v0.9.4 Fix spmatrix.A

CHANGELOG.md

@@ -1,3 +1,7 @@
+### Version 0.9.4
+
+* Replace deprecated scipy `spmatrix.A` calls with `spmatrix.todense()`
+
 ### Version 0.9.3
 
 * Directly expose service functions `mkl_get_max_threads`, `mkl_set_num_threads`,

README.md

@@ -79,6 +79,8 @@ If A is sparse, it will return a sparse matrix unless `dense=True` is set.
 It will also convert a CSC matrix to a CSR matrix if necessary.
 
 #### Service Functions
+Several service functions are available and can be imported from the base `sparse_dot_mkl` package.
+
 `mkl_get_max_threads()` returns the maximum number of threads used by MKL as an integer
 
 `mkl_set_num_threads(n_threads)` will set a maximum number of threads hint for MKL 

setup.py

@@ -2,7 +2,7 @@
 from setuptools import setup, find_packages
 
 DISTNAME = 'sparse_dot_mkl'
-VERSION = '0.9.3'
+VERSION = '0.9.4'
 DESCRIPTION = "Intel MKL wrapper for sparse matrix multiplication"
 MAINTAINER = 'Chris Jackson'
 MAINTAINER_EMAIL = '[email protected]'

sparse_dot_mkl/__init__.py

@@ -1,4 +1,4 @@
-__version__ = '0.9.3'
+__version__ = '0.9.4'
 
 
 from sparse_dot_mkl.sparse_dot import (

sparse_dot_mkl/_mkl_interface/__init__.py

@@ -69,7 +69,13 @@ def _validate_dtype():
     test_array = _spsparse.random(
         5, 5, density=0.5, format="csc", dtype=_np.float32, random_state=50
     )
-    test_comparison = test_array.A
+    test_comparison = test_array.todense()
+
+    # Make sure this is an array and not np.matrix
+    try:
+        test_comparison = test_comparison.A
+    except AttributeError:
+        pass
 
     csc_ref, precision_flag, _ = _create_mkl_sparse(test_array)
 
@@ -84,7 +90,7 @@ def _validate_dtype():
                 output_type='csr_matrix'
             )
 
-        if not _np.allclose(test_comparison, final_array.A):
+        if not _np.allclose(test_comparison, final_array.todense()):
             raise ValueError("Match failed after matrix conversion")
         _destroy_mkl_handle(csr_ref)
     finally:

sparse_dot_mkl/tests/test_dense_dense.py

@@ -14,7 +14,7 @@ class TestDenseDenseMultiplication(unittest.TestCase):
 
     @classmethod
     def setUpClass(cls):
-        cls.MATRIX_1, cls.MATRIX_2 = MATRIX_1.copy().A, MATRIX_2.copy().A
+        cls.MATRIX_1, cls.MATRIX_2 = MATRIX_1.copy().todense(), MATRIX_2.copy().todense()
 
     def setUp(self):
         self.mat1 = self.MATRIX_1.copy()
@@ -123,8 +123,8 @@ class _ComplexMixin:
     @classmethod
     def setUpClass(cls):
         cls.MATRIX_1, cls.MATRIX_2 = make_matrixes(200, 100, 300, 0.05, dtype=np.cdouble)
-        cls.MATRIX_1 = cls.MATRIX_1.A
-        cls.MATRIX_2 = cls.MATRIX_2.A
+        cls.MATRIX_1 = cls.MATRIX_1.todense()
+        cls.MATRIX_2 = cls.MATRIX_2.todense()
 
     def test_inherits(self):
 

sparse_dot_mkl/tests/test_mkl.py

@@ -56,13 +56,13 @@ def np_almost_equal(a, b, **kwargs):
         a = a.toarray()
 
     if isinstance(a, np.matrix):
-        a = a.A
+        a = a.todense()
 
     if _spsparse.issparse(b):
         b = b.toarray()
 
     if isinstance(b, np.matrix):
-        b = b.A
+        b = b.todense()
 
     return npt.assert_array_almost_equal(a, b, **kwargs)
 
@@ -73,16 +73,16 @@ def setUp(self):
         self.mat1 = MATRIX_1_EMPTY.copy()
         self.mat2 = MATRIX_2.copy()
 
-        self.mat1_d = np.asarray(MATRIX_1_EMPTY.A, order="C")
-        self.mat2_d = np.asarray(MATRIX_2.A, order="C")
+        self.mat1_d = np.asarray(MATRIX_1_EMPTY.todense(), order="C")
+        self.mat2_d = np.asarray(MATRIX_2.todense(), order="C")
 
         self.mat1_zero = np.zeros((0, 300))
 
     def test_sparse_sparse(self):
         mat3 = dot_product_mkl(self.mat1, self.mat2)
         mat3_np = np.dot(self.mat1_d, self.mat2_d)
 
-        npt.assert_array_almost_equal(mat3_np, mat3.A)
+        npt.assert_array_almost_equal(mat3_np, mat3.todense())
 
     def test_sparse_dense(self):
         mat3 = dot_product_mkl(self.mat1, self.mat2_d)
@@ -141,8 +141,8 @@ def test_empty_handle(self):
             _destroy_mkl_handle(mkl_handle_empty)
 
     def test_3d_matrixes(self):
-        d1 = self.mat1.A.reshape(200, 300, 1)
-        d2 = self.mat2.A.reshape(300, 100, 1)
+        d1 = self.mat1.todense().A.reshape(200, 300, 1)
+        d2 = self.mat2.todense().A.reshape(300, 100, 1)
 
         with self.assertRaises(ValueError):
             dot_product_mkl(d1, d2)
@@ -199,7 +199,7 @@ def is_sparse_identical_internal(sparse_1, sparse_2):
         npt.assert_array_equal(sparse_1.indices, sparse_2.indices)
 
     def is_sparse_identical_A(self, sparse_1, sparse_2):
-        npt.assert_array_almost_equal(sparse_1.A, sparse_2.A)
+        npt.assert_array_almost_equal(sparse_1.todense(), sparse_2.todense())
 
     def test_create_export(self):
         mat1 = self.csc_constructor(self.mat1).copy()

sparse_dot_mkl/tests/test_qr_solver.py

@@ -12,7 +12,7 @@ class TestSparseSolver(unittest.TestCase):
     def setUpClass(cls):
         cls.A = _spsparse.diags((MATRIX_1.data[0:100].copy()), format="csr")
         cls.B = MATRIX_1.data[0:100].copy().reshape(-1, 1)
-        cls.X = np.linalg.lstsq(cls.A.A, cls.B, rcond=None)[0]
+        cls.X = np.linalg.lstsq(cls.A.todense(), cls.B, rcond=None)[0]
 
     def setUp(self):
         self.mat1 = self.A.copy()
@@ -25,15 +25,26 @@ def test_sparse_solver(self):
             npt.assert_array_almost_equal(self.mat3, mat3)
 
     def test_sparse_solver_single(self):
-        mat3 = sparse_qr_solve_mkl(self.mat1.astype(np.float32), self.mat2.astype(np.float32))
+        mat3 = sparse_qr_solve_mkl(
+            self.mat1.astype(np.float32),
+            self.mat2.astype(np.float32)
+        )
         npt.assert_array_almost_equal(self.mat3, mat3)
 
     def test_sparse_solver_cast_B(self):
-        mat3 = sparse_qr_solve_mkl(self.mat1, self.mat2.astype(np.float32), cast=True)
+        mat3 = sparse_qr_solve_mkl(
+            self.mat1,
+            self.mat2.astype(np.float32),
+            cast=True
+        )
         npt.assert_array_almost_equal(self.mat3, mat3)
 
     def test_sparse_solver_cast_A(self):
-        mat3 = sparse_qr_solve_mkl(self.mat1.astype(np.float32), self.mat2, cast=True)
+        mat3 = sparse_qr_solve_mkl(
+            self.mat1.astype(np.float32),
+            self.mat2,
+            cast=True
+        )
         npt.assert_array_almost_equal(self.mat3, mat3)
 
     def test_sparse_solver_cast_CSC(self):