scikit-garden · Oct 19, 2022 · Sep 13, 2023 · Sep 20, 2023 · Sep 20, 2023
Showing with 70 additions and 62 deletions.

+1 −0 .gitignore

+51 −44 setup.py

+18 −18 skgarden/forest.py
diff --git a/.gitignore b/.gitignore
@@ -11,3 +11,4 @@ dist/
 *.so
 site/
 .env/*
+.eggs/
diff --git a/setup.py b/setup.py
@@ -6,30 +6,33 @@
 from setuptools.command.build_ext import build_ext
 
 
-DISTNAME = 'scikit-garden'
+DISTNAME = "scikit-garden"
 DESCRIPTION = "A garden of scikit-learn compatible trees"
-URL = 'https://github.com/scikit-garden/scikit-garden'
-MAINTAINER = 'Manoj Kumar'
-MAINTAINER_EMAIL = 'mks542@nyu.edu'
-LICENSE = 'new BSD'
-VERSION = '0.1.3'
+URL = "https://github.com/scikit-garden/scikit-garden"
+MAINTAINER = "Manoj Kumar"
+MAINTAINER_EMAIL = "mks542@nyu.edu"
+LICENSE = "new BSD"
+VERSION = "0.1.3"
 
 libraries = []
-if os.name == 'posix':
-    libraries.append('m')
+if os.name == "posix":
+    libraries.append("m")
 
 extensions = []
-for name in ['_tree', '_splitter', '_criterion', '_utils']:
-    extensions.append(Extension(
-        'skgarden.mondrian.tree.{}'.format(name),
-        sources=['skgarden/mondrian/tree/{}.pyx'.format(name)],
-        libraries=libraries,
-        extra_compile_args=['-O3'],
-    ))
+for name in ["_tree", "_splitter", "_criterion", "_utils"]:
+    extensions.append(
+        Extension(
+            "skgarden.mondrian.tree.{}".format(name),
+            sources=["skgarden/mondrian/tree/{}.pyx".format(name)],
+            libraries=libraries,
+            extra_compile_args=["-O3"],
+        )
+    )
 
 
 class CustomBuildExtCommand(build_ext):
     """build_ext command for use when numpy headers are needed."""
+
     def run(self):
         # Import numpy here, only when headers are needed
         import numpy
@@ -43,36 +46,40 @@ def finalize_options(self):
         # Import Cython here, only when we need to cythonize extensions
         if self.distribution.ext_modules:
             from Cython.Build.Dependencies import cythonize
-            self.distribution.ext_modules[:] = cythonize(self.distribution.ext_modules, force=self.force)
+
+            self.distribution.ext_modules[:] = cythonize(
+                self.distribution.ext_modules, force=self.force
+            )
         super().finalize_options()
 
 
 if __name__ == "__main__":
-    setup(name=DISTNAME,
-          cmdclass={'build_ext': CustomBuildExtCommand},
-          maintainer=MAINTAINER,
-          maintainer_email=MAINTAINER_EMAIL,
-          packages=find_packages(),
-          include_package_data=True,
-          description=DESCRIPTION,
-          license=LICENSE,
-          url=URL,
-          version=VERSION,
-          zip_safe=False,  # the package can run out of an .egg file
-          classifiers=[
-              'Intended Audience :: Science/Research',
-              'Intended Audience :: Developers',
-              'License :: OSI Approved',
-              'Programming Language :: C',
-              'Programming Language :: Python',
-              'Topic :: Software Development',
-              'Topic :: Scientific/Engineering',
-              'Operating System :: Microsoft :: Windows',
-              'Operating System :: POSIX',
-              'Operating System :: Unix',
-              'Operating System :: MacOS'
-            ],
-          install_requires=["numpy", "scipy", "scikit-learn>=0.22", "six"],
-          setup_requires=["Cython>=0.23", "numpy", "setuptools>=18"],
-          ext_modules=extensions,
-          )
+    setup(
+        name=DISTNAME,
+        cmdclass={"build_ext": CustomBuildExtCommand},
+        maintainer=MAINTAINER,
+        maintainer_email=MAINTAINER_EMAIL,
+        packages=find_packages(),
+        include_package_data=True,
+        description=DESCRIPTION,
+        license=LICENSE,
+        url=URL,
+        version=VERSION,
+        zip_safe=False,  # the package can run out of an .egg file
+        classifiers=[
+            "Intended Audience :: Science/Research",
+            "Intended Audience :: Developers",
+            "License :: OSI Approved",
+            "Programming Language :: C",
+            "Programming Language :: Python",
+            "Topic :: Software Development",
+            "Topic :: Scientific/Engineering",
+            "Operating System :: Microsoft :: Windows",
+            "Operating System :: POSIX",
+            "Operating System :: Unix",
+            "Operating System :: MacOS",
+        ],
+        install_requires=["numpy", "scipy", "scikit-learn>=0.22", "six"],
+        setup_requires=["Cython>=0.23,<3.0.0", "numpy", "setuptools>=18"],
+        ext_modules=extensions,
+    )
diff --git a/skgarden/forest.py b/skgarden/forest.py
@@ -16,7 +16,6 @@
 from sklearn.utils import check_random_state
 from sklearn.utils import check_array
 from sklearn.utils import compute_sample_weight
-from sklearn.utils.fixes import _joblib_parallel_args
 from sklearn.utils.multiclass import check_classification_targets
 from sklearn.utils.validation import check_is_fitted
 from sklearn.utils.validation import _check_sample_weight
@@ -596,10 +595,11 @@ def apply(self, X):
             return the index of the leaf x ends up in.
         """
         X = self._validate_X_predict(X)
-        results = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
-                           **_joblib_parallel_args(prefer="threads"))(
-            delayed(tree.apply)(X, check_input=False)
-            for tree in self.estimators_)
+        results = Parallel(
+            n_jobs=self.n_jobs, 
+            verbose=self.verbose,
+            prefer="threads"
+        )(delayed(tree.apply)(X, check_input=False) for tree in self.estimators_)
 
         return np.array(results).T
 
@@ -623,10 +623,11 @@ def decision_path(self, X):
             gives the indicator value for the i-th estimator.
         """
         X = self._validate_X_predict(X)
-        indicators = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
-                              **_joblib_parallel_args(prefer='threads'))(
-            delayed(tree.decision_path)(X, check_input=False)
-            for tree in self.estimators_)
+        indicators = Parallel(
+            n_jobs=self.n_jobs, 
+            verbose=self.verbose, 
+            prefer='threads',
+        )(delayed(tree.decision_path)(X, check_input=False) for tree in self.estimators_)
 
         n_nodes = [0]
         n_nodes.extend([i.shape[1] for i in indicators])
@@ -739,9 +740,11 @@ def fit(self, X, y, sample_weight=None):
             # that case. However, for joblib 0.12+ we respect any
             # parallel_backend contexts set at a higher level,
             # since correctness does not rely on using threads.
-            trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
-                             **_joblib_parallel_args(prefer='threads'))(
-                delayed(_parallel_build_trees)(
+            trees = Parallel(
+                n_jobs=self.n_jobs, 
+                verbose=self.verbose, 
+                prefer='threads',
+            )(delayed(_parallel_build_trees)(
                     t, self, X, y, sample_weight, i, len(trees),
                     verbose=self.verbose, class_weight=self.class_weight,
                     n_samples_bootstrap=n_samples_bootstrap)
@@ -790,8 +793,7 @@ def feature_importances_(self):
         """
         check_is_fitted(self)
 
-        all_importances = Parallel(n_jobs=self.n_jobs,
-                                   **_joblib_parallel_args(prefer='threads'))(
+        all_importances = Parallel(n_jobs=self.n_jobs, prefer='threads')(
             delayed(getattr)(tree, 'feature_importances_')
             for tree in self.estimators_ if tree.tree_.node_count > 1)
 
@@ -1016,8 +1018,7 @@ def predict_proba(self, X):
         all_proba = [np.zeros((X.shape[0], j), dtype=np.float64)
                      for j in np.atleast_1d(self.n_classes_)]
         lock = threading.Lock()
-        Parallel(n_jobs=n_jobs, verbose=self.verbose,
-                 **_joblib_parallel_args(require="sharedmem"))(
+        Parallel(n_jobs=n_jobs, verbose=self.verbose, require="sharedmem")(
             delayed(_accumulate_prediction)(e.predict_proba, X, all_proba,
                                             lock)
             for e in self.estimators_)
@@ -1121,8 +1122,7 @@ def predict(self, X):
 
         # Parallel loop
         lock = threading.Lock()
-        Parallel(n_jobs=n_jobs, verbose=self.verbose,
-                 **_joblib_parallel_args(require="sharedmem"))(
+        Parallel(n_jobs=n_jobs, verbose=self.verbose, require="sharedmem")(
             delayed(_accumulate_prediction)(e.predict, X, [y_hat], lock)
             for e in self.estimators_)
-Original file line number
+Diff line change
@@ @@ -11,3 +11,4 @@ dist/ @@
     *.so
     site/
     .env/*
+    .eggs/