Merge branch 'main' into dev/pytorch_testing_2

Author: david-cortes-intel

.ci/pipeline/build-and-test-lnx.yml

@@ -37,7 +37,7 @@ steps:
       conda activate CB
       if [ -z "${DALROOT}" ]; then conda install -q -y -c conda-forge "dal-devel>=2025.6.1"; fi
       pip install -r dependencies-dev
-      pip list
+      conda list
     env:
       DALROOT: ${{ variables.DALROOT }}
     displayName: "Install develop requirements"
@@ -62,7 +62,7 @@ steps:
       # dpep installation is set to pypi to avoid conflict of numpy versions from pip and conda
       if [ $(echo $(PYTHON_VERSION) | grep '3.9\|3.10\|3.11\|3.12') ] && [ $(SKLEARN_VERSION) != "1.0" ] && [ -z ${NO_DPC} ]; then pip install dpctl==$DPCTL_VERSION dpnp==$DPNP_VERSION; fi
       if [ -z "${NO_DPC}" ]; then pip install dpcpp-cpp-rt==$DPCPP_RT_VERSION; fi
-      pip list
+      conda list
     env:
       NO_DPC: ${{ variables.NO_DPC }}
       DPCTL_VERSION: 0.21.0

.ci/pipeline/build-and-test-mac.yml

@@ -29,7 +29,7 @@ steps:
   - script: |
       source activate CB
       pip install -r dependencies-dev
-      pip list
+      conda list
     displayName: 'Install develop requirements'
   - script: |
       source activate CB
@@ -41,7 +41,7 @@ steps:
       bash .ci/scripts/setup_sklearn.sh $(SKLEARN_VERSION)
       pip install --upgrade -r requirements-test.txt
       pip install $(python .ci/scripts/get_compatible_scipy_version.py)
-      pip list
+      conda list
     displayName: 'Install testing requirements'
   - script: |
       source activate CB

.ci/pipeline/build-and-test-win.yml

@@ -25,8 +25,8 @@ steps:
       pip install --upgrade setuptools
       pip install cpufeature
       pip install -r dependencies-dev
-      pip list
       if not defined DALROOT conda install -q -y -c conda-forge "dal-devel>=2025.6.1"
+      conda list
     displayName: 'Install develop requirements'
     env:
       DALROOT: ${{ variables.DALROOT }}
@@ -51,7 +51,7 @@ steps:
       cd ..
       for /f "delims=" %%c in ('python s\.ci\scripts\get_compatible_scipy_version.py') do set SCIPY_VERSION=%%c
       pip install %SCIPY_VERSION%
-      pip list
+      conda list
     displayName: 'Install testing requirements'
   - script: |
       call activate CB

.circleci/run_xpu_tests.py

@@ -16,9 +16,11 @@
 # ==============================================================================
 
 # coding: utf-8
-import argparse
 import os
 
+os.environ["SCIPY_ARRAY_API"] = "1"
+import argparse
+
 import pytest
 
 if __name__ == "__main__":
@@ -83,8 +85,6 @@
     if args.json_report_file is not None:
         pytest_params += ["--json-report", f"--json-report-file={args.json_report_file}"]
 
-    os.environ["SCIPY_ARRAY_API"] = "1"
-
     if not args.no_intel_optimized:
         from sklearnex import patch_sklearn
 

.pre-commit-config.yaml

@@ -16,7 +16,7 @@
 
 repos:
   - repo: https://github.com/psf/black
-    rev: 25.11.0
+    rev: 25.12.0
     hooks:
       - id: black
   - repo: https://github.com/PyCQA/isort

deselected_tests.yaml

@@ -34,10 +34,6 @@ deselected_tests:
   - neighbors/tests/test_neighbors.py::test_nan_euclidean_support[KNeighborsClassifier-params0]
   - neighbors/tests/test_neighbors.py::test_nan_euclidean_support[KNeighborsRegressor-params1]
   - neighbors/tests/test_neighbors.py::test_nan_euclidean_support[LocalOutlierFactor-params6]
-  - neighbors/tests/test_neighbors.py::test_neighbor_classifiers_loocv[ball_tree-nn_model0]
-  - neighbors/tests/test_neighbors.py::test_neighbor_classifiers_loocv[brute-nn_model0]
-  - neighbors/tests/test_neighbors.py::test_neighbor_classifiers_loocv[kd_tree-nn_model0]
-  - neighbors/tests/test_neighbors.py::test_neighbor_classifiers_loocv[auto-nn_model0]
   # sklearn 1.7 unsupported features
   - tests/test_common.py::test_estimators[LinearRegression()-check_sample_weight_equivalence_on_dense_data]
 
@@ -396,6 +392,9 @@ deselected_tests:
   - metrics/tests/test_common.py::test_array_api_compliance
   - utils/tests/test_stats.py::test_weighted_percentile_array_api_consistency
 
+  # Tests for exact equality, but results have differences in the order of machine epsilon
+  - neighbors/tests/test_neighbors.py::test_KNeighborsClassifier_multioutput
+
   # --------------------------------------------------------
   # No need to test daal4py patching
 reduced_tests:
@@ -525,12 +524,8 @@ gpu:
   - tests/test_common.py::test_estimators[ExtraTreesClassifier()-check_class_weight_classifiers]
   - tests/test_common.py::test_estimators[ExtraTreesRegressor()-check_sample_weights_invariance(kind=zeros)]
   - tests/test_common.py::test_estimators[RandomForestRegressor()-check_regressor_data_not_an_array]
-
-  # GPU implementation of Extra Trees doesn't support sample_weights
-  # comparisons to GPU with sample weights will use different algorithms
-  - tests/test_common.py::test_estimators[ExtraTreesClassifier()-check_sample_weights_invariance(kind=ones)]
   - tests/test_common.py::test_estimators[ExtraTreesClassifier()-check_sample_weights_invariance(kind=zeros)]
-  - tests/test_common.py::test_estimators[ExtraTreesRegressor()-check_sample_weights_invariance(kind=ones)]
+  - ensemble/tests/test_forest.py::test_min_weight_fraction_leaf
 
   # RuntimeError: Device support is not implemented, failing as result of fallback to cpu false
   - svm/tests/test_svm.py::test_unfitted

doc/sources/array_api.rst

@@ -97,6 +97,10 @@ The following patched classes have support for array API inputs:
 - :obj:`sklearn.covariance.EmpiricalCovariance`
 - :obj:`sklearnex.covariance.IncrementalEmpiricalCovariance`
 - :obj:`sklearn.decomposition.PCA`
+- :obj:`sklearn.ensemble.ExtraTreesClassifier`
+- :obj:`sklearn.ensemble.ExtraTreesRegressor`
+- :obj:`sklearn.ensemble.RandomForestClassifier`
+- :obj:`sklearn.ensemble.RandomForestRegressor`
 - :obj:`sklearn.linear_model.LinearRegression`
 - :obj:`sklearn.linear_model.Ridge`
 - :obj:`sklearnex.linear_model.IncrementalLinearRegression`
@@ -108,6 +112,11 @@ The following patched classes have support for array API inputs:
     enabled in |sklearn|, when passing these classes as inputs, data will be transferred to host and then back to
     device instead of being used directly.
 
+    Result attributes of |sklearnex| classes which contain |sklearn| or |sklearnex| classes may not themselves be
+    array API compliant. For example, ensemble algorithms contain decision tree estimators result objects which
+    do not comply with the array API standard.
+
+
 
 Example usage
 =============

doc/sources/distributed-mode.rst

@@ -26,7 +26,7 @@ Several :doc:`GPU-supported algorithms <oneapi-gpu>`
 also provide distributed, multi-GPU computing capabilities via integration with |mpi4py|. The prerequisites
 match those of GPU computing, along with an MPI backend of your choice (`Intel MPI recommended
 <https://www.intel.com/content/www/us/en/developer/tools/oneapi/mpi-library.html>`_, available
-via the ``impi_rt`` python/conda package) and the |mpi4py| python package. If using |sklearnex|
+via the ``impi_rt`` / ``impi-rt`` python/conda package) and the |mpi4py| python package. If using |sklearnex|
 `installed from sources <https://github.com/uxlfoundation/scikit-learn-intelex/blob/main/INSTALL.md#build-from-sources>`_,
 ensure that the spmd_backend is built.
 
@@ -49,9 +49,9 @@ ensure that the spmd_backend is built.
         .. tab:: From Intel's pip Index
             ::
 
-                pip install --index-url https://software.repos.intel.com/python/pypi mpi4py impi_rt
+                pip install --index-url https://software.repos.intel.com/python/pypi mpi4py impi-rt
 
-  It also requires the MPI runtime executable (``mpiexec`` / ``mpirun``) to be from the same library that was used to compile |sklearnex| or from a compatible library. Intel's MPI runtime library is offered as a Python package ``impi_rt`` and will be installed together with the ``mpi4py`` package if executing the commands above, but otherwise, it can be installed separately from different distribution channels:
+  It also requires the MPI runtime executable (``mpiexec`` / ``mpirun``) to be from the same library that was used to compile |sklearnex| or from a compatible library. Intel's MPI runtime library is offered as a Python package ``impi_rt`` (conda) / ``impi-rt`` (PyPI) and will be installed together with the ``mpi4py`` package if executing the commands above, but otherwise, it can be installed separately from different distribution channels:
 
     .. tabs::
         .. tab:: From conda-forge
@@ -67,12 +67,12 @@ ensure that the spmd_backend is built.
         .. tab:: From PyPI
             ::
 
-                pip install impi_rt
+                pip install impi-rt
 
         .. tab:: From Intel's pip Index
             ::
 
-                pip install --index-url https://software.repos.intel.com/python/pypi impi_rt
+                pip install --index-url https://software.repos.intel.com/python/pypi impi-rt
 
 
   Using other MPI backends that are not MPICH-compatible (e.g. OpenMPI) requires building |sklearnex| from source with that backend, and using an |mpi4py| built with that same backend.

doc/sources/distributed_daal4py.rst

@@ -45,7 +45,7 @@ same algorithms to much larger problem sizes.
     the MPI runtime library managing the computations to be the same MPI backend library
     with which the |sklearnex| library was compiled, or to be ABI compatible with it.
     Distributions of the |sklearnex| in PyPI and conda-forge are both compiled with `Intel's MPI <https://www.intel.com/content/www/us/en/developer/tools/oneapi/mpi-library.html>`__
-    as MPI backend (offered as Python package ``impi_rt`` in both PyPI and conda): ::
+    as MPI backend (offered as Python package ``impi_rt`` in conda, or ``impi-rt`` in PyPI): ::
 
         conda install -c conda-forge impi_rt mpi=*=impi
 
@@ -77,7 +77,7 @@ same algorithms to much larger problem sizes.
         .. tab:: From Intel's pip Index
             ::
 
-                pip install --index-url https://software.repos.intel.com/python/pypi mpi4py impi_rt
+                pip install --index-url https://software.repos.intel.com/python/pypi mpi4py impi-rt
 
 
 Using distributed mode

onedal/covariance/incremental_covariance.py

@@ -18,13 +18,10 @@
 
 from daal4py.sklearn._utils import daal_check_version
 
-from .._config import _get_config
 from .._device_offload import supports_queue
 from ..common._backend import bind_default_backend
 from ..datatypes import from_table, return_type_constructor, to_table
 from ..utils import _sycl_queue_manager as QM
-from ..utils._array_api import _get_sycl_namespace
-from ..utils.validation import _check_array
 from .covariance import BaseEmpiricalCovariance