[MRG] Updated sklearn.utils.validation.check_array calls to use ensure_all_…

pmdarima/arima/arima.py

@@ -12,7 +12,6 @@
 import numbers
 import warnings
 from scipy.stats import gaussian_kde, norm
-from sklearn.utils.validation import check_array
 from statsmodels import api as sm
 
 from . import _validation as val
@@ -25,7 +24,7 @@
 from ..compat import matplotlib as mpl_compat
 from ..utils import if_has_delegate, is_iterable, check_endog, check_exog
 from ..utils.visualization import _get_plt
-from ..utils.array import diff_inv, diff
+from ..utils.array import diff_inv, diff, check_array
 
 # Get the version
 import pmdarima
@@ -804,7 +803,7 @@ def predict(self,
             # The confidence intervals may be a Pandas frame if it comes from
             # SARIMAX & we want Numpy. We will to duck type it so we don't add
             # new explicit requirements for the package
-            return f, check_array(conf_int, force_all_finite=False)
+            return f, check_array(conf_int, ensure_all_finite=False)
         return f
 
     def __getstate__(self):

pmdarima/utils/array.py

@@ -12,6 +12,26 @@
 from ..compat import DTYPE
 from ._array import C_intgrt_vec
 
+import sklearn
+from sklearn.utils import check_array as _check_array
+
+
+# patched version of sklearn.utils.validation.check_array
+# until sklearn minimal version for this project is 1.6
+# to remove deprecation warning and ensure compatibility with sklearn 1.8
+sklearn_ver = list(map(int, sklearn.__version__.split(".")))
+_new_check_array_api = sklearn_ver[0] >= 1 and sklearn_ver[1] >= 6
+
+
+def check_array(*args, ensure_all_finite=False, **kwargs):
+    if _new_check_array_api:
+        return _check_array(*args, ensure_all_finite=ensure_all_finite,
+                            **kwargs)
+    else:
+        return _check_array(*args, force_all_finite=ensure_all_finite,
+                            **kwargs)
+
+
 __all__ = [
     'as_series',
     'c',
@@ -176,10 +196,10 @@ def check_endog(
     y : np.ndarray or pd.Series, shape=(n_samples,)
         A 1d numpy ndarray
     """
-    endog = skval.check_array(
+    endog = check_array(
         y,
         ensure_2d=False,
-        force_all_finite=force_all_finite,
+        ensure_all_finite=force_all_finite,
         copy=copy,
         dtype=dtype,
     )
@@ -215,7 +235,7 @@ def check_exog(X, dtype=DTYPE, copy=True, force_all_finite=True):
         Whether a forced copy will be triggered. If copy=False, a copy might
         still be triggered by a conversion.
 
-    force_all_finite : bool, optional (default=True)
+    force_all_finite: bool, optional (default=True)
         Whether to raise an error on np.inf and np.nan in an array. The
         possibilities are:
 
@@ -239,12 +259,12 @@ def check_exog(X, dtype=DTYPE, copy=True, force_all_finite=True):
         return X
 
     # otherwise just a pass-through to the scikit-learn method
-    return skval.check_array(
+    return check_array(
         X,
         ensure_2d=True,
         dtype=DTYPE,
         copy=copy,
-        force_all_finite=force_all_finite,
+        ensure_all_finite=force_all_finite,
     )
 
 
@@ -330,7 +350,7 @@ def diff(x, lag=1, differences=1):
     if any(v < 1 for v in (lag, differences)):
         raise ValueError('lag and differences must be positive (> 0) integers')
 
-    x = skval.check_array(x, ensure_2d=False, dtype=DTYPE, copy=False)
+    x = check_array(x, ensure_2d=False, dtype=DTYPE, copy=False)
     fun = _diff_vector if x.ndim == 1 else _diff_matrix
     res = x
 
@@ -385,11 +405,11 @@ def _diff_inv_matrix(x, lag, differences, xi):
         if xi is None:
             xi = np.zeros((lag * differences, m), dtype=DTYPE)
         else:
-            xi = skval.check_array(
+            xi = check_array(
                 xi,
                 dtype=DTYPE,
                 copy=False,
-                force_all_finite=False,
+                ensure_all_finite=False,
                 ensure_2d=True,
             )
             if xi.shape != (lag * differences, m):
@@ -471,11 +491,11 @@ def diff_inv(x, lag=1, differences=1, xi=None):
     ----------
     .. [1] https://stat.ethz.ch/R-manual/R-devel/library/stats/html/diffinv.html
     """  # noqa: E501
-    x = skval.check_array(
+    x = check_array(
         x,
         dtype=DTYPE,
         copy=False,
-        force_all_finite=False,
+        ensure_all_finite=False,
         ensure_2d=False,
     )