def test_features_create_raw_dataframe(data_classification_balanced, feature_descriptor):
"""Testing if .create_raw_dataframe returns correct dataframe (the same that was provided as input to the
object). """
X, y = data_classification_balanced
f = Features(X, y, feature_descriptor)
expected_df = pd.concat([X, y], axis=1).drop(["Date"], axis=1)
cols = expected_df.columns
actual_df = f._create_raw_dataframe()[cols]
assert actual_df.equals(expected_df)
def test_features_create_raw_dataframe_preserving_index(data_classification_balanced, feature_descriptor):
"""Testing if create_raw_dataframe preserves the index of the DataFrame."""
X, y = data_classification_balanced
not_expected_df = pd.concat([X, y], axis=1).drop(["Date"], axis=1)
length = X.shape[0]
new_ind = list(range(100, length + 100))
X.index = new_ind
y.index = new_ind
f = Features(X, y, feature_descriptor)
expected_df = pd.concat([X, y], axis=1).drop(["Date"], axis=1)
expected_df.index = new_ind
cols = expected_df.columns
actual_df = f._create_raw_dataframe()[cols]
assert not actual_df.equals(not_expected_df)
assert actual_df.equals(expected_df)