Support for py313

.github/workflows/build-and-test.yml

@@ -12,7 +12,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest, windows-latest, macos-latest]
-        python-version: ["3.10", "3.11", "3.12"]
+        python-version: ["3.10", "3.11", "3.12", "3.13"]
     steps:
     - uses: actions/checkout@v4
     - name: Set up Python

.github/workflows/build-wheels.yml

@@ -34,6 +34,7 @@ jobs:
             cp310-manylinux_x86_64 cp310-manylinux_aarch64 cp310-macosx_x86_64 cp310-win_amd64
             cp311-manylinux_x86_64 cp311-manylinux_aarch64 cp311-macosx_x86_64 cp311-win_amd64
             cp312-manylinux_x86_64 cp312-manylinux_aarch64 cp312-macosx_x86_64 cp312-win_amd64
+            cp313-manylinux_x86_64 cp313-manylinux_aarch64 cp313-macosx_x86_64 cp313-win_amd64
       - name: Store artifacts
         uses: actions/upload-artifact@v4
         with:

.pre-commit-config.yaml

@@ -1,6 +1,6 @@
 repos:
 -   repo: https://github.com/ambv/black
-    rev: 20.8b1
+    rev: 24.8.0
     hooks:
     - id: black
       language_version: python3

build_tools/requirements.txt

@@ -1,10 +1,10 @@
 -r ../requirements.txt
 pytest
 pre-commit
-black==20.8b1
+black==24.8.0
 click==8.0.3
 flake8==3.8.4
 pytest-cov
 lightgbm
 xgboost
-cython>=0.28.5
+cython>=3.0,<3.1

deepforest/_binner.py

@@ -5,7 +5,6 @@
     https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_hist_gradient_boosting/binning.py
 """
 
-
 __all__ = ["Binner"]
 
 import numpy as np

deepforest/_estimator.py

@@ -1,6 +1,5 @@
 """A wrapper on the base estimator for the naming consistency."""
 
-
 __all__ = ["Estimator"]
 
 import numpy as np

deepforest/_io.py

@@ -3,7 +3,6 @@
 class is designed to support the partial mode in deep forest.
 """
 
-
 __all__ = ["Buffer"]
 
 import os

deepforest/_layer.py

@@ -1,6 +1,5 @@
 """Implementation of the cascade layer in deep forest."""
 
-
 __all__ = [
     "BaseCascadeLayer",
     "ClassificationCascadeLayer",

deepforest/_utils.py

@@ -1,6 +1,5 @@
 """Implement utilities used in deep forest."""
 
-
 import numpy as np
 from datetime import datetime
 

deepforest/cascade.py

@@ -1,6 +1,5 @@
 """Implementation of Deep Forest."""
 
-
 __all__ = ["CascadeForestClassifier", "CascadeForestRegressor"]
 
 import numbers
@@ -770,10 +769,12 @@ def fit(self, X, y, sample_weight=None):
         X, y = check_X_y(
             X,
             y,
-            multi_output=True
-            if type_of_target(y)
-            in ("continuous-multioutput", "multiclass-multioutput")
-            else False,
+            multi_output=(
+                True
+                if type_of_target(y)
+                in ("continuous-multioutput", "multiclass-multioutput")
+                else False
+            ),
         )
 
         self._check_input(X, y)
@@ -1427,10 +1428,12 @@ def fit(self, X, y, sample_weight=None):
         X, y = check_X_y(
             X,
             y,
-            multi_output=True
-            if type_of_target(y)
-            in ("continuous-multioutput", "multiclass-multioutput")
-            else False,
+            multi_output=(
+                True
+                if type_of_target(y)
+                in ("continuous-multioutput", "multiclass-multioutput")
+                else False
+            ),
         )
         # Check the input for classification
         y = self._encode_class_labels(y)
@@ -1639,10 +1642,12 @@ def fit(self, X, y, sample_weight=None):
         X, y = check_X_y(
             X,
             y,
-            multi_output=True
-            if type_of_target(y)
-            in ("continuous-multioutput", "multiclass-multioutput")
-            else False,
+            multi_output=(
+                True
+                if type_of_target(y)
+                in ("continuous-multioutput", "multiclass-multioutput")
+                else False
+            ),
         )
 
         # Check the input for regression

deepforest/forest.py

@@ -5,7 +5,6 @@
     https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/ensemble/_forest.py
 """
 
-
 __all__ = [
     "RandomForestClassifier",
     "RandomForestRegressor",
@@ -618,7 +617,11 @@ def predict_proba(self, X):
             for j in np.atleast_1d(self.n_classes_)
         ]
         lock = threading.Lock()
-        Parallel(n_jobs=n_jobs, verbose=self.verbose, require="sharedmem",)(
+        Parallel(
+            n_jobs=n_jobs,
+            verbose=self.verbose,
+            require="sharedmem",
+        )(
             delayed(_accumulate_prediction)(
                 self.features[i],
                 self.thresholds[i],
@@ -801,7 +804,11 @@ def predict(self, X):
 
         # Parallel loop
         lock = threading.Lock()
-        Parallel(n_jobs=n_jobs, verbose=self.verbose, require="sharedmem",)(
+        Parallel(
+            n_jobs=n_jobs,
+            verbose=self.verbose,
+            require="sharedmem",
+        )(
             delayed(_accumulate_prediction)(
                 self.features[i],
                 self.thresholds[i],

deepforest/tree/_splitter.pyx

@@ -939,7 +939,7 @@ cdef class BaseSparseSplitter(Splitter):
                                          end_negative, start_positive)
 
 
-cdef int compare_SIZE_t(const void* a, const void* b) nogil:
+cdef int compare_SIZE_t(const void* a, const void* b) noexcept nogil:
     """Comparison function for sort."""
     return <int>((<SIZE_t*>a)[0] - (<SIZE_t*>b)[0])
 

deepforest/tree/_tree.pyx

@@ -32,6 +32,7 @@ cdef extern from "numpy/arrayobject.h":
                                 int nd, np.npy_intp* dims,
                                 np.npy_intp* strides,
                                 void* data, int flags, object obj)
+    int PyArray_SetBaseObject(np.ndarray arr, PyObject* obj)
 
 # =============================================================================
 # Types and constants
@@ -137,8 +138,8 @@ cdef class DepthFirstTreeBuilder(TreeBuilder):
         cdef int init_leaf_capacity
 
         if tree.max_depth <= 10:
-            init_internal_capacity = (2 ** (tree.max_depth + 1)) - 1
-            init_leaf_capacity = (2 ** (tree.max_depth + 1)) - 1
+            init_internal_capacity = (1 << (tree.max_depth + 1)) - 1
+            init_leaf_capacity = (1 << (tree.max_depth + 1)) - 1
         else:
             init_internal_capacity = 2047
             init_leaf_capacity = 2047
@@ -905,7 +906,7 @@ cdef class Tree:
         cdef np.ndarray arr
         arr = np.PyArray_SimpleNewFromData(3, shape, np.NPY_DOUBLE, self.value)
         Py_INCREF(self)
-        arr.base = <PyObject*> self
+        PyArray_SetBaseObject(arr, <PyObject*> self)
         return arr
 
     cdef np.ndarray _get_node_ndarray(self):
@@ -924,7 +925,7 @@ cdef class Tree:
         arr = PyArray_NewFromDescr(<PyTypeObject *> np.ndarray,
                                    <np.dtype> NODE_DTYPE, 1, shape,
                                    strides, <void*> self.nodes,
-                                   np.NPY_DEFAULT, None)
+                                   0, None)
         Py_INCREF(self)
-        arr.base = <PyObject*> self
+        PyArray_SetBaseObject(arr, <PyObject*> self)
         return arr

deepforest/tree/tree.py

@@ -5,7 +5,6 @@
     https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/tree/_classes.py
 """
 
-
 __all__ = [
     "DecisionTreeClassifier",
     "DecisionTreeRegressor",

deepforest/utils/kfoldwrapper.py

@@ -2,7 +2,6 @@
 Implementation of the estimator wrapper to support customized base estimators.
 """
 
-
 __all__ = ["KFoldWrapper"]
 
 import copy

pyproject.toml

@@ -2,8 +2,8 @@
 requires = [
     "setuptools>=61,<70",
     "wheel",
-    "numpy==1.26.4",
-    "Cython>=0.28.5,<3.0",
+    "numpy>=2.1.1,<3",
+    "Cython>=3.0,<3.1",
     "oldest-supported-numpy",
     "scipy>=1.3.2",
 ]

requirements.txt

@@ -1,4 +1,4 @@
-numpy>=1.14.6
+numpy>=1.21,<3
 scipy>=1.1.0
 joblib>=0.11
 scikit-learn>=1.0

setup.py

@@ -104,17 +104,18 @@
             "Programming Language :: Python :: 3.10",
             "Programming Language :: Python :: 3.11",
             "Programming Language :: Python :: 3.12",
+            "Programming Language :: Python :: 3.13",
             "Topic :: Software Development",
             "Topic :: Scientific/Engineering",
             "Operating System :: Microsoft :: Windows",
             "Operating System :: Unix",
         ],
-        python_requires=">=3.10",
+        python_requires=">=3.10,<3.14",
         install_requires=[
-            "numpy>=1.14.6,<2.0",
+            "numpy>=1.21,<3",
             "scipy>=1.1.0",
             "joblib>=0.11",
             "scikit-learn>=1.0,<1.6",
         ],
-        setup_requires=["cython", "numpy>=1.21,<2.0"],
+        setup_requires=["Cython>=3.0,<3.1", "numpy>=1.21,<3"],
     )