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_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_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)
matrix, features = ft.dfs(entityset=es, target_entity="X")
feature = DFSTransformer()
feature.fit(X)
X_feature_matrix = feature.transform(X)
pd.testing.assert_frame_equal(matrix, X_feature_matrix)
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_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())