some new features

.venv/lib/python3.12/site-packages/sklearn/utils/_missing.py

@@ -0,0 +1,65 @@
+import math
+import numbers
+from contextlib import suppress
+
+
+def is_scalar_nan(x):
+    """Test if x is NaN.
+
+    This function is meant to overcome the issue that np.isnan does not allow
+    non-numerical types as input, and that np.nan is not float('nan').
+
+    Parameters
+    ----------
+    x : any type
+        Any scalar value.
+
+    Returns
+    -------
+    bool
+        Returns true if x is NaN, and false otherwise.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.utils._missing import is_scalar_nan
+    >>> is_scalar_nan(np.nan)
+    True
+    >>> is_scalar_nan(float("nan"))
+    True
+    >>> is_scalar_nan(None)
+    False
+    >>> is_scalar_nan("")
+    False
+    >>> is_scalar_nan([np.nan])
+    False
+    """
+    return (
+        not isinstance(x, numbers.Integral)
+        and isinstance(x, numbers.Real)
+        and math.isnan(x)
+    )
+
+
+def is_pandas_na(x):
+    """Test if x is pandas.NA.
+
+    We intentionally do not use this function to return `True` for `pd.NA` in
+    `is_scalar_nan`, because estimators that support `pd.NA` are the exception
+    rather than the rule at the moment. When `pd.NA` is more universally
+    supported, we may reconsider this decision.
+
+    Parameters
+    ----------
+    x : any type
+
+    Returns
+    -------
+    boolean
+    """
+    with suppress(ImportError):
+        from pandas import NA
+
+        return x is NA
+
+    return False