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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)
RANDOM_SEED = 90210
DEV_SAMPLE_SIZE = 1000
BUREAU_BALANCE = params.bureau_balance_filepath
BUREAU = params.bureau_filepath
CREDIT_CARD_BALANCE = params.credit_card_balance_filepath
INSTALLMENTS_PAYMENTS = params.installments_payments_filepath
POS_CASH_BALANCE = params.POS_CASH_balance_filepath
PREVIOUS_APPLICATION = params.previous_application_filepath
ID_COLUMN = 'SK_ID_CURR'
TARGET_COLUMN = 'TARGET'
TIMESTAMP_COLUMNS = []
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_GOODS_PRICE',
'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',
'APARTMENTS_MEDI',
'APARTMENTS_MODE',
'BASEMENTAREA_AVG',
'BASEMENTAREA_MEDI',
'BASEMENTAREA_MODE',
'COMMONAREA_AVG',
'COMMONAREA_MEDI',
'COMMONAREA_MODE',
'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',
'ELEVATORS_MEDI',
'ELEVATORS_MODE',
'ENTRANCES_AVG',
'ENTRANCES_MEDI',
'ENTRANCES_MODE',
'EXT_SOURCE_1',
'EXT_SOURCE_2',
'EXT_SOURCE_3',
'FLOORSMAX_AVG',
'FLOORSMAX_MEDI',
'FLOORSMAX_MODE',
'FLOORSMIN_AVG',
'FLOORSMIN_MEDI',
'FLOORSMIN_MODE',
'LANDAREA_AVG',
'LANDAREA_MEDI',
'LANDAREA_MODE',
'LIVINGAPARTMENTS_AVG',
'LIVINGAPARTMENTS_MEDI',
'LIVINGAPARTMENTS_MODE',
'LIVINGAREA_AVG',
'LIVINGAREA_MEDI',
'LIVINGAREA_MODE',
'NONLIVINGAPARTMENTS_AVG',
'NONLIVINGAPARTMENTS_MEDI',
'NONLIVINGAPARTMENTS_MODE',
'NONLIVINGAREA_AVG',
'NONLIVINGAREA_MEDI',
'NONLIVINGAREA_MODE',
'OBS_30_CNT_SOCIAL_CIRCLE',
'OBS_60_CNT_SOCIAL_CIRCLE',
'OWN_CAR_AGE',
'REGION_POPULATION_RELATIVE',
'REGION_RATING_CLIENT',
'REGION_RATING_CLIENT_W_CITY',
'TOTALAREA_MODE',
'YEARS_BEGINEXPLUATATION_AVG',
'YEARS_BEGINEXPLUATATION_MEDI',
'YEARS_BEGINEXPLUATATION_MODE',
'YEARS_BUILD_AVG',
'YEARS_BUILD_MEDI',
'YEARS_BUILD_MODE']
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']
AGGREGATION_RECIPIES = []
for agg in ['mean', 'size', 'var', 'min', 'max']:
for select in NUMERICAL_COLUMNS:
for group in [['CODE_GENDER'],
['CODE_GENDER', 'OCCUPATION_TYPE'],
['CODE_GENDER', 'FLAG_OWN_REALTY'],
['CODE_GENDER', 'ORGANIZATION_TYPE'],
['CODE_GENDER', 'OCCUPATION_TYPE', 'ORGANIZATION_TYPE'],
['FLAG_OWN_REALTY', 'NAME_HOUSING_TYPE'],
['FLAG_OWN_REALTY', 'OCCUPATION_TYPE', 'ORGANIZATION_TYPE'],
['OCCUPATION_TYPE', 'ORGANIZATION_TYPE'],
]:
AGGREGATION_RECIPIES.append({'groupby': group, 'select': select, 'agg': agg})
SOLUTION_CONFIG = AttrDict({
'pipeline': {'experiment_directory': params.experiment_directory
},
'preprocessing': {'fillna_value': params.fillna_value},
'dataframe_by_type_splitter': {'numerical_columns': NUMERICAL_COLUMNS,
'categorical_columns': CATEGORICAL_COLUMNS,
'timestamp_columns': TIMESTAMP_COLUMNS,
},
'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),
'learning_rate': parameter_eval(params.lgbm__learning_rate),
'max_depth': parameter_eval(params.lgbm__max_depth),
'subsample': parameter_eval(params.lgbm__subsample),
'colsample_bytree': parameter_eval(params.lgbm__colsample_bytree),
'min_child_weight': parameter_eval(params.lgbm__min_child_weight),
'reg_lambda': parameter_eval(params.lgbm__reg_lambda),
'reg_alpha': parameter_eval(params.lgbm__reg_alpha),
'subsample_freq': parameter_eval(params.lgbm__subsample_freq),
'max_bin': parameter_eval(params.lgbm__max_bin),
'min_child_samples': parameter_eval(params.lgbm__min_child_samples),
'num_leaves': parameter_eval(params.lgbm__num_leaves),
'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')}
},
},
},
'bureau': {'filepath': BUREAU,
'id_columns': ('SK_ID_CURR', 'SK_ID_CURR'),
'groupby_aggregations': [
{'groupby': ['SK_ID_CURR'], 'select': 'DAYS_CREDIT', 'agg': 'count'}, # 1
{'groupby': ['SK_ID_CURR'], 'select': 'CREDIT_TYPE', 'agg': 'nunique'}, # 2
{'groupby': ['SK_ID_CURR'], 'select': 'CNT_CREDIT_PROLONG', 'agg': 'mean'}, # 10
{'groupby': ['SK_ID_CURR'], 'select': 'CREDIT_DAY_OVERDUE', 'agg': 'count'},
{'groupby': ['SK_ID_CURR'], 'select': 'CREDIT_ACTIVE', 'agg': 'size'},
{'groupby': ['SK_ID_CURR'], 'select': 'AMT_CREDIT_SUM', 'agg': 'count'},
]},
'clipper': {'min_val': 0,
'max_val': 1
},
'groupby_aggregation': {'groupby_aggregations': AGGREGATION_RECIPIES
},
})