def test_featuretools_index(mock_calculate_feature_matrix, mock_dfs, X_y_multi):
X, y = X_y_multi
X_pd = pd.DataFrame(X)
X_new_index = X_pd.copy()
index = [i for i in range(len(X))]
new_index = [i * 2 for i in index]
X_new_index['index'] = new_index
mock_calculate_feature_matrix.return_value = pd.DataFrame({})
# check if _make_entity_set keeps the intended index
feature = DFSTransformer()
feature.fit(X_new_index)
feature.transform(X_new_index)
arg_es = mock_dfs.call_args[1]['entityset'].entities[0].df['index']
arg_tr = mock_calculate_feature_matrix.call_args[1]['entityset'].entities[0].df['index']
assert arg_es.to_list() == new_index
assert arg_tr.to_list() == new_index
# check if _make_entity_set fills in the proper index values
feature.fit(X_pd)
feature.transform(X_pd)
arg_es = mock_dfs.call_args[1]['entityset'].entities[0].df['index']
arg_tr = mock_calculate_feature_matrix.call_args[1]['entityset'].entities[0].df['index']
assert arg_es.to_list() == index
assert arg_tr.to_list() == index
def test_dfs_sets_max_depth_1(mock_dfs, X_y_multi):
X, y = X_y_multi
X_pd = pd.DataFrame(X)
feature = DFSTransformer()
feature.fit(X_pd, y)
_, kwargs = mock_dfs.call_args
assert kwargs['max_depth'] == 1
def test_ft_woodwork_custom_overrides_returned_by_components(X_df):
y = pd.Series([1, 2, 1])
override_types = [Integer, Double, Categorical, Datetime, Boolean]
for logical_type in override_types:
try:
X = ww.DataTable(X_df.copy(), logical_types={0: logical_type})
except TypeError:
continue
dft = DFSTransformer()
dft.fit(X, y)
transformed = dft.transform(X, y)
assert isinstance(transformed, ww.DataTable)
if logical_type == Datetime:
assert transformed.logical_types == {'DAY(0)': Integer, 'MONTH(0)': Integer, 'WEEKDAY(0)': Integer, 'YEAR(0)': Integer}
else:
assert transformed.logical_types == {'0': logical_type}
def test_transform_subset(X_y_binary, X_y_multi, X_y_regression):
datasets = locals()
for dataset in datasets.values():
X, y = dataset
X_pd = pd.DataFrame(X)
X_pd.columns = X_pd.columns.astype(str)
X_fit = X_pd.iloc[: len(X) // 3]
X_transform = X_pd.iloc[len(X) // 3:]
es = ft.EntitySet()
es = es.entity_from_dataframe(entity_id="X", dataframe=X_transform, index='index', make_index=True)
feature_matrix, features = ft.dfs(entityset=es, target_entity="X")
feature = DFSTransformer()
feature.fit(X_fit)
X_t = feature.transform(X_transform)
assert_frame_equal(feature_matrix, X_t.to_dataframe())
def test_numeric_columns(X_y_multi):
X, y = X_y_multi
X_pd = pd.DataFrame(X)
feature = DFSTransformer()
feature.fit(X_pd, y)
feature.transform(X_pd)
def test_describe_component():
enc = OneHotEncoder()
imputer = Imputer()
simple_imputer = SimpleImputer("mean")
column_imputer = PerColumnImputer({"a": "mean", "b": ("constant", 100)})
scaler = StandardScaler()
feature_selection_clf = RFClassifierSelectFromModel(n_estimators=10, number_features=5, percent_features=0.3, threshold=-np.inf)
feature_selection_reg = RFRegressorSelectFromModel(n_estimators=10, number_features=5, percent_features=0.3, threshold=-np.inf)
drop_col_transformer = DropColumns(columns=['col_one', 'col_two'])
drop_null_transformer = DropNullColumns()
datetime = DateTimeFeaturizer()
text_featurizer = TextFeaturizer()
lsa = LSA()
pca = PCA()
lda = LinearDiscriminantAnalysis()
ft = DFSTransformer()
us = Undersampler()
assert enc.describe(return_dict=True) == {'name': 'One Hot Encoder', 'parameters': {'top_n': 10,
'features_to_encode': None,
'categories': None,
'drop': 'if_binary',
'handle_unknown': 'ignore',
'handle_missing': 'error'}}
assert imputer.describe(return_dict=True) == {'name': 'Imputer', 'parameters': {'categorical_impute_strategy': "most_frequent",
'categorical_fill_value': None,
'numeric_impute_strategy': "mean",
'numeric_fill_value': None}}
assert simple_imputer.describe(return_dict=True) == {'name': 'Simple Imputer', 'parameters': {'impute_strategy': 'mean', 'fill_value': None}}
assert column_imputer.describe(return_dict=True) == {'name': 'Per Column Imputer', 'parameters': {'impute_strategies': {'a': 'mean', 'b': ('constant', 100)}, 'default_impute_strategy': 'most_frequent'}}
assert scaler.describe(return_dict=True) == {'name': 'Standard Scaler', 'parameters': {}}
assert feature_selection_clf.describe(return_dict=True) == {'name': 'RF Classifier Select From Model', 'parameters': {'number_features': 5, 'n_estimators': 10, 'max_depth': None, 'percent_features': 0.3, 'threshold': -np.inf, 'n_jobs': -1}}
assert feature_selection_reg.describe(return_dict=True) == {'name': 'RF Regressor Select From Model', 'parameters': {'number_features': 5, 'n_estimators': 10, 'max_depth': None, 'percent_features': 0.3, 'threshold': -np.inf, 'n_jobs': -1}}
assert drop_col_transformer.describe(return_dict=True) == {'name': 'Drop Columns Transformer', 'parameters': {'columns': ['col_one', 'col_two']}}
assert drop_null_transformer.describe(return_dict=True) == {'name': 'Drop Null Columns Transformer', 'parameters': {'pct_null_threshold': 1.0}}
assert datetime.describe(return_dict=True) == {'name': 'DateTime Featurization Component',
'parameters': {'features_to_extract': ['year', 'month', 'day_of_week', 'hour'],
'encode_as_categories': False}}
assert text_featurizer.describe(return_dict=True) == {'name': 'Text Featurization Component', 'parameters': {}}
assert lsa.describe(return_dict=True) == {'name': 'LSA Transformer', 'parameters': {}}
assert pca.describe(return_dict=True) == {'name': 'PCA Transformer', 'parameters': {'n_components': None, 'variance': 0.95}}
assert lda.describe(return_dict=True) == {'name': 'Linear Discriminant Analysis Transformer', 'parameters': {'n_components': None}}
assert ft.describe(return_dict=True) == {'name': 'DFS Transformer', 'parameters': {"index": "index"}}
assert us.describe(return_dict=True) == {'name': 'Undersampler', 'parameters': {"balanced_ratio": 4, "min_samples": 100, "min_percentage": 0.1}}
# testing estimators
base_classifier = BaselineClassifier()
base_regressor = BaselineRegressor()
lr_classifier = LogisticRegressionClassifier()
en_classifier = ElasticNetClassifier()
en_regressor = ElasticNetRegressor()
et_classifier = ExtraTreesClassifier(n_estimators=10, max_features="auto")
et_regressor = ExtraTreesRegressor(n_estimators=10, max_features="auto")
rf_classifier = RandomForestClassifier(n_estimators=10, max_depth=3)
rf_regressor = RandomForestRegressor(n_estimators=10, max_depth=3)
linear_regressor = LinearRegressor()
svm_classifier = SVMClassifier()
svm_regressor = SVMRegressor()
assert base_classifier.describe(return_dict=True) == {'name': 'Baseline Classifier', 'parameters': {'strategy': 'mode'}}
assert base_regressor.describe(return_dict=True) == {'name': 'Baseline Regressor', 'parameters': {'strategy': 'mean'}}
assert lr_classifier.describe(return_dict=True) == {'name': 'Logistic Regression Classifier', 'parameters': {'penalty': 'l2', 'C': 1.0, 'n_jobs': -1, 'multi_class': 'auto', 'solver': 'lbfgs'}}
assert en_classifier.describe(return_dict=True) == {'name': 'Elastic Net Classifier', 'parameters': {'alpha': 0.5, 'l1_ratio': 0.5, 'n_jobs': -1, 'max_iter': 1000, "loss": 'log', 'penalty': 'elasticnet'}}
assert en_regressor.describe(return_dict=True) == {'name': 'Elastic Net Regressor', 'parameters': {'alpha': 0.5, 'l1_ratio': 0.5, 'max_iter': 1000, 'normalize': False}}
assert et_classifier.describe(return_dict=True) == {'name': 'Extra Trees Classifier', 'parameters': {'n_estimators': 10, 'max_features': 'auto', 'max_depth': 6, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_jobs': -1}}
assert et_regressor.describe(return_dict=True) == {'name': 'Extra Trees Regressor', 'parameters': {'n_estimators': 10, 'max_features': 'auto', 'max_depth': 6, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_jobs': -1}}
assert rf_classifier.describe(return_dict=True) == {'name': 'Random Forest Classifier', 'parameters': {'n_estimators': 10, 'max_depth': 3, 'n_jobs': -1}}
assert rf_regressor.describe(return_dict=True) == {'name': 'Random Forest Regressor', 'parameters': {'n_estimators': 10, 'max_depth': 3, 'n_jobs': -1}}
assert linear_regressor.describe(return_dict=True) == {'name': 'Linear Regressor', 'parameters': {'fit_intercept': True, 'normalize': False, 'n_jobs': -1}}
assert svm_classifier.describe(return_dict=True) == {'name': 'SVM Classifier', 'parameters': {'C': 1.0, 'kernel': 'rbf', 'gamma': 'scale', 'probability': True}}
assert svm_regressor.describe(return_dict=True) == {'name': 'SVM Regressor', 'parameters': {'C': 1.0, 'kernel': 'rbf', 'gamma': 'scale'}}
try:
xgb_classifier = XGBoostClassifier(eta=0.1, min_child_weight=1, max_depth=3, n_estimators=75)
xgb_regressor = XGBoostRegressor(eta=0.1, min_child_weight=1, max_depth=3, n_estimators=75)
assert xgb_classifier.describe(return_dict=True) == {'name': 'XGBoost Classifier', 'parameters': {'eta': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'n_estimators': 75}}
assert xgb_regressor.describe(return_dict=True) == {'name': 'XGBoost Regressor', 'parameters': {'eta': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'n_estimators': 75}}
except ImportError:
pass
try:
cb_classifier = CatBoostClassifier()
cb_regressor = CatBoostRegressor()
assert cb_classifier.describe(return_dict=True) == {'name': 'CatBoost Classifier', 'parameters': {'allow_writing_files': False, 'n_estimators': 10, 'eta': 0.03, 'max_depth': 6, 'bootstrap_type': None, 'silent': True}}
assert cb_regressor.describe(return_dict=True) == {'name': 'CatBoost Regressor', 'parameters': {'allow_writing_files': False, 'n_estimators': 10, 'eta': 0.03, 'max_depth': 6, 'bootstrap_type': None, 'silent': False}}
except ImportError:
pass
try:
lg_classifier = LightGBMClassifier()
lg_regressor = LightGBMRegressor()
assert lg_classifier.describe(return_dict=True) == {'name': 'LightGBM Classifier', 'parameters': {'boosting_type': 'gbdt', 'learning_rate': 0.1, 'n_estimators': 100, 'max_depth': 0, 'num_leaves': 31,
'min_child_samples': 20, 'n_jobs': -1, 'bagging_fraction': 0.9, 'bagging_freq': 0}}
assert lg_regressor.describe(return_dict=True) == {'name': 'LightGBM Regressor', 'parameters': {'boosting_type': 'gbdt', 'learning_rate': 0.1, 'n_estimators': 20, 'max_depth': 0, 'num_leaves': 31,
'min_child_samples': 20, 'n_jobs': -1, 'bagging_fraction': 0.9, 'bagging_freq': 0}}
except ImportError:
pass
def test_transform(X_y_binary, X_y_multi, X_y_regression):
datasets = locals()
for dataset in datasets.values():
X, y = dataset
X_pd = pd.DataFrame(X)
X_pd.columns = X_pd.columns.astype(str)
es = ft.EntitySet()
es = es.entity_from_dataframe(entity_id="X", dataframe=X_pd, index='index', make_index=True)
feature_matrix, features = ft.dfs(entityset=es, target_entity="X")
feature = DFSTransformer()
feature.fit(X)
X_t = feature.transform(X)
assert_frame_equal(feature_matrix, X_t.to_dataframe())
assert features == feature.features
feature.fit(X, y)
feature.transform(X)
X_ww = ww.DataTable(X_pd)
feature.fit(X_ww)
feature.transform(X_ww)
def test_index_errors(X_y_binary):
with pytest.raises(TypeError, match="Index provided must be string"):
DFSTransformer(index=0)
with pytest.raises(TypeError, match="Index provided must be string"):
DFSTransformer(index=None)
