def test_manual_pipeline(sampled_app_train_test, sampled_app_roles,
binary_task):
train, test = sampled_app_train_test
pd_dataset = PandasDataset(train,
roles_parser(sampled_app_roles),
task=binary_task)
selector_iterator = FoldsIterator(pd_dataset, 1)
pipe = LGBSimpleFeatures()
model0 = BoostLGBM(default_params={
"learning_rate": 0.05,
"num_leaves": 64,
"seed": 0,
"num_threads": 5,
})
mbie = ModelBasedImportanceEstimator()
selector = ImportanceCutoffSelector(pipe, model0, mbie, cutoff=10)
selector.fit(selector_iterator)
pipe = LGBSimpleFeatures()
params_tuner1 = OptunaTuner(n_trials=10, timeout=300)
model1 = BoostLGBM(default_params={
"learning_rate": 0.05,
"num_leaves": 128
})
params_tuner2 = OptunaTuner(n_trials=100, timeout=300)
model2 = BoostLGBM(default_params={
"learning_rate": 0.025,
"num_leaves": 64
})
total = MLPipeline(
[(model1, params_tuner1), (model2, params_tuner2)],
pre_selection=selector,
features_pipeline=pipe,
post_selection=None,
)
train_valid = FoldsIterator(pd_dataset)
total.fit_predict(train_valid)
total.predict(pd_dataset)
with open("automl.pickle", "wb") as f:
pickle.dump(total, f)
with open("automl.pickle", "rb") as f:
total = pickle.load(f)
total.predict(pd_dataset)
os.remove("automl.pickle")
def test_manual_pipeline():
# Read data from file
logging.debug('Read data from file')
data = pd.read_csv('../example_data/test_data_files/sampled_app_train.csv',
usecols=[
'TARGET', 'NAME_CONTRACT_TYPE', 'AMT_CREDIT',
'NAME_TYPE_SUITE', 'AMT_GOODS_PRICE', 'DAYS_BIRTH',
'DAYS_EMPLOYED'
])
# Fix dates and convert to date type
logging.debug('Fix dates and convert to date type')
data['BIRTH_DATE'] = np.datetime64(
'2018-01-01') + data['DAYS_BIRTH'].astype(np.dtype('timedelta64[D]'))
data['EMP_DATE'] = np.datetime64('2018-01-01') + np.clip(
data['DAYS_EMPLOYED'], None, 0).astype(np.dtype('timedelta64[D]'))
data.drop(['DAYS_BIRTH', 'DAYS_EMPLOYED'], axis=1, inplace=True)
# Create folds
logging.debug('Create folds')
data['__fold__'] = np.random.randint(0, 5, len(data))
# Print data head
logging.debug('Print data head')
print(data.head())
# # Set roles for columns
logging.debug('Set roles for columns')
check_roles = {
TargetRole(): 'TARGET',
CategoryRole(dtype=str): ['NAME_CONTRACT_TYPE', 'NAME_TYPE_SUITE'],
NumericRole(np.float32): ['AMT_CREDIT', 'AMT_GOODS_PRICE'],
DatetimeRole(seasonality=['y', 'm', 'wd']): ['BIRTH_DATE', 'EMP_DATE'],
FoldsRole(): '__fold__'
}
# create Task
task = Task('binary')
# # Creating PandasDataSet
logging.debug('Creating PandasDataset')
start_time = time.time()
pd_dataset = PandasDataset(data, roles_parser(check_roles), task=task)
logging.debug(
'PandasDataset created. Time = {:.3f} sec'.format(time.time() -
start_time))
# # Print pandas dataset feature roles
logging.debug('Print pandas dataset feature roles')
roles = pd_dataset.roles
for role in roles:
logging.debug('{}: {}'.format(role, roles[role]))
# # Feature selection part
logging.debug('Feature selection part')
selector_iterator = FoldsIterator(pd_dataset, 1)
logging.debug('Selection iterator created')
model = BoostLGBM()
pipe = LGBSimpleFeatures()
logging.debug('Pipe and model created')
model0 = BoostLGBM(default_params={
'learning_rate': 0.05,
'num_leaves': 64,
'seed': 0,
'num_threads': 5
})
mbie = ModelBasedImportanceEstimator()
selector = ImportanceCutoffSelector(pipe, model0, mbie, cutoff=10)
start_time = time.time()
selector.fit(selector_iterator)
logging.debug(
'Feature selector fitted. Time = {:.3f} sec'.format(time.time() -
start_time))
logging.debug('Feature selector scores:')
logging.debug('\n{}'.format(selector.get_features_score()))
# # Build AutoML pipeline
logging.debug('Start building AutoML pipeline')
pipe = LGBSimpleFeatures()
logging.debug('Pipe created')
params_tuner1 = OptunaTuner(n_trials=10, timeout=300)
model1 = BoostLGBM(default_params={
'learning_rate': 0.05,
'num_leaves': 128
})
logging.debug('Tuner1 and model1 created')
params_tuner2 = OptunaTuner(n_trials=100, timeout=300)
model2 = BoostLGBM(default_params={
'learning_rate': 0.025,
'num_leaves': 64
})
logging.debug('Tuner2 and model2 created')
total = MLPipeline([(model1, params_tuner1), (model2, params_tuner2)],
pre_selection=selector,
features_pipeline=pipe,
post_selection=None)
logging.debug('Finished building AutoML pipeline')
# # Create full train iterator
logging.debug('Full train valid iterator creation')
train_valid = FoldsIterator(pd_dataset)
logging.debug('Full train valid iterator created')
# # Fit predict using pipeline
logging.debug('Start AutoML pipeline fit_predict')
start_time = time.time()
pred = total.fit_predict(train_valid)
logging.debug(
'Fit_predict finished. Time = {:.3f} sec'.format(time.time() -
start_time))
# # Check preds
logging.debug('Preds:')
logging.debug('\n{}'.format(pred))
logging.debug('Preds.shape = {}'.format(pred.shape))
# # Predict full train dataset
logging.debug('Predict full train dataset')
start_time = time.time()
train_pred = total.predict(pd_dataset)
logging.debug('Predict finished. Time = {:.3f} sec'.format(time.time() -
start_time))
logging.debug('Preds:')
logging.debug('\n{}'.format(train_pred))
logging.debug('Preds.shape = {}'.format(train_pred.shape))
logging.debug('Pickle automl')
with open('automl.pickle', 'wb') as f:
pickle.dump(total, f)
logging.debug('Load pickled automl')
with open('automl.pickle', 'rb') as f:
total = pickle.load(f)
logging.debug('Predict loaded automl')
train_pred = total.predict(pd_dataset)
os.remove('automl.pickle')
# # Check preds feature names
logging.debug('Preds features: {}'.format(train_pred.features))
# # Check model feature scores
logging.debug('Feature scores for model_1:\n{}'.format(
model1.get_features_score()))
logging.debug('Feature scores for model_2:\n{}'.format(
model2.get_features_score()))
pre_selection=selector,
features_pipeline=pipe,
post_selection=None,
)
print("Finished building AutoML pipeline")
# # Create full train iterator
print("Full train valid iterator creation")
train_valid = FoldsIterator(pd_dataset)
print("Full train valid iterator created")
# # Fit predict using pipeline
print("Start AutoML pipeline fit_predict")
start_time = time.time()
pred = total.fit_predict(train_valid)
print("Fit_predict finished. Time = {:.3f} sec".format(time.time() -
start_time))
# # Check preds
print("Preds:")
print("\n{}".format(pred))
print("Preds.shape = {}".format(pred.shape))
# # Predict full train dataset
print("Predict full train dataset")
start_time = time.time()
train_pred = total.predict(pd_dataset)
print("Predict finished. Time = {:.3f} sec".format(time.time() - start_time))
print("Preds:")
print("\n{}".format(train_pred))
