add internal script for validate_data and sklearn version constant, u…

…pdate gnb and gwnb scripts accordingly

wnb/_utils.py

@@ -0,0 +1,19 @@
+from typing import Any
+
+import sklearn
+from packaging import version
+from sklearn.utils import check_array
+
+__all__ = ["SKLEARN_V1_6_OR_LATER", "validate_data"]
+
+
+SKLEARN_V1_6_OR_LATER = version.parse(sklearn.__version__) >= version.parse("1.6")
+
+
+if SKLEARN_V1_6_OR_LATER:
+    from sklearn.utils.validation import validate_data
+else:
+
+    def validate_data(estimator, X, **kwargs: Any):
+        kwargs.pop("reset", None)
+        return check_array(X, estimator=estimator, **kwargs)

wnb/gnb.py

@@ -7,23 +7,13 @@
 
 import numpy as np
 import pandas as pd
-import sklearn
-from packaging import version
 from scipy.special import logsumexp
 from sklearn.base import BaseEstimator, ClassifierMixin
 from sklearn.exceptions import DataConversionWarning
-from sklearn.utils import as_float_array, check_array
+from sklearn.utils import as_float_array
 from sklearn.utils.multiclass import check_classification_targets
 from sklearn.utils.validation import check_is_fitted
 
-if version.parse(sklearn.__version__) >= version.parse("1.6"):
-    from sklearn.utils.validation import validate_data
-else:
-
-    def validate_data(estimator, X, **kwargs):
-        return check_array(X, estimator=estimator, **kwargs)
-
-
 if sys.version_info >= (3, 11):
     from typing import Self
 else:
@@ -34,6 +24,7 @@ def validate_data(estimator, X, **kwargs):
 from wnb.stats.base import DistMixin
 from wnb.stats.typing import DistributionLike
 
+from ._utils import SKLEARN_V1_6_OR_LATER, validate_data
 from .typing import ArrayLike, Float, MatrixLike
 
 __all__ = ["GeneralNB"]
@@ -93,7 +84,7 @@ def __init__(
         self.distributions = distributions
         self.alpha = alpha
 
-    if version.parse(sklearn.__version__) >= version.parse("1.6"):
+    if SKLEARN_V1_6_OR_LATER:
 
         def __sklearn_tags__(self):
             tags = super().__sklearn_tags__()

wnb/gwnb.py

@@ -8,29 +8,20 @@
 
 import numpy as np
 import pandas as pd
-import sklearn
-from packaging import version
 from scipy.special import logsumexp
 from scipy.stats import norm
 from sklearn.base import BaseEstimator, ClassifierMixin
 from sklearn.exceptions import DataConversionWarning
-from sklearn.utils import as_float_array, check_array, deprecated
+from sklearn.utils import as_float_array, deprecated
 from sklearn.utils.multiclass import check_classification_targets, type_of_target
 from sklearn.utils.validation import check_is_fitted
 
-if version.parse(sklearn.__version__) >= version.parse("1.6"):
-    from sklearn.utils.validation import validate_data
-else:
-
-    def validate_data(estimator, X, **kwargs):
-        return check_array(X, estimator=estimator, **kwargs)
-
-
 if sys.version_info >= (3, 11):
     from typing import Self
 else:
     from typing_extensions import Self
 
+from ._utils import SKLEARN_V1_6_OR_LATER, validate_data
 from .typing import ArrayLike, Float, Int, MatrixLike
 
 __all__ = ["GaussianWNB"]
@@ -121,7 +112,7 @@ def __init__(
         self.C = C
         self.learning_hist = learning_hist
 
-    if version.parse(sklearn.__version__) >= version.parse("1.6"):
+    if SKLEARN_V1_6_OR_LATER:
 
         def __sklearn_tags__(self):
             tags = super().__sklearn_tags__()
@@ -138,7 +129,7 @@ def _check_inputs(self, X, y) -> None:
 
         # Check that the dataset has only two unique labels
         if (y_type := type_of_target(y)) != "binary":
-            if version.parse(sklearn.__version__) >= version.parse("1.6"):
+            if SKLEARN_V1_6_OR_LATER:
                 msg = f"Only binary classification is supported. The type of the target is {y_type}."
             else:
                 msg = "Unknown label type: non-binary"