Bumpy Cython version. Fix Numpy 2.0 changes

k_means_constrained/k_means_constrained_.py

@@ -146,7 +146,7 @@ def k_means_constrained(X, n_clusters, size_min=None, size_max=None, init='k-mea
 
     # Validate init array
     if hasattr(init, '__array__'):
-        init = check_array(init, dtype=X.dtype.type, copy=True)
+        init = check_array(init, dtype=X.dtype.type)
         _validate_center_shape(X, n_clusters, init)
 
         if n_init != 1:

k_means_constrained/sklearn_import/cluster/_k_means.pyx

@@ -1,4 +1,5 @@
 # cython: profile=True
+# distutils: define_macros=NPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION
 # Profiling is enabled by default as the overhead does not seem to be measurable
 # on this specific use case.
 

k_means_constrained/sklearn_import/metrics/pairwise.py

@@ -207,7 +207,7 @@ def pairwise_distances_argmin_min(X, Y, axis=1, metric="euclidean",
     # Allocate output arrays
     indices = np.empty(X.shape[0], dtype=np.intp)
     values = np.empty(X.shape[0])
-    values.fill(np.infty)
+    values.fill(np.inf)
 
     for chunk_x in gen_batches(X.shape[0], batch_size):
         X_chunk = X[chunk_x, :]

k_means_constrained/sklearn_import/metrics/pairwise_fast.pyx

@@ -1,6 +1,7 @@
 #cython: boundscheck=False
 #cython: cdivision=True
 #cython: wraparound=False
+# distutils: define_macros=NPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION
 
 # Author: Andreas Mueller <[email protected]>
 #         Lars Buitinck

k_means_constrained/sklearn_import/utils/sparsefuncs_fast.pyx

@@ -8,6 +8,7 @@
 
 #!python
 #cython: boundscheck=False, wraparound=False, cdivision=True
+# distutils: define_macros=NPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION
 
 from libc.math cimport fabs, sqrt, pow
 cimport numpy as np

k_means_constrained/sklearn_import/utils/validation.py

@@ -12,7 +12,7 @@
 
 
 def check_array(array, accept_sparse=False, dtype="numeric", order=None,
-                copy=False, force_all_finite=True, ensure_2d=True,
+                force_all_finite=True, ensure_2d=True,
                 allow_nd=False, ensure_min_samples=1, ensure_min_features=1,
                 warn_on_dtype=False, estimator=None):
     """Input validation on an array, list, sparse matrix or similar.
@@ -46,15 +46,10 @@ def check_array(array, accept_sparse=False, dtype="numeric", order=None,
 
     order : 'F', 'C' or None (default=None)
         Whether an array will be forced to be fortran or c-style.
-        When order is None (default), then if copy=False, nothing is ensured
-        about the memory layout of the output array; otherwise (copy=True)
-        the memory layout of the returned array is kept as close as possible
+        When order is None (default), the memory layout of the
+        returned array is kept as close as possible
         to the original array.
 
-    copy : boolean (default=False)
-        Whether a forced copy will be triggered. If copy=False, a copy might
-        be triggered by a conversion.
-
     force_all_finite : boolean (default=True)
         Whether to raise an error on np.inf and np.nan in X.
 
@@ -131,10 +126,10 @@ def check_array(array, accept_sparse=False, dtype="numeric", order=None,
     context = " by %s" % estimator_name if estimator is not None else ""
 
     if sp.issparse(array):
-        array = _ensure_sparse_format(array, accept_sparse, dtype, copy,
-                                      force_all_finite)
+        array = _ensure_sparse_format(array, accept_sparse, dtype,
+                                      force_all_finite, copy=True)
     else:
-        array = np.array(array, dtype=dtype, order=order, copy=copy)
+        array = np.array(array, dtype=dtype, order=order, copy=True)
 
         if ensure_2d:
             if array.ndim == 1:
@@ -145,7 +140,7 @@ def check_array(array, accept_sparse=False, dtype="numeric", order=None,
                     "if it contains a single sample.".format(array))
             array = np.atleast_2d(array)
             # To ensure that array flags are maintained
-            array = np.array(array, dtype=dtype, order=order, copy=copy)
+            array = np.array(array, dtype=dtype, order=order, copy=True)
 
         # make sure we actually converted to numeric:
         if dtype_numeric and array.dtype.kind == "O":

requirements-dev.txt

@@ -1,3 +1,4 @@
+-r requirements.txt
 pytest>=5.1
 pandas>=1.0.4
 twine
@@ -7,4 +8,4 @@ numpydoc
 bump2version
 nose
 scikit-learn>=0.24.2
-cython
+cython>=3.0.11

tests/test_kmeans_constrained_from_sklearn.py

@@ -30,7 +30,7 @@ def test_labels_assignment_and_inertia():
     noisy_centers = centers + rng.normal(size=centers.shape)
     labels_gold = - np.ones(n_samples, dtype=int)
     mindist = np.empty(n_samples)
-    mindist.fill(np.infty)
+    mindist.fill(np.inf)
     for center_id in range(n_clusters):
         dist = np.sum((X - noisy_centers[center_id]) ** 2, axis=1)
         labels_gold[dist < mindist] = center_id
@@ -234,7 +234,7 @@ def test_transform():
     X_new = km.transform(km.cluster_centers_)
 
     for c in range(n_clusters):
-        assert_equal(X_new[c, c], 0)
+        assert_array_almost_equal(X_new[c, c], 0)
         for c2 in range(n_clusters):
             if c != c2:
                 assert X_new[c, c2] > 0