def test_pass_categorical_and_numeric_columns_to_pipeline(
dask_client, data_type, include_categorical):
# Prepare the training data
X, y = sklearn.datasets.make_classification()
feat_type = None
if 'pandas' in data_type:
X = pd.DataFrame(X)
y = pd.DataFrame(y, dtype="category")
if include_categorical:
cat_name = X.shape[1]
X[cat_name] = 'A'
X[cat_name] = X[cat_name].astype('category')
elif 'numpy' in data_type:
if include_categorical:
feat_type = ['numerical' for x in range(np.shape(X)[1])]
feat_type.append('categorical')
temporal = np.zeros((X.shape[0], X.shape[1] + 1))
temporal[:, :-1] = X
X = temporal
else:
pytest.fail()
X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(
X, y, test_size=0.5, random_state=3)
seed = 3
automl = AutoSklearnClassifier(
time_left_for_this_task=120,
# Time left for task plays no role
# only per run time limit
per_run_time_limit=30,
ensemble_size=0,
dask_client=dask_client,
include_estimators=['random_forest'],
seed=seed,
)
config = automl.get_configuration_space(
X_train,
y_train,
feat_type=feat_type,
X_test=X_test,
y_test=y_test,
).get_default_configuration()
pipeline, run_info, run_value = automl.fit_pipeline(X=X_train,
y=y_train,
config=config,
feat_type=feat_type,
X_test=X_test,
y_test=y_test)
# We should produce a decent result
assert run_value.cost < 0.4, f"{run_value}/{run_value.additional_info}"
prediction = pipeline.predict(
automl.automl_.InputValidator.feature_validator.transform(X))
assert np.shape(prediction)[0], np.shape(y)[0]
if include_categorical:
expected_dict = {i: 'numerical' for i in range(np.shape(X)[1] - 1)}
expected_dict[X.shape[1] - 1] = 'categorical'
else:
expected_dict = {i: 'numerical' for i in range(np.shape(X)[1])}
assert expected_dict == pipeline.named_steps[
'data_preprocessing'].feat_type
def test_pass_categorical_and_numeric_columns_to_pipeline(
dask_client, data_type, include_categorical):
# Prepare the training data
X, y = sklearn.datasets.make_classification(random_state=0)
X = cast(np.ndarray, X)
n_features = X.shape[1]
# If categorical, insert a row of 'categorical' '0's at last col
if include_categorical:
X = np.insert(X, n_features, values=0, axis=1)
if data_type == 'pandas':
X = pd.DataFrame(X)
y = pd.DataFrame(y, dtype="category")
# Set the last column to categorical
if include_categorical:
X.loc[:, n_features] = X.loc[:, n_features].astype(
'category') # type: ignore
# Specify the feature_types
if data_type == 'numpy' and include_categorical:
feat_type = ['numerical'] * n_features + ['categorical']
else:
feat_type = None
# Create the splits
X_train, X_test, y_train, y_test = model_selection.train_test_split(
X, y, test_size=0.5, random_state=3)
# Create Estimator
# Time left for task plays no role for fit_pipeline
automl = AutoSklearnClassifier(
delete_tmp_folder_after_terminate=False,
time_left_for_this_task=120,
per_run_time_limit=30,
ensemble_size=0,
seed=0,
dask_client=dask_client,
include={'classifier': ['random_forest']},
)
config_space = automl.get_configuration_space(
X_train,
y_train,
X_test=X_test,
y_test=y_test,
feat_type=feat_type,
)
config = config_space.get_default_configuration()
pipeline, _, run_value = automl.fit_pipeline(
X=X_train,
y=y_train,
X_test=X_test,
y_test=y_test,
config=config,
feat_type=feat_type,
)
assert pipeline is not None, "Expected a pipeline from automl.fit_pipeline"
feature_validator = automl.automl_.InputValidator.feature_validator # type: ignore
transformed_X_test = feature_validator.transform(X_test)
predictions = pipeline.predict(transformed_X_test)
# We should produce a half decent result
assert run_value.cost < 0.40, f"Run value:\n {run_value}"
# Outputs should be the correct length
assert np.shape(predictions)[0] == np.shape(y_test)[0]
n_columns = np.shape(X)[1]
if include_categorical:
expected_feat_types = {
i: feature_type
for i, feature_type in enumerate(['numerical'] * (n_columns - 1) +
['categorical'])
}
else:
expected_feat_types = {
i: feature_type
for i, feature_type in enumerate(['numerical'] * n_columns)
}
pipeline_feat_types = pipeline.named_steps[
'data_preprocessor'].choice.feat_type
assert expected_feat_types == pipeline_feat_types