Merge branch 'master' into trainer_kwargs

CHANGELOG.md

@@ -41,7 +41,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - 
 - 
 - 
-- 
+- [BUG] nn models make forecast without inverse_transform ([#541](https://github.com/tinkoff-ai/etna/pull/541))
 
 ## [1.6.3] - 2022-02-14
 

etna/models/nn/deepar.py

@@ -175,4 +175,5 @@ def forecast(self, ts: TSDataset) -> TSDataset:
         # shape (segments, encoder_length)
 
         ts.loc[:, pd.IndexSlice[:, "target"]] = predicts.T[-len(ts.df) :]
+        ts.inverse_transform()
         return ts

etna/models/nn/tft.py

@@ -182,4 +182,5 @@ def forecast(self, ts: TSDataset) -> TSDataset:
         # shape (segments, encoder_length)
 
         ts.loc[:, pd.IndexSlice[:, "target"]] = predicts.T[-len(ts.df) :]
+        ts.inverse_transform()
         return ts

tests/test_models/nn/test_deepar.py

@@ -9,6 +9,7 @@
 from etna.transforms import AddConstTransform
 from etna.transforms import DateFlagsTransform
 from etna.transforms import PytorchForecastingTransform
+from etna.transforms import StandardScalerTransform
 
 
 def test_fit_wrong_order_transform(weekly_period_df):
@@ -68,6 +69,46 @@ def test_deepar_model_run_weekly_overfit(weekly_period_df, horizon):
     assert mae(ts_test, ts_pred) < 0.2207
 
 
+@pytest.mark.long
+@pytest.mark.parametrize("horizon", [8])
+def test_deepar_model_run_weekly_overfit_with_scaler(weekly_period_df, horizon):
+    """
+    Given: I have dataframe with 2 segments with weekly seasonality with known future
+    When: I use scale transformations
+    Then: I get {horizon} periods per dataset as a forecast and they "the same" as past
+    """
+
+    ts_start = sorted(set(weekly_period_df.timestamp))[-horizon]
+    train, test = (
+        weekly_period_df[lambda x: x.timestamp < ts_start],
+        weekly_period_df[lambda x: x.timestamp >= ts_start],
+    )
+
+    ts_train = TSDataset(TSDataset.to_dataset(train), "D")
+    ts_test = TSDataset(TSDataset.to_dataset(test), "D")
+    std = StandardScalerTransform(in_column="target")
+    dft = DateFlagsTransform(day_number_in_week=True, day_number_in_month=False, out_column="regressor_dateflags")
+    pft = PytorchForecastingTransform(
+        max_encoder_length=21,
+        max_prediction_length=horizon,
+        time_varying_known_reals=["time_idx"],
+        time_varying_known_categoricals=["regressor_dateflags_day_number_in_week"],
+        time_varying_unknown_reals=["target"],
+        target_normalizer=GroupNormalizer(groups=["segment"]),
+    )
+
+    ts_train.fit_transform([std, dft, pft])
+
+    model = DeepARModel(max_epochs=300, learning_rate=[0.1])
+    ts_pred = ts_train.make_future(horizon)
+    model.fit(ts_train)
+    ts_pred = model.forecast(ts_pred)
+
+    mae = MAE("macro")
+
+    assert mae(ts_test, ts_pred) < 0.2207
+
+
 def test_forecast_without_make_future(weekly_period_df):
     ts = TSDataset(TSDataset.to_dataset(weekly_period_df), "D")
     pft = PytorchForecastingTransform(

tests/test_models/nn/test_tft.py

@@ -6,6 +6,7 @@
 from etna.transforms import AddConstTransform
 from etna.transforms import DateFlagsTransform
 from etna.transforms import PytorchForecastingTransform
+from etna.transforms import StandardScalerTransform
 
 
 def test_fit_wrong_order_transform(weekly_period_df):
@@ -68,6 +69,47 @@ def test_tft_model_run_weekly_overfit(weekly_period_df, horizon):
     assert mae(ts_test, ts_pred) < 0.24
 
 
+@pytest.mark.long
+@pytest.mark.parametrize("horizon", [8])
+def test_tft_model_run_weekly_overfit_with_scaler(weekly_period_df, horizon):
+    """
+    Given: I have dataframe with 2 segments with weekly seasonality with known future
+    When: I use scale transformations
+    Then: I get {horizon} periods per dataset as a forecast and they "the same" as past
+    """
+
+    ts_start = sorted(set(weekly_period_df.timestamp))[-horizon]
+    train, test = (
+        weekly_period_df[lambda x: x.timestamp < ts_start],
+        weekly_period_df[lambda x: x.timestamp >= ts_start],
+    )
+
+    ts_train = TSDataset(TSDataset.to_dataset(train), "D")
+    ts_test = TSDataset(TSDataset.to_dataset(test), "D")
+    std = StandardScalerTransform(in_column="target")
+    dft = DateFlagsTransform(day_number_in_week=True, day_number_in_month=False, out_column="regressor_dateflag")
+    pft = PytorchForecastingTransform(
+        max_encoder_length=21,
+        min_encoder_length=21,
+        max_prediction_length=horizon,
+        time_varying_known_reals=["time_idx"],
+        time_varying_known_categoricals=["regressor_dateflag_day_number_in_week"],
+        time_varying_unknown_reals=["target"],
+        static_categoricals=["segment"],
+        target_normalizer=None,
+    )
+
+    ts_train.fit_transform([std, dft, pft])
+
+    model = TFTModel(max_epochs=300, learning_rate=[0.1])
+    ts_pred = ts_train.make_future(horizon)
+    model.fit(ts_train)
+    ts_pred = model.forecast(ts_pred)
+
+    mae = MAE("macro")
+    assert mae(ts_test, ts_pred) < 0.24
+
+
 def test_forecast_without_make_future(weekly_period_df):
     ts = TSDataset(TSDataset.to_dataset(weekly_period_df), "D")
     pft = PytorchForecastingTransform(