"""test sparse matrix construction functions""" import numpy as np from numpy import array from numpy.testing import (assert_equal, assert_, assert_array_equal, assert_array_almost_equal_nulp) import pytest from pytest import raises as assert_raises from scipy._lib._testutils import check_free_memory from scipy._lib._util import check_random_state from scipy.sparse import (csr_matrix, coo_matrix, csr_array, coo_array, sparray, spmatrix, _construct as construct) from scipy.sparse._construct import rand as sprand sparse_formats = ['csr','csc','coo','bsr','dia','lil','dok'] #TODO check whether format=XXX is respected def _sprandn(m, n, density=0.01, format="coo", dtype=None, random_state=None): # Helper function for testing. random_state = check_random_state(random_state) data_rvs = random_state.standard_normal return construct.random(m, n, density, format, dtype, random_state, data_rvs) def _sprandn_array(m, n, density=0.01, format="coo", dtype=None, random_state=None): # Helper function for testing. random_state = check_random_state(random_state) data_sampler = random_state.standard_normal return construct.random_array((m, n), density=density, format=format, dtype=dtype, random_state=random_state, data_sampler=data_sampler) class TestConstructUtils: def test_spdiags(self): diags1 = array([[1, 2, 3, 4, 5]]) diags2 = array([[1, 2, 3, 4, 5], [6, 7, 8, 9,10]]) diags3 = array([[1, 2, 3, 4, 5], [6, 7, 8, 9,10], [11,12,13,14,15]]) cases = [] cases.append((diags1, 0, 1, 1, [[1]])) cases.append((diags1, [0], 1, 1, [[1]])) cases.append((diags1, [0], 2, 1, [[1],[0]])) cases.append((diags1, [0], 1, 2, [[1,0]])) cases.append((diags1, [1], 1, 2, [[0,2]])) cases.append((diags1,[-1], 1, 2, [[0,0]])) cases.append((diags1, [0], 2, 2, [[1,0],[0,2]])) cases.append((diags1,[-1], 2, 2, [[0,0],[1,0]])) cases.append((diags1, [3], 2, 2, [[0,0],[0,0]])) cases.append((diags1, [0], 3, 4, [[1,0,0,0],[0,2,0,0],[0,0,3,0]])) cases.append((diags1, [1], 3, 4, [[0,2,0,0],[0,0,3,0],[0,0,0,4]])) cases.append((diags1, [2], 3, 5, [[0,0,3,0,0],[0,0,0,4,0],[0,0,0,0,5]])) cases.append((diags2, [0,2], 3, 3, [[1,0,8],[0,2,0],[0,0,3]])) cases.append((diags2, [-1,0], 3, 4, [[6,0,0,0],[1,7,0,0],[0,2,8,0]])) cases.append((diags2, [2,-3], 6, 6, [[0,0,3,0,0,0], [0,0,0,4,0,0], [0,0,0,0,5,0], [6,0,0,0,0,0], [0,7,0,0,0,0], [0,0,8,0,0,0]])) cases.append((diags3, [-1,0,1], 6, 6, [[6,12, 0, 0, 0, 0], [1, 7,13, 0, 0, 0], [0, 2, 8,14, 0, 0], [0, 0, 3, 9,15, 0], [0, 0, 0, 4,10, 0], [0, 0, 0, 0, 5, 0]])) cases.append((diags3, [-4,2,-1], 6, 5, [[0, 0, 8, 0, 0], [11, 0, 0, 9, 0], [0,12, 0, 0,10], [0, 0,13, 0, 0], [1, 0, 0,14, 0], [0, 2, 0, 0,15]])) cases.append((diags3, [-1, 1, 2], len(diags3[0]), len(diags3[0]), [[0, 7, 13, 0, 0], [1, 0, 8, 14, 0], [0, 2, 0, 9, 15], [0, 0, 3, 0, 10], [0, 0, 0, 4, 0]])) for d, o, m, n, result in cases: if len(d[0]) == m and m == n: assert_equal(construct.spdiags(d, o).toarray(), result) assert_equal(construct.spdiags(d, o, m, n).toarray(), result) assert_equal(construct.spdiags(d, o, (m, n)).toarray(), result) def test_diags(self): a = array([1, 2, 3, 4, 5]) b = array([6, 7, 8, 9, 10]) c = array([11, 12, 13, 14, 15]) cases = [] cases.append((a[:1], 0, (1, 1), [[1]])) cases.append(([a[:1]], [0], (1, 1), [[1]])) cases.append(([a[:1]], [0], (2, 1), [[1],[0]])) cases.append(([a[:1]], [0], (1, 2), [[1,0]])) cases.append(([a[:1]], [1], (1, 2), [[0,1]])) cases.append(([a[:2]], [0], (2, 2), [[1,0],[0,2]])) cases.append(([a[:1]],[-1], (2, 2), [[0,0],[1,0]])) cases.append(([a[:3]], [0], (3, 4), [[1,0,0,0],[0,2,0,0],[0,0,3,0]])) cases.append(([a[:3]], [1], (3, 4), [[0,1,0,0],[0,0,2,0],[0,0,0,3]])) cases.append(([a[:1]], [-2], (3, 5), [[0,0,0,0,0],[0,0,0,0,0],[1,0,0,0,0]])) cases.append(([a[:2]], [-1], (3, 5), [[0,0,0,0,0],[1,0,0,0,0],[0,2,0,0,0]])) cases.append(([a[:3]], [0], (3, 5), [[1,0,0,0,0],[0,2,0,0,0],[0,0,3,0,0]])) cases.append(([a[:3]], [1], (3, 5), [[0,1,0,0,0],[0,0,2,0,0],[0,0,0,3,0]])) cases.append(([a[:3]], [2], (3, 5), [[0,0,1,0,0],[0,0,0,2,0],[0,0,0,0,3]])) cases.append(([a[:2]], [3], (3, 5), [[0,0,0,1,0],[0,0,0,0,2],[0,0,0,0,0]])) cases.append(([a[:1]], [4], (3, 5), [[0,0,0,0,1],[0,0,0,0,0],[0,0,0,0,0]])) cases.append(([a[:1]], [-4], (5, 3), [[0,0,0],[0,0,0],[0,0,0],[0,0,0],[1,0,0]])) cases.append(([a[:2]], [-3], (5, 3), [[0,0,0],[0,0,0],[0,0,0],[1,0,0],[0,2,0]])) cases.append(([a[:3]], [-2], (5, 3), [[0,0,0],[0,0,0],[1,0,0],[0,2,0],[0,0,3]])) cases.append(([a[:3]], [-1], (5, 3), [[0,0,0],[1,0,0],[0,2,0],[0,0,3],[0,0,0]])) cases.append(([a[:3]], [0], (5, 3), [[1,0,0],[0,2,0],[0,0,3],[0,0,0],[0,0,0]])) cases.append(([a[:2]], [1], (5, 3), [[0,1,0],[0,0,2],[0,0,0],[0,0,0],[0,0,0]])) cases.append(([a[:1]], [2], (5, 3), [[0,0,1],[0,0,0],[0,0,0],[0,0,0],[0,0,0]])) cases.append(([a[:3],b[:1]], [0,2], (3, 3), [[1,0,6],[0,2,0],[0,0,3]])) cases.append(([a[:2],b[:3]], [-1,0], (3, 4), [[6,0,0,0],[1,7,0,0],[0,2,8,0]])) cases.append(([a[:4],b[:3]], [2,-3], (6, 6), [[0,0,1,0,0,0], [0,0,0,2,0,0], [0,0,0,0,3,0], [6,0,0,0,0,4], [0,7,0,0,0,0], [0,0,8,0,0,0]])) cases.append(([a[:4],b,c[:4]], [-1,0,1], (5, 5), [[6,11, 0, 0, 0], [1, 7,12, 0, 0], [0, 2, 8,13, 0], [0, 0, 3, 9,14], [0, 0, 0, 4,10]])) cases.append(([a[:2],b[:3],c], [-4,2,-1], (6, 5), [[0, 0, 6, 0, 0], [11, 0, 0, 7, 0], [0,12, 0, 0, 8], [0, 0,13, 0, 0], [1, 0, 0,14, 0], [0, 2, 0, 0,15]])) # too long arrays are OK cases.append(([a], [0], (1, 1), [[1]])) cases.append(([a[:3],b], [0,2], (3, 3), [[1, 0, 6], [0, 2, 0], [0, 0, 3]])) cases.append(( np.array([[1, 2, 3], [4, 5, 6]]), [0,-1], (3, 3), [[1, 0, 0], [4, 2, 0], [0, 5, 3]] )) # scalar case: broadcasting cases.append(([1,-2,1], [1,0,-1], (3, 3), [[-2, 1, 0], [1, -2, 1], [0, 1, -2]])) for d, o, shape, result in cases: err_msg = f"{d!r} {o!r} {shape!r} {result!r}" assert_equal(construct.diags(d, offsets=o, shape=shape).toarray(), result, err_msg=err_msg) if (shape[0] == shape[1] and hasattr(d[0], '__len__') and len(d[0]) <= max(shape)): # should be able to find the shape automatically assert_equal(construct.diags(d, offsets=o).toarray(), result, err_msg=err_msg) def test_diags_default(self): a = array([1, 2, 3, 4, 5]) assert_equal(construct.diags(a).toarray(), np.diag(a)) def test_diags_default_bad(self): a = array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6]]) assert_raises(ValueError, construct.diags, a) def test_diags_bad(self): a = array([1, 2, 3, 4, 5]) b = array([6, 7, 8, 9, 10]) c = array([11, 12, 13, 14, 15]) cases = [] cases.append(([a[:0]], 0, (1, 1))) cases.append(([a[:4],b,c[:3]], [-1,0,1], (5, 5))) cases.append(([a[:2],c,b[:3]], [-4,2,-1], (6, 5))) cases.append(([a[:2],c,b[:3]], [-4,2,-1], None)) cases.append(([], [-4,2,-1], None)) cases.append(([1], [-5], (4, 4))) cases.append(([a], 0, None)) for d, o, shape in cases: assert_raises(ValueError, construct.diags, d, offsets=o, shape=shape) assert_raises(TypeError, construct.diags, [[None]], offsets=[0]) def test_diags_vs_diag(self): # Check that # # diags([a, b, ...], [i, j, ...]) == diag(a, i) + diag(b, j) + ... # np.random.seed(1234) for n_diags in [1, 2, 3, 4, 5, 10]: n = 1 + n_diags//2 + np.random.randint(0, 10) offsets = np.arange(-n+1, n-1) np.random.shuffle(offsets) offsets = offsets[:n_diags] diagonals = [np.random.rand(n - abs(q)) for q in offsets] mat = construct.diags(diagonals, offsets=offsets) dense_mat = sum([np.diag(x, j) for x, j in zip(diagonals, offsets)]) assert_array_almost_equal_nulp(mat.toarray(), dense_mat) if len(offsets) == 1: mat = construct.diags(diagonals[0], offsets=offsets[0]) dense_mat = np.diag(diagonals[0], offsets[0]) assert_array_almost_equal_nulp(mat.toarray(), dense_mat) def test_diags_dtype(self): x = construct.diags([2.2], offsets=[0], shape=(2, 2), dtype=int) assert_equal(x.dtype, int) assert_equal(x.toarray(), [[2, 0], [0, 2]]) def test_diags_one_diagonal(self): d = list(range(5)) for k in range(-5, 6): assert_equal(construct.diags(d, offsets=k).toarray(), construct.diags([d], offsets=[k]).toarray()) def test_diags_empty(self): x = construct.diags([]) assert_equal(x.shape, (0, 0)) @pytest.mark.parametrize("identity", [construct.identity, construct.eye_array]) def test_identity(self, identity): assert_equal(identity(1).toarray(), [[1]]) assert_equal(identity(2).toarray(), [[1,0],[0,1]]) I = identity(3, dtype='int8', format='dia') assert_equal(I.dtype, np.dtype('int8')) assert_equal(I.format, 'dia') for fmt in sparse_formats: I = identity(3, format=fmt) assert_equal(I.format, fmt) assert_equal(I.toarray(), [[1,0,0],[0,1,0],[0,0,1]]) @pytest.mark.parametrize("eye", [construct.eye, construct.eye_array]) def test_eye(self, eye): assert_equal(eye(1,1).toarray(), [[1]]) assert_equal(eye(2,3).toarray(), [[1,0,0],[0,1,0]]) assert_equal(eye(3,2).toarray(), [[1,0],[0,1],[0,0]]) assert_equal(eye(3,3).toarray(), [[1,0,0],[0,1,0],[0,0,1]]) assert_equal(eye(3,3,dtype='int16').dtype, np.dtype('int16')) for m in [3, 5]: for n in [3, 5]: for k in range(-5,6): # scipy.sparse.eye deviates from np.eye here. np.eye will # create arrays of all 0's when the diagonal offset is # greater than the size of the array. For sparse arrays # this makes less sense, especially as it results in dia # arrays with negative diagonals. Therefore sp.sparse.eye # validates that diagonal offsets fall within the shape of # the array. See gh-18555. if (k > 0 and k > n) or (k < 0 and abs(k) > m): with pytest.raises( ValueError, match="Offset.*out of bounds" ): eye(m, n, k=k) else: assert_equal( eye(m, n, k=k).toarray(), np.eye(m, n, k=k) ) if m == n: assert_equal( eye(m, k=k).toarray(), np.eye(m, n, k=k) ) @pytest.mark.parametrize("eye", [construct.eye, construct.eye_array]) def test_eye_one(self, eye): assert_equal(eye(1).toarray(), [[1]]) assert_equal(eye(2).toarray(), [[1,0],[0,1]]) I = eye(3, dtype='int8', format='dia') assert_equal(I.dtype, np.dtype('int8')) assert_equal(I.format, 'dia') for fmt in sparse_formats: I = eye(3, format=fmt) assert_equal(I.format, fmt) assert_equal(I.toarray(), [[1,0,0],[0,1,0],[0,0,1]]) def test_eye_array_vs_matrix(self): assert isinstance(construct.eye_array(3), sparray) assert not isinstance(construct.eye(3), sparray) def test_kron(self): cases = [] cases.append(array([[0]])) cases.append(array([[-1]])) cases.append(array([[4]])) cases.append(array([[10]])) cases.append(array([[0],[0]])) cases.append(array([[0,0]])) cases.append(array([[1,2],[3,4]])) cases.append(array([[0,2],[5,0]])) cases.append(array([[0,2,-6],[8,0,14]])) cases.append(array([[5,4],[0,0],[6,0]])) cases.append(array([[5,4,4],[1,0,0],[6,0,8]])) cases.append(array([[0,1,0,2,0,5,8]])) cases.append(array([[0.5,0.125,0,3.25],[0,2.5,0,0]])) # test all cases with some formats for a in cases: ca = csr_array(a) for b in cases: cb = csr_array(b) expected = np.kron(a, b) for fmt in sparse_formats[1:4]: result = construct.kron(ca, cb, format=fmt) assert_equal(result.format, fmt) assert_array_equal(result.toarray(), expected) assert isinstance(result, sparray) # test one case with all formats a = cases[-1] b = cases[-3] ca = csr_array(a) cb = csr_array(b) expected = np.kron(a, b) for fmt in sparse_formats: result = construct.kron(ca, cb, format=fmt) assert_equal(result.format, fmt) assert_array_equal(result.toarray(), expected) assert isinstance(result, sparray) # check that spmatrix returned when both inputs are spmatrix result = construct.kron(csr_matrix(a), csr_matrix(b), format=fmt) assert_equal(result.format, fmt) assert_array_equal(result.toarray(), expected) assert isinstance(result, spmatrix) def test_kron_large(self): n = 2**16 a = construct.diags_array([1], shape=(1, n), offsets=n-1) b = construct.diags_array([1], shape=(n, 1), offsets=1-n) construct.kron(a, a) construct.kron(b, b) def test_kronsum(self): cases = [] cases.append(array([[0]])) cases.append(array([[-1]])) cases.append(array([[4]])) cases.append(array([[10]])) cases.append(array([[1,2],[3,4]])) cases.append(array([[0,2],[5,0]])) cases.append(array([[0,2,-6],[8,0,14],[0,3,0]])) cases.append(array([[1,0,0],[0,5,-1],[4,-2,8]])) # test all cases with default format for a in cases: for b in cases: result = construct.kronsum(csr_array(a), csr_array(b)).toarray() expected = (np.kron(np.eye(b.shape[0]), a) + np.kron(b, np.eye(a.shape[0]))) assert_array_equal(result, expected) # check that spmatrix returned when both inputs are spmatrix result = construct.kronsum(csr_matrix(a), csr_matrix(b)).toarray() assert_array_equal(result, expected) @pytest.mark.parametrize("coo_cls", [coo_matrix, coo_array]) def test_vstack(self, coo_cls): A = coo_cls([[1,2],[3,4]]) B = coo_cls([[5,6]]) expected = array([[1, 2], [3, 4], [5, 6]]) assert_equal(construct.vstack([A, B]).toarray(), expected) assert_equal(construct.vstack([A, B], dtype=np.float32).dtype, np.float32) assert_equal(construct.vstack([A.todok(), B.todok()]).toarray(), expected) assert_equal(construct.vstack([A.tocsr(), B.tocsr()]).toarray(), expected) result = construct.vstack([A.tocsr(), B.tocsr()], format="csr", dtype=np.float32) assert_equal(result.dtype, np.float32) assert_equal(result.indices.dtype, np.int32) assert_equal(result.indptr.dtype, np.int32) assert_equal(construct.vstack([A.tocsc(), B.tocsc()]).toarray(), expected) result = construct.vstack([A.tocsc(), B.tocsc()], format="csc", dtype=np.float32) assert_equal(result.dtype, np.float32) assert_equal(result.indices.dtype, np.int32) assert_equal(result.indptr.dtype, np.int32) def test_vstack_matrix_or_array(self): A = [[1,2],[3,4]] B = [[5,6]] assert isinstance(construct.vstack([coo_array(A), coo_array(B)]), sparray) assert isinstance(construct.vstack([coo_array(A), coo_matrix(B)]), sparray) assert isinstance(construct.vstack([coo_matrix(A), coo_array(B)]), sparray) assert isinstance(construct.vstack([coo_matrix(A), coo_matrix(B)]), spmatrix) @pytest.mark.parametrize("coo_cls", [coo_matrix, coo_array]) def test_hstack(self, coo_cls): A = coo_cls([[1,2],[3,4]]) B = coo_cls([[5],[6]]) expected = array([[1, 2, 5], [3, 4, 6]]) assert_equal(construct.hstack([A, B]).toarray(), expected) assert_equal(construct.hstack([A, B], dtype=np.float32).dtype, np.float32) assert_equal(construct.hstack([A.todok(), B.todok()]).toarray(), expected) assert_equal(construct.hstack([A.tocsc(), B.tocsc()]).toarray(), expected) assert_equal(construct.hstack([A.tocsc(), B.tocsc()], dtype=np.float32).dtype, np.float32) assert_equal(construct.hstack([A.tocsr(), B.tocsr()]).toarray(), expected) assert_equal(construct.hstack([A.tocsr(), B.tocsr()], dtype=np.float32).dtype, np.float32) def test_hstack_matrix_or_array(self): A = [[1,2],[3,4]] B = [[5],[6]] assert isinstance(construct.hstack([coo_array(A), coo_array(B)]), sparray) assert isinstance(construct.hstack([coo_array(A), coo_matrix(B)]), sparray) assert isinstance(construct.hstack([coo_matrix(A), coo_array(B)]), sparray) assert isinstance(construct.hstack([coo_matrix(A), coo_matrix(B)]), spmatrix) @pytest.mark.parametrize("block_array", (construct.bmat, construct.block_array)) def test_block_creation(self, block_array): A = coo_array([[1, 2], [3, 4]]) B = coo_array([[5],[6]]) C = coo_array([[7]]) D = coo_array((0, 0)) expected = array([[1, 2, 5], [3, 4, 6], [0, 0, 7]]) assert_equal(block_array([[A, B], [None, C]]).toarray(), expected) E = csr_array((1, 2), dtype=np.int32) assert_equal(block_array([[A.tocsr(), B.tocsr()], [E, C.tocsr()]]).toarray(), expected) assert_equal(block_array([[A.tocsc(), B.tocsc()], [E.tocsc(), C.tocsc()]]).toarray(), expected) expected = array([[1, 2, 0], [3, 4, 0], [0, 0, 7]]) assert_equal(block_array([[A, None], [None, C]]).toarray(), expected) assert_equal(block_array([[A.tocsr(), E.T.tocsr()], [E, C.tocsr()]]).toarray(), expected) assert_equal(block_array([[A.tocsc(), E.T.tocsc()], [E.tocsc(), C.tocsc()]]).toarray(), expected) Z = csr_array((1, 1), dtype=np.int32) expected = array([[0, 5], [0, 6], [7, 0]]) assert_equal(block_array([[None, B], [C, None]]).toarray(), expected) assert_equal(block_array([[E.T.tocsr(), B.tocsr()], [C.tocsr(), Z]]).toarray(), expected) assert_equal(block_array([[E.T.tocsc(), B.tocsc()], [C.tocsc(), Z.tocsc()]]).toarray(), expected) expected = np.empty((0, 0)) assert_equal(block_array([[None, None]]).toarray(), expected) assert_equal(block_array([[None, D], [D, None]]).toarray(), expected) # test bug reported in gh-5976 expected = array([[7]]) assert_equal(block_array([[None, D], [C, None]]).toarray(), expected) # test failure cases with assert_raises(ValueError) as excinfo: block_array([[A], [B]]) excinfo.match(r'Got blocks\[1,0\]\.shape\[1\] == 1, expected 2') with assert_raises(ValueError) as excinfo: block_array([[A.tocsr()], [B.tocsr()]]) excinfo.match(r'incompatible dimensions for axis 1') with assert_raises(ValueError) as excinfo: block_array([[A.tocsc()], [B.tocsc()]]) excinfo.match(r'Mismatching dimensions along axis 1: ({1, 2}|{2, 1})') with assert_raises(ValueError) as excinfo: block_array([[A, C]]) excinfo.match(r'Got blocks\[0,1\]\.shape\[0\] == 1, expected 2') with assert_raises(ValueError) as excinfo: block_array([[A.tocsr(), C.tocsr()]]) excinfo.match(r'Mismatching dimensions along axis 0: ({1, 2}|{2, 1})') with assert_raises(ValueError) as excinfo: block_array([[A.tocsc(), C.tocsc()]]) excinfo.match(r'incompatible dimensions for axis 0') def test_block_return_type(self): block = construct.block_array # csr format ensures we hit _compressed_sparse_stack # shape of F,G ensure we hit _stack_along_minor_axis # list version ensure we hit the path with neither helper function Fl, Gl = [[1, 2],[3, 4]], [[7], [5]] Fm, Gm = csr_matrix(Fl), csr_matrix(Gl) assert isinstance(block([[None, Fl], [Gl, None]], format="csr"), sparray) assert isinstance(block([[None, Fm], [Gm, None]], format="csr"), sparray) assert isinstance(block([[Fm, Gm]], format="csr"), sparray) def test_bmat_return_type(self): """This can be removed after sparse matrix is removed""" bmat = construct.bmat # check return type. if any input _is_array output array, else matrix Fl, Gl = [[1, 2],[3, 4]], [[7], [5]] Fm, Gm = csr_matrix(Fl), csr_matrix(Gl) Fa, Ga = csr_array(Fl), csr_array(Gl) assert isinstance(bmat([[Fa, Ga]], format="csr"), sparray) assert isinstance(bmat([[Fm, Gm]], format="csr"), spmatrix) assert isinstance(bmat([[None, Fa], [Ga, None]], format="csr"), sparray) assert isinstance(bmat([[None, Fm], [Ga, None]], format="csr"), sparray) assert isinstance(bmat([[None, Fm], [Gm, None]], format="csr"), spmatrix) assert isinstance(bmat([[None, Fl], [Gl, None]], format="csr"), spmatrix) # type returned by _compressed_sparse_stack (all csr) assert isinstance(bmat([[Ga, Ga]], format="csr"), sparray) assert isinstance(bmat([[Gm, Ga]], format="csr"), sparray) assert isinstance(bmat([[Ga, Gm]], format="csr"), sparray) assert isinstance(bmat([[Gm, Gm]], format="csr"), spmatrix) # shape is 2x2 so no _stack_along_minor_axis assert isinstance(bmat([[Fa, Fm]], format="csr"), sparray) assert isinstance(bmat([[Fm, Fm]], format="csr"), spmatrix) # type returned by _compressed_sparse_stack (all csc) assert isinstance(bmat([[Gm.tocsc(), Ga.tocsc()]], format="csc"), sparray) assert isinstance(bmat([[Gm.tocsc(), Gm.tocsc()]], format="csc"), spmatrix) # shape is 2x2 so no _stack_along_minor_axis assert isinstance(bmat([[Fa.tocsc(), Fm.tocsc()]], format="csr"), sparray) assert isinstance(bmat([[Fm.tocsc(), Fm.tocsc()]], format="csr"), spmatrix) # type returned when mixed input assert isinstance(bmat([[Gl, Ga]], format="csr"), sparray) assert isinstance(bmat([[Gm.tocsc(), Ga]], format="csr"), sparray) assert isinstance(bmat([[Gm.tocsc(), Gm]], format="csr"), spmatrix) assert isinstance(bmat([[Gm, Gm]], format="csc"), spmatrix) @pytest.mark.slow @pytest.mark.xfail_on_32bit("Can't create large array for test") def test_concatenate_int32_overflow(self): """ test for indptr overflow when concatenating matrices """ check_free_memory(30000) n = 33000 A = csr_array(np.ones((n, n), dtype=bool)) B = A.copy() C = construct._compressed_sparse_stack((A, B), axis=0, return_spmatrix=False) assert_(np.all(np.equal(np.diff(C.indptr), n))) assert_equal(C.indices.dtype, np.int64) assert_equal(C.indptr.dtype, np.int64) def test_block_diag_basic(self): """ basic test for block_diag """ A = coo_array([[1,2],[3,4]]) B = coo_array([[5],[6]]) C = coo_array([[7]]) expected = array([[1, 2, 0, 0], [3, 4, 0, 0], [0, 0, 5, 0], [0, 0, 6, 0], [0, 0, 0, 7]]) assert_equal(construct.block_diag((A, B, C)).toarray(), expected) def test_block_diag_scalar_1d_args(self): """ block_diag with scalar and 1d arguments """ # one 1d matrix and a scalar assert_array_equal(construct.block_diag([[2,3], 4]).toarray(), [[2, 3, 0], [0, 0, 4]]) # 1d sparse arrays A = coo_array([1,0,3]) B = coo_array([0,4]) assert_array_equal(construct.block_diag([A, B]).toarray(), [[1, 0, 3, 0, 0], [0, 0, 0, 0, 4]]) def test_block_diag_1(self): """ block_diag with one matrix """ assert_equal(construct.block_diag([[1, 0]]).toarray(), array([[1, 0]])) assert_equal(construct.block_diag([[[1, 0]]]).toarray(), array([[1, 0]])) assert_equal(construct.block_diag([[[1], [0]]]).toarray(), array([[1], [0]])) # just on scalar assert_equal(construct.block_diag([1]).toarray(), array([[1]])) def test_block_diag_sparse_arrays(self): """ block_diag with sparse arrays """ A = coo_array([[1, 2, 3]], shape=(1, 3)) B = coo_array([[4, 5]], shape=(1, 2)) assert_equal(construct.block_diag([A, B]).toarray(), array([[1, 2, 3, 0, 0], [0, 0, 0, 4, 5]])) A = coo_array([[1], [2], [3]], shape=(3, 1)) B = coo_array([[4], [5]], shape=(2, 1)) assert_equal(construct.block_diag([A, B]).toarray(), array([[1, 0], [2, 0], [3, 0], [0, 4], [0, 5]])) def test_block_diag_return_type(self): A, B = coo_array([[1, 2, 3]]), coo_matrix([[2, 3, 4]]) assert isinstance(construct.block_diag([A, A]), sparray) assert isinstance(construct.block_diag([A, B]), sparray) assert isinstance(construct.block_diag([B, A]), sparray) assert isinstance(construct.block_diag([B, B]), spmatrix) def test_random_sampling(self): # Simple sanity checks for sparse random sampling. for f in sprand, _sprandn: for t in [np.float32, np.float64, np.longdouble, np.int32, np.int64, np.complex64, np.complex128]: x = f(5, 10, density=0.1, dtype=t) assert_equal(x.dtype, t) assert_equal(x.shape, (5, 10)) assert_equal(x.nnz, 5) x1 = f(5, 10, density=0.1, random_state=4321) assert_equal(x1.dtype, np.float64) x2 = f(5, 10, density=0.1, random_state=np.random.RandomState(4321)) assert_array_equal(x1.data, x2.data) assert_array_equal(x1.row, x2.row) assert_array_equal(x1.col, x2.col) for density in [0.0, 0.1, 0.5, 1.0]: x = f(5, 10, density=density) assert_equal(x.nnz, int(density * np.prod(x.shape))) for fmt in ['coo', 'csc', 'csr', 'lil']: x = f(5, 10, format=fmt) assert_equal(x.format, fmt) assert_raises(ValueError, lambda: f(5, 10, 1.1)) assert_raises(ValueError, lambda: f(5, 10, -0.1)) def test_rand(self): # Simple distributional checks for sparse.rand. random_states = [None, 4321, np.random.RandomState()] try: gen = np.random.default_rng() random_states.append(gen) except AttributeError: pass for random_state in random_states: x = sprand(10, 20, density=0.5, dtype=np.float64, random_state=random_state) assert_(np.all(np.less_equal(0, x.data))) assert_(np.all(np.less_equal(x.data, 1))) def test_randn(self): # Simple distributional checks for sparse.randn. # Statistically, some of these should be negative # and some should be greater than 1. random_states = [None, 4321, np.random.RandomState()] try: gen = np.random.default_rng() random_states.append(gen) except AttributeError: pass for rs in random_states: x = _sprandn(10, 20, density=0.5, dtype=np.float64, random_state=rs) assert_(np.any(np.less(x.data, 0))) assert_(np.any(np.less(1, x.data))) x = _sprandn_array(10, 20, density=0.5, dtype=np.float64, random_state=rs) assert_(np.any(np.less(x.data, 0))) assert_(np.any(np.less(1, x.data))) def test_random_accept_str_dtype(self): # anything that np.dtype can convert to a dtype should be accepted # for the dtype construct.random(10, 10, dtype='d') construct.random_array((10, 10), dtype='d') def test_random_sparse_matrix_returns_correct_number_of_non_zero_elements(self): # A 10 x 10 matrix, with density of 12.65%, should have 13 nonzero elements. # 10 x 10 x 0.1265 = 12.65, which should be rounded up to 13, not 12. sparse_matrix = construct.random(10, 10, density=0.1265) assert_equal(sparse_matrix.count_nonzero(),13) # check random_array sparse_array = construct.random_array((10, 10), density=0.1265) assert_equal(sparse_array.count_nonzero(),13) assert isinstance(sparse_array, sparray) # check big size shape = (2**33, 2**33) sparse_array = construct.random_array(shape, density=2.7105e-17) assert_equal(sparse_array.count_nonzero(),2000) def test_diags_array(): """Tests of diags_array that do not rely on diags wrapper.""" diag = np.arange(1, 5) assert_array_equal(construct.diags_array(diag).toarray(), np.diag(diag)) assert_array_equal( construct.diags_array(diag, offsets=2).toarray(), np.diag(diag, k=2) ) assert_array_equal( construct.diags_array(diag, offsets=2, shape=(4, 4)).toarray(), np.diag(diag, k=2)[:4, :4] ) # Offset outside bounds when shape specified with pytest.raises(ValueError, match=".*out of bounds"): construct.diags(np.arange(1, 5), 5, shape=(4, 4))