def test_classification_models_fit_correct(data_fixture, request):
data = request.getfixturevalue(data_fixture)
train_data, test_data = train_test_data_setup(data=data)
roc_threshold = 0.95
logger = default_log('default_test_logger')
with OperationTypesRepository() as repo:
model_names, _ = repo.suitable_operation(
task_type=TaskTypesEnum.classification,
data_type=data.data_type,
tags=['ml'])
for model_name in model_names:
logger.info(f"Test classification model: {model_name}.")
model = Model(operation_type=model_name)
_, train_predicted = model.fit(data=train_data)
test_pred = model.predict(fitted_operation=_,
data=test_data,
is_fit_pipeline_stage=False)
roc_on_test = get_roc_auc(valid_data=test_data,
predicted_data=test_pred)
if model_name not in ['bernb', 'multinb']:
assert roc_on_test >= roc_threshold
else:
assert roc_on_test >= 0.5
def test_log_clustering_fit_correct(data_fixture, request):
data = request.getfixturevalue(data_fixture)
train_data, test_data = train_test_data_setup(data=data)
# Scaling pipeline. Fit predict it
scaling_pipeline = Pipeline(PrimaryNode('normalization'))
scaling_pipeline.fit(train_data)
scaled_data = scaling_pipeline.predict(train_data)
kmeans = Model(operation_type='kmeans')
_, train_predicted = kmeans.fit(data=scaled_data)
assert all(np.unique(train_predicted.predict) == [0, 1])
def test_svc_fit_correct(data_fixture, request):
data = request.getfixturevalue(data_fixture)
train_data, test_data = train_test_data_setup(data=data)
# Scaling pipeline. Fit predict it
scaling_pipeline = Pipeline(PrimaryNode('normalization'))
scaling_pipeline.fit(train_data)
scaled_data = scaling_pipeline.predict(train_data)
svc = Model(operation_type='svc')
_, train_predicted = svc.fit(data=scaled_data)
roc_on_train = get_roc_auc(valid_data=train_data,
predicted_data=train_predicted)
roc_threshold = 0.95
assert roc_on_train >= roc_threshold
def test_random_forest_fit_correct(data_fixture, request):
data = request.getfixturevalue(data_fixture)
train_data, test_data = train_test_data_setup(data=data)
# Scaling chain. Fit predict it
scaling_chain = Chain(PrimaryNode('normalization'))
scaling_chain.fit(train_data)
scaled_data = scaling_chain.predict(train_data)
random_forest = Model(operation_type='rf')
_, train_predicted = random_forest.fit(data=scaled_data)
roc_on_train = get_roc_auc(valid_data=train_data,
predicted_data=train_predicted)
roc_threshold = 0.95
assert roc_on_train >= roc_threshold
def test_log_regression_fit_correct(classification_dataset):
data = classification_dataset
train_data, test_data = train_test_data_setup(data=data)
# Scaling chain. Fit predict it
scaling_chain = Chain(PrimaryNode('normalization'))
scaling_chain.fit(train_data)
scaled_data = scaling_chain.predict(train_data)
log_reg = Model(operation_type='logit')
_, train_predicted = log_reg.fit(data=scaled_data)
roc_on_train = get_roc_auc(valid_data=train_data,
predicted_data=train_predicted)
roc_threshold = 0.95
assert roc_on_train >= roc_threshold
def test_node_factory_log_reg_correct(data_setup):
model_type = 'logit'
node = PrimaryNode(operation_type=model_type)
expected_model = Model(operation_type=model_type).__class__
actual_model = node.operation.__class__
assert node.__class__ == PrimaryNode
assert expected_model == actual_model
def test_ts_models_fit_correct():
train_data, test_data = get_ts_data(forecast_length=5)
logger = default_log('default_test_logger')
with OperationTypesRepository() as repo:
model_names, _ = repo.suitable_operation(
task_type=TaskTypesEnum.ts_forecasting, tags=['time_series'])
for model_name in model_names:
logger.info(f"Test time series model: {model_name}.")
model = Model(operation_type=model_name)
_, train_predicted = model.fit(data=deepcopy(train_data))
test_pred = model.predict(fitted_operation=_,
data=test_data,
is_fit_pipeline_stage=False)
mae_value_test = mean_absolute_error(y_true=test_data.target,
y_pred=test_pred.predict[0])
mae_threshold = np.var(test_data.target) * 2
assert mae_value_test < mae_threshold
def test_regression_models_fit_correct():
data = get_synthetic_regression_data(n_samples=1000, random_state=42)
train_data, test_data = train_test_data_setup(data)
logger = default_log('default_test_logger')
with OperationTypesRepository() as repo:
model_names, _ = repo.suitable_operation(
task_type=TaskTypesEnum.regression, tags=['ml'])
for model_name in model_names:
logger.info(f"Test regression model: {model_name}.")
model = Model(operation_type=model_name)
_, train_predicted = model.fit(data=train_data)
test_pred = model.predict(fitted_operation=_,
data=test_data,
is_fit_pipeline_stage=False)
rmse_value_test = mean_squared_error(y_true=test_data.target,
y_pred=test_pred.predict)
rmse_threshold = np.std(test_data.target)**2
assert rmse_value_test < rmse_threshold
def test_logger_write_logs_correctly():
test_file_path = str(os.path.dirname(__file__))
test_log_file = os.path.join(test_file_path, 'test_log.log')
test_log = default_log('test_log', log_file=test_log_file)
# Model data preparation
file = os.path.join('../data', 'advanced_classification.csv')
data = InputData.from_csv(os.path.join(test_file_path, file))
train_data, test_data = train_test_data_setup(data=data)
try:
knn = Model(operation_type='knnreg', log=test_log)
model, _ = knn.fit(train_data, is_fit_chain_stage=True)
except Exception:
print('Captured error')
if os.path.exists(test_log_file):
with open(test_log_file, 'r') as file:
content = file.readlines()
release_log(logger=test_log, log_file=test_log_file)
assert 'Can not find evaluation strategy' in content[0]
def get_operation(self) -> Operation:
"""
Factory method returns the desired object of the 'Data_operation' or
'Model' class which depends on model_type variable
"""
if self.operation_type == 'model':
operation = Model(operation_type=self.operation_name)
elif self.operation_type == 'data_operation':
operation = DataOperation(operation_type=self.operation_name)
else:
raise ValueError(f'Operation type {self.operation_type} is not supported')
return operation
