fix statistics

CHANGELOG.md

@@ -31,14 +31,14 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Fixed
 -
--
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+- Fixed adding unnecessary lag=1 in statistics ([#523](https://github.com/tinkoff-ai/etna/pull/523))
+- Fixed wrong MeanTransform behaviour when using alpha parameter ([#523](https://github.com/tinkoff-ai/etna/pull/523))
 - Fix processing add_noise=True parameter in datasets generation ([#520](https://github.com/tinkoff-ai/etna/pull/520))
--
--
--
--
--
+- 
+- 
+- 
+- 
+- 
 
 ## [1.6.2] - 2022-02-09
 ### Added

etna/transforms/math/statistics.py

@@ -77,9 +77,8 @@ def transform(self, df: pd.DataFrame) -> pd.DataFrame:
         """
         features = (
             df.xs(self.in_column, level=1, axis=1)
-            .shift(1)
             .rolling(
-                window=self.seasonality * self.window if self.window != -1 else len(df) - 1,
+                window=self.seasonality * self.window if self.window != -1 else len(df),
                 min_periods=self.min_required_len,
             )
             .aggregate(self._aggregate_window)
@@ -167,7 +166,7 @@ def transform(self, df: pd.DataFrame) -> pd.DataFrame:
         result: pd.DataFrame
             dataframe with results
         """
-        size = self.window if self.window != -1 else len(df) - 1
+        size = self.window if self.window != -1 else len(df)
         self._alpha_range = [self.alpha ** i for i in range(0, size)]
         return super().transform(df=df)
 
@@ -177,7 +176,7 @@ def _aggregate_window(self, series: pd.Series) -> float:
             raise ValueError("Something went wrong generating the alphas!")
         tmp_series = self._get_required_lags(series)
         size = len(tmp_series)
-        tmp = tmp_series * self._alpha_range[-size:]
+        tmp = tmp_series * self._alpha_range[:size]
         return tmp.mean(**self.kwargs)
 
 

tests/test_transforms/test_encoders/conftest.py

@@ -44,11 +44,11 @@ def transformed_simple_df() -> pd.DataFrame:
     df_1["segment"] = "Moscow"
     df_1["target"] = [1.0, 2.0, 3.0, 4.0, 5.0, np.NAN, np.NAN]
     df_1["exog"] = [6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0]
-    df_1["regressor_segment_mean"] = [0, 1, 1.5, 2, 2.5, 3, 3]
+    df_1["regressor_segment_mean"] = [1, 1.5, 2, 2.5, 3, 3, 3]
     df_2["segment"] = "Omsk"
     df_2["target"] = [10.0, 20.0, 30.0, 40.0, 50.0, np.NAN, np.NAN]
     df_2["exog"] = [60.0, 70.0, 80.0, 90.0, 100.0, 110.0, 120.0]
-    df_2["regressor_segment_mean"] = [0.0, 10.0, 15.0, 20.0, 25.0, 30, 30]
+    df_2["regressor_segment_mean"] = [10.0, 15.0, 20.0, 25.0, 30, 30, 30]
     classic_df = pd.concat([df_1, df_2], ignore_index=True)
     df = TSDataset.to_dataset(classic_df)
     return df

tests/test_transforms/test_math/test_statistics_transform.py

@@ -79,12 +79,19 @@ def test_interface_quantile(simple_df_for_agg: pd.DataFrame, out_column: str):
 @pytest.mark.parametrize(
     "window,seasonality,alpha,periods,fill_na,expected",
     (
-        (10, 1, 1, 1, 0, np.array([0, 0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4])),
-        (-1, 1, 1, 1, 0, np.array([0, 0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4])),
-        (3, 1, 1, 1, -17, np.array([-17, 0, 0.5, 1, 2, 3, 4, 5, 6, 7])),
-        (3, 1, 0.5, 1, -17, np.array([-17, 0, 0.5, 2.5 / 3, 4.25 / 3, 2, 7.75 / 3, 9.5 / 3, 11.25 / 3, 13 / 3])),
-        (3, 1, 0.5, 3, -12, np.array([-12, -12, -12, 2.5 / 3, 4.25 / 3, 2, 7.75 / 3, 9.5 / 3, 11.25 / 3, 13 / 3])),
-        (3, 2, 1, 1, -17, np.array([-17, 0, 1, 1, 2, 2, 3, 4, 5, 6])),
+        (10, 1, 1, 1, 0, np.array([0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5])),
+        (-1, 1, 1, 1, 0, np.array([0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5])),
+        (3, 1, 1, 1, -17, np.array([0, 0.5, 1, 2, 3, 4, 5, 6, 7, 8])),
+        (3, 1, 0.5, 1, -17, np.array([0, 0.5, 2.5 / 3, 4.25 / 3, 2, 7.75 / 3, 9.5 / 3, 11.25 / 3, 13 / 3, 14.75 / 3])),
+        (
+            3,
+            1,
+            0.5,
+            3,
+            -12,
+            np.array([-12, -12, 2.5 / 3, 4.25 / 3, 2, 7.75 / 3, 9.5 / 3, 11.25 / 3, 13 / 3, 14.75 / 3]),
+        ),
+        (3, 2, 1, 1, -17, np.array([0, 1, 1, 2, 2, 3, 4, 5, 6, 7])),
     ),
 )
 def test_mean_feature(
@@ -115,8 +122,8 @@ def test_mean_feature(
     (
         (10, 1, 1, 0, np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])),
         (-1, 1, 1, 0, np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])),
-        (3, 1, 1, -17, np.array([-17, 0, 0, 0, 1, 2, 3, 4, 5, 6])),
-        (3, 2, 1, -17, np.array([-17, 0, 1, 0, 1, 0, 1, 2, 3, 4])),
+        (3, 1, 1, -17, np.array([0, 0, 0, 1, 2, 3, 4, 5, 6, 7])),
+        (3, 2, 1, -17, np.array([0, 1, 0, 1, 0, 1, 2, 3, 4, 5])),
     ),
 )
 def test_min_feature(
@@ -138,9 +145,9 @@ def test_min_feature(
 @pytest.mark.parametrize(
     "window,periods,fill_na,expected",
     (
-        (10, 1, 0, np.array([0, 0, 1, 2, 3, 4, 5, 6, 7, 8])),
-        (-1, 1, 0, np.array([0, 0, 1, 2, 3, 4, 5, 6, 7, 8])),
-        (3, 2, -17, np.array([-17, -17, 1, 2, 3, 4, 5, 6, 7, 8])),
+        (10, 1, 0, np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])),
+        (-1, 1, 0, np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])),
+        (3, 2, -17, np.array([-17, 1, 2, 3, 4, 5, 6, 7, 8, 9])),
     ),
 )
 def test_max_feature(simple_df_for_agg: pd.DataFrame, window: int, periods: int, fill_na: float, expected: np.array):
@@ -155,8 +162,8 @@ def test_max_feature(simple_df_for_agg: pd.DataFrame, window: int, periods: int,
 @pytest.mark.parametrize(
     "window,periods,fill_na,expected",
     (
-        (3, 3, -17, np.array([-17, -17, -17, 1, 2, 3, 4, 5, 6, 7])),
-        (-1, 1, -17, np.array([-17, 0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4])),
+        (3, 3, -17, np.array([-17, -17, 1, 2, 3, 4, 5, 6, 7, 8])),
+        (-1, 1, -17, np.array([0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5])),
     ),
 )
 def test_median_feature(simple_df_for_agg: pd.DataFrame, window: int, periods: int, fill_na: float, expected: np.array):
@@ -171,8 +178,8 @@ def test_median_feature(simple_df_for_agg: pd.DataFrame, window: int, periods: i
 @pytest.mark.parametrize(
     "window,periods,fill_na,expected",
     (
-        (3, 3, -17, np.array([-17, -17, -17, 1, 1, 1, 1, 1, 1, 1])),
-        (3, 1, -17, np.array([-17, -17, np.sqrt(0.5 ** 2 * 2), 1, 1, 1, 1, 1, 1, 1])),
+        (3, 3, -17, np.array([-17, -17, 1, 1, 1, 1, 1, 1, 1, 1])),
+        (3, 1, -17, np.array([-17, np.sqrt(0.5 ** 2 * 2), 1, 1, 1, 1, 1, 1, 1, 1])),
     ),
 )
 def test_std_feature(simple_df_for_agg: pd.DataFrame, window: int, periods: int, fill_na: float, expected: np.array):
@@ -187,9 +194,9 @@ def test_std_feature(simple_df_for_agg: pd.DataFrame, window: int, periods: int,
 @pytest.mark.parametrize(
     "window,periods,fill_na,expected",
     (
-        (3, 3, -17, [-17, -17, -17, 4 / 3, 2 / 3, 2 / 3, 8 / 3, 2, 14 / 9, 10 / 9]),
-        (4, 1, -17, [-17, 0, 1, 4 / 3, 1.25, 1, 2.25, 2.75, 2, 1.5]),
-        (-1, 1, 0, [0, 0, 1, 4 / 3, 1.25, 1.44, 7 / 3, 138 / 49, 2.625, 208 / 81]),
+        (3, 3, -17, [-17, -17, 4 / 3, 2 / 3, 2 / 3, 8 / 3, 2, 14 / 9, 10 / 9, 22 / 9]),
+        (4, 1, -17, [0, 1, 4 / 3, 1.25, 1, 2.25, 2.75, 2, 1.5, 9.5 / 4]),
+        (-1, 1, 0, [0, 1, 4 / 3, 1.25, 1.44, 7 / 3, 138 / 49, 2.625, 208 / 81, 27 / 10]),
     ),
 )
 def test_mad_transform(df_for_agg: pd.DataFrame, window: int, periods: int, fill_na: float, expected: np.ndarray):
@@ -202,7 +209,7 @@ def test_mad_transform(df_for_agg: pd.DataFrame, window: int, periods: int, fill
 
 @pytest.mark.parametrize(
     "window,periods,fill_na,expected",
-    ((3, 3, -17, [-17, -17, -17, 4 / 3, -17, -17, -17, 2, 14 / 9, 10 / 9]),),
+    ((3, 3, -17, [-17, -17, 4 / 3, -17, -17, -17, 2, 14 / 9, 10 / 9, 22 / 9]),),
 )
 def test_mad_transform_with_nans(
     df_for_agg_with_nan: pd.DataFrame, window: int, periods: int, fill_na: float, expected: np.ndarray