Fix/code maintenance after dependencies update (#2722)

* fix: simplified logic after increase in minimym version for statsmodels

* fix: simplified logic after increase of minimum version for sklearn

* update changelog

* Update CHANGELOG.md

---------

Co-authored-by: Dennis Bader <[email protected]>

Author: madtoinou

CHANGELOG.md

@@ -34,6 +34,7 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
 
 - Bumped minimum scikit-learn version from `1.0.1` to `1.6.0`. This was required due to sklearn deprecating `_get_tags` in favor of `BaseEstimator.__sklearn_tags__` in version 1.7. This leads to increasing both sklearn and XGBoost minimum supported version to 1.6 and 2.1.4 respectively. [#2659](https://github.com/unit8co/darts/pull/2659) by [Jonas Blanc](https://github.com/jonasblanc)
 - Bumped minimum xgboost version from `1.6.0` to `2.1.4` for the same reason as bumping the minimum sklearn version. [#2659](https://github.com/unit8co/darts/pull/2659) by [Jonas Blanc](https://github.com/jonasblanc)
+- Various code changes to reflect the updated dependencies versions (sklearn, statsmodel) and eliminate various warnings. [#2722](https://github.com/unit8co/darts/pull/2722) by [Antoine Madrona](https://github.com/madtoinou)
 
 ### For developers of the library:
 

darts/models/forecasting/arima.py

@@ -20,7 +20,6 @@
     from typing_extensions import TypeAlias
 
 import numpy as np
-from statsmodels import __version_tuple__ as statsmodels_version
 from statsmodels.tsa.arima.model import ARIMA as staARIMA
 
 from darts.logging import get_logger
@@ -31,9 +30,6 @@
 
 logger = get_logger(__name__)
 
-# Check whether we are running statsmodels >= 0.13.5 or not:
-statsmodels_above_0135 = statsmodels_version > (0, 13, 5)
-
 
 IntOrIntSequence: TypeAlias = Union[int, Sequence[int]]
 
@@ -137,15 +133,11 @@ def encode_year(idx):
         self.seasonal_order = seasonal_order
         self.trend = trend
         self.model = None
-        if statsmodels_above_0135:
-            self._random_state = (
-                random_state
-                if random_state is None
-                else np.random.RandomState(random_state)
-            )
-        else:
-            self._random_state = None
-            np.random.seed(random_state if random_state is not None else 0)
+        self._random_state = (
+            random_state
+            if random_state is None
+            else np.random.RandomState(random_state)
+        )
 
     @property
     def supports_multivariate(self) -> bool:

darts/utils/multioutput.py

@@ -1,27 +1,18 @@
 from typing import Optional
 
-from sklearn import __version__ as sklearn_version
 from sklearn.base import is_classifier
 from sklearn.multioutput import MultiOutputRegressor as sk_MultiOutputRegressor
 from sklearn.multioutput import _fit_estimator
 from sklearn.utils.multiclass import check_classification_targets
-from sklearn.utils.validation import has_fit_parameter
+from sklearn.utils.parallel import Parallel, delayed
+from sklearn.utils.validation import (
+    _check_method_params,
+    has_fit_parameter,
+    validate_data,
+)
 
 from darts.logging import get_logger, raise_log
 
-if sklearn_version >= "1.4":
-    # sklearn renamed `_check_fit_params` to `_check_method_params` in v1.4
-    from sklearn.utils.validation import _check_method_params
-else:
-    from sklearn.utils.validation import _check_fit_params as _check_method_params
-
-if sklearn_version >= "1.3":
-    # delayed was moved from sklearn.utils.fixes to sklearn.utils.parallel in v1.3
-    from sklearn.utils.parallel import Parallel, delayed
-else:
-    from joblib import Parallel
-    from sklearn.utils.fixes import delayed
-
 logger = get_logger(__name__)
 
 
@@ -78,8 +69,7 @@ def fit(self, X, y, sample_weight=None, **fit_params):
                 ValueError("The base estimator should implement a fit method"),
                 logger=logger,
             )
-
-        y = self._validate_data(X="no_validation", y=y, multi_output=True)
+        y = validate_data(self.estimator, X="no_validation", y=y, multi_output=True)
 
         if is_classifier(self):
             check_classification_targets(y)