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pipeline_config.py
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pipeline_config.py
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import os
from attrdict import AttrDict
from deepsense import neptune
from .utils import read_params, parameter_eval
ctx = neptune.Context()
params = read_params(ctx, fallback_file='../neptune.yaml')
RANDOM_SEED = 90210
DEV_SAMPLE_SIZE = 1000
ID_COLUMNS = ['SK_ID_CURR']
TARGET_COLUMNS = ['TARGET']
CATEGORICAL_COLUMNS = ['CODE_GENDER',
'EMERGENCYSTATE_MODE',
'FLAG_CONT_MOBILE',
'FLAG_DOCUMENT_3',
'FLAG_DOCUMENT_4',
'FLAG_DOCUMENT_5',
'FLAG_DOCUMENT_6',
'FLAG_DOCUMENT_7',
'FLAG_DOCUMENT_8',
'FLAG_DOCUMENT_9',
'FLAG_DOCUMENT_11',
'FLAG_DOCUMENT_18',
'FLAG_EMAIL',
'FLAG_EMP_PHONE',
'FLAG_MOBIL',
'FLAG_OWN_CAR',
'FLAG_OWN_REALTY',
'FLAG_PHONE',
'FLAG_WORK_PHONE',
'FONDKAPREMONT_MODE',
'HOUR_APPR_PROCESS_START',
'HOUSETYPE_MODE',
'LIVE_CITY_NOT_WORK_CITY',
'LIVE_REGION_NOT_WORK_REGION',
'NAME_CONTRACT_TYPE',
'NAME_TYPE_SUITE',
'NAME_INCOME_TYPE',
'NAME_EDUCATION_TYPE',
'NAME_FAMILY_STATUS',
'NAME_HOUSING_TYPE',
'OCCUPATION_TYPE',
'ORGANIZATION_TYPE',
'REG_CITY_NOT_LIVE_CITY',
'REG_CITY_NOT_WORK_CITY',
'REG_REGION_NOT_LIVE_REGION',
'REG_REGION_NOT_WORK_REGION',
'WALLSMATERIAL_MODE',
'WEEKDAY_APPR_PROCESS_START']
NUMERICAL_COLUMNS = ['AMT_ANNUITY',
'AMT_CREDIT',
'AMT_INCOME_TOTAL',
'AMT_REQ_CREDIT_BUREAU_HOUR',
'AMT_REQ_CREDIT_BUREAU_DAY',
'AMT_REQ_CREDIT_BUREAU_WEEK',
'AMT_REQ_CREDIT_BUREAU_MON',
'AMT_REQ_CREDIT_BUREAU_QRT',
'AMT_REQ_CREDIT_BUREAU_YEAR',
'APARTMENTS_AVG',
'BASEMENTAREA_AVG',
'COMMONAREA_AVG',
'CNT_CHILDREN',
'CNT_FAM_MEMBERS',
'DAYS_BIRTH',
'DAYS_EMPLOYED',
'DAYS_ID_PUBLISH',
'DAYS_LAST_PHONE_CHANGE',
'DAYS_REGISTRATION',
'DEF_30_CNT_SOCIAL_CIRCLE',
'DEF_60_CNT_SOCIAL_CIRCLE',
'ELEVATORS_AVG',
'ENTRANCES_AVG',
'EXT_SOURCE_1',
'EXT_SOURCE_2',
'EXT_SOURCE_3',
'FLOORSMAX_AVG',
'FLOORSMIN_AVG',
'LANDAREA_AVG',
'LIVINGAPARTMENTS_AVG',
'LIVINGAREA_AVG',
'NONLIVINGAPARTMENTS_AVG',
'NONLIVINGAREA_AVG',
'OBS_30_CNT_SOCIAL_CIRCLE',
'OWN_CAR_AGE',
'REGION_POPULATION_RELATIVE',
'REGION_RATING_CLIENT',
'TOTALAREA_MODE',
'YEARS_BEGINEXPLUATATION_AVG',
'YEARS_BUILD_AVG']
USELESS_COLUMNS = ['FLAG_DOCUMENT_10',
'FLAG_DOCUMENT_12',
'FLAG_DOCUMENT_13',
'FLAG_DOCUMENT_14',
'FLAG_DOCUMENT_15',
'FLAG_DOCUMENT_16',
'FLAG_DOCUMENT_17',
'FLAG_DOCUMENT_19',
'FLAG_DOCUMENT_2',
'FLAG_DOCUMENT_20',
'FLAG_DOCUMENT_21']
HIGHLY_CORRELATED_NUMERICAL_COLUMNS = ['AMT_GOODS_PRICE',
'APARTMENTS_MEDI',
'APARTMENTS_MODE',
'BASEMENTAREA_MEDI',
'BASEMENTAREA_MODE',
'COMMONAREA_MEDI',
'COMMONAREA_MODE',
'ELEVATORS_MEDI',
'ELEVATORS_MODE',
'ENTRANCES_MEDI',
'ENTRANCES_MODE',
'FLAG_EMP_PHONE',
'FLOORSMAX_MEDI',
'FLOORSMAX_MODE',
'FLOORSMIN_MEDI',
'FLOORSMIN_MODE',
'LANDAREA_MEDI',
'LANDAREA_MODE',
'LIVINGAPARTMENTS_MEDI',
'LIVINGAPARTMENTS_MODE',
'LIVINGAREA_MEDI',
'LIVINGAREA_MODE',
'NONLIVINGAPARTMENTS_MEDI',
'NONLIVINGAPARTMENTS_MODE',
'NONLIVINGAREA_MEDI',
'NONLIVINGAREA_MODE',
'OBS_60_CNT_SOCIAL_CIRCLE',
'REGION_RATING_CLIENT_W_CITY',
'YEARS_BEGINEXPLUATATION_MEDI',
'YEARS_BEGINEXPLUATATION_MODE',
'YEARS_BUILD_MEDI',
'YEARS_BUILD_MODE']
APPLICATION_AGGREGATION_RECIPIES = [
(['CODE_GENDER', 'NAME_EDUCATION_TYPE'], [('AMT_ANNUITY', 'max'),
('AMT_CREDIT', 'max'),
('EXT_SOURCE_1', 'mean'),
('EXT_SOURCE_2', 'mean'),
('OWN_CAR_AGE', 'max'),
('OWN_CAR_AGE', 'sum')]),
(['CODE_GENDER', 'ORGANIZATION_TYPE'], [('AMT_ANNUITY', 'mean'),
('AMT_INCOME_TOTAL', 'mean'),
('DAYS_REGISTRATION', 'mean'),
('EXT_SOURCE_1', 'mean')]),
(['CODE_GENDER', 'REG_CITY_NOT_WORK_CITY'], [('AMT_ANNUITY', 'mean'),
('CNT_CHILDREN', 'mean'),
('DAYS_ID_PUBLISH', 'mean')]),
(['CODE_GENDER', 'NAME_EDUCATION_TYPE', 'OCCUPATION_TYPE', 'REG_CITY_NOT_WORK_CITY'], [('EXT_SOURCE_1', 'mean'),
('EXT_SOURCE_2', 'mean')]),
(['NAME_EDUCATION_TYPE', 'OCCUPATION_TYPE'], [('AMT_CREDIT', 'mean'),
('AMT_REQ_CREDIT_BUREAU_YEAR', 'mean'),
('APARTMENTS_AVG', 'mean'),
('BASEMENTAREA_AVG', 'mean'),
('EXT_SOURCE_1', 'mean'),
('EXT_SOURCE_2', 'mean'),
('EXT_SOURCE_3', 'mean'),
('NONLIVINGAREA_AVG', 'mean'),
('OWN_CAR_AGE', 'mean'),
('YEARS_BUILD_AVG', 'mean')]),
(['NAME_EDUCATION_TYPE', 'OCCUPATION_TYPE', 'REG_CITY_NOT_WORK_CITY'], [('ELEVATORS_AVG', 'mean'),
('EXT_SOURCE_1', 'mean')]),
(['OCCUPATION_TYPE'], [('AMT_ANNUITY', 'mean'),
('CNT_CHILDREN', 'mean'),
('CNT_FAM_MEMBERS', 'mean'),
('DAYS_BIRTH', 'mean'),
('DAYS_EMPLOYED', 'mean'),
('DAYS_ID_PUBLISH', 'mean'),
('DAYS_REGISTRATION', 'mean'),
('EXT_SOURCE_1', 'mean'),
('EXT_SOURCE_2', 'mean'),
('EXT_SOURCE_3', 'mean')]),
]
BUREAU_AGGREGATION_RECIPIES = [('CREDIT_TYPE', 'count'),
('CREDIT_ACTIVE', 'size')
]
for agg in ['mean', 'min', 'max', 'sum', 'var']:
for select in ['AMT_ANNUITY',
'AMT_CREDIT_SUM',
'AMT_CREDIT_SUM_DEBT',
'AMT_CREDIT_SUM_LIMIT',
'AMT_CREDIT_SUM_OVERDUE',
'AMT_CREDIT_MAX_OVERDUE',
'CNT_CREDIT_PROLONG',
'CREDIT_DAY_OVERDUE',
'DAYS_CREDIT',
'DAYS_CREDIT_ENDDATE',
'DAYS_CREDIT_UPDATE'
]:
BUREAU_AGGREGATION_RECIPIES.append((select, agg))
BUREAU_AGGREGATION_RECIPIES = [(['SK_ID_CURR'], BUREAU_AGGREGATION_RECIPIES)]
CREDIT_CARD_BALANCE_AGGREGATION_RECIPIES = []
for agg in ['mean', 'min', 'max', 'sum', 'var']:
for select in ['AMT_BALANCE',
'AMT_CREDIT_LIMIT_ACTUAL',
'AMT_DRAWINGS_ATM_CURRENT',
'AMT_DRAWINGS_CURRENT',
'AMT_DRAWINGS_OTHER_CURRENT',
'AMT_DRAWINGS_POS_CURRENT',
'AMT_PAYMENT_CURRENT',
'CNT_DRAWINGS_ATM_CURRENT',
'CNT_DRAWINGS_CURRENT',
'CNT_DRAWINGS_OTHER_CURRENT',
'CNT_INSTALMENT_MATURE_CUM',
'MONTHS_BALANCE',
'SK_DPD',
'SK_DPD_DEF'
]:
CREDIT_CARD_BALANCE_AGGREGATION_RECIPIES.append((select, agg))
CREDIT_CARD_BALANCE_AGGREGATION_RECIPIES = [(['SK_ID_CURR'], CREDIT_CARD_BALANCE_AGGREGATION_RECIPIES)]
INSTALLMENTS_PAYMENTS_AGGREGATION_RECIPIES = []
for agg in ['mean', 'min', 'max', 'sum', 'var']:
for select in ['AMT_INSTALMENT',
'AMT_PAYMENT',
'DAYS_ENTRY_PAYMENT',
'DAYS_INSTALMENT',
'NUM_INSTALMENT_NUMBER',
'NUM_INSTALMENT_VERSION'
]:
INSTALLMENTS_PAYMENTS_AGGREGATION_RECIPIES.append((select, agg))
INSTALLMENTS_PAYMENTS_AGGREGATION_RECIPIES = [(['SK_ID_CURR'], INSTALLMENTS_PAYMENTS_AGGREGATION_RECIPIES)]
POS_CASH_BALANCE_AGGREGATION_RECIPIES = []
for agg in ['mean', 'min', 'max', 'sum', 'var']:
for select in ['MONTHS_BALANCE',
'SK_DPD',
'SK_DPD_DEF'
]:
POS_CASH_BALANCE_AGGREGATION_RECIPIES.append((select, agg))
POS_CASH_BALANCE_AGGREGATION_RECIPIES = [(['SK_ID_CURR'], POS_CASH_BALANCE_AGGREGATION_RECIPIES)]
PREVIOUS_APPLICATION_AGGREGATION_RECIPIES = []
for agg in ['mean', 'min', 'max', 'sum', 'var']:
for select in ['AMT_ANNUITY',
'AMT_APPLICATION',
'AMT_CREDIT',
'AMT_DOWN_PAYMENT',
'AMT_GOODS_PRICE',
'CNT_PAYMENT',
'DAYS_DECISION',
'HOUR_APPR_PROCESS_START',
'RATE_DOWN_PAYMENT'
]:
PREVIOUS_APPLICATION_AGGREGATION_RECIPIES.append((select, agg))
PREVIOUS_APPLICATION_AGGREGATION_RECIPIES = [(['SK_ID_CURR'], PREVIOUS_APPLICATION_AGGREGATION_RECIPIES)]
SOLUTION_CONFIG = AttrDict({
'pipeline': {'experiment_directory': params.experiment_directory
},
'preprocessing': {'impute_missing': {'fill_missing': params.fill_missing,
'fill_value': params.fill_value},
'categorical_encoder': {'categorical_columns': CATEGORICAL_COLUMNS
},
},
'applications': {'columns': {'categorical_columns': CATEGORICAL_COLUMNS,
'numerical_columns': NUMERICAL_COLUMNS
},
'aggregations': {'groupby_aggregations': APPLICATION_AGGREGATION_RECIPIES
}
},
'bureau': {'table_name': 'bureau',
'id_columns': ('SK_ID_CURR', 'SK_ID_CURR'),
'groupby_aggregations': BUREAU_AGGREGATION_RECIPIES
},
'credit_card_balance': {'table_name': 'credit_card_balance',
'id_columns': ('SK_ID_CURR', 'SK_ID_CURR'),
'groupby_aggregations': CREDIT_CARD_BALANCE_AGGREGATION_RECIPIES
},
'installments_payments': {'table_name': 'installments_payments',
'id_columns': ('SK_ID_CURR', 'SK_ID_CURR'),
'groupby_aggregations': INSTALLMENTS_PAYMENTS_AGGREGATION_RECIPIES
},
'pos_cash_balance': {'table_name': 'POS_CASH_balance',
'id_columns': ('SK_ID_CURR', 'SK_ID_CURR'),
'groupby_aggregations': POS_CASH_BALANCE_AGGREGATION_RECIPIES
},
'previous_applications': {'table_name': 'previous_application',
'id_columns': ('SK_ID_CURR', 'SK_ID_CURR'),
'groupby_aggregations': PREVIOUS_APPLICATION_AGGREGATION_RECIPIES
},
'light_gbm': {'device': parameter_eval(params.lgbm__device),
'boosting_type': parameter_eval(params.lgbm__boosting_type),
'objective': parameter_eval(params.lgbm__objective),
'metric': parameter_eval(params.lgbm__metric),
'scale_pos_weight': parameter_eval(params.lgbm__scale_pos_weight),
'learning_rate': parameter_eval(params.lgbm__learning_rate),
'max_bin': parameter_eval(params.lgbm__max_bin),
'max_depth': parameter_eval(params.lgbm__max_depth),
'num_leaves': parameter_eval(params.lgbm__num_leaves),
'min_child_samples': parameter_eval(params.lgbm__min_child_samples),
'subsample': parameter_eval(params.lgbm__subsample),
'colsample_bytree': parameter_eval(params.lgbm__colsample_bytree),
'subsample_freq': parameter_eval(params.lgbm__subsample_freq),
'min_gain_to_split': parameter_eval(params.lgbm__min_gain_to_split),
'reg_lambda': parameter_eval(params.lgbm__reg_lambda),
'reg_alpha': parameter_eval(params.lgbm__reg_alpha),
'nthread': parameter_eval(params.num_workers),
'number_boosting_rounds': parameter_eval(params.lgbm__number_boosting_rounds),
'early_stopping_rounds': parameter_eval(params.lgbm__early_stopping_rounds),
'verbose': parameter_eval(params.verbose),
},
'xgboost': {'booster': parameter_eval(params.xgb__booster),
'objective': parameter_eval(params.xgb__objective),
'tree_method': parameter_eval(params.xgb__tree_method),
'eval_metric': parameter_eval(params.xgb__eval_metric),
'eta': parameter_eval(params.xgb__eta),
'max_depth': parameter_eval(params.xgb__max_depth),
'subsample': parameter_eval(params.xgb__subsample),
'colsample_bytree': parameter_eval(params.xgb__colsample_bytree),
'colsample_bylevel': parameter_eval(params.xgb__colsample_bylevel),
'min_child_weight': parameter_eval(params.xgb__min_child_weight),
'lambda': parameter_eval(params.xgb__lambda),
'alpha': parameter_eval(params.xgb__alpha),
'max_bin': parameter_eval(params.xgb__max_bin),
'num_leaves': parameter_eval(params.xgb__max_leaves),
'nthread': parameter_eval(params.num_workers),
'nrounds': parameter_eval(params.xgb__nrounds),
'early_stopping_rounds': parameter_eval(params.xgb__early_stopping_rounds),
'verbose': parameter_eval(params.verbose)
},
'random_forest': {'n_estimators': parameter_eval(params.rf__n_estimators),
'criterion': parameter_eval(params.rf__criterion),
'max_features': parameter_eval(params.rf__max_features),
'min_samples_split': parameter_eval(params.rf__min_samples_split),
'min_samples_leaf': parameter_eval(params.rf__min_samples_leaf),
'n_jobs': parameter_eval(params.num_workers),
'random_state': RANDOM_SEED,
'verbose': parameter_eval(params.verbose),
'class_weight': parameter_eval(params.rf__class_weight),
},
'logistic_regression': {'penalty': parameter_eval(params.lr__penalty),
'tol': parameter_eval(params.lr__tol),
'C': parameter_eval(params.lr__C),
'fit_intercept': parameter_eval(params.lr__fit_intercept),
'class_weight': parameter_eval(params.lr__class_weight),
'random_state': RANDOM_SEED,
'solver': parameter_eval(params.lr__solver),
'max_iter': parameter_eval(params.lr__max_iter),
'verbose': parameter_eval(params.verbose),
'n_jobs': parameter_eval(params.num_workers),
},
'svc': {'kernel': parameter_eval(params.svc__kernel),
'C': parameter_eval(params.svc__C),
'degree': parameter_eval(params.svc__degree),
'gamma': parameter_eval(params.svc__gamma),
'coef0': parameter_eval(params.svc__coef0),
'probability': parameter_eval(params.svc__probability),
'tol': parameter_eval(params.svc__tol),
'max_iter': parameter_eval(params.svc__max_iter),
'verbose': parameter_eval(params.verbose),
'random_state': RANDOM_SEED,
},
'random_search': {'light_gbm': {'n_runs': params.lgbm_random_search_runs,
'callbacks':
{'neptune_monitor': {'name': 'light_gbm'},
'persist_results': {'filepath': os.path.join(params.experiment_directory,
'random_search_light_gbm.pkl')}
},
},
'xgboost': {'n_runs': params.xgb_random_search_runs,
'callbacks':
{'neptune_monitor': {'name': 'xgboost'},
'persist_results': {'filepath': os.path.join(params.experiment_directory,
'random_search_xgboost.pkl')}
},
},
'random_forest': {'n_runs': params.rf_random_search_runs,
'callbacks':
{'neptune_monitor': {'name': 'random_forest'},
'persist_results':
{'filepath': os.path.join(params.experiment_directory,
'random_search_random_forest.pkl')}
},
},
'logistic_regression': {'n_runs': params.lr_random_search_runs,
'callbacks':
{'neptune_monitor': {'name': 'logistic_regression'},
'persist_results':
{'filepath': os.path.join(params.experiment_directory,
'random_search_logistic_regression.pkl')}
},
},
'svc': {'n_runs': params.svc_random_search_runs,
'callbacks': {'neptune_monitor': {'name': 'svc'},
'persist_results': {'filepath': os.path.join(params.experiment_directory,
'random_search_svc.pkl')}
},
},
},
})