Add default params_to_tune for MedianOutliersTransform, DensityOutliersTransform and PredictionIntervalOutliersTransform

CHANGELOG.md

@@ -52,6 +52,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Add default `params_to_tune` for `HoltWintersModel`, `HoltModel` and `SimpleExpSmoothingModel` ([#1209](https://github.com/tinkoff-ai/etna/pull/1209))
 - 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))
 ### 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/outliers/point_outliers.py

@@ -5,6 +5,7 @@
 from typing import Union
 
 import pandas as pd
+from typing_extensions import Literal
 
 from etna import SETTINGS
 from etna.analysis import absolute_difference_distance
@@ -18,6 +19,12 @@
 if SETTINGS.prophet_required:
     from etna.models import ProphetModel
 
+if SETTINGS.auto_required:
+    from optuna.distributions import BaseDistribution
+    from optuna.distributions import CategoricalDistribution
+    from optuna.distributions import IntUniformDistribution
+    from optuna.distributions import UniformDistribution
+
 
 class MedianOutliersTransform(OutliersTransform):
     """Transform that uses :py:func:`~etna.analysis.outliers.median_outliers.get_anomalies_median` to find anomalies in data.
@@ -59,6 +66,19 @@ def detect_outliers(self, ts: TSDataset) -> Dict[str, List[pd.Timestamp]]:
         """
         return get_anomalies_median(ts=ts, in_column=self.in_column, window_size=self.window_size, alpha=self.alpha)
 
+    def params_to_tune(self) -> Dict[str, "BaseDistribution"]:
+        """Get default grid for tuning hyperparameters.
+
+        Returns
+        -------
+        :
+            Grid to tune.
+        """
+        return {
+            "window_size": IntUniformDistribution(low=3, high=30),
+            "alpha": UniformDistribution(low=0.5, high=5),
+        }
+
 
 class DensityOutliersTransform(OutliersTransform):
     """Transform that uses :py:func:`~etna.analysis.outliers.density_outliers.get_anomalies_density` to find anomalies in data.
@@ -120,14 +140,28 @@ def detect_outliers(self, ts: TSDataset) -> Dict[str, List[pd.Timestamp]]:
             distance_func=self.distance_func,
         )
 
+    def params_to_tune(self) -> Dict[str, "BaseDistribution"]:
+        """Get default grid for tuning hyperparameters.
+
+        Returns
+        -------
+        :
+            Grid to tune.
+        """
+        return {
+            "window_size": IntUniformDistribution(low=3, high=30),
+            "distance_coef": UniformDistribution(low=0.5, high=5),
+            "n_neighbors": IntUniformDistribution(low=1, high=10),
+        }
+
 
 class PredictionIntervalOutliersTransform(OutliersTransform):
     """Transform that uses :py:func:`~etna.analysis.outliers.prediction_interval_outliers.get_anomalies_prediction_interval` to find anomalies in data."""
 
     def __init__(
         self,
         in_column: str,
-        model: Union[Type["ProphetModel"], Type["SARIMAXModel"]],
+        model: Union[Literal["prophet"], Literal["sarimax"], Type["ProphetModel"], Type["SARIMAXModel"]],
         interval_width: float = 0.95,
         **model_kwargs,
     ):
@@ -149,8 +183,20 @@ def __init__(
         self.model = model
         self.interval_width = interval_width
         self.model_kwargs = model_kwargs
+        self._model_type = self._get_model_type(model)
         super().__init__(in_column=in_column)
 
+    @staticmethod
+    def _get_model_type(
+        model: Union[Literal["prophet"], Literal["sarimax"], Type["ProphetModel"], Type["SARIMAXModel"]]
+    ) -> Union[Type["ProphetModel"], Type["SARIMAXModel"]]:
+        if isinstance(model, str):
+            if model == "prophet":
+                return ProphetModel
+            elif model == "sarimax":
+                return SARIMAXModel
+        return model
+
     def detect_outliers(self, ts: TSDataset) -> Dict[str, List[pd.Timestamp]]:
         """Call :py:func:`~etna.analysis.outliers.prediction_interval_outliers.get_anomalies_prediction_interval` function with self parameters.
 
@@ -166,12 +212,25 @@ def detect_outliers(self, ts: TSDataset) -> Dict[str, List[pd.Timestamp]]:
         """
         return get_anomalies_prediction_interval(
             ts=ts,
-            model=self.model,
+            model=self._model_type,
             interval_width=self.interval_width,
             in_column=self.in_column,
             **self.model_kwargs,
         )
 
+    def params_to_tune(self) -> Dict[str, "BaseDistribution"]:
+        """Get default grid for tuning hyperparameters.
+
+        Returns
+        -------
+        :
+            Grid to tune.
+        """
+        return {
+            "interval_width": UniformDistribution(low=0.8, high=1.0),
+            "model": CategoricalDistribution(["prophet", "sarimax"]),
+        }
+
 
 __all__ = [
     "MedianOutliersTransform",

tests/test_transforms/test_outliers/test_outliers_transform.py

@@ -11,6 +11,7 @@
 from etna.transforms import DensityOutliersTransform
 from etna.transforms import MedianOutliersTransform
 from etna.transforms import PredictionIntervalOutliersTransform
+from tests.test_transforms.utils import assert_sampling_is_valid
 from tests.test_transforms.utils import assert_transformation_equals_loaded_original
 from tests.utils import select_segments_subset
 
@@ -226,3 +227,17 @@ def test_save_load(transform, outliers_solid_tsds):
 )
 def test_save_load_prediction_interval(transform, outliers_solid_tsds):
     assert_transformation_equals_loaded_original(transform=transform, ts=outliers_solid_tsds)
+
+
+@pytest.mark.parametrize(
+    "transform",
+    (
+        MedianOutliersTransform(in_column="target"),
+        DensityOutliersTransform(in_column="target"),
+        PredictionIntervalOutliersTransform(in_column="target", model="sarimax"),
+    ),
+)
+def test_params_to_tune(transform, outliers_solid_tsds):
+    ts = outliers_solid_tsds
+    assert len(transform.params_to_tune()) > 0
+    assert_sampling_is_valid(transform=transform, ts=ts)