Fix target leakage in MeanSegmentEncoderTransform

CHANGELOG.md

@@ -42,7 +42,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Optimize memory usage in `TFTNativeModel` by eliminating copying during making samples ([#494](https://github.com/etna-team/etna/pull/494))
 - Optimize memory usage in `DeepStateModel` and `DeepARNativeModel` by eliminating copying during making samples ([#499](https://github.com/etna-team/etna/pull/499))
 - Fix working with NaN target in `MeanEncoderTransform` ([#492](https://github.com/etna-team/etna/pull/492))
-- 
+- Fix `target` leakage in `MeanSegmentEncoderTransform` ([#503](https://github.com/etna-team/etna/pull/503))
 - 
 - 
 - 

etna/transforms/encoders/mean_segment_encoder.py

@@ -1,24 +1,34 @@
-import reprlib
-from typing import Dict
 from typing import List
-from typing import Optional
 
 import numpy as np
 import pandas as pd
 
 from etna.transforms import IrreversibleTransform
-from etna.transforms.math.statistics import MeanTransform
+from etna.transforms.encoders.mean_encoder import MeanEncoderTransform
 
 
 class MeanSegmentEncoderTransform(IrreversibleTransform):
     """Makes expanding mean target encoding of the segment. Creates column 'segment_mean'."""
 
-    idx = pd.IndexSlice
+    _segment_column = "segment_column"
+    out_column = "segment_mean"
 
     def __init__(self):
         super().__init__(required_features=["target"])
-        self.mean_encoder = MeanTransform(in_column="target", window=-1, out_column="segment_mean")
-        self.global_means: Optional[Dict[str, float]] = None
+        self._mean_encoder = MeanEncoderTransform(
+            in_column=self._segment_column, mode="per-segment", out_column=self.out_column, smoothing=0
+        )
+
+    def _add_segment_column(self, df):
+        segments = df.columns.get_level_values("segment").unique()
+        flatten_segments = np.repeat(segments.values[np.newaxis, :], len(df), axis=0)
+        segment_values = pd.DataFrame(
+            data=flatten_segments,
+            columns=pd.MultiIndex.from_product([segments, [self._segment_column]]),
+            index=df.index,
+        )
+        df = pd.concat([df, segment_values], axis=1).sort_index(axis=1)
+        return df
 
     def _fit(self, df: pd.DataFrame) -> "MeanSegmentEncoderTransform":
         """
@@ -34,10 +44,8 @@ def _fit(self, df: pd.DataFrame) -> "MeanSegmentEncoderTransform":
         :
             Fitted transform
         """
-        self.mean_encoder._fit(df)
-        mean_values = df.loc[:, self.idx[:, "target"]].mean().to_dict()
-        mean_values = {key[0]: value for key, value in mean_values.items()}
-        self.global_means = mean_values
+        df = self._add_segment_column(df)
+        self._mean_encoder._fit(df)
         return self
 
     def _transform(self, df: pd.DataFrame) -> pd.DataFrame:
@@ -61,25 +69,11 @@ def _transform(self, df: pd.DataFrame) -> pd.DataFrame:
         NotImplementedError:
             If there are segments that weren't present during training.
         """
-        if self.global_means is None:
-            raise ValueError("The transform isn't fitted!")
-
-        segments = df.columns.get_level_values("segment").unique().tolist()
-        new_segments = set(segments) - self.global_means.keys()
-        if len(new_segments) > 0:
-            raise NotImplementedError(
-                f"This transform can't process segments that weren't present on train data: {reprlib.repr(new_segments)}"
-            )
-
-        df = self.mean_encoder._transform(df)
-        segment = segments[0]
-        nan_timestamps = df[df.loc[:, self.idx[segment, "target"]].isna()].index
-        values_to_set = np.array([self.global_means[x] for x in segments])
-        # repetition isn't necessary for pandas >= 1.2
-        values_to_set = np.repeat(values_to_set[np.newaxis, :], len(nan_timestamps), axis=0)
-        df.loc[nan_timestamps, self.idx[:, "segment_mean"]] = values_to_set
-        return df
+        df = self._add_segment_column(df)
+        df_transformed = self._mean_encoder._transform(df)
+        df_transformed = df_transformed.drop(columns=[self._segment_column], level="feature")
+        return df_transformed
 
     def get_regressors_info(self) -> List[str]:
         """Return the list with regressors created by the transform."""
-        return ["segment_mean"]
+        return [self.out_column]

tests/test_transforms/test_encoders/conftest.py

@@ -1,38 +1,34 @@
 import numpy as np
-import pandas as pd
 import pytest
 
 from etna.datasets import TSDataset
+from etna.datasets import generate_ar_df
 
 
 @pytest.fixture
-def simple_ts() -> TSDataset:
-    df_1 = pd.DataFrame.from_dict({"timestamp": pd.date_range("2021-06-01", "2021-06-07", freq="D")})
-    df_2 = pd.DataFrame.from_dict({"timestamp": pd.date_range("2021-06-01", "2021-06-07", freq="D")})
-    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_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]
-    classic_df = pd.concat([df_1, df_2], ignore_index=True)
-    df = TSDataset.to_dataset(classic_df)
-    ts = TSDataset(df, freq="D")
+def mean_segment_encoder_ts() -> TSDataset:
+    df = generate_ar_df(n_segments=2, start_time="2001-01-01", periods=5)
+    df["target"] = [0.0, 1.0, np.NaN, 3.0, 4.0] + [np.NaN, 1.0, 2.0, 3.0, 4.0]
+
+    ts = TSDataset(df=df, freq="D")
+    return ts
+
+
+@pytest.fixture
+def expected_mean_segment_encoder_ts() -> TSDataset:
+    df = generate_ar_df(n_segments=2, start_time="2001-01-01", periods=5)
+    df["target"] = [0.0, 1.0, np.NaN, 3.0, 4.0] + [np.NaN, 1.0, 2.0, 3.0, 4.0]
+    df["segment_mean"] = [np.NaN, 0, 0.5, 0.5, 1.33] + [np.NaN, np.NaN, 1, 1.5, 2.0]
+
+    ts = TSDataset(df=df, freq="D")
     return ts
 
 
 @pytest.fixture
-def transformed_simple_df() -> pd.DataFrame:
-    df_1 = pd.DataFrame.from_dict({"timestamp": pd.date_range("2021-06-01", "2021-06-07", freq="D")})
-    df_2 = pd.DataFrame.from_dict({"timestamp": pd.date_range("2021-06-01", "2021-06-07", freq="D")})
-    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["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["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
+def expected_make_future_mean_segment_encoder_ts() -> TSDataset:
+    df = generate_ar_df(start_time="2001-01-06", periods=2, n_segments=2)
+    df["target"] = [np.NaN, np.NaN] + [np.NaN, np.NaN]
+    df["segment_mean"] = [2.0, 2.0] + [2.5, 2.5]
+
+    ts = TSDataset(df=df, freq="D")
+    return ts

tests/test_transforms/test_encoders/test_mean_encoder_transform.py

@@ -32,7 +32,6 @@ def expected_micro_category_ts() -> TSDataset:
     df = generate_ar_df(start_time="2001-01-01", periods=6, n_segments=2)
     df.rename(columns={"target": "mean_encoded_regressor"}, inplace=True)
     df["mean_encoded_regressor"] = [np.NaN, np.NaN, np.NaN, 1.5, 2.75, 2.25] + [np.NaN, np.NaN, 6.25, 7, 7.625, np.NaN]
-
     ts = TSDataset(df, freq="D")
     return ts
 
@@ -151,28 +150,14 @@ def expected_multiple_nan_target_category_ts() -> TSDataset:
 
 
 @pytest.fixture
-def mean_segment_encoder_ts() -> TSDataset:
-    df = generate_ar_df(n_segments=1, start_time="2001-01-01", periods=5)
-    df["target"] = [0, 1, np.NaN, 3, 4]
-
-    df_exog = generate_ar_df(n_segments=1, start_time="2001-01-01", periods=10)
-    df_exog.rename(columns={"target": "segment_feature"}, inplace=True)
-    df_exog["segment_feature"] = "segment_0"
-
-    ts = TSDataset(df=df, df_exog=df_exog, freq="D", known_future="all")
-
-    return ts
-
-
-@pytest.fixture
-def expected_mean_segment_encoder_ts() -> TSDataset:
-    df = generate_ar_df(n_segments=1, start_time="2001-01-01", periods=5)
-    df.rename(columns={"target": "segment_mean"}, inplace=True)
-    df["segment_mean"] = [np.NaN, 0, 0.5, 0.5, 1.33]
+def mean_segment_encoder_ts(mean_segment_encoder_ts) -> TSDataset:
+    df = generate_ar_df(n_segments=2, start_time="2001-01-01", periods=7)
+    df = df.drop(columns=["target"])
+    df["segment_feature"] = ["segment_0"] * 7 + ["segment_1"] * 7
+    df_wide = TSDataset.to_dataset(df)
+    mean_segment_encoder_ts.add_columns_from_pandas(df_wide, update_exog=True, regressors=["segment_feature"])
 
-    ts = TSDataset(df=df, freq="D")
-
-    return ts
+    return mean_segment_encoder_ts
 
 
 @pytest.fixture
@@ -407,7 +392,7 @@ def test_mean_segment_encoder(mean_segment_encoder_ts, expected_mean_segment_enc
     mean_encoder.fit_transform(mean_segment_encoder_ts)
     assert_frame_equal(
         mean_segment_encoder_ts.df.loc[:, pd.IndexSlice[:, "segment_mean"]],
-        expected_mean_segment_encoder_ts.df,
+        expected_mean_segment_encoder_ts.df.loc[:, pd.IndexSlice[:, "segment_mean"]],
         atol=0.01,
     )
 

tests/test_transforms/test_encoders/test_mean_segment_encoder_transform.py

@@ -1,6 +1,7 @@
 import numpy as np
 import pandas as pd
 import pytest
+from pandas.testing import assert_frame_equal
 
 from etna.datasets import TSDataset
 from etna.metrics import R2
@@ -10,44 +11,31 @@
 from tests.utils import select_segments_subset
 
 
-@pytest.mark.parametrize("expected_global_means", [{"Moscow": 3, "Omsk": 30}])
-def test_mean_segment_encoder_fit(simple_ts, expected_global_means):
+def test_mean_segment_encoder_transform(mean_segment_encoder_ts, expected_mean_segment_encoder_ts):
     encoder = MeanSegmentEncoderTransform()
-    encoder.fit(simple_ts)
-    assert encoder.global_means == expected_global_means
+    transformed_df = encoder.fit_transform(mean_segment_encoder_ts).to_pandas()
+    assert_frame_equal(transformed_df, expected_mean_segment_encoder_ts.to_pandas(), atol=0.01)
 
 
-def test_mean_segment_encoder_transform(simple_ts, transformed_simple_df):
-    encoder = MeanSegmentEncoderTransform()
-    transformed_df = encoder.fit_transform(simple_ts).to_pandas()
-    transformed_simple_df.index.freq = "D"
-    pd.testing.assert_frame_equal(transformed_simple_df, transformed_df)
-
-
-def test_subset_segments(simple_ts):
-    train_ts = simple_ts
-    test_df = simple_ts.loc[:, pd.IndexSlice["Omsk", :]]
-    test_ts = TSDataset(df=test_df, freq=simple_ts.freq)
-    transform = MeanSegmentEncoderTransform()
-
-    transform.fit(train_ts)
-    transformed_test_df = transform.transform(test_ts).to_pandas()
+def test_make_future_mean_segment_encoder_transform(
+    mean_segment_encoder_ts, expected_make_future_mean_segment_encoder_ts
+):
+    mean_segment_encoder = MeanSegmentEncoderTransform()
+    mean_segment_encoder.fit_transform(mean_segment_encoder_ts)
+    future_ts = mean_segment_encoder_ts.make_future(future_steps=2, transforms=[mean_segment_encoder])
 
-    segments = sorted(transformed_test_df.columns.get_level_values("segment").unique())
-    features = sorted(transformed_test_df.columns.get_level_values("feature").unique())
-    assert segments == ["Omsk"]
-    assert features == ["exog", "segment_mean", "target"]
+    assert_frame_equal(future_ts.to_pandas(), expected_make_future_mean_segment_encoder_ts.to_pandas())
 
 
-def test_not_fitted_error(simple_ts):
+def test_not_fitted_error(mean_segment_encoder_ts):
     encoder = MeanSegmentEncoderTransform()
     with pytest.raises(ValueError, match="The transform isn't fitted"):
-        encoder.transform(simple_ts)
+        encoder.transform(mean_segment_encoder_ts)
 
 
-def test_new_segments_error(simple_ts):
-    train_ts = select_segments_subset(ts=simple_ts, segments=["Moscow"])
-    test_ts = select_segments_subset(ts=simple_ts, segments=["Omsk"])
+def test_new_segments_error(mean_segment_encoder_ts):
+    train_ts = select_segments_subset(ts=mean_segment_encoder_ts, segments=["segment_0"])
+    test_ts = select_segments_subset(ts=mean_segment_encoder_ts, segments=["segment_1"])
     transform = MeanSegmentEncoderTransform()
 
     transform.fit(train_ts)

tests/test_transforms/test_encoders/test_segment_encoder_transform.py

@@ -2,54 +2,36 @@
 import pandas as pd
 import pytest
 
-from etna.datasets import TSDataset
 from etna.transforms import SegmentEncoderTransform
 from tests.test_transforms.utils import assert_transformation_equals_loaded_original
 from tests.utils import select_segments_subset
 
 
-def test_segment_encoder_transform(simple_ts):
+def test_segment_encoder_transform(mean_segment_encoder_ts):
     transform = SegmentEncoderTransform()
-    transformed_df = transform.fit_transform(simple_ts).to_pandas()
+    transformed_df = transform.fit_transform(mean_segment_encoder_ts).to_pandas()
     assert (
         len(transformed_df.loc[:, pd.IndexSlice[:, "segment_code"]].columns) == 2
     ), "Number of columns not the same as segments"
-    assert len(simple_ts.to_pandas()) == len(transformed_df), "Row missing"
+    assert len(mean_segment_encoder_ts.to_pandas()) == len(transformed_df), "Row missing"
     codes = set()
-    for segment in simple_ts.segments:
+    for segment in mean_segment_encoder_ts.segments:
         column = transformed_df.loc[:, pd.IndexSlice[segment, "segment_code"]]
         assert column.dtype == "category", "Column type is not category"
         assert np.all(column == column.iloc[0]), "Values are not the same for the whole column"
         codes.add(column.iloc[0])
     assert codes == {0, 1}, "Codes are not 0 and 1"
 
 
-def test_subset_segments(simple_ts):
-    train_ts = simple_ts
-    test_df = simple_ts.loc[:, pd.IndexSlice["Omsk", :]]
-    test_ts = TSDataset(df=test_df, freq=simple_ts.freq)
-    transform = SegmentEncoderTransform()
-
-    transform.fit(train_ts)
-    transformed_test_df = transform.transform(test_ts).to_pandas()
-
-    segments = sorted(transformed_test_df.columns.get_level_values("segment").unique())
-    features = sorted(transformed_test_df.columns.get_level_values("feature").unique())
-    assert segments == ["Omsk"]
-    assert features == ["exog", "segment_code", "target"]
-    values = transformed_test_df.loc[:, pd.IndexSlice[:, "segment_code"]]
-    assert np.all(values == values.iloc[0])
-
-
-def test_not_fitted_error(simple_ts):
+def test_not_fitted_error(mean_segment_encoder_ts):
     encoder = SegmentEncoderTransform()
     with pytest.raises(ValueError, match="The transform isn't fitted"):
-        encoder.transform(simple_ts)
+        encoder.transform(mean_segment_encoder_ts)
 
 
-def test_new_segments_error(simple_ts):
-    train_ts = select_segments_subset(ts=simple_ts, segments=["Moscow"])
-    test_ts = select_segments_subset(ts=simple_ts, segments=["Omsk"])
+def test_new_segments_error(mean_segment_encoder_ts):
+    train_ts = select_segments_subset(ts=mean_segment_encoder_ts, segments=["segment_0"])
+    test_ts = select_segments_subset(ts=mean_segment_encoder_ts, segments=["segment_1"])
     transform = SegmentEncoderTransform()
 
     transform.fit(train_ts)