Add handling sklearn version in OneHotEncoderTransform and `Hierarc…

…hicalClustering` (#529)

CHANGELOG.md

@@ -49,7 +49,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - 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))
 - 
-- 
+- Add handling scikit-learn version >= 1.4 in `OneHotEncoderTransform` and `HierarchicalClustering` ([#529](https://github.com/etna-team/etna/pull/529))
 - 
 - 
 - 

etna/clustering/hierarchical/base.py

@@ -5,6 +5,7 @@
 from typing import Union
 
 import pandas as pd
+from sklearn import __version__ as sklearn_version
 from sklearn.cluster import AgglomerativeClustering
 
 from etna.clustering.base import Clustering
@@ -81,9 +82,16 @@ def build_clustering_algo(
         """
         self.n_clusters = n_clusters
         self.linkage = ClusteringLinkageMode(linkage).name
+
+        sklearn_version_tuple = tuple(map(int, sklearn_version.split(".")))
+        if sklearn_version_tuple < (1, 2):
+            clustering_algo_params["affinity"] = "precomputed"
+        else:
+            clustering_algo_params["metric"] = "precomputed"
         self.clustering_algo = AgglomerativeClustering(
-            n_clusters=self.n_clusters, affinity="precomputed", linkage=self.linkage, **clustering_algo_params
+            n_clusters=self.n_clusters, linkage=self.linkage, **clustering_algo_params
         )
+
         self.clusters = None
         self.segment2cluster = None
         self.centroids_df = None

etna/transforms/encoders/categorical.py

@@ -5,6 +5,7 @@
 
 import numpy as np
 import pandas as pd
+from sklearn import __version__ as sklearn_version
 from sklearn import preprocessing
 from sklearn.utils._encode import _check_unknown
 from sklearn.utils._encode import _encode
@@ -215,7 +216,15 @@ def __init__(self, in_column: str, out_column: Optional[str] = None, return_type
         self.in_column = in_column
         self.out_column = out_column
         self.return_type = ReturnType(return_type)
-        self.ohe = preprocessing.OneHotEncoder(handle_unknown="ignore", sparse=False, dtype=int)
+
+        sklearn_version_tuple = tuple(map(int, sklearn_version.split(".")))
+        encoder_params = {}
+        if sklearn_version_tuple < (1, 2):
+            encoder_params["sparse"] = False
+        else:
+            encoder_params["sparse_output"] = False
+        self.ohe = preprocessing.OneHotEncoder(handle_unknown="ignore", dtype=int, **encoder_params)
+
         self.in_column_regressor: Optional[bool] = None
 
     def get_regressors_info(self) -> List[str]: