Fixed bugs in type specifications from Numpy 1.25 release (#1047)

* attempted fix for matrix type erroring

* fix spec of csr_array

* add a random seed

* remove isspmatrix

* fix a type check

* black

graspologic/embed/svd.py

@@ -7,11 +7,11 @@
 import scipy
 import scipy.sparse as sp
 import sklearn
+from scipy.sparse import csr_array
 from scipy.stats import norm
 from typing_extensions import Literal
 
 from graspologic.types import List, Tuple
-from graspologic.utils import is_almost_symmetric
 
 SvdAlgorithmType = Literal["full", "truncated", "randomized", "eigsh"]
 
@@ -107,7 +107,7 @@ def select_dimension(
         pp.918-930.
     """
     # Handle input data
-    if not isinstance(X, np.ndarray) and not sp.isspmatrix_csr(X):
+    if not isinstance(X, (np.ndarray, csr_array)):
         msg = "X must be a numpy array or scipy.sparse.csr_array, not {}.".format(
             type(X)
         )

graspologic/utils/utils.py

@@ -12,7 +12,7 @@
 import scipy.sparse
 from beartype import beartype
 from scipy.optimize import linear_sum_assignment
-from scipy.sparse import csgraph, csr_array, diags, isspmatrix_csr
+from scipy.sparse import csgraph, csr_array, diags
 from scipy.sparse.csgraph import connected_components
 from sklearn.metrics import confusion_matrix
 from sklearn.utils import check_array, check_consistent_length, column_or_1d
@@ -43,7 +43,7 @@ def average_matrices(
     """
     if isinstance(matrices[0], np.ndarray):
         return np.mean(matrices, axis=0)  # type: ignore
-    elif isspmatrix_csr(matrices[0]):
+    elif isinstance(matrices[0], csr_array):
         return np.sum(matrices) / len(matrices)
 
     raise TypeError(f"Unexpected type {matrices}")
@@ -275,11 +275,11 @@ def is_almost_symmetric(
     Raises
     ------
     TypeError
-        If the provided graph is not a numpy.ndarray or scipy.sparse.spmatrix
+        If the provided graph is not a numpy.ndarray or scipy.sparse.csr_array
     """
     if (x.ndim != 2) or (x.shape[0] != x.shape[1]):
         return False
-    if isinstance(x, (np.ndarray, scipy.sparse.spmatrix)):
+    if isinstance(x, (np.ndarray, csr_array)):
         return abs(x - x.T).max() <= atol
     else:
         raise TypeError("input a correct matrix type.")
@@ -836,7 +836,7 @@ def augment_diagonal(
     degrees = (in_degrees + out_degrees) / 2
     diag = weight * degrees / divisor
 
-    graph += diags(diag) if isspmatrix_csr(graph) else np.diag(diag)
+    graph += diags(diag) if isinstance(graph, csr_array) else np.diag(diag)
 
     return graph
 

tests/partition/test_leiden.py

@@ -193,8 +193,10 @@ def test_correct_types(self):
         node_ids = partitions.keys()
         for node_id in node_ids:
             self.assertTrue(
-                isinstance(node_id, (np.int32, np.intc)),
-                f"{node_id} has {type(node_id)} should be an np.int32/np.intc",
+                isinstance(
+                    node_id, np.integer
+                ),  # this is the preferred numpy typecheck
+                f"{node_id} has {type(node_id)} should be an int",
             )
 
     def test_hierarchical(self):

tests/test_spectral_embed.py

@@ -164,6 +164,7 @@ def test_directed_vertex_direction(self):
     def test_directed_correct_latent_positions(self):
         # setup
         ase = AdjacencySpectralEmbed(n_components=3)
+        np.random.seed(8888)
         P = np.array([[0.9, 0.1, 0.1], [0.3, 0.6, 0.1], [0.1, 0.5, 0.6]])
         M, labels = sbm([200, 200, 200], P, directed=True, return_labels=True)