def test_invalid_window_size(self):
window_size = -1
df = pd.DataFrame.from_dict({"x0": [0, 1, 2, 3, 4, 5]})
ma_feature = MovingAverage(window_size=window_size)
with pytest.raises(ValueError):
ma_feature.fit_transform(df)
def test_random_ts_and_window_size(self, df: pd.DataFrame,
window_size: int):
ma_feature = MovingAverage(window_size=window_size)
df_ma = ma_feature.fit_transform(df)
expected_df_ma = self._correct_ma(df, window_size)
testing.assert_frame_equal(expected_df_ma, df_ma)
def test_positive_window_size(self):
window_size = 2
df = pd.DataFrame.from_dict({"x": [0, 1, 2, 3, 4, 5]})
ma_feature = MovingAverage(window_size=window_size)
df_ma = ma_feature.fit_transform(df)
output_name = "x__" + ma_feature.__class__.__name__
expected_df_ma = pd.DataFrame.from_dict(
{output_name: [np.nan, 0.5, 1.5, 2.5, 3.5, 4.5]})
testing.assert_frame_equal(expected_df_ma, df_ma, check_names=False)
def test_multi_columns_window_size(self):
window_size = 2
df = pd.DataFrame.from_dict({
"x0": [0, 1, 2, 3, 4, 5],
"x1": [7, 8, 9, 10, 11, 12]
})
ma_feature = MovingAverage(window_size=window_size)
feature_name = ma_feature.__class__.__name__
df_ma = ma_feature.fit_transform(df)
expected_df_ma = pd.DataFrame({
f"x0__{feature_name}": [np.nan, 0.5, 1.5, 2.5, 3.5, 4.5],
f"x1__{feature_name}": [np.nan, 7.5, 8.5, 9.5, 10.5, 11.5],
})
testing.assert_frame_equal(expected_df_ma, df_ma, check_names=False)