post_selection=None,
)
print("Pipeline1 created")
print("Create AutoML pipeline...")
automl = AutoML(
reader,
[
[pipeline_lvl1],
],
skip_conn=False,
)
print("AutoML pipeline created...")
print("Start AutoML pipeline fit_predict...")
start_time = time.time()
oof_pred = automl.fit_predict(train_data, roles={"target": target})
print("AutoML pipeline fitted and predicted. Time = {:.3f} sec".format(
time.time() - start_time))
test_pred = automl.predict(test_data)
print("Prediction for test data:\n{}\nShape = {}".format(
test_pred, test_pred.shape))
print("Check scores...")
print("OOF score: {}".format(
task.metric_func(train_data[target].values, oof_pred.data[:, 0])))
print("TEST score: {}".format(
task.metric_func(test_data[target].values, test_pred.data[:, 0])))
def test_different_losses_and_metrics():
np.random.seed(42)
logging.basicConfig(format='[%(asctime)s] (%(levelname)s): %(message)s',
level=logging.DEBUG)
logging.debug('Load data...')
data = pd.read_csv('../example_data/test_data_files/sampled_app_train.csv')
logging.debug('Data loaded')
logging.debug('Features modification from user side...')
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['constant'] = 1
data['allnan'] = np.nan
data.drop(['DAYS_BIRTH', 'DAYS_EMPLOYED'], axis=1, inplace=True)
logging.debug('Features modification finished')
logging.debug('Split data...')
train_data, test_data = train_test_split(data,
test_size=2000,
stratify=data['TARGET'],
random_state=13)
train_data.reset_index(drop=True, inplace=True)
test_data.reset_index(drop=True, inplace=True)
logging.debug(
'Data splitted. Parts sizes: train_data = {}, test_data = {}'.format(
train_data.shape, test_data.shape))
for task_params, target in zip([{
'name': 'binary'
}, {
'name': 'binary',
'metric': roc_auc_score
}, {
'name': 'reg',
'loss': 'mse',
'metric': 'r2'
}, {
'name': 'reg',
'loss': 'rmsle',
'metric': 'rmsle'
}, {
'name': 'reg',
'loss': 'quantile',
'loss_params': {
'q': .9
},
'metric': 'quantile',
'metric_params': {
'q': .9
}
}], ['TARGET', 'TARGET', 'AMT_CREDIT', 'AMT_CREDIT', 'AMT_CREDIT']):
logging.debug('Create task..')
task = Task(**task_params)
logging.debug('Task created')
logging.debug('Create reader...')
reader = PandasToPandasReader(task, cv=5, random_state=1)
logging.debug('Reader created')
# pipeline 1 level parts
logging.debug('Start creation pipeline_1...')
pipe = LGBSimpleFeatures()
logging.debug('\t ParamsTuner2 and Model2...')
model2 = BoostLGBM(default_params={
'learning_rate': 0.025,
'num_leaves': 64,
'seed': 2,
'num_threads': 5
})
logging.debug('\t Tuner2 and model2 created')
logging.debug('\t Pipeline1...')
pipeline_lvl1 = MLPipeline(
[model2],
pre_selection=None, # selector,
features_pipeline=pipe,
post_selection=None)
logging.debug('Pipeline1 created')
logging.debug('Create AutoML pipeline...')
automl = AutoML(reader, [
[pipeline_lvl1],
], skip_conn=False)
logging.debug('AutoML pipeline created...')
logging.debug('Start AutoML pipeline fit_predict...')
start_time = time.time()
oof_pred = automl.fit_predict(train_data, roles={'target': target})
logging.debug(
'AutoML pipeline fitted and predicted. Time = {:.3f} sec'.format(
time.time() - start_time))
test_pred = automl.predict(test_data)
logging.debug('Prediction for test data:\n{}\nShape = {}'.format(
test_pred, test_pred.shape))
logging.debug('Check scores...')
logging.debug('OOF score: {}'.format(
task.metric_func(train_data[target].values, oof_pred.data[:, 0])))
logging.debug('TEST score: {}'.format(
task.metric_func(test_data[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_data)
logging.debug('TEST score, loaded: {}'.format(
roc_auc_score(test_data['TARGET'].values, test_pred.data[:, 0])))
os.remove('automl.pickle')
