demo7.py

#!/usr/bin/env python
# coding: utf-8
import logging
import os
import pickle

import numpy as np
import pandas as pd
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import train_test_split

from lightautoml.automl.presets.tabular_presets import TabularAutoML
from lightautoml.dataset.roles import DatetimeRole
from lightautoml.tasks import Task


def test_tabular_automl_preset_without_params():
    np.random.seed(42)
    logging.basicConfig(format='[%(asctime)s] (%(levelname)s): %(message)s', level=logging.DEBUG)

    data = pd.read_csv('../example_data/test_data_files/sampled_app_train.csv')

    data['BIRTH_DATE'] = (np.datetime64('2018-01-01') + data['DAYS_BIRTH'].astype(np.dtype('timedelta64[D]'))).astype(str)
    data['EMP_DATE'] = (np.datetime64('2018-01-01') + np.clip(data['DAYS_EMPLOYED'], None, 0).astype(np.dtype('timedelta64[D]'))
                        ).astype(str)

    data['report_dt'] = np.datetime64('2018-01-01')

    data['constant'] = 1
    data['allnan'] = np.nan

    data.drop(['DAYS_BIRTH', 'DAYS_EMPLOYED'], axis=1, inplace=True)

    train, test = train_test_split(data, test_size=2000, random_state=42)

    roles = {'target': 'TARGET',
             DatetimeRole(base_date=True, seasonality=(), base_feats=False): 'report_dt',
             }

    task = Task('binary', )

    automl = TabularAutoML(task=task, timeout=3600, )
    oof_pred = automl.fit_predict(train, roles=roles)
    test_pred = automl.predict(test)

    not_nan = np.any(~np.isnan(oof_pred.data), axis=1)

    logging.debug('Check scores...')
    print('OOF score: {}'.format(roc_auc_score(train[roles['target']].values[not_nan], oof_pred.data[not_nan][:, 0])))
    print('TEST score: {}'.format(roc_auc_score(test[roles['target']].values, test_pred.data[:, 0])))
    logging.debug('Pickle automl')
    with open('automl.pickle', 'wb') as f:
        pickle.dump(automl, f)

    logging.debug('Load pickled automl')
    with open('automl.pickle', 'rb') as f:
        automl = pickle.load(f)

    logging.debug('Predict loaded automl')
    test_pred = automl.predict(test)
    logging.debug('TEST score, loaded: {}'.format(roc_auc_score(test['TARGET'].values, test_pred.data[:, 0])))

    os.remove('automl.pickle')