fix: fix using numpy.warnings in sklearn.preprocessing.PowerTransform…

…._check_input

etna/transforms/math/power.py

@@ -1,8 +1,11 @@
+import functools
 from typing import Dict
 from typing import List
 from typing import Optional
 from typing import Union
+from unittest.mock import patch
 
+import pandas as pd
 from sklearn.preprocessing import PowerTransformer
 
 from etna.distributions import BaseDistribution
@@ -11,6 +14,20 @@
 from etna.transforms.math.sklearn import TransformMode
 
 
+def _replace_warnings_decorator(func):
+    @functools.wraps(func)
+    def inner(*args, **kwargs):
+        import warnings
+
+        import numpy as np
+
+        np.warnings = warnings
+
+        return func(*args, **kwargs)
+
+    return inner
+
+
 class YeoJohnsonTransform(SklearnTransform):
     """YeoJohnsonTransform applies Yeo-Johns transformation to a DataFrame.
 
@@ -61,6 +78,36 @@ def __init__(
             mode=mode,
         )
 
+    def _fit(self, df: pd.DataFrame) -> "SklearnTransform":
+        """
+        Fit transformer with data from df.
+
+        Here we are going to make a `patch <https://github.com/scikit-learn/scikit-learn/pull/23654>`_.
+        """
+        new_check_input = _replace_warnings_decorator(PowerTransformer._check_input)
+        with patch("sklearn.preprocessing.PowerTransformer._check_input", new=new_check_input):
+            return super()._fit(df=df)
+
+    def _transform(self, df: pd.DataFrame) -> pd.DataFrame:
+        """
+        Fit transformer with data from df.
+
+        Here we are going to make a `patch <https://github.com/scikit-learn/scikit-learn/pull/23654>`_.
+        """
+        new_check_input = _replace_warnings_decorator(PowerTransformer._check_input)
+        with patch("sklearn.preprocessing.PowerTransformer._check_input", new=new_check_input):
+            return super()._transform(df=df)
+
+    def _inverse_transform(self, df: pd.DataFrame) -> pd.DataFrame:
+        """
+        Fit transformer with data from df.
+
+        Here we are going to make a `patch <https://github.com/scikit-learn/scikit-learn/pull/23654>`_.
+        """
+        new_check_input = _replace_warnings_decorator(PowerTransformer._check_input)
+        with patch("sklearn.preprocessing.PowerTransformer._check_input", new=new_check_input):
+            return super()._inverse_transform(df=df)
+
     def params_to_tune(self) -> Dict[str, BaseDistribution]:
         """Get default grid for tuning hyperparameters.
 
@@ -130,6 +177,36 @@ def __init__(
             mode=mode,
         )
 
+    def _fit(self, df: pd.DataFrame) -> "SklearnTransform":
+        """
+        Fit transformer with data from df.
+
+        Here we are going to make a `patch <https://github.com/scikit-learn/scikit-learn/pull/23654>`_.
+        """
+        new_check_input = _replace_warnings_decorator(PowerTransformer._check_input)
+        with patch("sklearn.preprocessing.PowerTransformer._check_input", new=new_check_input):
+            return super()._fit(df=df)
+
+    def _transform(self, df: pd.DataFrame) -> pd.DataFrame:
+        """
+        Fit transformer with data from df.
+
+        Here we are going to make a `patch <https://github.com/scikit-learn/scikit-learn/pull/23654>`_.
+        """
+        new_check_input = _replace_warnings_decorator(PowerTransformer._check_input)
+        with patch("sklearn.preprocessing.PowerTransformer._check_input", new=new_check_input):
+            return super()._transform(df=df)
+
+    def _inverse_transform(self, df: pd.DataFrame) -> pd.DataFrame:
+        """
+        Fit transformer with data from df.
+
+        Here we are going to make a `patch <https://github.com/scikit-learn/scikit-learn/pull/23654>`_.
+        """
+        new_check_input = _replace_warnings_decorator(PowerTransformer._check_input)
+        with patch("sklearn.preprocessing.PowerTransformer._check_input", new=new_check_input):
+            return super()._inverse_transform(df=df)
+
     def params_to_tune(self) -> Dict[str, BaseDistribution]:
         """Get default grid for tuning hyperparameters.