import numpy as np
import pandas as pd
# 导入我自定义的模块
import sys
sys.path.append("./code_libs")
import alpha_tools as at
Checking data_tools dependencies... Checking numpy >= 1.20.3... Find that numpy == 1.21.5... Checking pandas >= 1.3.4... Find that pandas == 1.3.5... Checking bottleneck >= 1.3.4... Find that bottleneck == 1.3.4... Checking graphviz >= 0.20... Find that graphviz == 0.20... Checking scikit-learn >= 1.0.1... Find that scikit-learn == 1.0.2... Checking xlrd >= 2.0.1... Find that xlrd == 2.0.1... Checking psutil >= 5.8.0... Find that psutil == 5.9.0... Checking openpyxl >= 3.0.9... Find that openpyxl == 3.0.9... Checking xlsxwriter >= 3.0.3... Find that xlsxwriter == 3.0.3... Checking data_tools dependencies finished. Checking tree_tools dependencies... Checking numpy >= 1.20.3... Find that numpy == 1.21.5... Checking pandas >= 1.3.4... Find that pandas == 1.3.5... Checking bottleneck >= 1.3.4... Find that bottleneck == 1.3.4... Checking graphviz >= 0.20... Find that graphviz == 0.20... Checking scikit-learn >= 1.0.1... Find that scikit-learn == 1.0.2... Checking xlrd >= 2.0.1... Find that xlrd == 2.0.1... Checking psutil >= 5.8.0... Find that psutil == 5.9.0... Checking openpyxl >= 3.0.9... Find that openpyxl == 3.0.9... Checking xlsxwriter >= 3.0.3... Find that xlsxwriter == 3.0.3... Checking scipy >= 1.7.1... Find that scipy == 1.7.3... Checking imbalanced-learn >= 0.9.0... Find that imbalanced-learn == 0.9.0... Checking matplotlib >= 3.4.2... Find that matplotlib == 3.5.1... Checking statsmodels >= 0.13.1... Find that statsmodels == 0.13.1... Checking scikit-optimize >= 0.9.0... Find that scikit-optimize == 0.9.0... Checking hyperopt >= 0.2.7... Find that hyperopt == 0.2.7... Checking lxml >= 4.6.3... Find that lxml == 4.7.1... Checking sklearn2pmml >= 0.83.0... Find that sklearn2pmml == 0.83.0... Checking sklearn_pandas >= 2.2.0... Find that sklearn_pandas == 2.2.0... Checking xgboost >= 1.6.1... Find that xgboost == 1.6.1... Checking lightgbm >= 3.3.2... Find that lightgbm == 3.3.2... Checking catboost >= 1.0.4... Find that catboost == 1.0.4... Checking pikepdf >= 5.1.3... Find that pikepdf == 5.1.3... Checking reportlab >= 3.5.68... Find that reportlab == 3.6.5... Checking svglib >= 1.3.0... Find that svglib == 1.3.0... Checking tree_tools dependencies finished. Checking model_tools dependencies... Checking numpy >= 1.20.3... Find that numpy == 1.21.5... Checking pandas >= 1.3.4... Find that pandas == 1.3.5... Checking bottleneck >= 1.3.4... Find that bottleneck == 1.3.4... Checking graphviz >= 0.20... Find that graphviz == 0.20... Checking scikit-learn >= 1.0.1... Find that scikit-learn == 1.0.2... Checking xlrd >= 2.0.1... Find that xlrd == 2.0.1... Checking psutil >= 5.8.0... Find that psutil == 5.9.0... Checking openpyxl >= 3.0.9... Find that openpyxl == 3.0.9... Checking xlsxwriter >= 3.0.3... Find that xlsxwriter == 3.0.3... Checking scipy >= 1.7.1... Find that scipy == 1.7.3... Checking imbalanced-learn >= 0.9.0... Find that imbalanced-learn == 0.9.0... Checking matplotlib >= 3.4.2... Find that matplotlib == 3.5.1... Checking statsmodels >= 0.13.1... Find that statsmodels == 0.13.1... Checking scikit-optimize >= 0.9.0... Find that scikit-optimize == 0.9.0... Checking hyperopt >= 0.2.7... Find that hyperopt == 0.2.7... Checking lxml >= 4.6.3... Find that lxml == 4.7.1... Checking sklearn2pmml >= 0.83.0... Find that sklearn2pmml == 0.83.0... Checking sklearn_pandas >= 2.2.0... Find that sklearn_pandas == 2.2.0... Checking model_tools dependencies finished.
# 导入数据
df_ = pd.read_csv("application_record.csv")
df_response = pd.read_csv("credit_record.csv")
# 添加“年龄”变量
df_['Age'] = -(df_['DAYS_BIRTH']) // 365
# 定义标签
df_["target"] = df_["ID"].map(df_response.groupby("ID")["STATUS"].apply(
lambda x: 1 if ({"2", "3", "4", "5"} & set(x)) else 2 if ({"1"} & set(x)) else 0))
# 定义切片
df_['birth_year'] = df_["Age"].map(lambda x: str(2021 - x) + "0101")
df_data = df_[df_["target"].notnull()]
# 划分数据集
from sklearn.model_selection import train_test_split
df_train, df_test = train_test_split(
df_data, test_size=0.3, stratify=df_data["target"], random_state=42)
# 首先定义一下之前data_flow用过的参数，便于后续直接调用
data_params = {
"response": "target", # 基础定义
"split_col_name": "birth_year", "use_train_time": True, # 时间切片
"add_info": "ID", # 主键添加
"enable_single_threshold": False, # 关闭单一值过滤
# enable_iv_limit=True, iv_threshold=[0.01, np.inf], # 暂不启用iv过滤
"customized_groups": {
"OCCUPATION_TYPE": [
"Cleaning staff|Cooking staff|Drivers|Laborers|Low-skill Laborers|Security staff|Waiters/barmen staff",
"Accountants|Core staff|HR staff|Medicine staff|Private service staff|Realty agents|Sales staff|Secretaries",
"Managers|High skill tech staff|IT staff",
],
"CNT_CHILDREN": [-np.inf, 0, np.inf],
},
"exclude_column": ["ID", "weight"], # 排除权重
"min_group_percent": 0,
}
at.Analysis.model_on_data(
df_train, "./模型分析demo/v12/v12.xlsx", test_data=df_test,
data_flow={
**data_params,
**{
"save_or_return": False # 跳过csv的io读写提升性能
},
},
model_flow={
# 默认流程即为评分卡建模，采用statsmodels的逻辑回归
"model_type_path": "statsmodels.api.Logit"
}
)
INFO Prepare kwargs...
INFO Checking train_data...
INFO Checking response...
INFO Checking test_data...
INFO Checking response...
INFO Checking columns...
INFO Checking dtypes...
WARNING Setting format_time_edge to True !
INFO Prepare run...
INFO Run: DAYS_BIRTH
INFO Run: NAME_HOUSING_TYPE
INFO Run: FLAG_OWN_REALTY
INFO Run: NAME_EDUCATION_TYPE
INFO Run: CNT_FAM_MEMBERS
INFO Run: FLAG_WORK_PHONE
INFO Run: Age
INFO Run: CODE_GENDER
INFO Run: FLAG_PHONE
INFO Run: NAME_FAMILY_STATUS
INFO Run: FLAG_MOBIL
INFO Run: CNT_CHILDREN
INFO Run: NAME_INCOME_TYPE
INFO Run: OCCUPATION_TYPE
INFO Run: FLAG_EMAIL
INFO Run: DAYS_EMPLOYED
INFO Run: FLAG_OWN_CAR
INFO Run: AMT_INCOME_TOTAL
INFO Selecting...
INFO Match train: FLAG_WORK_PHONE_asC
INFO Match train: FLAG_OWN_REALTY_asD
INFO Match train: CODE_GENDER_asD
INFO Match train: Age_asC
INFO Match train: NAME_EDUCATION_TYPE_asD
INFO Match train: NAME_HOUSING_TYPE_asD
INFO Match train: DAYS_BIRTH_asC
INFO Match train: CNT_FAM_MEMBERS_asC
INFO Match train: NAME_FAMILY_STATUS_asD
INFO Match train: CNT_CHILDREN_asC
INFO Match train: FLAG_PHONE_asC
INFO Match train: OCCUPATION_TYPE_asD
INFO Match train: FLAG_EMAIL_asC
INFO Match train: FLAG_OWN_CAR_asD
INFO Match train: NAME_INCOME_TYPE_asD
INFO Match train: DAYS_EMPLOYED_asC
INFO Match train: AMT_INCOME_TOTAL_asC
INFO Creating train data...
INFO Creating train corr detail...
INFO Creating (-inf, 1972-01-01] corr...
INFO Creating (1972-01-01, 1984-01-01] corr...
INFO Creating (1984-01-01, inf) corr...
INFO Creating all corr...
INFO Creating train cross table...
INFO Creating train all desc tables...
INFO Match test: DAYS_BIRTH_asC
INFO Match test: NAME_HOUSING_TYPE_asD
INFO Match test: FLAG_OWN_REALTY_asD
INFO Match test: FLAG_WORK_PHONE_asC
INFO Match test: NAME_EDUCATION_TYPE_asD
INFO Match test: CNT_FAM_MEMBERS_asC
INFO Match test: CODE_GENDER_asD
INFO Match test: Age_asC
INFO Match test: FLAG_PHONE_asC
INFO Match test: NAME_FAMILY_STATUS_asD
INFO Match test: NAME_INCOME_TYPE_asD
INFO Match test: CNT_CHILDREN_asC
INFO Match test: OCCUPATION_TYPE_asD
INFO Match test: DAYS_EMPLOYED_asC
INFO Match test: FLAG_EMAIL_asC
INFO Match test: FLAG_OWN_CAR_asD
INFO Match test: AMT_INCOME_TOTAL_asC
INFO Creating test data...
INFO Creating test corr detail...
INFO Creating (-inf, 1972-01-01] corr...
INFO Creating (1972-01-01, 1984-01-01] corr...
INFO Creating (1984-01-01, inf) corr...
INFO Creating all corr...
INFO Creating test cross table...
INFO Creating test all desc tables...
INFO Match train test: FLAG_OWN_REALTY_asD
INFO Match train test: NAME_FAMILY_STATUS_asD
INFO Match train test: DAYS_EMPLOYED_asC
INFO Match train test: NAME_INCOME_TYPE_asD
INFO Match train test: NAME_HOUSING_TYPE_asD
INFO Match train test: NAME_EDUCATION_TYPE_asD
INFO Match train test: CODE_GENDER_asD
INFO Match train test: OCCUPATION_TYPE_asD
INFO Match train test: AMT_INCOME_TOTAL_asC
INFO Match train test: FLAG_WORK_PHONE_asC
INFO Match train test: FLAG_OWN_CAR_asD
INFO Match train test: FLAG_PHONE_asC
INFO Match train test: DAYS_BIRTH_asC
INFO Match train test: CNT_CHILDREN_asC
INFO Match train test: FLAG_EMAIL_asC
INFO Match train test: Age_asC
INFO Match train test: CNT_FAM_MEMBERS_asC
INFO Saving info...
INFO Saving desc...
INFO Saving cross...
INFO Saving unique...
INFO Saving summary...
INFO Saving corr summary...
INFO Saving stable_summary...
INFO Saving train_stable_iv_ks...
INFO Saving train_stable_psi...
INFO Saving train_stable_quantile...
INFO Saving test_stable_iv_ks...
INFO Saving test_stable_psi...
INFO Saving test_stable_quantile...
INFO Saving stable_cross_psi...
INFO Saving detail...
INFO Saving draft...
INFO Saving drop...
INFO Saving json...
INFO Saving setting...
INFO Adding runtime to excel...
INFO Saving directory...
INFO Saving...
INFO Total: 25.07 s
INFO Prepare kwargs...
WARNING Using response as split_col_name !
INFO Checking train_data...
INFO Checking response...
INFO Checking test_data...
INFO Checking response...
INFO Checking columns...
INFO Checking dtypes...
WARNING Find that nan_mode is None, setting nan_mode to default_null_mode !
WARNING Find that convert_score is True, setting precision to 0 !
INFO Model linear flow...
WARNING Find that convert_score is True & save_score_pmml is True, setting save_model_pmml to False !
INFO Selecting by corr...
INFO Total: 17...
INFO Selected: 15...
WARNING Found n_jobs not set, setting to 2 !
WARNING Auto setting lightgbm params...
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'max_depth': 3, 'n_estimators': 30, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': 'auto'}
INFO Using variables: 15...
INFO Training LGBMClassifier...
INFO Filtering importance by LGBMClassifier ...
INFO Total: 15...
INFO Selected: 15...
INFO Round: 1; Forward Stepwise: WOE_FLAG_OWN_REALTY_asD; Selected: 2
INFO Round: 1; Forward Stepwise: WOE_NAME_FAMILY_STATUS_asD; Selected: 3
INFO Round: 1; Forward Stepwise: WOE_DAYS_EMPLOYED_asC; Selected: 4
INFO Round: 1; Forward Stepwise: WOE_NAME_INCOME_TYPE_asD; Selected: 4
INFO Round: 1; Forward Stepwise: WOE_NAME_HOUSING_TYPE_asD; Selected: 5
INFO Round: 1; Forward Stepwise: WOE_NAME_EDUCATION_TYPE_asD; Selected: 6
INFO Round: 1; Forward Stepwise: WOE_CODE_GENDER_asD; Selected: 7
INFO Round: 1; Forward Stepwise: WOE_OCCUPATION_TYPE_asD; Selected: 7
INFO Round: 1; Forward Stepwise: WOE_AMT_INCOME_TOTAL_asC; Selected: 7
INFO Round: 1; Forward Stepwise: WOE_FLAG_WORK_PHONE_asC; Selected: 7
INFO Round: 1; Forward Stepwise: WOE_FLAG_OWN_CAR_asD; Selected: 7
INFO Round: 1; Forward Stepwise: WOE_FLAG_PHONE_asC; Selected: 7
INFO Round: 1; Forward Stepwise: WOE_DAYS_BIRTH_asC; Selected: 7
INFO Round: 1; Forward Stepwise: WOE_CNT_CHILDREN_asC; Selected: 7
INFO Round: 1; Forward Stepwise: WOE_FLAG_EMAIL_asC; Selected: 7
INFO Round: 2; Forward Stepwise: WOE_NAME_INCOME_TYPE_asD; Selected: 7
INFO Round: 2; Forward Stepwise: WOE_OCCUPATION_TYPE_asD; Selected: 7
INFO Round: 2; Forward Stepwise: WOE_AMT_INCOME_TOTAL_asC; Selected: 7
INFO Round: 2; Forward Stepwise: WOE_FLAG_WORK_PHONE_asC; Selected: 7
INFO Round: 2; Forward Stepwise: WOE_FLAG_OWN_CAR_asD; Selected: 7
INFO Round: 2; Forward Stepwise: WOE_FLAG_PHONE_asC; Selected: 7
INFO Round: 2; Forward Stepwise: WOE_DAYS_BIRTH_asC; Selected: 7
INFO Round: 2; Forward Stepwise: WOE_CNT_CHILDREN_asC; Selected: 7
INFO Round: 2; Forward Stepwise: WOE_FLAG_EMAIL_asC; Selected: 7
INFO Round: 3; Forward Stepwise: WOE_NAME_INCOME_TYPE_asD; Selected: 7
INFO Round: 3; Forward Stepwise: WOE_OCCUPATION_TYPE_asD; Selected: 7
INFO Round: 3; Forward Stepwise: WOE_AMT_INCOME_TOTAL_asC; Selected: 7
INFO Round: 3; Forward Stepwise: WOE_FLAG_WORK_PHONE_asC; Selected: 7
INFO Round: 3; Forward Stepwise: WOE_FLAG_OWN_CAR_asD; Selected: 7
INFO Round: 3; Forward Stepwise: WOE_FLAG_PHONE_asC; Selected: 7
INFO Round: 3; Forward Stepwise: WOE_DAYS_BIRTH_asC; Selected: 7
INFO Round: 3; Forward Stepwise: WOE_CNT_CHILDREN_asC; Selected: 7
INFO Round: 3; Forward Stepwise: WOE_FLAG_EMAIL_asC; Selected: 7
INFO Model variables: ['Intercept', 'WOE_FLAG_OWN_REALTY_asD', 'WOE_NAME_FAMILY_STATUS_asD', 'WOE_DAYS_EMPLOYED_asC', 'WOE_NAME_HOUSING_TYPE_asD', 'WOE_NAME_EDUCATION_TYPE_asD', 'WOE_CODE_GENDER_asD'] !
INFO Building model...
INFO Saving var_desc...
INFO Saving var_cross...
INFO Saving var_unique...
INFO Saving var_summary...
INFO Saving woe_corr...
INFO Saving train_stable_summary...
INFO Saving test_stable_summary...
Optimization terminated successfully.
Current function value: 0.091927
Iterations 8
INFO Saving train_var_stable_iv_ks... INFO Saving train_var_stable_psi... INFO Saving train_var_stable_quantile... INFO Saving test_var_stable_iv_ks... INFO Saving test_var_stable_psi... INFO Saving test_var_stable_quantile... INFO Saving var_stable_cross_psi... INFO Saving var_detail... INFO Saving var_draft... INFO Saving var_drop... INFO Prepare scorecard... INFO Adding scorecard_description to excel... INFO Converting train proba to score... INFO Converting woe to score... INFO Checking scorecard equation... INFO Saving train_var_corr... INFO Saving train vif... INFO Getting detail score... INFO Saving train results... INFO Getting total score... INFO Creating uncut info... INFO Drawing train_score_uncut_ks_graph... INFO Drawing train_score_uncut_count_chart1... INFO Drawing train_score_uncut_count_chart2... INFO Drawing train_score_uncut_count_chart3... INFO Drawing train_score_uncut_cap_chart1... INFO Drawing train_score_uncut_cap_chart2... INFO Drawing train_score_uncut_cap_chart3... INFO Creating cut info... INFO Drawing train_score_cut_ks_graph... INFO Drawing train_score_cut_rate_chart... INFO Drawing train_score_cut_count_chart1... INFO Drawing train_score_cut_count_chart2... INFO Drawing train_score_cut_count_chart3... INFO Drawing train_score_cut_cap_chart1... INFO Drawing train_score_cut_cap_chart2... INFO Drawing train_score_cut_cap_chart3... INFO Creating qcut info... INFO Drawing train_score_qcut_ks_graph... INFO Drawing train_score_qcut_rate_chart... INFO Drawing train_score_qcut_count_chart1... INFO Drawing train_score_qcut_count_chart2... INFO Drawing train_score_qcut_count_chart3... INFO Drawing train_score_qcut_cap_chart1... INFO Drawing train_score_qcut_cap_chart2... INFO Drawing train_score_qcut_cap_chart3... INFO Creating precision recall fscore... INFO Drawing train_classification_chart... INFO Drawing train_classification_chart... INFO Drawing roc... INFO Creating results summary... INFO Drawing ap... INFO Converting test proba to score... INFO Converting woe to score... INFO Checking scorecard equation... INFO Saving test_var_corr... INFO Saving test vif... INFO Getting detail score... INFO Saving test results... INFO Getting total score... INFO Creating uncut info... INFO Drawing test_score_uncut_ks_graph... INFO Drawing test_score_uncut_count_chart1... INFO Drawing test_score_uncut_count_chart2... INFO Drawing test_score_uncut_count_chart3... INFO Drawing test_score_uncut_cap_chart1... INFO Drawing test_score_uncut_cap_chart2... INFO Drawing test_score_uncut_cap_chart3... INFO Creating cut info... INFO Drawing test_score_cut_ks_graph... INFO Drawing test_score_cut_rate_chart... INFO Drawing test_score_cut_count_chart1... INFO Drawing test_score_cut_count_chart2... INFO Drawing test_score_cut_count_chart3... INFO Drawing test_score_cut_cap_chart1... INFO Drawing test_score_cut_cap_chart2... INFO Drawing test_score_cut_cap_chart3... INFO Creating qcut info... INFO Drawing test_score_qcut_ks_graph... INFO Drawing test_score_qcut_rate_chart... INFO Drawing test_score_qcut_count_chart1... INFO Drawing test_score_qcut_count_chart2... INFO Drawing test_score_qcut_count_chart3... INFO Drawing test_score_qcut_cap_chart1... INFO Drawing test_score_qcut_cap_chart2... INFO Drawing test_score_qcut_cap_chart3... INFO Creating precision recall fscore... INFO Drawing test_classification_chart... INFO Drawing test_classification_chart... INFO Drawing roc... INFO Creating results summary... INFO Drawing ap... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi... INFO Drawing cut_psi_chart... INFO Drawing cut_psi_chart... INFO Drawing qcut_psi_chart... INFO Drawing qcut_psi_chart... INFO Saving model score gap... INFO Creating pmml... INFO Adding score_pmml to excel... INFO Adding run_score_pmml_in_java to excel... INFO Creating sql... INFO Creating check unique sql... INFO Creating unique table sql... INFO Creating count sql... INFO Adding model_sql to excel... INFO Adding run_sql_in_dataframe to excel... INFO target | 3 | cv flow... INFO Prepare scorecard... INFO Converting train proba to score... INFO Saving train results... INFO Getting total score... INFO Creating uncut info... INFO Creating cut info... INFO Creating qcut info... INFO Creating precision recall fscore... INFO Creating results summary... INFO Converting test proba to score... INFO Saving test results... INFO Getting total score... INFO Creating uncut info... INFO Creating cut info... INFO Creating qcut info... INFO Creating precision recall fscore... INFO Creating results summary... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi... INFO Prepare scorecard... INFO Converting train proba to score... INFO Saving train results... INFO Getting total score... INFO Creating uncut info... INFO Creating cut info... INFO Creating qcut info... INFO Creating precision recall fscore... INFO Creating results summary... INFO Converting test proba to score... INFO Saving test results... INFO Getting total score... INFO Creating uncut info... INFO Creating cut info... INFO Creating qcut info... INFO Creating precision recall fscore... INFO Creating results summary... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi... INFO Prepare scorecard... INFO Converting train proba to score... INFO Saving train results... INFO Getting total score... INFO Creating uncut info... INFO Creating cut info... INFO Creating qcut info... INFO Creating precision recall fscore... INFO Creating results summary... INFO Converting test proba to score... INFO Saving test results... INFO Getting total score... INFO Creating uncut info... INFO Creating cut info... INFO Creating qcut info... INFO Creating precision recall fscore... INFO Creating results summary... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi... INFO Saving cross validate... INFO Drawing cv compare graph... INFO Drawing cv_compare_ks_chart... INFO Drawing cv_compare_auc_chart... INFO Drawing cv_compare_ap_chart... INFO Drawing cv_compare_logloss_chart... INFO Drawing cv_compare_r2_chart... INFO Drawing cv_compare_mse_chart... INFO Drawing cv_compare_mdp_chart... INFO Drawing cv_compare_psi_chart... INFO Drawing cv_compare_mpg_chart... INFO Saving model pkl... INFO Saving json... INFO Saving setting... INFO Adding runtime to excel... INFO Saving directory... INFO Saving... INFO Total: 43.70 s INFO Total: 68.89 s
at.Analysis.model_flow(
df_train, "./模型分析demo/v1-lgb/v1-lgb.xlsx", test_data=df_test,
# 目标定义 & 切片定义
response="target", split_col_name="birth_year", use_train_time=True,
exclude_column=["ID", "weight"], # 排除权重
add_info="ID", # 主键添加
# 指定使用训练的class并不进行打分转化
model_type_path="lightgbm.sklearn.LGBMClassifier",
convert_score=False,
)
INFO Prepare kwargs...
WARNING Setting format_time_edge to True !
INFO Checking train_data...
INFO Checking response...
INFO Checking test_data...
INFO Checking response...
INFO Checking columns...
INFO Checking dtypes...
WARNING Setting allow_nan to True by lightgbm.sklearn !
WARNING Setting allow_discrete to True by lightgbm.sklearn !
WARNING Pre: WOE_ not found, change pre to "" !
WARNING Unable to find nan data, setting allow_nan to False !
WARNING Find that allow_nan is True or allow_discrete is True, setting enable_stepwise to False !
WARNING Find that allow_nan is True or allow_discrete is True, setting enable_corr_filter to False !
WARNING Find that convert_score is False, setting keep_raw_score to False !
WARNING Find that convert_score is False, setting save_score_desc to False !
WARNING Find that convert_score is False, setting save_score_pmml to False !
INFO Model optional flow...
WARNING Check /home/conda_env/模型分析demo/v1-lgb/Data_v1-lgb.xlsx exists !
WARNING Found n_jobs not set, setting to 2 !
WARNING Auto setting lightgbm params...
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'max_depth': 3, 'n_estimators': 30, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 9, 10, 13, 14, 15, 16, 17]}
INFO Using variables: 18...
INFO Training LGBMClassifier...
INFO Filtering importance by LGBMClassifier ...
INFO Total: 18...
INFO Selected: 16...
INFO Model variables: ['AMT_INCOME_TOTAL', 'Age', 'CNT_CHILDREN', 'CNT_FAM_MEMBERS', 'CODE_GENDER', 'DAYS_BIRTH', 'DAYS_EMPLOYED', 'FLAG_OWN_CAR', 'FLAG_OWN_REALTY', 'FLAG_PHONE', 'FLAG_WORK_PHONE', 'NAME_EDUCATION_TYPE', 'NAME_FAMILY_STATUS', 'NAME_HOUSING_TYPE', 'NAME_INCOME_TYPE', 'OCCUPATION_TYPE'] !
WARNING Found n_jobs not set, setting to 2 !
WARNING Auto setting lightgbm params...
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO [FIT_TRANSFORM] ['AMT_INCOME_TOTAL']: 0.000623 secs
INFO [FIT_TRANSFORM] ['Age']: 0.00057 secs
INFO [FIT_TRANSFORM] ['CNT_CHILDREN']: 0.000485 secs
INFO [FIT_TRANSFORM] ['CNT_FAM_MEMBERS']: 0.000544 secs
INFO [FIT_TRANSFORM] ['CODE_GENDER']: 0.018621 secs
INFO [FIT_TRANSFORM] ['DAYS_BIRTH']: 0.000951 secs
INFO [FIT_TRANSFORM] ['DAYS_EMPLOYED']: 0.000863 secs
INFO [FIT_TRANSFORM] ['FLAG_OWN_CAR']: 0.01844 secs
INFO [FIT_TRANSFORM] ['FLAG_OWN_REALTY']: 0.077458 secs
INFO [FIT_TRANSFORM] ['FLAG_PHONE']: 0.001095 secs
INFO [FIT_TRANSFORM] ['FLAG_WORK_PHONE']: 0.000804 secs
INFO [FIT_TRANSFORM] ['NAME_EDUCATION_TYPE']: 0.018547 secs
INFO [FIT_TRANSFORM] ['NAME_FAMILY_STATUS']: 0.07721 secs
INFO [FIT_TRANSFORM] ['NAME_HOUSING_TYPE']: 0.018178 secs
INFO [FIT_TRANSFORM] ['NAME_INCOME_TYPE']: 0.075108 secs
INFO [FIT_TRANSFORM] ['OCCUPATION_TYPE']: 0.018787 secs
INFO [TRANSFORM] ['AMT_INCOME_TOTAL']: 0.000686 secs
INFO [TRANSFORM] ['Age']: 0.000646 secs
INFO [TRANSFORM] ['CNT_CHILDREN']: 0.000495 secs
INFO [TRANSFORM] ['CNT_FAM_MEMBERS']: 0.000837 secs
INFO [TRANSFORM] ['CODE_GENDER']: 0.011462 secs
INFO [TRANSFORM] ['DAYS_BIRTH']: 0.000746 secs
INFO [TRANSFORM] ['DAYS_EMPLOYED']: 0.000544 secs
INFO [TRANSFORM] ['FLAG_OWN_CAR']: 0.075736 secs
INFO [TRANSFORM] ['FLAG_OWN_REALTY']: 0.011396 secs
INFO [TRANSFORM] ['FLAG_PHONE']: 0.000591 secs
INFO [TRANSFORM] ['FLAG_WORK_PHONE']: 0.000494 secs
INFO [TRANSFORM] ['NAME_EDUCATION_TYPE']: 0.01143 secs
INFO [TRANSFORM] ['NAME_FAMILY_STATUS']: 0.070248 secs
INFO [TRANSFORM] ['NAME_HOUSING_TYPE']: 0.011703 secs
INFO [TRANSFORM] ['NAME_INCOME_TYPE']: 0.012116 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE']: 0.075671 secs
INFO [TRANSFORM] ['AMT_INCOME_TOTAL']: 0.000643 secs
INFO [TRANSFORM] ['Age']: 0.000785 secs
INFO [TRANSFORM] ['CNT_CHILDREN']: 0.000499 secs
INFO [TRANSFORM] ['CNT_FAM_MEMBERS']: 0.000465 secs
INFO [TRANSFORM] ['CODE_GENDER']: 0.085902 secs
INFO [TRANSFORM] ['DAYS_BIRTH']: 0.000626 secs
INFO [TRANSFORM] ['DAYS_EMPLOYED']: 0.000469 secs
INFO [TRANSFORM] ['FLAG_OWN_CAR']: 0.005398 secs
INFO [TRANSFORM] ['FLAG_OWN_REALTY']: 0.006208 secs
INFO [TRANSFORM] ['FLAG_PHONE']: 0.000771 secs
INFO [TRANSFORM] ['FLAG_WORK_PHONE']: 0.000497 secs
INFO [TRANSFORM] ['NAME_EDUCATION_TYPE']: 0.005973 secs
INFO [TRANSFORM] ['NAME_FAMILY_STATUS']: 0.005635 secs
INFO [TRANSFORM] ['NAME_HOUSING_TYPE']: 0.005824 secs
INFO [TRANSFORM] ['NAME_INCOME_TYPE']: 0.005702 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE']: 0.006489 secs
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Drawing train_score_uncut_ks_graph...
INFO Drawing train_score_uncut_count_chart1...
INFO Drawing train_score_uncut_count_chart2...
INFO Drawing train_score_uncut_count_chart3...
INFO Drawing train_score_uncut_cap_chart1...
INFO Drawing train_score_uncut_cap_chart2...
INFO Drawing train_score_uncut_cap_chart3...
INFO Creating cut info...
INFO Drawing train_score_cut_ks_graph...
INFO Drawing train_score_cut_rate_chart...
INFO Drawing train_score_cut_count_chart1...
INFO Drawing train_score_cut_count_chart2...
INFO Drawing train_score_cut_count_chart3...
INFO Drawing train_score_cut_cap_chart1...
INFO Drawing train_score_cut_cap_chart2...
INFO Drawing train_score_cut_cap_chart3...
INFO Creating qcut info...
INFO Drawing train_score_qcut_ks_graph...
INFO Drawing train_score_qcut_rate_chart...
INFO Drawing train_score_qcut_count_chart1...
INFO Drawing train_score_qcut_count_chart2...
INFO Drawing train_score_qcut_count_chart3...
INFO Drawing train_score_qcut_cap_chart1...
INFO Drawing train_score_qcut_cap_chart2...
INFO Drawing train_score_qcut_cap_chart3...
INFO Creating precision recall fscore...
INFO Drawing train_classification_chart...
INFO Drawing train_classification_chart...
INFO Drawing roc...
INFO Creating results summary...
INFO Drawing ap...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Drawing test_score_uncut_ks_graph...
INFO Drawing test_score_uncut_count_chart1...
INFO Drawing test_score_uncut_count_chart2...
INFO Drawing test_score_uncut_count_chart3...
INFO Drawing test_score_uncut_cap_chart1...
INFO Drawing test_score_uncut_cap_chart2...
INFO Drawing test_score_uncut_cap_chart3...
INFO Creating cut info...
INFO Drawing test_score_cut_ks_graph...
INFO Drawing test_score_cut_rate_chart...
INFO Drawing test_score_cut_count_chart1...
INFO Drawing test_score_cut_count_chart2...
INFO Drawing test_score_cut_count_chart3...
INFO Drawing test_score_cut_cap_chart1...
INFO Drawing test_score_cut_cap_chart2...
INFO Drawing test_score_cut_cap_chart3...
INFO Creating qcut info...
INFO Drawing test_score_qcut_ks_graph...
INFO Drawing test_score_qcut_rate_chart...
INFO Drawing test_score_qcut_count_chart1...
INFO Drawing test_score_qcut_count_chart2...
INFO Drawing test_score_qcut_count_chart3...
INFO Drawing test_score_qcut_cap_chart1...
INFO Drawing test_score_qcut_cap_chart2...
INFO Drawing test_score_qcut_cap_chart3...
INFO Creating precision recall fscore...
INFO Drawing test_classification_chart...
INFO Drawing test_classification_chart...
INFO Drawing roc...
INFO Creating results summary...
INFO Drawing ap...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Drawing cut_psi_chart...
INFO Drawing cut_psi_chart...
INFO Drawing qcut_psi_chart...
INFO Drawing qcut_psi_chart...
INFO Total: 25.82 s
INFO Saving var_drop...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Auto setting lightgbm params...
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Auto setting lightgbm params...
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Auto setting lightgbm params...
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO Drawing cv compare graph...
INFO Drawing cv_compare_ks_chart...
INFO Drawing cv_compare_auc_chart...
INFO Drawing cv_compare_ap_chart...
INFO Drawing cv_compare_logloss_chart...
INFO Drawing cv_compare_r2_chart...
INFO Drawing cv_compare_mse_chart...
INFO Drawing cv_compare_mdp_chart...
INFO Drawing cv_compare_psi_chart...
INFO Drawing cv_compare_mpg_chart...
INFO Saving model pkl...
INFO Saving model pmml...
INFO Adding model_pmml to excel...
INFO Adding run_model_pmml_in_java to excel...
INFO Saving json...
INFO Saving setting...
INFO Adding runtime to excel...
INFO Saving directory...
INFO Saving...
INFO Total: 47.51 s
INFO Total: 47.60 s
at.mt.__metrics__
{'binary': {'ks': None,
'log_loss': {'eps': 1e-15,
'normalize': True,
'sample_weight': None,
'labels': None},
'r2_score': {'sample_weight': None, 'multioutput': 'uniform_average'},
'mean_squared_error': {'sample_weight': None,
'multioutput': 'uniform_average'},
'roc_auc_score': {'average': 'macro', 'sample_weight': None},
'roc_curve': {'pos_label': None,
'sample_weight': None,
'drop_intermediate': True},
'average_precision_score': {'average': 'macro', 'sample_weight': None},
'confusion_matrix': {'labels': None, 'sample_weight': None},
'classification_report': {'f1_formulate': <function <lambda> at 0x7f3092de4860>,
'summary_name': ('precision', 'recall', 'f1'),
'summary_display_func': ('max', 'mean'),
'display_func_range': ('mean', 'max', 'min', 'std', 'sum')},
'max_distribution_percent': None,
'psi': None},
'regression': {'r2_score': {'sample_weight': None,
'multioutput': 'uniform_average'},
'mean_squared_error': {'sample_weight': None,
'multioutput': 'uniform_average'},
'max_distribution_percent': None,
'psi': None}}at.mt.__pattern__["model_module_path"]
('sklearn.tree',
'sklearn.ensemble',
'imblearn.ensemble',
'lightgbm.sklearn',
'lightgbm',
'xgboost.sklearn',
'xgboost',
'catboost',
'statsmodels.api',
'sklearn.linear_model',
'sklearn.cluster',
'sklearn.neural_network',
'sklearn.calibration',
'sklearn.neighbors',
'sklearn.isotonic',
'sklearn.discriminant_analysis',
'sklearn.cross_decomposition',
'sklearn.svm',
'sklearn.gaussian_process',
'sklearn.mixture',
'sklearn.naive_bayes',
'sklearn2pmml.ensemble',
'optional.model',
'optional.n_model',
'optional.d_model',
'optional.nd_model')# 默认支持缺失值的模型
at.mt.__pattern__["allow_nan"]
('xgboost.sklearn',
'xgboost',
'lightgbm.sklearn',
'lightgbm',
'optional.n_model',
'optional.nd_model')# 默认支持离散值的模型
at.mt.__pattern__["allow_discrete"]
('xgboost.sklearn',
'xgboost',
'lightgbm.sklearn',
'lightgbm',
'catboost',
'optional.d_model',
'optional.nd_model')# 未指定填充方式时的默认填充模式
at.mt.__pattern__["default_null_mode"]
'min'
# train无缺失，但test存在缺失时，数值变量默认填充值
at.mt.__pattern__["nan_not_found"]
-999999
# train无缺失，但test存在缺失时，离散变量默认填充值
at.mt.__pattern__["null_value"]
'missing'
at.Analysis.model_flow(
df_train, "./模型分析demo/v2-lr/v2-lr.xlsx", test_data=df_test,
# 目标定义 & 切片定义
response="target", split_col_name="birth_year", use_train_time=True,
exclude_column=["ID", "weight", "DAYS_BIRTH"], # 排除权重
add_info="ID", # 主键添加
task_type="regression", # 调整为 regression
# 非分类器会自动把 convert_score 调整为 False
model_type_path="sklearn.linear_model.LinearRegression",
)
INFO Prepare kwargs...
WARNING Setting format_time_edge to True !
INFO Checking train_data...
INFO Checking response...
INFO Checking test_data...
INFO Checking response...
INFO Checking columns...
INFO Checking dtypes...
WARNING Setting nan_mode to min !
WARNING Pre: WOE_ not found, change pre to "" !
WARNING Find that task_type != binary, setting convert_score to False !
WARNING Find that convert_score is False, setting keep_raw_score to False !
WARNING Find that convert_score is False, setting save_score_desc to False !
WARNING Find that convert_score is False, setting save_score_pmml to False !
INFO Model optional flow...
WARNING Check /home/conda_env/模型分析demo/v2-lr/Data_v2-lr.xlsx exists !
INFO Selecting by corr...
INFO Total: 9...
INFO Selected: 7...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'max_depth': 3, 'n_estimators': 30, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': 'auto'}
INFO Using variables: 7...
INFO Training LGBMClassifier...
INFO Filtering importance by LGBMClassifier ...
INFO Total: 7...
INFO Selected: 7...
INFO Model variables: ['AMT_INCOME_TOTAL', 'Age', 'CNT_CHILDREN', 'DAYS_EMPLOYED', 'FLAG_EMAIL', 'FLAG_PHONE', 'FLAG_WORK_PHONE'] !
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_jobs': 2}
INFO Using fit params: {'sample_weight': None}
INFO Using variables: 7...
INFO Training LinearRegression...
INFO [FIT_TRANSFORM] ['AMT_INCOME_TOTAL']: 0.000454 secs
INFO [FIT_TRANSFORM] ['Age']: 0.000393 secs
INFO [FIT_TRANSFORM] ['CNT_CHILDREN']: 0.000224 secs
INFO [FIT_TRANSFORM] ['DAYS_EMPLOYED']: 0.000387 secs
INFO [FIT_TRANSFORM] ['FLAG_EMAIL']: 0.000558 secs
INFO [FIT_TRANSFORM] ['FLAG_PHONE']: 0.00059 secs
INFO [FIT_TRANSFORM] ['FLAG_WORK_PHONE']: 0.000211 secs
INFO [TRANSFORM] ['AMT_INCOME_TOTAL']: 0.000237 secs
INFO [TRANSFORM] ['Age']: 0.000481 secs
INFO [TRANSFORM] ['CNT_CHILDREN']: 0.000178 secs
INFO [TRANSFORM] ['DAYS_EMPLOYED']: 0.001419 secs
INFO [TRANSFORM] ['FLAG_EMAIL']: 0.000162 secs
INFO [TRANSFORM] ['FLAG_PHONE']: 0.000171 secs
INFO [TRANSFORM] ['FLAG_WORK_PHONE']: 0.000162 secs
INFO [TRANSFORM] ['AMT_INCOME_TOTAL']: 0.000166 secs
INFO [TRANSFORM] ['Age']: 0.000135 secs
INFO [TRANSFORM] ['CNT_CHILDREN']: 0.000134 secs
INFO [TRANSFORM] ['DAYS_EMPLOYED']: 0.000124 secs
INFO [TRANSFORM] ['FLAG_EMAIL']: 0.000136 secs
INFO [TRANSFORM] ['FLAG_PHONE']: 0.000128 secs
INFO [TRANSFORM] ['FLAG_WORK_PHONE']: 0.000127 secs
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Drawing cut_psi_chart...
INFO Drawing cut_psi_chart...
INFO Drawing qcut_psi_chart...
INFO Drawing qcut_psi_chart...
INFO Saving var_drop...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_jobs': 2}
INFO Using fit params: {'sample_weight': None}
INFO Using variables: 7...
INFO Training LinearRegression...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_jobs': 2}
INFO Using fit params: {'sample_weight': None}
INFO Using variables: 7...
INFO Training LinearRegression...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_jobs': 2}
INFO Using fit params: {'sample_weight': None}
INFO Using variables: 7...
INFO Training LinearRegression...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO Drawing cv compare graph...
INFO Drawing cv_compare_r2_chart...
INFO Drawing cv_compare_mse_chart...
INFO Drawing cv_compare_mdp_chart...
INFO Drawing cv_compare_psi_chart...
INFO Drawing cv_compare_mpg_chart...
INFO Saving model pkl...
INFO Saving model pmml...
/home/conda_env/miniconda3/lib/python3.9/subprocess.py:941: RuntimeWarning: line buffering (buffering=1) isn't supported in binary mode, the default buffer size will be used
self.stdout = io.open(c2pread, 'rb', bufsize)
/home/conda_env/miniconda3/lib/python3.9/subprocess.py:946: RuntimeWarning: line buffering (buffering=1) isn't supported in binary mode, the default buffer size will be used
self.stderr = io.open(errread, 'rb', bufsize)
INFO Adding model_pmml to excel...
INFO Adding run_model_pmml_in_java to excel...
INFO Saving json...
INFO Saving setting...
INFO Adding runtime to excel...
INFO Saving directory...
INFO Saving...
INFO Total: 4.69 s
INFO Total: 4.73 s
at.Analysis.model_on_data(
df_train, "./模型分析demo/v1-scorecard-more/v1-scorecard-more.xlsx",
# 同时入参两个数据集：测试、全量
test_data=[df_test, df_data], train_name="开发", test_names=["测试", "全量"],
data_flow={
**data_params,
**{
"save_or_return": False # 跳过csv的io读写提升性能
},
},
model_flow={
# 默认流程即为评分卡建模，采用statsmodels的逻辑回归
"model_type_path": "statsmodels.api.Logit"
}
)
INFO Prepare kwargs...
INFO Checking train_data...
INFO Checking response...
INFO Checking test_data...
INFO Checking response...
INFO Checking columns...
INFO Checking dtypes...
WARNING Setting format_time_edge to True !
INFO Prepare run...
INFO Run: CNT_FAM_MEMBERS
INFO Run: OCCUPATION_TYPE
INFO Run: DAYS_BIRTH
INFO Run: FLAG_OWN_REALTY
INFO Run: CODE_GENDER
INFO Run: FLAG_MOBIL
INFO Run: NAME_EDUCATION_TYPE
INFO Run: NAME_FAMILY_STATUS
INFO Run: FLAG_PHONE
INFO Run: AMT_INCOME_TOTAL
INFO Run: NAME_HOUSING_TYPE
INFO Run: Age
INFO Run: DAYS_EMPLOYED
INFO Run: NAME_INCOME_TYPE
INFO Run: FLAG_WORK_PHONE
INFO Run: FLAG_EMAIL
INFO Run: CNT_CHILDREN
INFO Run: FLAG_OWN_CAR
INFO Selecting...
INFO Match train: OCCUPATION_TYPE_asD
INFO Match train: CNT_FAM_MEMBERS_asC
INFO Match train: FLAG_OWN_REALTY_asD
INFO Match train: CODE_GENDER_asD
INFO Match train: FLAG_PHONE_asC
INFO Match train: NAME_FAMILY_STATUS_asD
INFO Match train: NAME_EDUCATION_TYPE_asD
INFO Match train: DAYS_BIRTH_asC
INFO Match train: NAME_HOUSING_TYPE_asD
INFO Match train: NAME_INCOME_TYPE_asD
INFO Match train: CNT_CHILDREN_asC
INFO Match train: Age_asC
INFO Match train: AMT_INCOME_TOTAL_asC
INFO Match train: FLAG_WORK_PHONE_asC
INFO Match train: FLAG_OWN_CAR_asD
INFO Match train: FLAG_EMAIL_asC
INFO Match train: DAYS_EMPLOYED_asC
INFO Creating train data...
INFO Creating train corr detail...
INFO Creating (-inf, 1972-01-01] corr...
INFO Creating (1972-01-01, 1984-01-01] corr...
INFO Creating (1984-01-01, inf) corr...
INFO Creating all corr...
INFO Creating train cross table...
INFO Creating train all desc tables...
INFO Match test: CNT_FAM_MEMBERS_asC
INFO Match test: OCCUPATION_TYPE_asD
INFO Match test: DAYS_BIRTH_asC
INFO Match test: FLAG_OWN_REALTY_asD
INFO Match test: CODE_GENDER_asD
INFO Match test: NAME_EDUCATION_TYPE_asD
INFO Match test: NAME_FAMILY_STATUS_asD
INFO Match test: FLAG_PHONE_asC
INFO Match test: AMT_INCOME_TOTAL_asC
INFO Match test: NAME_HOUSING_TYPE_asD
INFO Match test: Age_asC
INFO Match test: DAYS_EMPLOYED_asC
INFO Match test: NAME_INCOME_TYPE_asD
INFO Match test: FLAG_WORK_PHONE_asC
INFO Match test: FLAG_EMAIL_asC
INFO Match test: FLAG_OWN_CAR_asD
INFO Match test: CNT_CHILDREN_asC
INFO Creating test data...
INFO Creating test corr detail...
INFO Creating (-inf, 1972-01-01] corr...
INFO Creating (1972-01-01, 1984-01-01] corr...
INFO Creating (1984-01-01, inf) corr...
INFO Creating all corr...
INFO Creating test cross table...
INFO Creating test all desc tables...
INFO Match train test: FLAG_OWN_REALTY_asD
INFO Match train test: NAME_FAMILY_STATUS_asD
INFO Match train test: DAYS_EMPLOYED_asC
INFO Match train test: NAME_INCOME_TYPE_asD
INFO Match train test: NAME_HOUSING_TYPE_asD
INFO Match train test: CODE_GENDER_asD
INFO Match train test: AMT_INCOME_TOTAL_asC
INFO Match train test: NAME_EDUCATION_TYPE_asD
INFO Match train test: OCCUPATION_TYPE_asD
INFO Match train test: FLAG_OWN_CAR_asD
INFO Match train test: FLAG_WORK_PHONE_asC
INFO Match train test: FLAG_PHONE_asC
INFO Match train test: DAYS_BIRTH_asC
INFO Match train test: FLAG_EMAIL_asC
INFO Match train test: Age_asC
INFO Match train test: CNT_CHILDREN_asC
INFO Match train test: CNT_FAM_MEMBERS_asC
INFO Saving info...
INFO Saving desc...
INFO Saving cross...
INFO Saving unique...
INFO Saving summary...
INFO Saving corr summary...
INFO Saving stable_summary...
INFO Saving train_stable_iv_ks...
INFO Saving train_stable_psi...
INFO Saving train_stable_quantile...
INFO Saving test_stable_iv_ks...
INFO Saving test_stable_psi...
INFO Saving test_stable_quantile...
INFO Saving stable_cross_psi...
INFO Saving detail...
INFO Saving draft...
INFO Saving drop...
INFO Saving json...
INFO Saving setting...
INFO Adding runtime to excel...
INFO Saving directory...
INFO Saving...
INFO Total: 9.03 s
INFO Prepare kwargs...
WARNING Using response as split_col_name !
INFO Checking train_data...
INFO Checking response...
INFO Checking test_data...
INFO Checking response...
INFO Checking columns...
INFO Checking dtypes...
WARNING Setting nan_mode to min !
WARNING Find that convert_score is True, setting precision to 0 !
INFO Model linear flow...
WARNING Find that convert_score is True & save_score_pmml is True, setting save_model_pmml to False !
INFO Selecting by corr...
INFO Total: 17...
INFO Selected: 15...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'max_depth': 3, 'n_estimators': 30, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': 'auto'}
INFO Using variables: 15...
INFO Training LGBMClassifier...
INFO Filtering importance by LGBMClassifier ...
INFO Total: 15...
INFO Selected: 15...
INFO Round: 1; Forward Stepwise: WOE_FLAG_OWN_REALTY_asD; Selected: 2
INFO Round: 1; Forward Stepwise: WOE_NAME_FAMILY_STATUS_asD; Selected: 3
INFO Round: 1; Forward Stepwise: WOE_DAYS_EMPLOYED_asC; Selected: 4
INFO Round: 1; Forward Stepwise: WOE_NAME_INCOME_TYPE_asD; Selected: 4
INFO Round: 1; Forward Stepwise: WOE_NAME_HOUSING_TYPE_asD; Selected: 5
INFO Round: 1; Forward Stepwise: WOE_NAME_EDUCATION_TYPE_asD; Selected: 6
INFO Round: 1; Forward Stepwise: WOE_CODE_GENDER_asD; Selected: 7
INFO Round: 1; Forward Stepwise: WOE_OCCUPATION_TYPE_asD; Selected: 7
INFO Round: 1; Forward Stepwise: WOE_AMT_INCOME_TOTAL_asC; Selected: 7
INFO Round: 1; Forward Stepwise: WOE_FLAG_WORK_PHONE_asC; Selected: 7
INFO Round: 1; Forward Stepwise: WOE_FLAG_OWN_CAR_asD; Selected: 7
INFO Round: 1; Forward Stepwise: WOE_FLAG_PHONE_asC; Selected: 7
INFO Round: 1; Forward Stepwise: WOE_DAYS_BIRTH_asC; Selected: 7
INFO Round: 1; Forward Stepwise: WOE_CNT_CHILDREN_asC; Selected: 7
INFO Round: 1; Forward Stepwise: WOE_FLAG_EMAIL_asC; Selected: 7
INFO Round: 2; Forward Stepwise: WOE_NAME_INCOME_TYPE_asD; Selected: 7
INFO Round: 2; Forward Stepwise: WOE_OCCUPATION_TYPE_asD; Selected: 7
INFO Round: 2; Forward Stepwise: WOE_AMT_INCOME_TOTAL_asC; Selected: 7
INFO Round: 2; Forward Stepwise: WOE_FLAG_WORK_PHONE_asC; Selected: 7
INFO Round: 2; Forward Stepwise: WOE_FLAG_OWN_CAR_asD; Selected: 7
INFO Round: 2; Forward Stepwise: WOE_FLAG_PHONE_asC; Selected: 7
INFO Round: 2; Forward Stepwise: WOE_DAYS_BIRTH_asC; Selected: 7
INFO Round: 2; Forward Stepwise: WOE_CNT_CHILDREN_asC; Selected: 7
INFO Round: 2; Forward Stepwise: WOE_FLAG_EMAIL_asC; Selected: 7
INFO Round: 3; Forward Stepwise: WOE_NAME_INCOME_TYPE_asD; Selected: 7
INFO Round: 3; Forward Stepwise: WOE_OCCUPATION_TYPE_asD; Selected: 7
INFO Round: 3; Forward Stepwise: WOE_AMT_INCOME_TOTAL_asC; Selected: 7
INFO Round: 3; Forward Stepwise: WOE_FLAG_WORK_PHONE_asC; Selected: 7
INFO Round: 3; Forward Stepwise: WOE_FLAG_OWN_CAR_asD; Selected: 7
INFO Round: 3; Forward Stepwise: WOE_FLAG_PHONE_asC; Selected: 7
INFO Round: 3; Forward Stepwise: WOE_DAYS_BIRTH_asC; Selected: 7
INFO Round: 3; Forward Stepwise: WOE_CNT_CHILDREN_asC; Selected: 7
INFO Round: 3; Forward Stepwise: WOE_FLAG_EMAIL_asC; Selected: 7
INFO Model variables: ['Intercept', 'WOE_FLAG_OWN_REALTY_asD', 'WOE_NAME_FAMILY_STATUS_asD', 'WOE_DAYS_EMPLOYED_asC', 'WOE_NAME_HOUSING_TYPE_asD', 'WOE_NAME_EDUCATION_TYPE_asD', 'WOE_CODE_GENDER_asD'] !
INFO Building model...
INFO Saving var_desc...
INFO Saving var_cross...
INFO Saving var_unique...
INFO Saving var_summary...
INFO Saving woe_corr...
INFO Saving train_stable_summary...
INFO Saving test_stable_summary...
INFO Saving train_var_stable_iv_ks...
INFO Saving train_var_stable_psi...
INFO Saving train_var_stable_quantile...
INFO Saving test_var_stable_iv_ks...
INFO Saving test_var_stable_psi...
INFO Saving test_var_stable_quantile...
INFO Saving var_stable_cross_psi...
INFO Saving var_detail...
Optimization terminated successfully.
Current function value: 0.091927
Iterations 8
INFO Saving var_draft... INFO Saving var_drop... INFO Prepare scorecard... INFO Adding scorecard_description to excel... INFO Converting train proba to score... INFO Converting woe to score... INFO Checking scorecard equation... INFO Saving train_var_corr... INFO Saving train vif... INFO Getting detail score... INFO Saving train results... INFO Getting total score... INFO Creating uncut info... INFO Drawing train_score_uncut_ks_graph... INFO Drawing train_score_uncut_count_chart1... INFO Drawing train_score_uncut_count_chart2... INFO Drawing train_score_uncut_count_chart3... INFO Drawing train_score_uncut_cap_chart1... INFO Drawing train_score_uncut_cap_chart2... INFO Drawing train_score_uncut_cap_chart3... INFO Creating cut info... INFO Drawing train_score_cut_ks_graph... INFO Drawing train_score_cut_rate_chart... INFO Drawing train_score_cut_count_chart1... INFO Drawing train_score_cut_count_chart2... INFO Drawing train_score_cut_count_chart3... INFO Drawing train_score_cut_cap_chart1... INFO Drawing train_score_cut_cap_chart2... INFO Drawing train_score_cut_cap_chart3... INFO Creating qcut info... INFO Drawing train_score_qcut_ks_graph... INFO Drawing train_score_qcut_rate_chart... INFO Drawing train_score_qcut_count_chart1... INFO Drawing train_score_qcut_count_chart2... INFO Drawing train_score_qcut_count_chart3... INFO Drawing train_score_qcut_cap_chart1... INFO Drawing train_score_qcut_cap_chart2... INFO Drawing train_score_qcut_cap_chart3... INFO Creating precision recall fscore... INFO Drawing train_classification_chart... INFO Drawing train_classification_chart... INFO Drawing roc... INFO Creating results summary... INFO Drawing ap... INFO Converting test proba to score... INFO Converting woe to score... INFO Checking scorecard equation... INFO Saving test_var_corr... INFO Saving test vif... INFO Getting detail score... INFO Saving test results... INFO Getting total score... INFO Creating uncut info... INFO Drawing test_score_uncut_ks_graph... INFO Drawing test_score_uncut_count_chart1... INFO Drawing test_score_uncut_count_chart2... INFO Drawing test_score_uncut_count_chart3... INFO Drawing test_score_uncut_cap_chart1... INFO Drawing test_score_uncut_cap_chart2... INFO Drawing test_score_uncut_cap_chart3... INFO Creating cut info... INFO Drawing test_score_cut_ks_graph... INFO Drawing test_score_cut_rate_chart... INFO Drawing test_score_cut_count_chart1... INFO Drawing test_score_cut_count_chart2... INFO Drawing test_score_cut_count_chart3... INFO Drawing test_score_cut_cap_chart1... INFO Drawing test_score_cut_cap_chart2... INFO Drawing test_score_cut_cap_chart3... INFO Creating qcut info... INFO Drawing test_score_qcut_ks_graph... INFO Drawing test_score_qcut_rate_chart... INFO Drawing test_score_qcut_count_chart1... INFO Drawing test_score_qcut_count_chart2... INFO Drawing test_score_qcut_count_chart3... INFO Drawing test_score_qcut_cap_chart1... INFO Drawing test_score_qcut_cap_chart2... INFO Drawing test_score_qcut_cap_chart3... INFO Creating precision recall fscore... INFO Drawing test_classification_chart... INFO Drawing test_classification_chart... INFO Drawing roc... INFO Creating results summary... INFO Drawing ap... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi... INFO Drawing cut_psi_chart... INFO Drawing cut_psi_chart... INFO Drawing qcut_psi_chart... INFO Drawing qcut_psi_chart... INFO Saving model score gap... INFO Creating pmml... INFO Adding score_pmml to excel... INFO Adding run_score_pmml_in_java to excel... INFO Creating sql... INFO Creating check unique sql... INFO Creating unique table sql... INFO Creating count sql... INFO Adding model_sql to excel... INFO Adding run_sql_in_dataframe to excel... INFO target | 3 | cv flow... INFO Prepare scorecard... INFO Converting train proba to score... INFO Saving train results... INFO Getting total score... INFO Creating uncut info... INFO Creating cut info... INFO Creating qcut info... INFO Creating precision recall fscore... INFO Creating results summary... INFO Converting test proba to score... INFO Saving test results... INFO Getting total score... INFO Creating uncut info... INFO Creating cut info... INFO Creating qcut info... INFO Creating precision recall fscore... INFO Creating results summary... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi... INFO Prepare scorecard... INFO Converting train proba to score... INFO Saving train results... INFO Getting total score... INFO Creating uncut info... INFO Creating cut info... INFO Creating qcut info... INFO Creating precision recall fscore... INFO Creating results summary... INFO Converting test proba to score... INFO Saving test results... INFO Getting total score... INFO Creating uncut info... INFO Creating cut info... INFO Creating qcut info... INFO Creating precision recall fscore... INFO Creating results summary... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi... INFO Prepare scorecard... INFO Converting train proba to score... INFO Saving train results... INFO Getting total score... INFO Creating uncut info... INFO Creating cut info... INFO Creating qcut info... INFO Creating precision recall fscore... INFO Creating results summary... INFO Converting test proba to score... INFO Saving test results... INFO Getting total score... INFO Creating uncut info... INFO Creating cut info... INFO Creating qcut info... INFO Creating precision recall fscore... INFO Creating results summary... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi... INFO Saving cross validate... INFO Drawing cv compare graph... INFO Drawing cv_compare_ks_chart... INFO Drawing cv_compare_auc_chart... INFO Drawing cv_compare_ap_chart... INFO Drawing cv_compare_logloss_chart... INFO Drawing cv_compare_r2_chart... INFO Drawing cv_compare_mse_chart... INFO Drawing cv_compare_mdp_chart... INFO Drawing cv_compare_psi_chart... INFO Drawing cv_compare_mpg_chart... INFO Saving model pkl... INFO Saving json... INFO Saving setting... INFO Adding runtime to excel... INFO Saving directory... INFO Saving... INFO Total: 19.18 s WARNING Find that recover_from_pkl is True, setting recover_pkl to /home/conda_env/模型分析demo/v1-scorecard-more/Data_v1-scorecard-more-开发-测试_data.pkl ! INFO Prepare kwargs... INFO Checking train_data... INFO Checking response... INFO Checking test_data... INFO Checking response... INFO Checking columns... INFO Checking dtypes... WARNING customized_groups: "CNT_FAM_MEMBERS", "FLAG_PHONE", "AMT_INCOME_TOTAL", "OCCUPATION_TYPE", "Age", "DAYS_BIRTH", "NAME_INCOME_TYPE", "FLAG_WORK_PHONE", "FLAG_EMAIL", "CNT_CHILDREN", "FLAG_OWN_CAR" not in data ! WARNING exclude_column: "weight" not in data ! WARNING Setting format_time_edge to True ! INFO Match train: NAME_HOUSING_TYPE_asD INFO Match train: DAYS_EMPLOYED_asC INFO Match train: FLAG_OWN_REALTY_asD INFO Match train: CODE_GENDER_asD INFO Match train: NAME_EDUCATION_TYPE_asD INFO Match train: NAME_FAMILY_STATUS_asD INFO Creating train data... INFO Creating train corr detail... INFO Creating (-inf, 1972-01-01] corr... INFO Creating (1972-01-01, 1984-01-01] corr... INFO Creating (1984-01-01, inf) corr... INFO Creating all corr... INFO Creating train cross table... INFO Creating train all desc tables... INFO Match test: NAME_HOUSING_TYPE_asD INFO Match test: DAYS_EMPLOYED_asC INFO Match test: FLAG_OWN_REALTY_asD INFO Match test: CODE_GENDER_asD INFO Match test: NAME_EDUCATION_TYPE_asD INFO Match test: NAME_FAMILY_STATUS_asD INFO Creating test data... INFO Creating test corr detail... INFO Creating (-inf, 1972-01-01] corr... INFO Creating (1972-01-01, 1984-01-01] corr... INFO Creating (1984-01-01, inf) corr... INFO Creating all corr... INFO Creating test cross table... INFO Creating test all desc tables... INFO Match train test: NAME_FAMILY_STATUS_asD INFO Match train test: NAME_EDUCATION_TYPE_asD INFO Match train test: DAYS_EMPLOYED_asC INFO Match train test: CODE_GENDER_asD INFO Match train test: NAME_HOUSING_TYPE_asD INFO Match train test: FLAG_OWN_REALTY_asD INFO Saving info... INFO Saving desc... INFO Saving cross... INFO Saving unique... INFO Saving summary... INFO Saving corr summary... INFO Saving stable_summary... INFO Saving train_stable_iv_ks... INFO Saving train_stable_psi... INFO Saving train_stable_quantile... INFO Saving test_stable_iv_ks... INFO Saving test_stable_psi... INFO Saving test_stable_quantile... INFO Saving stable_cross_psi... INFO Saving detail... INFO Saving draft... INFO Saving json... INFO Saving setting... INFO Adding runtime to excel... INFO Saving directory... INFO Saving... INFO Total: 5.96 s WARNING Find that recover_from_pkl is True, setting recover_pkl to /home/conda_env/模型分析demo/v1-scorecard-more/Model_v1-scorecard-more-开发-测试_model.pkl ! WARNING Find that recover_from_pkl is True, setting filter_variable to False ! INFO Prepare kwargs... WARNING Using response as split_col_name ! INFO Checking train_data... INFO Checking response... INFO Checking test_data... INFO Checking response... INFO Checking columns... INFO Checking dtypes... WARNING Find that filter_variable is False, setting zero_imp_filter to False ! WARNING Find that filter_variable is False, setting enable_stepwise to False ! WARNING Find that filter_variable is False, setting enable_corr_filter to False ! WARNING Find that filter_variable is False, setting limit_var_num to inf ! INFO Model linear flow... WARNING Find that convert_score is True & save_score_pmml is True, setting save_model_pmml to False ! INFO Model variables: ['Intercept', 'WOE_FLAG_OWN_REALTY_asD', 'WOE_NAME_FAMILY_STATUS_asD', 'WOE_DAYS_EMPLOYED_asC', 'WOE_NAME_HOUSING_TYPE_asD', 'WOE_NAME_EDUCATION_TYPE_asD', 'WOE_CODE_GENDER_asD'] ! INFO Saving var_desc... INFO Saving var_cross... INFO Saving var_unique... INFO Saving var_summary... INFO Saving woe_corr... INFO Saving train_stable_summary... INFO Saving test_stable_summary... INFO Saving train_var_stable_iv_ks... INFO Saving train_var_stable_psi... INFO Saving train_var_stable_quantile... INFO Saving test_var_stable_iv_ks... INFO Saving test_var_stable_psi... INFO Saving test_var_stable_quantile... INFO Saving var_stable_cross_psi... INFO Saving var_detail... INFO Saving var_draft... INFO Saving var_drop... INFO Prepare scorecard... INFO Adding scorecard_description to excel... INFO Converting train proba to score... INFO Converting woe to score... INFO Checking scorecard equation... INFO Saving train_var_corr... INFO Saving train vif... INFO Getting detail score... INFO Saving train results... INFO Getting total score... INFO Creating uncut info... INFO Drawing train_score_uncut_ks_graph... INFO Drawing train_score_uncut_count_chart1... INFO Drawing train_score_uncut_count_chart2... INFO Drawing train_score_uncut_count_chart3... INFO Drawing train_score_uncut_cap_chart1... INFO Drawing train_score_uncut_cap_chart2... INFO Drawing train_score_uncut_cap_chart3... INFO Creating cut info... INFO Drawing train_score_cut_ks_graph... INFO Drawing train_score_cut_rate_chart... INFO Drawing train_score_cut_count_chart1... INFO Drawing train_score_cut_count_chart2... INFO Drawing train_score_cut_count_chart3... INFO Drawing train_score_cut_cap_chart1... INFO Drawing train_score_cut_cap_chart2... INFO Drawing train_score_cut_cap_chart3... INFO Creating qcut info... INFO Drawing train_score_qcut_ks_graph... INFO Drawing train_score_qcut_rate_chart... INFO Drawing train_score_qcut_count_chart1... INFO Drawing train_score_qcut_count_chart2... INFO Drawing train_score_qcut_count_chart3... INFO Drawing train_score_qcut_cap_chart1... INFO Drawing train_score_qcut_cap_chart2... INFO Drawing train_score_qcut_cap_chart3... INFO Creating precision recall fscore... INFO Drawing train_classification_chart... INFO Drawing train_classification_chart... INFO Drawing roc... INFO Creating results summary... INFO Drawing ap... INFO Converting test proba to score... INFO Converting woe to score... INFO Checking scorecard equation... INFO Saving test_var_corr... INFO Saving test vif... INFO Getting detail score... INFO Saving test results... INFO Getting total score... INFO Creating uncut info... INFO Drawing test_score_uncut_ks_graph... INFO Drawing test_score_uncut_count_chart1... INFO Drawing test_score_uncut_count_chart2... INFO Drawing test_score_uncut_count_chart3... INFO Drawing test_score_uncut_cap_chart1... INFO Drawing test_score_uncut_cap_chart2... INFO Drawing test_score_uncut_cap_chart3... INFO Creating cut info... INFO Drawing test_score_cut_ks_graph... INFO Drawing test_score_cut_rate_chart... INFO Drawing test_score_cut_count_chart1... INFO Drawing test_score_cut_count_chart2... INFO Drawing test_score_cut_count_chart3... INFO Drawing test_score_cut_cap_chart1... INFO Drawing test_score_cut_cap_chart2... INFO Drawing test_score_cut_cap_chart3... INFO Creating qcut info... INFO Drawing test_score_qcut_ks_graph... INFO Drawing test_score_qcut_rate_chart... INFO Drawing test_score_qcut_count_chart1... INFO Drawing test_score_qcut_count_chart2... INFO Drawing test_score_qcut_count_chart3... INFO Drawing test_score_qcut_cap_chart1... INFO Drawing test_score_qcut_cap_chart2... INFO Drawing test_score_qcut_cap_chart3... INFO Creating precision recall fscore... INFO Drawing test_classification_chart... INFO Drawing test_classification_chart... INFO Drawing roc... INFO Creating results summary... INFO Drawing ap... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi... INFO Drawing cut_psi_chart... INFO Drawing cut_psi_chart... INFO Drawing qcut_psi_chart... INFO Drawing qcut_psi_chart... INFO Saving model score gap... INFO Creating pmml... INFO Adding score_pmml to excel... INFO Adding run_score_pmml_in_java to excel... INFO Creating sql... INFO Creating check unique sql... INFO Creating unique table sql... INFO Creating count sql... INFO Adding model_sql to excel... INFO Adding run_sql_in_dataframe to excel... INFO target | 3 | cv flow... INFO Prepare scorecard... INFO Converting train proba to score... INFO Saving train results... INFO Getting total score... INFO Creating uncut info... INFO Creating cut info... INFO Creating qcut info... INFO Creating precision recall fscore... INFO Creating results summary... INFO Converting test proba to score... INFO Saving test results... INFO Getting total score... INFO Creating uncut info... INFO Creating cut info... INFO Creating qcut info... INFO Creating precision recall fscore... INFO Creating results summary... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi... INFO Prepare scorecard... INFO Converting train proba to score... INFO Saving train results... INFO Getting total score... INFO Creating uncut info... INFO Creating cut info... INFO Creating qcut info... INFO Creating precision recall fscore... INFO Creating results summary... INFO Converting test proba to score... INFO Saving test results... INFO Getting total score... INFO Creating uncut info... INFO Creating cut info... INFO Creating qcut info... INFO Creating precision recall fscore... INFO Creating results summary... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi... INFO Prepare scorecard... INFO Converting train proba to score... INFO Saving train results... INFO Getting total score... INFO Creating uncut info... INFO Creating cut info... INFO Creating qcut info... INFO Creating precision recall fscore... INFO Creating results summary... INFO Converting test proba to score... INFO Saving test results... INFO Getting total score... INFO Creating uncut info... INFO Creating cut info... INFO Creating qcut info... INFO Creating precision recall fscore... INFO Creating results summary... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi... INFO Saving cross validate... INFO Drawing cv compare graph... INFO Drawing cv_compare_ks_chart... INFO Drawing cv_compare_auc_chart... INFO Drawing cv_compare_ap_chart... INFO Drawing cv_compare_logloss_chart... INFO Drawing cv_compare_r2_chart... INFO Drawing cv_compare_mse_chart... INFO Drawing cv_compare_mdp_chart... INFO Drawing cv_compare_psi_chart... INFO Drawing cv_compare_mpg_chart... INFO Saving model pkl... INFO Saving json... INFO Saving setting... INFO Adding runtime to excel... INFO Saving directory... INFO Saving... INFO Total: 16.18 s INFO Total: 66.07 s
at.Analysis.model_flow(
df_train, "./模型分析demo/v1-lgb-more/v1-lgb-more.xlsx",
test_data=[df_test, df_data], train_name="开发", test_names=["测试", "全量"],
# 目标定义 & 切片定义
response="target", split_col_name="birth_year", use_train_time=True,
exclude_column=["ID", "weight"], # 排除权重
add_info="ID", # 主键添加
# 指定使用训练的class并不进行打分转化
model_type_path="lightgbm.sklearn.LGBMClassifier",
convert_score=False,
)
INFO Prepare kwargs...
WARNING Setting format_time_edge to True !
INFO Checking train_data...
INFO Checking response...
INFO Checking test_data...
INFO Checking response...
INFO Checking columns...
INFO Checking dtypes...
WARNING Setting allow_nan to True by lightgbm.sklearn !
WARNING Setting allow_discrete to True by lightgbm.sklearn !
WARNING Pre: WOE_ not found, change pre to "" !
WARNING Unable to find nan data, setting allow_nan to False !
WARNING Find that allow_nan is True or allow_discrete is True, setting enable_stepwise to False !
WARNING Find that allow_nan is True or allow_discrete is True, setting enable_corr_filter to False !
WARNING Find that convert_score is False, setting keep_raw_score to False !
WARNING Find that convert_score is False, setting save_score_desc to False !
WARNING Find that convert_score is False, setting save_score_pmml to False !
INFO Model optional flow...
WARNING Check /home/conda_env/模型分析demo/v1-lgb-more/Data_v1-lgb-more-开发-测试.xlsx exists !
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'max_depth': 3, 'n_estimators': 30, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 9, 10, 13, 14, 15, 16, 17]}
INFO Using variables: 18...
INFO Training LGBMClassifier...
INFO Filtering importance by LGBMClassifier ...
INFO Total: 18...
INFO Selected: 16...
INFO Model variables: ['AMT_INCOME_TOTAL', 'Age', 'CNT_CHILDREN', 'CNT_FAM_MEMBERS', 'CODE_GENDER', 'DAYS_BIRTH', 'DAYS_EMPLOYED', 'FLAG_OWN_CAR', 'FLAG_OWN_REALTY', 'FLAG_PHONE', 'FLAG_WORK_PHONE', 'NAME_EDUCATION_TYPE', 'NAME_FAMILY_STATUS', 'NAME_HOUSING_TYPE', 'NAME_INCOME_TYPE', 'OCCUPATION_TYPE'] !
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO [FIT_TRANSFORM] ['AMT_INCOME_TOTAL']: 0.000113 secs
INFO [FIT_TRANSFORM] ['Age']: 7.9e-05 secs
INFO [FIT_TRANSFORM] ['CNT_CHILDREN']: 9.4e-05 secs
INFO [FIT_TRANSFORM] ['CNT_FAM_MEMBERS']: 7.9e-05 secs
INFO [FIT_TRANSFORM] ['CODE_GENDER']: 0.02006 secs
INFO [FIT_TRANSFORM] ['DAYS_BIRTH']: 0.000153 secs
INFO [FIT_TRANSFORM] ['DAYS_EMPLOYED']: 6.3e-05 secs
INFO [FIT_TRANSFORM] ['FLAG_OWN_CAR']: 0.01895 secs
INFO [FIT_TRANSFORM] ['FLAG_OWN_REALTY']: 0.021502 secs
INFO [FIT_TRANSFORM] ['FLAG_PHONE']: 0.001273 secs
INFO [FIT_TRANSFORM] ['FLAG_WORK_PHONE']: 7e-05 secs
INFO [FIT_TRANSFORM] ['NAME_EDUCATION_TYPE']: 0.019162 secs
INFO [FIT_TRANSFORM] ['NAME_FAMILY_STATUS']: 0.02098 secs
INFO [FIT_TRANSFORM] ['NAME_HOUSING_TYPE']: 0.02084 secs
INFO [FIT_TRANSFORM] ['NAME_INCOME_TYPE']: 0.020211 secs
INFO [FIT_TRANSFORM] ['OCCUPATION_TYPE']: 0.020722 secs
INFO [TRANSFORM] ['AMT_INCOME_TOTAL']: 0.000156 secs
INFO [TRANSFORM] ['Age']: 9.2e-05 secs
INFO [TRANSFORM] ['CNT_CHILDREN']: 7.4e-05 secs
INFO [TRANSFORM] ['CNT_FAM_MEMBERS']: 0.00014 secs
INFO [TRANSFORM] ['CODE_GENDER']: 0.011554 secs
INFO [TRANSFORM] ['DAYS_BIRTH']: 0.000293 secs
INFO [TRANSFORM] ['DAYS_EMPLOYED']: 0.000162 secs
INFO [TRANSFORM] ['FLAG_OWN_CAR']: 0.011333 secs
INFO [TRANSFORM] ['FLAG_OWN_REALTY']: 0.011454 secs
INFO [TRANSFORM] ['FLAG_PHONE']: 0.000311 secs
INFO [TRANSFORM] ['FLAG_WORK_PHONE']: 8.5e-05 secs
INFO [TRANSFORM] ['NAME_EDUCATION_TYPE']: 0.012984 secs
INFO [TRANSFORM] ['NAME_FAMILY_STATUS']: 0.011524 secs
INFO [TRANSFORM] ['NAME_HOUSING_TYPE']: 0.012529 secs
INFO [TRANSFORM] ['NAME_INCOME_TYPE']: 0.012884 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE']: 0.015233 secs
INFO [TRANSFORM] ['AMT_INCOME_TOTAL']: 0.000126 secs
INFO [TRANSFORM] ['Age']: 6.3e-05 secs
INFO [TRANSFORM] ['CNT_CHILDREN']: 6e-05 secs
INFO [TRANSFORM] ['CNT_FAM_MEMBERS']: 0.000143 secs
INFO [TRANSFORM] ['CODE_GENDER']: 0.004926 secs
INFO [TRANSFORM] ['DAYS_BIRTH']: 0.000122 secs
INFO [TRANSFORM] ['DAYS_EMPLOYED']: 6.8e-05 secs
INFO [TRANSFORM] ['FLAG_OWN_CAR']: 0.005331 secs
INFO [TRANSFORM] ['FLAG_OWN_REALTY']: 0.005001 secs
INFO [TRANSFORM] ['FLAG_PHONE']: 0.000111 secs
INFO [TRANSFORM] ['FLAG_WORK_PHONE']: 6.8e-05 secs
INFO [TRANSFORM] ['NAME_EDUCATION_TYPE']: 0.005402 secs
INFO [TRANSFORM] ['NAME_FAMILY_STATUS']: 0.006028 secs
INFO [TRANSFORM] ['NAME_HOUSING_TYPE']: 0.00524 secs
INFO [TRANSFORM] ['NAME_INCOME_TYPE']: 0.005387 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE']: 0.005733 secs
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Drawing train_score_uncut_ks_graph...
INFO Drawing train_score_uncut_count_chart1...
INFO Drawing train_score_uncut_count_chart2...
INFO Drawing train_score_uncut_count_chart3...
INFO Drawing train_score_uncut_cap_chart1...
INFO Drawing train_score_uncut_cap_chart2...
INFO Drawing train_score_uncut_cap_chart3...
INFO Creating cut info...
INFO Drawing train_score_cut_ks_graph...
INFO Drawing train_score_cut_rate_chart...
INFO Drawing train_score_cut_count_chart1...
INFO Drawing train_score_cut_count_chart2...
INFO Drawing train_score_cut_count_chart3...
INFO Drawing train_score_cut_cap_chart1...
INFO Drawing train_score_cut_cap_chart2...
INFO Drawing train_score_cut_cap_chart3...
INFO Creating qcut info...
INFO Drawing train_score_qcut_ks_graph...
INFO Drawing train_score_qcut_rate_chart...
INFO Drawing train_score_qcut_count_chart1...
INFO Drawing train_score_qcut_count_chart2...
INFO Drawing train_score_qcut_count_chart3...
INFO Drawing train_score_qcut_cap_chart1...
INFO Drawing train_score_qcut_cap_chart2...
INFO Drawing train_score_qcut_cap_chart3...
INFO Creating precision recall fscore...
INFO Drawing train_classification_chart...
INFO Drawing train_classification_chart...
INFO Drawing roc...
INFO Creating results summary...
INFO Drawing ap...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Drawing test_score_uncut_ks_graph...
INFO Drawing test_score_uncut_count_chart1...
INFO Drawing test_score_uncut_count_chart2...
INFO Drawing test_score_uncut_count_chart3...
INFO Drawing test_score_uncut_cap_chart1...
INFO Drawing test_score_uncut_cap_chart2...
INFO Drawing test_score_uncut_cap_chart3...
INFO Creating cut info...
INFO Drawing test_score_cut_ks_graph...
INFO Drawing test_score_cut_rate_chart...
INFO Drawing test_score_cut_count_chart1...
INFO Drawing test_score_cut_count_chart2...
INFO Drawing test_score_cut_count_chart3...
INFO Drawing test_score_cut_cap_chart1...
INFO Drawing test_score_cut_cap_chart2...
INFO Drawing test_score_cut_cap_chart3...
INFO Creating qcut info...
INFO Drawing test_score_qcut_ks_graph...
INFO Drawing test_score_qcut_rate_chart...
INFO Drawing test_score_qcut_count_chart1...
INFO Drawing test_score_qcut_count_chart2...
INFO Drawing test_score_qcut_count_chart3...
INFO Drawing test_score_qcut_cap_chart1...
INFO Drawing test_score_qcut_cap_chart2...
INFO Drawing test_score_qcut_cap_chart3...
INFO Creating precision recall fscore...
INFO Drawing test_classification_chart...
INFO Drawing test_classification_chart...
INFO Drawing roc...
INFO Creating results summary...
INFO Drawing ap...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Drawing cut_psi_chart...
INFO Drawing cut_psi_chart...
INFO Drawing qcut_psi_chart...
INFO Drawing qcut_psi_chart...
INFO Total: 10.96 s
INFO Saving var_drop...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO Drawing cv compare graph...
INFO Drawing cv_compare_ks_chart...
INFO Drawing cv_compare_auc_chart...
INFO Drawing cv_compare_ap_chart...
INFO Drawing cv_compare_logloss_chart...
INFO Drawing cv_compare_r2_chart...
INFO Drawing cv_compare_mse_chart...
INFO Drawing cv_compare_mdp_chart...
INFO Drawing cv_compare_psi_chart...
INFO Drawing cv_compare_mpg_chart...
INFO Saving model pkl...
INFO Saving model pmml...
/home/conda_env/miniconda3/lib/python3.9/subprocess.py:941: RuntimeWarning: line buffering (buffering=1) isn't supported in binary mode, the default buffer size will be used
self.stdout = io.open(c2pread, 'rb', bufsize)
/home/conda_env/miniconda3/lib/python3.9/subprocess.py:946: RuntimeWarning: line buffering (buffering=1) isn't supported in binary mode, the default buffer size will be used
self.stderr = io.open(errread, 'rb', bufsize)
INFO Adding model_pmml to excel...
INFO Adding run_model_pmml_in_java to excel...
INFO Saving json...
INFO Saving setting...
INFO Adding runtime to excel...
INFO Saving directory...
INFO Saving...
INFO Total: 17.99 s
WARNING Find that recover_from_pkl is True, setting recover_pkl to /home/conda_env/模型分析demo/v1-lgb-more/Model_v1-lgb-more-开发-测试_model.pkl !
WARNING Find that recover_from_pkl is True, setting filter_variable to False !
INFO Prepare kwargs...
WARNING Setting format_time_edge to True !
INFO Checking train_data...
INFO Checking response...
INFO Checking test_data...
INFO Checking response...
INFO Checking columns...
INFO Checking dtypes...
WARNING Setting allow_nan to True by lightgbm.sklearn !
WARNING Unable to find nan data, setting allow_nan to False !
WARNING Find that filter_variable is False, setting zero_imp_filter to False !
WARNING Find that filter_variable is False, setting enable_stepwise to False !
WARNING Find that filter_variable is False, setting enable_corr_filter to False !
WARNING Find that filter_variable is False, setting limit_var_num to inf !
INFO Model optional flow...
WARNING Check /home/conda_env/模型分析demo/v1-lgb-more/Data_v1-lgb-more-开发-全量.xlsx exists !
INFO Model variables: ['AMT_INCOME_TOTAL', 'Age', 'CNT_CHILDREN', 'CNT_FAM_MEMBERS', 'CODE_GENDER', 'DAYS_BIRTH', 'DAYS_EMPLOYED', 'FLAG_OWN_CAR', 'FLAG_OWN_REALTY', 'FLAG_PHONE', 'FLAG_WORK_PHONE', 'NAME_EDUCATION_TYPE', 'NAME_FAMILY_STATUS', 'NAME_HOUSING_TYPE', 'NAME_INCOME_TYPE', 'OCCUPATION_TYPE'] !
INFO [TRANSFORM] ['AMT_INCOME_TOTAL']: 0.000173 secs
INFO [TRANSFORM] ['Age']: 0.000145 secs
INFO [TRANSFORM] ['CNT_CHILDREN']: 7e-05 secs
INFO [TRANSFORM] ['CNT_FAM_MEMBERS']: 7.8e-05 secs
INFO [TRANSFORM] ['CODE_GENDER']: 0.01188 secs
INFO [TRANSFORM] ['DAYS_BIRTH']: 0.000169 secs
INFO [TRANSFORM] ['DAYS_EMPLOYED']: 8.3e-05 secs
INFO [TRANSFORM] ['FLAG_OWN_CAR']: 0.01168 secs
INFO [TRANSFORM] ['FLAG_OWN_REALTY']: 0.011731 secs
INFO [TRANSFORM] ['FLAG_PHONE']: 0.001943 secs
INFO [TRANSFORM] ['FLAG_WORK_PHONE']: 0.00012 secs
INFO [TRANSFORM] ['NAME_EDUCATION_TYPE']: 0.011784 secs
INFO [TRANSFORM] ['NAME_FAMILY_STATUS']: 0.01232 secs
INFO [TRANSFORM] ['NAME_HOUSING_TYPE']: 0.012263 secs
INFO [TRANSFORM] ['NAME_INCOME_TYPE']: 0.012315 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE']: 0.014607 secs
INFO [TRANSFORM] ['AMT_INCOME_TOTAL']: 0.000143 secs
INFO [TRANSFORM] ['Age']: 7.8e-05 secs
INFO [TRANSFORM] ['CNT_CHILDREN']: 9.6e-05 secs
INFO [TRANSFORM] ['CNT_FAM_MEMBERS']: 8.9e-05 secs
INFO [TRANSFORM] ['CODE_GENDER']: 0.016006 secs
INFO [TRANSFORM] ['DAYS_BIRTH']: 0.000165 secs
INFO [TRANSFORM] ['DAYS_EMPLOYED']: 8.3e-05 secs
INFO [TRANSFORM] ['FLAG_OWN_CAR']: 0.016089 secs
INFO [TRANSFORM] ['FLAG_OWN_REALTY']: 0.01686 secs
INFO [TRANSFORM] ['FLAG_PHONE']: 0.000136 secs
INFO [TRANSFORM] ['FLAG_WORK_PHONE']: 7.3e-05 secs
INFO [TRANSFORM] ['NAME_EDUCATION_TYPE']: 0.017034 secs
INFO [TRANSFORM] ['NAME_FAMILY_STATUS']: 0.016727 secs
INFO [TRANSFORM] ['NAME_HOUSING_TYPE']: 0.017639 secs
INFO [TRANSFORM] ['NAME_INCOME_TYPE']: 0.018483 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE']: 0.019547 secs
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Drawing train_score_uncut_ks_graph...
INFO Drawing train_score_uncut_count_chart1...
INFO Drawing train_score_uncut_count_chart2...
INFO Drawing train_score_uncut_count_chart3...
INFO Drawing train_score_uncut_cap_chart1...
INFO Drawing train_score_uncut_cap_chart2...
INFO Drawing train_score_uncut_cap_chart3...
INFO Creating cut info...
INFO Drawing train_score_cut_ks_graph...
INFO Drawing train_score_cut_rate_chart...
INFO Drawing train_score_cut_count_chart1...
INFO Drawing train_score_cut_count_chart2...
INFO Drawing train_score_cut_count_chart3...
INFO Drawing train_score_cut_cap_chart1...
INFO Drawing train_score_cut_cap_chart2...
INFO Drawing train_score_cut_cap_chart3...
INFO Creating qcut info...
INFO Drawing train_score_qcut_ks_graph...
INFO Drawing train_score_qcut_rate_chart...
INFO Drawing train_score_qcut_count_chart1...
INFO Drawing train_score_qcut_count_chart2...
INFO Drawing train_score_qcut_count_chart3...
INFO Drawing train_score_qcut_cap_chart1...
INFO Drawing train_score_qcut_cap_chart2...
INFO Drawing train_score_qcut_cap_chart3...
INFO Creating precision recall fscore...
INFO Drawing train_classification_chart...
INFO Drawing train_classification_chart...
INFO Drawing roc...
INFO Creating results summary...
INFO Drawing ap...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Drawing test_score_uncut_ks_graph...
INFO Drawing test_score_uncut_count_chart1...
INFO Drawing test_score_uncut_count_chart2...
INFO Drawing test_score_uncut_count_chart3...
INFO Drawing test_score_uncut_cap_chart1...
INFO Drawing test_score_uncut_cap_chart2...
INFO Drawing test_score_uncut_cap_chart3...
INFO Creating cut info...
INFO Drawing test_score_cut_ks_graph...
INFO Drawing test_score_cut_rate_chart...
INFO Drawing test_score_cut_count_chart1...
INFO Drawing test_score_cut_count_chart2...
INFO Drawing test_score_cut_count_chart3...
INFO Drawing test_score_cut_cap_chart1...
INFO Drawing test_score_cut_cap_chart2...
INFO Drawing test_score_cut_cap_chart3...
INFO Creating qcut info...
INFO Drawing test_score_qcut_ks_graph...
INFO Drawing test_score_qcut_rate_chart...
INFO Drawing test_score_qcut_count_chart1...
INFO Drawing test_score_qcut_count_chart2...
INFO Drawing test_score_qcut_count_chart3...
INFO Drawing test_score_qcut_cap_chart1...
INFO Drawing test_score_qcut_cap_chart2...
INFO Drawing test_score_qcut_cap_chart3...
INFO Creating precision recall fscore...
INFO Drawing test_classification_chart...
INFO Drawing test_classification_chart...
INFO Drawing roc...
INFO Creating results summary...
INFO Drawing ap...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Drawing cut_psi_chart...
INFO Drawing cut_psi_chart...
INFO Drawing qcut_psi_chart...
INFO Drawing qcut_psi_chart...
INFO Total: 11.29 s
INFO Saving var_drop...
INFO birth_year | 3 | cv flow...
INFO [TRANSFORM] ['AMT_INCOME_TOTAL']: 0.000173 secs
INFO [TRANSFORM] ['Age']: 0.000151 secs
INFO [TRANSFORM] ['CNT_CHILDREN']: 0.000123 secs
INFO [TRANSFORM] ['CNT_FAM_MEMBERS']: 7.8e-05 secs
INFO [TRANSFORM] ['CODE_GENDER']: 0.008309 secs
INFO [TRANSFORM] ['DAYS_BIRTH']: 0.000146 secs
INFO [TRANSFORM] ['DAYS_EMPLOYED']: 9.2e-05 secs
INFO [TRANSFORM] ['FLAG_OWN_CAR']: 0.007447 secs
INFO [TRANSFORM] ['FLAG_OWN_REALTY']: 0.008227 secs
INFO [TRANSFORM] ['FLAG_PHONE']: 9e-05 secs
INFO [TRANSFORM] ['FLAG_WORK_PHONE']: 5.8e-05 secs
INFO [TRANSFORM] ['NAME_EDUCATION_TYPE']: 0.008212 secs
INFO [TRANSFORM] ['NAME_FAMILY_STATUS']: 0.008383 secs
INFO [TRANSFORM] ['NAME_HOUSING_TYPE']: 0.011118 secs
INFO [TRANSFORM] ['NAME_INCOME_TYPE']: 0.010107 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE']: 0.010068 secs
INFO [TRANSFORM] ['AMT_INCOME_TOTAL']: 0.000141 secs
INFO [TRANSFORM] ['Age']: 0.00021 secs
INFO [TRANSFORM] ['CNT_CHILDREN']: 6.8e-05 secs
INFO [TRANSFORM] ['CNT_FAM_MEMBERS']: 8.4e-05 secs
INFO [TRANSFORM] ['CODE_GENDER']: 0.00394 secs
INFO [TRANSFORM] ['DAYS_BIRTH']: 8.1e-05 secs
INFO [TRANSFORM] ['DAYS_EMPLOYED']: 6.1e-05 secs
INFO [TRANSFORM] ['FLAG_OWN_CAR']: 0.0042 secs
INFO [TRANSFORM] ['FLAG_OWN_REALTY']: 0.004233 secs
INFO [TRANSFORM] ['FLAG_PHONE']: 0.000111 secs
INFO [TRANSFORM] ['FLAG_WORK_PHONE']: 0.000155 secs
INFO [TRANSFORM] ['NAME_EDUCATION_TYPE']: 0.004561 secs
INFO [TRANSFORM] ['NAME_FAMILY_STATUS']: 0.005336 secs
INFO [TRANSFORM] ['NAME_HOUSING_TYPE']: 0.004363 secs
INFO [TRANSFORM] ['NAME_INCOME_TYPE']: 0.004537 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE']: 0.004724 secs
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO [TRANSFORM] ['AMT_INCOME_TOTAL']: 0.000125 secs
INFO [TRANSFORM] ['Age']: 6.4e-05 secs
INFO [TRANSFORM] ['CNT_CHILDREN']: 8.4e-05 secs
INFO [TRANSFORM] ['CNT_FAM_MEMBERS']: 6.7e-05 secs
INFO [TRANSFORM] ['CODE_GENDER']: 0.007827 secs
INFO [TRANSFORM] ['DAYS_BIRTH']: 0.000136 secs
INFO [TRANSFORM] ['DAYS_EMPLOYED']: 6.5e-05 secs
INFO [TRANSFORM] ['FLAG_OWN_CAR']: 0.009865 secs
INFO [TRANSFORM] ['FLAG_OWN_REALTY']: 0.007965 secs
INFO [TRANSFORM] ['FLAG_PHONE']: 0.000157 secs
INFO [TRANSFORM] ['FLAG_WORK_PHONE']: 9.4e-05 secs
INFO [TRANSFORM] ['NAME_EDUCATION_TYPE']: 0.008337 secs
INFO [TRANSFORM] ['NAME_FAMILY_STATUS']: 0.008132 secs
INFO [TRANSFORM] ['NAME_HOUSING_TYPE']: 0.009601 secs
INFO [TRANSFORM] ['NAME_INCOME_TYPE']: 0.010023 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE']: 0.009893 secs
INFO [TRANSFORM] ['AMT_INCOME_TOTAL']: 0.000113 secs
INFO [TRANSFORM] ['Age']: 0.00012 secs
INFO [TRANSFORM] ['CNT_CHILDREN']: 6.5e-05 secs
INFO [TRANSFORM] ['CNT_FAM_MEMBERS']: 7.3e-05 secs
INFO [TRANSFORM] ['CODE_GENDER']: 0.004985 secs
INFO [TRANSFORM] ['DAYS_BIRTH']: 0.000177 secs
INFO [TRANSFORM] ['DAYS_EMPLOYED']: 8.3e-05 secs
INFO [TRANSFORM] ['FLAG_OWN_CAR']: 0.003944 secs
INFO [TRANSFORM] ['FLAG_OWN_REALTY']: 0.005893 secs
INFO [TRANSFORM] ['FLAG_PHONE']: 0.000143 secs
INFO [TRANSFORM] ['FLAG_WORK_PHONE']: 7.1e-05 secs
INFO [TRANSFORM] ['NAME_EDUCATION_TYPE']: 0.004077 secs
INFO [TRANSFORM] ['NAME_FAMILY_STATUS']: 0.006119 secs
INFO [TRANSFORM] ['NAME_HOUSING_TYPE']: 0.003912 secs
INFO [TRANSFORM] ['NAME_INCOME_TYPE']: 0.004759 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE']: 0.005056 secs
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO [TRANSFORM] ['AMT_INCOME_TOTAL']: 0.000131 secs
INFO [TRANSFORM] ['Age']: 0.00122 secs
INFO [TRANSFORM] ['CNT_CHILDREN']: 0.00027 secs
INFO [TRANSFORM] ['CNT_FAM_MEMBERS']: 9e-05 secs
INFO [TRANSFORM] ['CODE_GENDER']: 0.007708 secs
INFO [TRANSFORM] ['DAYS_BIRTH']: 0.000338 secs
INFO [TRANSFORM] ['DAYS_EMPLOYED']: 7.3e-05 secs
INFO [TRANSFORM] ['FLAG_OWN_CAR']: 0.00826 secs
INFO [TRANSFORM] ['FLAG_OWN_REALTY']: 0.008157 secs
INFO [TRANSFORM] ['FLAG_PHONE']: 0.000102 secs
INFO [TRANSFORM] ['FLAG_WORK_PHONE']: 9.1e-05 secs
INFO [TRANSFORM] ['NAME_EDUCATION_TYPE']: 0.008398 secs
INFO [TRANSFORM] ['NAME_FAMILY_STATUS']: 0.008383 secs
INFO [TRANSFORM] ['NAME_HOUSING_TYPE']: 0.008676 secs
INFO [TRANSFORM] ['NAME_INCOME_TYPE']: 0.008231 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE']: 0.0092 secs
INFO [TRANSFORM] ['AMT_INCOME_TOTAL']: 0.000106 secs
INFO [TRANSFORM] ['Age']: 6e-05 secs
INFO [TRANSFORM] ['CNT_CHILDREN']: 6.1e-05 secs
INFO [TRANSFORM] ['CNT_FAM_MEMBERS']: 0.000271 secs
INFO [TRANSFORM] ['CODE_GENDER']: 0.004261 secs
INFO [TRANSFORM] ['DAYS_BIRTH']: 6.7e-05 secs
INFO [TRANSFORM] ['DAYS_EMPLOYED']: 5.8e-05 secs
INFO [TRANSFORM] ['FLAG_OWN_CAR']: 0.003992 secs
INFO [TRANSFORM] ['FLAG_OWN_REALTY']: 0.003872 secs
INFO [TRANSFORM] ['FLAG_PHONE']: 0.000136 secs
INFO [TRANSFORM] ['FLAG_WORK_PHONE']: 8.7e-05 secs
INFO [TRANSFORM] ['NAME_EDUCATION_TYPE']: 0.004066 secs
INFO [TRANSFORM] ['NAME_FAMILY_STATUS']: 0.004324 secs
INFO [TRANSFORM] ['NAME_HOUSING_TYPE']: 0.004113 secs
INFO [TRANSFORM] ['NAME_INCOME_TYPE']: 0.004058 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE']: 0.005868 secs
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO Drawing cv compare graph...
INFO Drawing cv_compare_ks_chart...
INFO Drawing cv_compare_auc_chart...
INFO Drawing cv_compare_ap_chart...
INFO Drawing cv_compare_logloss_chart...
INFO Drawing cv_compare_r2_chart...
INFO Drawing cv_compare_mse_chart...
INFO Drawing cv_compare_mdp_chart...
INFO Drawing cv_compare_psi_chart...
INFO Drawing cv_compare_mpg_chart...
INFO Saving model pkl...
INFO Saving model pmml...
/home/conda_env/miniconda3/lib/python3.9/subprocess.py:941: RuntimeWarning: line buffering (buffering=1) isn't supported in binary mode, the default buffer size will be used
self.stdout = io.open(c2pread, 'rb', bufsize)
/home/conda_env/miniconda3/lib/python3.9/subprocess.py:946: RuntimeWarning: line buffering (buffering=1) isn't supported in binary mode, the default buffer size will be used
self.stderr = io.open(errread, 'rb', bufsize)
INFO Adding model_pmml to excel...
INFO Adding run_model_pmml_in_java to excel...
INFO Saving json...
INFO Saving setting...
INFO Adding runtime to excel...
INFO Saving directory...
INFO Saving...
INFO Total: 18.22 s
INFO Total: 37.91 s
at.Analysis.model_flow(
df_train, "./模型分析demo/v3-lgb/v3-lgb.xlsx", test_data=df_test,
# 目标定义 & 切片定义
response="target", split_col_name="birth_year", use_train_time=True,
exclude_column=["ID", "weight"], # 排除权重
add_info="ID", # 主键添加
# 指定使用训练的class并不进行打分转化
model_type_path="lightgbm.sklearn.LGBMClassifier",
convert_score=False,
model_filter_order=["corr", "rank"],
)
INFO Prepare kwargs...
WARNING Setting format_time_edge to True !
INFO Checking train_data...
INFO Checking response...
INFO Checking test_data...
INFO Checking response...
INFO Checking columns...
INFO Checking dtypes...
WARNING Setting allow_nan to True by lightgbm.sklearn !
WARNING Setting allow_discrete to True by lightgbm.sklearn !
WARNING Pre: WOE_ not found, change pre to "" !
WARNING Unable to find nan data, setting allow_nan to False !
WARNING Find that allow_nan is True or allow_discrete is True, setting enable_stepwise to False !
WARNING Find that allow_nan is True or allow_discrete is True, setting enable_corr_filter to False !
WARNING Find that convert_score is False, setting keep_raw_score to False !
WARNING Find that convert_score is False, setting save_score_desc to False !
WARNING Find that convert_score is False, setting save_score_pmml to False !
INFO Model optional flow...
WARNING Check /home/conda_env/模型分析demo/v3-lgb/Data_v3-lgb.xlsx exists !
INFO Model variables: ['AMT_INCOME_TOTAL', 'Age', 'CNT_CHILDREN', 'CNT_FAM_MEMBERS', 'CODE_GENDER', 'DAYS_BIRTH', 'DAYS_EMPLOYED', 'FLAG_EMAIL', 'FLAG_MOBIL', 'FLAG_OWN_CAR', 'FLAG_OWN_REALTY', 'FLAG_PHONE', 'FLAG_WORK_PHONE', 'NAME_EDUCATION_TYPE', 'NAME_FAMILY_STATUS', 'NAME_HOUSING_TYPE', 'NAME_INCOME_TYPE', 'OCCUPATION_TYPE'] !
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 9, 10, 13, 14, 15, 16, 17]}
INFO Using variables: 18...
INFO Training LGBMClassifier...
INFO [FIT_TRANSFORM] ['AMT_INCOME_TOTAL']: 0.001351 secs
INFO [FIT_TRANSFORM] ['Age']: 0.000128 secs
INFO [FIT_TRANSFORM] ['CNT_CHILDREN']: 6.7e-05 secs
INFO [FIT_TRANSFORM] ['CNT_FAM_MEMBERS']: 7.5e-05 secs
INFO [FIT_TRANSFORM] ['CODE_GENDER']: 0.01885 secs
INFO [FIT_TRANSFORM] ['DAYS_BIRTH']: 0.000139 secs
INFO [FIT_TRANSFORM] ['DAYS_EMPLOYED']: 0.00026 secs
INFO [FIT_TRANSFORM] ['FLAG_EMAIL']: 0.000103 secs
INFO [FIT_TRANSFORM] ['FLAG_MOBIL']: 0.000114 secs
INFO [FIT_TRANSFORM] ['FLAG_OWN_CAR']: 0.018752 secs
INFO [FIT_TRANSFORM] ['FLAG_OWN_REALTY']: 0.0207 secs
INFO [FIT_TRANSFORM] ['FLAG_PHONE']: 0.000142 secs
INFO [FIT_TRANSFORM] ['FLAG_WORK_PHONE']: 5.9e-05 secs
INFO [FIT_TRANSFORM] ['NAME_EDUCATION_TYPE']: 0.01953 secs
INFO [FIT_TRANSFORM] ['NAME_FAMILY_STATUS']: 0.019331 secs
INFO [FIT_TRANSFORM] ['NAME_HOUSING_TYPE']: 0.021695 secs
INFO [FIT_TRANSFORM] ['NAME_INCOME_TYPE']: 0.019271 secs
INFO [FIT_TRANSFORM] ['OCCUPATION_TYPE']: 0.017771 secs
INFO [TRANSFORM] ['AMT_INCOME_TOTAL']: 0.000148 secs
INFO [TRANSFORM] ['Age']: 0.000149 secs
INFO [TRANSFORM] ['CNT_CHILDREN']: 0.000102 secs
INFO [TRANSFORM] ['CNT_FAM_MEMBERS']: 0.000191 secs
INFO [TRANSFORM] ['CODE_GENDER']: 0.011311 secs
INFO [TRANSFORM] ['DAYS_BIRTH']: 0.000133 secs
INFO [TRANSFORM] ['DAYS_EMPLOYED']: 6.5e-05 secs
INFO [TRANSFORM] ['FLAG_EMAIL']: 0.000263 secs
INFO [TRANSFORM] ['FLAG_MOBIL']: 7.8e-05 secs
INFO [TRANSFORM] ['FLAG_OWN_CAR']: 0.011771 secs
INFO [TRANSFORM] ['FLAG_OWN_REALTY']: 0.012094 secs
INFO [TRANSFORM] ['FLAG_PHONE']: 0.000189 secs
INFO [TRANSFORM] ['FLAG_WORK_PHONE']: 0.000202 secs
INFO [TRANSFORM] ['NAME_EDUCATION_TYPE']: 0.012779 secs
INFO [TRANSFORM] ['NAME_FAMILY_STATUS']: 0.013942 secs
INFO [TRANSFORM] ['NAME_HOUSING_TYPE']: 0.012566 secs
INFO [TRANSFORM] ['NAME_INCOME_TYPE']: 0.013192 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE']: 0.015614 secs
INFO [TRANSFORM] ['AMT_INCOME_TOTAL']: 0.000122 secs
INFO [TRANSFORM] ['Age']: 8.1e-05 secs
INFO [TRANSFORM] ['CNT_CHILDREN']: 6.1e-05 secs
INFO [TRANSFORM] ['CNT_FAM_MEMBERS']: 7.3e-05 secs
INFO [TRANSFORM] ['CODE_GENDER']: 0.005252 secs
INFO [TRANSFORM] ['DAYS_BIRTH']: 8.5e-05 secs
INFO [TRANSFORM] ['DAYS_EMPLOYED']: 5.9e-05 secs
INFO [TRANSFORM] ['FLAG_EMAIL']: 0.00018 secs
INFO [TRANSFORM] ['FLAG_MOBIL']: 0.000114 secs
INFO [TRANSFORM] ['FLAG_OWN_CAR']: 0.005164 secs
INFO [TRANSFORM] ['FLAG_OWN_REALTY']: 0.005112 secs
INFO [TRANSFORM] ['FLAG_PHONE']: 0.000122 secs
INFO [TRANSFORM] ['FLAG_WORK_PHONE']: 0.000346 secs
INFO [TRANSFORM] ['NAME_EDUCATION_TYPE']: 0.005481 secs
INFO [TRANSFORM] ['NAME_FAMILY_STATUS']: 0.006932 secs
INFO [TRANSFORM] ['NAME_HOUSING_TYPE']: 0.005272 secs
INFO [TRANSFORM] ['NAME_INCOME_TYPE']: 0.005491 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE']: 0.005691 secs
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Drawing train_score_uncut_ks_graph...
INFO Drawing train_score_uncut_count_chart1...
INFO Drawing train_score_uncut_count_chart2...
INFO Drawing train_score_uncut_count_chart3...
INFO Drawing train_score_uncut_cap_chart1...
INFO Drawing train_score_uncut_cap_chart2...
INFO Drawing train_score_uncut_cap_chart3...
INFO Creating cut info...
INFO Drawing train_score_cut_ks_graph...
INFO Drawing train_score_cut_rate_chart...
INFO Drawing train_score_cut_count_chart1...
INFO Drawing train_score_cut_count_chart2...
INFO Drawing train_score_cut_count_chart3...
INFO Drawing train_score_cut_cap_chart1...
INFO Drawing train_score_cut_cap_chart2...
INFO Drawing train_score_cut_cap_chart3...
INFO Creating qcut info...
INFO Drawing train_score_qcut_ks_graph...
INFO Drawing train_score_qcut_rate_chart...
INFO Drawing train_score_qcut_count_chart1...
INFO Drawing train_score_qcut_count_chart2...
INFO Drawing train_score_qcut_count_chart3...
INFO Drawing train_score_qcut_cap_chart1...
INFO Drawing train_score_qcut_cap_chart2...
INFO Drawing train_score_qcut_cap_chart3...
INFO Creating precision recall fscore...
INFO Drawing train_classification_chart...
INFO Drawing train_classification_chart...
INFO Drawing roc...
INFO Creating results summary...
INFO Drawing ap...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Drawing test_score_uncut_ks_graph...
INFO Drawing test_score_uncut_count_chart1...
INFO Drawing test_score_uncut_count_chart2...
INFO Drawing test_score_uncut_count_chart3...
INFO Drawing test_score_uncut_cap_chart1...
INFO Drawing test_score_uncut_cap_chart2...
INFO Drawing test_score_uncut_cap_chart3...
INFO Creating cut info...
INFO Drawing test_score_cut_ks_graph...
INFO Drawing test_score_cut_rate_chart...
INFO Drawing test_score_cut_count_chart1...
INFO Drawing test_score_cut_count_chart2...
INFO Drawing test_score_cut_count_chart3...
INFO Drawing test_score_cut_cap_chart1...
INFO Drawing test_score_cut_cap_chart2...
INFO Drawing test_score_cut_cap_chart3...
INFO Creating qcut info...
INFO Drawing test_score_qcut_ks_graph...
INFO Drawing test_score_qcut_rate_chart...
INFO Drawing test_score_qcut_count_chart1...
INFO Drawing test_score_qcut_count_chart2...
INFO Drawing test_score_qcut_count_chart3...
INFO Drawing test_score_qcut_cap_chart1...
INFO Drawing test_score_qcut_cap_chart2...
INFO Drawing test_score_qcut_cap_chart3...
INFO Creating precision recall fscore...
INFO Drawing test_classification_chart...
INFO Drawing test_classification_chart...
INFO Drawing roc...
INFO Creating results summary...
INFO Drawing ap...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Drawing cut_psi_chart...
INFO Drawing cut_psi_chart...
INFO Drawing qcut_psi_chart...
INFO Drawing qcut_psi_chart...
INFO Total: 10.89 s
INFO Saving var_drop...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 9, 10, 13, 14, 15, 16, 17]}
INFO Using variables: 18...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 9, 10, 13, 14, 15, 16, 17]}
INFO Using variables: 18...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 9, 10, 13, 14, 15, 16, 17]}
INFO Using variables: 18...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO Drawing cv compare graph...
INFO Drawing cv_compare_ks_chart...
INFO Drawing cv_compare_auc_chart...
INFO Drawing cv_compare_ap_chart...
INFO Drawing cv_compare_logloss_chart...
INFO Drawing cv_compare_r2_chart...
INFO Drawing cv_compare_mse_chart...
INFO Drawing cv_compare_mdp_chart...
INFO Drawing cv_compare_psi_chart...
INFO Drawing cv_compare_mpg_chart...
INFO Saving model pkl...
INFO Saving model pmml...
/home/conda_env/miniconda3/lib/python3.9/subprocess.py:941: RuntimeWarning: line buffering (buffering=1) isn't supported in binary mode, the default buffer size will be used
self.stdout = io.open(c2pread, 'rb', bufsize)
/home/conda_env/miniconda3/lib/python3.9/subprocess.py:946: RuntimeWarning: line buffering (buffering=1) isn't supported in binary mode, the default buffer size will be used
self.stderr = io.open(errread, 'rb', bufsize)
INFO Adding model_pmml to excel...
INFO Adding run_model_pmml_in_java to excel...
INFO Saving json...
INFO Saving setting...
INFO Adding runtime to excel...
INFO Saving directory...
INFO Saving...
INFO Total: 17.80 s
INFO Total: 17.85 s
discrete_cols = df_train.dtypes[df_train.dtypes == object].index.tolist()
at.Analysis.model_flow(
df_train, "./模型分析demo/v4-lgb/v4-lgb.xlsx", test_data=df_test,
# 目标定义 & 切片定义
response="target", split_col_name="birth_year", use_train_time=True,
exclude_column=["ID", "weight"] + discrete_cols, # 排除权重和离散值
add_info="ID", # 主键添加
# 指定使用训练的class并不进行打分转化
model_type_path="lightgbm.sklearn.LGBMClassifier",
convert_score=False,
)
INFO Prepare kwargs...
WARNING Setting format_time_edge to True !
INFO Checking train_data...
INFO Checking response...
INFO Checking test_data...
INFO Checking response...
INFO Checking columns...
INFO Checking dtypes...
WARNING Setting allow_nan to True by lightgbm.sklearn !
WARNING Setting allow_discrete to True by lightgbm.sklearn !
WARNING Pre: WOE_ not found, change pre to "" !
WARNING Unable to find nan data, setting allow_nan to False !
WARNING Unable to find discrete data, setting allow_discrete to False !
WARNING Find that convert_score is False, setting keep_raw_score to False !
WARNING Find that convert_score is False, setting save_score_desc to False !
WARNING Find that convert_score is False, setting save_score_pmml to False !
INFO Model optional flow...
WARNING Check /home/conda_env/模型分析demo/v4-lgb/Data_v4-lgb.xlsx exists !
INFO Selecting by corr...
INFO Total: 10...
INFO Selected: 7...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'max_depth': 3, 'n_estimators': 30, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': 'auto'}
INFO Using variables: 7...
INFO Training LGBMClassifier...
INFO Filtering importance by LGBMClassifier ...
INFO Total: 7...
INFO Selected: 6...
INFO Model variables: ['AMT_INCOME_TOTAL', 'Age', 'CNT_CHILDREN', 'DAYS_EMPLOYED', 'FLAG_PHONE', 'FLAG_WORK_PHONE'] !
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': 'auto'}
INFO Using variables: 6...
INFO Training LGBMClassifier...
INFO [FIT_TRANSFORM] ['AMT_INCOME_TOTAL']: 0.000124 secs
INFO [FIT_TRANSFORM] ['Age']: 9e-05 secs
INFO [FIT_TRANSFORM] ['CNT_CHILDREN']: 6.1e-05 secs
INFO [FIT_TRANSFORM] ['DAYS_EMPLOYED']: 0.000121 secs
INFO [FIT_TRANSFORM] ['FLAG_PHONE']: 0.001366 secs
INFO [FIT_TRANSFORM] ['FLAG_WORK_PHONE']: 0.001265 secs
INFO [TRANSFORM] ['AMT_INCOME_TOTAL']: 0.000248 secs
INFO [TRANSFORM] ['Age']: 0.000196 secs
INFO [TRANSFORM] ['CNT_CHILDREN']: 0.000272 secs
INFO [TRANSFORM] ['DAYS_EMPLOYED']: 0.000496 secs
INFO [TRANSFORM] ['FLAG_PHONE']: 7.6e-05 secs
INFO [TRANSFORM] ['FLAG_WORK_PHONE']: 7.5e-05 secs
INFO [TRANSFORM] ['AMT_INCOME_TOTAL']: 0.000121 secs
INFO [TRANSFORM] ['Age']: 0.000125 secs
INFO [TRANSFORM] ['CNT_CHILDREN']: 7e-05 secs
INFO [TRANSFORM] ['DAYS_EMPLOYED']: 6.2e-05 secs
INFO [TRANSFORM] ['FLAG_PHONE']: 6.1e-05 secs
INFO [TRANSFORM] ['FLAG_WORK_PHONE']: 6.6e-05 secs
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Drawing train_score_uncut_ks_graph...
INFO Drawing train_score_uncut_count_chart1...
INFO Drawing train_score_uncut_count_chart2...
INFO Drawing train_score_uncut_count_chart3...
INFO Drawing train_score_uncut_cap_chart1...
INFO Drawing train_score_uncut_cap_chart2...
INFO Drawing train_score_uncut_cap_chart3...
INFO Creating cut info...
INFO Drawing train_score_cut_ks_graph...
INFO Drawing train_score_cut_rate_chart...
INFO Drawing train_score_cut_count_chart1...
INFO Drawing train_score_cut_count_chart2...
INFO Drawing train_score_cut_count_chart3...
INFO Drawing train_score_cut_cap_chart1...
INFO Drawing train_score_cut_cap_chart2...
INFO Drawing train_score_cut_cap_chart3...
INFO Creating qcut info...
INFO Drawing train_score_qcut_ks_graph...
INFO Drawing train_score_qcut_rate_chart...
INFO Drawing train_score_qcut_count_chart1...
INFO Drawing train_score_qcut_count_chart2...
INFO Drawing train_score_qcut_count_chart3...
INFO Drawing train_score_qcut_cap_chart1...
INFO Drawing train_score_qcut_cap_chart2...
INFO Drawing train_score_qcut_cap_chart3...
INFO Creating precision recall fscore...
INFO Drawing train_classification_chart...
INFO Drawing train_classification_chart...
INFO Drawing roc...
INFO Creating results summary...
INFO Drawing ap...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Drawing test_score_uncut_ks_graph...
INFO Drawing test_score_uncut_count_chart1...
INFO Drawing test_score_uncut_count_chart2...
INFO Drawing test_score_uncut_count_chart3...
INFO Drawing test_score_uncut_cap_chart1...
INFO Drawing test_score_uncut_cap_chart2...
INFO Drawing test_score_uncut_cap_chart3...
INFO Creating cut info...
INFO Drawing test_score_cut_ks_graph...
INFO Drawing test_score_cut_rate_chart...
INFO Drawing test_score_cut_count_chart1...
INFO Drawing test_score_cut_count_chart2...
INFO Drawing test_score_cut_count_chart3...
INFO Drawing test_score_cut_cap_chart1...
INFO Drawing test_score_cut_cap_chart2...
INFO Drawing test_score_cut_cap_chart3...
INFO Creating qcut info...
INFO Drawing test_score_qcut_ks_graph...
INFO Drawing test_score_qcut_rate_chart...
INFO Drawing test_score_qcut_count_chart1...
INFO Drawing test_score_qcut_count_chart2...
INFO Drawing test_score_qcut_count_chart3...
INFO Drawing test_score_qcut_cap_chart1...
INFO Drawing test_score_qcut_cap_chart2...
INFO Drawing test_score_qcut_cap_chart3...
INFO Creating precision recall fscore...
INFO Drawing test_classification_chart...
INFO Drawing test_classification_chart...
INFO Drawing roc...
INFO Creating results summary...
INFO Drawing ap...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Drawing cut_psi_chart...
INFO Drawing cut_psi_chart...
INFO Drawing qcut_psi_chart...
INFO Drawing qcut_psi_chart...
INFO Total: 10.69 s
INFO Saving var_drop...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': 'auto'}
INFO Using variables: 6...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': 'auto'}
INFO Using variables: 6...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': 'auto'}
INFO Using variables: 6...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO Drawing cv compare graph...
INFO Drawing cv_compare_ks_chart...
INFO Drawing cv_compare_auc_chart...
INFO Drawing cv_compare_ap_chart...
INFO Drawing cv_compare_logloss_chart...
INFO Drawing cv_compare_r2_chart...
INFO Drawing cv_compare_mse_chart...
INFO Drawing cv_compare_mdp_chart...
INFO Drawing cv_compare_psi_chart...
INFO Drawing cv_compare_mpg_chart...
INFO Saving model pkl...
INFO Saving model pmml...
/home/conda_env/miniconda3/lib/python3.9/subprocess.py:941: RuntimeWarning: line buffering (buffering=1) isn't supported in binary mode, the default buffer size will be used
self.stdout = io.open(c2pread, 'rb', bufsize)
/home/conda_env/miniconda3/lib/python3.9/subprocess.py:946: RuntimeWarning: line buffering (buffering=1) isn't supported in binary mode, the default buffer size will be used
self.stderr = io.open(errread, 'rb', bufsize)
INFO Adding model_pmml to excel...
INFO Adding run_model_pmml_in_java to excel...
INFO Saving json...
INFO Saving setting...
INFO Adding runtime to excel...
INFO Saving directory...
INFO Saving...
INFO Total: 17.51 s
INFO Total: 17.56 s
at.mt.__pattern__["tree_default_params"]
{'max_depth': 3, 'n_estimators': 30}at.Analysis.model_on_data(
df_train, "./模型分析demo/v5-gls/v5-gls.xlsx", test_data=df_test,
data_flow={
**data_params,
**{
"save_ori": True, # 数据集仅保留原始数据，会自动添加"ORI_"
"save_or_return": False # 跳过csv的io读写提升性能
},
},
model_flow={
# 更换模型，非discrete模型会自动关闭convert_score
"model_type_path": "statsmodels.api.GLS",
"pvalue_filter_limit": 0.5, # 变量较少，此处调整pvalue阈值避免无变量
# stepwise时按ORI_取值，enable_coef_negative取None跳过，check_coef_consistency取True开启
"model_filter_order": ["corr", "stepwise"],
}
)
INFO Prepare kwargs...
INFO Checking train_data...
INFO Checking response...
INFO Checking test_data...
INFO Checking response...
INFO Checking columns...
INFO Checking dtypes...
WARNING Find that flow_data_type is ORI_, setting save_woe_data to False !
WARNING Setting format_time_edge to True !
INFO Prepare run...
INFO Run: CNT_FAM_MEMBERS
INFO Run: OCCUPATION_TYPE
INFO Run: DAYS_BIRTH
INFO Run: FLAG_OWN_REALTY
INFO Run: CODE_GENDER
INFO Run: FLAG_MOBIL
INFO Run: NAME_EDUCATION_TYPE
INFO Run: NAME_FAMILY_STATUS
INFO Run: FLAG_PHONE
INFO Run: AMT_INCOME_TOTAL
INFO Run: NAME_HOUSING_TYPE
INFO Run: Age
INFO Run: DAYS_EMPLOYED
INFO Run: NAME_INCOME_TYPE
INFO Run: FLAG_WORK_PHONE
INFO Run: FLAG_EMAIL
INFO Run: CNT_CHILDREN
INFO Run: FLAG_OWN_CAR
INFO Selecting...
INFO Match train: CODE_GENDER_asD
INFO Match train: OCCUPATION_TYPE_asD
INFO Match train: FLAG_OWN_REALTY_asD
INFO Match train: CNT_FAM_MEMBERS_asC
INFO Match train: FLAG_PHONE_asC
INFO Match train: NAME_FAMILY_STATUS_asD
INFO Match train: NAME_EDUCATION_TYPE_asD
INFO Match train: DAYS_BIRTH_asC
INFO Match train: NAME_HOUSING_TYPE_asD
INFO Match train: NAME_INCOME_TYPE_asD
INFO Match train: FLAG_WORK_PHONE_asC
INFO Match train: AMT_INCOME_TOTAL_asC
INFO Match train: Age_asC
INFO Match train: FLAG_EMAIL_asC
INFO Match train: CNT_CHILDREN_asC
INFO Match train: FLAG_OWN_CAR_asD
INFO Match train: DAYS_EMPLOYED_asC
INFO Creating train data...
INFO Creating train corr detail...
INFO Creating (-inf, 1972-01-01] corr...
INFO Creating (1972-01-01, 1984-01-01] corr...
INFO Creating (1984-01-01, inf) corr...
INFO Creating all corr...
INFO Creating train cross table...
INFO Creating train all desc tables...
INFO Match test: CNT_FAM_MEMBERS_asC
INFO Match test: OCCUPATION_TYPE_asD
INFO Match test: DAYS_BIRTH_asC
INFO Match test: FLAG_OWN_REALTY_asD
INFO Match test: CODE_GENDER_asD
INFO Match test: NAME_EDUCATION_TYPE_asD
INFO Match test: NAME_FAMILY_STATUS_asD
INFO Match test: FLAG_PHONE_asC
INFO Match test: AMT_INCOME_TOTAL_asC
INFO Match test: NAME_HOUSING_TYPE_asD
INFO Match test: Age_asC
INFO Match test: DAYS_EMPLOYED_asC
INFO Match test: NAME_INCOME_TYPE_asD
INFO Match test: FLAG_WORK_PHONE_asC
INFO Match test: FLAG_EMAIL_asC
INFO Match test: CNT_CHILDREN_asC
INFO Match test: FLAG_OWN_CAR_asD
INFO Creating test data...
INFO Creating test corr detail...
INFO Creating (-inf, 1972-01-01] corr...
INFO Creating (1972-01-01, 1984-01-01] corr...
INFO Creating (1984-01-01, inf) corr...
INFO Creating all corr...
INFO Creating test cross table...
INFO Creating test all desc tables...
INFO Match train test: FLAG_OWN_REALTY_asD
INFO Match train test: NAME_FAMILY_STATUS_asD
INFO Match train test: DAYS_EMPLOYED_asC
INFO Match train test: NAME_INCOME_TYPE_asD
INFO Match train test: NAME_HOUSING_TYPE_asD
INFO Match train test: CODE_GENDER_asD
INFO Match train test: OCCUPATION_TYPE_asD
INFO Match train test: NAME_EDUCATION_TYPE_asD
INFO Match train test: AMT_INCOME_TOTAL_asC
INFO Match train test: FLAG_WORK_PHONE_asC
INFO Match train test: FLAG_OWN_CAR_asD
INFO Match train test: FLAG_PHONE_asC
INFO Match train test: DAYS_BIRTH_asC
INFO Match train test: Age_asC
INFO Match train test: FLAG_EMAIL_asC
INFO Match train test: CNT_CHILDREN_asC
INFO Match train test: CNT_FAM_MEMBERS_asC
INFO Saving info...
INFO Saving desc...
INFO Saving cross...
INFO Saving unique...
INFO Saving summary...
INFO Saving corr summary...
INFO Saving stable_summary...
INFO Saving train_stable_iv_ks...
INFO Saving train_stable_psi...
INFO Saving train_stable_quantile...
INFO Saving test_stable_iv_ks...
INFO Saving test_stable_psi...
INFO Saving test_stable_quantile...
INFO Saving stable_cross_psi...
INFO Saving detail...
INFO Saving draft...
INFO Saving drop...
INFO Saving json...
INFO Saving setting...
INFO Adding runtime to excel...
INFO Saving directory...
INFO Saving...
INFO Total: 9.06 s
INFO Prepare kwargs...
WARNING Using response as split_col_name !
WARNING Pre: WOE_ not found, and ORI_ found, change pre to ORI_ !
INFO Checking train_data...
INFO Checking response...
INFO Checking test_data...
INFO Checking response...
INFO Checking columns...
INFO Checking dtypes...
WARNING Setting nan_mode to min !
WARNING Find that is_stats_model is True and GLS is not statsmodels.discrete.discrete_model.BinaryModel, setting convert_score to False !
WARNING Find that convert_score is False, setting keep_raw_score to False !
WARNING Find that convert_score is False, setting save_score_desc to False !
WARNING Find that convert_score is False, setting save_score_pmml to False !
INFO Model linear flow...
INFO Selecting by corr...
INFO Total: 9...
INFO Selected: 7...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'max_depth': 3, 'n_estimators': 30, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': 'auto'}
INFO Using variables: 7...
INFO Training LGBMClassifier...
INFO Filtering importance by LGBMClassifier ...
INFO Total: 7...
INFO Selected: 7...
INFO Caching var coef...
INFO Caching ORI_DAYS_EMPLOYED_asC single coef...
INFO Caching ORI_AMT_INCOME_TOTAL_asC single coef...
INFO Caching ORI_FLAG_WORK_PHONE_asC single coef...
INFO Caching ORI_FLAG_PHONE_asC single coef...
INFO Caching ORI_DAYS_BIRTH_asC single coef...
INFO Caching ORI_CNT_CHILDREN_asC single coef...
INFO Caching ORI_FLAG_EMAIL_asC single coef...
INFO Caching Intercept single coef...
INFO Round: 1; Forward Stepwise: ORI_DAYS_EMPLOYED_asC; Selected: 2
INFO Round: 1; Forward Stepwise: ORI_AMT_INCOME_TOTAL_asC; Selected: 2
INFO Round: 1; Forward Stepwise: ORI_FLAG_WORK_PHONE_asC; Selected: 3
INFO Round: 1; Forward Stepwise: ORI_FLAG_PHONE_asC; Selected: 4
INFO Round: 1; Forward Stepwise: ORI_DAYS_BIRTH_asC; Selected: 5
INFO Round: 1; Forward Stepwise: ORI_CNT_CHILDREN_asC; Selected: 5
INFO Round: 1; Forward Stepwise: ORI_FLAG_EMAIL_asC; Selected: 5
INFO Round: 2; Forward Stepwise: ORI_AMT_INCOME_TOTAL_asC; Selected: 5
INFO Round: 2; Forward Stepwise: ORI_CNT_CHILDREN_asC; Selected: 5
INFO Round: 2; Forward Stepwise: ORI_FLAG_EMAIL_asC; Selected: 5
INFO Round: 3; Forward Stepwise: ORI_AMT_INCOME_TOTAL_asC; Selected: 5
INFO Round: 3; Forward Stepwise: ORI_CNT_CHILDREN_asC; Selected: 5
INFO Round: 3; Forward Stepwise: ORI_FLAG_EMAIL_asC; Selected: 5
INFO Model variables: ['Intercept', 'ORI_DAYS_EMPLOYED_asC', 'ORI_FLAG_WORK_PHONE_asC', 'ORI_FLAG_PHONE_asC', 'ORI_DAYS_BIRTH_asC'] !
INFO Building model...
INFO Saving var_desc...
INFO Saving var_cross...
INFO Saving var_unique...
INFO Saving var_summary...
INFO Saving ori_corr...
INFO Saving train_stable_summary...
INFO Saving test_stable_summary...
INFO Saving train_var_stable_iv_ks...
INFO Saving train_var_stable_psi...
INFO Saving train_var_stable_quantile...
INFO Saving test_var_stable_iv_ks...
INFO Saving test_var_stable_psi...
INFO Saving test_var_stable_quantile...
INFO Saving var_stable_cross_psi...
INFO Saving var_detail...
INFO Saving var_draft...
INFO Saving var_drop...
INFO Saving train_var_corr...
INFO Saving train vif...
INFO Getting detail score...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Drawing train_score_uncut_ks_graph...
INFO Drawing train_score_uncut_count_chart1...
INFO Drawing train_score_uncut_count_chart2...
INFO Drawing train_score_uncut_count_chart3...
INFO Drawing train_score_uncut_cap_chart1...
INFO Drawing train_score_uncut_cap_chart2...
INFO Drawing train_score_uncut_cap_chart3...
INFO Creating cut info...
INFO Drawing train_score_cut_ks_graph...
INFO Drawing train_score_cut_rate_chart...
INFO Drawing train_score_cut_count_chart1...
INFO Drawing train_score_cut_count_chart2...
INFO Drawing train_score_cut_count_chart3...
INFO Drawing train_score_cut_cap_chart1...
INFO Drawing train_score_cut_cap_chart2...
INFO Drawing train_score_cut_cap_chart3...
INFO Creating qcut info...
INFO Drawing train_score_qcut_ks_graph...
INFO Drawing train_score_qcut_rate_chart...
INFO Drawing train_score_qcut_count_chart1...
INFO Drawing train_score_qcut_count_chart2...
INFO Drawing train_score_qcut_count_chart3...
INFO Drawing train_score_qcut_cap_chart1...
INFO Drawing train_score_qcut_cap_chart2...
INFO Drawing train_score_qcut_cap_chart3...
INFO Creating precision recall fscore...
INFO Drawing train_classification_chart...
INFO Drawing train_classification_chart...
INFO Drawing roc...
INFO Creating results summary...
INFO Drawing ap...
INFO Saving test_var_corr...
INFO Saving test vif...
INFO Getting detail score...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Drawing test_score_uncut_ks_graph...
INFO Drawing test_score_uncut_count_chart1...
INFO Drawing test_score_uncut_count_chart2...
INFO Drawing test_score_uncut_count_chart3...
INFO Drawing test_score_uncut_cap_chart1...
INFO Drawing test_score_uncut_cap_chart2...
INFO Drawing test_score_uncut_cap_chart3...
INFO Creating cut info...
INFO Drawing test_score_cut_ks_graph...
INFO Drawing test_score_cut_rate_chart...
INFO Drawing test_score_cut_count_chart1...
INFO Drawing test_score_cut_count_chart2...
INFO Drawing test_score_cut_count_chart3...
INFO Drawing test_score_cut_cap_chart1...
INFO Drawing test_score_cut_cap_chart2...
INFO Drawing test_score_cut_cap_chart3...
INFO Creating qcut info...
INFO Drawing test_score_qcut_ks_graph...
INFO Drawing test_score_qcut_rate_chart...
INFO Drawing test_score_qcut_count_chart1...
INFO Drawing test_score_qcut_count_chart2...
INFO Drawing test_score_qcut_count_chart3...
INFO Drawing test_score_qcut_cap_chart1...
INFO Drawing test_score_qcut_cap_chart2...
INFO Drawing test_score_qcut_cap_chart3...
INFO Creating precision recall fscore...
INFO Drawing test_classification_chart...
INFO Drawing test_classification_chart...
INFO Drawing roc...
INFO Creating results summary...
INFO Drawing ap...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Drawing cut_psi_chart...
INFO Drawing cut_psi_chart...
INFO Drawing qcut_psi_chart...
INFO Drawing qcut_psi_chart...
INFO target | 3 | cv flow...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO Drawing cv compare graph...
INFO Drawing cv_compare_ks_chart...
INFO Drawing cv_compare_auc_chart...
INFO Drawing cv_compare_ap_chart...
INFO Drawing cv_compare_logloss_chart...
INFO Drawing cv_compare_r2_chart...
INFO Drawing cv_compare_mse_chart...
INFO Drawing cv_compare_mdp_chart...
INFO Drawing cv_compare_psi_chart...
INFO Drawing cv_compare_mpg_chart...
INFO Saving model pkl...
INFO Saving json...
INFO Saving setting...
INFO Adding runtime to excel...
INFO Saving directory...
INFO Saving...
INFO Total: 14.71 s
INFO Total: 23.86 s
discrete_cols = df_train.dtypes[df_train.dtypes == object].index.tolist()
at.Analysis.model_flow(
df_train, "./模型分析demo/v6-lgb/v6-lgb.xlsx", test_data=df_test,
# 目标定义 & 切片定义
response="target", split_col_name="birth_year", use_train_time=True,
exclude_column=["ID", "weight"], # 排除权重和离散值
add_info="ID", # 主键添加
# 指定使用训练的class并不进行打分转化
model_type_path="lightgbm.sklearn.LGBMClassifier",
convert_score=False,
customized_variables=discrete_cols,
)
WARNING Find that recover_from_json is None and customized_variables is not empty, setting filter_variable to False !
INFO Prepare kwargs...
WARNING Setting format_time_edge to True !
INFO Checking train_data...
INFO Checking response...
INFO Checking test_data...
INFO Checking response...
INFO Checking columns...
INFO Checking dtypes...
WARNING Setting allow_nan to True by lightgbm.sklearn !
WARNING Setting allow_discrete to True by lightgbm.sklearn !
WARNING Unable to find nan data, setting allow_nan to False !
WARNING Find that filter_variable is False, setting zero_imp_filter to False !
WARNING Find that filter_variable is False, setting enable_stepwise to False !
WARNING Find that filter_variable is False, setting enable_corr_filter to False !
WARNING Find that filter_variable is False, setting limit_var_num to inf !
WARNING Find that convert_score is False, setting keep_raw_score to False !
WARNING Find that convert_score is False, setting save_score_desc to False !
WARNING Find that convert_score is False, setting save_score_pmml to False !
INFO Model optional flow...
WARNING Check /home/conda_env/模型分析demo/v6-lgb/Data_v6-lgb.xlsx exists !
INFO Model variables: ['CODE_GENDER', 'FLAG_OWN_CAR', 'FLAG_OWN_REALTY', 'NAME_INCOME_TYPE', 'NAME_EDUCATION_TYPE', 'NAME_FAMILY_STATUS', 'NAME_HOUSING_TYPE', 'OCCUPATION_TYPE'] !
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [0, 1, 2, 3, 4, 5, 6, 7]}
INFO Using variables: 8...
INFO Training LGBMClassifier...
INFO [FIT_TRANSFORM] ['CODE_GENDER']: 0.018618 secs
INFO [FIT_TRANSFORM] ['FLAG_OWN_CAR']: 0.019903 secs
INFO [FIT_TRANSFORM] ['FLAG_OWN_REALTY']: 0.0203 secs
INFO [FIT_TRANSFORM] ['NAME_INCOME_TYPE']: 0.020431 secs
INFO [FIT_TRANSFORM] ['NAME_EDUCATION_TYPE']: 0.020034 secs
INFO [FIT_TRANSFORM] ['NAME_FAMILY_STATUS']: 0.02233 secs
INFO [FIT_TRANSFORM] ['NAME_HOUSING_TYPE']: 0.020196 secs
INFO [FIT_TRANSFORM] ['OCCUPATION_TYPE']: 0.019022 secs
INFO [TRANSFORM] ['CODE_GENDER']: 0.01204 secs
INFO [TRANSFORM] ['FLAG_OWN_CAR']: 0.012493 secs
INFO [TRANSFORM] ['FLAG_OWN_REALTY']: 0.012632 secs
INFO [TRANSFORM] ['NAME_INCOME_TYPE']: 0.012978 secs
INFO [TRANSFORM] ['NAME_EDUCATION_TYPE']: 0.012779 secs
INFO [TRANSFORM] ['NAME_FAMILY_STATUS']: 0.014557 secs
INFO [TRANSFORM] ['NAME_HOUSING_TYPE']: 0.012711 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE']: 0.016715 secs
INFO [TRANSFORM] ['CODE_GENDER']: 0.005002 secs
INFO [TRANSFORM] ['FLAG_OWN_CAR']: 0.005611 secs
INFO [TRANSFORM] ['FLAG_OWN_REALTY']: 0.005267 secs
INFO [TRANSFORM] ['NAME_INCOME_TYPE']: 0.006002 secs
INFO [TRANSFORM] ['NAME_EDUCATION_TYPE']: 0.005521 secs
INFO [TRANSFORM] ['NAME_FAMILY_STATUS']: 0.005132 secs
INFO [TRANSFORM] ['NAME_HOUSING_TYPE']: 0.005163 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE']: 0.006638 secs
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Drawing train_score_uncut_ks_graph...
INFO Drawing train_score_uncut_count_chart1...
INFO Drawing train_score_uncut_count_chart2...
INFO Drawing train_score_uncut_count_chart3...
INFO Drawing train_score_uncut_cap_chart1...
INFO Drawing train_score_uncut_cap_chart2...
INFO Drawing train_score_uncut_cap_chart3...
INFO Creating cut info...
INFO Drawing train_score_cut_ks_graph...
INFO Drawing train_score_cut_rate_chart...
INFO Drawing train_score_cut_count_chart1...
INFO Drawing train_score_cut_count_chart2...
INFO Drawing train_score_cut_count_chart3...
INFO Drawing train_score_cut_cap_chart1...
INFO Drawing train_score_cut_cap_chart2...
INFO Drawing train_score_cut_cap_chart3...
INFO Creating qcut info...
INFO Drawing train_score_qcut_ks_graph...
INFO Drawing train_score_qcut_rate_chart...
INFO Drawing train_score_qcut_count_chart1...
INFO Drawing train_score_qcut_count_chart2...
INFO Drawing train_score_qcut_count_chart3...
INFO Drawing train_score_qcut_cap_chart1...
INFO Drawing train_score_qcut_cap_chart2...
INFO Drawing train_score_qcut_cap_chart3...
INFO Creating precision recall fscore...
INFO Drawing train_classification_chart...
INFO Drawing train_classification_chart...
INFO Drawing roc...
INFO Creating results summary...
INFO Drawing ap...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Drawing test_score_uncut_ks_graph...
INFO Drawing test_score_uncut_count_chart1...
INFO Drawing test_score_uncut_count_chart2...
INFO Drawing test_score_uncut_count_chart3...
INFO Drawing test_score_uncut_cap_chart1...
INFO Drawing test_score_uncut_cap_chart2...
INFO Drawing test_score_uncut_cap_chart3...
INFO Creating cut info...
INFO Drawing test_score_cut_ks_graph...
INFO Drawing test_score_cut_rate_chart...
INFO Drawing test_score_cut_count_chart1...
INFO Drawing test_score_cut_count_chart2...
INFO Drawing test_score_cut_count_chart3...
INFO Drawing test_score_cut_cap_chart1...
INFO Drawing test_score_cut_cap_chart2...
INFO Drawing test_score_cut_cap_chart3...
INFO Creating qcut info...
INFO Drawing test_score_qcut_ks_graph...
INFO Drawing test_score_qcut_rate_chart...
INFO Drawing test_score_qcut_count_chart1...
INFO Drawing test_score_qcut_count_chart2...
INFO Drawing test_score_qcut_count_chart3...
INFO Drawing test_score_qcut_cap_chart1...
INFO Drawing test_score_qcut_cap_chart2...
INFO Drawing test_score_qcut_cap_chart3...
INFO Creating precision recall fscore...
INFO Drawing test_classification_chart...
INFO Drawing test_classification_chart...
INFO Drawing roc...
INFO Creating results summary...
INFO Drawing ap...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Drawing cut_psi_chart...
INFO Drawing cut_psi_chart...
INFO Drawing qcut_psi_chart...
INFO Drawing qcut_psi_chart...
INFO Total: 11.32 s
INFO Saving var_drop...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [0, 1, 2, 3, 4, 5, 6, 7]}
INFO Using variables: 8...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [0, 1, 2, 3, 4, 5, 6, 7]}
INFO Using variables: 8...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [0, 1, 2, 3, 4, 5, 6, 7]}
INFO Using variables: 8...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO Drawing cv compare graph...
INFO Drawing cv_compare_ks_chart...
INFO Drawing cv_compare_auc_chart...
INFO Drawing cv_compare_ap_chart...
INFO Drawing cv_compare_logloss_chart...
INFO Drawing cv_compare_r2_chart...
INFO Drawing cv_compare_mse_chart...
INFO Drawing cv_compare_mdp_chart...
INFO Drawing cv_compare_psi_chart...
INFO Drawing cv_compare_mpg_chart...
INFO Saving model pkl...
INFO Saving model pmml...
/home/conda_env/miniconda3/lib/python3.9/subprocess.py:941: RuntimeWarning: line buffering (buffering=1) isn't supported in binary mode, the default buffer size will be used
self.stdout = io.open(c2pread, 'rb', bufsize)
/home/conda_env/miniconda3/lib/python3.9/subprocess.py:946: RuntimeWarning: line buffering (buffering=1) isn't supported in binary mode, the default buffer size will be used
self.stderr = io.open(errread, 'rb', bufsize)
INFO Adding model_pmml to excel...
INFO Adding run_model_pmml_in_java to excel...
INFO Saving json...
INFO Saving setting...
INFO Adding runtime to excel...
INFO Saving directory...
INFO Saving...
INFO Total: 18.49 s
INFO Total: 18.54 s
not_discrete_cols = df_train.dtypes[df_train.dtypes != object].index.tolist()
at.Analysis.model_on_data(
df_train, "./模型分析demo/v6-lgb-oh/v6-lgb-oh.xlsx", test_data=df_test,
data_flow={
**data_params,
**{
"save_one_hot": True, # 数据集仅保留原始数据，会自动添加"ORI_"
"save_or_return": False, # 跳过csv的io读写提升性能
"exclude_column": ["ID", "weight"] + not_discrete_cols, # 排除权重和数值变量
},
},
model_flow={
"response": "target", "split_col_name": "birth_year", "use_train_time": True,
"exclude_column": ["ID", "weight"], # 排除权重和离散值
"add_info": "ID", # 主键添加
# 指定使用训练的class并不进行打分转化
"model_type_path": "lightgbm.sklearn.LGBMClassifier", "convert_score": False,
"filter_variable": False, # 数据one-hot之后变量名会变化，data_flow已经限制范围了，直接设置filter_variables能达到一样的效果
}
)
INFO Prepare kwargs...
INFO Checking train_data...
INFO Checking response...
INFO Checking test_data...
INFO Checking response...
INFO Checking columns...
INFO Checking dtypes...
WARNING Find that save_one_hot is True, setting save_woe_data to False !
WARNING Setting format_time_edge to True !
INFO Prepare run...
INFO Run: OCCUPATION_TYPE
INFO Run: NAME_HOUSING_TYPE
INFO Run: NAME_INCOME_TYPE
INFO Run: FLAG_OWN_REALTY
INFO Run: CODE_GENDER
INFO Run: NAME_EDUCATION_TYPE
INFO Run: FLAG_OWN_CAR
INFO Run: NAME_FAMILY_STATUS
INFO Selecting...
INFO Match train: CODE_GENDER_asD
INFO Match train: OCCUPATION_TYPE_asD
INFO Match train: FLAG_OWN_REALTY_asD
INFO Match train: NAME_HOUSING_TYPE_asD
INFO Match train: NAME_INCOME_TYPE_asD
INFO Match train: NAME_EDUCATION_TYPE_asD
INFO Match train: FLAG_OWN_CAR_asD
INFO Match train: NAME_FAMILY_STATUS_asD
INFO Creating train data...
INFO Creating train corr detail...
INFO Creating (-inf, 1972-01-01] corr...
INFO Creating (1972-01-01, 1984-01-01] corr...
INFO Creating (1984-01-01, inf) corr...
INFO Creating all corr...
INFO Creating train cross table...
INFO Creating train all desc tables...
INFO Match test: OCCUPATION_TYPE_asD
INFO Match test: NAME_HOUSING_TYPE_asD
INFO Match test: NAME_INCOME_TYPE_asD
INFO Match test: FLAG_OWN_REALTY_asD
INFO Match test: CODE_GENDER_asD
INFO Match test: NAME_EDUCATION_TYPE_asD
INFO Match test: FLAG_OWN_CAR_asD
INFO Match test: NAME_FAMILY_STATUS_asD
INFO Creating test data...
INFO Creating test corr detail...
INFO Creating (-inf, 1972-01-01] corr...
INFO Creating (1972-01-01, 1984-01-01] corr...
INFO Creating (1984-01-01, inf) corr...
INFO Creating all corr...
INFO Creating test cross table...
INFO Creating test all desc tables...
INFO Match train test: FLAG_OWN_REALTY_asD
INFO Match train test: NAME_FAMILY_STATUS_asD
INFO Match train test: NAME_INCOME_TYPE_asD
INFO Match train test: NAME_EDUCATION_TYPE_asD
INFO Match train test: NAME_HOUSING_TYPE_asD
INFO Match train test: FLAG_OWN_CAR_asD
INFO Match train test: OCCUPATION_TYPE_asD
INFO Match train test: CODE_GENDER_asD
INFO Saving info...
INFO Saving desc...
INFO Saving cross...
INFO Saving unique...
INFO Saving summary...
INFO Saving corr summary...
INFO Saving stable_summary...
INFO Saving train_stable_iv_ks...
INFO Saving train_stable_psi...
INFO Saving test_stable_iv_ks...
INFO Saving test_stable_psi...
INFO Saving stable_cross_psi...
INFO Saving detail...
INFO Saving draft...
INFO Saving drop...
INFO Saving json...
INFO Saving setting...
INFO Adding runtime to excel...
INFO Saving directory...
INFO Saving...
INFO Total: 7.13 s
INFO Prepare kwargs...
WARNING Setting format_time_edge to True !
WARNING exclude_column: "weight" not in data !
INFO Checking train_data...
INFO Checking response...
INFO Checking test_data...
INFO Checking response...
INFO Checking columns...
INFO Checking dtypes...
WARNING Setting allow_nan to True by lightgbm.sklearn !
WARNING Setting allow_discrete to True by lightgbm.sklearn !
WARNING Pre: WOE_ not found, change pre to "" !
WARNING Unable to find nan data, setting allow_nan to False !
WARNING Unable to find discrete data, setting allow_discrete to False !
WARNING Find that filter_variable is False, setting zero_imp_filter to False !
WARNING Find that filter_variable is False, setting enable_stepwise to False !
WARNING Find that filter_variable is False, setting enable_corr_filter to False !
WARNING Find that filter_variable is False, setting limit_var_num to inf !
WARNING Find that convert_score is False, setting keep_raw_score to False !
WARNING Find that convert_score is False, setting save_score_desc to False !
WARNING Find that convert_score is False, setting save_score_pmml to False !
INFO Model optional flow...
INFO Model variables: ['CODE_GENDER_asD：F', 'CODE_GENDER_asD：M', 'FLAG_OWN_CAR_asD：N', 'FLAG_OWN_CAR_asD：Y', 'FLAG_OWN_REALTY_asD：N', 'FLAG_OWN_REALTY_asD：Y', 'NAME_EDUCATION_TYPE_asD：Academic degree', 'NAME_EDUCATION_TYPE_asD：Higher education', 'NAME_EDUCATION_TYPE_asD：Incomplete higher', 'NAME_EDUCATION_TYPE_asD：Lower secondary', 'NAME_EDUCATION_TYPE_asD：Secondary / secondary special', 'NAME_FAMILY_STATUS_asD：Civil marriage', 'NAME_FAMILY_STATUS_asD：Married', 'NAME_FAMILY_STATUS_asD：Separated', 'NAME_FAMILY_STATUS_asD：Single / not married', 'NAME_FAMILY_STATUS_asD：Widow', 'NAME_HOUSING_TYPE_asD：Co-op apartment', 'NAME_HOUSING_TYPE_asD：House / apartment', 'NAME_HOUSING_TYPE_asD：Municipal apartment', 'NAME_HOUSING_TYPE_asD：Office apartment', 'NAME_HOUSING_TYPE_asD：Rented apartment', 'NAME_HOUSING_TYPE_asD：With parents', 'NAME_INCOME_TYPE_asD：Commercial associate', 'NAME_INCOME_TYPE_asD：Pensioner', 'NAME_INCOME_TYPE_asD：State servant', 'NAME_INCOME_TYPE_asD：Student', 'NAME_INCOME_TYPE_asD：Working', 'OCCUPATION_TYPE_asD：Accountants', 'OCCUPATION_TYPE_asD：Cleaning staff', 'OCCUPATION_TYPE_asD：Cooking staff', 'OCCUPATION_TYPE_asD：Core staff', 'OCCUPATION_TYPE_asD：Drivers', 'OCCUPATION_TYPE_asD：HR staff', 'OCCUPATION_TYPE_asD：High skill tech staff', 'OCCUPATION_TYPE_asD：IT staff', 'OCCUPATION_TYPE_asD：Laborers', 'OCCUPATION_TYPE_asD：Low-skill Laborers', 'OCCUPATION_TYPE_asD：Managers', 'OCCUPATION_TYPE_asD：Medicine staff', 'OCCUPATION_TYPE_asD：Private service staff', 'OCCUPATION_TYPE_asD：Realty agents', 'OCCUPATION_TYPE_asD：Sales staff', 'OCCUPATION_TYPE_asD：Secretaries', 'OCCUPATION_TYPE_asD：Security staff', 'OCCUPATION_TYPE_asD：Waiters/barmen staff', 'OCCUPATION_TYPE_asD：missing'] !
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': 'auto'}
INFO Using variables: 46...
INFO Training LGBMClassifier...
INFO [FIT_TRANSFORM] ['CODE_GENDER_asD：F']: 0.000151 secs
INFO [FIT_TRANSFORM] ['CODE_GENDER_asD：M']: 9.9e-05 secs
INFO [FIT_TRANSFORM] ['FLAG_OWN_CAR_asD：N']: 7.2e-05 secs
INFO [FIT_TRANSFORM] ['FLAG_OWN_CAR_asD：Y']: 7.4e-05 secs
INFO [FIT_TRANSFORM] ['FLAG_OWN_REALTY_asD：N']: 7.8e-05 secs
INFO [FIT_TRANSFORM] ['FLAG_OWN_REALTY_asD：Y']: 6.8e-05 secs
INFO [FIT_TRANSFORM] ['NAME_EDUCATION_TYPE_asD：Academic degree']: 8.4e-05 secs
INFO [FIT_TRANSFORM] ['NAME_EDUCATION_TYPE_asD：Higher education']: 6.1e-05 secs
INFO [FIT_TRANSFORM] ['NAME_EDUCATION_TYPE_asD：Incomplete higher']: 8.1e-05 secs
INFO [FIT_TRANSFORM] ['NAME_EDUCATION_TYPE_asD：Lower secondary']: 6.7e-05 secs
INFO [FIT_TRANSFORM] ['NAME_EDUCATION_TYPE_asD：Secondary / secondary special']: 6.8e-05 secs
INFO [FIT_TRANSFORM] ['NAME_FAMILY_STATUS_asD：Civil marriage']: 7.6e-05 secs
INFO [FIT_TRANSFORM] ['NAME_FAMILY_STATUS_asD：Married']: 6.8e-05 secs
INFO [FIT_TRANSFORM] ['NAME_FAMILY_STATUS_asD：Separated']: 6.9e-05 secs
INFO [FIT_TRANSFORM] ['NAME_FAMILY_STATUS_asD：Single / not married']: 7.1e-05 secs
INFO [FIT_TRANSFORM] ['NAME_FAMILY_STATUS_asD：Widow']: 6.5e-05 secs
INFO [FIT_TRANSFORM] ['NAME_HOUSING_TYPE_asD：Co-op apartment']: 6.7e-05 secs
INFO [FIT_TRANSFORM] ['NAME_HOUSING_TYPE_asD：House / apartment']: 7e-05 secs
INFO [FIT_TRANSFORM] ['NAME_HOUSING_TYPE_asD：Municipal apartment']: 7.5e-05 secs
INFO [FIT_TRANSFORM] ['NAME_HOUSING_TYPE_asD：Office apartment']: 7.3e-05 secs
INFO [FIT_TRANSFORM] ['NAME_HOUSING_TYPE_asD：Rented apartment']: 7.2e-05 secs
INFO [FIT_TRANSFORM] ['NAME_HOUSING_TYPE_asD：With parents']: 0.000512 secs
INFO [FIT_TRANSFORM] ['NAME_INCOME_TYPE_asD：Commercial associate']: 9.4e-05 secs
INFO [FIT_TRANSFORM] ['NAME_INCOME_TYPE_asD：Pensioner']: 9.4e-05 secs
INFO [FIT_TRANSFORM] ['NAME_INCOME_TYPE_asD：State servant']: 0.000135 secs
INFO [FIT_TRANSFORM] ['NAME_INCOME_TYPE_asD：Student']: 0.000102 secs
INFO [FIT_TRANSFORM] ['NAME_INCOME_TYPE_asD：Working']: 6.9e-05 secs
INFO [FIT_TRANSFORM] ['OCCUPATION_TYPE_asD：Accountants']: 8.7e-05 secs
INFO [FIT_TRANSFORM] ['OCCUPATION_TYPE_asD：Cleaning staff']: 6.7e-05 secs
INFO [FIT_TRANSFORM] ['OCCUPATION_TYPE_asD：Cooking staff']: 7.1e-05 secs
INFO [FIT_TRANSFORM] ['OCCUPATION_TYPE_asD：Core staff']: 0.000694 secs
INFO [FIT_TRANSFORM] ['OCCUPATION_TYPE_asD：Drivers']: 8.5e-05 secs
INFO [FIT_TRANSFORM] ['OCCUPATION_TYPE_asD：HR staff']: 0.000736 secs
INFO [FIT_TRANSFORM] ['OCCUPATION_TYPE_asD：High skill tech staff']: 0.000762 secs
INFO [FIT_TRANSFORM] ['OCCUPATION_TYPE_asD：IT staff']: 0.000121 secs
INFO [FIT_TRANSFORM] ['OCCUPATION_TYPE_asD：Laborers']: 7.2e-05 secs
INFO [FIT_TRANSFORM] ['OCCUPATION_TYPE_asD：Low-skill Laborers']: 8.4e-05 secs
INFO [FIT_TRANSFORM] ['OCCUPATION_TYPE_asD：Managers']: 7.1e-05 secs
INFO [FIT_TRANSFORM] ['OCCUPATION_TYPE_asD：Medicine staff']: 6.6e-05 secs
INFO [FIT_TRANSFORM] ['OCCUPATION_TYPE_asD：Private service staff']: 8e-05 secs
INFO [FIT_TRANSFORM] ['OCCUPATION_TYPE_asD：Realty agents']: 8.3e-05 secs
INFO [FIT_TRANSFORM] ['OCCUPATION_TYPE_asD：Sales staff']: 0.000209 secs
INFO [FIT_TRANSFORM] ['OCCUPATION_TYPE_asD：Secretaries']: 0.000509 secs
INFO [FIT_TRANSFORM] ['OCCUPATION_TYPE_asD：Security staff']: 0.000145 secs
INFO [FIT_TRANSFORM] ['OCCUPATION_TYPE_asD：Waiters/barmen staff']: 0.000263 secs
INFO [FIT_TRANSFORM] ['OCCUPATION_TYPE_asD：missing']: 0.000194 secs
INFO [TRANSFORM] ['CODE_GENDER_asD：F']: 0.000138 secs
INFO [TRANSFORM] ['CODE_GENDER_asD：M']: 6.5e-05 secs
INFO [TRANSFORM] ['FLAG_OWN_CAR_asD：N']: 7.8e-05 secs
INFO [TRANSFORM] ['FLAG_OWN_CAR_asD：Y']: 6.9e-05 secs
INFO [TRANSFORM] ['FLAG_OWN_REALTY_asD：N']: 0.000134 secs
INFO [TRANSFORM] ['FLAG_OWN_REALTY_asD：Y']: 7.7e-05 secs
INFO [TRANSFORM] ['NAME_EDUCATION_TYPE_asD：Academic degree']: 6.3e-05 secs
INFO [TRANSFORM] ['NAME_EDUCATION_TYPE_asD：Higher education']: 7.1e-05 secs
INFO [TRANSFORM] ['NAME_EDUCATION_TYPE_asD：Incomplete higher']: 9.9e-05 secs
INFO [TRANSFORM] ['NAME_EDUCATION_TYPE_asD：Lower secondary']: 7.5e-05 secs
INFO [TRANSFORM] ['NAME_EDUCATION_TYPE_asD：Secondary / secondary special']: 6.9e-05 secs
INFO [TRANSFORM] ['NAME_FAMILY_STATUS_asD：Civil marriage']: 7e-05 secs
INFO [TRANSFORM] ['NAME_FAMILY_STATUS_asD：Married']: 0.0001 secs
INFO [TRANSFORM] ['NAME_FAMILY_STATUS_asD：Separated']: 6.8e-05 secs
INFO [TRANSFORM] ['NAME_FAMILY_STATUS_asD：Single / not married']: 6.7e-05 secs
INFO [TRANSFORM] ['NAME_FAMILY_STATUS_asD：Widow']: 7.1e-05 secs
INFO [TRANSFORM] ['NAME_HOUSING_TYPE_asD：Co-op apartment']: 6.9e-05 secs
INFO [TRANSFORM] ['NAME_HOUSING_TYPE_asD：House / apartment']: 7.5e-05 secs
INFO [TRANSFORM] ['NAME_HOUSING_TYPE_asD：Municipal apartment']: 7.6e-05 secs
INFO [TRANSFORM] ['NAME_HOUSING_TYPE_asD：Office apartment']: 0.000156 secs
INFO [TRANSFORM] ['NAME_HOUSING_TYPE_asD：Rented apartment']: 8.8e-05 secs
INFO [TRANSFORM] ['NAME_HOUSING_TYPE_asD：With parents']: 7.1e-05 secs
INFO [TRANSFORM] ['NAME_INCOME_TYPE_asD：Commercial associate']: 6.6e-05 secs
INFO [TRANSFORM] ['NAME_INCOME_TYPE_asD：Pensioner']: 6.4e-05 secs
INFO [TRANSFORM] ['NAME_INCOME_TYPE_asD：State servant']: 7.1e-05 secs
INFO [TRANSFORM] ['NAME_INCOME_TYPE_asD：Student']: 7.2e-05 secs
INFO [TRANSFORM] ['NAME_INCOME_TYPE_asD：Working']: 6.4e-05 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE_asD：Accountants']: 7.9e-05 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE_asD：Cleaning staff']: 0.000134 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE_asD：Cooking staff']: 0.000597 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE_asD：Core staff']: 0.000107 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE_asD：Drivers']: 8.1e-05 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE_asD：HR staff']: 0.000103 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE_asD：High skill tech staff']: 9.3e-05 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE_asD：IT staff']: 8.2e-05 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE_asD：Laborers']: 7.1e-05 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE_asD：Low-skill Laborers']: 6.8e-05 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE_asD：Managers']: 8.1e-05 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE_asD：Medicine staff']: 6.4e-05 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE_asD：Private service staff']: 6.8e-05 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE_asD：Realty agents']: 6.3e-05 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE_asD：Sales staff']: 8.1e-05 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE_asD：Secretaries']: 7.9e-05 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE_asD：Security staff']: 7.3e-05 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE_asD：Waiters/barmen staff']: 7.9e-05 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE_asD：missing']: 7.5e-05 secs
INFO [TRANSFORM] ['CODE_GENDER_asD：F']: 0.000136 secs
INFO [TRANSFORM] ['CODE_GENDER_asD：M']: 6.5e-05 secs
INFO [TRANSFORM] ['FLAG_OWN_CAR_asD：N']: 0.00014 secs
INFO [TRANSFORM] ['FLAG_OWN_CAR_asD：Y']: 0.002041 secs
INFO [TRANSFORM] ['FLAG_OWN_REALTY_asD：N']: 0.00011 secs
INFO [TRANSFORM] ['FLAG_OWN_REALTY_asD：Y']: 8.9e-05 secs
INFO [TRANSFORM] ['NAME_EDUCATION_TYPE_asD：Academic degree']: 7.4e-05 secs
INFO [TRANSFORM] ['NAME_EDUCATION_TYPE_asD：Higher education']: 0.00011 secs
INFO [TRANSFORM] ['NAME_EDUCATION_TYPE_asD：Incomplete higher']: 6.1e-05 secs
INFO [TRANSFORM] ['NAME_EDUCATION_TYPE_asD：Lower secondary']: 6e-05 secs
INFO [TRANSFORM] ['NAME_EDUCATION_TYPE_asD：Secondary / secondary special']: 7.1e-05 secs
INFO [TRANSFORM] ['NAME_FAMILY_STATUS_asD：Civil marriage']: 6.9e-05 secs
INFO [TRANSFORM] ['NAME_FAMILY_STATUS_asD：Married']: 7.8e-05 secs
INFO [TRANSFORM] ['NAME_FAMILY_STATUS_asD：Separated']: 8.1e-05 secs
INFO [TRANSFORM] ['NAME_FAMILY_STATUS_asD：Single / not married']: 7.1e-05 secs
INFO [TRANSFORM] ['NAME_FAMILY_STATUS_asD：Widow']: 8.6e-05 secs
INFO [TRANSFORM] ['NAME_HOUSING_TYPE_asD：Co-op apartment']: 6.5e-05 secs
INFO [TRANSFORM] ['NAME_HOUSING_TYPE_asD：House / apartment']: 6.6e-05 secs
INFO [TRANSFORM] ['NAME_HOUSING_TYPE_asD：Municipal apartment']: 9.5e-05 secs
INFO [TRANSFORM] ['NAME_HOUSING_TYPE_asD：Office apartment']: 6.9e-05 secs
INFO [TRANSFORM] ['NAME_HOUSING_TYPE_asD：Rented apartment']: 6.9e-05 secs
INFO [TRANSFORM] ['NAME_HOUSING_TYPE_asD：With parents']: 6.8e-05 secs
INFO [TRANSFORM] ['NAME_INCOME_TYPE_asD：Commercial associate']: 6.7e-05 secs
INFO [TRANSFORM] ['NAME_INCOME_TYPE_asD：Pensioner']: 9.7e-05 secs
INFO [TRANSFORM] ['NAME_INCOME_TYPE_asD：State servant']: 6.7e-05 secs
INFO [TRANSFORM] ['NAME_INCOME_TYPE_asD：Student']: 6.7e-05 secs
INFO [TRANSFORM] ['NAME_INCOME_TYPE_asD：Working']: 8.8e-05 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE_asD：Accountants']: 0.000143 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE_asD：Cleaning staff']: 0.000516 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE_asD：Cooking staff']: 7.5e-05 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE_asD：Core staff']: 7.1e-05 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE_asD：Drivers']: 6.6e-05 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE_asD：HR staff']: 8.1e-05 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE_asD：High skill tech staff']: 0.000108 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE_asD：IT staff']: 7.5e-05 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE_asD：Laborers']: 7.9e-05 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE_asD：Low-skill Laborers']: 8e-05 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE_asD：Managers']: 7.3e-05 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE_asD：Medicine staff']: 8.8e-05 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE_asD：Private service staff']: 9.5e-05 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE_asD：Realty agents']: 9e-05 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE_asD：Sales staff']: 6.8e-05 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE_asD：Secretaries']: 0.000125 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE_asD：Security staff']: 0.000509 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE_asD：Waiters/barmen staff']: 0.000311 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE_asD：missing']: 0.000486 secs
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Drawing train_score_uncut_ks_graph...
INFO Drawing train_score_uncut_count_chart1...
INFO Drawing train_score_uncut_count_chart2...
INFO Drawing train_score_uncut_count_chart3...
INFO Drawing train_score_uncut_cap_chart1...
INFO Drawing train_score_uncut_cap_chart2...
INFO Drawing train_score_uncut_cap_chart3...
INFO Creating cut info...
INFO Drawing train_score_cut_ks_graph...
INFO Drawing train_score_cut_rate_chart...
INFO Drawing train_score_cut_count_chart1...
INFO Drawing train_score_cut_count_chart2...
INFO Drawing train_score_cut_count_chart3...
INFO Drawing train_score_cut_cap_chart1...
INFO Drawing train_score_cut_cap_chart2...
INFO Drawing train_score_cut_cap_chart3...
INFO Creating qcut info...
INFO Drawing train_score_qcut_ks_graph...
INFO Drawing train_score_qcut_rate_chart...
INFO Drawing train_score_qcut_count_chart1...
INFO Drawing train_score_qcut_count_chart2...
INFO Drawing train_score_qcut_count_chart3...
INFO Drawing train_score_qcut_cap_chart1...
INFO Drawing train_score_qcut_cap_chart2...
INFO Drawing train_score_qcut_cap_chart3...
INFO Creating precision recall fscore...
INFO Drawing train_classification_chart...
INFO Drawing train_classification_chart...
INFO Drawing roc...
INFO Creating results summary...
INFO Drawing ap...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Drawing test_score_uncut_ks_graph...
INFO Drawing test_score_uncut_count_chart1...
INFO Drawing test_score_uncut_count_chart2...
INFO Drawing test_score_uncut_count_chart3...
INFO Drawing test_score_uncut_cap_chart1...
INFO Drawing test_score_uncut_cap_chart2...
INFO Drawing test_score_uncut_cap_chart3...
INFO Creating cut info...
INFO Drawing test_score_cut_ks_graph...
INFO Drawing test_score_cut_rate_chart...
INFO Drawing test_score_cut_count_chart1...
INFO Drawing test_score_cut_count_chart2...
INFO Drawing test_score_cut_count_chart3...
INFO Drawing test_score_cut_cap_chart1...
INFO Drawing test_score_cut_cap_chart2...
INFO Drawing test_score_cut_cap_chart3...
INFO Creating qcut info...
INFO Drawing test_score_qcut_ks_graph...
INFO Drawing test_score_qcut_rate_chart...
INFO Drawing test_score_qcut_count_chart1...
INFO Drawing test_score_qcut_count_chart2...
INFO Drawing test_score_qcut_count_chart3...
INFO Drawing test_score_qcut_cap_chart1...
INFO Drawing test_score_qcut_cap_chart2...
INFO Drawing test_score_qcut_cap_chart3...
INFO Creating precision recall fscore...
INFO Drawing test_classification_chart...
INFO Drawing test_classification_chart...
INFO Drawing roc...
INFO Creating results summary...
INFO Drawing ap...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Drawing cut_psi_chart...
INFO Drawing cut_psi_chart...
INFO Drawing qcut_psi_chart...
INFO Drawing qcut_psi_chart...
INFO Total: 10.87 s
INFO Saving var_desc...
INFO Saving var_cross...
INFO Saving var_unique...
INFO Saving var_summary...
WARNING Empty var_summary !
INFO Saving train_stable_summary...
WARNING Empty train_var_stable_summary !
INFO Saving test_stable_summary...
WARNING Empty test_var_stable_summary !
INFO Saving train_var_stable_iv_ks...
INFO Saving train_var_stable_psi...
INFO Saving test_var_stable_iv_ks...
INFO Saving test_var_stable_psi...
INFO Saving var_stable_cross_psi...
INFO Saving var_detail...
INFO Saving var_draft...
INFO Saving var_drop...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': 'auto'}
INFO Using variables: 46...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': 'auto'}
INFO Using variables: 46...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': 'auto'}
INFO Using variables: 46...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO Drawing cv compare graph...
INFO Drawing cv_compare_ks_chart...
INFO Drawing cv_compare_auc_chart...
INFO Drawing cv_compare_ap_chart...
INFO Drawing cv_compare_logloss_chart...
INFO Drawing cv_compare_r2_chart...
INFO Drawing cv_compare_mse_chart...
INFO Drawing cv_compare_mdp_chart...
INFO Drawing cv_compare_psi_chart...
INFO Drawing cv_compare_mpg_chart...
INFO Saving model pkl...
INFO Saving model pmml...
/home/conda_env/miniconda3/lib/python3.9/subprocess.py:941: RuntimeWarning: line buffering (buffering=1) isn't supported in binary mode, the default buffer size will be used
self.stdout = io.open(c2pread, 'rb', bufsize)
/home/conda_env/miniconda3/lib/python3.9/subprocess.py:946: RuntimeWarning: line buffering (buffering=1) isn't supported in binary mode, the default buffer size will be used
self.stderr = io.open(errread, 'rb', bufsize)
INFO Adding model_pmml to excel...
INFO Adding run_model_pmml_in_java to excel...
INFO Saving json...
INFO Saving setting...
INFO Adding runtime to excel...
INFO Saving directory...
INFO Saving...
INFO Total: 18.14 s
INFO Total: 25.35 s
at.Analysis.model_flow(
df_train, "./模型分析demo/v7-lgb/v7-lgb.xlsx", test_data=df_test,
# 目标定义 & 切片定义
response="target", split_col_name="birth_year", use_train_time=True,
exclude_column=["ID", "weight"], # 排除权重
add_info="ID", # 主键添加
# 指定使用训练的class并不进行打分转化
model_type_path="lightgbm.sklearn.LGBMClassifier",
convert_score=False,
customized_parameter={
"n_estimators": 200 # 默认100，尝试增加迭代次数
}
)
INFO Prepare kwargs...
WARNING Setting format_time_edge to True !
INFO Checking train_data...
INFO Checking response...
INFO Checking test_data...
INFO Checking response...
INFO Checking columns...
INFO Checking dtypes...
WARNING Setting allow_nan to True by lightgbm.sklearn !
WARNING Setting allow_discrete to True by lightgbm.sklearn !
WARNING Pre: WOE_ not found, change pre to "" !
WARNING Unable to find nan data, setting allow_nan to False !
WARNING Find that allow_nan is True or allow_discrete is True, setting enable_stepwise to False !
WARNING Find that allow_nan is True or allow_discrete is True, setting enable_corr_filter to False !
WARNING Find that convert_score is False, setting keep_raw_score to False !
WARNING Find that convert_score is False, setting save_score_desc to False !
WARNING Find that convert_score is False, setting save_score_pmml to False !
INFO Model optional flow...
WARNING Check /home/conda_env/模型分析demo/v7-lgb/Data_v7-lgb.xlsx exists !
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'max_depth': 3, 'n_estimators': 30, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 9, 10, 13, 14, 15, 16, 17]}
INFO Using variables: 18...
INFO Training LGBMClassifier...
INFO Filtering importance by LGBMClassifier ...
INFO Total: 18...
INFO Selected: 16...
INFO Model variables: ['AMT_INCOME_TOTAL', 'Age', 'CNT_CHILDREN', 'CNT_FAM_MEMBERS', 'CODE_GENDER', 'DAYS_BIRTH', 'DAYS_EMPLOYED', 'FLAG_OWN_CAR', 'FLAG_OWN_REALTY', 'FLAG_PHONE', 'FLAG_WORK_PHONE', 'NAME_EDUCATION_TYPE', 'NAME_FAMILY_STATUS', 'NAME_HOUSING_TYPE', 'NAME_INCOME_TYPE', 'OCCUPATION_TYPE'] !
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 200, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO [FIT_TRANSFORM] ['AMT_INCOME_TOTAL']: 0.000132 secs
INFO [FIT_TRANSFORM] ['Age']: 7.6e-05 secs
INFO [FIT_TRANSFORM] ['CNT_CHILDREN']: 7e-05 secs
INFO [FIT_TRANSFORM] ['CNT_FAM_MEMBERS']: 8.9e-05 secs
INFO [FIT_TRANSFORM] ['CODE_GENDER']: 0.018599 secs
INFO [FIT_TRANSFORM] ['DAYS_BIRTH']: 0.00015 secs
INFO [FIT_TRANSFORM] ['DAYS_EMPLOYED']: 7.6e-05 secs
INFO [FIT_TRANSFORM] ['FLAG_OWN_CAR']: 0.019438 secs
INFO [FIT_TRANSFORM] ['FLAG_OWN_REALTY']: 0.021 secs
INFO [FIT_TRANSFORM] ['FLAG_PHONE']: 0.000149 secs
INFO [FIT_TRANSFORM] ['FLAG_WORK_PHONE']: 6.1e-05 secs
INFO [FIT_TRANSFORM] ['NAME_EDUCATION_TYPE']: 0.020115 secs
INFO [FIT_TRANSFORM] ['NAME_FAMILY_STATUS']: 0.019289 secs
INFO [FIT_TRANSFORM] ['NAME_HOUSING_TYPE']: 0.019384 secs
INFO [FIT_TRANSFORM] ['NAME_INCOME_TYPE']: 0.020671 secs
INFO [FIT_TRANSFORM] ['OCCUPATION_TYPE']: 0.017788 secs
INFO [TRANSFORM] ['AMT_INCOME_TOTAL']: 0.000141 secs
INFO [TRANSFORM] ['Age']: 8.6e-05 secs
INFO [TRANSFORM] ['CNT_CHILDREN']: 0.001894 secs
INFO [TRANSFORM] ['CNT_FAM_MEMBERS']: 0.00012 secs
INFO [TRANSFORM] ['CODE_GENDER']: 0.011295 secs
INFO [TRANSFORM] ['DAYS_BIRTH']: 0.000141 secs
INFO [TRANSFORM] ['DAYS_EMPLOYED']: 8.5e-05 secs
INFO [TRANSFORM] ['FLAG_OWN_CAR']: 0.011489 secs
INFO [TRANSFORM] ['FLAG_OWN_REALTY']: 0.012979 secs
INFO [TRANSFORM] ['FLAG_PHONE']: 0.000179 secs
INFO [TRANSFORM] ['FLAG_WORK_PHONE']: 8.6e-05 secs
INFO [TRANSFORM] ['NAME_EDUCATION_TYPE']: 0.012382 secs
INFO [TRANSFORM] ['NAME_FAMILY_STATUS']: 0.012682 secs
INFO [TRANSFORM] ['NAME_HOUSING_TYPE']: 0.011881 secs
INFO [TRANSFORM] ['NAME_INCOME_TYPE']: 0.011926 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE']: 0.013818 secs
INFO [TRANSFORM] ['AMT_INCOME_TOTAL']: 0.00017 secs
INFO [TRANSFORM] ['Age']: 0.000139 secs
INFO [TRANSFORM] ['CNT_CHILDREN']: 6.6e-05 secs
INFO [TRANSFORM] ['CNT_FAM_MEMBERS']: 7.1e-05 secs
INFO [TRANSFORM] ['CODE_GENDER']: 0.004909 secs
INFO [TRANSFORM] ['DAYS_BIRTH']: 0.000147 secs
INFO [TRANSFORM] ['DAYS_EMPLOYED']: 6.8e-05 secs
INFO [TRANSFORM] ['FLAG_OWN_CAR']: 0.005004 secs
INFO [TRANSFORM] ['FLAG_OWN_REALTY']: 0.005092 secs
INFO [TRANSFORM] ['FLAG_PHONE']: 0.000104 secs
INFO [TRANSFORM] ['FLAG_WORK_PHONE']: 8.1e-05 secs
INFO [TRANSFORM] ['NAME_EDUCATION_TYPE']: 0.00536 secs
INFO [TRANSFORM] ['NAME_FAMILY_STATUS']: 0.00716 secs
INFO [TRANSFORM] ['NAME_HOUSING_TYPE']: 0.006344 secs
INFO [TRANSFORM] ['NAME_INCOME_TYPE']: 0.005417 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE']: 0.006381 secs
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Drawing train_score_uncut_ks_graph...
INFO Drawing train_score_uncut_count_chart1...
INFO Drawing train_score_uncut_count_chart2...
INFO Drawing train_score_uncut_count_chart3...
INFO Drawing train_score_uncut_cap_chart1...
INFO Drawing train_score_uncut_cap_chart2...
INFO Drawing train_score_uncut_cap_chart3...
INFO Creating cut info...
INFO Drawing train_score_cut_ks_graph...
INFO Drawing train_score_cut_rate_chart...
INFO Drawing train_score_cut_count_chart1...
INFO Drawing train_score_cut_count_chart2...
INFO Drawing train_score_cut_count_chart3...
INFO Drawing train_score_cut_cap_chart1...
INFO Drawing train_score_cut_cap_chart2...
INFO Drawing train_score_cut_cap_chart3...
INFO Creating qcut info...
INFO Drawing train_score_qcut_ks_graph...
INFO Drawing train_score_qcut_rate_chart...
INFO Drawing train_score_qcut_count_chart1...
INFO Drawing train_score_qcut_count_chart2...
INFO Drawing train_score_qcut_count_chart3...
INFO Drawing train_score_qcut_cap_chart1...
INFO Drawing train_score_qcut_cap_chart2...
INFO Drawing train_score_qcut_cap_chart3...
INFO Creating precision recall fscore...
INFO Drawing train_classification_chart...
INFO Drawing train_classification_chart...
INFO Drawing roc...
INFO Creating results summary...
INFO Drawing ap...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Drawing test_score_uncut_ks_graph...
INFO Drawing test_score_uncut_count_chart1...
INFO Drawing test_score_uncut_count_chart2...
INFO Drawing test_score_uncut_count_chart3...
INFO Drawing test_score_uncut_cap_chart1...
INFO Drawing test_score_uncut_cap_chart2...
INFO Drawing test_score_uncut_cap_chart3...
INFO Creating cut info...
INFO Drawing test_score_cut_ks_graph...
INFO Drawing test_score_cut_rate_chart...
INFO Drawing test_score_cut_count_chart1...
INFO Drawing test_score_cut_count_chart2...
INFO Drawing test_score_cut_count_chart3...
INFO Drawing test_score_cut_cap_chart1...
INFO Drawing test_score_cut_cap_chart2...
INFO Drawing test_score_cut_cap_chart3...
INFO Creating qcut info...
INFO Drawing test_score_qcut_ks_graph...
INFO Drawing test_score_qcut_rate_chart...
INFO Drawing test_score_qcut_count_chart1...
INFO Drawing test_score_qcut_count_chart2...
INFO Drawing test_score_qcut_count_chart3...
INFO Drawing test_score_qcut_cap_chart1...
INFO Drawing test_score_qcut_cap_chart2...
INFO Drawing test_score_qcut_cap_chart3...
INFO Creating precision recall fscore...
INFO Drawing test_classification_chart...
INFO Drawing test_classification_chart...
INFO Drawing roc...
INFO Creating results summary...
INFO Drawing ap...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Drawing cut_psi_chart...
INFO Drawing cut_psi_chart...
INFO Drawing qcut_psi_chart...
INFO Drawing qcut_psi_chart...
INFO Total: 11.14 s
INFO Saving var_drop...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 200, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 200, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 200, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO Drawing cv compare graph...
INFO Drawing cv_compare_ks_chart...
INFO Drawing cv_compare_auc_chart...
INFO Drawing cv_compare_ap_chart...
INFO Drawing cv_compare_logloss_chart...
INFO Drawing cv_compare_r2_chart...
INFO Drawing cv_compare_mse_chart...
INFO Drawing cv_compare_mdp_chart...
INFO Drawing cv_compare_psi_chart...
INFO Drawing cv_compare_mpg_chart...
INFO Saving model pkl...
INFO Saving model pmml...
/home/conda_env/miniconda3/lib/python3.9/subprocess.py:941: RuntimeWarning: line buffering (buffering=1) isn't supported in binary mode, the default buffer size will be used
self.stdout = io.open(c2pread, 'rb', bufsize)
/home/conda_env/miniconda3/lib/python3.9/subprocess.py:946: RuntimeWarning: line buffering (buffering=1) isn't supported in binary mode, the default buffer size will be used
self.stderr = io.open(errread, 'rb', bufsize)
INFO Adding model_pmml to excel...
INFO Adding run_model_pmml_in_java to excel...
INFO Saving json...
INFO Saving setting...
INFO Adding runtime to excel...
INFO Saving directory...
INFO Saving...
INFO Total: 18.76 s
INFO Total: 18.81 s
print(at.mt.Api.get_model_template())
def run_logisticregression(train_data, train_response, train_all, test_all, kwargs):
from sklearn.linear_model import LogisticRegression
params = {}
model_obj = template_fit_model(
LogisticRegression, params, train_data, train_response, kwargs)
train_p = template_use_model(model_obj, train_all, kwargs)
test_p = template_use_model(model_obj, test_all, kwargs)
kwargs["model_obj"] = model_obj
return train_p, test_p, kwargs
def run_torch_net(train_data, train_response, train_all, test_all, kwargs):
# 当 params = {} 时会自动使用 customized_parameter 来调参
params = {}
# not_discrete_cols = df_train.dtypes[df_train.dtypes != object].index.tolist()
# num_variable = len(set(not_discrete_cols) - set(["ID", "target", "weight"]))
# 10
# TO DO 测试一下编译之后能不能移到后面导入
import numpy as np
import torch
import torch.nn.functional as F
from skorch import NeuralNetClassifier
class Classifier(torch.nn.Module):
def __init__(self):
super(Classifier, self).__init__()
var_num = len(train_data.columns)
self.first_layer = torch.nn.Linear(var_num, var_num * 2)
self.second_layer = torch.nn.Linear(var_num * 2, var_num * 4)
self.final_layer = torch.nn.Linear(var_num * 4, 2)
def forward(self, x_batch):
X = self.first_layer(x_batch)
# X = F.relu(X)
X = torch.sigmoid(X)
X = self.second_layer(X)
X = torch.sigmoid(X)
# X = F.relu(X)
X = F.dropout(X, 0.15)
X = self.final_layer(X)
# X = F.relu(X)
X = torch.sigmoid(X)
return F.softmax(X, dim=1)
net = NeuralNetClassifier(
module=Classifier,
criterion=torch.nn.CrossEntropyLoss,
optimizer=torch.optim.Adam,
# train_split=skorch.dataset.CVSplit(cv=5, stratified=True),
# verbose=0,
**params
)
# 使用模型函数fit拟合数据来训练模型
net.fit(
train_data.values.astype(np.float32),
train_response.values.astype(np.int64)
)
# 通过查看 net.classes_ 来确认 p1的index的位置
train_p = net.predict_proba(train_all.values.astype(np.float32))[:, 1]
if test_all is not None:
test_p = net.predict_proba(test_all.values.astype(np.float32))[:, 1]
else:
test_p = None
# 将模型对象缓存
kwargs["model_obj"] = net
return train_p, test_p, kwargs
not_discrete_cols = df_train.dtypes[df_train.dtypes != object].index.tolist()
at.Analysis.model_flow(
df_train, "./模型分析demo/v8-torch/v8-torch.xlsx", test_data=df_test,
# 目标定义 & 切片定义
response="target", split_col_name="birth_year", use_train_time=True,
exclude_column=["ID", "weight"], # 排除权重
add_info="ID", # 主键添加
model_func_definition=run_torch_net,
customized_variables=not_discrete_cols,
customized_parameter={
"max_epochs": 10,
"lr": 0.1,
},
precision=30,
)
WARNING Find that recover_from_json is None and customized_variables is not empty, setting filter_variable to False ! INFO Prepare kwargs... WARNING Setting format_time_edge to True ! INFO Checking train_data... INFO Checking response... INFO Checking test_data... INFO Checking response... INFO Checking columns... INFO Checking dtypes... WARNING Setting nan_mode to min ! WARNING Find that filter_variable is False, setting zero_imp_filter to False ! WARNING Find that filter_variable is False, setting enable_stepwise to False ! WARNING Find that filter_variable is False, setting enable_corr_filter to False ! WARNING Find that filter_variable is False, setting limit_var_num to inf ! WARNING Find that is_stats_model is False and predict_proba not in optional.model, setting convert_score to False ! WARNING Find that convert_score is False, setting keep_raw_score to False ! WARNING Find that convert_score is False, setting save_score_desc to False ! WARNING Find that convert_score is False, setting save_score_pmml to False ! INFO Model optional flow... WARNING Check /home/conda_env/模型分析demo/v8-torch/Data_v8-torch.xlsx exists ! INFO Model variables: ['CNT_CHILDREN', 'AMT_INCOME_TOTAL', 'DAYS_BIRTH', 'DAYS_EMPLOYED', 'FLAG_MOBIL', 'FLAG_WORK_PHONE', 'FLAG_PHONE', 'FLAG_EMAIL', 'CNT_FAM_MEMBERS', 'Age'] !
epoch train_loss valid_acc valid_loss dur
------- ------------ ----------- ------------ ------
1 0.4991 0.9810 0.4973 0.2400
2 0.4972 0.9810 0.4973 0.2392
3 0.4972 0.9810 0.4973 0.2489
4 0.4972 0.9810 0.4973 0.2615
5 0.4972 0.9810 0.4973 0.2437
6 0.4972 0.9810 0.4973 0.2531
7 0.4972 0.9810 0.4973 0.2514
8 0.4972 0.9810 0.4973 0.2702
9 0.4972 0.9810 0.4973 0.2569
10 0.4972 0.9810 0.4973 0.2744
INFO Saving train results... INFO Getting total score... INFO Creating uncut info... INFO Drawing train_score_uncut_ks_graph... INFO Drawing train_score_uncut_count_chart1... INFO Drawing train_score_uncut_count_chart2... INFO Drawing train_score_uncut_count_chart3... INFO Drawing train_score_uncut_cap_chart1... INFO Drawing train_score_uncut_cap_chart2... INFO Drawing train_score_uncut_cap_chart3... INFO Creating cut info... INFO Drawing train_score_cut_ks_graph... INFO Drawing train_score_cut_rate_chart... INFO Drawing train_score_cut_count_chart1... INFO Drawing train_score_cut_count_chart2... INFO Drawing train_score_cut_count_chart3... INFO Drawing train_score_cut_cap_chart1... INFO Drawing train_score_cut_cap_chart2... INFO Drawing train_score_cut_cap_chart3... INFO Creating qcut info... INFO Drawing train_score_qcut_ks_graph... INFO Drawing train_score_qcut_rate_chart... INFO Drawing train_score_qcut_count_chart1... INFO Drawing train_score_qcut_count_chart2... INFO Drawing train_score_qcut_count_chart3... INFO Drawing train_score_qcut_cap_chart1... INFO Drawing train_score_qcut_cap_chart2... INFO Drawing train_score_qcut_cap_chart3... INFO Creating precision recall fscore... INFO Drawing train_classification_chart... INFO Drawing train_classification_chart... INFO Drawing roc... INFO Creating results summary... INFO Drawing ap... INFO Saving test results... INFO Getting total score... WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) ! INFO Creating uncut info... INFO Drawing test_score_uncut_ks_graph... INFO Drawing test_score_uncut_count_chart1... INFO Drawing test_score_uncut_count_chart2... INFO Drawing test_score_uncut_count_chart3... INFO Drawing test_score_uncut_cap_chart1... INFO Drawing test_score_uncut_cap_chart2... INFO Drawing test_score_uncut_cap_chart3... INFO Creating cut info... INFO Drawing test_score_cut_ks_graph... INFO Drawing test_score_cut_rate_chart... INFO Drawing test_score_cut_count_chart1... INFO Drawing test_score_cut_count_chart2... INFO Drawing test_score_cut_count_chart3... INFO Drawing test_score_cut_cap_chart1... INFO Drawing test_score_cut_cap_chart2... INFO Drawing test_score_cut_cap_chart3... INFO Creating qcut info... INFO Drawing test_score_qcut_ks_graph... INFO Drawing test_score_qcut_rate_chart... INFO Drawing test_score_qcut_count_chart1... INFO Drawing test_score_qcut_count_chart2... INFO Drawing test_score_qcut_count_chart3... INFO Drawing test_score_qcut_cap_chart1... INFO Drawing test_score_qcut_cap_chart2... INFO Drawing test_score_qcut_cap_chart3... INFO Creating precision recall fscore... INFO Drawing test_classification_chart... INFO Drawing test_classification_chart... INFO Drawing roc... INFO Creating results summary... INFO Drawing ap... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi... INFO Drawing cut_psi_chart... INFO Drawing cut_psi_chart... INFO Drawing qcut_psi_chart... INFO Drawing qcut_psi_chart... INFO Total: 13.16 s INFO Saving var_drop... INFO birth_year | 3 | cv flow...
epoch train_loss valid_acc valid_loss dur
------- ------------ ----------- ------------ ------
1 0.4989 0.9817 0.4970 0.1679
2 0.4970 0.9817 0.4970 0.1797
3 0.4970 0.9817 0.4970 0.1739
4 0.4970 0.9817 0.4970 0.1720
5 0.4970 0.9817 0.4970 0.1698
6 0.4970 0.9817 0.4970 0.1675
7 0.4970 0.9817 0.4970 0.1825
8 0.4970 0.9817 0.4970 0.1662
9 0.4970 0.9817 0.4970 0.1611
10 0.4970 0.9817 0.4970 0.1635
INFO Saving train results... INFO Getting total score... WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) ! INFO Creating uncut info... INFO Creating cut info... INFO Creating qcut info... INFO Creating precision recall fscore... INFO Creating results summary... INFO Saving test results... INFO Getting total score... WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) ! INFO Creating uncut info... INFO Creating cut info... INFO Creating qcut info... INFO Creating precision recall fscore... INFO Creating results summary... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi...
epoch train_loss valid_acc valid_loss dur
------- ------------ ----------- ------------ ------
1 0.4994 0.9802 0.4977 0.1714
2 0.4977 0.9802 0.4977 0.1707
3 0.4977 0.9802 0.4977 0.1800
4 0.4977 0.9802 0.4977 0.1791
5 0.4977 0.9802 0.4977 0.1775
6 0.4977 0.9802 0.4977 0.1728
7 0.4977 0.9802 0.4977 0.1806
8 0.4977 0.9802 0.4977 0.1689
9 0.4977 0.9802 0.4977 0.1694
10 0.4977 0.9802 0.4977 0.1742
INFO Saving train results... INFO Getting total score... WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) ! INFO Creating uncut info... INFO Creating cut info... INFO Creating qcut info... INFO Creating precision recall fscore... INFO Creating results summary... INFO Saving test results... INFO Getting total score... WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) ! INFO Creating uncut info... INFO Creating cut info... INFO Creating qcut info... INFO Creating precision recall fscore... INFO Creating results summary... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi...
epoch train_loss valid_acc valid_loss dur
------- ------------ ----------- ------------ ------
1 0.4998 0.9819 0.4969 0.1673
2 0.4970 0.9819 0.4969 0.1665
3 0.4970 0.9819 0.4969 0.1741
4 0.4970 0.9819 0.4969 0.1767
5 0.4970 0.9819 0.4969 0.1719
6 0.4970 0.9819 0.4969 0.1734
7 0.4970 0.9819 0.4969 0.1706
8 0.4970 0.9819 0.4969 0.1721
9 0.4970 0.9819 0.4969 0.1737
10 0.4970 0.9819 0.4969 0.1817
INFO Saving train results... INFO Getting total score... WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) ! INFO Creating uncut info... INFO Creating cut info... INFO Creating qcut info... INFO Creating precision recall fscore... INFO Creating results summary... INFO Saving test results... INFO Getting total score... WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) ! INFO Creating uncut info... INFO Creating cut info... INFO Creating qcut info... INFO Creating precision recall fscore... INFO Creating results summary... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi... INFO Saving cross validate... INFO Drawing cv compare graph... INFO Drawing cv_compare_ks_chart... INFO Drawing cv_compare_auc_chart... INFO Drawing cv_compare_ap_chart... INFO Drawing cv_compare_logloss_chart... INFO Drawing cv_compare_r2_chart... INFO Drawing cv_compare_mse_chart... INFO Drawing cv_compare_mdp_chart... INFO Drawing cv_compare_psi_chart... INFO Drawing cv_compare_mpg_chart... INFO Saving model pkl... ERROR Can't pickle local object 'run_torch_net.<locals>.Classifier' INFO Saving model pmml... ERROR INFO Saving json... INFO Saving setting... INFO Adding runtime to excel... INFO Saving directory... INFO Saving... INFO Total: 22.87 s INFO Total: 22.92 s
at.Analysis.model_flow(
df_train, "./模型分析demo/v9-lgb/v9-lgb.xlsx", test_data=df_test,
# 目标定义 & 切片定义
response="target", split_col_name="birth_year", use_train_time=True,
exclude_column=["ID", "weight"], # 排除权重
add_info="ID", # 主键添加
# 指定使用训练的class并不进行打分转化
model_type_path="lightgbm.sklearn.LGBMClassifier",
convert_score=False,
customized_parameter={
"n_estimators": [10, 20, 30, 40, 50],
"learning_rate": [0.05, 0.08, 0.10]
}
)
INFO Prepare kwargs...
WARNING Setting format_time_edge to True !
INFO Checking train_data...
INFO Checking response...
INFO Checking test_data...
INFO Checking response...
INFO Checking columns...
INFO Checking dtypes...
WARNING Setting allow_nan to True by lightgbm.sklearn !
WARNING Setting allow_discrete to True by lightgbm.sklearn !
WARNING Pre: WOE_ not found, change pre to "" !
WARNING Unable to find nan data, setting allow_nan to False !
WARNING Find that allow_nan is True or allow_discrete is True, setting enable_stepwise to False !
WARNING Find that allow_nan is True or allow_discrete is True, setting enable_corr_filter to False !
WARNING Find that model_search_method not in (order, order-random), setting auto_search_loop to 1 !
WARNING Find that convert_score is False, setting keep_raw_score to False !
WARNING Find that convert_score is False, setting save_score_desc to False !
WARNING Find that convert_score is False, setting save_score_pmml to False !
INFO Model optional flow...
WARNING Check /home/conda_env/模型分析demo/v9-lgb/Data_v9-lgb.xlsx exists !
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'max_depth': 3, 'n_estimators': 30, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 9, 10, 13, 14, 15, 16, 17]}
INFO Using variables: 18...
INFO Training LGBMClassifier...
INFO Filtering importance by LGBMClassifier ...
INFO Total: 18...
INFO Selected: 16...
INFO Model variables: ['AMT_INCOME_TOTAL', 'Age', 'CNT_CHILDREN', 'CNT_FAM_MEMBERS', 'CODE_GENDER', 'DAYS_BIRTH', 'DAYS_EMPLOYED', 'FLAG_OWN_CAR', 'FLAG_OWN_REALTY', 'FLAG_PHONE', 'FLAG_WORK_PHONE', 'NAME_EDUCATION_TYPE', 'NAME_FAMILY_STATUS', 'NAME_HOUSING_TYPE', 'NAME_INCOME_TYPE', 'OCCUPATION_TYPE'] !
INFO Auto params cv...
INFO Auto search loop: 1...
INFO ALL params: {'n_estimators': 10, 'learning_rate': 0.05}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 10, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 10, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 10, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 10, 'learning_rate': 0.08}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 10, 'learning_rate': 0.08, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 10, 'learning_rate': 0.08, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 10, 'learning_rate': 0.08, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 10, 'learning_rate': 0.1}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 10, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 10, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 10, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 20, 'learning_rate': 0.05}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 20, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 20, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 20, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 20, 'learning_rate': 0.08}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 20, 'learning_rate': 0.08, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 20, 'learning_rate': 0.08, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 20, 'learning_rate': 0.08, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 20, 'learning_rate': 0.1}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 20, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 20, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 20, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 30, 'learning_rate': 0.05}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 30, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 30, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 30, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 30, 'learning_rate': 0.08}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 30, 'learning_rate': 0.08, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 30, 'learning_rate': 0.08, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 30, 'learning_rate': 0.08, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 30, 'learning_rate': 0.1}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 30, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 30, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 30, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 40, 'learning_rate': 0.05}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 40, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 40, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 40, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 40, 'learning_rate': 0.08}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 40, 'learning_rate': 0.08, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 40, 'learning_rate': 0.08, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 40, 'learning_rate': 0.08, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 40, 'learning_rate': 0.1}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 40, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 40, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 40, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 50, 'learning_rate': 0.05}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 50, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 50, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 50, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 50, 'learning_rate': 0.08}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 50, 'learning_rate': 0.08, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 50, 'learning_rate': 0.08, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 50, 'learning_rate': 0.08, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 50, 'learning_rate': 0.1}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 50, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 50, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 50, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO Drawing cv search graph...
INFO Drawing params_search_chart...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 10, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO [FIT_TRANSFORM] ['AMT_INCOME_TOTAL']: 0.000874 secs
INFO [FIT_TRANSFORM] ['Age']: 7.5e-05 secs
INFO [FIT_TRANSFORM] ['CNT_CHILDREN']: 7.2e-05 secs
INFO [FIT_TRANSFORM] ['CNT_FAM_MEMBERS']: 6.7e-05 secs
INFO [FIT_TRANSFORM] ['CODE_GENDER']: 0.018694 secs
INFO [FIT_TRANSFORM] ['DAYS_BIRTH']: 0.000135 secs
INFO [FIT_TRANSFORM] ['DAYS_EMPLOYED']: 0.000109 secs
INFO [FIT_TRANSFORM] ['FLAG_OWN_CAR']: 0.01979 secs
INFO [FIT_TRANSFORM] ['FLAG_OWN_REALTY']: 0.019664 secs
INFO [FIT_TRANSFORM] ['FLAG_PHONE']: 0.000111 secs
INFO [FIT_TRANSFORM] ['FLAG_WORK_PHONE']: 0.000101 secs
INFO [FIT_TRANSFORM] ['NAME_EDUCATION_TYPE']: 0.019242 secs
INFO [FIT_TRANSFORM] ['NAME_FAMILY_STATUS']: 0.019244 secs
INFO [FIT_TRANSFORM] ['NAME_HOUSING_TYPE']: 0.019365 secs
INFO [FIT_TRANSFORM] ['NAME_INCOME_TYPE']: 0.019341 secs
INFO [FIT_TRANSFORM] ['OCCUPATION_TYPE']: 0.017799 secs
INFO [TRANSFORM] ['AMT_INCOME_TOTAL']: 0.000119 secs
INFO [TRANSFORM] ['Age']: 0.00012 secs
INFO [TRANSFORM] ['CNT_CHILDREN']: 8.9e-05 secs
INFO [TRANSFORM] ['CNT_FAM_MEMBERS']: 0.000109 secs
INFO [TRANSFORM] ['CODE_GENDER']: 0.011498 secs
INFO [TRANSFORM] ['DAYS_BIRTH']: 0.000991 secs
INFO [TRANSFORM] ['DAYS_EMPLOYED']: 0.00014 secs
INFO [TRANSFORM] ['FLAG_OWN_CAR']: 0.011565 secs
INFO [TRANSFORM] ['FLAG_OWN_REALTY']: 0.011643 secs
INFO [TRANSFORM] ['FLAG_PHONE']: 0.001018 secs
INFO [TRANSFORM] ['FLAG_WORK_PHONE']: 9.4e-05 secs
INFO [TRANSFORM] ['NAME_EDUCATION_TYPE']: 0.012664 secs
INFO [TRANSFORM] ['NAME_FAMILY_STATUS']: 0.012369 secs
INFO [TRANSFORM] ['NAME_HOUSING_TYPE']: 0.011765 secs
INFO [TRANSFORM] ['NAME_INCOME_TYPE']: 0.012976 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE']: 0.014279 secs
INFO [TRANSFORM] ['AMT_INCOME_TOTAL']: 0.000125 secs
INFO [TRANSFORM] ['Age']: 0.00013 secs
INFO [TRANSFORM] ['CNT_CHILDREN']: 8.5e-05 secs
INFO [TRANSFORM] ['CNT_FAM_MEMBERS']: 7.5e-05 secs
INFO [TRANSFORM] ['CODE_GENDER']: 0.005169 secs
INFO [TRANSFORM] ['DAYS_BIRTH']: 0.000105 secs
INFO [TRANSFORM] ['DAYS_EMPLOYED']: 7.4e-05 secs
INFO [TRANSFORM] ['FLAG_OWN_CAR']: 0.004997 secs
INFO [TRANSFORM] ['FLAG_OWN_REALTY']: 0.005075 secs
INFO [TRANSFORM] ['FLAG_PHONE']: 9.8e-05 secs
INFO [TRANSFORM] ['FLAG_WORK_PHONE']: 7.7e-05 secs
INFO [TRANSFORM] ['NAME_EDUCATION_TYPE']: 0.005358 secs
INFO [TRANSFORM] ['NAME_FAMILY_STATUS']: 0.005783 secs
INFO [TRANSFORM] ['NAME_HOUSING_TYPE']: 0.00504 secs
INFO [TRANSFORM] ['NAME_INCOME_TYPE']: 0.00526 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE']: 0.006445 secs
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Drawing train_score_uncut_ks_graph...
INFO Drawing train_score_uncut_count_chart1...
INFO Drawing train_score_uncut_count_chart2...
INFO Drawing train_score_uncut_count_chart3...
INFO Drawing train_score_uncut_cap_chart1...
INFO Drawing train_score_uncut_cap_chart2...
INFO Drawing train_score_uncut_cap_chart3...
INFO Creating cut info...
INFO Drawing train_score_cut_ks_graph...
INFO Drawing train_score_cut_rate_chart...
INFO Drawing train_score_cut_count_chart1...
INFO Drawing train_score_cut_count_chart2...
INFO Drawing train_score_cut_count_chart3...
INFO Drawing train_score_cut_cap_chart1...
INFO Drawing train_score_cut_cap_chart2...
INFO Drawing train_score_cut_cap_chart3...
INFO Creating qcut info...
INFO Drawing train_score_qcut_ks_graph...
INFO Drawing train_score_qcut_rate_chart...
INFO Drawing train_score_qcut_count_chart1...
INFO Drawing train_score_qcut_count_chart2...
INFO Drawing train_score_qcut_count_chart3...
INFO Drawing train_score_qcut_cap_chart1...
INFO Drawing train_score_qcut_cap_chart2...
INFO Drawing train_score_qcut_cap_chart3...
INFO Creating precision recall fscore...
INFO Drawing train_classification_chart...
INFO Drawing train_classification_chart...
INFO Drawing roc...
INFO Creating results summary...
INFO Drawing ap...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Drawing test_score_uncut_ks_graph...
INFO Drawing test_score_uncut_count_chart1...
INFO Drawing test_score_uncut_count_chart2...
INFO Drawing test_score_uncut_count_chart3...
INFO Drawing test_score_uncut_cap_chart1...
INFO Drawing test_score_uncut_cap_chart2...
INFO Drawing test_score_uncut_cap_chart3...
INFO Creating cut info...
INFO Drawing test_score_cut_ks_graph...
INFO Drawing test_score_cut_rate_chart...
INFO Drawing test_score_cut_count_chart1...
INFO Drawing test_score_cut_count_chart2...
INFO Drawing test_score_cut_count_chart3...
INFO Drawing test_score_cut_cap_chart1...
INFO Drawing test_score_cut_cap_chart2...
INFO Drawing test_score_cut_cap_chart3...
INFO Creating qcut info...
INFO Drawing test_score_qcut_ks_graph...
INFO Drawing test_score_qcut_rate_chart...
INFO Drawing test_score_qcut_count_chart1...
INFO Drawing test_score_qcut_count_chart2...
INFO Drawing test_score_qcut_count_chart3...
INFO Drawing test_score_qcut_cap_chart1...
INFO Drawing test_score_qcut_cap_chart2...
INFO Drawing test_score_qcut_cap_chart3...
INFO Creating precision recall fscore...
INFO Drawing test_classification_chart...
INFO Drawing test_classification_chart...
INFO Drawing roc...
INFO Creating results summary...
INFO Drawing ap...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Drawing cut_psi_chart...
INFO Drawing cut_psi_chart...
INFO Drawing qcut_psi_chart...
INFO Drawing qcut_psi_chart...
INFO Total: 10.19 s
INFO Saving var_drop...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 10, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 10, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 10, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO Drawing cv compare graph...
INFO Drawing cv_compare_ks_chart...
INFO Drawing cv_compare_auc_chart...
INFO Drawing cv_compare_ap_chart...
INFO Drawing cv_compare_logloss_chart...
INFO Drawing cv_compare_r2_chart...
INFO Drawing cv_compare_mse_chart...
INFO Drawing cv_compare_mdp_chart...
INFO Drawing cv_compare_psi_chart...
INFO Drawing cv_compare_mpg_chart...
INFO Saving model pkl...
INFO Saving model pmml...
/home/conda_env/miniconda3/lib/python3.9/subprocess.py:941: RuntimeWarning: line buffering (buffering=1) isn't supported in binary mode, the default buffer size will be used
self.stdout = io.open(c2pread, 'rb', bufsize)
/home/conda_env/miniconda3/lib/python3.9/subprocess.py:946: RuntimeWarning: line buffering (buffering=1) isn't supported in binary mode, the default buffer size will be used
self.stderr = io.open(errread, 'rb', bufsize)
INFO Adding model_pmml to excel...
INFO Adding run_model_pmml_in_java to excel...
INFO Saving json...
INFO Saving setting...
INFO Adding runtime to excel...
INFO Saving directory...
INFO Saving...
INFO Total: 46.02 s
INFO Total: 46.07 s
at.Analysis.model_flow(
df_train, "./模型分析demo/v10-lgb/v10-lgb.xlsx", test_data=df_test,
# 目标定义 & 切片定义
response="target", split_col_name="birth_year", use_train_time=True,
exclude_column=["ID", "weight"], # 排除权重
add_info="ID", # 主键添加
# 指定使用训练的class并不进行打分转化
model_type_path="lightgbm.sklearn.LGBMClassifier",
convert_score=False,
customized_parameter={
"n_estimators": [10, 20, 30, 40, 50],
"learning_rate": [0.05, 0.08, 0.10]
},
model_search_target={"binary": "ks"},
model_search_params={"binary": ["max", -1]},
sample_weight_name="weight",
)
INFO Prepare kwargs...
WARNING Setting format_time_edge to True !
INFO Checking train_data...
INFO Checking response...
INFO Checking test_data...
INFO Checking response...
INFO Checking columns...
INFO Checking dtypes...
WARNING Setting allow_nan to True by lightgbm.sklearn !
WARNING Setting allow_discrete to True by lightgbm.sklearn !
WARNING Pre: WOE_ not found, change pre to "" !
WARNING Unable to find nan data, setting allow_nan to False !
WARNING Find that allow_nan is True or allow_discrete is True, setting enable_stepwise to False !
WARNING Find that allow_nan is True or allow_discrete is True, setting enable_corr_filter to False !
WARNING Find that model_search_method not in (order, order-random), setting auto_search_loop to 1 !
WARNING Find that convert_score is False, setting keep_raw_score to False !
WARNING Find that convert_score is False, setting save_score_desc to False !
WARNING Find that convert_score is False, setting save_score_pmml to False !
INFO Model optional flow...
WARNING Check /home/conda_env/模型分析demo/v10-lgb/Data_v10-lgb.xlsx exists !
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'max_depth': 3, 'n_estimators': 30, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 9, 10, 13, 14, 15, 16, 17]}
INFO Using variables: 18...
INFO Training LGBMClassifier...
INFO Filtering importance by LGBMClassifier ...
INFO Total: 18...
INFO Selected: 16...
INFO Model variables: ['AMT_INCOME_TOTAL', 'Age', 'CNT_CHILDREN', 'CNT_FAM_MEMBERS', 'CODE_GENDER', 'DAYS_BIRTH', 'DAYS_EMPLOYED', 'FLAG_EMAIL', 'FLAG_OWN_CAR', 'FLAG_OWN_REALTY', 'FLAG_PHONE', 'FLAG_WORK_PHONE', 'NAME_EDUCATION_TYPE', 'NAME_FAMILY_STATUS', 'NAME_HOUSING_TYPE', 'OCCUPATION_TYPE'] !
INFO Auto params cv...
INFO Auto search loop: 1...
INFO ALL params: {'n_estimators': 10, 'learning_rate': 0.05}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 10, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 10, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 10, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 10, 'learning_rate': 0.08}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 10, 'learning_rate': 0.08, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 10, 'learning_rate': 0.08, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 10, 'learning_rate': 0.08, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 10, 'learning_rate': 0.1}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 10, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 10, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 10, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 20, 'learning_rate': 0.05}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 20, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 20, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 20, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 20, 'learning_rate': 0.08}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 20, 'learning_rate': 0.08, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 20, 'learning_rate': 0.08, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 20, 'learning_rate': 0.08, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 20, 'learning_rate': 0.1}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 20, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 20, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 20, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 30, 'learning_rate': 0.05}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 30, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 30, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 30, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 30, 'learning_rate': 0.08}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 30, 'learning_rate': 0.08, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 30, 'learning_rate': 0.08, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 30, 'learning_rate': 0.08, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 30, 'learning_rate': 0.1}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 30, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 30, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 30, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 40, 'learning_rate': 0.05}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 40, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 40, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 40, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 40, 'learning_rate': 0.08}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 40, 'learning_rate': 0.08, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 40, 'learning_rate': 0.08, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 40, 'learning_rate': 0.08, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 40, 'learning_rate': 0.1}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 40, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 40, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 40, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 50, 'learning_rate': 0.05}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 50, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 50, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 50, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 50, 'learning_rate': 0.08}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 50, 'learning_rate': 0.08, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 50, 'learning_rate': 0.08, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 50, 'learning_rate': 0.08, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 50, 'learning_rate': 0.1}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 50, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 50, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 50, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO Drawing cv search graph...
INFO Drawing params_search_chart...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 50, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO [FIT_TRANSFORM] ['AMT_INCOME_TOTAL']: 0.000201 secs
INFO [FIT_TRANSFORM] ['Age']: 7.3e-05 secs
INFO [FIT_TRANSFORM] ['CNT_CHILDREN']: 6.2e-05 secs
INFO [FIT_TRANSFORM] ['CNT_FAM_MEMBERS']: 9.3e-05 secs
INFO [FIT_TRANSFORM] ['CODE_GENDER']: 0.019595 secs
INFO [FIT_TRANSFORM] ['DAYS_BIRTH']: 0.000159 secs
INFO [FIT_TRANSFORM] ['DAYS_EMPLOYED']: 7e-05 secs
INFO [FIT_TRANSFORM] ['FLAG_EMAIL']: 0.000142 secs
INFO [FIT_TRANSFORM] ['FLAG_OWN_CAR']: 0.020268 secs
INFO [FIT_TRANSFORM] ['FLAG_OWN_REALTY']: 0.022323 secs
INFO [FIT_TRANSFORM] ['FLAG_PHONE']: 0.000166 secs
INFO [FIT_TRANSFORM] ['FLAG_WORK_PHONE']: 0.000127 secs
INFO [FIT_TRANSFORM] ['NAME_EDUCATION_TYPE']: 0.018786 secs
INFO [FIT_TRANSFORM] ['NAME_FAMILY_STATUS']: 0.019112 secs
INFO [FIT_TRANSFORM] ['NAME_HOUSING_TYPE']: 0.021237 secs
INFO [FIT_TRANSFORM] ['OCCUPATION_TYPE']: 0.021535 secs
INFO [TRANSFORM] ['AMT_INCOME_TOTAL']: 0.000129 secs
INFO [TRANSFORM] ['Age']: 0.000149 secs
INFO [TRANSFORM] ['CNT_CHILDREN']: 0.000137 secs
INFO [TRANSFORM] ['CNT_FAM_MEMBERS']: 0.000113 secs
INFO [TRANSFORM] ['CODE_GENDER']: 0.01171 secs
INFO [TRANSFORM] ['DAYS_BIRTH']: 0.001024 secs
INFO [TRANSFORM] ['DAYS_EMPLOYED']: 0.00012 secs
INFO [TRANSFORM] ['FLAG_EMAIL']: 9.8e-05 secs
INFO [TRANSFORM] ['FLAG_OWN_CAR']: 0.011761 secs
INFO [TRANSFORM] ['FLAG_OWN_REALTY']: 0.012816 secs
INFO [TRANSFORM] ['FLAG_PHONE']: 0.000217 secs
INFO [TRANSFORM] ['FLAG_WORK_PHONE']: 0.000146 secs
INFO [TRANSFORM] ['NAME_EDUCATION_TYPE']: 0.014497 secs
INFO [TRANSFORM] ['NAME_FAMILY_STATUS']: 0.014447 secs
INFO [TRANSFORM] ['NAME_HOUSING_TYPE']: 0.012382 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE']: 0.015224 secs
INFO [TRANSFORM] ['AMT_INCOME_TOTAL']: 0.000141 secs
INFO [TRANSFORM] ['Age']: 0.000141 secs
INFO [TRANSFORM] ['CNT_CHILDREN']: 0.002131 secs
INFO [TRANSFORM] ['CNT_FAM_MEMBERS']: 0.00027 secs
INFO [TRANSFORM] ['CODE_GENDER']: 0.005306 secs
INFO [TRANSFORM] ['DAYS_BIRTH']: 0.000133 secs
INFO [TRANSFORM] ['DAYS_EMPLOYED']: 8.1e-05 secs
INFO [TRANSFORM] ['FLAG_EMAIL']: 0.000244 secs
INFO [TRANSFORM] ['FLAG_OWN_CAR']: 0.005656 secs
INFO [TRANSFORM] ['FLAG_OWN_REALTY']: 0.005099 secs
INFO [TRANSFORM] ['FLAG_PHONE']: 0.000133 secs
INFO [TRANSFORM] ['FLAG_WORK_PHONE']: 0.000154 secs
INFO [TRANSFORM] ['NAME_EDUCATION_TYPE']: 0.008329 secs
INFO [TRANSFORM] ['NAME_FAMILY_STATUS']: 0.005936 secs
INFO [TRANSFORM] ['NAME_HOUSING_TYPE']: 0.006043 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE']: 0.007842 secs
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Drawing train_score_uncut_ks_graph...
INFO Drawing train_score_uncut_count_chart1...
INFO Drawing train_score_uncut_count_chart2...
INFO Drawing train_score_uncut_count_chart3...
INFO Drawing train_score_uncut_cap_chart1...
INFO Drawing train_score_uncut_cap_chart2...
INFO Drawing train_score_uncut_cap_chart3...
INFO Creating cut info...
INFO Drawing train_score_cut_ks_graph...
INFO Drawing train_score_cut_rate_chart...
INFO Drawing train_score_cut_count_chart1...
INFO Drawing train_score_cut_count_chart2...
INFO Drawing train_score_cut_count_chart3...
INFO Drawing train_score_cut_cap_chart1...
INFO Drawing train_score_cut_cap_chart2...
INFO Drawing train_score_cut_cap_chart3...
INFO Creating qcut info...
INFO Drawing train_score_qcut_ks_graph...
INFO Drawing train_score_qcut_rate_chart...
INFO Drawing train_score_qcut_count_chart1...
INFO Drawing train_score_qcut_count_chart2...
INFO Drawing train_score_qcut_count_chart3...
INFO Drawing train_score_qcut_cap_chart1...
INFO Drawing train_score_qcut_cap_chart2...
INFO Drawing train_score_qcut_cap_chart3...
INFO Creating precision recall fscore...
INFO Drawing train_classification_chart...
INFO Drawing train_classification_chart...
INFO Drawing roc...
INFO Creating results summary...
INFO Drawing ap...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Drawing test_score_uncut_ks_graph...
INFO Drawing test_score_uncut_count_chart1...
INFO Drawing test_score_uncut_count_chart2...
INFO Drawing test_score_uncut_count_chart3...
INFO Drawing test_score_uncut_cap_chart1...
INFO Drawing test_score_uncut_cap_chart2...
INFO Drawing test_score_uncut_cap_chart3...
INFO Creating cut info...
INFO Drawing test_score_cut_ks_graph...
INFO Drawing test_score_cut_rate_chart...
INFO Drawing test_score_cut_count_chart1...
INFO Drawing test_score_cut_count_chart2...
INFO Drawing test_score_cut_count_chart3...
INFO Drawing test_score_cut_cap_chart1...
INFO Drawing test_score_cut_cap_chart2...
INFO Drawing test_score_cut_cap_chart3...
INFO Creating qcut info...
INFO Drawing test_score_qcut_ks_graph...
INFO Drawing test_score_qcut_rate_chart...
INFO Drawing test_score_qcut_count_chart1...
INFO Drawing test_score_qcut_count_chart2...
INFO Drawing test_score_qcut_count_chart3...
INFO Drawing test_score_qcut_cap_chart1...
INFO Drawing test_score_qcut_cap_chart2...
INFO Drawing test_score_qcut_cap_chart3...
INFO Creating precision recall fscore...
INFO Drawing test_classification_chart...
INFO Drawing test_classification_chart...
INFO Drawing roc...
INFO Creating results summary...
INFO Drawing ap...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Drawing cut_psi_chart...
INFO Drawing cut_psi_chart...
INFO Drawing qcut_psi_chart...
INFO Drawing qcut_psi_chart...
INFO Total: 11.38 s
INFO Saving var_drop...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 50, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 50, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 50, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': array([1., 1., 1., ..., 1., 1., 1.]), 'feature_name': 'auto', 'categorical_feature': [4, 8, 9, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO Drawing cv compare graph...
INFO Drawing cv_compare_ks_chart...
INFO Drawing cv_compare_auc_chart...
INFO Drawing cv_compare_ap_chart...
INFO Drawing cv_compare_logloss_chart...
INFO Drawing cv_compare_r2_chart...
INFO Drawing cv_compare_mse_chart...
INFO Drawing cv_compare_mdp_chart...
INFO Drawing cv_compare_psi_chart...
INFO Drawing cv_compare_mpg_chart...
INFO Saving model pkl...
INFO Saving model pmml...
/home/conda_env/miniconda3/lib/python3.9/subprocess.py:941: RuntimeWarning: line buffering (buffering=1) isn't supported in binary mode, the default buffer size will be used
self.stdout = io.open(c2pread, 'rb', bufsize)
/home/conda_env/miniconda3/lib/python3.9/subprocess.py:946: RuntimeWarning: line buffering (buffering=1) isn't supported in binary mode, the default buffer size will be used
self.stderr = io.open(errread, 'rb', bufsize)
INFO Adding model_pmml to excel...
INFO Adding run_model_pmml_in_java to excel...
INFO Saving json...
INFO Saving setting...
INFO Adding runtime to excel...
INFO Saving directory...
INFO Saving...
INFO Total: 51.62 s
INFO Total: 51.67 s
at.Analysis.model_flow(
df_train, "./模型分析demo/v11-lgb/v11-lgb.xlsx", test_data=df_test,
# 目标定义 & 切片定义
response="target", split_col_name="birth_year", use_train_time=True,
exclude_column=["ID", "weight"], # 排除权重
add_info="ID", # 主键添加
# 指定使用训练的class并不进行打分转化
model_type_path="lightgbm.sklearn.LGBMClassifier",
convert_score=False,
customized_parameter={
"n_estimators": list(range(5, 200, 10)),
"learning_rate": [i/100 for i in range(5, 15, 1)],
}
)
INFO Prepare kwargs...
WARNING Setting format_time_edge to True !
INFO Checking train_data...
INFO Checking response...
INFO Checking test_data...
INFO Checking response...
INFO Checking columns...
INFO Checking dtypes...
WARNING Setting allow_nan to True by lightgbm.sklearn !
WARNING Setting allow_discrete to True by lightgbm.sklearn !
WARNING Pre: WOE_ not found, change pre to "" !
WARNING Unable to find nan data, setting allow_nan to False !
WARNING Find that allow_nan is True or allow_discrete is True, setting enable_stepwise to False !
WARNING Find that allow_nan is True or allow_discrete is True, setting enable_corr_filter to False !
WARNING Find that model_search_method not in (order, order-random), setting auto_search_loop to 1 !
WARNING Find that convert_score is False, setting keep_raw_score to False !
WARNING Find that convert_score is False, setting save_score_desc to False !
WARNING Find that convert_score is False, setting save_score_pmml to False !
INFO Model optional flow...
WARNING Check /home/conda_env/模型分析demo/v11-lgb/Data_v11-lgb.xlsx exists !
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'max_depth': 3, 'n_estimators': 30, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 9, 10, 13, 14, 15, 16, 17]}
INFO Using variables: 18...
INFO Training LGBMClassifier...
INFO Filtering importance by LGBMClassifier ...
INFO Total: 18...
INFO Selected: 16...
INFO Model variables: ['AMT_INCOME_TOTAL', 'Age', 'CNT_CHILDREN', 'CNT_FAM_MEMBERS', 'CODE_GENDER', 'DAYS_BIRTH', 'DAYS_EMPLOYED', 'FLAG_OWN_CAR', 'FLAG_OWN_REALTY', 'FLAG_PHONE', 'FLAG_WORK_PHONE', 'NAME_EDUCATION_TYPE', 'NAME_FAMILY_STATUS', 'NAME_HOUSING_TYPE', 'NAME_INCOME_TYPE', 'OCCUPATION_TYPE'] !
INFO Auto params cv...
INFO Auto search loop: 1...
INFO ALL params: {'n_estimators': 5, 'learning_rate': 0.05}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 5, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 5, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 5, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 5, 'learning_rate': 0.06}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 5, 'learning_rate': 0.06, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 5, 'learning_rate': 0.06, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 5, 'learning_rate': 0.06, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 5, 'learning_rate': 0.07}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 5, 'learning_rate': 0.07, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 5, 'learning_rate': 0.07, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 5, 'learning_rate': 0.07, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 5, 'learning_rate': 0.08}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 5, 'learning_rate': 0.08, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 5, 'learning_rate': 0.08, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 5, 'learning_rate': 0.08, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 5, 'learning_rate': 0.09}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 5, 'learning_rate': 0.09, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 5, 'learning_rate': 0.09, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 5, 'learning_rate': 0.09, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 5, 'learning_rate': 0.1}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 5, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 5, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 5, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 5, 'learning_rate': 0.11}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 5, 'learning_rate': 0.11, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 5, 'learning_rate': 0.11, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 5, 'learning_rate': 0.11, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 5, 'learning_rate': 0.12}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 5, 'learning_rate': 0.12, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 5, 'learning_rate': 0.12, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 5, 'learning_rate': 0.12, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 5, 'learning_rate': 0.13}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 5, 'learning_rate': 0.13, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 5, 'learning_rate': 0.13, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 5, 'learning_rate': 0.13, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 5, 'learning_rate': 0.14}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 5, 'learning_rate': 0.14, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 5, 'learning_rate': 0.14, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 5, 'learning_rate': 0.14, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 15, 'learning_rate': 0.05}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 15, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 15, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 15, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 15, 'learning_rate': 0.06}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 15, 'learning_rate': 0.06, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 15, 'learning_rate': 0.06, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 15, 'learning_rate': 0.06, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 15, 'learning_rate': 0.07}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 15, 'learning_rate': 0.07, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 15, 'learning_rate': 0.07, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 15, 'learning_rate': 0.07, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 15, 'learning_rate': 0.08}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 15, 'learning_rate': 0.08, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 15, 'learning_rate': 0.08, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 15, 'learning_rate': 0.08, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 15, 'learning_rate': 0.09}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 15, 'learning_rate': 0.09, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 15, 'learning_rate': 0.09, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 15, 'learning_rate': 0.09, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 15, 'learning_rate': 0.1}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 15, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 15, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 15, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 15, 'learning_rate': 0.11}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 15, 'learning_rate': 0.11, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 15, 'learning_rate': 0.11, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 15, 'learning_rate': 0.11, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 15, 'learning_rate': 0.12}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 15, 'learning_rate': 0.12, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 15, 'learning_rate': 0.12, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 15, 'learning_rate': 0.12, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 15, 'learning_rate': 0.13}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 15, 'learning_rate': 0.13, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 15, 'learning_rate': 0.13, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 15, 'learning_rate': 0.13, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 15, 'learning_rate': 0.14}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 15, 'learning_rate': 0.14, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 15, 'learning_rate': 0.14, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 15, 'learning_rate': 0.14, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 25, 'learning_rate': 0.05}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 25, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 25, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 25, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 25, 'learning_rate': 0.06}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 25, 'learning_rate': 0.06, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 25, 'learning_rate': 0.06, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 25, 'learning_rate': 0.06, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 25, 'learning_rate': 0.07}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 25, 'learning_rate': 0.07, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 25, 'learning_rate': 0.07, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 25, 'learning_rate': 0.07, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 25, 'learning_rate': 0.08}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 25, 'learning_rate': 0.08, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 25, 'learning_rate': 0.08, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 25, 'learning_rate': 0.08, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 25, 'learning_rate': 0.09}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 25, 'learning_rate': 0.09, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 25, 'learning_rate': 0.09, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 25, 'learning_rate': 0.09, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 25, 'learning_rate': 0.1}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 25, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 25, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 25, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 25, 'learning_rate': 0.11}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 25, 'learning_rate': 0.11, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 25, 'learning_rate': 0.11, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 25, 'learning_rate': 0.11, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 25, 'learning_rate': 0.12}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 25, 'learning_rate': 0.12, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 25, 'learning_rate': 0.12, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 25, 'learning_rate': 0.12, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 25, 'learning_rate': 0.13}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 25, 'learning_rate': 0.13, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 25, 'learning_rate': 0.13, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 25, 'learning_rate': 0.13, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 25, 'learning_rate': 0.14}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 25, 'learning_rate': 0.14, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 25, 'learning_rate': 0.14, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 25, 'learning_rate': 0.14, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 35, 'learning_rate': 0.05}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 35, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 35, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 35, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 35, 'learning_rate': 0.06}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 35, 'learning_rate': 0.06, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 35, 'learning_rate': 0.06, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 35, 'learning_rate': 0.06, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 35, 'learning_rate': 0.07}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 35, 'learning_rate': 0.07, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 35, 'learning_rate': 0.07, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 35, 'learning_rate': 0.07, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 35, 'learning_rate': 0.08}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 35, 'learning_rate': 0.08, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 35, 'learning_rate': 0.08, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 35, 'learning_rate': 0.08, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 35, 'learning_rate': 0.09}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 35, 'learning_rate': 0.09, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 35, 'learning_rate': 0.09, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 35, 'learning_rate': 0.09, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 35, 'learning_rate': 0.1}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 35, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 35, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 35, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 35, 'learning_rate': 0.11}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 35, 'learning_rate': 0.11, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 35, 'learning_rate': 0.11, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 35, 'learning_rate': 0.11, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 35, 'learning_rate': 0.12}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 35, 'learning_rate': 0.12, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 35, 'learning_rate': 0.12, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 35, 'learning_rate': 0.12, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 35, 'learning_rate': 0.13}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 35, 'learning_rate': 0.13, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 35, 'learning_rate': 0.13, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 35, 'learning_rate': 0.13, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 35, 'learning_rate': 0.14}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 35, 'learning_rate': 0.14, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 35, 'learning_rate': 0.14, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 35, 'learning_rate': 0.14, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 45, 'learning_rate': 0.05}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 45, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 45, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 45, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 45, 'learning_rate': 0.06}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 45, 'learning_rate': 0.06, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 45, 'learning_rate': 0.06, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 45, 'learning_rate': 0.06, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 45, 'learning_rate': 0.07}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 45, 'learning_rate': 0.07, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 45, 'learning_rate': 0.07, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 45, 'learning_rate': 0.07, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 45, 'learning_rate': 0.08}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 45, 'learning_rate': 0.08, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 45, 'learning_rate': 0.08, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 45, 'learning_rate': 0.08, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 45, 'learning_rate': 0.09}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 45, 'learning_rate': 0.09, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 45, 'learning_rate': 0.09, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 45, 'learning_rate': 0.09, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 45, 'learning_rate': 0.1}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 45, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 45, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 45, 'learning_rate': 0.1, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 45, 'learning_rate': 0.11}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 45, 'learning_rate': 0.11, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 45, 'learning_rate': 0.11, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 45, 'learning_rate': 0.11, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 45, 'learning_rate': 0.12}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 45, 'learning_rate': 0.12, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 45, 'learning_rate': 0.12, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 45, 'learning_rate': 0.12, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 45, 'learning_rate': 0.13}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 45, 'learning_rate': 0.13, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 45, 'learning_rate': 0.13, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 45, 'learning_rate': 0.13, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 45, 'learning_rate': 0.14}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 45, 'learning_rate': 0.14, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 45, 'learning_rate': 0.14, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 45, 'learning_rate': 0.14, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 55, 'learning_rate': 0.05}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 55, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 55, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 55, 'learning_rate': 0.05, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 55, 'learning_rate': 0.06}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 55, 'learning_rate': 0.06, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 55, 'learning_rate': 0.06, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 55, 'learning_rate': 0.06, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO Drawing cv search graph...
INFO Drawing params_search_chart...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 5, 'learning_rate': 0.06, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO [FIT_TRANSFORM] ['AMT_INCOME_TOTAL']: 0.000122 secs
INFO [FIT_TRANSFORM] ['Age']: 6.3e-05 secs
INFO [FIT_TRANSFORM] ['CNT_CHILDREN']: 6.1e-05 secs
INFO [FIT_TRANSFORM] ['CNT_FAM_MEMBERS']: 6.4e-05 secs
INFO [FIT_TRANSFORM] ['CODE_GENDER']: 0.018901 secs
INFO [FIT_TRANSFORM] ['DAYS_BIRTH']: 0.001318 secs
INFO [FIT_TRANSFORM] ['DAYS_EMPLOYED']: 0.000194 secs
INFO [FIT_TRANSFORM] ['FLAG_OWN_CAR']: 0.019772 secs
INFO [FIT_TRANSFORM] ['FLAG_OWN_REALTY']: 0.021782 secs
INFO [FIT_TRANSFORM] ['FLAG_PHONE']: 0.001121 secs
INFO [FIT_TRANSFORM] ['FLAG_WORK_PHONE']: 6.6e-05 secs
INFO [FIT_TRANSFORM] ['NAME_EDUCATION_TYPE']: 0.021156 secs
INFO [FIT_TRANSFORM] ['NAME_FAMILY_STATUS']: 0.020246 secs
INFO [FIT_TRANSFORM] ['NAME_HOUSING_TYPE']: 0.020276 secs
INFO [FIT_TRANSFORM] ['NAME_INCOME_TYPE']: 0.02078 secs
INFO [FIT_TRANSFORM] ['OCCUPATION_TYPE']: 0.018974 secs
INFO [TRANSFORM] ['AMT_INCOME_TOTAL']: 0.000142 secs
INFO [TRANSFORM] ['Age']: 0.000143 secs
INFO [TRANSFORM] ['CNT_CHILDREN']: 0.001299 secs
INFO [TRANSFORM] ['CNT_FAM_MEMBERS']: 0.000354 secs
INFO [TRANSFORM] ['CODE_GENDER']: 0.011745 secs
INFO [TRANSFORM] ['DAYS_BIRTH']: 0.000188 secs
INFO [TRANSFORM] ['DAYS_EMPLOYED']: 0.000171 secs
INFO [TRANSFORM] ['FLAG_OWN_CAR']: 0.011451 secs
INFO [TRANSFORM] ['FLAG_OWN_REALTY']: 0.012903 secs
INFO [TRANSFORM] ['FLAG_PHONE']: 0.001422 secs
INFO [TRANSFORM] ['FLAG_WORK_PHONE']: 7.6e-05 secs
INFO [TRANSFORM] ['NAME_EDUCATION_TYPE']: 0.012988 secs
INFO [TRANSFORM] ['NAME_FAMILY_STATUS']: 0.013955 secs
INFO [TRANSFORM] ['NAME_HOUSING_TYPE']: 0.011453 secs
INFO [TRANSFORM] ['NAME_INCOME_TYPE']: 0.012127 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE']: 0.013028 secs
INFO [TRANSFORM] ['AMT_INCOME_TOTAL']: 0.00012 secs
INFO [TRANSFORM] ['Age']: 9.4e-05 secs
INFO [TRANSFORM] ['CNT_CHILDREN']: 6e-05 secs
INFO [TRANSFORM] ['CNT_FAM_MEMBERS']: 6.4e-05 secs
INFO [TRANSFORM] ['CODE_GENDER']: 0.004913 secs
INFO [TRANSFORM] ['DAYS_BIRTH']: 9.5e-05 secs
INFO [TRANSFORM] ['DAYS_EMPLOYED']: 0.000159 secs
INFO [TRANSFORM] ['FLAG_OWN_CAR']: 0.00555 secs
INFO [TRANSFORM] ['FLAG_OWN_REALTY']: 0.006072 secs
INFO [TRANSFORM] ['FLAG_PHONE']: 0.000131 secs
INFO [TRANSFORM] ['FLAG_WORK_PHONE']: 6.4e-05 secs
INFO [TRANSFORM] ['NAME_EDUCATION_TYPE']: 0.005105 secs
INFO [TRANSFORM] ['NAME_FAMILY_STATUS']: 0.006503 secs
INFO [TRANSFORM] ['NAME_HOUSING_TYPE']: 0.005266 secs
INFO [TRANSFORM] ['NAME_INCOME_TYPE']: 0.005181 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE']: 0.00557 secs
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Drawing train_score_uncut_ks_graph...
INFO Drawing train_score_uncut_count_chart1...
INFO Drawing train_score_uncut_count_chart2...
INFO Drawing train_score_uncut_count_chart3...
INFO Drawing train_score_uncut_cap_chart1...
INFO Drawing train_score_uncut_cap_chart2...
INFO Drawing train_score_uncut_cap_chart3...
INFO Creating cut info...
INFO Drawing train_score_cut_ks_graph...
INFO Drawing train_score_cut_rate_chart...
INFO Drawing train_score_cut_count_chart1...
INFO Drawing train_score_cut_count_chart2...
INFO Drawing train_score_cut_count_chart3...
INFO Drawing train_score_cut_cap_chart1...
INFO Drawing train_score_cut_cap_chart2...
INFO Drawing train_score_cut_cap_chart3...
INFO Creating qcut info...
INFO Drawing train_score_qcut_ks_graph...
INFO Drawing train_score_qcut_rate_chart...
INFO Drawing train_score_qcut_count_chart1...
INFO Drawing train_score_qcut_count_chart2...
INFO Drawing train_score_qcut_count_chart3...
INFO Drawing train_score_qcut_cap_chart1...
INFO Drawing train_score_qcut_cap_chart2...
INFO Drawing train_score_qcut_cap_chart3...
INFO Creating precision recall fscore...
INFO Drawing train_classification_chart...
INFO Drawing train_classification_chart...
INFO Drawing roc...
INFO Creating results summary...
INFO Drawing ap...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Drawing test_score_uncut_ks_graph...
INFO Drawing test_score_uncut_count_chart1...
INFO Drawing test_score_uncut_count_chart2...
INFO Drawing test_score_uncut_count_chart3...
INFO Drawing test_score_uncut_cap_chart1...
INFO Drawing test_score_uncut_cap_chart2...
INFO Drawing test_score_uncut_cap_chart3...
INFO Creating cut info...
INFO Drawing test_score_cut_ks_graph...
INFO Drawing test_score_cut_rate_chart...
INFO Drawing test_score_cut_count_chart1...
INFO Drawing test_score_cut_count_chart2...
INFO Drawing test_score_cut_count_chart3...
INFO Drawing test_score_cut_cap_chart1...
INFO Drawing test_score_cut_cap_chart2...
INFO Drawing test_score_cut_cap_chart3...
INFO Creating qcut info...
INFO Drawing test_score_qcut_ks_graph...
INFO Drawing test_score_qcut_rate_chart...
INFO Drawing test_score_qcut_count_chart1...
INFO Drawing test_score_qcut_count_chart2...
INFO Drawing test_score_qcut_count_chart3...
INFO Drawing test_score_qcut_cap_chart1...
INFO Drawing test_score_qcut_cap_chart2...
INFO Drawing test_score_qcut_cap_chart3...
INFO Creating precision recall fscore...
INFO Drawing test_classification_chart...
INFO Drawing test_classification_chart...
INFO Drawing roc...
INFO Creating results summary...
INFO Drawing ap...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Drawing cut_psi_chart...
INFO Drawing cut_psi_chart...
INFO Drawing qcut_psi_chart...
INFO Drawing qcut_psi_chart...
INFO Total: 10.06 s
INFO Saving var_drop...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 5, 'learning_rate': 0.06, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 5, 'learning_rate': 0.06, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 5, 'learning_rate': 0.06, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO Drawing cv compare graph...
INFO Drawing cv_compare_ks_chart...
INFO Drawing cv_compare_auc_chart...
INFO Drawing cv_compare_ap_chart...
INFO Drawing cv_compare_logloss_chart...
INFO Drawing cv_compare_r2_chart...
INFO Drawing cv_compare_mse_chart...
INFO Drawing cv_compare_mdp_chart...
INFO Drawing cv_compare_psi_chart...
INFO Drawing cv_compare_mpg_chart...
INFO Saving model pkl...
INFO Saving model pmml...
/home/conda_env/miniconda3/lib/python3.9/subprocess.py:941: RuntimeWarning: line buffering (buffering=1) isn't supported in binary mode, the default buffer size will be used
self.stdout = io.open(c2pread, 'rb', bufsize)
/home/conda_env/miniconda3/lib/python3.9/subprocess.py:946: RuntimeWarning: line buffering (buffering=1) isn't supported in binary mode, the default buffer size will be used
self.stderr = io.open(errread, 'rb', bufsize)
INFO Adding model_pmml to excel...
INFO Adding run_model_pmml_in_java to excel...
INFO Saving json...
INFO Saving setting...
INFO Adding runtime to excel...
INFO Saving directory...
INFO Saving...
INFO Total: 118.91 s
INFO Total: 118.96 s
at.Analysis.model_flow(
df_train, "./模型分析demo/v12-lgb/v12-lgb.xlsx", test_data=df_test,
# 目标定义 & 切片定义
response="target", split_col_name="birth_year", use_train_time=True,
exclude_column=["ID", "weight"], # 排除权重
add_info="ID", # 主键添加
# 指定使用训练的class并不进行打分转化
model_type_path="lightgbm.sklearn.LGBMClassifier",
convert_score=False,
customized_parameter={
"n_estimators": list(range(5, 200, 10)),
"learning_rate": [i/100 for i in range(5, 15, 1)],
"max_depth": [3, 4, 5, -1],
"min_child_samples": list(range(20, 55, 5)),
"min_child_weight": [i/1000 for i in range(1, 11, 1)],
},
model_search_method="order",
model_search_params={"binary": ["max", -1]}
)
INFO Prepare kwargs...
WARNING Setting format_time_edge to True !
INFO Checking train_data...
INFO Checking response...
INFO Checking test_data...
INFO Checking response...
INFO Checking columns...
INFO Checking dtypes...
WARNING Setting allow_nan to True by lightgbm.sklearn !
WARNING Setting allow_discrete to True by lightgbm.sklearn !
WARNING Pre: WOE_ not found, change pre to "" !
WARNING Unable to find nan data, setting allow_nan to False !
WARNING Find that allow_nan is True or allow_discrete is True, setting enable_stepwise to False !
WARNING Find that allow_nan is True or allow_discrete is True, setting enable_corr_filter to False !
WARNING Find that convert_score is False, setting keep_raw_score to False !
WARNING Find that convert_score is False, setting save_score_desc to False !
WARNING Find that convert_score is False, setting save_score_pmml to False !
INFO Model optional flow...
WARNING Check /home/conda_env/模型分析demo/v12-lgb/Data_v12-lgb.xlsx exists !
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'max_depth': 3, 'n_estimators': 30, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 9, 10, 13, 14, 15, 16, 17]}
INFO Using variables: 18...
INFO Training LGBMClassifier...
INFO Filtering importance by LGBMClassifier ...
INFO Total: 18...
INFO Selected: 16...
INFO Model variables: ['AMT_INCOME_TOTAL', 'Age', 'CNT_CHILDREN', 'CNT_FAM_MEMBERS', 'CODE_GENDER', 'DAYS_BIRTH', 'DAYS_EMPLOYED', 'FLAG_OWN_CAR', 'FLAG_OWN_REALTY', 'FLAG_PHONE', 'FLAG_WORK_PHONE', 'NAME_EDUCATION_TYPE', 'NAME_FAMILY_STATUS', 'NAME_HOUSING_TYPE', 'NAME_INCOME_TYPE', 'OCCUPATION_TYPE'] !
INFO Auto params cv...
INFO Auto search loop: 1...
/tmp/ipykernel_45055/185663500.py:1: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
at.Analysis.model_flow(
INFO ALL params: {'n_estimators': 5, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 5, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 5, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 5, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 15, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 15, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 15, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 15, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 25, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 25, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 25, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 25, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 35, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 35, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 35, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 35, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 45, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 45, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 45, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 45, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 55, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 55, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 55, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 55, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 65, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 65, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 65, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 65, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 75, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 75, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 75, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 75, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 85, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 85, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 85, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 85, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 95, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 95, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 95, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 95, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 105, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 105, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 105, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 105, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 115, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 115, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 115, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 115, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 125, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 125, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 125, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 125, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 135, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 135, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 135, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 135, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 145, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 145, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 145, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 145, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 155, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 155, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 155, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 155, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 165, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 165, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 165, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 165, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 175, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 175, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 175, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 175, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 185, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 185, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 185, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 185, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 195, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.05, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO Auto search n_estimators...
INFO roc_auc_score; n_estimators: 5; max: 0.7054496069706926
INFO roc_auc_score; n_estimators: 15; max: 0.7401149364191927
INFO roc_auc_score; n_estimators: 25; max: 0.7613715997801144
INFO roc_auc_score; n_estimators: 35; max: 0.7750284298736776
INFO roc_auc_score; n_estimators: 45; max: 0.7913186658964141
INFO roc_auc_score; n_estimators: 55; max: 0.7995883904471894
INFO roc_auc_score; n_estimators: 65; max: 0.8054500265998243
INFO roc_auc_score; n_estimators: 75; max: 0.8126983039056748
INFO roc_auc_score; n_estimators: 85; max: 0.8211813399317682
INFO roc_auc_score; n_estimators: 95; max: 0.8308033193596833
INFO roc_auc_score; n_estimators: 105; max: 0.8414131757019813
INFO roc_auc_score; n_estimators: 115; max: 0.8454930199356475
INFO roc_auc_score; n_estimators: 125; max: 0.8491806274947535
INFO roc_auc_score; n_estimators: 135; max: 0.8514783301185079
INFO roc_auc_score; n_estimators: 145; max: 0.8554705184890926
INFO roc_auc_score; n_estimators: 155; max: 0.860208364514109
INFO roc_auc_score; n_estimators: 165; max: 0.865420974276268
INFO roc_auc_score; n_estimators: 175; max: 0.8676911678791618
INFO roc_auc_score; n_estimators: 185; max: 0.8714691122652467
INFO roc_auc_score; n_estimators: 195; max: 0.87655396826951
INFO Auto select n_estimators: 195
/tmp/ipykernel_45055/185663500.py:1: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
at.Analysis.model_flow(
INFO ALL params: {'n_estimators': 195, 'learning_rate': 0.06, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.06, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.06, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.06, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 195, 'learning_rate': 0.07, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.07, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.07, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.07, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 195, 'learning_rate': 0.08, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.08, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.08, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.08, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 195, 'learning_rate': 0.09, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.09, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.09, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.09, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 195, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 195, 'learning_rate': 0.11, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.11, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.11, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.11, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 195, 'learning_rate': 0.12, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.12, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.12, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.12, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 195, 'learning_rate': 0.13, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.13, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.13, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.13, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': 3, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO Auto search learning_rate...
INFO roc_auc_score; learning_rate: 0.05; max: 0.87655396826951
INFO roc_auc_score; learning_rate: 0.06; max: 0.8872174438897569
INFO roc_auc_score; learning_rate: 0.07; max: 0.9013805558466214
INFO roc_auc_score; learning_rate: 0.08; max: 0.9147177928858042
INFO roc_auc_score; learning_rate: 0.09; max: 0.92813158365128
INFO roc_auc_score; learning_rate: 0.1; max: 0.9366350877409254
INFO roc_auc_score; learning_rate: 0.11; max: 0.9404524189621485
INFO roc_auc_score; learning_rate: 0.12; max: 0.9494214091556284
INFO roc_auc_score; learning_rate: 0.13; max: 0.9545355925517292
INFO roc_auc_score; learning_rate: 0.14; max: 0.9621562016531167
INFO Auto select learning_rate: 0.14
/tmp/ipykernel_45055/185663500.py:1: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
at.Analysis.model_flow(
INFO ALL params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': 4, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': 4, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': 4, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': 4, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': 5, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': 5, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': 5, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': 5, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO Auto search max_depth...
INFO roc_auc_score; max_depth: 3; max: 0.9621562016531167
INFO roc_auc_score; max_depth: 4; max: 0.9916023890679799
INFO roc_auc_score; max_depth: 5; max: 0.9950484162254453
INFO roc_auc_score; max_depth: -1; max: 0.9968954438067635
INFO Auto select max_depth: -1
/tmp/ipykernel_45055/185663500.py:1: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
at.Analysis.model_flow(
INFO ALL params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 25, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 25, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 25, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 25, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 30, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 30, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 30, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 30, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 35, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 35, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 35, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 35, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 40, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 40, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 40, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 40, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 45, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 45, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 45, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 45, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 50, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 50, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 50, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 50, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO Auto search min_child_samples...
INFO roc_auc_score; min_child_samples: 20; max: 0.9968954438067635
INFO roc_auc_score; min_child_samples: 25; max: 0.996816413653613
INFO roc_auc_score; min_child_samples: 30; max: 0.996842990165292
INFO roc_auc_score; min_child_samples: 35; max: 0.9967404141553029
INFO roc_auc_score; min_child_samples: 40; max: 0.9968637384945852
INFO roc_auc_score; min_child_samples: 45; max: 0.9968446220563599
INFO roc_auc_score; min_child_samples: 50; max: 0.9968168799082038
INFO Auto select min_child_samples: 20
/tmp/ipykernel_45055/185663500.py:1: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
at.Analysis.model_flow(
INFO ALL params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.002}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.002, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.002, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.002, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.003}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.003, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.003, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.003, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.004}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.004, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.004, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.004, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.005}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.005, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.005, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.005, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.006}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.006, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.006, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.006, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.007}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.007, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.007, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.007, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.008}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.008, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.008, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.008, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.009}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.009, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.009, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.009, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.01}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.01, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.01, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.01, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO Auto search min_child_weight...
INFO roc_auc_score; min_child_weight: 0.001; max: 0.9968954438067635
INFO roc_auc_score; min_child_weight: 0.002; max: 0.9968954438067635
INFO roc_auc_score; min_child_weight: 0.003; max: 0.9968954438067635
INFO roc_auc_score; min_child_weight: 0.004; max: 0.9968954438067635
INFO roc_auc_score; min_child_weight: 0.005; max: 0.9968954438067635
INFO roc_auc_score; min_child_weight: 0.006; max: 0.9968954438067635
INFO roc_auc_score; min_child_weight: 0.007; max: 0.9968954438067635
INFO roc_auc_score; min_child_weight: 0.008; max: 0.9968954438067635
INFO roc_auc_score; min_child_weight: 0.009; max: 0.9968954438067635
INFO roc_auc_score; min_child_weight: 0.01; max: 0.9968954438067635
WARNING Useless min_child_weight: [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01]
WARNING Reset min_child_weight to 0.001
INFO Auto search loop: 2...
INFO ALL params: {'n_estimators': 5, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 5, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 5, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 5, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 15, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 15, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 15, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 15, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 25, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 25, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 25, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 25, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 35, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 35, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 35, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 35, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 45, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 45, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 45, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 45, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 55, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 55, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 55, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 55, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 65, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 65, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 65, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 65, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 75, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 75, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 75, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 75, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 85, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 85, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 85, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 85, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 95, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 95, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 95, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 95, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 105, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 105, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 105, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 105, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 115, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 115, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 115, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 115, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 125, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 125, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 125, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 125, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 135, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 135, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 135, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 135, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 145, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 145, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 145, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 145, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 155, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 155, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 155, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 155, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 165, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 165, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 165, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 165, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 175, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 175, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 175, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 175, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 185, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 185, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 185, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 185, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO Auto search n_estimators...
INFO roc_auc_score; n_estimators: 5; max: 0.8490571317623999
INFO roc_auc_score; n_estimators: 15; max: 0.9495035190966745
INFO roc_auc_score; n_estimators: 25; max: 0.9743177657653216
INFO roc_auc_score; n_estimators: 35; max: 0.9876382374151714
INFO roc_auc_score; n_estimators: 45; max: 0.9917485931249577
INFO roc_auc_score; n_estimators: 55; max: 0.9934564438883376
INFO roc_auc_score; n_estimators: 65; max: 0.9941633022885988
INFO roc_auc_score; n_estimators: 75; max: 0.9949668982553219
INFO roc_auc_score; n_estimators: 85; max: 0.9954548336846563
INFO roc_auc_score; n_estimators: 95; max: 0.9958863523084964
INFO roc_auc_score; n_estimators: 105; max: 0.9961201789858123
INFO roc_auc_score; n_estimators: 115; max: 0.9963097114769967
INFO roc_auc_score; n_estimators: 125; max: 0.9964919004583748
INFO roc_auc_score; n_estimators: 135; max: 0.9966135929065891
INFO roc_auc_score; n_estimators: 145; max: 0.9966905249140809
INFO roc_auc_score; n_estimators: 155; max: 0.9967814445592985
INFO roc_auc_score; n_estimators: 165; max: 0.9968304012913387
INFO roc_auc_score; n_estimators: 175; max: 0.9968635053672896
INFO roc_auc_score; n_estimators: 185; max: 0.9968777261323108
INFO roc_auc_score; n_estimators: 195; max: 0.9968954438067635
INFO Auto select n_estimators: 195
INFO ALL params: {'n_estimators': 195, 'learning_rate': 0.05, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.05, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.05, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.05, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 195, 'learning_rate': 0.06, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.06, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.06, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.06, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 195, 'learning_rate': 0.07, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.07, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.07, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.07, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 195, 'learning_rate': 0.08, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.08, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.08, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.08, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 195, 'learning_rate': 0.09, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.09, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.09, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.09, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 195, 'learning_rate': 0.1, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.1, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.1, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.1, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 195, 'learning_rate': 0.11, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.11, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.11, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.11, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 195, 'learning_rate': 0.12, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.12, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.12, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.12, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 195, 'learning_rate': 0.13, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.13, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.13, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.13, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO Auto search learning_rate...
INFO roc_auc_score; learning_rate: 0.05; max: 0.9948265213666259
INFO roc_auc_score; learning_rate: 0.06; max: 0.9953066812884106
INFO roc_auc_score; learning_rate: 0.07; max: 0.9956967032536644
INFO roc_auc_score; learning_rate: 0.08; max: 0.9960641118712615
INFO roc_auc_score; learning_rate: 0.09; max: 0.9962978219849297
INFO roc_auc_score; learning_rate: 0.1; max: 0.996545752863619
INFO roc_auc_score; learning_rate: 0.11; max: 0.9967259602629862
INFO roc_auc_score; learning_rate: 0.12; max: 0.9966788685493094
INFO roc_auc_score; learning_rate: 0.13; max: 0.9967641931394368
INFO roc_auc_score; learning_rate: 0.14; max: 0.9968954438067635
INFO Auto select learning_rate: 0.14
INFO Auto search max_depth...
INFO roc_auc_score; max_depth: 3; max: 0.9621562016531167
INFO roc_auc_score; max_depth: 4; max: 0.9916023890679799
INFO roc_auc_score; max_depth: 5; max: 0.9950484162254453
INFO roc_auc_score; max_depth: -1; max: 0.9968954438067635
INFO Auto select max_depth: -1
INFO Drawing cv search graph...
INFO Drawing params_search_chart...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO [FIT_TRANSFORM] ['AMT_INCOME_TOTAL']: 0.000118 secs
INFO [FIT_TRANSFORM] ['Age']: 7.3e-05 secs
INFO [FIT_TRANSFORM] ['CNT_CHILDREN']: 7.6e-05 secs
INFO [FIT_TRANSFORM] ['CNT_FAM_MEMBERS']: 0.000106 secs
INFO [FIT_TRANSFORM] ['CODE_GENDER']: 0.021126 secs
INFO [FIT_TRANSFORM] ['DAYS_BIRTH']: 0.000168 secs
INFO [FIT_TRANSFORM] ['DAYS_EMPLOYED']: 0.000137 secs
INFO [FIT_TRANSFORM] ['FLAG_OWN_CAR']: 0.020402 secs
INFO [FIT_TRANSFORM] ['FLAG_OWN_REALTY']: 0.019935 secs
INFO [FIT_TRANSFORM] ['FLAG_PHONE']: 0.000189 secs
INFO [FIT_TRANSFORM] ['FLAG_WORK_PHONE']: 7.1e-05 secs
INFO [FIT_TRANSFORM] ['NAME_EDUCATION_TYPE']: 0.026569 secs
INFO [FIT_TRANSFORM] ['NAME_FAMILY_STATUS']: 0.027534 secs
INFO [FIT_TRANSFORM] ['NAME_HOUSING_TYPE']: 0.021775 secs
INFO [FIT_TRANSFORM] ['NAME_INCOME_TYPE']: 0.022049 secs
INFO [FIT_TRANSFORM] ['OCCUPATION_TYPE']: 0.020273 secs
INFO [TRANSFORM] ['AMT_INCOME_TOTAL']: 0.000186 secs
INFO [TRANSFORM] ['Age']: 0.000178 secs
INFO [TRANSFORM] ['CNT_CHILDREN']: 9.7e-05 secs
INFO [TRANSFORM] ['CNT_FAM_MEMBERS']: 0.000112 secs
INFO [TRANSFORM] ['CODE_GENDER']: 0.011546 secs
INFO [TRANSFORM] ['DAYS_BIRTH']: 0.000126 secs
INFO [TRANSFORM] ['DAYS_EMPLOYED']: 8e-05 secs
INFO [TRANSFORM] ['FLAG_OWN_CAR']: 0.011677 secs
INFO [TRANSFORM] ['FLAG_OWN_REALTY']: 0.011106 secs
INFO [TRANSFORM] ['FLAG_PHONE']: 0.00014 secs
INFO [TRANSFORM] ['FLAG_WORK_PHONE']: 0.000278 secs
INFO [TRANSFORM] ['NAME_EDUCATION_TYPE']: 0.013192 secs
INFO [TRANSFORM] ['NAME_FAMILY_STATUS']: 0.012107 secs
INFO [TRANSFORM] ['NAME_HOUSING_TYPE']: 0.011589 secs
INFO [TRANSFORM] ['NAME_INCOME_TYPE']: 0.012411 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE']: 0.013504 secs
INFO [TRANSFORM] ['AMT_INCOME_TOTAL']: 0.000135 secs
INFO [TRANSFORM] ['Age']: 8.5e-05 secs
INFO [TRANSFORM] ['CNT_CHILDREN']: 6.2e-05 secs
INFO [TRANSFORM] ['CNT_FAM_MEMBERS']: 0.000247 secs
INFO [TRANSFORM] ['CODE_GENDER']: 0.005391 secs
INFO [TRANSFORM] ['DAYS_BIRTH']: 9e-05 secs
INFO [TRANSFORM] ['DAYS_EMPLOYED']: 0.000153 secs
INFO [TRANSFORM] ['FLAG_OWN_CAR']: 0.005486 secs
INFO [TRANSFORM] ['FLAG_OWN_REALTY']: 0.004894 secs
INFO [TRANSFORM] ['FLAG_PHONE']: 0.000325 secs
INFO [TRANSFORM] ['FLAG_WORK_PHONE']: 0.000271 secs
INFO [TRANSFORM] ['NAME_EDUCATION_TYPE']: 0.005318 secs
INFO [TRANSFORM] ['NAME_FAMILY_STATUS']: 0.005085 secs
INFO [TRANSFORM] ['NAME_HOUSING_TYPE']: 0.005356 secs
INFO [TRANSFORM] ['NAME_INCOME_TYPE']: 0.005416 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE']: 0.005934 secs
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Drawing train_score_uncut_ks_graph...
INFO Drawing train_score_uncut_count_chart1...
INFO Drawing train_score_uncut_count_chart2...
INFO Drawing train_score_uncut_count_chart3...
INFO Drawing train_score_uncut_cap_chart1...
INFO Drawing train_score_uncut_cap_chart2...
INFO Drawing train_score_uncut_cap_chart3...
INFO Creating cut info...
INFO Drawing train_score_cut_ks_graph...
INFO Drawing train_score_cut_rate_chart...
INFO Drawing train_score_cut_count_chart1...
INFO Drawing train_score_cut_count_chart2...
INFO Drawing train_score_cut_count_chart3...
INFO Drawing train_score_cut_cap_chart1...
INFO Drawing train_score_cut_cap_chart2...
INFO Drawing train_score_cut_cap_chart3...
INFO Creating qcut info...
INFO Drawing train_score_qcut_ks_graph...
INFO Drawing train_score_qcut_rate_chart...
INFO Drawing train_score_qcut_count_chart1...
INFO Drawing train_score_qcut_count_chart2...
INFO Drawing train_score_qcut_count_chart3...
INFO Drawing train_score_qcut_cap_chart1...
INFO Drawing train_score_qcut_cap_chart2...
INFO Drawing train_score_qcut_cap_chart3...
INFO Creating precision recall fscore...
INFO Drawing train_classification_chart...
INFO Drawing train_classification_chart...
INFO Drawing roc...
INFO Creating results summary...
INFO Drawing ap...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Drawing test_score_uncut_ks_graph...
INFO Drawing test_score_uncut_count_chart1...
INFO Drawing test_score_uncut_count_chart2...
INFO Drawing test_score_uncut_count_chart3...
INFO Drawing test_score_uncut_cap_chart1...
INFO Drawing test_score_uncut_cap_chart2...
INFO Drawing test_score_uncut_cap_chart3...
INFO Creating cut info...
INFO Drawing test_score_cut_ks_graph...
INFO Drawing test_score_cut_rate_chart...
INFO Drawing test_score_cut_count_chart1...
INFO Drawing test_score_cut_count_chart2...
INFO Drawing test_score_cut_count_chart3...
INFO Drawing test_score_cut_cap_chart1...
INFO Drawing test_score_cut_cap_chart2...
INFO Drawing test_score_cut_cap_chart3...
INFO Creating qcut info...
INFO Drawing test_score_qcut_ks_graph...
INFO Drawing test_score_qcut_rate_chart...
INFO Drawing test_score_qcut_count_chart1...
INFO Drawing test_score_qcut_count_chart2...
INFO Drawing test_score_qcut_count_chart3...
INFO Drawing test_score_qcut_cap_chart1...
INFO Drawing test_score_qcut_cap_chart2...
INFO Drawing test_score_qcut_cap_chart3...
INFO Creating precision recall fscore...
INFO Drawing test_classification_chart...
INFO Drawing test_classification_chart...
INFO Drawing roc...
INFO Creating results summary...
INFO Drawing ap...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Drawing cut_psi_chart...
INFO Drawing cut_psi_chart...
INFO Drawing qcut_psi_chart...
INFO Drawing qcut_psi_chart...
INFO Total: 10.97 s
INFO Saving var_drop...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 195, 'learning_rate': 0.14, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO Drawing cv compare graph...
INFO Drawing cv_compare_ks_chart...
INFO Drawing cv_compare_auc_chart...
INFO Drawing cv_compare_ap_chart...
INFO Drawing cv_compare_logloss_chart...
INFO Drawing cv_compare_r2_chart...
INFO Drawing cv_compare_mse_chart...
INFO Drawing cv_compare_mdp_chart...
INFO Drawing cv_compare_psi_chart...
INFO Drawing cv_compare_mpg_chart...
INFO Saving model pkl...
INFO Saving model pmml...
/home/conda_env/miniconda3/lib/python3.9/subprocess.py:941: RuntimeWarning: line buffering (buffering=1) isn't supported in binary mode, the default buffer size will be used
self.stdout = io.open(c2pread, 'rb', bufsize)
/home/conda_env/miniconda3/lib/python3.9/subprocess.py:946: RuntimeWarning: line buffering (buffering=1) isn't supported in binary mode, the default buffer size will be used
self.stderr = io.open(errread, 'rb', bufsize)
INFO Adding model_pmml to excel...
INFO Adding run_model_pmml_in_java to excel...
INFO Saving json...
INFO Saving setting...
INFO Adding runtime to excel...
INFO Saving directory...
INFO Saving...
INFO Total: 209.20 s
INFO Total: 209.26 s
at.Analysis.model_flow(
df_train, "./模型分析demo/v13-lgb/v13-lgb.xlsx", test_data=df_test,
# 目标定义 & 切片定义
response="target", split_col_name="birth_year", use_train_time=True,
exclude_column=["ID", "weight"], # 排除权重
add_info="ID", # 主键添加
# 指定使用训练的class并不进行打分转化
model_type_path="lightgbm.sklearn.LGBMClassifier",
convert_score=False,
customized_parameter={
"n_estimators": list(range(5, 200, 10)),
"learning_rate": [i/100 for i in range(5, 15, 1)],
"max_depth": [3, 4, 5, -1],
"min_child_samples": list(range(20, 55, 5)),
"min_child_weight": [i/1000 for i in range(1, 11, 1)],
},
model_search_method="random",
model_search_params={"binary": ["max", -1]},
)
INFO Prepare kwargs...
WARNING Setting format_time_edge to True !
INFO Checking train_data...
INFO Checking response...
INFO Checking test_data...
INFO Checking response...
INFO Checking columns...
INFO Checking dtypes...
WARNING Setting allow_nan to True by lightgbm.sklearn !
WARNING Setting allow_discrete to True by lightgbm.sklearn !
WARNING Pre: WOE_ not found, change pre to "" !
WARNING Unable to find nan data, setting allow_nan to False !
WARNING Find that allow_nan is True or allow_discrete is True, setting enable_stepwise to False !
WARNING Find that allow_nan is True or allow_discrete is True, setting enable_corr_filter to False !
WARNING Find that model_search_method not in (order, order-random), setting auto_search_loop to 1 !
WARNING Find that convert_score is False, setting keep_raw_score to False !
WARNING Find that convert_score is False, setting save_score_desc to False !
WARNING Find that convert_score is False, setting save_score_pmml to False !
INFO Model optional flow...
WARNING Check /home/conda_env/模型分析demo/v13-lgb/Data_v13-lgb.xlsx exists !
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'max_depth': 3, 'n_estimators': 30, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 9, 10, 13, 14, 15, 16, 17]}
INFO Using variables: 18...
INFO Training LGBMClassifier...
INFO Filtering importance by LGBMClassifier ...
INFO Total: 18...
INFO Selected: 16...
INFO Model variables: ['AMT_INCOME_TOTAL', 'Age', 'CNT_CHILDREN', 'CNT_FAM_MEMBERS', 'CODE_GENDER', 'DAYS_BIRTH', 'DAYS_EMPLOYED', 'FLAG_OWN_CAR', 'FLAG_OWN_REALTY', 'FLAG_PHONE', 'FLAG_WORK_PHONE', 'NAME_EDUCATION_TYPE', 'NAME_FAMILY_STATUS', 'NAME_HOUSING_TYPE', 'NAME_INCOME_TYPE', 'OCCUPATION_TYPE'] !
INFO Auto params cv...
INFO Auto search loop: 1...
INFO ALL params: {'n_estimators': 35, 'learning_rate': 0.112, 'max_depth': 2, 'min_child_samples': 25, 'min_child_weight': 0.01}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 35, 'learning_rate': 0.112, 'max_depth': 2, 'min_child_samples': 25, 'min_child_weight': 0.01, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 35, 'learning_rate': 0.112, 'max_depth': 2, 'min_child_samples': 25, 'min_child_weight': 0.01, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 35, 'learning_rate': 0.112, 'max_depth': 2, 'min_child_samples': 25, 'min_child_weight': 0.01, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 107, 'learning_rate': 0.109, 'max_depth': 5, 'min_child_samples': 30, 'min_child_weight': 0.007}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 107, 'learning_rate': 0.109, 'max_depth': 5, 'min_child_samples': 30, 'min_child_weight': 0.007, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 107, 'learning_rate': 0.109, 'max_depth': 5, 'min_child_samples': 30, 'min_child_weight': 0.007, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 107, 'learning_rate': 0.109, 'max_depth': 5, 'min_child_samples': 30, 'min_child_weight': 0.007, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 58, 'learning_rate': 0.089, 'max_depth': 5, 'min_child_samples': 29, 'min_child_weight': 0.008}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 58, 'learning_rate': 0.089, 'max_depth': 5, 'min_child_samples': 29, 'min_child_weight': 0.008, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 58, 'learning_rate': 0.089, 'max_depth': 5, 'min_child_samples': 29, 'min_child_weight': 0.008, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 58, 'learning_rate': 0.089, 'max_depth': 5, 'min_child_samples': 29, 'min_child_weight': 0.008, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 41, 'learning_rate': 0.125, 'max_depth': 3, 'min_child_samples': 45, 'min_child_weight': 0.003}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 41, 'learning_rate': 0.125, 'max_depth': 3, 'min_child_samples': 45, 'min_child_weight': 0.003, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 41, 'learning_rate': 0.125, 'max_depth': 3, 'min_child_samples': 45, 'min_child_weight': 0.003, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 41, 'learning_rate': 0.125, 'max_depth': 3, 'min_child_samples': 45, 'min_child_weight': 0.003, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 85, 'learning_rate': 0.058, 'max_depth': 5, 'min_child_samples': 50, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 85, 'learning_rate': 0.058, 'max_depth': 5, 'min_child_samples': 50, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 85, 'learning_rate': 0.058, 'max_depth': 5, 'min_child_samples': 50, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 85, 'learning_rate': 0.058, 'max_depth': 5, 'min_child_samples': 50, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 75, 'learning_rate': 0.13, 'max_depth': -1, 'min_child_samples': 23, 'min_child_weight': 0.007}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 75, 'learning_rate': 0.13, 'max_depth': -1, 'min_child_samples': 23, 'min_child_weight': 0.007, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 75, 'learning_rate': 0.13, 'max_depth': -1, 'min_child_samples': 23, 'min_child_weight': 0.007, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 75, 'learning_rate': 0.13, 'max_depth': -1, 'min_child_samples': 23, 'min_child_weight': 0.007, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 144, 'learning_rate': 0.067, 'max_depth': 2, 'min_child_samples': 22, 'min_child_weight': 0.005}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 144, 'learning_rate': 0.067, 'max_depth': 2, 'min_child_samples': 22, 'min_child_weight': 0.005, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 144, 'learning_rate': 0.067, 'max_depth': 2, 'min_child_samples': 22, 'min_child_weight': 0.005, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 144, 'learning_rate': 0.067, 'max_depth': 2, 'min_child_samples': 22, 'min_child_weight': 0.005, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 120, 'learning_rate': 0.084, 'max_depth': 1, 'min_child_samples': 39, 'min_child_weight': 0.005}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 120, 'learning_rate': 0.084, 'max_depth': 1, 'min_child_samples': 39, 'min_child_weight': 0.005, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 120, 'learning_rate': 0.084, 'max_depth': 1, 'min_child_samples': 39, 'min_child_weight': 0.005, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 120, 'learning_rate': 0.084, 'max_depth': 1, 'min_child_samples': 39, 'min_child_weight': 0.005, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 135, 'learning_rate': 0.075, 'max_depth': 2, 'min_child_samples': 47, 'min_child_weight': 0.002}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 135, 'learning_rate': 0.075, 'max_depth': 2, 'min_child_samples': 47, 'min_child_weight': 0.002, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 135, 'learning_rate': 0.075, 'max_depth': 2, 'min_child_samples': 47, 'min_child_weight': 0.002, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 135, 'learning_rate': 0.075, 'max_depth': 2, 'min_child_samples': 47, 'min_child_weight': 0.002, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 66, 'learning_rate': 0.113, 'max_depth': 5, 'min_child_samples': 40, 'min_child_weight': 0.008}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 66, 'learning_rate': 0.113, 'max_depth': 5, 'min_child_samples': 40, 'min_child_weight': 0.008, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 66, 'learning_rate': 0.113, 'max_depth': 5, 'min_child_samples': 40, 'min_child_weight': 0.008, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 66, 'learning_rate': 0.113, 'max_depth': 5, 'min_child_samples': 40, 'min_child_weight': 0.008, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 31, 'learning_rate': 0.101, 'max_depth': 1, 'min_child_samples': 49, 'min_child_weight': 0.007}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 31, 'learning_rate': 0.101, 'max_depth': 1, 'min_child_samples': 49, 'min_child_weight': 0.007, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 31, 'learning_rate': 0.101, 'max_depth': 1, 'min_child_samples': 49, 'min_child_weight': 0.007, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 31, 'learning_rate': 0.101, 'max_depth': 1, 'min_child_samples': 49, 'min_child_weight': 0.007, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 98, 'learning_rate': 0.111, 'max_depth': -1, 'min_child_samples': 37, 'min_child_weight': 0.006}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 98, 'learning_rate': 0.111, 'max_depth': -1, 'min_child_samples': 37, 'min_child_weight': 0.006, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 98, 'learning_rate': 0.111, 'max_depth': -1, 'min_child_samples': 37, 'min_child_weight': 0.006, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 98, 'learning_rate': 0.111, 'max_depth': -1, 'min_child_samples': 37, 'min_child_weight': 0.006, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 187, 'learning_rate': 0.13, 'max_depth': 1, 'min_child_samples': 27, 'min_child_weight': 0.01}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 187, 'learning_rate': 0.13, 'max_depth': 1, 'min_child_samples': 27, 'min_child_weight': 0.01, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 187, 'learning_rate': 0.13, 'max_depth': 1, 'min_child_samples': 27, 'min_child_weight': 0.01, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 187, 'learning_rate': 0.13, 'max_depth': 1, 'min_child_samples': 27, 'min_child_weight': 0.01, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 9, 'learning_rate': 0.135, 'max_depth': -1, 'min_child_samples': 46, 'min_child_weight': 0.008}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 9, 'learning_rate': 0.135, 'max_depth': -1, 'min_child_samples': 46, 'min_child_weight': 0.008, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 9, 'learning_rate': 0.135, 'max_depth': -1, 'min_child_samples': 46, 'min_child_weight': 0.008, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 9, 'learning_rate': 0.135, 'max_depth': -1, 'min_child_samples': 46, 'min_child_weight': 0.008, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 135, 'learning_rate': 0.064, 'max_depth': 3, 'min_child_samples': 39, 'min_child_weight': 0.008}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 135, 'learning_rate': 0.064, 'max_depth': 3, 'min_child_samples': 39, 'min_child_weight': 0.008, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 135, 'learning_rate': 0.064, 'max_depth': 3, 'min_child_samples': 39, 'min_child_weight': 0.008, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 135, 'learning_rate': 0.064, 'max_depth': 3, 'min_child_samples': 39, 'min_child_weight': 0.008, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 115, 'learning_rate': 0.056, 'max_depth': 1, 'min_child_samples': 33, 'min_child_weight': 0.009}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 115, 'learning_rate': 0.056, 'max_depth': 1, 'min_child_samples': 33, 'min_child_weight': 0.009, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 115, 'learning_rate': 0.056, 'max_depth': 1, 'min_child_samples': 33, 'min_child_weight': 0.009, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 115, 'learning_rate': 0.056, 'max_depth': 1, 'min_child_samples': 33, 'min_child_weight': 0.009, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 19, 'learning_rate': 0.14, 'max_depth': 0, 'min_child_samples': 31, 'min_child_weight': 0.006}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 19, 'learning_rate': 0.14, 'max_depth': 0, 'min_child_samples': 31, 'min_child_weight': 0.006, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 19, 'learning_rate': 0.14, 'max_depth': 0, 'min_child_samples': 31, 'min_child_weight': 0.006, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 19, 'learning_rate': 0.14, 'max_depth': 0, 'min_child_samples': 31, 'min_child_weight': 0.006, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 155, 'learning_rate': 0.134, 'max_depth': 0, 'min_child_samples': 26, 'min_child_weight': 0.004}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 155, 'learning_rate': 0.134, 'max_depth': 0, 'min_child_samples': 26, 'min_child_weight': 0.004, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 155, 'learning_rate': 0.134, 'max_depth': 0, 'min_child_samples': 26, 'min_child_weight': 0.004, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 155, 'learning_rate': 0.134, 'max_depth': 0, 'min_child_samples': 26, 'min_child_weight': 0.004, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 167, 'learning_rate': 0.134, 'max_depth': 4, 'min_child_samples': 41, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 167, 'learning_rate': 0.134, 'max_depth': 4, 'min_child_samples': 41, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 167, 'learning_rate': 0.134, 'max_depth': 4, 'min_child_samples': 41, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 167, 'learning_rate': 0.134, 'max_depth': 4, 'min_child_samples': 41, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 72, 'learning_rate': 0.11, 'max_depth': 3, 'min_child_samples': 27, 'min_child_weight': 0.007}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 72, 'learning_rate': 0.11, 'max_depth': 3, 'min_child_samples': 27, 'min_child_weight': 0.007, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 72, 'learning_rate': 0.11, 'max_depth': 3, 'min_child_samples': 27, 'min_child_weight': 0.007, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 72, 'learning_rate': 0.11, 'max_depth': 3, 'min_child_samples': 27, 'min_child_weight': 0.007, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 155, 'learning_rate': 0.085, 'max_depth': 2, 'min_child_samples': 29, 'min_child_weight': 0.004}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 155, 'learning_rate': 0.085, 'max_depth': 2, 'min_child_samples': 29, 'min_child_weight': 0.004, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 155, 'learning_rate': 0.085, 'max_depth': 2, 'min_child_samples': 29, 'min_child_weight': 0.004, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 155, 'learning_rate': 0.085, 'max_depth': 2, 'min_child_samples': 29, 'min_child_weight': 0.004, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 176, 'learning_rate': 0.112, 'max_depth': -1, 'min_child_samples': 35, 'min_child_weight': 0.002}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 176, 'learning_rate': 0.112, 'max_depth': -1, 'min_child_samples': 35, 'min_child_weight': 0.002, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 176, 'learning_rate': 0.112, 'max_depth': -1, 'min_child_samples': 35, 'min_child_weight': 0.002, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 176, 'learning_rate': 0.112, 'max_depth': -1, 'min_child_samples': 35, 'min_child_weight': 0.002, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 158, 'learning_rate': 0.122, 'max_depth': 0, 'min_child_samples': 41, 'min_child_weight': 0.006}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 158, 'learning_rate': 0.122, 'max_depth': 0, 'min_child_samples': 41, 'min_child_weight': 0.006, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 158, 'learning_rate': 0.122, 'max_depth': 0, 'min_child_samples': 41, 'min_child_weight': 0.006, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 158, 'learning_rate': 0.122, 'max_depth': 0, 'min_child_samples': 41, 'min_child_weight': 0.006, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 130, 'learning_rate': 0.058, 'max_depth': 4, 'min_child_samples': 29, 'min_child_weight': 0.009}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 130, 'learning_rate': 0.058, 'max_depth': 4, 'min_child_samples': 29, 'min_child_weight': 0.009, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 130, 'learning_rate': 0.058, 'max_depth': 4, 'min_child_samples': 29, 'min_child_weight': 0.009, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 130, 'learning_rate': 0.058, 'max_depth': 4, 'min_child_samples': 29, 'min_child_weight': 0.009, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 70, 'learning_rate': 0.063, 'max_depth': -1, 'min_child_samples': 46, 'min_child_weight': 0.009}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 70, 'learning_rate': 0.063, 'max_depth': -1, 'min_child_samples': 46, 'min_child_weight': 0.009, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 70, 'learning_rate': 0.063, 'max_depth': -1, 'min_child_samples': 46, 'min_child_weight': 0.009, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 70, 'learning_rate': 0.063, 'max_depth': -1, 'min_child_samples': 46, 'min_child_weight': 0.009, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 182, 'learning_rate': 0.114, 'max_depth': 0, 'min_child_samples': 28, 'min_child_weight': 0.005}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 182, 'learning_rate': 0.114, 'max_depth': 0, 'min_child_samples': 28, 'min_child_weight': 0.005, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 182, 'learning_rate': 0.114, 'max_depth': 0, 'min_child_samples': 28, 'min_child_weight': 0.005, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 182, 'learning_rate': 0.114, 'max_depth': 0, 'min_child_samples': 28, 'min_child_weight': 0.005, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 136, 'learning_rate': 0.099, 'max_depth': 5, 'min_child_samples': 35, 'min_child_weight': 0.007}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 136, 'learning_rate': 0.099, 'max_depth': 5, 'min_child_samples': 35, 'min_child_weight': 0.007, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 136, 'learning_rate': 0.099, 'max_depth': 5, 'min_child_samples': 35, 'min_child_weight': 0.007, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 136, 'learning_rate': 0.099, 'max_depth': 5, 'min_child_samples': 35, 'min_child_weight': 0.007, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 83, 'learning_rate': 0.064, 'max_depth': 5, 'min_child_samples': 20, 'min_child_weight': 0.004}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 83, 'learning_rate': 0.064, 'max_depth': 5, 'min_child_samples': 20, 'min_child_weight': 0.004, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 83, 'learning_rate': 0.064, 'max_depth': 5, 'min_child_samples': 20, 'min_child_weight': 0.004, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 83, 'learning_rate': 0.064, 'max_depth': 5, 'min_child_samples': 20, 'min_child_weight': 0.004, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 40, 'learning_rate': 0.067, 'max_depth': 2, 'min_child_samples': 29, 'min_child_weight': 0.008}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 40, 'learning_rate': 0.067, 'max_depth': 2, 'min_child_samples': 29, 'min_child_weight': 0.008, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 40, 'learning_rate': 0.067, 'max_depth': 2, 'min_child_samples': 29, 'min_child_weight': 0.008, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 40, 'learning_rate': 0.067, 'max_depth': 2, 'min_child_samples': 29, 'min_child_weight': 0.008, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 86, 'learning_rate': 0.095, 'max_depth': 1, 'min_child_samples': 46, 'min_child_weight': 0.007}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 86, 'learning_rate': 0.095, 'max_depth': 1, 'min_child_samples': 46, 'min_child_weight': 0.007, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 86, 'learning_rate': 0.095, 'max_depth': 1, 'min_child_samples': 46, 'min_child_weight': 0.007, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 86, 'learning_rate': 0.095, 'max_depth': 1, 'min_child_samples': 46, 'min_child_weight': 0.007, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 34, 'learning_rate': 0.124, 'max_depth': 0, 'min_child_samples': 37, 'min_child_weight': 0.01}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 34, 'learning_rate': 0.124, 'max_depth': 0, 'min_child_samples': 37, 'min_child_weight': 0.01, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 34, 'learning_rate': 0.124, 'max_depth': 0, 'min_child_samples': 37, 'min_child_weight': 0.01, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 34, 'learning_rate': 0.124, 'max_depth': 0, 'min_child_samples': 37, 'min_child_weight': 0.01, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 75, 'learning_rate': 0.105, 'max_depth': 4, 'min_child_samples': 20, 'min_child_weight': 0.002}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 75, 'learning_rate': 0.105, 'max_depth': 4, 'min_child_samples': 20, 'min_child_weight': 0.002, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 75, 'learning_rate': 0.105, 'max_depth': 4, 'min_child_samples': 20, 'min_child_weight': 0.002, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 75, 'learning_rate': 0.105, 'max_depth': 4, 'min_child_samples': 20, 'min_child_weight': 0.002, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 102, 'learning_rate': 0.111, 'max_depth': 5, 'min_child_samples': 50, 'min_child_weight': 0.003}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 102, 'learning_rate': 0.111, 'max_depth': 5, 'min_child_samples': 50, 'min_child_weight': 0.003, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 102, 'learning_rate': 0.111, 'max_depth': 5, 'min_child_samples': 50, 'min_child_weight': 0.003, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 102, 'learning_rate': 0.111, 'max_depth': 5, 'min_child_samples': 50, 'min_child_weight': 0.003, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 20, 'learning_rate': 0.069, 'max_depth': 5, 'min_child_samples': 40, 'min_child_weight': 0.004}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 20, 'learning_rate': 0.069, 'max_depth': 5, 'min_child_samples': 40, 'min_child_weight': 0.004, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 20, 'learning_rate': 0.069, 'max_depth': 5, 'min_child_samples': 40, 'min_child_weight': 0.004, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 20, 'learning_rate': 0.069, 'max_depth': 5, 'min_child_samples': 40, 'min_child_weight': 0.004, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 117, 'learning_rate': 0.064, 'max_depth': -1, 'min_child_samples': 46, 'min_child_weight': 0.01}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 117, 'learning_rate': 0.064, 'max_depth': -1, 'min_child_samples': 46, 'min_child_weight': 0.01, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 117, 'learning_rate': 0.064, 'max_depth': -1, 'min_child_samples': 46, 'min_child_weight': 0.01, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 117, 'learning_rate': 0.064, 'max_depth': -1, 'min_child_samples': 46, 'min_child_weight': 0.01, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 121, 'learning_rate': 0.067, 'max_depth': 1, 'min_child_samples': 44, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 121, 'learning_rate': 0.067, 'max_depth': 1, 'min_child_samples': 44, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 121, 'learning_rate': 0.067, 'max_depth': 1, 'min_child_samples': 44, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 121, 'learning_rate': 0.067, 'max_depth': 1, 'min_child_samples': 44, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 123, 'learning_rate': 0.09, 'max_depth': -1, 'min_child_samples': 35, 'min_child_weight': 0.009}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 123, 'learning_rate': 0.09, 'max_depth': -1, 'min_child_samples': 35, 'min_child_weight': 0.009, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 123, 'learning_rate': 0.09, 'max_depth': -1, 'min_child_samples': 35, 'min_child_weight': 0.009, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 123, 'learning_rate': 0.09, 'max_depth': -1, 'min_child_samples': 35, 'min_child_weight': 0.009, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 93, 'learning_rate': 0.074, 'max_depth': 0, 'min_child_samples': 42, 'min_child_weight': 0.006}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 93, 'learning_rate': 0.074, 'max_depth': 0, 'min_child_samples': 42, 'min_child_weight': 0.006, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 93, 'learning_rate': 0.074, 'max_depth': 0, 'min_child_samples': 42, 'min_child_weight': 0.006, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 93, 'learning_rate': 0.074, 'max_depth': 0, 'min_child_samples': 42, 'min_child_weight': 0.006, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 52, 'learning_rate': 0.058, 'max_depth': 2, 'min_child_samples': 39, 'min_child_weight': 0.007}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 52, 'learning_rate': 0.058, 'max_depth': 2, 'min_child_samples': 39, 'min_child_weight': 0.007, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 52, 'learning_rate': 0.058, 'max_depth': 2, 'min_child_samples': 39, 'min_child_weight': 0.007, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 52, 'learning_rate': 0.058, 'max_depth': 2, 'min_child_samples': 39, 'min_child_weight': 0.007, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 44, 'learning_rate': 0.053, 'max_depth': 0, 'min_child_samples': 36, 'min_child_weight': 0.008}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 44, 'learning_rate': 0.053, 'max_depth': 0, 'min_child_samples': 36, 'min_child_weight': 0.008, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 44, 'learning_rate': 0.053, 'max_depth': 0, 'min_child_samples': 36, 'min_child_weight': 0.008, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 44, 'learning_rate': 0.053, 'max_depth': 0, 'min_child_samples': 36, 'min_child_weight': 0.008, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 34, 'learning_rate': 0.123, 'max_depth': 3, 'min_child_samples': 48, 'min_child_weight': 0.003}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 34, 'learning_rate': 0.123, 'max_depth': 3, 'min_child_samples': 48, 'min_child_weight': 0.003, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 34, 'learning_rate': 0.123, 'max_depth': 3, 'min_child_samples': 48, 'min_child_weight': 0.003, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 34, 'learning_rate': 0.123, 'max_depth': 3, 'min_child_samples': 48, 'min_child_weight': 0.003, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 57, 'learning_rate': 0.095, 'max_depth': 0, 'min_child_samples': 26, 'min_child_weight': 0.007}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 57, 'learning_rate': 0.095, 'max_depth': 0, 'min_child_samples': 26, 'min_child_weight': 0.007, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 57, 'learning_rate': 0.095, 'max_depth': 0, 'min_child_samples': 26, 'min_child_weight': 0.007, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 57, 'learning_rate': 0.095, 'max_depth': 0, 'min_child_samples': 26, 'min_child_weight': 0.007, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 13, 'learning_rate': 0.111, 'max_depth': 0, 'min_child_samples': 41, 'min_child_weight': 0.006}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 13, 'learning_rate': 0.111, 'max_depth': 0, 'min_child_samples': 41, 'min_child_weight': 0.006, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 13, 'learning_rate': 0.111, 'max_depth': 0, 'min_child_samples': 41, 'min_child_weight': 0.006, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 13, 'learning_rate': 0.111, 'max_depth': 0, 'min_child_samples': 41, 'min_child_weight': 0.006, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 186, 'learning_rate': 0.06, 'max_depth': 1, 'min_child_samples': 28, 'min_child_weight': 0.002}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 186, 'learning_rate': 0.06, 'max_depth': 1, 'min_child_samples': 28, 'min_child_weight': 0.002, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 186, 'learning_rate': 0.06, 'max_depth': 1, 'min_child_samples': 28, 'min_child_weight': 0.002, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 186, 'learning_rate': 0.06, 'max_depth': 1, 'min_child_samples': 28, 'min_child_weight': 0.002, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 9, 'learning_rate': 0.121, 'max_depth': 1, 'min_child_samples': 27, 'min_child_weight': 0.004}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 9, 'learning_rate': 0.121, 'max_depth': 1, 'min_child_samples': 27, 'min_child_weight': 0.004, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 9, 'learning_rate': 0.121, 'max_depth': 1, 'min_child_samples': 27, 'min_child_weight': 0.004, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 9, 'learning_rate': 0.121, 'max_depth': 1, 'min_child_samples': 27, 'min_child_weight': 0.004, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 29, 'learning_rate': 0.102, 'max_depth': 0, 'min_child_samples': 25, 'min_child_weight': 0.005}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 29, 'learning_rate': 0.102, 'max_depth': 0, 'min_child_samples': 25, 'min_child_weight': 0.005, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 29, 'learning_rate': 0.102, 'max_depth': 0, 'min_child_samples': 25, 'min_child_weight': 0.005, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 29, 'learning_rate': 0.102, 'max_depth': 0, 'min_child_samples': 25, 'min_child_weight': 0.005, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 29, 'learning_rate': 0.075, 'max_depth': -1, 'min_child_samples': 49, 'min_child_weight': 0.002}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 29, 'learning_rate': 0.075, 'max_depth': -1, 'min_child_samples': 49, 'min_child_weight': 0.002, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 29, 'learning_rate': 0.075, 'max_depth': -1, 'min_child_samples': 49, 'min_child_weight': 0.002, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 29, 'learning_rate': 0.075, 'max_depth': -1, 'min_child_samples': 49, 'min_child_weight': 0.002, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 30, 'learning_rate': 0.101, 'max_depth': 4, 'min_child_samples': 31, 'min_child_weight': 0.003}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 30, 'learning_rate': 0.101, 'max_depth': 4, 'min_child_samples': 31, 'min_child_weight': 0.003, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 30, 'learning_rate': 0.101, 'max_depth': 4, 'min_child_samples': 31, 'min_child_weight': 0.003, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 30, 'learning_rate': 0.101, 'max_depth': 4, 'min_child_samples': 31, 'min_child_weight': 0.003, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 149, 'learning_rate': 0.067, 'max_depth': 5, 'min_child_samples': 38, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 149, 'learning_rate': 0.067, 'max_depth': 5, 'min_child_samples': 38, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 149, 'learning_rate': 0.067, 'max_depth': 5, 'min_child_samples': 38, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 149, 'learning_rate': 0.067, 'max_depth': 5, 'min_child_samples': 38, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 58, 'learning_rate': 0.115, 'max_depth': -1, 'min_child_samples': 35, 'min_child_weight': 0.005}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 58, 'learning_rate': 0.115, 'max_depth': -1, 'min_child_samples': 35, 'min_child_weight': 0.005, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 58, 'learning_rate': 0.115, 'max_depth': -1, 'min_child_samples': 35, 'min_child_weight': 0.005, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 58, 'learning_rate': 0.115, 'max_depth': -1, 'min_child_samples': 35, 'min_child_weight': 0.005, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 82, 'learning_rate': 0.083, 'max_depth': -1, 'min_child_samples': 37, 'min_child_weight': 0.01}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 82, 'learning_rate': 0.083, 'max_depth': -1, 'min_child_samples': 37, 'min_child_weight': 0.01, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 82, 'learning_rate': 0.083, 'max_depth': -1, 'min_child_samples': 37, 'min_child_weight': 0.01, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 82, 'learning_rate': 0.083, 'max_depth': -1, 'min_child_samples': 37, 'min_child_weight': 0.01, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 184, 'learning_rate': 0.072, 'max_depth': 2, 'min_child_samples': 48, 'min_child_weight': 0.002}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 184, 'learning_rate': 0.072, 'max_depth': 2, 'min_child_samples': 48, 'min_child_weight': 0.002, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 184, 'learning_rate': 0.072, 'max_depth': 2, 'min_child_samples': 48, 'min_child_weight': 0.002, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 184, 'learning_rate': 0.072, 'max_depth': 2, 'min_child_samples': 48, 'min_child_weight': 0.002, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 23, 'learning_rate': 0.097, 'max_depth': 4, 'min_child_samples': 44, 'min_child_weight': 0.006}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 23, 'learning_rate': 0.097, 'max_depth': 4, 'min_child_samples': 44, 'min_child_weight': 0.006, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 23, 'learning_rate': 0.097, 'max_depth': 4, 'min_child_samples': 44, 'min_child_weight': 0.006, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 23, 'learning_rate': 0.097, 'max_depth': 4, 'min_child_samples': 44, 'min_child_weight': 0.006, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 118, 'learning_rate': 0.064, 'max_depth': 3, 'min_child_samples': 41, 'min_child_weight': 0.002}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 118, 'learning_rate': 0.064, 'max_depth': 3, 'min_child_samples': 41, 'min_child_weight': 0.002, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 118, 'learning_rate': 0.064, 'max_depth': 3, 'min_child_samples': 41, 'min_child_weight': 0.002, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 118, 'learning_rate': 0.064, 'max_depth': 3, 'min_child_samples': 41, 'min_child_weight': 0.002, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 31, 'learning_rate': 0.062, 'max_depth': 5, 'min_child_samples': 27, 'min_child_weight': 0.004}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 31, 'learning_rate': 0.062, 'max_depth': 5, 'min_child_samples': 27, 'min_child_weight': 0.004, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 31, 'learning_rate': 0.062, 'max_depth': 5, 'min_child_samples': 27, 'min_child_weight': 0.004, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 31, 'learning_rate': 0.062, 'max_depth': 5, 'min_child_samples': 27, 'min_child_weight': 0.004, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 9, 'learning_rate': 0.092, 'max_depth': 5, 'min_child_samples': 26, 'min_child_weight': 0.002}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 9, 'learning_rate': 0.092, 'max_depth': 5, 'min_child_samples': 26, 'min_child_weight': 0.002, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 9, 'learning_rate': 0.092, 'max_depth': 5, 'min_child_samples': 26, 'min_child_weight': 0.002, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 9, 'learning_rate': 0.092, 'max_depth': 5, 'min_child_samples': 26, 'min_child_weight': 0.002, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 51, 'learning_rate': 0.128, 'max_depth': 1, 'min_child_samples': 45, 'min_child_weight': 0.003}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 51, 'learning_rate': 0.128, 'max_depth': 1, 'min_child_samples': 45, 'min_child_weight': 0.003, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 51, 'learning_rate': 0.128, 'max_depth': 1, 'min_child_samples': 45, 'min_child_weight': 0.003, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 51, 'learning_rate': 0.128, 'max_depth': 1, 'min_child_samples': 45, 'min_child_weight': 0.003, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 145, 'learning_rate': 0.073, 'max_depth': 2, 'min_child_samples': 38, 'min_child_weight': 0.004}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 145, 'learning_rate': 0.073, 'max_depth': 2, 'min_child_samples': 38, 'min_child_weight': 0.004, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 145, 'learning_rate': 0.073, 'max_depth': 2, 'min_child_samples': 38, 'min_child_weight': 0.004, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 145, 'learning_rate': 0.073, 'max_depth': 2, 'min_child_samples': 38, 'min_child_weight': 0.004, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 110, 'learning_rate': 0.07, 'max_depth': 4, 'min_child_samples': 44, 'min_child_weight': 0.001}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 110, 'learning_rate': 0.07, 'max_depth': 4, 'min_child_samples': 44, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 110, 'learning_rate': 0.07, 'max_depth': 4, 'min_child_samples': 44, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 110, 'learning_rate': 0.07, 'max_depth': 4, 'min_child_samples': 44, 'min_child_weight': 0.001, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 161, 'learning_rate': 0.107, 'max_depth': 3, 'min_child_samples': 40, 'min_child_weight': 0.01}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 161, 'learning_rate': 0.107, 'max_depth': 3, 'min_child_samples': 40, 'min_child_weight': 0.01, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 161, 'learning_rate': 0.107, 'max_depth': 3, 'min_child_samples': 40, 'min_child_weight': 0.01, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 161, 'learning_rate': 0.107, 'max_depth': 3, 'min_child_samples': 40, 'min_child_weight': 0.01, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 99, 'learning_rate': 0.106, 'max_depth': 2, 'min_child_samples': 20, 'min_child_weight': 0.005}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 99, 'learning_rate': 0.106, 'max_depth': 2, 'min_child_samples': 20, 'min_child_weight': 0.005, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 99, 'learning_rate': 0.106, 'max_depth': 2, 'min_child_samples': 20, 'min_child_weight': 0.005, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 99, 'learning_rate': 0.106, 'max_depth': 2, 'min_child_samples': 20, 'min_child_weight': 0.005, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 73, 'learning_rate': 0.122, 'max_depth': 4, 'min_child_samples': 25, 'min_child_weight': 0.007}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 73, 'learning_rate': 0.122, 'max_depth': 4, 'min_child_samples': 25, 'min_child_weight': 0.007, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 73, 'learning_rate': 0.122, 'max_depth': 4, 'min_child_samples': 25, 'min_child_weight': 0.007, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 73, 'learning_rate': 0.122, 'max_depth': 4, 'min_child_samples': 25, 'min_child_weight': 0.007, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 169, 'learning_rate': 0.123, 'max_depth': 3, 'min_child_samples': 22, 'min_child_weight': 0.002}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 169, 'learning_rate': 0.123, 'max_depth': 3, 'min_child_samples': 22, 'min_child_weight': 0.002, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 169, 'learning_rate': 0.123, 'max_depth': 3, 'min_child_samples': 22, 'min_child_weight': 0.002, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 169, 'learning_rate': 0.123, 'max_depth': 3, 'min_child_samples': 22, 'min_child_weight': 0.002, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 94, 'learning_rate': 0.138, 'max_depth': 0, 'min_child_samples': 43, 'min_child_weight': 0.01}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 94, 'learning_rate': 0.138, 'max_depth': 0, 'min_child_samples': 43, 'min_child_weight': 0.01, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 94, 'learning_rate': 0.138, 'max_depth': 0, 'min_child_samples': 43, 'min_child_weight': 0.01, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 94, 'learning_rate': 0.138, 'max_depth': 0, 'min_child_samples': 43, 'min_child_weight': 0.01, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 75, 'learning_rate': 0.092, 'max_depth': -1, 'min_child_samples': 26, 'min_child_weight': 0.009}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 75, 'learning_rate': 0.092, 'max_depth': -1, 'min_child_samples': 26, 'min_child_weight': 0.009, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 75, 'learning_rate': 0.092, 'max_depth': -1, 'min_child_samples': 26, 'min_child_weight': 0.009, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 75, 'learning_rate': 0.092, 'max_depth': -1, 'min_child_samples': 26, 'min_child_weight': 0.009, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 152, 'learning_rate': 0.051, 'max_depth': 1, 'min_child_samples': 36, 'min_child_weight': 0.007}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 152, 'learning_rate': 0.051, 'max_depth': 1, 'min_child_samples': 36, 'min_child_weight': 0.007, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 152, 'learning_rate': 0.051, 'max_depth': 1, 'min_child_samples': 36, 'min_child_weight': 0.007, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 152, 'learning_rate': 0.051, 'max_depth': 1, 'min_child_samples': 36, 'min_child_weight': 0.007, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 173, 'learning_rate': 0.108, 'max_depth': 0, 'min_child_samples': 35, 'min_child_weight': 0.009}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 173, 'learning_rate': 0.108, 'max_depth': 0, 'min_child_samples': 35, 'min_child_weight': 0.009, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 173, 'learning_rate': 0.108, 'max_depth': 0, 'min_child_samples': 35, 'min_child_weight': 0.009, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 173, 'learning_rate': 0.108, 'max_depth': 0, 'min_child_samples': 35, 'min_child_weight': 0.009, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 69, 'learning_rate': 0.13, 'max_depth': 1, 'min_child_samples': 30, 'min_child_weight': 0.007}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 69, 'learning_rate': 0.13, 'max_depth': 1, 'min_child_samples': 30, 'min_child_weight': 0.007, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 69, 'learning_rate': 0.13, 'max_depth': 1, 'min_child_samples': 30, 'min_child_weight': 0.007, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 69, 'learning_rate': 0.13, 'max_depth': 1, 'min_child_samples': 30, 'min_child_weight': 0.007, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 157, 'learning_rate': 0.127, 'max_depth': 3, 'min_child_samples': 36, 'min_child_weight': 0.005}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 157, 'learning_rate': 0.127, 'max_depth': 3, 'min_child_samples': 36, 'min_child_weight': 0.005, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 157, 'learning_rate': 0.127, 'max_depth': 3, 'min_child_samples': 36, 'min_child_weight': 0.005, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 157, 'learning_rate': 0.127, 'max_depth': 3, 'min_child_samples': 36, 'min_child_weight': 0.005, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 157, 'learning_rate': 0.128, 'max_depth': 0, 'min_child_samples': 46, 'min_child_weight': 0.006}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 157, 'learning_rate': 0.128, 'max_depth': 0, 'min_child_samples': 46, 'min_child_weight': 0.006, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 157, 'learning_rate': 0.128, 'max_depth': 0, 'min_child_samples': 46, 'min_child_weight': 0.006, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 157, 'learning_rate': 0.128, 'max_depth': 0, 'min_child_samples': 46, 'min_child_weight': 0.006, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 96, 'learning_rate': 0.068, 'max_depth': 0, 'min_child_samples': 20, 'min_child_weight': 0.008}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 96, 'learning_rate': 0.068, 'max_depth': 0, 'min_child_samples': 20, 'min_child_weight': 0.008, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 96, 'learning_rate': 0.068, 'max_depth': 0, 'min_child_samples': 20, 'min_child_weight': 0.008, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 96, 'learning_rate': 0.068, 'max_depth': 0, 'min_child_samples': 20, 'min_child_weight': 0.008, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 35, 'learning_rate': 0.134, 'max_depth': 0, 'min_child_samples': 20, 'min_child_weight': 0.01}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 35, 'learning_rate': 0.134, 'max_depth': 0, 'min_child_samples': 20, 'min_child_weight': 0.01, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 35, 'learning_rate': 0.134, 'max_depth': 0, 'min_child_samples': 20, 'min_child_weight': 0.01, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 35, 'learning_rate': 0.134, 'max_depth': 0, 'min_child_samples': 20, 'min_child_weight': 0.01, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 76, 'learning_rate': 0.11, 'max_depth': 3, 'min_child_samples': 25, 'min_child_weight': 0.004}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 76, 'learning_rate': 0.11, 'max_depth': 3, 'min_child_samples': 25, 'min_child_weight': 0.004, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 76, 'learning_rate': 0.11, 'max_depth': 3, 'min_child_samples': 25, 'min_child_weight': 0.004, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 76, 'learning_rate': 0.11, 'max_depth': 3, 'min_child_samples': 25, 'min_child_weight': 0.004, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 18, 'learning_rate': 0.059, 'max_depth': 0, 'min_child_samples': 34, 'min_child_weight': 0.004}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 18, 'learning_rate': 0.059, 'max_depth': 0, 'min_child_samples': 34, 'min_child_weight': 0.004, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 18, 'learning_rate': 0.059, 'max_depth': 0, 'min_child_samples': 34, 'min_child_weight': 0.004, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 18, 'learning_rate': 0.059, 'max_depth': 0, 'min_child_samples': 34, 'min_child_weight': 0.004, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 96, 'learning_rate': 0.134, 'max_depth': 3, 'min_child_samples': 26, 'min_child_weight': 0.008}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 96, 'learning_rate': 0.134, 'max_depth': 3, 'min_child_samples': 26, 'min_child_weight': 0.008, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 96, 'learning_rate': 0.134, 'max_depth': 3, 'min_child_samples': 26, 'min_child_weight': 0.008, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 96, 'learning_rate': 0.134, 'max_depth': 3, 'min_child_samples': 26, 'min_child_weight': 0.008, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO ALL params: {'n_estimators': 15, 'learning_rate': 0.072, 'max_depth': 4, 'min_child_samples': 49, 'min_child_weight': 0.006}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 15, 'learning_rate': 0.072, 'max_depth': 4, 'min_child_samples': 49, 'min_child_weight': 0.006, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 15, 'learning_rate': 0.072, 'max_depth': 4, 'min_child_samples': 49, 'min_child_weight': 0.006, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 15, 'learning_rate': 0.072, 'max_depth': 4, 'min_child_samples': 49, 'min_child_weight': 0.006, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO Drawing cv search graph...
INFO Drawing params_search_chart...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 182, 'learning_rate': 0.114, 'max_depth': 0, 'min_child_samples': 28, 'min_child_weight': 0.005, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO [FIT_TRANSFORM] ['AMT_INCOME_TOTAL']: 0.00013 secs
INFO [FIT_TRANSFORM] ['Age']: 6.8e-05 secs
INFO [FIT_TRANSFORM] ['CNT_CHILDREN']: 9.6e-05 secs
INFO [FIT_TRANSFORM] ['CNT_FAM_MEMBERS']: 9.4e-05 secs
INFO [FIT_TRANSFORM] ['CODE_GENDER']: 0.018857 secs
INFO [FIT_TRANSFORM] ['DAYS_BIRTH']: 0.000148 secs
INFO [FIT_TRANSFORM] ['DAYS_EMPLOYED']: 6.3e-05 secs
INFO [FIT_TRANSFORM] ['FLAG_OWN_CAR']: 0.019563 secs
INFO [FIT_TRANSFORM] ['FLAG_OWN_REALTY']: 0.019964 secs
INFO [FIT_TRANSFORM] ['FLAG_PHONE']: 0.000742 secs
INFO [FIT_TRANSFORM] ['FLAG_WORK_PHONE']: 9.7e-05 secs
INFO [FIT_TRANSFORM] ['NAME_EDUCATION_TYPE']: 0.018716 secs
INFO [FIT_TRANSFORM] ['NAME_FAMILY_STATUS']: 0.019679 secs
INFO [FIT_TRANSFORM] ['NAME_HOUSING_TYPE']: 0.019325 secs
INFO [FIT_TRANSFORM] ['NAME_INCOME_TYPE']: 0.018796 secs
INFO [FIT_TRANSFORM] ['OCCUPATION_TYPE']: 0.018859 secs
INFO [TRANSFORM] ['AMT_INCOME_TOTAL']: 0.000153 secs
INFO [TRANSFORM] ['Age']: 0.000195 secs
INFO [TRANSFORM] ['CNT_CHILDREN']: 7.1e-05 secs
INFO [TRANSFORM] ['CNT_FAM_MEMBERS']: 0.000105 secs
INFO [TRANSFORM] ['CODE_GENDER']: 0.011863 secs
INFO [TRANSFORM] ['DAYS_BIRTH']: 0.000324 secs
INFO [TRANSFORM] ['DAYS_EMPLOYED']: 0.000137 secs
INFO [TRANSFORM] ['FLAG_OWN_CAR']: 0.011442 secs
INFO [TRANSFORM] ['FLAG_OWN_REALTY']: 0.012064 secs
INFO [TRANSFORM] ['FLAG_PHONE']: 0.001206 secs
INFO [TRANSFORM] ['FLAG_WORK_PHONE']: 7e-05 secs
INFO [TRANSFORM] ['NAME_EDUCATION_TYPE']: 0.012357 secs
INFO [TRANSFORM] ['NAME_FAMILY_STATUS']: 0.012143 secs
INFO [TRANSFORM] ['NAME_HOUSING_TYPE']: 0.012047 secs
INFO [TRANSFORM] ['NAME_INCOME_TYPE']: 0.012201 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE']: 0.013462 secs
INFO [TRANSFORM] ['AMT_INCOME_TOTAL']: 0.000125 secs
INFO [TRANSFORM] ['Age']: 9.3e-05 secs
INFO [TRANSFORM] ['CNT_CHILDREN']: 0.000128 secs
INFO [TRANSFORM] ['CNT_FAM_MEMBERS']: 0.000567 secs
INFO [TRANSFORM] ['CODE_GENDER']: 0.005051 secs
INFO [TRANSFORM] ['DAYS_BIRTH']: 8.1e-05 secs
INFO [TRANSFORM] ['DAYS_EMPLOYED']: 6e-05 secs
INFO [TRANSFORM] ['FLAG_OWN_CAR']: 0.00529 secs
INFO [TRANSFORM] ['FLAG_OWN_REALTY']: 0.005147 secs
INFO [TRANSFORM] ['FLAG_PHONE']: 0.000622 secs
INFO [TRANSFORM] ['FLAG_WORK_PHONE']: 6.9e-05 secs
INFO [TRANSFORM] ['NAME_EDUCATION_TYPE']: 0.00562 secs
INFO [TRANSFORM] ['NAME_FAMILY_STATUS']: 0.005723 secs
INFO [TRANSFORM] ['NAME_HOUSING_TYPE']: 0.005449 secs
INFO [TRANSFORM] ['NAME_INCOME_TYPE']: 0.007839 secs
INFO [TRANSFORM] ['OCCUPATION_TYPE']: 0.006574 secs
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Drawing train_score_uncut_ks_graph...
INFO Drawing train_score_uncut_count_chart1...
INFO Drawing train_score_uncut_count_chart2...
INFO Drawing train_score_uncut_count_chart3...
INFO Drawing train_score_uncut_cap_chart1...
INFO Drawing train_score_uncut_cap_chart2...
INFO Drawing train_score_uncut_cap_chart3...
INFO Creating cut info...
INFO Drawing train_score_cut_ks_graph...
INFO Drawing train_score_cut_rate_chart...
INFO Drawing train_score_cut_count_chart1...
INFO Drawing train_score_cut_count_chart2...
INFO Drawing train_score_cut_count_chart3...
INFO Drawing train_score_cut_cap_chart1...
INFO Drawing train_score_cut_cap_chart2...
INFO Drawing train_score_cut_cap_chart3...
INFO Creating qcut info...
INFO Drawing train_score_qcut_ks_graph...
INFO Drawing train_score_qcut_rate_chart...
INFO Drawing train_score_qcut_count_chart1...
INFO Drawing train_score_qcut_count_chart2...
INFO Drawing train_score_qcut_count_chart3...
INFO Drawing train_score_qcut_cap_chart1...
INFO Drawing train_score_qcut_cap_chart2...
INFO Drawing train_score_qcut_cap_chart3...
INFO Creating precision recall fscore...
INFO Drawing train_classification_chart...
INFO Drawing train_classification_chart...
INFO Drawing roc...
INFO Creating results summary...
INFO Drawing ap...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Drawing test_score_uncut_ks_graph...
INFO Drawing test_score_uncut_count_chart1...
INFO Drawing test_score_uncut_count_chart2...
INFO Drawing test_score_uncut_count_chart3...
INFO Drawing test_score_uncut_cap_chart1...
INFO Drawing test_score_uncut_cap_chart2...
INFO Drawing test_score_uncut_cap_chart3...
INFO Creating cut info...
INFO Drawing test_score_cut_ks_graph...
INFO Drawing test_score_cut_rate_chart...
INFO Drawing test_score_cut_count_chart1...
INFO Drawing test_score_cut_count_chart2...
INFO Drawing test_score_cut_count_chart3...
INFO Drawing test_score_cut_cap_chart1...
INFO Drawing test_score_cut_cap_chart2...
INFO Drawing test_score_cut_cap_chart3...
INFO Creating qcut info...
INFO Drawing test_score_qcut_ks_graph...
INFO Drawing test_score_qcut_rate_chart...
INFO Drawing test_score_qcut_count_chart1...
INFO Drawing test_score_qcut_count_chart2...
INFO Drawing test_score_qcut_count_chart3...
INFO Drawing test_score_qcut_cap_chart1...
INFO Drawing test_score_qcut_cap_chart2...
INFO Drawing test_score_qcut_cap_chart3...
INFO Creating precision recall fscore...
INFO Drawing test_classification_chart...
INFO Drawing test_classification_chart...
INFO Drawing roc...
INFO Creating results summary...
INFO Drawing ap...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Drawing cut_psi_chart...
INFO Drawing cut_psi_chart...
INFO Drawing qcut_psi_chart...
INFO Drawing qcut_psi_chart...
INFO Total: 11.14 s
INFO Saving var_drop...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 182, 'learning_rate': 0.114, 'max_depth': 0, 'min_child_samples': 28, 'min_child_weight': 0.005, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 182, 'learning_rate': 0.114, 'max_depth': 0, 'min_child_samples': 28, 'min_child_weight': 0.005, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_estimators': 182, 'learning_rate': 0.114, 'max_depth': 0, 'min_child_samples': 28, 'min_child_weight': 0.005, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': [4, 7, 8, 11, 12, 13, 14, 15]}
INFO Using variables: 16...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO Drawing cv compare graph...
INFO Drawing cv_compare_ks_chart...
INFO Drawing cv_compare_auc_chart...
INFO Drawing cv_compare_ap_chart...
INFO Drawing cv_compare_logloss_chart...
INFO Drawing cv_compare_r2_chart...
INFO Drawing cv_compare_mse_chart...
INFO Drawing cv_compare_mdp_chart...
INFO Drawing cv_compare_psi_chart...
INFO Drawing cv_compare_mpg_chart...
INFO Saving model pkl...
INFO Saving model pmml...
/home/conda_env/miniconda3/lib/python3.9/subprocess.py:941: RuntimeWarning: line buffering (buffering=1) isn't supported in binary mode, the default buffer size will be used
self.stdout = io.open(c2pread, 'rb', bufsize)
/home/conda_env/miniconda3/lib/python3.9/subprocess.py:946: RuntimeWarning: line buffering (buffering=1) isn't supported in binary mode, the default buffer size will be used
self.stderr = io.open(errread, 'rb', bufsize)
INFO Adding model_pmml to excel...
INFO Adding run_model_pmml_in_java to excel...
INFO Saving json...
INFO Saving setting...
INFO Adding runtime to excel...
INFO Saving directory...
INFO Saving...
INFO Total: 196.28 s
INFO Total: 196.33 s
at.Analysis.model_flow(
df_train, "./模型分析demo/v14-knn/v14-knn.xlsx", test_data=df_test,
# 目标定义 & 切片定义
response="target", split_col_name="birth_year", use_train_time=True,
exclude_column=["ID", "weight"], # 排除权重
add_info="ID", # 主键添加
# 指定使用训练的class并不进行打分转化
model_type_path="sklearn.neighbors.KNeighborsClassifier",
convert_score=False,
customized_parameter={
"n_neighbors": [3, 4, 5, 6, 7, 8],
"weights": ['uniform', 'distance'],
"leaf_size": [20, 30, 40, 50],
},
model_search_method="tpe",
model_search_params={"binary": ["max", -1]},
)
INFO Prepare kwargs...
WARNING Setting format_time_edge to True !
INFO Checking train_data...
INFO Checking response...
INFO Checking test_data...
INFO Checking response...
INFO Checking columns...
INFO Checking dtypes...
WARNING Setting nan_mode to min !
WARNING Pre: WOE_ not found, change pre to "" !
WARNING Find that model_search_method not in (order, order-random), setting auto_search_loop to 1 !
WARNING Find that convert_score is False, setting keep_raw_score to False !
WARNING Find that convert_score is False, setting save_score_desc to False !
WARNING Find that convert_score is False, setting save_score_pmml to False !
INFO Model optional flow...
WARNING Check /home/conda_env/模型分析demo/v14-knn/Data_v14-knn.xlsx exists !
INFO Selecting by corr...
INFO Total: 10...
INFO Selected: 7...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'max_depth': 3, 'n_estimators': 30, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': 'auto'}
INFO Using variables: 7...
INFO Training LGBMClassifier...
INFO Filtering importance by LGBMClassifier ...
INFO Total: 7...
INFO Selected: 6...
INFO Model variables: ['AMT_INCOME_TOTAL', 'Age', 'CNT_CHILDREN', 'DAYS_EMPLOYED', 'FLAG_PHONE', 'FLAG_WORK_PHONE'] !
INFO Auto params cv...
INFO Auto search loop: 1...
0%| | 0/36 [00:00<?, ?trial/s, best loss=?]
INFO build_posterior_wrapper took 0.001021 seconds
INFO TPE using 0 trials
INFO ALL params: {'n_neighbors': 6, 'weights': 'distance', 'leaf_size': 41}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 6, 'weights': 'distance', 'leaf_size': 41, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 6, 'weights': 'distance', 'leaf_size': 41, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 6, 'weights': 'distance', 'leaf_size': 41, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
3%|▎ | 1/36 [00:01<00:57, 1.65s/trial, best loss: -0.9808088067603966]
INFO build_posterior_wrapper took 0.001112 seconds
INFO TPE using 1/1 trials with best loss -0.980809
INFO ALL params: {'n_neighbors': 6, 'weights': 'uniform', 'leaf_size': 27}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 6, 'weights': 'uniform', 'leaf_size': 27, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 6, 'weights': 'uniform', 'leaf_size': 27, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 6, 'weights': 'uniform', 'leaf_size': 27, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
6%|▌ | 2/36 [00:03<00:56, 1.66s/trial, best loss: -0.9808088067603966]
INFO build_posterior_wrapper took 0.000963 seconds
INFO TPE using 2/2 trials with best loss -0.980809
INFO ALL params: {'n_neighbors': 8, 'weights': 'uniform', 'leaf_size': 25}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 8, 'weights': 'uniform', 'leaf_size': 25, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 8, 'weights': 'uniform', 'leaf_size': 25, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 8, 'weights': 'uniform', 'leaf_size': 25, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
8%|▊ | 3/36 [00:04<00:54, 1.65s/trial, best loss: -0.9808088067603966]
INFO build_posterior_wrapper took 0.000978 seconds
INFO TPE using 3/3 trials with best loss -0.980809
INFO ALL params: {'n_neighbors': 4, 'weights': 'uniform', 'leaf_size': 41}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 4, 'weights': 'uniform', 'leaf_size': 41, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 4, 'weights': 'uniform', 'leaf_size': 41, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 4, 'weights': 'uniform', 'leaf_size': 41, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
11%|█ | 4/36 [00:06<00:51, 1.61s/trial, best loss: -0.9808088067603966]
INFO build_posterior_wrapper took 0.000888 seconds
INFO TPE using 4/4 trials with best loss -0.980809
INFO ALL params: {'n_neighbors': 4, 'weights': 'distance', 'leaf_size': 45}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 4, 'weights': 'distance', 'leaf_size': 45, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 4, 'weights': 'distance', 'leaf_size': 45, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 4, 'weights': 'distance', 'leaf_size': 45, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
14%|█▍ | 5/36 [00:08<00:49, 1.59s/trial, best loss: -0.9808088067603966]
INFO build_posterior_wrapper took 0.001111 seconds
INFO TPE using 5/5 trials with best loss -0.980809
INFO ALL params: {'n_neighbors': 8, 'weights': 'distance', 'leaf_size': 36}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 8, 'weights': 'distance', 'leaf_size': 36, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 8, 'weights': 'distance', 'leaf_size': 36, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 8, 'weights': 'distance', 'leaf_size': 36, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
17%|█▋ | 6/36 [00:09<00:48, 1.61s/trial, best loss: -0.9879391514987147]
INFO build_posterior_wrapper took 0.000959 seconds
INFO TPE using 6/6 trials with best loss -0.987939
INFO ALL params: {'n_neighbors': 6, 'weights': 'uniform', 'leaf_size': 47}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 6, 'weights': 'uniform', 'leaf_size': 47, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 6, 'weights': 'uniform', 'leaf_size': 47, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 6, 'weights': 'uniform', 'leaf_size': 47, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
19%|█▉ | 7/36 [00:11<00:46, 1.61s/trial, best loss: -0.9879391514987147]
INFO build_posterior_wrapper took 0.000849 seconds
INFO TPE using 7/7 trials with best loss -0.987939
INFO ALL params: {'n_neighbors': 5, 'weights': 'distance', 'leaf_size': 27}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 5, 'weights': 'distance', 'leaf_size': 27, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 5, 'weights': 'distance', 'leaf_size': 27, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 5, 'weights': 'distance', 'leaf_size': 27, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
22%|██▏ | 8/36 [00:12<00:44, 1.58s/trial, best loss: -0.9879391514987147]
INFO build_posterior_wrapper took 0.000889 seconds
INFO TPE using 8/8 trials with best loss -0.987939
INFO ALL params: {'n_neighbors': 7, 'weights': 'uniform', 'leaf_size': 43}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 7, 'weights': 'uniform', 'leaf_size': 43, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 7, 'weights': 'uniform', 'leaf_size': 43, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 7, 'weights': 'uniform', 'leaf_size': 43, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
25%|██▌ | 9/36 [00:14<00:43, 1.62s/trial, best loss: -0.9879391514987147]
INFO build_posterior_wrapper took 0.000835 seconds
INFO TPE using 9/9 trials with best loss -0.987939
INFO ALL params: {'n_neighbors': 5, 'weights': 'distance', 'leaf_size': 42}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 5, 'weights': 'distance', 'leaf_size': 42, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 5, 'weights': 'distance', 'leaf_size': 42, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 5, 'weights': 'distance', 'leaf_size': 42, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
28%|██▊ | 10/36 [00:16<00:41, 1.61s/trial, best loss: -0.9879391514987147]
INFO build_posterior_wrapper took 0.000824 seconds
INFO TPE using 10/10 trials with best loss -0.987939
INFO ALL params: {'n_neighbors': 3, 'weights': 'distance', 'leaf_size': 48}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 3, 'weights': 'distance', 'leaf_size': 48, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 3, 'weights': 'distance', 'leaf_size': 48, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 3, 'weights': 'distance', 'leaf_size': 48, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
31%|███ | 11/36 [00:17<00:39, 1.58s/trial, best loss: -0.9879391514987147]
INFO build_posterior_wrapper took 0.000819 seconds
INFO TPE using 11/11 trials with best loss -0.987939
INFO ALL params: {'n_neighbors': 4, 'weights': 'uniform', 'leaf_size': 46}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 4, 'weights': 'uniform', 'leaf_size': 46, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 4, 'weights': 'uniform', 'leaf_size': 46, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 4, 'weights': 'uniform', 'leaf_size': 46, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
33%|███▎ | 12/36 [00:19<00:37, 1.58s/trial, best loss: -0.9879391514987147]
INFO build_posterior_wrapper took 0.000808 seconds
INFO TPE using 12/12 trials with best loss -0.987939
INFO build_posterior_wrapper took 0.000771 seconds
INFO TPE using 13/13 trials with best loss -0.987939
INFO ALL params: {'n_neighbors': 7, 'weights': 'distance', 'leaf_size': 22}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 7, 'weights': 'distance', 'leaf_size': 22, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 7, 'weights': 'distance', 'leaf_size': 22, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 7, 'weights': 'distance', 'leaf_size': 22, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
39%|███▉ | 14/36 [00:20<00:27, 1.23s/trial, best loss: -0.9879391514987147]
INFO build_posterior_wrapper took 0.000815 seconds
INFO TPE using 14/14 trials with best loss -0.987939
INFO ALL params: {'n_neighbors': 7, 'weights': 'uniform', 'leaf_size': 33}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 7, 'weights': 'uniform', 'leaf_size': 33, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 7, 'weights': 'uniform', 'leaf_size': 33, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 7, 'weights': 'uniform', 'leaf_size': 33, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
42%|████▏ | 15/36 [00:22<00:27, 1.33s/trial, best loss: -0.9879391514987147]
INFO build_posterior_wrapper took 0.001153 seconds
INFO TPE using 15/15 trials with best loss -0.987939
INFO ALL params: {'n_neighbors': 5, 'weights': 'distance', 'leaf_size': 36}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 5, 'weights': 'distance', 'leaf_size': 36, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 5, 'weights': 'distance', 'leaf_size': 36, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 5, 'weights': 'distance', 'leaf_size': 36, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
44%|████▍ | 16/36 [00:23<00:27, 1.37s/trial, best loss: -0.9879391514987147]
INFO build_posterior_wrapper took 0.000867 seconds
INFO TPE using 16/16 trials with best loss -0.987939
INFO ALL params: {'n_neighbors': 8, 'weights': 'uniform', 'leaf_size': 24}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 8, 'weights': 'uniform', 'leaf_size': 24, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 8, 'weights': 'uniform', 'leaf_size': 24, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 8, 'weights': 'uniform', 'leaf_size': 24, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
47%|████▋ | 17/36 [00:25<00:26, 1.39s/trial, best loss: -0.9879391514987147]
INFO build_posterior_wrapper took 0.000796 seconds
INFO TPE using 17/17 trials with best loss -0.987939
INFO build_posterior_wrapper took 0.000712 seconds
INFO TPE using 18/18 trials with best loss -0.987939
INFO ALL params: {'n_neighbors': 3, 'weights': 'distance', 'leaf_size': 20}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 3, 'weights': 'distance', 'leaf_size': 20, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 3, 'weights': 'distance', 'leaf_size': 20, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 3, 'weights': 'distance', 'leaf_size': 20, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
53%|█████▎ | 19/36 [00:26<00:19, 1.13s/trial, best loss: -0.9879391514987147]
INFO build_posterior_wrapper took 0.000874 seconds
INFO TPE using 19/19 trials with best loss -0.987939
INFO ALL params: {'n_neighbors': 4, 'weights': 'uniform', 'leaf_size': 22}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 4, 'weights': 'uniform', 'leaf_size': 22, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 4, 'weights': 'uniform', 'leaf_size': 22, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 4, 'weights': 'uniform', 'leaf_size': 22, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
56%|█████▌ | 20/36 [00:28<00:19, 1.22s/trial, best loss: -0.9879391514987147]
INFO build_posterior_wrapper took 0.000820 seconds
INFO TPE using 20/20 trials with best loss -0.987939
INFO ALL params: {'n_neighbors': 8, 'weights': 'distance', 'leaf_size': 37}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 8, 'weights': 'distance', 'leaf_size': 37, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 8, 'weights': 'distance', 'leaf_size': 37, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 8, 'weights': 'distance', 'leaf_size': 37, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
58%|█████▊ | 21/36 [00:30<00:20, 1.34s/trial, best loss: -0.9879391514987147]
INFO build_posterior_wrapper took 0.000792 seconds
INFO TPE using 21/21 trials with best loss -0.987939
INFO build_posterior_wrapper took 0.000828 seconds
INFO TPE using 22/22 trials with best loss -0.987939
INFO ALL params: {'n_neighbors': 8, 'weights': 'distance', 'leaf_size': 38}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 8, 'weights': 'distance', 'leaf_size': 38, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 8, 'weights': 'distance', 'leaf_size': 38, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 8, 'weights': 'distance', 'leaf_size': 38, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
64%|██████▍ | 23/36 [00:31<00:14, 1.11s/trial, best loss: -0.9879391514987147]
INFO build_posterior_wrapper took 0.000803 seconds
INFO TPE using 23/23 trials with best loss -0.987939
INFO ALL params: {'n_neighbors': 8, 'weights': 'distance', 'leaf_size': 31}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 8, 'weights': 'distance', 'leaf_size': 31, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 8, 'weights': 'distance', 'leaf_size': 31, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 8, 'weights': 'distance', 'leaf_size': 31, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
67%|██████▋ | 24/36 [00:33<00:14, 1.24s/trial, best loss: -0.9879391514987147]
INFO build_posterior_wrapper took 0.000819 seconds
INFO TPE using 24/24 trials with best loss -0.987939
INFO ALL params: {'n_neighbors': 7, 'weights': 'distance', 'leaf_size': 32}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 7, 'weights': 'distance', 'leaf_size': 32, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 7, 'weights': 'distance', 'leaf_size': 32, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 7, 'weights': 'distance', 'leaf_size': 32, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
69%|██████▉ | 25/36 [00:34<00:14, 1.30s/trial, best loss: -0.9879391514987147]
INFO build_posterior_wrapper took 0.000808 seconds
INFO TPE using 25/25 trials with best loss -0.987939
INFO build_posterior_wrapper took 0.000688 seconds
INFO TPE using 26/26 trials with best loss -0.987939
INFO ALL params: {'n_neighbors': 7, 'weights': 'distance', 'leaf_size': 30}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 7, 'weights': 'distance', 'leaf_size': 30, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 7, 'weights': 'distance', 'leaf_size': 30, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 7, 'weights': 'distance', 'leaf_size': 30, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
75%|███████▌ | 27/36 [00:36<00:10, 1.11s/trial, best loss: -0.9879391514987147]
INFO build_posterior_wrapper took 0.000820 seconds
INFO TPE using 27/27 trials with best loss -0.987939
INFO ALL params: {'n_neighbors': 6, 'weights': 'distance', 'leaf_size': 50}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 6, 'weights': 'distance', 'leaf_size': 50, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 6, 'weights': 'distance', 'leaf_size': 50, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 6, 'weights': 'distance', 'leaf_size': 50, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
78%|███████▊ | 28/36 [00:38<00:09, 1.22s/trial, best loss: -0.9879391514987147]
INFO build_posterior_wrapper took 0.000838 seconds
INFO TPE using 28/28 trials with best loss -0.987939
INFO ALL params: {'n_neighbors': 8, 'weights': 'distance', 'leaf_size': 39}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 8, 'weights': 'distance', 'leaf_size': 39, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 8, 'weights': 'distance', 'leaf_size': 39, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 8, 'weights': 'distance', 'leaf_size': 39, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
81%|████████ | 29/36 [00:39<00:09, 1.34s/trial, best loss: -0.9879391514987147]
INFO build_posterior_wrapper took 0.000778 seconds
INFO TPE using 29/29 trials with best loss -0.987939
INFO ALL params: {'n_neighbors': 7, 'weights': 'distance', 'leaf_size': 39}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 7, 'weights': 'distance', 'leaf_size': 39, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 7, 'weights': 'distance', 'leaf_size': 39, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 7, 'weights': 'distance', 'leaf_size': 39, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
83%|████████▎ | 30/36 [00:41<00:08, 1.41s/trial, best loss: -0.9879391514987147]
INFO build_posterior_wrapper took 0.000807 seconds
INFO TPE using 30/30 trials with best loss -0.987939
INFO ALL params: {'n_neighbors': 6, 'weights': 'distance', 'leaf_size': 35}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 6, 'weights': 'distance', 'leaf_size': 35, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 6, 'weights': 'distance', 'leaf_size': 35, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 6, 'weights': 'distance', 'leaf_size': 35, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
86%|████████▌ | 31/36 [00:43<00:07, 1.46s/trial, best loss: -0.9879391514987147]
INFO build_posterior_wrapper took 0.001044 seconds
INFO TPE using 31/31 trials with best loss -0.987939
INFO ALL params: {'n_neighbors': 8, 'weights': 'distance', 'leaf_size': 40}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 8, 'weights': 'distance', 'leaf_size': 40, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 8, 'weights': 'distance', 'leaf_size': 40, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 8, 'weights': 'distance', 'leaf_size': 40, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
89%|████████▉ | 32/36 [00:44<00:06, 1.53s/trial, best loss: -0.9879391514987147]
INFO build_posterior_wrapper took 0.000848 seconds
INFO TPE using 32/32 trials with best loss -0.987939
INFO ALL params: {'n_neighbors': 6, 'weights': 'distance', 'leaf_size': 44}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 6, 'weights': 'distance', 'leaf_size': 44, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 6, 'weights': 'distance', 'leaf_size': 44, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 6, 'weights': 'distance', 'leaf_size': 44, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
92%|█████████▏| 33/36 [00:46<00:04, 1.55s/trial, best loss: -0.9879391514987147]
INFO build_posterior_wrapper took 0.000833 seconds
INFO TPE using 33/33 trials with best loss -0.987939
INFO ALL params: {'n_neighbors': 7, 'weights': 'distance', 'leaf_size': 28}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 7, 'weights': 'distance', 'leaf_size': 28, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 7, 'weights': 'distance', 'leaf_size': 28, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 7, 'weights': 'distance', 'leaf_size': 28, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
94%|█████████▍| 34/36 [00:48<00:03, 1.59s/trial, best loss: -0.9879391514987147]
INFO build_posterior_wrapper took 0.000864 seconds
INFO TPE using 34/34 trials with best loss -0.987939
INFO ALL params: {'n_neighbors': 8, 'weights': 'distance', 'leaf_size': 41}...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 8, 'weights': 'distance', 'leaf_size': 41, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 8, 'weights': 'distance', 'leaf_size': 41, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'n_neighbors': 8, 'weights': 'distance', 'leaf_size': 41, 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
97%|█████████▋| 35/36 [00:49<00:01, 1.59s/trial, best loss: -0.9879391514987147]
INFO build_posterior_wrapper took 0.002857 seconds INFO TPE using 35/35 trials with best loss -0.987939
100%|██████████| 36/36 [00:49<00:00, 1.38s/trial, best loss: -0.9879391514987147]
INFO Drawing cv search graph...
INFO Drawing params_search_chart...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'leaf_size': 36, 'n_neighbors': 8, 'weights': 'distance', 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO [FIT_TRANSFORM] ['AMT_INCOME_TOTAL']: 0.001133 secs
INFO [FIT_TRANSFORM] ['Age']: 0.000281 secs
INFO [FIT_TRANSFORM] ['CNT_CHILDREN']: 0.000213 secs
INFO [FIT_TRANSFORM] ['DAYS_EMPLOYED']: 0.000251 secs
INFO [FIT_TRANSFORM] ['FLAG_PHONE']: 0.000283 secs
INFO [FIT_TRANSFORM] ['FLAG_WORK_PHONE']: 0.000245 secs
INFO [TRANSFORM] ['AMT_INCOME_TOTAL']: 0.000303 secs
INFO [TRANSFORM] ['Age']: 0.001819 secs
INFO [TRANSFORM] ['CNT_CHILDREN']: 0.000626 secs
INFO [TRANSFORM] ['DAYS_EMPLOYED']: 0.00062 secs
INFO [TRANSFORM] ['FLAG_PHONE']: 0.00053 secs
INFO [TRANSFORM] ['FLAG_WORK_PHONE']: 0.000165 secs
INFO [TRANSFORM] ['AMT_INCOME_TOTAL']: 0.000265 secs
INFO [TRANSFORM] ['Age']: 0.001023 secs
INFO [TRANSFORM] ['CNT_CHILDREN']: 0.000141 secs
INFO [TRANSFORM] ['DAYS_EMPLOYED']: 0.000131 secs
INFO [TRANSFORM] ['FLAG_PHONE']: 0.000137 secs
INFO [TRANSFORM] ['FLAG_WORK_PHONE']: 0.000135 secs
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Drawing train_score_uncut_ks_graph...
INFO Drawing train_score_uncut_count_chart1...
INFO Drawing train_score_uncut_count_chart2...
INFO Drawing train_score_uncut_count_chart3...
INFO Drawing train_score_uncut_cap_chart1...
INFO Drawing train_score_uncut_cap_chart2...
INFO Drawing train_score_uncut_cap_chart3...
INFO Creating cut info...
INFO Drawing train_score_cut_ks_graph...
INFO Drawing train_score_cut_rate_chart...
INFO Drawing train_score_cut_count_chart1...
INFO Drawing train_score_cut_count_chart2...
INFO Drawing train_score_cut_count_chart3...
INFO Drawing train_score_cut_cap_chart1...
INFO Drawing train_score_cut_cap_chart2...
INFO Drawing train_score_cut_cap_chart3...
INFO Creating qcut info...
INFO Drawing train_score_qcut_ks_graph...
INFO Drawing train_score_qcut_rate_chart...
INFO Drawing train_score_qcut_count_chart1...
INFO Drawing train_score_qcut_count_chart2...
INFO Drawing train_score_qcut_count_chart3...
INFO Drawing train_score_qcut_cap_chart1...
INFO Drawing train_score_qcut_cap_chart2...
INFO Drawing train_score_qcut_cap_chart3...
INFO Creating precision recall fscore...
INFO Drawing train_classification_chart...
INFO Drawing train_classification_chart...
INFO Drawing roc...
INFO Creating results summary...
INFO Drawing ap...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Drawing test_score_uncut_ks_graph...
INFO Drawing test_score_uncut_count_chart1...
INFO Drawing test_score_uncut_count_chart2...
INFO Drawing test_score_uncut_count_chart3...
INFO Drawing test_score_uncut_cap_chart1...
INFO Drawing test_score_uncut_cap_chart2...
INFO Drawing test_score_uncut_cap_chart3...
INFO Creating cut info...
INFO Drawing test_score_cut_ks_graph...
INFO Drawing test_score_cut_rate_chart...
INFO Drawing test_score_cut_count_chart1...
INFO Drawing test_score_cut_count_chart2...
INFO Drawing test_score_cut_count_chart3...
INFO Drawing test_score_cut_cap_chart1...
INFO Drawing test_score_cut_cap_chart2...
INFO Drawing test_score_cut_cap_chart3...
INFO Creating qcut info...
INFO Drawing test_score_qcut_ks_graph...
INFO Drawing test_score_qcut_rate_chart...
INFO Drawing test_score_qcut_count_chart1...
INFO Drawing test_score_qcut_count_chart2...
INFO Drawing test_score_qcut_count_chart3...
INFO Drawing test_score_qcut_cap_chart1...
INFO Drawing test_score_qcut_cap_chart2...
INFO Drawing test_score_qcut_cap_chart3...
INFO Creating precision recall fscore...
INFO Drawing test_classification_chart...
INFO Drawing test_classification_chart...
INFO Drawing roc...
INFO Creating results summary...
INFO Drawing ap...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Drawing cut_psi_chart...
INFO Drawing cut_psi_chart...
INFO Drawing qcut_psi_chart...
INFO Drawing qcut_psi_chart...
INFO Total: 9.77 s
INFO Saving var_drop...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'leaf_size': 36, 'n_neighbors': 8, 'weights': 'distance', 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) !
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'leaf_size': 36, 'n_neighbors': 8, 'weights': 'distance', 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
INFO Using model params: {'leaf_size': 36, 'n_neighbors': 8, 'weights': 'distance', 'n_jobs': 2}
INFO Using variables: 6...
INFO Training KNeighborsClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO Drawing cv compare graph...
INFO Drawing cv_compare_ks_chart...
INFO Drawing cv_compare_auc_chart...
INFO Drawing cv_compare_ap_chart...
INFO Drawing cv_compare_logloss_chart...
INFO Drawing cv_compare_r2_chart...
INFO Drawing cv_compare_mse_chart...
INFO Drawing cv_compare_mdp_chart...
INFO Drawing cv_compare_psi_chart...
INFO Drawing cv_compare_mpg_chart...
INFO Saving model pkl...
INFO Saving model pmml...
/home/conda_env/miniconda3/lib/python3.9/subprocess.py:941: RuntimeWarning: line buffering (buffering=1) isn't supported in binary mode, the default buffer size will be used
self.stdout = io.open(c2pread, 'rb', bufsize)
/home/conda_env/miniconda3/lib/python3.9/subprocess.py:946: RuntimeWarning: line buffering (buffering=1) isn't supported in binary mode, the default buffer size will be used
self.stderr = io.open(errread, 'rb', bufsize)
ERROR The JPMML-SkLearn conversion application has failed. The Java executable should have printed more information about the failure into its standard output and/or standard error streams
INFO Saving json...
INFO Saving setting...
INFO Adding runtime to excel...
INFO Saving directory...
INFO Saving...
Standard output is empty Standard error: Apr 12, 2022 4:34:52 PM org.jpmml.sklearn.Main run INFO: Parsing PKL.. Apr 12, 2022 4:34:52 PM org.jpmml.sklearn.Main run SEVERE: Failed to parse PKL net.razorvine.pickle.PickleException: expected zero arguments for construction of ClassDict (for sklearn.metrics._dist_metrics.newObj). This happens when an unsupported/unregistered class is being unpickled that requires construction arguments. Fix it by registering a custom IObjectConstructor for this class. at net.razorvine.pickle.objects.ClassDictConstructor.construct(ClassDictConstructor.java:23) at net.razorvine.pickle.Unpickler.load_reduce(Unpickler.java:759) at net.razorvine.pickle.Unpickler.dispatch(Unpickler.java:199) at org.jpmml.python.PickleUtil$1.dispatch(PickleUtil.java:83) at net.razorvine.pickle.Unpickler.load(Unpickler.java:109) at org.jpmml.python.PickleUtil.unpickle(PickleUtil.java:104) at org.jpmml.sklearn.Main.run(Main.java:155) at org.jpmml.sklearn.Main.main(Main.java:143) Exception in thread "main" net.razorvine.pickle.PickleException: expected zero arguments for construction of ClassDict (for sklearn.metrics._dist_metrics.newObj). This happens when an unsupported/unregistered class is being unpickled that requires construction arguments. Fix it by registering a custom IObjectConstructor for this class. at net.razorvine.pickle.objects.ClassDictConstructor.construct(ClassDictConstructor.java:23) at net.razorvine.pickle.Unpickler.load_reduce(Unpickler.java:759) at net.razorvine.pickle.Unpickler.dispatch(Unpickler.java:199) at org.jpmml.python.PickleUtil$1.dispatch(PickleUtil.java:83) at net.razorvine.pickle.Unpickler.load(Unpickler.java:109) at org.jpmml.python.PickleUtil.unpickle(PickleUtil.java:104) at org.jpmml.sklearn.Main.run(Main.java:155) at org.jpmml.sklearn.Main.main(Main.java:143)
INFO Total: 64.64 s INFO Total: 64.69 s
df_train, df_test = df_train.copy(), df_test.copy()
df_train["Age-swap"], df_test["Age-swap"] = df_train["Age"].copy(), df_test["Age"].copy()
df_train["sample1"] = np.random.rand(df_train.index.size)
df_train["sample2"] = np.random.uniform(0, 1, df_train.index.size)
df_test["sample1"] = np.random.rand(df_test.index.size)
df_test["sample2"] = np.random.uniform(0, 1, df_test.index.size)
at.Analysis.model_on_data(
df_train, "./模型分析demo/v15/v15.xlsx", test_data=df_test,
data_flow={
**data_params, **{
"save_or_return": False, # 跳过csv的io读写提升性能
"add_info": ["ID", "sample1", "sample2", "Age-swap"], # model_flow才能读到
},
},
model_flow={
"split_col_name": "birth_year", "use_train_time": True,
"exclude_column": ["ID", "weight"], # 排除权重
"swap_compare_name": ["sample1", "sample2", "Age-swap"], # 直接伪造数据假设有三列需要swap
}
)
INFO Prepare kwargs...
INFO Checking train_data...
INFO Checking response...
INFO Checking test_data...
INFO Checking response...
INFO Checking columns...
INFO Checking dtypes...
WARNING Setting format_time_edge to True !
INFO Prepare run...
INFO Run: CNT_FAM_MEMBERS
INFO Run: OCCUPATION_TYPE
INFO Run: FLAG_PHONE
INFO Run: AMT_INCOME_TOTAL
INFO Run: NAME_HOUSING_TYPE
INFO Run: Age
INFO Run: DAYS_BIRTH
INFO Run: DAYS_EMPLOYED
INFO Run: NAME_INCOME_TYPE
INFO Run: FLAG_WORK_PHONE
INFO Run: FLAG_OWN_REALTY
INFO Run: CODE_GENDER
INFO Run: FLAG_MOBIL
INFO Run: CNT_CHILDREN
INFO Run: FLAG_EMAIL
INFO Run: NAME_EDUCATION_TYPE
INFO Run: FLAG_OWN_CAR
INFO Run: NAME_FAMILY_STATUS
INFO Selecting...
INFO Match train: FLAG_PHONE_asC
INFO Match train: OCCUPATION_TYPE_asD
INFO Match train: CNT_FAM_MEMBERS_asC
INFO Match train: NAME_HOUSING_TYPE_asD
INFO Match train: FLAG_OWN_REALTY_asD
INFO Match train: NAME_INCOME_TYPE_asD
INFO Match train: FLAG_WORK_PHONE_asC
INFO Match train: AMT_INCOME_TOTAL_asC
INFO Match train: CODE_GENDER_asD
INFO Match train: DAYS_EMPLOYED_asC
INFO Match train: CNT_CHILDREN_asC
INFO Match train: Age_asC
INFO Match train: FLAG_EMAIL_asC
INFO Match train: FLAG_OWN_CAR_asD
INFO Match train: NAME_EDUCATION_TYPE_asD
INFO Match train: DAYS_BIRTH_asC
INFO Match train: NAME_FAMILY_STATUS_asD
INFO Creating train data...
INFO Creating train corr detail...
INFO Creating (-inf, 1972-01-01] corr...
INFO Creating (1972-01-01, 1984-01-01] corr...
INFO Creating (1984-01-01, inf) corr...
INFO Creating all corr...
INFO Creating train cross table...
INFO Creating train all desc tables...
INFO Match test: CNT_FAM_MEMBERS_asC
INFO Match test: OCCUPATION_TYPE_asD
INFO Match test: FLAG_PHONE_asC
INFO Match test: AMT_INCOME_TOTAL_asC
INFO Match test: NAME_HOUSING_TYPE_asD
INFO Match test: Age_asC
INFO Match test: DAYS_BIRTH_asC
INFO Match test: DAYS_EMPLOYED_asC
INFO Match test: NAME_INCOME_TYPE_asD
INFO Match test: FLAG_WORK_PHONE_asC
INFO Match test: FLAG_OWN_REALTY_asD
INFO Match test: CODE_GENDER_asD
INFO Match test: CNT_CHILDREN_asC
INFO Match test: FLAG_EMAIL_asC
INFO Match test: NAME_EDUCATION_TYPE_asD
INFO Match test: FLAG_OWN_CAR_asD
INFO Match test: NAME_FAMILY_STATUS_asD
INFO Creating test data...
INFO Creating test corr detail...
INFO Creating (-inf, 1972-01-01] corr...
INFO Creating (1972-01-01, 1984-01-01] corr...
INFO Creating (1984-01-01, inf) corr...
INFO Creating all corr...
INFO Creating test cross table...
INFO Creating test all desc tables...
INFO Match train test: FLAG_OWN_REALTY_asD
INFO Match train test: NAME_FAMILY_STATUS_asD
INFO Match train test: DAYS_EMPLOYED_asC
INFO Match train test: NAME_INCOME_TYPE_asD
INFO Match train test: NAME_EDUCATION_TYPE_asD
INFO Match train test: NAME_HOUSING_TYPE_asD
INFO Match train test: AMT_INCOME_TOTAL_asC
INFO Match train test: FLAG_WORK_PHONE_asC
INFO Match train test: CODE_GENDER_asD
INFO Match train test: FLAG_OWN_CAR_asD
INFO Match train test: OCCUPATION_TYPE_asD
INFO Match train test: FLAG_PHONE_asC
INFO Match train test: Age_asC
INFO Match train test: DAYS_BIRTH_asC
INFO Match train test: CNT_CHILDREN_asC
INFO Match train test: FLAG_EMAIL_asC
INFO Match train test: CNT_FAM_MEMBERS_asC
INFO Saving info...
INFO Saving desc...
INFO Saving cross...
INFO Saving unique...
INFO Saving summary...
INFO Saving corr summary...
INFO Saving stable_summary...
INFO Saving train_stable_iv_ks...
INFO Saving train_stable_psi...
INFO Saving train_stable_quantile...
INFO Saving test_stable_iv_ks...
INFO Saving test_stable_psi...
INFO Saving test_stable_quantile...
INFO Saving stable_cross_psi...
INFO Saving detail...
INFO Saving draft...
INFO Saving drop...
INFO Saving json...
INFO Saving setting...
INFO Adding runtime to excel...
INFO Saving directory...
INFO Saving...
INFO Total: 9.93 s
INFO Prepare kwargs...
WARNING Setting format_time_edge to True !
WARNING exclude_column: "weight" not in data !
INFO Checking train_data...
INFO Checking response...
INFO Checking test_data...
INFO Checking response...
INFO Checking columns...
INFO Checking dtypes...
WARNING Setting nan_mode to min !
WARNING Find that convert_score is True, setting precision to 0 !
INFO Model linear flow...
WARNING Find that convert_score is True & save_score_pmml is True, setting save_model_pmml to False !
INFO Selecting by corr...
INFO Total: 17...
INFO Selected: 15...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'max_depth': 3, 'n_estimators': 30, 'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': 'auto'}
INFO Using variables: 15...
INFO Training LGBMClassifier...
INFO Filtering importance by LGBMClassifier ...
INFO Total: 15...
INFO Selected: 15...
INFO Round: 1; Forward Stepwise: WOE_FLAG_OWN_REALTY_asD; Selected: 2
INFO Round: 1; Forward Stepwise: WOE_NAME_FAMILY_STATUS_asD; Selected: 3
INFO Round: 1; Forward Stepwise: WOE_DAYS_EMPLOYED_asC; Selected: 4
INFO Round: 1; Forward Stepwise: WOE_NAME_INCOME_TYPE_asD; Selected: 4
INFO Round: 1; Forward Stepwise: WOE_NAME_HOUSING_TYPE_asD; Selected: 5
INFO Round: 1; Forward Stepwise: WOE_NAME_EDUCATION_TYPE_asD; Selected: 6
INFO Round: 1; Forward Stepwise: WOE_CODE_GENDER_asD; Selected: 7
INFO Round: 1; Forward Stepwise: WOE_OCCUPATION_TYPE_asD; Selected: 7
INFO Round: 1; Forward Stepwise: WOE_AMT_INCOME_TOTAL_asC; Selected: 7
INFO Round: 1; Forward Stepwise: WOE_FLAG_WORK_PHONE_asC; Selected: 7
INFO Round: 1; Forward Stepwise: WOE_FLAG_OWN_CAR_asD; Selected: 7
INFO Round: 1; Forward Stepwise: WOE_FLAG_PHONE_asC; Selected: 7
INFO Round: 1; Forward Stepwise: WOE_DAYS_BIRTH_asC; Selected: 7
INFO Round: 1; Forward Stepwise: WOE_CNT_CHILDREN_asC; Selected: 7
INFO Round: 1; Forward Stepwise: WOE_FLAG_EMAIL_asC; Selected: 7
INFO Round: 2; Forward Stepwise: WOE_NAME_INCOME_TYPE_asD; Selected: 7
INFO Round: 2; Forward Stepwise: WOE_OCCUPATION_TYPE_asD; Selected: 7
INFO Round: 2; Forward Stepwise: WOE_AMT_INCOME_TOTAL_asC; Selected: 7
INFO Round: 2; Forward Stepwise: WOE_FLAG_WORK_PHONE_asC; Selected: 7
INFO Round: 2; Forward Stepwise: WOE_FLAG_OWN_CAR_asD; Selected: 7
INFO Round: 2; Forward Stepwise: WOE_FLAG_PHONE_asC; Selected: 7
INFO Round: 2; Forward Stepwise: WOE_DAYS_BIRTH_asC; Selected: 7
INFO Round: 2; Forward Stepwise: WOE_CNT_CHILDREN_asC; Selected: 7
INFO Round: 2; Forward Stepwise: WOE_FLAG_EMAIL_asC; Selected: 7
INFO Round: 3; Forward Stepwise: WOE_NAME_INCOME_TYPE_asD; Selected: 7
INFO Round: 3; Forward Stepwise: WOE_OCCUPATION_TYPE_asD; Selected: 7
INFO Round: 3; Forward Stepwise: WOE_AMT_INCOME_TOTAL_asC; Selected: 7
INFO Round: 3; Forward Stepwise: WOE_FLAG_WORK_PHONE_asC; Selected: 7
INFO Round: 3; Forward Stepwise: WOE_FLAG_OWN_CAR_asD; Selected: 7
INFO Round: 3; Forward Stepwise: WOE_FLAG_PHONE_asC; Selected: 7
INFO Round: 3; Forward Stepwise: WOE_DAYS_BIRTH_asC; Selected: 7
INFO Round: 3; Forward Stepwise: WOE_CNT_CHILDREN_asC; Selected: 7
INFO Round: 3; Forward Stepwise: WOE_FLAG_EMAIL_asC; Selected: 7
INFO Model variables: ['Intercept', 'WOE_FLAG_OWN_REALTY_asD', 'WOE_NAME_FAMILY_STATUS_asD', 'WOE_DAYS_EMPLOYED_asC', 'WOE_NAME_HOUSING_TYPE_asD', 'WOE_NAME_EDUCATION_TYPE_asD', 'WOE_CODE_GENDER_asD'] !
INFO Building model...
INFO Saving var_desc...
INFO Saving var_cross...
INFO Saving var_unique...
INFO Saving var_summary...
INFO Saving woe_corr...
INFO Saving train_stable_summary...
INFO Saving test_stable_summary...
INFO Saving train_var_stable_iv_ks...
INFO Saving train_var_stable_psi...
INFO Saving train_var_stable_quantile...
INFO Saving test_var_stable_iv_ks...
INFO Saving test_var_stable_psi...
INFO Saving test_var_stable_quantile...
INFO Saving var_stable_cross_psi...
INFO Saving var_detail...
INFO Saving var_draft...
Optimization terminated successfully.
Current function value: 0.091927
Iterations 8
INFO Saving var_drop... INFO Prepare scorecard... INFO Adding scorecard_description to excel... INFO Converting train proba to score... INFO Converting woe to score... INFO Checking scorecard equation... INFO Saving train_var_corr... INFO Saving train vif... INFO Getting detail score... INFO Saving train results... INFO Getting total score... INFO Creating uncut info... INFO Drawing train_score_uncut_ks_graph... INFO Drawing train_score_uncut_count_chart1... INFO Drawing train_score_uncut_count_chart2... INFO Drawing train_score_uncut_count_chart3... INFO Drawing train_score_uncut_cap_chart1... INFO Drawing train_score_uncut_cap_chart2... INFO Drawing train_score_uncut_cap_chart3... INFO Creating cut info... INFO Drawing train_score_cut_ks_graph... INFO Drawing train_score_cut_rate_chart... INFO Drawing train_score_cut_count_chart1... INFO Drawing train_score_cut_count_chart2... INFO Drawing train_score_cut_count_chart3... INFO Drawing train_score_cut_cap_chart1... INFO Drawing train_score_cut_cap_chart2... INFO Drawing train_score_cut_cap_chart3... INFO Creating qcut info... INFO Drawing train_score_qcut_ks_graph... INFO Drawing train_score_qcut_rate_chart... INFO Drawing train_score_qcut_count_chart1... INFO Drawing train_score_qcut_count_chart2... INFO Drawing train_score_qcut_count_chart3... INFO Drawing train_score_qcut_cap_chart1... INFO Drawing train_score_qcut_cap_chart2... INFO Drawing train_score_qcut_cap_chart3... INFO Creating precision recall fscore... INFO Drawing train_classification_chart... INFO Drawing train_classification_chart... INFO Drawing roc... INFO Creating results summary... INFO Drawing ap... INFO Converting test proba to score... INFO Converting woe to score... INFO Checking scorecard equation... INFO Saving test_var_corr... INFO Saving test vif... INFO Getting detail score... INFO Saving test results... INFO Getting total score... INFO Creating uncut info... INFO Drawing test_score_uncut_ks_graph... INFO Drawing test_score_uncut_count_chart1... INFO Drawing test_score_uncut_count_chart2... INFO Drawing test_score_uncut_count_chart3... INFO Drawing test_score_uncut_cap_chart1... INFO Drawing test_score_uncut_cap_chart2... INFO Drawing test_score_uncut_cap_chart3... INFO Creating cut info... INFO Drawing test_score_cut_ks_graph... INFO Drawing test_score_cut_rate_chart... INFO Drawing test_score_cut_count_chart1... INFO Drawing test_score_cut_count_chart2... INFO Drawing test_score_cut_count_chart3... INFO Drawing test_score_cut_cap_chart1... INFO Drawing test_score_cut_cap_chart2... INFO Drawing test_score_cut_cap_chart3... INFO Creating qcut info... INFO Drawing test_score_qcut_ks_graph... INFO Drawing test_score_qcut_rate_chart... INFO Drawing test_score_qcut_count_chart1... INFO Drawing test_score_qcut_count_chart2... INFO Drawing test_score_qcut_count_chart3... INFO Drawing test_score_qcut_cap_chart1... INFO Drawing test_score_qcut_cap_chart2... INFO Drawing test_score_qcut_cap_chart3... INFO Creating precision recall fscore... INFO Drawing test_classification_chart... INFO Drawing test_classification_chart... INFO Drawing roc... INFO Creating results summary... INFO Drawing ap... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi... INFO Drawing cut_psi_chart... INFO Drawing cut_psi_chart... INFO Drawing qcut_psi_chart... INFO Drawing qcut_psi_chart... INFO Getting swap compare... INFO Getting swap 1... INFO Drawing train swap scatter... INFO Getting train swap count... INFO Drawing test swap scatter... INFO Getting test swap count... INFO Getting swap psi... INFO Getting swap 2... INFO Drawing train swap scatter... INFO Getting train swap count... INFO Drawing test swap scatter... INFO Getting test swap count... INFO Getting swap psi... INFO Getting swap 3... INFO Drawing train swap scatter... INFO Getting train swap count... INFO Drawing test swap scatter... INFO Getting test swap count... INFO Getting swap psi... INFO Saving model score gap... INFO Creating pmml... INFO Adding score_pmml to excel... INFO Adding run_score_pmml_in_java to excel... INFO Creating sql... INFO Creating check unique sql... INFO Creating unique table sql... INFO Creating count sql... INFO Adding model_sql to excel... INFO Adding run_sql_in_dataframe to excel... INFO birth_year | 3 | cv flow... INFO Prepare scorecard... INFO Converting train proba to score... INFO Saving train results... INFO Getting total score... INFO Creating uncut info... INFO Creating cut info... INFO Creating qcut info... INFO Creating precision recall fscore... INFO Creating results summary... INFO Converting test proba to score... INFO Saving test results... INFO Getting total score... INFO Creating uncut info... INFO Creating cut info... INFO Creating qcut info... INFO Creating precision recall fscore... INFO Creating results summary... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi... INFO Prepare scorecard... INFO Converting train proba to score... INFO Saving train results... INFO Getting total score... INFO Creating uncut info... INFO Creating cut info... INFO Creating qcut info... INFO Creating precision recall fscore... INFO Creating results summary... INFO Converting test proba to score... INFO Saving test results... INFO Getting total score... INFO Creating uncut info... INFO Creating cut info... INFO Creating qcut info... INFO Creating precision recall fscore... INFO Creating results summary... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi... INFO Prepare scorecard... INFO Converting train proba to score... INFO Saving train results... INFO Getting total score... INFO Creating uncut info... INFO Creating cut info... INFO Creating qcut info... INFO Creating precision recall fscore... INFO Creating results summary... INFO Converting test proba to score... INFO Saving test results... INFO Getting total score... INFO Creating uncut info... INFO Creating cut info... INFO Creating qcut info... INFO Creating precision recall fscore... INFO Creating results summary... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi... INFO Saving cross validate... INFO Drawing cv compare graph... INFO Drawing cv_compare_ks_chart... INFO Drawing cv_compare_auc_chart... INFO Drawing cv_compare_ap_chart... INFO Drawing cv_compare_logloss_chart... INFO Drawing cv_compare_r2_chart... INFO Drawing cv_compare_mse_chart... INFO Drawing cv_compare_mdp_chart... INFO Drawing cv_compare_psi_chart... INFO Drawing cv_compare_mpg_chart... INFO Saving model pkl... INFO Saving json... INFO Saving setting... INFO Adding runtime to excel... INFO Saving directory... INFO Saving... INFO Total: 23.61 s INFO Total: 33.64 s
df_train, df_test = df_train.copy(), df_test.copy()
df_train["sample1"] = np.random.rand(df_train.index.size)
df_train["sample2"] = np.random.uniform(0, 1, df_train.index.size)
df_test["sample1"] = np.random.rand(df_test.index.size)
df_test["sample2"] = np.random.uniform(0, 1, df_test.index.size)
at.Analysis.model_flow(
df_train, "./模型分析demo/v16/v16.xlsx", test_data=df_test,
# 目标定义 & 切片定义
response="target", split_col_name="birth_year", use_train_time=True,
exclude_column=["ID", "weight"], # 排除权重
add_info="ID", # 主键添加
exists_proba_name="Age", # 假设此处Age是需要进行评估的模型结果
swap_compare_name=["sample1", "sample2"], # 直接伪造数据假设这两个维度需要swap
)
INFO Prepare kwargs... WARNING Setting format_time_edge to True ! INFO Checking train_data... INFO Checking response... INFO Checking test_data... INFO Checking response... INFO Checking columns... INFO Checking dtypes... WARNING Setting nan_mode to min ! WARNING Find that exists_proba_name is int, setting convert_score to False, setting precision to 0 ! WARNING Find that convert_score is False, setting keep_raw_score to False ! WARNING Find that convert_score is False, setting save_score_desc to False ! WARNING Find that convert_score is False, setting save_score_pmml to False ! INFO Model optional flow... INFO Saving train results... INFO Getting total score... INFO Creating uncut info... INFO Drawing train_score_uncut_ks_graph... INFO Drawing train_score_uncut_count_chart1... INFO Drawing train_score_uncut_count_chart2... INFO Drawing train_score_uncut_count_chart3... INFO Drawing train_score_uncut_cap_chart1... INFO Drawing train_score_uncut_cap_chart2... INFO Drawing train_score_uncut_cap_chart3... INFO Creating cut info... INFO Drawing train_score_cut_ks_graph... INFO Drawing train_score_cut_rate_chart... INFO Drawing train_score_cut_count_chart1... INFO Drawing train_score_cut_count_chart2... INFO Drawing train_score_cut_count_chart3... INFO Drawing train_score_cut_cap_chart1... INFO Drawing train_score_cut_cap_chart2... INFO Drawing train_score_cut_cap_chart3... INFO Creating qcut info... INFO Drawing train_score_qcut_ks_graph... INFO Drawing train_score_qcut_rate_chart... INFO Drawing train_score_qcut_count_chart1... INFO Drawing train_score_qcut_count_chart2... INFO Drawing train_score_qcut_count_chart3... INFO Drawing train_score_qcut_cap_chart1... INFO Drawing train_score_qcut_cap_chart2... INFO Drawing train_score_qcut_cap_chart3... INFO Creating precision recall fscore... INFO Drawing train_classification_chart... INFO Drawing train_classification_chart... INFO Drawing roc... INFO Creating results summary... INFO Drawing ap... INFO Saving test results... INFO Getting total score... WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) ! INFO Creating uncut info... INFO Drawing test_score_uncut_ks_graph... INFO Drawing test_score_uncut_count_chart1... INFO Drawing test_score_uncut_count_chart2... INFO Drawing test_score_uncut_count_chart3... INFO Drawing test_score_uncut_cap_chart1... INFO Drawing test_score_uncut_cap_chart2... INFO Drawing test_score_uncut_cap_chart3... INFO Creating cut info... INFO Drawing test_score_cut_ks_graph... INFO Drawing test_score_cut_rate_chart... INFO Drawing test_score_cut_count_chart1... INFO Drawing test_score_cut_count_chart2... INFO Drawing test_score_cut_count_chart3... INFO Drawing test_score_cut_cap_chart1... INFO Drawing test_score_cut_cap_chart2... INFO Drawing test_score_cut_cap_chart3... INFO Creating qcut info... INFO Drawing test_score_qcut_ks_graph... INFO Drawing test_score_qcut_rate_chart... INFO Drawing test_score_qcut_count_chart1... INFO Drawing test_score_qcut_count_chart2... INFO Drawing test_score_qcut_count_chart3... INFO Drawing test_score_qcut_cap_chart1... INFO Drawing test_score_qcut_cap_chart2... INFO Drawing test_score_qcut_cap_chart3... INFO Creating precision recall fscore... INFO Drawing test_classification_chart... INFO Drawing test_classification_chart... INFO Drawing roc... INFO Creating results summary... INFO Drawing ap... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi... INFO Drawing cut_psi_chart... INFO Drawing cut_psi_chart... INFO Drawing qcut_psi_chart... INFO Drawing qcut_psi_chart... INFO Getting swap compare... INFO Getting swap 1... INFO Drawing train swap scatter... INFO Getting train swap count... INFO Drawing test swap scatter... INFO Getting test swap count... INFO Getting swap psi... INFO Getting swap 2... INFO Drawing train swap scatter... INFO Getting train swap count... INFO Drawing test swap scatter... INFO Getting test swap count... INFO Getting swap psi... INFO Saving json... INFO Saving setting... INFO Adding runtime to excel... INFO Saving directory... INFO Saving... INFO Total: 15.00 s INFO Total: 15.05 s
df_train.drop(["sample1", "sample2", "Age-swap"], axis=1, inplace=True)
df_test.drop(["sample1", "sample2", "Age-swap"], axis=1, inplace=True)
at.Analysis.model_flow(
df_train, "./模型分析demo/vr-torch/vr-torch.xlsx", test_data=df_test,
recover_from_json="./模型分析demo/v8-torch/Model_v8-torch.json"
)
WARNING /home/conda_env/模型分析demo/v8-torch/Model_v8-torch_model.pkl not exists, setting recover_from_pkl to False ! INFO Prepare kwargs... WARNING Setting format_time_edge to True ! INFO Checking train_data... INFO Checking response... INFO Checking test_data... INFO Checking response... INFO Checking columns... INFO Checking dtypes... WARNING Find that filter_variable is False, setting zero_imp_filter to False ! WARNING Find that filter_variable is False, setting enable_stepwise to False ! WARNING Find that filter_variable is False, setting enable_corr_filter to False ! WARNING Find that filter_variable is False, setting limit_var_num to inf ! INFO Model optional flow... WARNING Check /home/conda_env/模型分析demo/vr-torch/Data_vr-torch.xlsx exists ! INFO Model variables: ['CNT_CHILDREN', 'AMT_INCOME_TOTAL', 'DAYS_BIRTH', 'DAYS_EMPLOYED', 'FLAG_MOBIL', 'FLAG_WORK_PHONE', 'FLAG_PHONE', 'FLAG_EMAIL', 'CNT_FAM_MEMBERS', 'Age'] !
epoch train_loss valid_acc valid_loss dur
------- ------------ ----------- ------------ ------
1 0.4989 0.9810 0.4973 0.2592
2 0.4972 0.9810 0.4973 0.3054
3 0.4972 0.9810 0.4973 0.2572
4 0.4972 0.9810 0.4973 0.2506
5 0.4972 0.9810 0.4973 0.2443
6 0.4972 0.9810 0.4973 0.2531
7 0.4972 0.9810 0.4973 0.2538
8 0.4972 0.9810 0.4973 0.2563
9 0.4972 0.9810 0.4973 0.2560
10 0.4972 0.9810 0.4973 0.2589
INFO Saving train results... INFO Getting total score... WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) ! INFO Creating uncut info... INFO Drawing train_score_uncut_ks_graph... INFO Drawing train_score_uncut_count_chart1... INFO Drawing train_score_uncut_count_chart2... INFO Drawing train_score_uncut_count_chart3... INFO Drawing train_score_uncut_cap_chart1... INFO Drawing train_score_uncut_cap_chart2... INFO Drawing train_score_uncut_cap_chart3... INFO Creating cut info... INFO Drawing train_score_cut_ks_graph... INFO Drawing train_score_cut_rate_chart... INFO Drawing train_score_cut_count_chart1... INFO Drawing train_score_cut_count_chart2... INFO Drawing train_score_cut_count_chart3... INFO Drawing train_score_cut_cap_chart1... INFO Drawing train_score_cut_cap_chart2... INFO Drawing train_score_cut_cap_chart3... INFO Creating qcut info... INFO Drawing train_score_qcut_ks_graph... INFO Drawing train_score_qcut_rate_chart... INFO Drawing train_score_qcut_count_chart1... INFO Drawing train_score_qcut_count_chart2... INFO Drawing train_score_qcut_count_chart3... INFO Drawing train_score_qcut_cap_chart1... INFO Drawing train_score_qcut_cap_chart2... INFO Drawing train_score_qcut_cap_chart3... INFO Creating precision recall fscore... INFO Drawing train_classification_chart... INFO Drawing train_classification_chart... INFO Drawing roc... INFO Creating results summary... INFO Drawing ap... INFO Saving test results... INFO Getting total score... WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) ! INFO Creating uncut info... INFO Drawing test_score_uncut_ks_graph... INFO Drawing test_score_uncut_count_chart1... INFO Drawing test_score_uncut_count_chart2... INFO Drawing test_score_uncut_count_chart3... INFO Drawing test_score_uncut_cap_chart1... INFO Drawing test_score_uncut_cap_chart2... INFO Drawing test_score_uncut_cap_chart3... INFO Creating cut info... INFO Drawing test_score_cut_ks_graph... INFO Drawing test_score_cut_rate_chart... INFO Drawing test_score_cut_count_chart1... INFO Drawing test_score_cut_count_chart2... INFO Drawing test_score_cut_count_chart3... INFO Drawing test_score_cut_cap_chart1... INFO Drawing test_score_cut_cap_chart2... INFO Drawing test_score_cut_cap_chart3... INFO Creating qcut info... INFO Drawing test_score_qcut_ks_graph... INFO Drawing test_score_qcut_rate_chart... INFO Drawing test_score_qcut_count_chart1... INFO Drawing test_score_qcut_count_chart2... INFO Drawing test_score_qcut_count_chart3... INFO Drawing test_score_qcut_cap_chart1... INFO Drawing test_score_qcut_cap_chart2... INFO Drawing test_score_qcut_cap_chart3... INFO Creating precision recall fscore... INFO Drawing test_classification_chart... INFO Drawing test_classification_chart... INFO Drawing roc... INFO Creating results summary... INFO Drawing ap... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi... INFO Drawing cut_psi_chart... INFO Drawing cut_psi_chart... INFO Drawing qcut_psi_chart... INFO Drawing qcut_psi_chart... INFO Total: 12.65 s INFO Saving var_drop... INFO birth_year | 3 | cv flow...
epoch train_loss valid_acc valid_loss dur
------- ------------ ----------- ------------ ------
1 0.4992 0.9817 0.4970 0.1657
2 0.4970 0.9817 0.4970 0.1685
3 0.4970 0.9817 0.4970 0.1672
4 0.4970 0.9817 0.4970 0.1691
5 0.4970 0.9817 0.4970 0.1616
6 0.4970 0.9817 0.4970 0.1653
7 0.4970 0.9817 0.4970 0.1598
8 0.4970 0.9817 0.4970 0.1670
9 0.4970 0.9817 0.4970 0.1644
10 0.4970 0.9817 0.4970 0.1693
INFO Saving train results... INFO Getting total score... WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) ! INFO Creating uncut info... INFO Creating cut info... INFO Creating qcut info... INFO Creating precision recall fscore... INFO Creating results summary... INFO Saving test results... INFO Getting total score... INFO Creating uncut info... INFO Creating cut info... INFO Creating qcut info... INFO Creating precision recall fscore... INFO Creating results summary... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi...
epoch train_loss valid_acc valid_loss dur
------- ------------ ----------- ------------ ------
1 0.5000 0.9802 0.4977 0.1647
2 0.4977 0.9802 0.4977 0.1658
3 0.4977 0.9802 0.4977 0.1669
4 0.4977 0.9802 0.4977 0.1750
5 0.4977 0.9802 0.4977 0.1742
6 0.4977 0.9802 0.4977 0.1668
7 0.4977 0.9802 0.4977 0.1649
8 0.4977 0.9802 0.4977 0.1798
9 0.4977 0.9802 0.4977 0.1790
10 0.4977 0.9802 0.4977 0.1672
INFO Saving train results... INFO Getting total score... WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) ! INFO Creating uncut info... INFO Creating cut info... INFO Creating qcut info... INFO Creating precision recall fscore... INFO Creating results summary... INFO Saving test results... INFO Getting total score... WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) ! INFO Creating uncut info... INFO Creating cut info... INFO Creating qcut info... INFO Creating precision recall fscore... INFO Creating results summary... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi...
epoch train_loss valid_acc valid_loss dur
------- ------------ ----------- ------------ ------
1 0.4991 0.9819 0.4969 0.1718
2 0.4970 0.9819 0.4969 0.1863
3 0.4970 0.9819 0.4969 0.1714
4 0.4970 0.9819 0.4969 0.1894
5 0.4970 0.9819 0.4969 0.1676
6 0.4970 0.9819 0.4969 0.1729
7 0.4970 0.9819 0.4969 0.1940
8 0.4970 0.9819 0.4969 0.1806
9 0.4970 0.9819 0.4969 0.1756
10 0.4970 0.9819 0.4969 0.2019
INFO Saving train results... INFO Getting total score... WARNING Find that auto_resize_score is True and np.corrcoef(target, proba)[0, 1] < 0, replace proba with 100 / (proba + 1) ! INFO Creating uncut info... INFO Creating cut info... INFO Creating qcut info... INFO Creating precision recall fscore... INFO Creating results summary... INFO Saving test results... INFO Getting total score... INFO Creating uncut info... INFO Creating cut info... INFO Creating qcut info... INFO Creating precision recall fscore... INFO Creating results summary... INFO Calculating Equidistant-based psi... INFO Calculating Equivalent-based psi... INFO Saving cross validate... INFO Drawing cv compare graph... INFO Drawing cv_compare_ks_chart... INFO Drawing cv_compare_auc_chart... INFO Drawing cv_compare_ap_chart... INFO Drawing cv_compare_logloss_chart... INFO Drawing cv_compare_r2_chart... INFO Drawing cv_compare_mse_chart... INFO Drawing cv_compare_mdp_chart... INFO Drawing cv_compare_psi_chart... INFO Drawing cv_compare_mpg_chart... INFO Saving model pkl... ERROR Can't pickle local object 'run_torch_net.<locals>.Classifier' INFO Saving model pmml... ERROR INFO Saving json... INFO Saving setting... INFO Adding runtime to excel... INFO Saving directory... INFO Saving... INFO Total: 22.27 s INFO Total: 22.33 s
ori_name = "./模型分析demo/v4-lgb/v4-lgb.xlsx"
new_output = "./模型分析demo/vr-lgb/vr-lgb.xlsx"
at.Analysis.model_flow(
df_train, new_output,
test_data=[df_test, df_data], train_name="开发", test_names=["测试", "全量"],
recover_path=ori_name,
# 继承 v4 并进行参数搜索，映射到多个数据集
customized_parameter={
"n_estimators": [10, 20, 30, 40, 50],
"learning_rate": [0.05, 0.08, 0.10]
}
)
WARNING Overwriting: customized_parameter found, disable recover from pkl !
WARNING Overwriting found, setting recover_from_pkl to False !
INFO Prepare kwargs...
WARNING Setting format_time_edge to True !
INFO Checking train_data...
INFO Checking response...
INFO Checking test_data...
INFO Checking response...
INFO Checking columns...
INFO Checking dtypes...
WARNING Setting allow_nan to True by lightgbm.sklearn !
WARNING Setting allow_discrete to True by lightgbm.sklearn !
WARNING Unable to find nan data, setting allow_nan to False !
WARNING Unable to find discrete data, setting allow_discrete to False !
INFO Model optional flow...
WARNING Check /home/conda_env/模型分析demo/vr-lgb/Data_vr-lgb-开发-测试.xlsx exists !
INFO Model variables: ['AMT_INCOME_TOTAL', 'Age', 'CNT_CHILDREN', 'DAYS_EMPLOYED', 'FLAG_PHONE', 'FLAG_WORK_PHONE'] !
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': 'auto'}
INFO Using variables: 6...
INFO Training LGBMClassifier...
INFO [FIT_TRANSFORM] ['AMT_INCOME_TOTAL']: 0.000191 secs
INFO [FIT_TRANSFORM] ['Age']: 9.8e-05 secs
INFO [FIT_TRANSFORM] ['CNT_CHILDREN']: 9.1e-05 secs
INFO [FIT_TRANSFORM] ['DAYS_EMPLOYED']: 0.000102 secs
INFO [FIT_TRANSFORM] ['FLAG_PHONE']: 8.9e-05 secs
INFO [FIT_TRANSFORM] ['FLAG_WORK_PHONE']: 6.9e-05 secs
INFO [TRANSFORM] ['AMT_INCOME_TOTAL']: 0.000205 secs
INFO [TRANSFORM] ['Age']: 0.001095 secs
INFO [TRANSFORM] ['CNT_CHILDREN']: 0.000233 secs
INFO [TRANSFORM] ['DAYS_EMPLOYED']: 8.5e-05 secs
INFO [TRANSFORM] ['FLAG_PHONE']: 7.5e-05 secs
INFO [TRANSFORM] ['FLAG_WORK_PHONE']: 8e-05 secs
INFO [TRANSFORM] ['AMT_INCOME_TOTAL']: 0.000139 secs
INFO [TRANSFORM] ['Age']: 0.000129 secs
INFO [TRANSFORM] ['CNT_CHILDREN']: 5.8e-05 secs
INFO [TRANSFORM] ['DAYS_EMPLOYED']: 5.8e-05 secs
INFO [TRANSFORM] ['FLAG_PHONE']: 5.8e-05 secs
INFO [TRANSFORM] ['FLAG_WORK_PHONE']: 9.9e-05 secs
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Drawing train_score_uncut_ks_graph...
INFO Drawing train_score_uncut_count_chart1...
INFO Drawing train_score_uncut_count_chart2...
INFO Drawing train_score_uncut_count_chart3...
INFO Drawing train_score_uncut_cap_chart1...
INFO Drawing train_score_uncut_cap_chart2...
INFO Drawing train_score_uncut_cap_chart3...
INFO Creating cut info...
INFO Drawing train_score_cut_ks_graph...
INFO Drawing train_score_cut_rate_chart...
INFO Drawing train_score_cut_count_chart1...
INFO Drawing train_score_cut_count_chart2...
INFO Drawing train_score_cut_count_chart3...
INFO Drawing train_score_cut_cap_chart1...
INFO Drawing train_score_cut_cap_chart2...
INFO Drawing train_score_cut_cap_chart3...
INFO Creating qcut info...
INFO Drawing train_score_qcut_ks_graph...
INFO Drawing train_score_qcut_rate_chart...
INFO Drawing train_score_qcut_count_chart1...
INFO Drawing train_score_qcut_count_chart2...
INFO Drawing train_score_qcut_count_chart3...
INFO Drawing train_score_qcut_cap_chart1...
INFO Drawing train_score_qcut_cap_chart2...
INFO Drawing train_score_qcut_cap_chart3...
INFO Creating precision recall fscore...
INFO Drawing train_classification_chart...
INFO Drawing train_classification_chart...
INFO Drawing roc...
INFO Creating results summary...
INFO Drawing ap...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Drawing test_score_uncut_ks_graph...
INFO Drawing test_score_uncut_count_chart1...
INFO Drawing test_score_uncut_count_chart2...
INFO Drawing test_score_uncut_count_chart3...
INFO Drawing test_score_uncut_cap_chart1...
INFO Drawing test_score_uncut_cap_chart2...
INFO Drawing test_score_uncut_cap_chart3...
INFO Creating cut info...
INFO Drawing test_score_cut_ks_graph...
INFO Drawing test_score_cut_rate_chart...
INFO Drawing test_score_cut_count_chart1...
INFO Drawing test_score_cut_count_chart2...
INFO Drawing test_score_cut_count_chart3...
INFO Drawing test_score_cut_cap_chart1...
INFO Drawing test_score_cut_cap_chart2...
INFO Drawing test_score_cut_cap_chart3...
INFO Creating qcut info...
INFO Drawing test_score_qcut_ks_graph...
INFO Drawing test_score_qcut_rate_chart...
INFO Drawing test_score_qcut_count_chart1...
INFO Drawing test_score_qcut_count_chart2...
INFO Drawing test_score_qcut_count_chart3...
INFO Drawing test_score_qcut_cap_chart1...
INFO Drawing test_score_qcut_cap_chart2...
INFO Drawing test_score_qcut_cap_chart3...
INFO Creating precision recall fscore...
INFO Drawing test_classification_chart...
INFO Drawing test_classification_chart...
INFO Drawing roc...
INFO Creating results summary...
INFO Drawing ap...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Drawing cut_psi_chart...
INFO Drawing cut_psi_chart...
INFO Drawing qcut_psi_chart...
INFO Drawing qcut_psi_chart...
INFO Total: 10.67 s
INFO Saving var_drop...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': 'auto'}
INFO Using variables: 6...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': 'auto'}
INFO Using variables: 6...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': 'auto'}
INFO Using variables: 6...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO Drawing cv compare graph...
INFO Drawing cv_compare_ks_chart...
INFO Drawing cv_compare_auc_chart...
INFO Drawing cv_compare_ap_chart...
INFO Drawing cv_compare_logloss_chart...
INFO Drawing cv_compare_r2_chart...
INFO Drawing cv_compare_mse_chart...
INFO Drawing cv_compare_mdp_chart...
INFO Drawing cv_compare_psi_chart...
INFO Drawing cv_compare_mpg_chart...
INFO Saving model pkl...
INFO Saving model pmml...
/home/conda_env/miniconda3/lib/python3.9/subprocess.py:941: RuntimeWarning: line buffering (buffering=1) isn't supported in binary mode, the default buffer size will be used
self.stdout = io.open(c2pread, 'rb', bufsize)
/home/conda_env/miniconda3/lib/python3.9/subprocess.py:946: RuntimeWarning: line buffering (buffering=1) isn't supported in binary mode, the default buffer size will be used
self.stderr = io.open(errread, 'rb', bufsize)
INFO Adding model_pmml to excel...
INFO Adding run_model_pmml_in_java to excel...
INFO Saving json...
INFO Saving setting...
INFO Adding runtime to excel...
INFO Saving directory...
INFO Saving...
INFO Total: 17.50 s
WARNING Overwriting: customized_parameter found, disable recover from pkl !
WARNING Overwriting found, setting recover_from_pkl to False !
INFO Prepare kwargs...
WARNING Setting format_time_edge to True !
INFO Checking train_data...
INFO Checking response...
INFO Checking test_data...
INFO Checking response...
INFO Checking columns...
INFO Checking dtypes...
WARNING Setting allow_nan to True by lightgbm.sklearn !
WARNING Setting allow_discrete to True by lightgbm.sklearn !
WARNING Unable to find nan data, setting allow_nan to False !
WARNING Unable to find discrete data, setting allow_discrete to False !
INFO Model optional flow...
WARNING Check /home/conda_env/模型分析demo/vr-lgb/Data_vr-lgb-开发-全量.xlsx exists !
INFO Model variables: ['AMT_INCOME_TOTAL', 'Age', 'CNT_CHILDREN', 'DAYS_EMPLOYED', 'FLAG_PHONE', 'FLAG_WORK_PHONE'] !
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': 'auto'}
INFO Using variables: 6...
INFO Training LGBMClassifier...
INFO [FIT_TRANSFORM] ['AMT_INCOME_TOTAL']: 0.000144 secs
INFO [FIT_TRANSFORM] ['Age']: 7.1e-05 secs
INFO [FIT_TRANSFORM] ['CNT_CHILDREN']: 9e-05 secs
INFO [FIT_TRANSFORM] ['DAYS_EMPLOYED']: 8.3e-05 secs
INFO [FIT_TRANSFORM] ['FLAG_PHONE']: 6.2e-05 secs
INFO [FIT_TRANSFORM] ['FLAG_WORK_PHONE']: 6.5e-05 secs
INFO [TRANSFORM] ['AMT_INCOME_TOTAL']: 0.000326 secs
INFO [TRANSFORM] ['Age']: 0.000171 secs
INFO [TRANSFORM] ['CNT_CHILDREN']: 0.000107 secs
INFO [TRANSFORM] ['DAYS_EMPLOYED']: 0.000104 secs
INFO [TRANSFORM] ['FLAG_PHONE']: 8.7e-05 secs
INFO [TRANSFORM] ['FLAG_WORK_PHONE']: 8e-05 secs
INFO [TRANSFORM] ['AMT_INCOME_TOTAL']: 0.000169 secs
INFO [TRANSFORM] ['Age']: 0.000185 secs
INFO [TRANSFORM] ['CNT_CHILDREN']: 6.9e-05 secs
INFO [TRANSFORM] ['DAYS_EMPLOYED']: 8.7e-05 secs
INFO [TRANSFORM] ['FLAG_PHONE']: 7.3e-05 secs
INFO [TRANSFORM] ['FLAG_WORK_PHONE']: 8.4e-05 secs
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Drawing train_score_uncut_ks_graph...
INFO Drawing train_score_uncut_count_chart1...
INFO Drawing train_score_uncut_count_chart2...
INFO Drawing train_score_uncut_count_chart3...
INFO Drawing train_score_uncut_cap_chart1...
INFO Drawing train_score_uncut_cap_chart2...
INFO Drawing train_score_uncut_cap_chart3...
INFO Creating cut info...
INFO Drawing train_score_cut_ks_graph...
INFO Drawing train_score_cut_rate_chart...
INFO Drawing train_score_cut_count_chart1...
INFO Drawing train_score_cut_count_chart2...
INFO Drawing train_score_cut_count_chart3...
INFO Drawing train_score_cut_cap_chart1...
INFO Drawing train_score_cut_cap_chart2...
INFO Drawing train_score_cut_cap_chart3...
INFO Creating qcut info...
INFO Drawing train_score_qcut_ks_graph...
INFO Drawing train_score_qcut_rate_chart...
INFO Drawing train_score_qcut_count_chart1...
INFO Drawing train_score_qcut_count_chart2...
INFO Drawing train_score_qcut_count_chart3...
INFO Drawing train_score_qcut_cap_chart1...
INFO Drawing train_score_qcut_cap_chart2...
INFO Drawing train_score_qcut_cap_chart3...
INFO Creating precision recall fscore...
INFO Drawing train_classification_chart...
INFO Drawing train_classification_chart...
INFO Drawing roc...
INFO Creating results summary...
INFO Drawing ap...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Drawing test_score_uncut_ks_graph...
INFO Drawing test_score_uncut_count_chart1...
INFO Drawing test_score_uncut_count_chart2...
INFO Drawing test_score_uncut_count_chart3...
INFO Drawing test_score_uncut_cap_chart1...
INFO Drawing test_score_uncut_cap_chart2...
INFO Drawing test_score_uncut_cap_chart3...
INFO Creating cut info...
INFO Drawing test_score_cut_ks_graph...
INFO Drawing test_score_cut_rate_chart...
INFO Drawing test_score_cut_count_chart1...
INFO Drawing test_score_cut_count_chart2...
INFO Drawing test_score_cut_count_chart3...
INFO Drawing test_score_cut_cap_chart1...
INFO Drawing test_score_cut_cap_chart2...
INFO Drawing test_score_cut_cap_chart3...
INFO Creating qcut info...
INFO Drawing test_score_qcut_ks_graph...
INFO Drawing test_score_qcut_rate_chart...
INFO Drawing test_score_qcut_count_chart1...
INFO Drawing test_score_qcut_count_chart2...
INFO Drawing test_score_qcut_count_chart3...
INFO Drawing test_score_qcut_cap_chart1...
INFO Drawing test_score_qcut_cap_chart2...
INFO Drawing test_score_qcut_cap_chart3...
INFO Creating precision recall fscore...
INFO Drawing test_classification_chart...
INFO Drawing test_classification_chart...
INFO Drawing roc...
INFO Creating results summary...
INFO Drawing ap...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Drawing cut_psi_chart...
INFO Drawing cut_psi_chart...
INFO Drawing qcut_psi_chart...
INFO Drawing qcut_psi_chart...
INFO Total: 11.39 s
INFO Saving var_drop...
INFO birth_year | 3 | cv flow...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': 'auto'}
INFO Using variables: 6...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': 'auto'}
INFO Using variables: 6...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
WARNING Found n_jobs not set, setting to 2 !
WARNING Setting model params: use_missing to True !
WARNING Setting model params: zero_as_missing to False !
INFO Using model params: {'n_jobs': 2, 'use_missing': True, 'zero_as_missing': False}
INFO Using fit params: {'sample_weight': None, 'feature_name': 'auto', 'categorical_feature': 'auto'}
INFO Using variables: 6...
INFO Training LGBMClassifier...
INFO Saving train results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Saving test results...
INFO Getting total score...
INFO Creating uncut info...
INFO Creating cut info...
INFO Creating qcut info...
INFO Creating precision recall fscore...
INFO Creating results summary...
INFO Calculating Equidistant-based psi...
INFO Calculating Equivalent-based psi...
INFO Saving cross validate...
INFO Drawing cv compare graph...
INFO Drawing cv_compare_ks_chart...
INFO Drawing cv_compare_auc_chart...
INFO Drawing cv_compare_ap_chart...
INFO Drawing cv_compare_logloss_chart...
INFO Drawing cv_compare_r2_chart...
INFO Drawing cv_compare_mse_chart...
INFO Drawing cv_compare_mdp_chart...
INFO Drawing cv_compare_psi_chart...
INFO Drawing cv_compare_mpg_chart...
INFO Saving model pkl...
INFO Saving model pmml...
/home/conda_env/miniconda3/lib/python3.9/subprocess.py:941: RuntimeWarning: line buffering (buffering=1) isn't supported in binary mode, the default buffer size will be used
self.stdout = io.open(c2pread, 'rb', bufsize)
/home/conda_env/miniconda3/lib/python3.9/subprocess.py:946: RuntimeWarning: line buffering (buffering=1) isn't supported in binary mode, the default buffer size will be used
self.stderr = io.open(errread, 'rb', bufsize)
INFO Adding model_pmml to excel...
INFO Adding run_model_pmml_in_java to excel...
INFO Saving json...
INFO Saving setting...
INFO Adding runtime to excel...
INFO Saving directory...
INFO Saving...
INFO Total: 18.41 s
INFO Total: 42.53 s
at.dt.Api.zip_dir_files("./模型分析demo/vr-lgb.zip", "./模型分析demo/vr-lgb")
INFO Saving ./模型分析demo/vr-lgb/Model_vr-lgb-开发-测试.xlsx... INFO Saving ./模型分析demo/vr-lgb/Model_vr-lgb-开发-测试.json... INFO Saving ./模型分析demo/vr-lgb/Model_vr-lgb-开发-测试_model.pkl... INFO Saving ./模型分析demo/vr-lgb/Model_vr-lgb-开发-全量.json... INFO Saving ./模型分析demo/vr-lgb/Model_vr-lgb-开发-全量_model.pkl... INFO Saving ./模型分析demo/vr-lgb/Model_vr-lgb-开发-全量.xlsx...
help(at.Report.create_model_report)
Help on function in module alpha_tools:
create_model_report(new_excel_name, excel_path_list, excel_name_list, train_test_dir_cn=None)
Usage:
---------
create_model_report(
"xxx.xlsx", [
"file1.xlsx", "file2.xlsx", ...
], [
"filename1", "filename2", ...
], ["train_display_name", "test_display_name"]
)
---------
help(at.Report.create_model_report2)
Help on function in module alpha_tools:
create_model_report2(new_excel_name, output_name, train_name, test_names)
Usage:
---------
Example1:
---------
create_model_report2(
"xxx.xlsx", "report.xlsx", "train", ["test1", "test2", ...]
)
---------
Example2:
---------
create_model_report2(
"xxx.xlsx", {
"name1": "report1.xlsx",
"name2": "report2.xlsx",
...
}, "train", ["test1", "test2", ...]
)
---------
at.Report.create_model_report(
"./模型分析demo/vm-模型对比/vm-模型对比.xlsx",
[
"./模型分析demo/v1-lgb/v1-lgb.xlsx", "./模型分析demo/v2-lr/v2-lr.xlsx",
"./模型分析demo/v3-lgb/v3-lgb.xlsx", "./模型分析demo/v4-lgb/v4-lgb.xlsx",
"./模型分析demo/v5-gls/v5-gls.xlsx", "./模型分析demo/v6-lgb/v6-lgb.xlsx",
"./模型分析demo/v6-lgb-oh/v6-lgb-oh.xlsx", "./模型分析demo/v7-lgb/v7-lgb.xlsx",
"./模型分析demo/v8-torch/v8-torch.xlsx", "./模型分析demo/v9-lgb/v9-lgb.xlsx",
"./模型分析demo/v10-lgb/v10-lgb.xlsx", "./模型分析demo/v11-lgb/v11-lgb.xlsx",
"./模型分析demo/v12-lgb/v12-lgb.xlsx", "./模型分析demo/v13-lgb/v13-lgb.xlsx",
"./模型分析demo/v14-knn/v14-knn.xlsx",
], [
"v1-lgb", "v2-lr", "v3-lgb", "v4-lgb", "v5-gls",
"v6-lgb", "v6-lgb-oh", "v7-lgb", "v8-torch", "v9-lgb",
"v10-lgb", "v11-lgb", "v12-lgb", "v13-lgb", "v14-knn",
]
)
INFO Loading ./模型分析demo/v1-lgb/Model_v1-lgb.xlsx... INFO Loading ./模型分析demo/v2-lr/Model_v2-lr.xlsx... INFO Loading ./模型分析demo/v3-lgb/Model_v3-lgb.xlsx... INFO Loading ./模型分析demo/v4-lgb/Model_v4-lgb.xlsx... INFO Loading ./模型分析demo/v5-gls/Model_v5-gls.xlsx... INFO Loading ./模型分析demo/v6-lgb/Model_v6-lgb.xlsx... INFO Loading ./模型分析demo/v6-lgb-oh/Model_v6-lgb-oh.xlsx... INFO Loading ./模型分析demo/v7-lgb/Model_v7-lgb.xlsx... INFO Loading ./模型分析demo/v8-torch/Model_v8-torch.xlsx... INFO Loading ./模型分析demo/v9-lgb/Model_v9-lgb.xlsx... INFO Loading ./模型分析demo/v10-lgb/Model_v10-lgb.xlsx... INFO Loading ./模型分析demo/v11-lgb/Model_v11-lgb.xlsx... INFO Loading ./模型分析demo/v12-lgb/Model_v12-lgb.xlsx... INFO Loading ./模型分析demo/v13-lgb/Model_v13-lgb.xlsx... INFO Loading ./模型分析demo/v14-knn/Model_v14-knn.xlsx... INFO Creating books summary... INFO Saving... INFO Creating 模型评估-变量参数-v1-lgb... INFO Creating 模型评估-变量参数-v2-lr... INFO Creating 模型评估-变量参数-v3-lgb... INFO Creating 模型评估-变量参数-v4-lgb... INFO Creating 模型评估-变量参数-v5-gls... INFO Creating 模型评估-变量参数-v6-lgb... INFO Creating 模型评估-变量参数-v6-lgb-oh... INFO Creating 模型评估-变量参数-v7-lgb... INFO Creating 模型评估-变量参数-v8-torch... INFO Creating 模型评估-变量参数-v9-lgb... INFO Creating 模型评估-变量参数-v10-lgb... INFO Creating 模型评估-变量参数-v11-lgb... INFO Creating 模型评估-变量参数-v12-lgb... INFO Creating 模型评估-变量参数-v13-lgb... INFO Creating 模型评估-变量参数-v14-knn... INFO Creating 变量评估-分析汇总-v5-gls... INFO Creating 变量评估-分组详情-v5-gls... INFO Creating 模型应用-分数分布-等量分组-开发-v1-lgb... INFO Creating 模型应用-分数分布-等量分组-开发-v3-lgb... INFO Creating 模型应用-分数分布-等量分组-开发-v4-lgb... INFO Creating 模型应用-分数分布-等量分组-开发-v5-gls... INFO Creating 模型应用-分数分布-等量分组-开发-v6-lgb... INFO Creating 模型应用-分数分布-等量分组-开发-v6-lgb-oh... INFO Creating 模型应用-分数分布-等量分组-开发-v7-lgb... INFO Creating 模型应用-分数分布-等量分组-开发-v8-torch... INFO Creating 模型应用-分数分布-等量分组-开发-v9-lgb... INFO Creating 模型应用-分数分布-等量分组-开发-v10-lgb... INFO Creating 模型应用-分数分布-等量分组-开发-v11-lgb... INFO Creating 模型应用-分数分布-等量分组-开发-v12-lgb... INFO Creating 模型应用-分数分布-等量分组-开发-v13-lgb... INFO Creating 模型应用-分数分布-等量分组-开发-v14-knn... INFO Creating 模型应用-分数分布-等量分组-验证-v1-lgb... INFO Creating 模型应用-分数分布-等量分组-验证-v3-lgb... INFO Creating 模型应用-分数分布-等量分组-验证-v4-lgb... INFO Creating 模型应用-分数分布-等量分组-验证-v5-gls... INFO Creating 模型应用-分数分布-等量分组-验证-v6-lgb... INFO Creating 模型应用-分数分布-等量分组-验证-v6-lgb-oh... INFO Creating 模型应用-分数分布-等量分组-验证-v7-lgb... INFO Creating 模型应用-分数分布-等量分组-验证-v8-torch... INFO Creating 模型应用-分数分布-等量分组-验证-v9-lgb... INFO Creating 模型应用-分数分布-等量分组-验证-v10-lgb... INFO Creating 模型应用-分数分布-等量分组-验证-v11-lgb... INFO Creating 模型应用-分数分布-等量分组-验证-v12-lgb... INFO Creating 模型应用-分数分布-等量分组-验证-v13-lgb... INFO Creating 模型应用-分数分布-等量分组-验证-v14-knn... INFO Creating 模型评估-排序稳定性-开发-v1-lgb... INFO Creating 模型评估-排序稳定性-开发-v3-lgb... INFO Creating 模型评估-排序稳定性-开发-v4-lgb... INFO Creating 模型评估-排序稳定性-开发-v5-gls... INFO Creating 模型评估-排序稳定性-开发-v6-lgb... INFO Creating 模型评估-排序稳定性-开发-v6-lgb-oh... INFO Creating 模型评估-排序稳定性-开发-v7-lgb... INFO Creating 模型评估-排序稳定性-开发-v8-torch... INFO Creating 模型评估-排序稳定性-开发-v9-lgb... INFO Creating 模型评估-排序稳定性-开发-v10-lgb... INFO Creating 模型评估-排序稳定性-开发-v11-lgb... INFO Creating 模型评估-排序稳定性-开发-v12-lgb... INFO Creating 模型评估-排序稳定性-开发-v13-lgb... INFO Creating 模型评估-排序稳定性-开发-v14-knn... INFO Creating 模型评估-排序稳定性-验证-v1-lgb... INFO Creating 模型评估-排序稳定性-验证-v3-lgb... INFO Creating 模型评估-排序稳定性-验证-v4-lgb... INFO Creating 模型评估-排序稳定性-验证-v5-gls... INFO Creating 模型评估-排序稳定性-验证-v6-lgb... INFO Creating 模型评估-排序稳定性-验证-v6-lgb-oh... INFO Creating 模型评估-排序稳定性-验证-v7-lgb... INFO Creating 模型评估-排序稳定性-验证-v8-torch... INFO Creating 模型评估-排序稳定性-验证-v9-lgb... INFO Creating 模型评估-排序稳定性-验证-v10-lgb... INFO Creating 模型评估-排序稳定性-验证-v11-lgb... INFO Creating 模型评估-排序稳定性-验证-v12-lgb... INFO Creating 模型评估-排序稳定性-验证-v13-lgb... INFO Creating 模型评估-排序稳定性-验证-v14-knn... INFO Creating 模型评估-分布稳定性-开发-v1-lgb... INFO Creating 模型评估-分布稳定性-开发-v3-lgb... INFO Creating 模型评估-分布稳定性-开发-v4-lgb... INFO Creating 模型评估-分布稳定性-开发-v5-gls... INFO Creating 模型评估-分布稳定性-开发-v6-lgb... INFO Creating 模型评估-分布稳定性-开发-v6-lgb-oh... INFO Creating 模型评估-分布稳定性-开发-v7-lgb... INFO Creating 模型评估-分布稳定性-开发-v8-torch... INFO Creating 模型评估-分布稳定性-开发-v9-lgb... INFO Creating 模型评估-分布稳定性-开发-v10-lgb... INFO Creating 模型评估-分布稳定性-开发-v11-lgb... INFO Creating 模型评估-分布稳定性-开发-v12-lgb... INFO Creating 模型评估-分布稳定性-开发-v13-lgb... INFO Creating 模型评估-分布稳定性-开发-v14-knn... INFO Creating 模型评估-分布稳定性-验证-v1-lgb... INFO Creating 模型评估-分布稳定性-验证-v3-lgb... INFO Creating 模型评估-分布稳定性-验证-v4-lgb... INFO Creating 模型评估-分布稳定性-验证-v5-gls... INFO Creating 模型评估-分布稳定性-验证-v6-lgb... INFO Creating 模型评估-分布稳定性-验证-v6-lgb-oh... INFO Creating 模型评估-分布稳定性-验证-v7-lgb... INFO Creating 模型评估-分布稳定性-验证-v8-torch... INFO Creating 模型评估-分布稳定性-验证-v9-lgb... INFO Creating 模型评估-分布稳定性-验证-v10-lgb... INFO Creating 模型评估-分布稳定性-验证-v11-lgb... INFO Creating 模型评估-分布稳定性-验证-v12-lgb... INFO Creating 模型评估-分布稳定性-验证-v13-lgb... INFO Creating 模型评估-分布稳定性-验证-v14-knn... INFO Creating 模型评估-稳定性汇总-开发-v1-lgb... INFO Creating 模型评估-稳定性汇总-开发-v3-lgb... INFO Creating 模型评估-稳定性汇总-开发-v4-lgb... INFO Creating 模型评估-稳定性汇总-开发-v5-gls... INFO Creating 模型评估-稳定性汇总-开发-v6-lgb... INFO Creating 模型评估-稳定性汇总-开发-v6-lgb-oh... INFO Creating 模型评估-稳定性汇总-开发-v7-lgb... INFO Creating 模型评估-稳定性汇总-开发-v8-torch... INFO Creating 模型评估-稳定性汇总-开发-v9-lgb... INFO Creating 模型评估-稳定性汇总-开发-v10-lgb... INFO Creating 模型评估-稳定性汇总-开发-v11-lgb... INFO Creating 模型评估-稳定性汇总-开发-v12-lgb... INFO Creating 模型评估-稳定性汇总-开发-v13-lgb... INFO Creating 模型评估-稳定性汇总-开发-v14-knn... INFO Creating 模型评估-稳定性汇总-验证-v1-lgb... INFO Creating 模型评估-稳定性汇总-验证-v3-lgb... INFO Creating 模型评估-稳定性汇总-验证-v4-lgb... INFO Creating 模型评估-稳定性汇总-验证-v5-gls... INFO Creating 模型评估-稳定性汇总-验证-v6-lgb... INFO Creating 模型评估-稳定性汇总-验证-v6-lgb-oh... INFO Creating 模型评估-稳定性汇总-验证-v7-lgb... INFO Creating 模型评估-稳定性汇总-验证-v8-torch... INFO Creating 模型评估-稳定性汇总-验证-v9-lgb... INFO Creating 模型评估-稳定性汇总-验证-v10-lgb... INFO Creating 模型评估-稳定性汇总-验证-v11-lgb... INFO Creating 模型评估-稳定性汇总-验证-v12-lgb... INFO Creating 模型评估-稳定性汇总-验证-v13-lgb... INFO Creating 模型评估-稳定性汇总-验证-v14-knn... INFO Creating 模型评估-指标汇总-v1-lgb... INFO Creating 模型评估-指标汇总-v2-lr... INFO Creating 模型评估-指标汇总-v3-lgb... INFO Creating 模型评估-指标汇总-v4-lgb... INFO Creating 模型评估-指标汇总-v5-gls... INFO Creating 模型评估-指标汇总-v6-lgb... INFO Creating 模型评估-指标汇总-v6-lgb-oh... INFO Creating 模型评估-指标汇总-v7-lgb... INFO Creating 模型评估-指标汇总-v8-torch... INFO Creating 模型评估-指标汇总-v9-lgb... INFO Creating 模型评估-指标汇总-v10-lgb... INFO Creating 模型评估-指标汇总-v11-lgb... INFO Creating 模型评估-指标汇总-v12-lgb... INFO Creating 模型评估-指标汇总-v13-lgb... INFO Creating 模型评估-指标汇总-v14-knn... INFO Creating 模型应用-分数PSI明细-v1-lgb... INFO Creating 模型应用-分数PSI明细-v2-lr... INFO Creating 模型应用-分数PSI明细-v3-lgb... INFO Creating 模型应用-分数PSI明细-v4-lgb... INFO Creating 模型应用-分数PSI明细-v5-gls... INFO Creating 模型应用-分数PSI明细-v6-lgb... INFO Creating 模型应用-分数PSI明细-v6-lgb-oh... INFO Creating 模型应用-分数PSI明细-v7-lgb... INFO Creating 模型应用-分数PSI明细-v8-torch... INFO Creating 模型应用-分数PSI明细-v9-lgb... INFO Creating 模型应用-分数PSI明细-v10-lgb... INFO Creating 模型应用-分数PSI明细-v11-lgb... INFO Creating 模型应用-分数PSI明细-v12-lgb... INFO Creating 模型应用-分数PSI明细-v13-lgb... INFO Creating 模型应用-分数PSI明细-v14-knn... INFO Creating 模型应用-分数PSI汇总-v1-lgb... INFO Creating 模型应用-分数PSI汇总-v2-lr... INFO Creating 模型应用-分数PSI汇总-v3-lgb... INFO Creating 模型应用-分数PSI汇总-v4-lgb... INFO Creating 模型应用-分数PSI汇总-v5-gls... INFO Creating 模型应用-分数PSI汇总-v6-lgb... INFO Creating 模型应用-分数PSI汇总-v6-lgb-oh... INFO Creating 模型应用-分数PSI汇总-v7-lgb... INFO Creating 模型应用-分数PSI汇总-v8-torch... INFO Creating 模型应用-分数PSI汇总-v9-lgb... INFO Creating 模型应用-分数PSI汇总-v10-lgb... INFO Creating 模型应用-分数PSI汇总-v11-lgb... INFO Creating 模型应用-分数PSI汇总-v12-lgb... INFO Creating 模型应用-分数PSI汇总-v13-lgb... INFO Creating 模型应用-分数PSI汇总-v14-knn... INFO Creating 模型评估-等量分组-KS图-开发-v1-lgb... INFO Creating 模型评估-等量分组-KS图-开发-v3-lgb... INFO Creating 模型评估-等量分组-KS图-开发-v4-lgb... INFO Creating 模型评估-等量分组-KS图-开发-v5-gls... INFO Creating 模型评估-等量分组-KS图-开发-v6-lgb... INFO Creating 模型评估-等量分组-KS图-开发-v6-lgb-oh... INFO Creating 模型评估-等量分组-KS图-开发-v7-lgb... INFO Creating 模型评估-等量分组-KS图-开发-v8-torch... INFO Creating 模型评估-等量分组-KS图-开发-v9-lgb... INFO Creating 模型评估-等量分组-KS图-开发-v10-lgb... INFO Creating 模型评估-等量分组-KS图-开发-v11-lgb... INFO Creating 模型评估-等量分组-KS图-开发-v12-lgb... INFO Creating 模型评估-等量分组-KS图-开发-v13-lgb... INFO Creating 模型评估-等量分组-KS图-开发-v14-knn... INFO Creating 模型评估-等量分组-KS图-验证-v1-lgb... INFO Creating 模型评估-等量分组-KS图-验证-v3-lgb... INFO Creating 模型评估-等量分组-KS图-验证-v4-lgb... INFO Creating 模型评估-等量分组-KS图-验证-v5-gls... INFO Creating 模型评估-等量分组-KS图-验证-v6-lgb... INFO Creating 模型评估-等量分组-KS图-验证-v6-lgb-oh... INFO Creating 模型评估-等量分组-KS图-验证-v7-lgb... INFO Creating 模型评估-等量分组-KS图-验证-v8-torch... INFO Creating 模型评估-等量分组-KS图-验证-v9-lgb... INFO Creating 模型评估-等量分组-KS图-验证-v10-lgb... INFO Creating 模型评估-等量分组-KS图-验证-v11-lgb... INFO Creating 模型评估-等量分组-KS图-验证-v12-lgb... INFO Creating 模型评估-等量分组-KS图-验证-v13-lgb... INFO Creating 模型评估-等量分组-KS图-验证-v14-knn... INFO Creating 模型评估-ROC曲线-开发-v1-lgb... INFO Creating 模型评估-ROC曲线-开发-v3-lgb... INFO Creating 模型评估-ROC曲线-开发-v4-lgb... INFO Creating 模型评估-ROC曲线-开发-v5-gls... INFO Creating 模型评估-ROC曲线-开发-v6-lgb... INFO Creating 模型评估-ROC曲线-开发-v6-lgb-oh... INFO Creating 模型评估-ROC曲线-开发-v7-lgb... INFO Creating 模型评估-ROC曲线-开发-v8-torch... INFO Creating 模型评估-ROC曲线-开发-v9-lgb... INFO Creating 模型评估-ROC曲线-开发-v10-lgb... INFO Creating 模型评估-ROC曲线-开发-v11-lgb... INFO Creating 模型评估-ROC曲线-开发-v12-lgb... INFO Creating 模型评估-ROC曲线-开发-v13-lgb... INFO Creating 模型评估-ROC曲线-开发-v14-knn... INFO Creating 模型评估-ROC曲线-验证-v1-lgb... INFO Creating 模型评估-ROC曲线-验证-v3-lgb... INFO Creating 模型评估-ROC曲线-验证-v4-lgb... INFO Creating 模型评估-ROC曲线-验证-v5-gls... INFO Creating 模型评估-ROC曲线-验证-v6-lgb... INFO Creating 模型评估-ROC曲线-验证-v6-lgb-oh... INFO Creating 模型评估-ROC曲线-验证-v7-lgb... INFO Creating 模型评估-ROC曲线-验证-v8-torch... INFO Creating 模型评估-ROC曲线-验证-v9-lgb... INFO Creating 模型评估-ROC曲线-验证-v10-lgb... INFO Creating 模型评估-ROC曲线-验证-v11-lgb... INFO Creating 模型评估-ROC曲线-验证-v12-lgb... INFO Creating 模型评估-ROC曲线-验证-v13-lgb... INFO Creating 模型评估-ROC曲线-验证-v14-knn... INFO Saving... INFO Copy files... INFO Copying /home/conda_env/模型分析demo/v1-lgb/Model_v1-lgb.xlsx... INFO Copying /home/conda_env/模型分析demo/v2-lr/Model_v2-lr.xlsx... INFO Copying /home/conda_env/模型分析demo/v3-lgb/Model_v3-lgb.xlsx... INFO Copying /home/conda_env/模型分析demo/v4-lgb/Model_v4-lgb.xlsx... INFO Copying /home/conda_env/模型分析demo/v5-gls/Data_v5-gls.xlsx... INFO Copying /home/conda_env/模型分析demo/v5-gls/Model_v5-gls.xlsx... INFO Copying /home/conda_env/模型分析demo/v6-lgb/Model_v6-lgb.xlsx... INFO Copying /home/conda_env/模型分析demo/v6-lgb-oh/Data_v6-lgb-oh.xlsx... INFO Copying /home/conda_env/模型分析demo/v6-lgb-oh/Model_v6-lgb-oh.xlsx... INFO Copying /home/conda_env/模型分析demo/v7-lgb/Model_v7-lgb.xlsx... INFO Copying /home/conda_env/模型分析demo/v8-torch/Model_v8-torch.xlsx... INFO Copying /home/conda_env/模型分析demo/v9-lgb/Model_v9-lgb.xlsx... INFO Copying /home/conda_env/模型分析demo/v10-lgb/Model_v10-lgb.xlsx... INFO Copying /home/conda_env/模型分析demo/v11-lgb/Model_v11-lgb.xlsx... INFO Copying /home/conda_env/模型分析demo/v12-lgb/Model_v12-lgb.xlsx... INFO Copying /home/conda_env/模型分析demo/v13-lgb/Model_v13-lgb.xlsx... INFO Copying /home/conda_env/模型分析demo/v14-knn/Model_v14-knn.xlsx... INFO Creating csv path...
(['./模型分析demo/v1-lgb/v1-lgb.xlsx',
'./模型分析demo/v2-lr/v2-lr.xlsx',
'./模型分析demo/v3-lgb/v3-lgb.xlsx',
'./模型分析demo/v4-lgb/v4-lgb.xlsx',
'./模型分析demo/v5-gls/v5-gls.xlsx',
'./模型分析demo/v6-lgb/v6-lgb.xlsx',
'./模型分析demo/v6-lgb-oh/v6-lgb-oh.xlsx',
'./模型分析demo/v7-lgb/v7-lgb.xlsx',
'./模型分析demo/v8-torch/v8-torch.xlsx',
'./模型分析demo/v9-lgb/v9-lgb.xlsx',
'./模型分析demo/v10-lgb/v10-lgb.xlsx',
'./模型分析demo/v11-lgb/v11-lgb.xlsx',
'./模型分析demo/v12-lgb/v12-lgb.xlsx',
'./模型分析demo/v13-lgb/v13-lgb.xlsx',
'./模型分析demo/v14-knn/v14-knn.xlsx'],
['v1-lgb',
'v2-lr',
'v3-lgb',
'v4-lgb',
'v5-gls',
'v6-lgb',
'v6-lgb-oh',
'v7-lgb',
'v8-torch',
'v9-lgb',
'v10-lgb',
'v11-lgb',
'v12-lgb',
'v13-lgb',
'v14-knn'],
None,
{})