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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, safe_eval
ctx = neptune.Context()
params = read_params(ctx)
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']
TIMESTAMP_COLUMNS = []
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']
ID_COLUMNS = ['SK_ID_CURR']
TARGET_COLUMNS = ['TARGET']
DEV_SAMPLE_SIZE = int(10e4)
SOLUTION_CONFIG = AttrDict({
'env': {'cache_dirpath': params.experiment_dir
},
'dataframe_by_type_splitter': {'numerical_columns': NUMERICAL_COLUMNS,
'categorical_columns': CATEGORICAL_COLUMNS,
'timestamp_columns': TIMESTAMP_COLUMNS,
},
'light_gbm': {'boosting_type': safe_eval(params.lgbm__boosting_type),
'objective': safe_eval(params.lgbm__objective),
'metric': safe_eval(params.lgbm__metric),
'learning_rate': safe_eval(params.lgbm__learning_rate),
'max_depth': safe_eval(params.lgbm__max_depth),
'subsample': safe_eval(params.lgbm__subsample),
'colsample_bytree': safe_eval(params.lgbm__colsample_bytree),
'min_child_weight': safe_eval(params.lgbm__min_child_weight),
'reg_lambda': safe_eval(params.lgbm__reg_lambda),
'reg_alpha': safe_eval(params.lgbm__reg_alpha),
'subsample_freq': safe_eval(params.lgbm__subsample_freq),
'max_bin': safe_eval(params.lgbm__max_bin),
'min_child_samples': safe_eval(params.lgbm__min_child_samples),
'num_leaves': safe_eval(params.lgbm__num_leaves),
'nthread': safe_eval(params.num_workers),
'number_boosting_rounds': safe_eval(params.lgbm__number_boosting_rounds),
'early_stopping_rounds': safe_eval(params.lgbm__early_stopping_rounds),
'verbose': safe_eval(params.verbose)
},
'random_search': {'light_gbm': {'n_runs': params.lgbm_random_search_runs,
'callbacks': {'neptune_monitor': {'name': 'light_gbm'
},
'save_results': {'filepath': os.path.join(params.experiment_dir,
'random_search_light_gbm.pkl')
}
}
}
},
'clipper': {'min_val': 0,
'max_val': 1
}
})