Bugfixes and licensing

pyproject.toml

@@ -1,5 +1,5 @@
 [build-system]
-requires = ["setuptools", "Cython", "numpy", "wheel"]
+requires = ["setuptools", "Cython==3.1.0", "numpy", "wheel"]
 
 [tool.cython-lint]
 max-line-length = 127

requirements.txt

@@ -1,3 +1,3 @@
 numpy>=1.25.0
 matplotlib>=3.7.1
-cython>=3.0.0
+cython==3.1.0

setup.py

@@ -4,7 +4,7 @@
 import os
 
 NAME = "adaXT"
-VERSION = "1.5.0"
+VERSION = "1.5.1"
 DESCRIPTION = "A Python package for tree-based regression and classification"
 PROJECT_URLS = {
     "Documentation": "https://NiklasPfister.github.io/adaXT/",
@@ -140,6 +140,7 @@ def run_build():
         extensions = cythonize(extensions, **arg_dir)
     setup(
         name=NAME,
+        license="BSD-3-clause",
         version=VERSION,
         description=DESCRIPTION,
         long_description=LONG_DESCRIPTION,
@@ -160,7 +161,6 @@ def run_build():
         classifiers=[
             "Programming Language :: Python :: 3",
             "Intended Audience :: Science/Research",
-            "License :: OSI Approved :: BSD License",
             "Operating System :: OS Independent",
         ],
         extras_require=extras,

src/adaXT/decision_tree/_decision_tree.pyx

@@ -3,7 +3,7 @@ import sys
 
 cimport numpy as cnp
 ctypedef cnp.float64_t DOUBLE_t
-ctypedef cnp.int64_t LONG_t
+ctypedef cnp.int32_t INT32_T
 from libcpp cimport bool
 
 
@@ -16,7 +16,6 @@ from .nodes import DecisionNode
 
 # for c level definitions
 
-cimport cython
 from .nodes cimport DecisionNode, Node
 
 from ..utils cimport dsum
@@ -27,7 +26,7 @@ cdef double EPSILON = np.finfo('double').eps
 cdef class refit_object(Node):
     cdef public:
         list list_idx
-        bint is_left
+        bool is_left
 
     def __init__(
             self,
@@ -44,9 +43,7 @@ cdef class refit_object(Node):
     def add_idx(self, idx: int) -> None:
         self.list_idx.append(idx)
 
-
-@cython.auto_pickle(True)
-cdef class _DecisionTree():
+cdef class _DecisionTree:
     cdef public:
         object criteria
         object splitter
@@ -180,7 +177,7 @@ cdef class _DecisionTree():
     cdef void __fit_new_leaf_nodes(self, cnp.ndarray[DOUBLE_t, ndim=2] X,
                                    cnp.ndarray[DOUBLE_t, ndim=2] Y,
                                    cnp.ndarray[DOUBLE_t, ndim=1] sample_weight,
-                                   cnp.ndarray[LONG_t, ndim=1] sample_indices):
+                                   cnp.ndarray[INT32_T, ndim=1] sample_indices):
         cdef:
             int idx, n_objs, depth, cur_split_idx
             double cur_threshold
@@ -328,7 +325,7 @@ cdef class _DecisionTree():
                          cnp.ndarray[DOUBLE_t, ndim=2] X,
                          cnp.ndarray[DOUBLE_t, ndim=2] Y,
                          cnp.ndarray[DOUBLE_t, ndim=1] sample_weight,
-                         cnp.ndarray[LONG_t, ndim=1] sample_indices) -> None:
+                         cnp.ndarray[INT32_T, ndim=1] sample_indices) -> None:
 
         if self.root is None:
             raise ValueError("The tree has not been trained before trying to\
@@ -343,6 +340,10 @@ cdef class _DecisionTree():
         # Now squash all the DecisionNodes not visited
         self.__squash_tree()
 
+        # Make sure that predictor_instance points to the same root, if we have
+        # changed it
+        self.predictor_instance.root = self.root
+
 
 # From below here, it is the DepthTreeBuilder
 class queue_obj:

src/adaXT/predictor/predictor.pxd

@@ -7,7 +7,7 @@ cdef class Predictor():
         cnp.ndarray X
         cnp.ndarray Y
         int n_features
-        Node root
+    cdef public Node root
 
     cpdef dict predict_leaf(self, double[:, ::1] X)
 

src/adaXT/random_forest/random_forest.py

@@ -48,17 +48,17 @@ def get_sample_indices(
     Assumes there has been a previous call to self.__get_sample_indices on the
     RandomForest.
     """
+    indices = np.arange(0, X_n_rows, dtype=np.int32)
     if sampling == "resampling":
         ret = (
             gen.choice(
-                np.arange(0, X_n_rows),
+                indices,
                 size=sampling_args["size"],
                 replace=sampling_args["replace"],
             ),
             None,
         )
     elif sampling == "honest_tree":
-        indices = np.arange(0, X_n_rows)
         gen.shuffle(indices)
         if sampling_args["replace"]:
             resample_size0 = sampling_args["size"]
@@ -81,7 +81,6 @@ def get_sample_indices(
         )
         ret = (fit_indices, pred_indices)
     elif sampling == "honest_forest":
-        indices = np.arange(0, X_n_rows)
         if sampling_args["replace"]:
             resample_size0 = sampling_args["size"]
             resample_size1 = sampling_args["size"]
@@ -103,7 +102,7 @@ def get_sample_indices(
         )
         ret = (fit_indices, pred_indices)
     else:
-        ret = (np.arange(0, X_n_rows), None)
+        ret = (indices, None)
 
     if sampling_args["OOB"]:
         # Only fitting indices
@@ -164,7 +163,6 @@ def build_single_tree(
             Y=Y,
             sample_weight=sample_weight,
             sample_indices=prediction_indices)
-
     return tree
 
 
@@ -366,11 +364,11 @@ def __get_sampling_parameter(self, sampling_args: dict | None) -> dict:
                 sampling_args["split"] = np.min(
                     [int(0.5 * self.X_n_rows), self.X_n_rows - 1]
                 )
-            elif isinstance(sampling_args["size"], float):
+            elif isinstance(sampling_args["split"], float):
                 sampling_args["split"] = np.min(
                     [int(sampling_args["split"] * self.X_n_rows), self.X_n_rows - 1]
                 )
-            elif not isinstance(sampling_args["size"], int):
+            elif not isinstance(sampling_args["split"], (int, np.integer)):
                 raise ValueError(
                     "The provided sampling_args['split'] is not an integer or float as required."
                 )
@@ -380,7 +378,7 @@ def __get_sampling_parameter(self, sampling_args: dict | None) -> dict:
                 sampling_args["size"] = int(
                     sampling_args["size"] * sampling_args["split"]
                 )
-            elif not isinstance(sampling_args["size"], int):
+            elif not isinstance(sampling_args["size"], (np.integer, int)):
                 raise ValueError(
                     "The provided sampling_args['size'] is not an integer or float as required."
                 )