Add default params_to_tune for TimeSeriesImputerTransform

CHANGELOG.md

@@ -53,6 +53,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Add default `params_to_tune` for `RNNModel` and `MLPModel` ([#1218](https://github.com/tinkoff-ai/etna/pull/1218))
 - Add default `params_to_tune` for `DateFlagsTransform`, `TimeFlagsTransform`, `SpecialDaysTransform` and `FourierTransform` ([#1228](https://github.com/tinkoff-ai/etna/pull/1228))
 - Add default `params_to_tune` for `MedianOutliersTransform`, `DensityOutliersTransform` and `PredictionIntervalOutliersTransform` ([#1231](https://github.com/tinkoff-ai/etna/pull/1231))
+- Add default `params_to_tune` for `TimeSeriesImputerTransform` ([#1232](https://github.com/tinkoff-ai/etna/pull/1232))
 ### Fixed
 - Fix bug in `GaleShapleyFeatureSelectionTransform` with wrong number of remaining features ([#1110](https://github.com/tinkoff-ai/etna/pull/1110))
 - `ProphetModel` fails with additional seasonality set ([#1157](https://github.com/tinkoff-ai/etna/pull/1157))

etna/transforms/missing_values/imputation.py

@@ -1,13 +1,20 @@
 from enum import Enum
+from typing import Dict
 from typing import List
 from typing import Optional
 
 import numpy as np
 import pandas as pd
 
+from etna import SETTINGS
 from etna.transforms.base import OneSegmentTransform
 from etna.transforms.base import ReversiblePerSegmentWrapper
 
+if SETTINGS.auto_required:
+    from optuna.distributions import BaseDistribution
+    from optuna.distributions import CategoricalDistribution
+    from optuna.distributions import IntUniformDistribution
+
 
 class ImputerMode(str, Enum):
     """Enum for different imputation strategy."""
@@ -277,5 +284,27 @@ def get_regressors_info(self) -> List[str]:
         """Return the list with regressors created by the transform."""
         return []
 
+    def params_to_tune(self) -> Dict[str, "BaseDistribution"]:
+        """Get default grid for tuning hyperparameters.
+
+        This grid doesn't tune ``seasonality`` parameter. It expected to be set by the user.
+        Strategy "seasonal" is suggested only if ``seasonality`` is set higher than 1.
+
+        Returns
+        -------
+        :
+            Grid to tune.
+        """
+        if self.seasonality > 1:
+            return {
+                "strategy": CategoricalDistribution(["constant", "mean", "running_mean", "forward_fill", "seasonal"]),
+                "window": IntUniformDistribution(low=1, high=20),
+            }
+        else:
+            return {
+                "strategy": CategoricalDistribution(["constant", "mean", "running_mean", "forward_fill"]),
+                "window": IntUniformDistribution(low=1, high=20),
+            }
+
 
 __all__ = ["TimeSeriesImputerTransform"]

tests/test_transforms/test_missing_values/test_impute_transform.py

@@ -8,6 +8,7 @@
 from etna.models import NaiveModel
 from etna.transforms.missing_values import TimeSeriesImputerTransform
 from etna.transforms.missing_values.imputation import _OneSegmentTimeSeriesImputerTransform
+from tests.test_transforms.utils import assert_sampling_is_valid
 from tests.test_transforms.utils import assert_transformation_equals_loaded_original
 
 
@@ -393,3 +394,15 @@ def test_constant_fill_strategy(ts_with_missing_range_x_index_two_segments: TSDa
 def test_save_load(ts_to_fill):
     transform = TimeSeriesImputerTransform()
     assert_transformation_equals_loaded_original(transform=transform, ts=ts_to_fill)
+
+
+@pytest.mark.parametrize(
+    "transform, expected_strategy_length",
+    [(TimeSeriesImputerTransform(), 4), (TimeSeriesImputerTransform(seasonality=7), 5)],
+)
+def test_params_to_tune(transform, expected_strategy_length, ts_to_fill):
+    ts = ts_to_fill
+    grid = transform.params_to_tune()
+    assert len(grid) > 0
+    assert len(grid["strategy"].choices) == expected_strategy_length
+    assert_sampling_is_valid(transform=transform, ts=ts)

tests/test_transforms/test_missing_values/test_resample_transform.py

@@ -127,3 +127,8 @@ def test_get_regressors_info_not_fitted():
     transform = ResampleWithDistributionTransform(in_column="regressor_exog", distribution_column="target")
     with pytest.raises(ValueError, match="Fit the transform to get the correct regressors info!"):
         _ = transform.get_regressors_info()
+
+
+def test_params_to_tune():
+    transform = ResampleWithDistributionTransform(in_column="regressor_exog", distribution_column="target")
+    assert len(transform.params_to_tune()) == 0