[fix] update BasicStatistics and ``IncrementalBasicStatistics…

…`` to follow additional sklearn conventions (#2038)

* Update test_basic_statistics.py

* Update test_basic_statistics.py

* Update basic_statistics.py

* formatting

* forgotten switch

* handle all better

* make updates to IncBS

* n_features_in_ safety

* convert to 2d data

* add daal_check_version import

* formatting

sklearnex/basic_statistics/basic_statistics.py

@@ -14,6 +14,8 @@
 # limitations under the License.
 # ==============================================================================
 
+import warnings
+
 import numpy as np
 from sklearn.base import BaseEstimator
 from sklearn.utils import check_array
@@ -26,6 +28,9 @@
 from .._device_offload import dispatch
 from .._utils import PatchingConditionsChain
 
+if sklearn_check_version("1.2"):
+    from sklearn.utils._param_validation import StrOptions
+
 
 @control_n_jobs(decorated_methods=["fit"])
 class BasicStatistics(BaseEstimator):
@@ -62,23 +67,69 @@ class BasicStatistics(BaseEstimator):
     """
 
     def __init__(self, result_options="all"):
-        self.options = result_options
+        self.result_options = result_options
 
     _onedal_basic_statistics = staticmethod(onedal_BasicStatistics)
 
+    if sklearn_check_version("1.2"):
+        _parameter_constraints: dict = {
+            "result_options": [
+                StrOptions(
+                    {
+                        "all",
+                        "min",
+                        "max",
+                        "sum",
+                        "mean",
+                        "variance",
+                        "variation",
+                        "sum_squares",
+                        "standard_deviation",
+                        "sum_squares_centered",
+                        "second_order_raw_moment",
+                    }
+                ),
+                list,
+            ],
+        }
+
     def _save_attributes(self):
         assert hasattr(self, "_onedal_estimator")
 
-        if self.options == "all":
+        if self.result_options == "all":
             result_options = onedal_BasicStatistics.get_all_result_options()
         else:
-            result_options = self.options
+            result_options = self.result_options
 
         if isinstance(result_options, str):
-            setattr(self, result_options, getattr(self._onedal_estimator, result_options))
+            setattr(
+                self,
+                result_options + "_",
+                getattr(self._onedal_estimator, result_options),
+            )
         elif isinstance(result_options, list):
             for option in result_options:
-                setattr(self, option, getattr(self._onedal_estimator, option))
+                setattr(self, option + "_", getattr(self._onedal_estimator, option))
+
+    def __getattr__(self, attr):
+        if self.result_options == "all":
+            result_options = onedal_BasicStatistics.get_all_result_options()
+        else:
+            result_options = self.result_options
+        is_deprecated_attr = (
+            isinstance(result_options, str) and (attr == result_options)
+        ) or (isinstance(result_options, list) and (attr in result_options))
+        if is_deprecated_attr:
+            warnings.warn(
+                "Result attributes without a trailing underscore were deprecated in version 2025.1 and will be removed in 2026.0"
+            )
+            attr += "_"
+        if attr in self.__dict__:
+            return self.__dict__[attr]
+
+        raise AttributeError(
+            f"'{self.__class__.__name__}' object has no attribute '{attr}'"
+        )
 
     def _onedal_supported(self, method_name, *data):
         patching_status = PatchingConditionsChain(
@@ -90,6 +141,9 @@ def _onedal_supported(self, method_name, *data):
     _onedal_gpu_supported = _onedal_supported
 
     def _onedal_fit(self, X, sample_weight=None, queue=None):
+        if sklearn_check_version("1.2"):
+            self._validate_params()
+
         if sklearn_check_version("1.0"):
             X = self._validate_data(X, dtype=[np.float64, np.float32], ensure_2d=False)
         else:
@@ -99,16 +153,17 @@ def _onedal_fit(self, X, sample_weight=None, queue=None):
             sample_weight = _check_sample_weight(sample_weight, X)
 
         onedal_params = {
-            "result_options": self.options,
+            "result_options": self.result_options,
         }
 
         if not hasattr(self, "_onedal_estimator"):
             self._onedal_estimator = self._onedal_basic_statistics(**onedal_params)
         self._onedal_estimator.fit(X, sample_weight, queue)
         self._save_attributes()
+        self.n_features_in_ = X.shape[1] if len(X.shape) > 1 else 1
 
     def fit(self, X, y=None, *, sample_weight=None):
-        """Compute statistics with X, using minibatches of size batch_size.
+        """Calculate statistics of X.
 
         Parameters
         ----------

sklearnex/basic_statistics/incremental_basic_statistics.py

@@ -32,6 +32,7 @@
     from sklearn.utils._param_validation import Interval, StrOptions
 
 import numbers
+import warnings
 
 
 @control_n_jobs(decorated_methods=["partial_fit", "_onedal_finalize_fit"])
@@ -175,6 +176,9 @@ def _onedal_partial_fit(self, X, sample_weight=None, queue=None):
         self._need_to_finalize = True
 
     def _onedal_fit(self, X, sample_weight=None, queue=None):
+        if sklearn_check_version("1.2"):
+            self._validate_params()
+
         if sklearn_check_version("1.0"):
             X = self._validate_data(X, dtype=[np.float64, np.float32])
         else:
@@ -198,9 +202,6 @@ def _onedal_fit(self, X, sample_weight=None, queue=None):
             weights_batch = sample_weight[batch] if sample_weight is not None else None
             self._onedal_partial_fit(X_batch, weights_batch, queue=queue)
 
-        if sklearn_check_version("1.2"):
-            self._validate_params()
-
         self.n_features_in_ = X.shape[1]
 
         self._onedal_finalize_fit(queue=queue)
@@ -209,13 +210,18 @@ def _onedal_fit(self, X, sample_weight=None, queue=None):
 
     def __getattr__(self, attr):
         result_options = self.__dict__["result_options"]
+        sattr = attr.removesuffix("_")
         is_statistic_attr = (
-            isinstance(result_options, str) and (attr == result_options)
-        ) or (isinstance(result_options, list) and (attr in result_options))
+            isinstance(result_options, str) and (sattr == result_options)
+        ) or (isinstance(result_options, list) and (sattr in result_options))
         if is_statistic_attr:
             if self._need_to_finalize:
                 self._onedal_finalize_fit()
-            return getattr(self._onedal_estimator, attr)
+            if sattr == attr:
+                warnings.warn(
+                    "Result attributes without a trailing underscore were deprecated in version 2025.1 and will be removed in 2026.0"
+                )
+            return getattr(self._onedal_estimator, sattr)
         if attr in self.__dict__:
             return self.__dict__[attr]
 
@@ -256,7 +262,7 @@ def partial_fit(self, X, sample_weight=None):
         return self
 
     def fit(self, X, y=None, sample_weight=None):
-        """Compute statistics with X, using minibatches of size batch_size.
+        """Calculate statistics of X using minibatches of size batch_size.
 
         Parameters
         ----------

sklearnex/basic_statistics/tests/test_basic_statistics.py

@@ -18,6 +18,7 @@
 import pytest
 from numpy.testing import assert_allclose
 
+from daal4py.sklearn._utils import daal_check_version
 from onedal.basic_statistics.tests.test_basic_statistics import (
     expected_max,
     expected_mean,
@@ -249,3 +250,21 @@ def test_1d_input_on_random_data(dataframe, queue, option, data_size, weighted,
 
     tol = fp32tol if res.dtype == np.float32 else fp64tol
     assert_allclose(gtr, res, atol=tol)
+
+
+def test_warning():
+    basicstat = BasicStatistics("all")
+    data = np.array([0, 1])
+
+    basicstat.fit(data)
+    for i in basicstat._onedal_estimator.get_all_result_options():
+        with pytest.warns(
+            UserWarning,
+            match="Result attributes without a trailing underscore were deprecated in version 2025.1 and will be removed in 2026.0",
+        ) as warn_record:
+            getattr(basicstat, i)
+
+        if daal_check_version((2026, "P", 0)):
+            assert len(warn_record) == 0, i
+        else:
+            assert len(warn_record) == 1, i

sklearnex/basic_statistics/tests/test_incremental_basic_statistics.py

@@ -18,6 +18,7 @@
 import pytest
 from numpy.testing import assert_allclose
 
+from daal4py.sklearn._utils import daal_check_version
 from onedal.basic_statistics.tests.test_basic_statistics import (
     expected_max,
     expected_mean,
@@ -382,3 +383,22 @@ def test_fit_all_option_on_random_data(
             gtr = function(X)
         tol = fp32tol if res.dtype == np.float32 else fp64tol
         assert_allclose(gtr, res, atol=tol)
+
+
+def test_warning():
+    basicstat = IncrementalBasicStatistics("all")
+    # Only 2d inputs supported into IncrementalBasicStatistics
+    data = np.array([[0.0], [1.0]])
+
+    basicstat.fit(data)
+    for i in basicstat._onedal_estimator.get_all_result_options():
+        with pytest.warns(
+            UserWarning,
+            match="Result attributes without a trailing underscore were deprecated in version 2025.1 and will be removed in 2026.0",
+        ) as warn_record:
+            getattr(basicstat, i)
+
+        if daal_check_version((2026, "P", 0)):
+            assert len(warn_record) == 0, i
+        else:
+            assert len(warn_record) == 1, i