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eaiaiaiaoi/venv/lib/python2.7/site-packages/scipy/sparse/tests/test_base.py

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#
# Authors: Travis Oliphant, Ed Schofield, Robert Cimrman, Nathan Bell, and others
""" Test functions for sparse matrices. Each class in the "Matrix class
based tests" section become subclasses of the classes in the "Generic
tests" section. This is done by the functions in the "Tailored base
class for generic tests" section.
"""
from __future__ import division, print_function, absolute_import
__usage__ = """
Build sparse:
python setup.py build
Run tests if scipy is installed:
python -c 'import scipy;scipy.sparse.test()'
Run tests if sparse is not installed:
python tests/test_base.py
"""
import operator
import contextlib
import functools
from distutils.version import LooseVersion
import numpy as np
from scipy._lib.six import xrange, zip as izip
from numpy import (arange, zeros, array, dot, matrix, asmatrix, asarray,
vstack, ndarray, transpose, diag, kron, inf, conjugate,
int8, ComplexWarning)
import random
from numpy.testing import (assert_equal, assert_array_equal,
assert_array_almost_equal, assert_almost_equal, assert_,
assert_allclose)
from pytest import raises as assert_raises
from scipy._lib._numpy_compat import suppress_warnings
import scipy.linalg
import scipy.sparse as sparse
from scipy.sparse import (csc_matrix, csr_matrix, dok_matrix,
coo_matrix, lil_matrix, dia_matrix, bsr_matrix,
eye, isspmatrix, SparseEfficiencyWarning, issparse)
from scipy.sparse.sputils import supported_dtypes, isscalarlike, get_index_dtype
from scipy.sparse.linalg import splu, expm, inv
from scipy._lib._version import NumpyVersion
from scipy._lib.decorator import decorator
import pytest
def assert_in(member, collection, msg=None):
assert_(member in collection, msg=msg if msg is not None else "%r not found in %r" % (member, collection))
def assert_array_equal_dtype(x, y, **kwargs):
assert_(x.dtype == y.dtype)
assert_array_equal(x, y, **kwargs)
# Only test matmul operator (A @ B) when available (Python 3.5+)
TEST_MATMUL = hasattr(operator, 'matmul')
sup_complex = suppress_warnings()
sup_complex.filter(ComplexWarning)
def with_64bit_maxval_limit(maxval_limit=None, random=False, fixed_dtype=None,
downcast_maxval=None, assert_32bit=False):
"""
Monkeypatch the maxval threshold at which scipy.sparse switches to
64-bit index arrays, or make it (pseudo-)random.
"""
if maxval_limit is None:
maxval_limit = 10
if assert_32bit:
def new_get_index_dtype(arrays=(), maxval=None, check_contents=False):
tp = get_index_dtype(arrays, maxval, check_contents)
assert_equal(np.iinfo(tp).max, np.iinfo(np.int32).max)
assert_(tp == np.int32 or tp == np.intc)
return tp
elif fixed_dtype is not None:
def new_get_index_dtype(arrays=(), maxval=None, check_contents=False):
return fixed_dtype
elif random:
counter = np.random.RandomState(seed=1234)
def new_get_index_dtype(arrays=(), maxval=None, check_contents=False):
return (np.int32, np.int64)[counter.randint(2)]
else:
def new_get_index_dtype(arrays=(), maxval=None, check_contents=False):
dtype = np.int32
if maxval is not None:
if maxval > maxval_limit:
dtype = np.int64
for arr in arrays:
arr = np.asarray(arr)
if arr.dtype > np.int32:
if check_contents:
if arr.size == 0:
# a bigger type not needed
continue
elif np.issubdtype(arr.dtype, np.integer):
maxval = arr.max()
minval = arr.min()
if minval >= -maxval_limit and maxval <= maxval_limit:
# a bigger type not needed
continue
dtype = np.int64
return dtype
if downcast_maxval is not None:
def new_downcast_intp_index(arr):
if arr.max() > downcast_maxval:
raise AssertionError("downcast limited")
return arr.astype(np.intp)
@decorator
def deco(func, *a, **kw):
backup = []
modules = [scipy.sparse.bsr, scipy.sparse.coo, scipy.sparse.csc,
scipy.sparse.csr, scipy.sparse.dia, scipy.sparse.dok,
scipy.sparse.lil, scipy.sparse.sputils,
scipy.sparse.compressed, scipy.sparse.construct]
try:
for mod in modules:
backup.append((mod, 'get_index_dtype',
getattr(mod, 'get_index_dtype', None)))
setattr(mod, 'get_index_dtype', new_get_index_dtype)
if downcast_maxval is not None:
backup.append((mod, 'downcast_intp_index',
getattr(mod, 'downcast_intp_index', None)))
setattr(mod, 'downcast_intp_index', new_downcast_intp_index)
return func(*a, **kw)
finally:
for mod, name, oldfunc in backup:
if oldfunc is not None:
setattr(mod, name, oldfunc)
return deco
def todense(a):
if isinstance(a, np.ndarray) or isscalarlike(a):
return a
return a.todense()
class BinopTester(object):
# Custom type to test binary operations on sparse matrices.
def __add__(self, mat):
return "matrix on the right"
def __mul__(self, mat):
return "matrix on the right"
def __sub__(self, mat):
return "matrix on the right"
def __radd__(self, mat):
return "matrix on the left"
def __rmul__(self, mat):
return "matrix on the left"
def __rsub__(self, mat):
return "matrix on the left"
def __matmul__(self, mat):
return "matrix on the right"
def __rmatmul__(self, mat):
return "matrix on the left"
class BinopTester_with_shape(object):
# Custom type to test binary operations on sparse matrices
# with object which has shape attribute.
def __init__(self,shape):
self._shape = shape
def shape(self):
return self._shape
def ndim(self):
return len(self._shape)
def __add__(self, mat):
return "matrix on the right"
def __mul__(self, mat):
return "matrix on the right"
def __sub__(self, mat):
return "matrix on the right"
def __radd__(self, mat):
return "matrix on the left"
def __rmul__(self, mat):
return "matrix on the left"
def __rsub__(self, mat):
return "matrix on the left"
def __matmul__(self, mat):
return "matrix on the right"
def __rmatmul__(self, mat):
return "matrix on the left"
#------------------------------------------------------------------------------
# Generic tests
#------------------------------------------------------------------------------
# TODO check that spmatrix( ... , copy=X ) is respected
# TODO test prune
# TODO test has_sorted_indices
class _TestCommon(object):
"""test common functionality shared by all sparse formats"""
math_dtypes = supported_dtypes
@classmethod
def init_class(cls):
# Canonical data.
cls.dat = matrix([[1,0,0,2],[3,0,1,0],[0,2,0,0]],'d')
cls.datsp = cls.spmatrix(cls.dat)
# Some sparse and dense matrices with data for every supported
# dtype.
# This set union is a workaround for numpy#6295, which means that
# two np.int64 dtypes don't hash to the same value.
cls.checked_dtypes = set(supported_dtypes).union(cls.math_dtypes)
cls.dat_dtypes = {}
cls.datsp_dtypes = {}
for dtype in cls.checked_dtypes:
cls.dat_dtypes[dtype] = cls.dat.astype(dtype)
cls.datsp_dtypes[dtype] = cls.spmatrix(cls.dat.astype(dtype))
# Check that the original data is equivalent to the
# corresponding dat_dtypes & datsp_dtypes.
assert_equal(cls.dat, cls.dat_dtypes[np.float64])
assert_equal(cls.datsp.todense(),
cls.datsp_dtypes[np.float64].todense())
def test_bool(self):
def check(dtype):
datsp = self.datsp_dtypes[dtype]
assert_raises(ValueError, bool, datsp)
assert_(self.spmatrix([1]))
assert_(not self.spmatrix([0]))
if isinstance(self, TestDOK):
pytest.skip("Cannot create a rank <= 2 DOK matrix.")
for dtype in self.checked_dtypes:
check(dtype)
def test_bool_rollover(self):
# bool's underlying dtype is 1 byte, check that it does not
# rollover True -> False at 256.
dat = np.matrix([[True, False]])
datsp = self.spmatrix(dat)
for _ in range(10):
datsp = datsp + datsp
dat = dat + dat
assert_array_equal(dat, datsp.todense())
def test_eq(self):
sup = suppress_warnings()
sup.filter(SparseEfficiencyWarning)
@sup
@sup_complex
def check(dtype):
dat = self.dat_dtypes[dtype]
datsp = self.datsp_dtypes[dtype]
dat2 = dat.copy()
dat2[:,0] = 0
datsp2 = self.spmatrix(dat2)
datbsr = bsr_matrix(dat)
datcsr = csr_matrix(dat)
datcsc = csc_matrix(dat)
datlil = lil_matrix(dat)
# sparse/sparse
assert_array_equal_dtype(dat == dat2, (datsp == datsp2).todense())
# mix sparse types
assert_array_equal_dtype(dat == dat2, (datbsr == datsp2).todense())
assert_array_equal_dtype(dat == dat2, (datcsr == datsp2).todense())
assert_array_equal_dtype(dat == dat2, (datcsc == datsp2).todense())
assert_array_equal_dtype(dat == dat2, (datlil == datsp2).todense())
# sparse/dense
assert_array_equal_dtype(dat == datsp2, datsp2 == dat)
# sparse/scalar
assert_array_equal_dtype(dat == 0, (datsp == 0).todense())
assert_array_equal_dtype(dat == 1, (datsp == 1).todense())
assert_array_equal_dtype(dat == np.nan,
(datsp == np.nan).todense())
if not isinstance(self, (TestBSR, TestCSC, TestCSR)):
pytest.skip("Bool comparisons only implemented for BSR, CSC, and CSR.")
for dtype in self.checked_dtypes:
check(dtype)
def test_ne(self):
sup = suppress_warnings()
sup.filter(SparseEfficiencyWarning)
@sup
@sup_complex
def check(dtype):
dat = self.dat_dtypes[dtype]
datsp = self.datsp_dtypes[dtype]
dat2 = dat.copy()
dat2[:,0] = 0
datsp2 = self.spmatrix(dat2)
datbsr = bsr_matrix(dat)
datcsc = csc_matrix(dat)
datcsr = csr_matrix(dat)
datlil = lil_matrix(dat)
# sparse/sparse
assert_array_equal_dtype(dat != dat2, (datsp != datsp2).todense())
# mix sparse types
assert_array_equal_dtype(dat != dat2, (datbsr != datsp2).todense())
assert_array_equal_dtype(dat != dat2, (datcsc != datsp2).todense())
assert_array_equal_dtype(dat != dat2, (datcsr != datsp2).todense())
assert_array_equal_dtype(dat != dat2, (datlil != datsp2).todense())
# sparse/dense
assert_array_equal_dtype(dat != datsp2, datsp2 != dat)
# sparse/scalar
assert_array_equal_dtype(dat != 0, (datsp != 0).todense())
assert_array_equal_dtype(dat != 1, (datsp != 1).todense())
assert_array_equal_dtype(0 != dat, (0 != datsp).todense())
assert_array_equal_dtype(1 != dat, (1 != datsp).todense())
assert_array_equal_dtype(dat != np.nan,
(datsp != np.nan).todense())
if not isinstance(self, (TestBSR, TestCSC, TestCSR)):
pytest.skip("Bool comparisons only implemented for BSR, CSC, and CSR.")
for dtype in self.checked_dtypes:
check(dtype)
def test_lt(self):
sup = suppress_warnings()
sup.filter(SparseEfficiencyWarning)
@sup
@sup_complex
def check(dtype):
# data
dat = self.dat_dtypes[dtype]
datsp = self.datsp_dtypes[dtype]
dat2 = dat.copy()
dat2[:,0] = 0
datsp2 = self.spmatrix(dat2)
datcomplex = dat.astype(complex)
datcomplex[:,0] = 1 + 1j
datspcomplex = self.spmatrix(datcomplex)
datbsr = bsr_matrix(dat)
datcsc = csc_matrix(dat)
datcsr = csr_matrix(dat)
datlil = lil_matrix(dat)
# sparse/sparse
assert_array_equal_dtype(dat < dat2, (datsp < datsp2).todense())
assert_array_equal_dtype(datcomplex < dat2,
(datspcomplex < datsp2).todense())
# mix sparse types
assert_array_equal_dtype(dat < dat2, (datbsr < datsp2).todense())
assert_array_equal_dtype(dat < dat2, (datcsc < datsp2).todense())
assert_array_equal_dtype(dat < dat2, (datcsr < datsp2).todense())
assert_array_equal_dtype(dat < dat2, (datlil < datsp2).todense())
assert_array_equal_dtype(dat2 < dat, (datsp2 < datbsr).todense())
assert_array_equal_dtype(dat2 < dat, (datsp2 < datcsc).todense())
assert_array_equal_dtype(dat2 < dat, (datsp2 < datcsr).todense())
assert_array_equal_dtype(dat2 < dat, (datsp2 < datlil).todense())
# sparse/dense
assert_array_equal_dtype(dat < dat2, datsp < dat2)
assert_array_equal_dtype(datcomplex < dat2, datspcomplex < dat2)
# sparse/scalar
assert_array_equal_dtype((datsp < 2).todense(), dat < 2)
assert_array_equal_dtype((datsp < 1).todense(), dat < 1)
assert_array_equal_dtype((datsp < 0).todense(), dat < 0)
assert_array_equal_dtype((datsp < -1).todense(), dat < -1)
assert_array_equal_dtype((datsp < -2).todense(), dat < -2)
with np.errstate(invalid='ignore'):
assert_array_equal_dtype((datsp < np.nan).todense(),
dat < np.nan)
assert_array_equal_dtype((2 < datsp).todense(), 2 < dat)
assert_array_equal_dtype((1 < datsp).todense(), 1 < dat)
assert_array_equal_dtype((0 < datsp).todense(), 0 < dat)
assert_array_equal_dtype((-1 < datsp).todense(), -1 < dat)
assert_array_equal_dtype((-2 < datsp).todense(), -2 < dat)
# data
dat = self.dat_dtypes[dtype]
datsp = self.datsp_dtypes[dtype]
dat2 = dat.copy()
dat2[:,0] = 0
datsp2 = self.spmatrix(dat2)
# dense rhs
assert_array_equal_dtype(dat < datsp2, datsp < dat2)
if not isinstance(self, (TestBSR, TestCSC, TestCSR)):
pytest.skip("Bool comparisons only implemented for BSR, CSC, and CSR.")
for dtype in self.checked_dtypes:
check(dtype)
def test_gt(self):
sup = suppress_warnings()
sup.filter(SparseEfficiencyWarning)
@sup
@sup_complex
def check(dtype):
dat = self.dat_dtypes[dtype]
datsp = self.datsp_dtypes[dtype]
dat2 = dat.copy()
dat2[:,0] = 0
datsp2 = self.spmatrix(dat2)
datcomplex = dat.astype(complex)
datcomplex[:,0] = 1 + 1j
datspcomplex = self.spmatrix(datcomplex)
datbsr = bsr_matrix(dat)
datcsc = csc_matrix(dat)
datcsr = csr_matrix(dat)
datlil = lil_matrix(dat)
# sparse/sparse
assert_array_equal_dtype(dat > dat2, (datsp > datsp2).todense())
assert_array_equal_dtype(datcomplex > dat2,
(datspcomplex > datsp2).todense())
# mix sparse types
assert_array_equal_dtype(dat > dat2, (datbsr > datsp2).todense())
assert_array_equal_dtype(dat > dat2, (datcsc > datsp2).todense())
assert_array_equal_dtype(dat > dat2, (datcsr > datsp2).todense())
assert_array_equal_dtype(dat > dat2, (datlil > datsp2).todense())
assert_array_equal_dtype(dat2 > dat, (datsp2 > datbsr).todense())
assert_array_equal_dtype(dat2 > dat, (datsp2 > datcsc).todense())
assert_array_equal_dtype(dat2 > dat, (datsp2 > datcsr).todense())
assert_array_equal_dtype(dat2 > dat, (datsp2 > datlil).todense())
# sparse/dense
assert_array_equal_dtype(dat > dat2, datsp > dat2)
assert_array_equal_dtype(datcomplex > dat2, datspcomplex > dat2)
# sparse/scalar
assert_array_equal_dtype((datsp > 2).todense(), dat > 2)
assert_array_equal_dtype((datsp > 1).todense(), dat > 1)
assert_array_equal_dtype((datsp > 0).todense(), dat > 0)
assert_array_equal_dtype((datsp > -1).todense(), dat > -1)
assert_array_equal_dtype((datsp > -2).todense(), dat > -2)
with np.errstate(invalid='ignore'):
assert_array_equal_dtype((datsp > np.nan).todense(),
dat > np.nan)
assert_array_equal_dtype((2 > datsp).todense(), 2 > dat)
assert_array_equal_dtype((1 > datsp).todense(), 1 > dat)
assert_array_equal_dtype((0 > datsp).todense(), 0 > dat)
assert_array_equal_dtype((-1 > datsp).todense(), -1 > dat)
assert_array_equal_dtype((-2 > datsp).todense(), -2 > dat)
# data
dat = self.dat_dtypes[dtype]
datsp = self.datsp_dtypes[dtype]
dat2 = dat.copy()
dat2[:,0] = 0
datsp2 = self.spmatrix(dat2)
# dense rhs
assert_array_equal_dtype(dat > datsp2, datsp > dat2)
if not isinstance(self, (TestBSR, TestCSC, TestCSR)):
pytest.skip("Bool comparisons only implemented for BSR, CSC, and CSR.")
for dtype in self.checked_dtypes:
check(dtype)
def test_le(self):
sup = suppress_warnings()
sup.filter(SparseEfficiencyWarning)
@sup
@sup_complex
def check(dtype):
dat = self.dat_dtypes[dtype]
datsp = self.datsp_dtypes[dtype]
dat2 = dat.copy()
dat2[:,0] = 0
datsp2 = self.spmatrix(dat2)
datcomplex = dat.astype(complex)
datcomplex[:,0] = 1 + 1j
datspcomplex = self.spmatrix(datcomplex)
datbsr = bsr_matrix(dat)
datcsc = csc_matrix(dat)
datcsr = csr_matrix(dat)
datlil = lil_matrix(dat)
# sparse/sparse
assert_array_equal_dtype(dat <= dat2, (datsp <= datsp2).todense())
assert_array_equal_dtype(datcomplex <= dat2,
(datspcomplex <= datsp2).todense())
# mix sparse types
assert_array_equal_dtype((datbsr <= datsp2).todense(), dat <= dat2)
assert_array_equal_dtype((datcsc <= datsp2).todense(), dat <= dat2)
assert_array_equal_dtype((datcsr <= datsp2).todense(), dat <= dat2)
assert_array_equal_dtype((datlil <= datsp2).todense(), dat <= dat2)
assert_array_equal_dtype((datsp2 <= datbsr).todense(), dat2 <= dat)
assert_array_equal_dtype((datsp2 <= datcsc).todense(), dat2 <= dat)
assert_array_equal_dtype((datsp2 <= datcsr).todense(), dat2 <= dat)
assert_array_equal_dtype((datsp2 <= datlil).todense(), dat2 <= dat)
# sparse/dense
assert_array_equal_dtype(datsp <= dat2, dat <= dat2)
assert_array_equal_dtype(datspcomplex <= dat2, datcomplex <= dat2)
# sparse/scalar
assert_array_equal_dtype((datsp <= 2).todense(), dat <= 2)
assert_array_equal_dtype((datsp <= 1).todense(), dat <= 1)
assert_array_equal_dtype((datsp <= -1).todense(), dat <= -1)
assert_array_equal_dtype((datsp <= -2).todense(), dat <= -2)
assert_array_equal_dtype((2 <= datsp).todense(), 2 <= dat)
assert_array_equal_dtype((1 <= datsp).todense(), 1 <= dat)
assert_array_equal_dtype((-1 <= datsp).todense(), -1 <= dat)
assert_array_equal_dtype((-2 <= datsp).todense(), -2 <= dat)
# data
dat = self.dat_dtypes[dtype]
datsp = self.datsp_dtypes[dtype]
dat2 = dat.copy()
dat2[:,0] = 0
datsp2 = self.spmatrix(dat2)
# dense rhs
assert_array_equal_dtype(dat <= datsp2, datsp <= dat2)
if not isinstance(self, (TestBSR, TestCSC, TestCSR)):
pytest.skip("Bool comparisons only implemented for BSR, CSC, and CSR.")
for dtype in self.checked_dtypes:
check(dtype)
def test_ge(self):
sup = suppress_warnings()
sup.filter(SparseEfficiencyWarning)
@sup
@sup_complex
def check(dtype):
dat = self.dat_dtypes[dtype]
datsp = self.datsp_dtypes[dtype]
dat2 = dat.copy()
dat2[:,0] = 0
datsp2 = self.spmatrix(dat2)
datcomplex = dat.astype(complex)
datcomplex[:,0] = 1 + 1j
datspcomplex = self.spmatrix(datcomplex)
datbsr = bsr_matrix(dat)
datcsc = csc_matrix(dat)
datcsr = csr_matrix(dat)
datlil = lil_matrix(dat)
# sparse/sparse
assert_array_equal_dtype(dat >= dat2, (datsp >= datsp2).todense())
assert_array_equal_dtype(datcomplex >= dat2,
(datspcomplex >= datsp2).todense())
# mix sparse types
assert_array_equal_dtype((datbsr >= datsp2).todense(), dat >= dat2)
assert_array_equal_dtype((datcsc >= datsp2).todense(), dat >= dat2)
assert_array_equal_dtype((datcsr >= datsp2).todense(), dat >= dat2)
assert_array_equal_dtype((datlil >= datsp2).todense(), dat >= dat2)
assert_array_equal_dtype((datsp2 >= datbsr).todense(), dat2 >= dat)
assert_array_equal_dtype((datsp2 >= datcsc).todense(), dat2 >= dat)
assert_array_equal_dtype((datsp2 >= datcsr).todense(), dat2 >= dat)
assert_array_equal_dtype((datsp2 >= datlil).todense(), dat2 >= dat)
# sparse/dense
assert_array_equal_dtype(datsp >= dat2, dat >= dat2)
assert_array_equal_dtype(datspcomplex >= dat2, datcomplex >= dat2)
# sparse/scalar
assert_array_equal_dtype((datsp >= 2).todense(), dat >= 2)
assert_array_equal_dtype((datsp >= 1).todense(), dat >= 1)
assert_array_equal_dtype((datsp >= -1).todense(), dat >= -1)
assert_array_equal_dtype((datsp >= -2).todense(), dat >= -2)
assert_array_equal_dtype((2 >= datsp).todense(), 2 >= dat)
assert_array_equal_dtype((1 >= datsp).todense(), 1 >= dat)
assert_array_equal_dtype((-1 >= datsp).todense(), -1 >= dat)
assert_array_equal_dtype((-2 >= datsp).todense(), -2 >= dat)
# dense data
dat = self.dat_dtypes[dtype]
datsp = self.datsp_dtypes[dtype]
dat2 = dat.copy()
dat2[:,0] = 0
datsp2 = self.spmatrix(dat2)
# dense rhs
assert_array_equal_dtype(dat >= datsp2, datsp >= dat2)
if not isinstance(self, (TestBSR, TestCSC, TestCSR)):
pytest.skip("Bool comparisons only implemented for BSR, CSC, and CSR.")
for dtype in self.checked_dtypes:
check(dtype)
def test_empty(self):
# create empty matrices
assert_equal(self.spmatrix((3,3)).todense(), np.zeros((3,3)))
assert_equal(self.spmatrix((3,3)).nnz, 0)
assert_equal(self.spmatrix((3,3)).count_nonzero(), 0)
def test_count_nonzero(self):
expected = np.count_nonzero(self.datsp.toarray())
assert_equal(self.datsp.count_nonzero(), expected)
assert_equal(self.datsp.T.count_nonzero(), expected)
def test_invalid_shapes(self):
assert_raises(ValueError, self.spmatrix, (-1,3))
assert_raises(ValueError, self.spmatrix, (3,-1))
assert_raises(ValueError, self.spmatrix, (-1,-1))
def test_repr(self):
repr(self.datsp)
def test_str(self):
str(self.datsp)
def test_empty_arithmetic(self):
# Test manipulating empty matrices. Fails in SciPy SVN <= r1768
shape = (5, 5)
for mytype in [np.dtype('int32'), np.dtype('float32'),
np.dtype('float64'), np.dtype('complex64'),
np.dtype('complex128')]:
a = self.spmatrix(shape, dtype=mytype)
b = a + a
c = 2 * a
d = a * a.tocsc()
e = a * a.tocsr()
f = a * a.tocoo()
for m in [a,b,c,d,e,f]:
assert_equal(m.A, a.A*a.A)
# These fail in all revisions <= r1768:
assert_equal(m.dtype,mytype)
assert_equal(m.A.dtype,mytype)
def test_abs(self):
A = matrix([[-1, 0, 17],[0, -5, 0],[1, -4, 0],[0,0,0]],'d')
assert_equal(abs(A),abs(self.spmatrix(A)).todense())
def test_elementwise_power(self):
A = matrix([[-4, -3, -2],[-1, 0, 1],[2, 3, 4]], 'd')
assert_equal(np.power(A, 2), self.spmatrix(A).power(2).todense())
#it's element-wise power function, input has to be a scalar
assert_raises(NotImplementedError, self.spmatrix(A).power, A)
def test_neg(self):
A = matrix([[-1, 0, 17], [0, -5, 0], [1, -4, 0], [0, 0, 0]], 'd')
assert_equal(-A, (-self.spmatrix(A)).todense())
# see gh-5843
A = matrix([[True, False, False], [False, False, True]])
assert_raises(NotImplementedError, self.spmatrix(A).__neg__)
def test_real(self):
D = matrix([[1 + 3j, 2 - 4j]])
A = self.spmatrix(D)
assert_equal(A.real.todense(),D.real)
def test_imag(self):
D = matrix([[1 + 3j, 2 - 4j]])
A = self.spmatrix(D)
assert_equal(A.imag.todense(),D.imag)
def test_diagonal(self):
# Does the matrix's .diagonal() method work?
mats = []
mats.append([[1,0,2]])
mats.append([[1],[0],[2]])
mats.append([[0,1],[0,2],[0,3]])
mats.append([[0,0,1],[0,0,2],[0,3,0]])
mats.append(kron(mats[0],[[1,2]]))
mats.append(kron(mats[0],[[1],[2]]))
mats.append(kron(mats[1],[[1,2],[3,4]]))
mats.append(kron(mats[2],[[1,2],[3,4]]))
mats.append(kron(mats[3],[[1,2],[3,4]]))
mats.append(kron(mats[3],[[1,2,3,4]]))
for m in mats:
rows, cols = array(m).shape
sparse_mat = self.spmatrix(m)
for k in range(-rows + 1, cols):
assert_equal(sparse_mat.diagonal(k=k), diag(m, k=k))
assert_raises(ValueError, sparse_mat.diagonal, -rows)
assert_raises(ValueError, sparse_mat.diagonal, cols)
# Test all-zero matrix.
assert_equal(self.spmatrix((40, 16130)).diagonal(), np.zeros(40))
def test_reshape(self):
# This first example is taken from the lil_matrix reshaping test.
x = self.spmatrix([[1, 0, 7], [0, 0, 0], [0, 3, 0], [0, 0, 5]])
for order in ['C', 'F']:
for s in [(12, 1), (1, 12)]:
assert_array_equal(x.reshape(s, order=order).todense(),
x.todense().reshape(s, order=order))
# This example is taken from the stackoverflow answer at
# https://stackoverflow.com/q/16511879
x = self.spmatrix([[0, 10, 0, 0], [0, 0, 0, 0], [0, 20, 30, 40]])
y = x.reshape((2, 6)) # Default order is 'C'
desired = [[0, 10, 0, 0, 0, 0], [0, 0, 0, 20, 30, 40]]
assert_array_equal(y.A, desired)
# Reshape with negative indexes
y = x.reshape((2, -1))
assert_array_equal(y.A, desired)
y = x.reshape((-1, 6))
assert_array_equal(y.A, desired)
assert_raises(ValueError, x.reshape, (-1, -1))
# Reshape with star args
y = x.reshape(2, 6)
assert_array_equal(y.A, desired)
assert_raises(TypeError, x.reshape, 2, 6, not_an_arg=1)
# Reshape with same size is noop unless copy=True
y = x.reshape((3, 4))
assert_(y is x)
y = x.reshape((3, 4), copy=True)
assert_(y is not x)
# Ensure reshape did not alter original size
assert_array_equal(x.shape, (3, 4))
# Reshape in place
x.shape = (2, 6)
assert_array_equal(x.A, desired)
@pytest.mark.slow
def test_setdiag_comprehensive(self):
def dense_setdiag(a, v, k):
v = np.asarray(v)
if k >= 0:
n = min(a.shape[0], a.shape[1] - k)
if v.ndim != 0:
n = min(n, len(v))
v = v[:n]
i = np.arange(0, n)
j = np.arange(k, k + n)
a[i,j] = v
elif k < 0:
dense_setdiag(a.T, v, -k)
def check_setdiag(a, b, k):
# Check setting diagonal using a scalar, a vector of
# correct length, and too short or too long vectors
for r in [-1, len(np.diag(a, k)), 2, 30]:
if r < 0:
v = int(np.random.randint(1, 20, size=1))
else:
v = np.random.randint(1, 20, size=r)
dense_setdiag(a, v, k)
with suppress_warnings() as sup:
sup.filter(SparseEfficiencyWarning, "Changing the sparsity structure of a cs[cr]_matrix is expensive")
b.setdiag(v, k)
# check that dense_setdiag worked
d = np.diag(a, k)
if np.asarray(v).ndim == 0:
assert_array_equal(d, v, err_msg="%s %d" % (msg, r))
else:
n = min(len(d), len(v))
assert_array_equal(d[:n], v[:n], err_msg="%s %d" % (msg, r))
# check that sparse setdiag worked
assert_array_equal(b.A, a, err_msg="%s %d" % (msg, r))
# comprehensive test
np.random.seed(1234)
shapes = [(0,5), (5,0), (1,5), (5,1), (5,5)]
for dtype in [np.int8, np.float64]:
for m,n in shapes:
ks = np.arange(-m+1, n-1)
for k in ks:
msg = repr((dtype, m, n, k))
a = np.zeros((m, n), dtype=dtype)
b = self.spmatrix((m, n), dtype=dtype)
check_setdiag(a, b, k)
# check overwriting etc
for k2 in np.random.choice(ks, size=min(len(ks), 5)):
check_setdiag(a, b, k2)
def test_setdiag(self):
# simple test cases
m = self.spmatrix(np.eye(3))
values = [3, 2, 1]
with suppress_warnings() as sup:
sup.filter(SparseEfficiencyWarning,
"Changing the sparsity structure of a cs[cr]_matrix is expensive")
assert_raises(ValueError, m.setdiag, values, k=4)
m.setdiag(values)
assert_array_equal(m.diagonal(), values)
m.setdiag(values, k=1)
assert_array_equal(m.A, np.array([[3, 3, 0],
[0, 2, 2],
[0, 0, 1]]))
m.setdiag(values, k=-2)
assert_array_equal(m.A, np.array([[3, 3, 0],
[0, 2, 2],
[3, 0, 1]]))
m.setdiag((9,), k=2)
assert_array_equal(m.A[0,2], 9)
m.setdiag((9,), k=-2)
assert_array_equal(m.A[2,0], 9)
def test_nonzero(self):
A = array([[1, 0, 1],[0, 1, 1],[0, 0, 1]])
Asp = self.spmatrix(A)
A_nz = set([tuple(ij) for ij in transpose(A.nonzero())])
Asp_nz = set([tuple(ij) for ij in transpose(Asp.nonzero())])
assert_equal(A_nz, Asp_nz)
def test_numpy_nonzero(self):
# See gh-5987
A = array([[1, 0, 1], [0, 1, 1], [0, 0, 1]])
Asp = self.spmatrix(A)
A_nz = set([tuple(ij) for ij in transpose(np.nonzero(A))])
Asp_nz = set([tuple(ij) for ij in transpose(np.nonzero(Asp))])
assert_equal(A_nz, Asp_nz)
def test_getrow(self):
assert_array_equal(self.datsp.getrow(1).todense(), self.dat[1,:])
assert_array_equal(self.datsp.getrow(-1).todense(), self.dat[-1,:])
def test_getcol(self):
assert_array_equal(self.datsp.getcol(1).todense(), self.dat[:,1])
assert_array_equal(self.datsp.getcol(-1).todense(), self.dat[:,-1])
def test_sum(self):
np.random.seed(1234)
dat_1 = np.matrix([[0, 1, 2],
[3, -4, 5],
[-6, 7, 9]])
dat_2 = np.random.rand(5, 5)
dat_3 = np.array([[]])
dat_4 = np.zeros((40, 40))
dat_5 = sparse.rand(5, 5, density=1e-2).A
matrices = [dat_1, dat_2, dat_3, dat_4, dat_5]
def check(dtype, j):
dat = np.matrix(matrices[j], dtype=dtype)
datsp = self.spmatrix(dat, dtype=dtype)
with np.errstate(over='ignore'):
assert_array_almost_equal(dat.sum(), datsp.sum())
assert_equal(dat.sum().dtype, datsp.sum().dtype)
assert_(np.isscalar(datsp.sum(axis=None)))
assert_array_almost_equal(dat.sum(axis=None),
datsp.sum(axis=None))
assert_equal(dat.sum(axis=None).dtype,
datsp.sum(axis=None).dtype)
assert_array_almost_equal(dat.sum(axis=0), datsp.sum(axis=0))
assert_equal(dat.sum(axis=0).dtype, datsp.sum(axis=0).dtype)
assert_array_almost_equal(dat.sum(axis=1), datsp.sum(axis=1))
assert_equal(dat.sum(axis=1).dtype, datsp.sum(axis=1).dtype)
assert_array_almost_equal(dat.sum(axis=-2), datsp.sum(axis=-2))
assert_equal(dat.sum(axis=-2).dtype, datsp.sum(axis=-2).dtype)
assert_array_almost_equal(dat.sum(axis=-1), datsp.sum(axis=-1))
assert_equal(dat.sum(axis=-1).dtype, datsp.sum(axis=-1).dtype)
for dtype in self.checked_dtypes:
for j in range(len(matrices)):
check(dtype, j)
def test_sum_invalid_params(self):
out = np.asmatrix(np.zeros((1, 3)))
dat = np.matrix([[0, 1, 2],
[3, -4, 5],
[-6, 7, 9]])
datsp = self.spmatrix(dat)
assert_raises(ValueError, datsp.sum, axis=3)
assert_raises(TypeError, datsp.sum, axis=(0, 1))
assert_raises(TypeError, datsp.sum, axis=1.5)
assert_raises(ValueError, datsp.sum, axis=1, out=out)
def test_sum_dtype(self):
dat = np.matrix([[0, 1, 2],
[3, -4, 5],
[-6, 7, 9]])
datsp = self.spmatrix(dat)
def check(dtype):
dat_mean = dat.mean(dtype=dtype)
datsp_mean = datsp.mean(dtype=dtype)
assert_array_almost_equal(dat_mean, datsp_mean)
assert_equal(dat_mean.dtype, datsp_mean.dtype)
for dtype in self.checked_dtypes:
check(dtype)
def test_sum_out(self):
dat = np.matrix([[0, 1, 2],
[3, -4, 5],
[-6, 7, 9]])
datsp = self.spmatrix(dat)
dat_out = np.matrix(0)
datsp_out = np.matrix(0)
dat.sum(out=dat_out)
datsp.sum(out=datsp_out)
assert_array_almost_equal(dat_out, datsp_out)
dat_out = np.asmatrix(np.zeros((3, 1)))
datsp_out = np.asmatrix(np.zeros((3, 1)))
dat.sum(axis=1, out=dat_out)
datsp.sum(axis=1, out=datsp_out)
assert_array_almost_equal(dat_out, datsp_out)
def test_numpy_sum(self):
# See gh-5987
dat = np.matrix([[0, 1, 2],
[3, -4, 5],
[-6, 7, 9]])
datsp = self.spmatrix(dat)
dat_mean = np.sum(dat)
datsp_mean = np.sum(datsp)
assert_array_almost_equal(dat_mean, datsp_mean)
assert_equal(dat_mean.dtype, datsp_mean.dtype)
def test_mean(self):
def check(dtype):
dat = np.matrix([[0, 1, 2],
[3, -4, 5],
[-6, 7, 9]], dtype=dtype)
datsp = self.spmatrix(dat, dtype=dtype)
assert_array_almost_equal(dat.mean(), datsp.mean())
assert_equal(dat.mean().dtype, datsp.mean().dtype)
assert_(np.isscalar(datsp.mean(axis=None)))
assert_array_almost_equal(dat.mean(axis=None), datsp.mean(axis=None))
assert_equal(dat.mean(axis=None).dtype, datsp.mean(axis=None).dtype)
assert_array_almost_equal(dat.mean(axis=0), datsp.mean(axis=0))
assert_equal(dat.mean(axis=0).dtype, datsp.mean(axis=0).dtype)
assert_array_almost_equal(dat.mean(axis=1), datsp.mean(axis=1))
assert_equal(dat.mean(axis=1).dtype, datsp.mean(axis=1).dtype)
assert_array_almost_equal(dat.mean(axis=-2), datsp.mean(axis=-2))
assert_equal(dat.mean(axis=-2).dtype, datsp.mean(axis=-2).dtype)
assert_array_almost_equal(dat.mean(axis=-1), datsp.mean(axis=-1))
assert_equal(dat.mean(axis=-1).dtype, datsp.mean(axis=-1).dtype)
for dtype in self.checked_dtypes:
check(dtype)
def test_mean_invalid_params(self):
out = np.asmatrix(np.zeros((1, 3)))
dat = np.matrix([[0, 1, 2],
[3, -4, 5],
[-6, 7, 9]])
datsp = self.spmatrix(dat)
assert_raises(ValueError, datsp.mean, axis=3)
assert_raises(TypeError, datsp.mean, axis=(0, 1))
assert_raises(TypeError, datsp.mean, axis=1.5)
assert_raises(ValueError, datsp.mean, axis=1, out=out)
def test_mean_dtype(self):
dat = np.matrix([[0, 1, 2],
[3, -4, 5],
[-6, 7, 9]])
datsp = self.spmatrix(dat)
def check(dtype):
dat_mean = dat.mean(dtype=dtype)
datsp_mean = datsp.mean(dtype=dtype)
assert_array_almost_equal(dat_mean, datsp_mean)
assert_equal(dat_mean.dtype, datsp_mean.dtype)
for dtype in self.checked_dtypes:
check(dtype)
def test_mean_out(self):
dat = np.matrix([[0, 1, 2],
[3, -4, 5],
[-6, 7, 9]])
datsp = self.spmatrix(dat)
dat_out = np.matrix(0)
datsp_out = np.matrix(0)
dat.mean(out=dat_out)
datsp.mean(out=datsp_out)
assert_array_almost_equal(dat_out, datsp_out)
dat_out = np.asmatrix(np.zeros((3, 1)))
datsp_out = np.asmatrix(np.zeros((3, 1)))
dat.mean(axis=1, out=dat_out)
datsp.mean(axis=1, out=datsp_out)
assert_array_almost_equal(dat_out, datsp_out)
def test_numpy_mean(self):
# See gh-5987
dat = np.matrix([[0, 1, 2],
[3, -4, 5],
[-6, 7, 9]])
datsp = self.spmatrix(dat)
dat_mean = np.mean(dat)
datsp_mean = np.mean(datsp)
assert_array_almost_equal(dat_mean, datsp_mean)
assert_equal(dat_mean.dtype, datsp_mean.dtype)
def test_expm(self):
M = array([[1, 0, 2], [0, 0, 3], [-4, 5, 6]], float)
sM = self.spmatrix(M, shape=(3,3), dtype=float)
Mexp = scipy.linalg.expm(M)
N = array([[3., 0., 1.], [0., 2., 0.], [0., 0., 0.]])
sN = self.spmatrix(N, shape=(3,3), dtype=float)
Nexp = scipy.linalg.expm(N)
with suppress_warnings() as sup:
sup.filter(SparseEfficiencyWarning, "splu requires CSC matrix format")
sup.filter(SparseEfficiencyWarning,
"spsolve is more efficient when sparse b is in the CSC matrix format")
sup.filter(SparseEfficiencyWarning,
"spsolve requires A be CSC or CSR matrix format")
sMexp = expm(sM).todense()
sNexp = expm(sN).todense()
assert_array_almost_equal((sMexp - Mexp), zeros((3, 3)))
assert_array_almost_equal((sNexp - Nexp), zeros((3, 3)))
def test_inv(self):
def check(dtype):
M = array([[1, 0, 2], [0, 0, 3], [-4, 5, 6]], dtype)
with suppress_warnings() as sup:
sup.filter(SparseEfficiencyWarning,
"spsolve requires A be CSC or CSR matrix format")
sup.filter(SparseEfficiencyWarning,
"spsolve is more efficient when sparse b is in the CSC matrix format")
sup.filter(SparseEfficiencyWarning,
"splu requires CSC matrix format")
sM = self.spmatrix(M, shape=(3,3), dtype=dtype)
sMinv = inv(sM)
assert_array_almost_equal(sMinv.dot(sM).todense(), np.eye(3))
assert_raises(TypeError, inv, M)
for dtype in [float]:
check(dtype)
@sup_complex
def test_from_array(self):
A = array([[1,0,0],[2,3,4],[0,5,0],[0,0,0]])
assert_array_equal(self.spmatrix(A).toarray(), A)
A = array([[1.0 + 3j, 0, 0],
[0, 2.0 + 5, 0],
[0, 0, 0]])
assert_array_equal(self.spmatrix(A).toarray(), A)
assert_array_equal(self.spmatrix(A, dtype='int16').toarray(), A.astype('int16'))
@sup_complex
def test_from_matrix(self):
A = matrix([[1,0,0],[2,3,4],[0,5,0],[0,0,0]])
assert_array_equal(self.spmatrix(A).todense(), A)
A = matrix([[1.0 + 3j, 0, 0],
[0, 2.0 + 5, 0],
[0, 0, 0]])
assert_array_equal(self.spmatrix(A).toarray(), A)
assert_array_equal(self.spmatrix(A, dtype='int16').toarray(), A.astype('int16'))
@sup_complex
def test_from_list(self):
A = [[1,0,0],[2,3,4],[0,5,0],[0,0,0]]
assert_array_equal(self.spmatrix(A).todense(), A)
A = [[1.0 + 3j, 0, 0],
[0, 2.0 + 5, 0],
[0, 0, 0]]
assert_array_equal(self.spmatrix(A).toarray(), array(A))
assert_array_equal(self.spmatrix(A, dtype='int16').todense(), array(A).astype('int16'))
@sup_complex
def test_from_sparse(self):
D = array([[1,0,0],[2,3,4],[0,5,0],[0,0,0]])
S = csr_matrix(D)
assert_array_equal(self.spmatrix(S).toarray(), D)
S = self.spmatrix(D)
assert_array_equal(self.spmatrix(S).toarray(), D)
D = array([[1.0 + 3j, 0, 0],
[0, 2.0 + 5, 0],
[0, 0, 0]])
S = csr_matrix(D)
assert_array_equal(self.spmatrix(S).toarray(), D)
assert_array_equal(self.spmatrix(S, dtype='int16').toarray(), D.astype('int16'))
S = self.spmatrix(D)
assert_array_equal(self.spmatrix(S).toarray(), D)
assert_array_equal(self.spmatrix(S, dtype='int16').toarray(), D.astype('int16'))
# def test_array(self):
# """test array(A) where A is in sparse format"""
# assert_equal( array(self.datsp), self.dat )
def test_todense(self):
# Check C- or F-contiguous (default).
chk = self.datsp.todense()
assert_array_equal(chk, self.dat)
assert_(chk.flags.c_contiguous != chk.flags.f_contiguous)
# Check C-contiguous (with arg).
chk = self.datsp.todense(order='C')
assert_array_equal(chk, self.dat)
assert_(chk.flags.c_contiguous)
assert_(not chk.flags.f_contiguous)
# Check F-contiguous (with arg).
chk = self.datsp.todense(order='F')
assert_array_equal(chk, self.dat)
assert_(not chk.flags.c_contiguous)
assert_(chk.flags.f_contiguous)
# Check with out argument (array).
out = np.zeros(self.datsp.shape, dtype=self.datsp.dtype)
chk = self.datsp.todense(out=out)
assert_array_equal(self.dat, out)
assert_array_equal(self.dat, chk)
assert_(chk.base is out)
# Check with out array (matrix).
out = np.asmatrix(np.zeros(self.datsp.shape, dtype=self.datsp.dtype))
chk = self.datsp.todense(out=out)
assert_array_equal(self.dat, out)
assert_array_equal(self.dat, chk)
assert_(chk is out)
a = matrix([1.,2.,3.])
dense_dot_dense = a * self.dat
check = a * self.datsp.todense()
assert_array_equal(dense_dot_dense, check)
b = matrix([1.,2.,3.,4.]).T
dense_dot_dense = self.dat * b
check2 = self.datsp.todense() * b
assert_array_equal(dense_dot_dense, check2)
# Check bool data works.
spbool = self.spmatrix(self.dat, dtype=bool)
matbool = self.dat.astype(bool)
assert_array_equal(spbool.todense(), matbool)
def test_toarray(self):
# Check C- or F-contiguous (default).
dat = asarray(self.dat)
chk = self.datsp.toarray()
assert_array_equal(chk, dat)
assert_(chk.flags.c_contiguous != chk.flags.f_contiguous)
# Check C-contiguous (with arg).
chk = self.datsp.toarray(order='C')
assert_array_equal(chk, dat)
assert_(chk.flags.c_contiguous)
assert_(not chk.flags.f_contiguous)
# Check F-contiguous (with arg).
chk = self.datsp.toarray(order='F')
assert_array_equal(chk, dat)
assert_(not chk.flags.c_contiguous)
assert_(chk.flags.f_contiguous)
# Check with output arg.
out = np.zeros(self.datsp.shape, dtype=self.datsp.dtype)
self.datsp.toarray(out=out)
assert_array_equal(chk, dat)
# Check that things are fine when we don't initialize with zeros.
out[...] = 1.
self.datsp.toarray(out=out)
assert_array_equal(chk, dat)
a = array([1.,2.,3.])
dense_dot_dense = dot(a, dat)
check = dot(a, self.datsp.toarray())
assert_array_equal(dense_dot_dense, check)
b = array([1.,2.,3.,4.])
dense_dot_dense = dot(dat, b)
check2 = dot(self.datsp.toarray(), b)
assert_array_equal(dense_dot_dense, check2)
# Check bool data works.
spbool = self.spmatrix(self.dat, dtype=bool)
arrbool = dat.astype(bool)
assert_array_equal(spbool.toarray(), arrbool)
@sup_complex
def test_astype(self):
D = array([[2.0 + 3j, 0, 0],
[0, 4.0 + 5j, 0],
[0, 0, 0]])
S = self.spmatrix(D)
for x in supported_dtypes:
# Check correctly casted
D_casted = D.astype(x)
for copy in (True, False):
S_casted = S.astype(x, copy=copy)
assert_equal(S_casted.dtype, D_casted.dtype) # correct type
assert_equal(S_casted.toarray(), D_casted) # correct values
assert_equal(S_casted.format, S.format) # format preserved
# Check correctly copied
assert_(S_casted.astype(x, copy=False) is S_casted)
S_copied = S_casted.astype(x, copy=True)
assert_(S_copied is not S_casted)
def check_equal_but_not_same_array_attribute(attribute):
a = getattr(S_casted, attribute)
b = getattr(S_copied, attribute)
assert_array_equal(a, b)
assert_(a is not b)
i = (0,) * b.ndim
b_i = b[i]
b[i] = not b[i]
assert_(a[i] != b[i])
b[i] = b_i
if S_casted.format in ('csr', 'csc', 'bsr'):
for attribute in ('indices', 'indptr', 'data'):
check_equal_but_not_same_array_attribute(attribute)
elif S_casted.format == 'coo':
for attribute in ('row', 'col', 'data'):
check_equal_but_not_same_array_attribute(attribute)
elif S_casted.format == 'dia':
for attribute in ('offsets', 'data'):
check_equal_but_not_same_array_attribute(attribute)
def test_asfptype(self):
A = self.spmatrix(arange(6,dtype='int32').reshape(2,3))
assert_equal(A.dtype, np.dtype('int32'))
assert_equal(A.asfptype().dtype, np.dtype('float64'))
assert_equal(A.asfptype().format, A.format)
assert_equal(A.astype('int16').asfptype().dtype, np.dtype('float32'))
assert_equal(A.astype('complex128').asfptype().dtype, np.dtype('complex128'))
B = A.asfptype()
C = B.asfptype()
assert_(B is C)
def test_mul_scalar(self):
def check(dtype):
dat = self.dat_dtypes[dtype]
datsp = self.datsp_dtypes[dtype]
assert_array_equal(dat*2,(datsp*2).todense())
assert_array_equal(dat*17.3,(datsp*17.3).todense())
for dtype in self.math_dtypes:
check(dtype)
def test_rmul_scalar(self):
def check(dtype):
dat = self.dat_dtypes[dtype]
datsp = self.datsp_dtypes[dtype]
assert_array_equal(2*dat,(2*datsp).todense())
assert_array_equal(17.3*dat,(17.3*datsp).todense())
for dtype in self.math_dtypes:
check(dtype)
def test_add(self):
def check(dtype):
dat = self.dat_dtypes[dtype]
datsp = self.datsp_dtypes[dtype]
a = dat.copy()
a[0,2] = 2.0
b = datsp
c = b + a
assert_array_equal(c, b.todense() + a)
c = b + b.tocsr()
assert_array_equal(c.todense(),
b.todense() + b.todense())
# test broadcasting
c = b + a[0]
assert_array_equal(c, b.todense() + a[0])
for dtype in self.math_dtypes:
check(dtype)
def test_radd(self):
def check(dtype):
dat = self.dat_dtypes[dtype]
datsp = self.datsp_dtypes[dtype]
a = dat.copy()
a[0,2] = 2.0
b = datsp
c = a + b
assert_array_equal(c, a + b.todense())
for dtype in self.math_dtypes:
check(dtype)
def test_sub(self):
def check(dtype):
dat = self.dat_dtypes[dtype]
datsp = self.datsp_dtypes[dtype]
assert_array_equal((datsp - datsp).todense(),[[0,0,0,0],[0,0,0,0],[0,0,0,0]])
assert_array_equal((datsp - 0).todense(), dat)
A = self.spmatrix(matrix([[1,0,0,4],[-1,0,0,0],[0,8,0,-5]],'d'))
assert_array_equal((datsp - A).todense(),dat - A.todense())
assert_array_equal((A - datsp).todense(),A.todense() - dat)
# test broadcasting
assert_array_equal(datsp - dat[0], dat - dat[0])
for dtype in self.math_dtypes:
if dtype == np.dtype('bool'):
# boolean array subtraction deprecated in 1.9.0
continue
check(dtype)
def test_rsub(self):
def check(dtype):
dat = self.dat_dtypes[dtype]
datsp = self.datsp_dtypes[dtype]
assert_array_equal((dat - datsp),[[0,0,0,0],[0,0,0,0],[0,0,0,0]])
assert_array_equal((datsp - dat),[[0,0,0,0],[0,0,0,0],[0,0,0,0]])
assert_array_equal((0 - datsp).todense(), -dat)
A = self.spmatrix(matrix([[1,0,0,4],[-1,0,0,0],[0,8,0,-5]],'d'))
assert_array_equal((dat - A),dat - A.todense())
assert_array_equal((A - dat),A.todense() - dat)
assert_array_equal(A.todense() - datsp,A.todense() - dat)
assert_array_equal(datsp - A.todense(),dat - A.todense())
# test broadcasting
assert_array_equal(dat[0] - datsp, dat[0] - dat)
for dtype in self.math_dtypes:
if dtype == np.dtype('bool'):
# boolean array subtraction deprecated in 1.9.0
continue
check(dtype)
def test_add0(self):
def check(dtype):
dat = self.dat_dtypes[dtype]
datsp = self.datsp_dtypes[dtype]
# Adding 0 to a sparse matrix
assert_array_equal((datsp + 0).todense(), dat)
# use sum (which takes 0 as a starting value)
sumS = sum([k * datsp for k in range(1, 3)])
sumD = sum([k * dat for k in range(1, 3)])
assert_almost_equal(sumS.todense(), sumD)
for dtype in self.math_dtypes:
check(dtype)
def test_elementwise_multiply(self):
# real/real
A = array([[4,0,9],[2,-3,5]])
B = array([[0,7,0],[0,-4,0]])
Asp = self.spmatrix(A)
Bsp = self.spmatrix(B)
assert_almost_equal(Asp.multiply(Bsp).todense(), A*B) # sparse/sparse
assert_almost_equal(Asp.multiply(B).todense(), A*B) # sparse/dense
# complex/complex
C = array([[1-2j,0+5j,-1+0j],[4-3j,-3+6j,5]])
D = array([[5+2j,7-3j,-2+1j],[0-1j,-4+2j,9]])
Csp = self.spmatrix(C)
Dsp = self.spmatrix(D)
assert_almost_equal(Csp.multiply(Dsp).todense(), C*D) # sparse/sparse
assert_almost_equal(Csp.multiply(D).todense(), C*D) # sparse/dense
# real/complex
assert_almost_equal(Asp.multiply(Dsp).todense(), A*D) # sparse/sparse
assert_almost_equal(Asp.multiply(D).todense(), A*D) # sparse/dense
def test_elementwise_multiply_broadcast(self):
A = array([4])
B = array([[-9]])
C = array([1,-1,0])
D = array([[7,9,-9]])
E = array([[3],[2],[1]])
F = array([[8,6,3],[-4,3,2],[6,6,6]])
G = [1, 2, 3]
H = np.ones((3, 4))
J = H.T
K = array([[0]])
L = array([[[1,2],[0,1]]])
# Some arrays can't be cast as spmatrices (A,C,L) so leave
# them out.
Bsp = self.spmatrix(B)
Dsp = self.spmatrix(D)
Esp = self.spmatrix(E)
Fsp = self.spmatrix(F)
Hsp = self.spmatrix(H)
Hspp = self.spmatrix(H[0,None])
Jsp = self.spmatrix(J)
Jspp = self.spmatrix(J[:,0,None])
Ksp = self.spmatrix(K)
matrices = [A, B, C, D, E, F, G, H, J, K, L]
spmatrices = [Bsp, Dsp, Esp, Fsp, Hsp, Hspp, Jsp, Jspp, Ksp]
# sparse/sparse
for i in spmatrices:
for j in spmatrices:
try:
dense_mult = np.multiply(i.todense(), j.todense())
except ValueError:
assert_raises(ValueError, i.multiply, j)
continue
sp_mult = i.multiply(j)
assert_almost_equal(sp_mult.todense(), dense_mult)
# sparse/dense
for i in spmatrices:
for j in matrices:
try:
dense_mult = np.multiply(i.todense(), j)
except TypeError:
continue
except ValueError:
assert_raises(ValueError, i.multiply, j)
continue
sp_mult = i.multiply(j)
if isspmatrix(sp_mult):
assert_almost_equal(sp_mult.todense(), dense_mult)
else:
assert_almost_equal(sp_mult, dense_mult)
def test_elementwise_divide(self):
expected = [[1,np.nan,np.nan,1],
[1,np.nan,1,np.nan],
[np.nan,1,np.nan,np.nan]]
assert_array_equal(todense(self.datsp / self.datsp),expected)
denom = self.spmatrix(matrix([[1,0,0,4],[-1,0,0,0],[0,8,0,-5]],'d'))
expected = [[1,np.nan,np.nan,0.5],
[-3,np.nan,inf,np.nan],
[np.nan,0.25,np.nan,0]]
assert_array_equal(todense(self.datsp / denom), expected)
# complex
A = array([[1-2j,0+5j,-1+0j],[4-3j,-3+6j,5]])
B = array([[5+2j,7-3j,-2+1j],[0-1j,-4+2j,9]])
Asp = self.spmatrix(A)
Bsp = self.spmatrix(B)
assert_almost_equal(todense(Asp / Bsp), A/B)
# integer
A = array([[1,2,3],[-3,2,1]])
B = array([[0,1,2],[0,-2,3]])
Asp = self.spmatrix(A)
Bsp = self.spmatrix(B)
with np.errstate(divide='ignore'):
assert_array_equal(todense(Asp / Bsp), A / B)
# mismatching sparsity patterns
A = array([[0,1],[1,0]])
B = array([[1,0],[1,0]])
Asp = self.spmatrix(A)
Bsp = self.spmatrix(B)
with np.errstate(divide='ignore', invalid='ignore'):
assert_array_equal(np.array(todense(Asp / Bsp)), A / B)
def test_pow(self):
A = matrix([[1,0,2,0],[0,3,4,0],[0,5,0,0],[0,6,7,8]])
B = self.spmatrix(A)
for exponent in [0,1,2,3]:
assert_array_equal((B**exponent).todense(),A**exponent)
# invalid exponents
for exponent in [-1, 2.2, 1 + 3j]:
assert_raises(Exception, B.__pow__, exponent)
# nonsquare matrix
B = self.spmatrix(A[:3,:])
assert_raises(Exception, B.__pow__, 1)
def test_rmatvec(self):
M = self.spmatrix(matrix([[3,0,0],[0,1,0],[2,0,3.0],[2,3,0]]))
assert_array_almost_equal([1,2,3,4]*M, dot([1,2,3,4], M.toarray()))
row = matrix([[1,2,3,4]])
assert_array_almost_equal(row*M, row*M.todense())
def test_small_multiplication(self):
# test that A*x works for x with shape () (1,) (1,1) and (1,0)
A = self.spmatrix([[1],[2],[3]])
assert_(isspmatrix(A * array(1)))
assert_equal((A * array(1)).todense(), [[1],[2],[3]])
assert_equal(A * array([1]), array([1,2,3]))
assert_equal(A * array([[1]]), array([[1],[2],[3]]))
assert_equal(A * np.ones((1,0)), np.ones((3,0)))
def test_binop_custom_type(self):
# Non-regression test: previously, binary operations would raise
# NotImplementedError instead of returning NotImplemented
# (https://docs.python.org/library/constants.html#NotImplemented)
# so overloading Custom + matrix etc. didn't work.
A = self.spmatrix([[1], [2], [3]])
B = BinopTester()
assert_equal(A + B, "matrix on the left")
assert_equal(A - B, "matrix on the left")
assert_equal(A * B, "matrix on the left")
assert_equal(B + A, "matrix on the right")
assert_equal(B - A, "matrix on the right")
assert_equal(B * A, "matrix on the right")
if TEST_MATMUL:
assert_equal(eval('A @ B'), "matrix on the left")
assert_equal(eval('B @ A'), "matrix on the right")
def test_binop_custom_type_with_shape(self):
A = self.spmatrix([[1], [2], [3]])
B = BinopTester_with_shape((3,1))
assert_equal(A + B, "matrix on the left")
assert_equal(A - B, "matrix on the left")
assert_equal(A * B, "matrix on the left")
assert_equal(B + A, "matrix on the right")
assert_equal(B - A, "matrix on the right")
assert_equal(B * A, "matrix on the right")
if TEST_MATMUL:
assert_equal(eval('A @ B'), "matrix on the left")
assert_equal(eval('B @ A'), "matrix on the right")
def test_matmul(self):
if not TEST_MATMUL:
pytest.skip("matmul is only tested in Python 3.5+")
M = self.spmatrix(matrix([[3,0,0],[0,1,0],[2,0,3.0],[2,3,0]]))
B = self.spmatrix(matrix([[0,1],[1,0],[0,2]],'d'))
col = matrix([1,2,3]).T
# check matrix-vector
assert_array_almost_equal(operator.matmul(M, col),
M.todense() * col)
# check matrix-matrix
assert_array_almost_equal(operator.matmul(M, B).todense(),
(M * B).todense())
assert_array_almost_equal(operator.matmul(M.todense(), B),
(M * B).todense())
assert_array_almost_equal(operator.matmul(M, B.todense()),
(M * B).todense())
# check error on matrix-scalar
assert_raises(ValueError, operator.matmul, M, 1)
assert_raises(ValueError, operator.matmul, 1, M)
def test_matvec(self):
M = self.spmatrix(matrix([[3,0,0],[0,1,0],[2,0,3.0],[2,3,0]]))
col = matrix([1,2,3]).T
assert_array_almost_equal(M * col, M.todense() * col)
# check result dimensions (ticket #514)
assert_equal((M * array([1,2,3])).shape,(4,))
assert_equal((M * array([[1],[2],[3]])).shape,(4,1))
assert_equal((M * matrix([[1],[2],[3]])).shape,(4,1))
# check result type
assert_(isinstance(M * array([1,2,3]), ndarray))
assert_(isinstance(M * matrix([1,2,3]).T, matrix))
# ensure exception is raised for improper dimensions
bad_vecs = [array([1,2]), array([1,2,3,4]), array([[1],[2]]),
matrix([1,2,3]), matrix([[1],[2]])]
for x in bad_vecs:
assert_raises(ValueError, M.__mul__, x)
# Should this be supported or not?!
# flat = array([1,2,3])
# assert_array_almost_equal(M*flat, M.todense()*flat)
# Currently numpy dense matrices promote the result to a 1x3 matrix,
# whereas sparse matrices leave the result as a rank-1 array. Which
# is preferable?
# Note: the following command does not work. Both NumPy matrices
# and spmatrices should raise exceptions!
# assert_array_almost_equal(M*[1,2,3], M.todense()*[1,2,3])
# The current relationship between sparse matrix products and array
# products is as follows:
assert_array_almost_equal(M*array([1,2,3]), dot(M.A,[1,2,3]))
assert_array_almost_equal(M*[[1],[2],[3]], asmatrix(dot(M.A,[1,2,3])).T)
# Note that the result of M * x is dense if x has a singleton dimension.
# Currently M.matvec(asarray(col)) is rank-1, whereas M.matvec(col)
# is rank-2. Is this desirable?
def test_matmat_sparse(self):
a = matrix([[3,0,0],[0,1,0],[2,0,3.0],[2,3,0]])
a2 = array([[3,0,0],[0,1,0],[2,0,3.0],[2,3,0]])
b = matrix([[0,1],[1,0],[0,2]],'d')
asp = self.spmatrix(a)
bsp = self.spmatrix(b)
assert_array_almost_equal((asp*bsp).todense(), a*b)
assert_array_almost_equal(asp*b, a*b)
assert_array_almost_equal(a*bsp, a*b)
assert_array_almost_equal(a2*bsp, a*b)
# Now try performing cross-type multplication:
csp = bsp.tocsc()
c = b
assert_array_almost_equal((asp*csp).todense(), a*c)
assert_array_almost_equal(asp*c, a*c)
assert_array_almost_equal(a*csp, a*c)
assert_array_almost_equal(a2*csp, a*c)
csp = bsp.tocsr()
assert_array_almost_equal((asp*csp).todense(), a*c)
assert_array_almost_equal(asp*c, a*c)
assert_array_almost_equal(a*csp, a*c)
assert_array_almost_equal(a2*csp, a*c)
csp = bsp.tocoo()
assert_array_almost_equal((asp*csp).todense(), a*c)
assert_array_almost_equal(asp*c, a*c)
assert_array_almost_equal(a*csp, a*c)
assert_array_almost_equal(a2*csp, a*c)
# Test provided by Andy Fraser, 2006-03-26
L = 30
frac = .3
random.seed(0) # make runs repeatable
A = zeros((L,2))
for i in xrange(L):
for j in xrange(2):
r = random.random()
if r < frac:
A[i,j] = r/frac
A = self.spmatrix(A)
B = A*A.T
assert_array_almost_equal(B.todense(), A.todense() * A.T.todense())
assert_array_almost_equal(B.todense(), A.todense() * A.todense().T)
# check dimension mismatch 2x2 times 3x2
A = self.spmatrix([[1,2],[3,4]])
B = self.spmatrix([[1,2],[3,4],[5,6]])
assert_raises(ValueError, A.__mul__, B)
def test_matmat_dense(self):
a = matrix([[3,0,0],[0,1,0],[2,0,3.0],[2,3,0]])
asp = self.spmatrix(a)
# check both array and matrix types
bs = [array([[1,2],[3,4],[5,6]]), matrix([[1,2],[3,4],[5,6]])]
for b in bs:
result = asp*b
assert_(isinstance(result, type(b)))
assert_equal(result.shape, (4,2))
assert_equal(result, dot(a,b))
def test_sparse_format_conversions(self):
A = sparse.kron([[1,0,2],[0,3,4],[5,0,0]], [[1,2],[0,3]])
D = A.todense()
A = self.spmatrix(A)
for format in ['bsr','coo','csc','csr','dia','dok','lil']:
a = A.asformat(format)
assert_equal(a.format,format)
assert_array_equal(a.todense(), D)
b = self.spmatrix(D+3j).asformat(format)
assert_equal(b.format,format)
assert_array_equal(b.todense(), D+3j)
c = eval(format + '_matrix')(A)
assert_equal(c.format,format)
assert_array_equal(c.todense(), D)
for format in ['array', 'dense']:
a = A.asformat(format)
assert_array_equal(a, D)
b = self.spmatrix(D+3j).asformat(format)
assert_array_equal(b, D+3j)
def test_tobsr(self):
x = array([[1,0,2,0],[0,0,0,0],[0,0,4,5]])
y = array([[0,1,2],[3,0,5]])
A = kron(x,y)
Asp = self.spmatrix(A)
for format in ['bsr']:
fn = getattr(Asp, 'to' + format)
for X in [1, 2, 3, 6]:
for Y in [1, 2, 3, 4, 6, 12]:
assert_equal(fn(blocksize=(X,Y)).todense(), A)
def test_transpose(self):
dat_1 = self.dat
dat_2 = np.array([[]])
matrices = [dat_1, dat_2]
def check(dtype, j):
dat = np.matrix(matrices[j], dtype=dtype)
datsp = self.spmatrix(dat)
a = datsp.transpose()
b = dat.transpose()
assert_array_equal(a.todense(), b)
assert_array_equal(a.transpose().todense(), dat)
assert_equal(a.dtype, b.dtype)
# See gh-5987
empty = self.spmatrix((3, 4))
assert_array_equal(np.transpose(empty).todense(),
np.transpose(zeros((3, 4))))
assert_array_equal(empty.T.todense(), zeros((4, 3)))
assert_raises(ValueError, empty.transpose, axes=0)
for dtype in self.checked_dtypes:
for j in range(len(matrices)):
check(dtype, j)
def test_add_dense(self):
def check(dtype):
dat = self.dat_dtypes[dtype]
datsp = self.datsp_dtypes[dtype]
# adding a dense matrix to a sparse matrix
sum1 = dat + datsp
assert_array_equal(sum1, dat + dat)
sum2 = datsp + dat
assert_array_equal(sum2, dat + dat)
for dtype in self.math_dtypes:
check(dtype)
def test_sub_dense(self):
# subtracting a dense matrix to/from a sparse matrix
def check(dtype):
dat = self.dat_dtypes[dtype]
datsp = self.datsp_dtypes[dtype]
# Behavior is different for bool.
if dat.dtype == bool:
sum1 = dat - datsp
assert_array_equal(sum1, dat - dat)
sum2 = datsp - dat
assert_array_equal(sum2, dat - dat)
else:
# Manually add to avoid upcasting from scalar
# multiplication.
sum1 = (dat + dat + dat) - datsp
assert_array_equal(sum1, dat + dat)
sum2 = (datsp + datsp + datsp) - dat
assert_array_equal(sum2, dat + dat)
for dtype in self.math_dtypes:
if (dtype == np.dtype('bool')) and (
NumpyVersion(np.__version__) >= '1.9.0.dev'):
# boolean array subtraction deprecated in 1.9.0
continue
check(dtype)
def test_maximum_minimum(self):
A_dense = np.array([[1, 0, 3], [0, 4, 5], [0, 0, 0]])
B_dense = np.array([[1, 1, 2], [0, 3, 6], [1, -1, 0]])
A_dense_cpx = np.array([[1, 0, 3], [0, 4+2j, 5], [0, 1j, -1j]])
def check(dtype, dtype2, btype):
if np.issubdtype(dtype, np.complexfloating):
A = self.spmatrix(A_dense_cpx.astype(dtype))
else:
A = self.spmatrix(A_dense.astype(dtype))
if btype == 'scalar':
B = dtype2.type(1)
elif btype == 'scalar2':
B = dtype2.type(-1)
elif btype == 'dense':
B = B_dense.astype(dtype2)
elif btype == 'sparse':
B = self.spmatrix(B_dense.astype(dtype2))
else:
raise ValueError()
with suppress_warnings() as sup:
sup.filter(SparseEfficiencyWarning,
"Taking maximum .minimum. with > 0 .< 0. number results to a dense matrix")
max_s = A.maximum(B)
min_s = A.minimum(B)
max_d = np.maximum(todense(A), todense(B))
assert_array_equal(todense(max_s), max_d)
assert_equal(max_s.dtype, max_d.dtype)
min_d = np.minimum(todense(A), todense(B))
assert_array_equal(todense(min_s), min_d)
assert_equal(min_s.dtype, min_d.dtype)
for dtype in self.math_dtypes:
for dtype2 in [np.int8, np.float_, np.complex_]:
for btype in ['scalar', 'scalar2', 'dense', 'sparse']:
check(np.dtype(dtype), np.dtype(dtype2), btype)
def test_copy(self):
# Check whether the copy=True and copy=False keywords work
A = self.datsp
# check that copy preserves format
assert_equal(A.copy().format, A.format)
assert_equal(A.__class__(A,copy=True).format, A.format)
assert_equal(A.__class__(A,copy=False).format, A.format)
assert_equal(A.copy().todense(), A.todense())
assert_equal(A.__class__(A,copy=True).todense(), A.todense())
assert_equal(A.__class__(A,copy=False).todense(), A.todense())
# check that XXX_matrix.toXXX() works
toself = getattr(A,'to' + A.format)
assert_equal(toself().format, A.format)
assert_equal(toself(copy=True).format, A.format)
assert_equal(toself(copy=False).format, A.format)
assert_equal(toself().todense(), A.todense())
assert_equal(toself(copy=True).todense(), A.todense())
assert_equal(toself(copy=False).todense(), A.todense())
# check whether the data is copied?
# TODO: deal with non-indexable types somehow
B = A.copy()
try:
B[0,0] += 1
assert_(B[0,0] != A[0,0])
except NotImplementedError:
# not all sparse matrices can be indexed
pass
except TypeError:
# not all sparse matrices can be indexed
pass
# test that __iter__ is compatible with NumPy matrix
def test_iterator(self):
B = np.matrix(np.arange(50).reshape(5, 10))
A = self.spmatrix(B)
for x, y in zip(A, B):
assert_equal(x.todense(), y)
def test_size_zero_matrix_arithmetic(self):
# Test basic matrix arithmetic with shapes like (0,0), (10,0),
# (0, 3), etc.
mat = np.matrix([])
a = mat.reshape((0, 0))
b = mat.reshape((0, 1))
c = mat.reshape((0, 5))
d = mat.reshape((1, 0))
e = mat.reshape((5, 0))
f = np.matrix(np.ones([5, 5]))
asp = self.spmatrix(a)
bsp = self.spmatrix(b)
csp = self.spmatrix(c)
dsp = self.spmatrix(d)
esp = self.spmatrix(e)
fsp = self.spmatrix(f)
# matrix product.
assert_array_equal(asp.dot(asp).A, np.dot(a, a).A)
assert_array_equal(bsp.dot(dsp).A, np.dot(b, d).A)
assert_array_equal(dsp.dot(bsp).A, np.dot(d, b).A)
assert_array_equal(csp.dot(esp).A, np.dot(c, e).A)
assert_array_equal(csp.dot(fsp).A, np.dot(c, f).A)
assert_array_equal(esp.dot(csp).A, np.dot(e, c).A)
assert_array_equal(dsp.dot(csp).A, np.dot(d, c).A)
assert_array_equal(fsp.dot(esp).A, np.dot(f, e).A)
# bad matrix products
assert_raises(ValueError, dsp.dot, e)
assert_raises(ValueError, asp.dot, d)
# elemente-wise multiplication
assert_array_equal(asp.multiply(asp).A, np.multiply(a, a).A)
assert_array_equal(bsp.multiply(bsp).A, np.multiply(b, b).A)
assert_array_equal(dsp.multiply(dsp).A, np.multiply(d, d).A)
assert_array_equal(asp.multiply(a).A, np.multiply(a, a).A)
assert_array_equal(bsp.multiply(b).A, np.multiply(b, b).A)
assert_array_equal(dsp.multiply(d).A, np.multiply(d, d).A)
assert_array_equal(asp.multiply(6).A, np.multiply(a, 6).A)
assert_array_equal(bsp.multiply(6).A, np.multiply(b, 6).A)
assert_array_equal(dsp.multiply(6).A, np.multiply(d, 6).A)
# bad element-wise multiplication
assert_raises(ValueError, asp.multiply, c)
assert_raises(ValueError, esp.multiply, c)
# Addition
assert_array_equal(asp.__add__(asp).A, a.__add__(a).A)
assert_array_equal(bsp.__add__(bsp).A, b.__add__(b).A)
assert_array_equal(dsp.__add__(dsp).A, d.__add__(d).A)
# bad addition
assert_raises(ValueError, asp.__add__, dsp)
assert_raises(ValueError, bsp.__add__, asp)
def test_size_zero_conversions(self):
mat = np.matrix([])
a = mat.reshape((0, 0))
b = mat.reshape((0, 5))
c = mat.reshape((5, 0))
for m in [a, b, c]:
spm = self.spmatrix(m)
assert_array_equal(spm.tocoo().A, m)
assert_array_equal(spm.tocsr().A, m)
assert_array_equal(spm.tocsc().A, m)
assert_array_equal(spm.tolil().A, m)
assert_array_equal(spm.todok().A, m)
assert_array_equal(spm.tobsr().A, m)
def test_pickle(self):
import pickle
sup = suppress_warnings()
sup.filter(SparseEfficiencyWarning)
@sup
def check():
datsp = self.datsp.copy()
for protocol in range(pickle.HIGHEST_PROTOCOL):
sploaded = pickle.loads(pickle.dumps(datsp, protocol=protocol))
assert_equal(datsp.shape, sploaded.shape)
assert_array_equal(datsp.toarray(), sploaded.toarray())
assert_equal(datsp.format, sploaded.format)
for key, val in datsp.__dict__.items():
if isinstance(val, np.ndarray):
assert_array_equal(val, sploaded.__dict__[key])
else:
assert_(val == sploaded.__dict__[key])
check()
def test_unary_ufunc_overrides(self):
def check(name):
if name == "sign":
pytest.skip("sign conflicts with comparison op "
"support on Numpy")
if self.spmatrix in (dok_matrix, lil_matrix):
pytest.skip("Unary ops not implemented for dok/lil")
ufunc = getattr(np, name)
X = self.spmatrix(np.arange(20).reshape(4, 5) / 20.)
X0 = ufunc(X.toarray())
X2 = ufunc(X)
assert_array_equal(X2.toarray(), X0)
for name in ["sin", "tan", "arcsin", "arctan", "sinh", "tanh",
"arcsinh", "arctanh", "rint", "sign", "expm1", "log1p",
"deg2rad", "rad2deg", "floor", "ceil", "trunc", "sqrt",
"abs"]:
check(name)
def test_resize(self):
# resize(shape) resizes the matrix in-place
D = np.array([[1, 0, 3, 4],
[2, 0, 0, 0],
[3, 0, 0, 0]])
S = self.spmatrix(D)
assert_(S.resize((3, 2)) is None)
assert_array_equal(S.A, [[1, 0],
[2, 0],
[3, 0]])
S.resize((2, 2))
assert_array_equal(S.A, [[1, 0],
[2, 0]])
S.resize((3, 2))
assert_array_equal(S.A, [[1, 0],
[2, 0],
[0, 0]])
S.resize((3, 3))
assert_array_equal(S.A, [[1, 0, 0],
[2, 0, 0],
[0, 0, 0]])
# test no-op
S.resize((3, 3))
assert_array_equal(S.A, [[1, 0, 0],
[2, 0, 0],
[0, 0, 0]])
# test *args
S.resize(3, 2)
assert_array_equal(S.A, [[1, 0],
[2, 0],
[0, 0]])
for bad_shape in [1, (-1, 2), (2, -1), (1, 2, 3)]:
assert_raises(ValueError, S.resize, bad_shape)
class _TestInplaceArithmetic(object):
@pytest.mark.skipif(NumpyVersion(np.__version__) < "1.13.0",
reason="numpy version doesn't respect array priority")
def test_inplace_dense(self):
a = np.ones((3, 4))
b = self.spmatrix(a)
x = a.copy()
y = a.copy()
x += a
y += b
assert_array_equal(x, y)
x = a.copy()
y = a.copy()
x -= a
y -= b
assert_array_equal(x, y)
# This is matrix product, from __rmul__
assert_raises(ValueError, operator.imul, x, b)
x = a.copy()
y = a.copy()
x = x.dot(a.T)
y *= b.T
assert_array_equal(x, y)
# Matrix (non-elementwise) floor division is not defined
assert_raises(TypeError, operator.ifloordiv, x, b)
def test_imul_scalar(self):
def check(dtype):
dat = self.dat_dtypes[dtype]
datsp = self.datsp_dtypes[dtype]
# Avoid implicit casting.
if np.can_cast(type(2), dtype, casting='same_kind'):
a = datsp.copy()
a *= 2
b = dat.copy()
b *= 2
assert_array_equal(b, a.todense())
if np.can_cast(type(17.3), dtype, casting='same_kind'):
a = datsp.copy()
a *= 17.3
b = dat.copy()
b *= 17.3
assert_array_equal(b, a.todense())
for dtype in self.math_dtypes:
check(dtype)
def test_idiv_scalar(self):
def check(dtype):
dat = self.dat_dtypes[dtype]
datsp = self.datsp_dtypes[dtype]
if np.can_cast(type(2), dtype, casting='same_kind'):
a = datsp.copy()
a /= 2
b = dat.copy()
b /= 2
assert_array_equal(b, a.todense())
if np.can_cast(type(17.3), dtype, casting='same_kind'):
a = datsp.copy()
a /= 17.3
b = dat.copy()
b /= 17.3
assert_array_equal(b, a.todense())
for dtype in self.math_dtypes:
# /= should only be used with float dtypes to avoid implicit
# casting.
if not np.can_cast(dtype, np.int_):
check(dtype)
def test_inplace_success(self):
# Inplace ops should work even if a specialized version is not
# implemented, falling back to x = x <op> y
a = self.spmatrix(np.eye(5))
b = self.spmatrix(np.eye(5))
bp = self.spmatrix(np.eye(5))
b += a
bp = bp + a
assert_allclose(b.A, bp.A)
b *= a
bp = bp * a
assert_allclose(b.A, bp.A)
b -= a
bp = bp - a
assert_allclose(b.A, bp.A)
assert_raises(TypeError, operator.ifloordiv, a, b)
class _TestGetSet(object):
def test_getelement(self):
def check(dtype):
D = array([[1,0,0],
[4,3,0],
[0,2,0],
[0,0,0]], dtype=dtype)
A = self.spmatrix(D)
M,N = D.shape
for i in range(-M, M):
for j in range(-N, N):
assert_equal(A[i,j], D[i,j])
for ij in [(0,3),(-1,3),(4,0),(4,3),(4,-1), (1, 2, 3)]:
assert_raises((IndexError, TypeError), A.__getitem__, ij)
for dtype in supported_dtypes:
check(np.dtype(dtype))
def test_setelement(self):
def check(dtype):
A = self.spmatrix((3,4), dtype=dtype)
with suppress_warnings() as sup:
sup.filter(SparseEfficiencyWarning,
"Changing the sparsity structure of a cs[cr]_matrix is expensive")
A[0, 0] = dtype.type(0) # bug 870
A[1, 2] = dtype.type(4.0)
A[0, 1] = dtype.type(3)
A[2, 0] = dtype.type(2.0)
A[0,-1] = dtype.type(8)
A[-1,-2] = dtype.type(7)
A[0, 1] = dtype.type(5)
if dtype != np.bool_:
assert_array_equal(A.todense(),[[0,5,0,8],[0,0,4,0],[2,0,7,0]])
for ij in [(0,4),(-1,4),(3,0),(3,4),(3,-1)]:
assert_raises(IndexError, A.__setitem__, ij, 123.0)
for v in [[1,2,3], array([1,2,3])]:
assert_raises(ValueError, A.__setitem__, (0,0), v)
if (not np.issubdtype(dtype, np.complexfloating) and
dtype != np.bool_):
for v in [3j]:
assert_raises(TypeError, A.__setitem__, (0,0), v)
for dtype in supported_dtypes:
check(np.dtype(dtype))
def test_negative_index_assignment(self):
# Regression test for github issue 4428.
def check(dtype):
A = self.spmatrix((3, 10), dtype=dtype)
with suppress_warnings() as sup:
sup.filter(SparseEfficiencyWarning,
"Changing the sparsity structure of a cs[cr]_matrix is expensive")
A[0, -4] = 1
assert_equal(A[0, -4], 1)
for dtype in self.math_dtypes:
check(np.dtype(dtype))
def test_scalar_assign_2(self):
n, m = (5, 10)
def _test_set(i, j, nitems):
msg = "%r ; %r ; %r" % (i, j, nitems)
A = self.spmatrix((n, m))
with suppress_warnings() as sup:
sup.filter(SparseEfficiencyWarning,
"Changing the sparsity structure of a cs[cr]_matrix is expensive")
A[i, j] = 1
assert_almost_equal(A.sum(), nitems, err_msg=msg)
assert_almost_equal(A[i, j], 1, err_msg=msg)
# [i,j]
for i, j in [(2, 3), (-1, 8), (-1, -2), (array(-1), -2), (-1, array(-2)),
(array(-1), array(-2))]:
_test_set(i, j, 1)
def test_index_scalar_assign(self):
A = self.spmatrix((5, 5))
B = np.zeros((5, 5))
with suppress_warnings() as sup:
sup.filter(SparseEfficiencyWarning,
"Changing the sparsity structure of a cs[cr]_matrix is expensive")
for C in [A, B]:
C[0,1] = 1
C[3,0] = 4
C[3,0] = 9
assert_array_equal(A.toarray(), B)
class _TestSolve(object):
def test_solve(self):
# Test whether the lu_solve command segfaults, as reported by Nils
# Wagner for a 64-bit machine, 02 March 2005 (EJS)
n = 20
np.random.seed(0) # make tests repeatable
A = zeros((n,n), dtype=complex)
x = np.random.rand(n)
y = np.random.rand(n-1)+1j*np.random.rand(n-1)
r = np.random.rand(n)
for i in range(len(x)):
A[i,i] = x[i]
for i in range(len(y)):
A[i,i+1] = y[i]
A[i+1,i] = conjugate(y[i])
A = self.spmatrix(A)
with suppress_warnings() as sup:
sup.filter(SparseEfficiencyWarning, "splu requires CSC matrix format")
x = splu(A).solve(r)
assert_almost_equal(A*x,r)
class _TestSlicing(object):
def test_dtype_preservation(self):
assert_equal(self.spmatrix((1,10), dtype=np.int16)[0,1:5].dtype, np.int16)
assert_equal(self.spmatrix((1,10), dtype=np.int32)[0,1:5].dtype, np.int32)
assert_equal(self.spmatrix((1,10), dtype=np.float32)[0,1:5].dtype, np.float32)
assert_equal(self.spmatrix((1,10), dtype=np.float64)[0,1:5].dtype, np.float64)
def test_get_horiz_slice(self):
B = asmatrix(arange(50.).reshape(5,10))
A = self.spmatrix(B)
assert_array_equal(B[1,:], A[1,:].todense())
assert_array_equal(B[1,2:5], A[1,2:5].todense())
C = matrix([[1, 2, 1], [4, 0, 6], [0, 0, 0], [0, 0, 1]])
D = self.spmatrix(C)
assert_array_equal(C[1, 1:3], D[1, 1:3].todense())
# Now test slicing when a row contains only zeros
E = matrix([[1, 2, 1], [4, 0, 0], [0, 0, 0], [0, 0, 1]])
F = self.spmatrix(E)
assert_array_equal(E[1, 1:3], F[1, 1:3].todense())
assert_array_equal(E[2, -2:], F[2, -2:].A)
# The following should raise exceptions:
assert_raises(IndexError, A.__getitem__, (slice(None), 11))
assert_raises(IndexError, A.__getitem__, (6, slice(3, 7)))
def test_get_vert_slice(self):
B = asmatrix(arange(50.).reshape(5,10))
A = self.spmatrix(B)
assert_array_equal(B[2:5,0], A[2:5,0].todense())
assert_array_equal(B[:,1], A[:,1].todense())
C = matrix([[1, 2, 1], [4, 0, 6], [0, 0, 0], [0, 0, 1]])
D = self.spmatrix(C)
assert_array_equal(C[1:3, 1], D[1:3, 1].todense())
assert_array_equal(C[:, 2], D[:, 2].todense())
# Now test slicing when a column contains only zeros
E = matrix([[1, 0, 1], [4, 0, 0], [0, 0, 0], [0, 0, 1]])
F = self.spmatrix(E)
assert_array_equal(E[:, 1], F[:, 1].todense())
assert_array_equal(E[-2:, 2], F[-2:, 2].todense())
# The following should raise exceptions:
assert_raises(IndexError, A.__getitem__, (slice(None), 11))
assert_raises(IndexError, A.__getitem__, (6, slice(3, 7)))
def test_get_slices(self):
B = asmatrix(arange(50.).reshape(5,10))
A = self.spmatrix(B)
assert_array_equal(A[2:5,0:3].todense(), B[2:5,0:3])
assert_array_equal(A[1:,:-1].todense(), B[1:,:-1])
assert_array_equal(A[:-1,1:].todense(), B[:-1,1:])
# Now test slicing when a column contains only zeros
E = matrix([[1, 0, 1], [4, 0, 0], [0, 0, 0], [0, 0, 1]])
F = self.spmatrix(E)
assert_array_equal(E[1:2, 1:2], F[1:2, 1:2].todense())
assert_array_equal(E[:, 1:], F[:, 1:].todense())
def test_non_unit_stride_2d_indexing(self):
# Regression test -- used to silently ignore the stride.
v0 = np.random.rand(50, 50)
try:
v = self.spmatrix(v0)[0:25:2, 2:30:3]
except ValueError:
# if unsupported
raise pytest.skip("feature not implemented")
assert_array_equal(v.todense(),
v0[0:25:2, 2:30:3])
def test_slicing_2(self):
B = asmatrix(arange(50).reshape(5,10))
A = self.spmatrix(B)
# [i,j]
assert_equal(A[2,3], B[2,3])
assert_equal(A[-1,8], B[-1,8])
assert_equal(A[-1,-2],B[-1,-2])
assert_equal(A[array(-1),-2],B[-1,-2])
assert_equal(A[-1,array(-2)],B[-1,-2])
assert_equal(A[array(-1),array(-2)],B[-1,-2])
# [i,1:2]
assert_equal(A[2,:].todense(), B[2,:])
assert_equal(A[2,5:-2].todense(),B[2,5:-2])
assert_equal(A[array(2),5:-2].todense(),B[2,5:-2])
# [1:2,j]
assert_equal(A[:,2].todense(), B[:,2])
assert_equal(A[3:4,9].todense(), B[3:4,9])
assert_equal(A[1:4,-5].todense(),B[1:4,-5])
assert_equal(A[2:-1,3].todense(),B[2:-1,3])
assert_equal(A[2:-1,array(3)].todense(),B[2:-1,3])
# [1:2,1:2]
assert_equal(A[1:2,1:2].todense(),B[1:2,1:2])
assert_equal(A[4:,3:].todense(), B[4:,3:])
assert_equal(A[:4,:5].todense(), B[:4,:5])
assert_equal(A[2:-1,:5].todense(),B[2:-1,:5])
# [i]
assert_equal(A[1,:].todense(), B[1,:])
assert_equal(A[-2,:].todense(),B[-2,:])
assert_equal(A[array(-2),:].todense(),B[-2,:])
# [1:2]
assert_equal(A[1:4].todense(), B[1:4])
assert_equal(A[1:-2].todense(),B[1:-2])
# Check bug reported by Robert Cimrman:
# http://thread.gmane.org/gmane.comp.python.scientific.devel/7986 (dead link)
s = slice(int8(2),int8(4),None)
assert_equal(A[s,:].todense(), B[2:4,:])
assert_equal(A[:,s].todense(), B[:,2:4])
def test_slicing_3(self):
B = asmatrix(arange(50).reshape(5,10))
A = self.spmatrix(B)
s_ = np.s_
slices = [s_[:2], s_[1:2], s_[3:], s_[3::2],
s_[8:3:-1], s_[4::-2], s_[:5:-1],
0, 1, s_[:], s_[1:5], -1, -2, -5,
array(-1), np.int8(-3)]
def check_1(a):
x = A[a]
y = B[a]
if y.shape == ():
assert_equal(x, y, repr(a))
else:
if x.size == 0 and y.size == 0:
pass
else:
assert_array_equal(x.todense(), y, repr(a))
for j, a in enumerate(slices):
check_1(a)
def check_2(a, b):
# Indexing np.matrix with 0-d arrays seems to be broken,
# as they seem not to be treated as scalars.
# https://github.com/numpy/numpy/issues/3110
if isinstance(a, np.ndarray):
ai = int(a)
else:
ai = a
if isinstance(b, np.ndarray):
bi = int(b)
else:
bi = b
x = A[a, b]
y = B[ai, bi]
if y.shape == ():
assert_equal(x, y, repr((a, b)))
else:
if x.size == 0 and y.size == 0:
pass
else:
assert_array_equal(x.todense(), y, repr((a, b)))
for i, a in enumerate(slices):
for j, b in enumerate(slices):
check_2(a, b)
def test_ellipsis_slicing(self):
b = asmatrix(arange(50).reshape(5,10))
a = self.spmatrix(b)
assert_array_equal(a[...].A, b[...].A)
assert_array_equal(a[...,].A, b[...,].A)
assert_array_equal(a[1, ...].A, b[1, ...].A)
assert_array_equal(a[..., 1].A, b[..., 1].A)
assert_array_equal(a[1:, ...].A, b[1:, ...].A)
assert_array_equal(a[..., 1:].A, b[..., 1:].A)
assert_array_equal(a[1:, 1, ...].A, b[1:, 1, ...].A)
assert_array_equal(a[1, ..., 1:].A, b[1, ..., 1:].A)
# These return ints
assert_equal(a[1, 1, ...], b[1, 1, ...])
assert_equal(a[1, ..., 1], b[1, ..., 1])
@pytest.mark.skipif(NumpyVersion(np.__version__) >= '1.9.0.dev', reason="")
def test_multiple_ellipsis_slicing(self):
b = asmatrix(arange(50).reshape(5,10))
a = self.spmatrix(b)
assert_array_equal(a[..., ...].A, b[..., ...].A)
assert_array_equal(a[..., ..., ...].A, b[..., ..., ...].A)
assert_array_equal(a[1, ..., ...].A, b[1, ..., ...].A)
assert_array_equal(a[1:, ..., ...].A, b[1:, ..., ...].A)
assert_array_equal(a[..., ..., 1:].A, b[..., ..., 1:].A)
# Bug in NumPy's slicing
assert_array_equal(a[..., ..., 1].A, b[..., ..., 1].A.reshape((5,1)))
class _TestSlicingAssign(object):
def test_slice_scalar_assign(self):
A = self.spmatrix((5, 5))
B = np.zeros((5, 5))
with suppress_warnings() as sup:
sup.filter(SparseEfficiencyWarning,
"Changing the sparsity structure of a cs[cr]_matrix is expensive")
for C in [A, B]:
C[0:1,1] = 1
C[3:0,0] = 4
C[3:4,0] = 9
C[0,4:] = 1
C[3::-1,4:] = 9
assert_array_equal(A.toarray(), B)
def test_slice_assign_2(self):
n, m = (5, 10)
def _test_set(i, j):
msg = "i=%r; j=%r" % (i, j)
A = self.spmatrix((n, m))
with suppress_warnings() as sup:
sup.filter(SparseEfficiencyWarning,
"Changing the sparsity structure of a cs[cr]_matrix is expensive")
A[i, j] = 1
B = np.zeros((n, m))
B[i, j] = 1
assert_array_almost_equal(A.todense(), B, err_msg=msg)
# [i,1:2]
for i, j in [(2, slice(3)), (2, slice(None, 10, 4)), (2, slice(5, -2)),
(array(2), slice(5, -2))]:
_test_set(i, j)
def test_self_self_assignment(self):
# Tests whether a row of one lil_matrix can be assigned to
# another.
B = self.spmatrix((4,3))
with suppress_warnings() as sup:
sup.filter(SparseEfficiencyWarning,
"Changing the sparsity structure of a cs[cr]_matrix is expensive")
B[0,0] = 2
B[1,2] = 7
B[2,1] = 3
B[3,0] = 10
A = B / 10
B[0,:] = A[0,:]
assert_array_equal(A[0,:].A, B[0,:].A)
A = B / 10
B[:,:] = A[:1,:1]
assert_array_equal(np.zeros((4,3)) + A[0,0], B.A)
A = B / 10
B[:-1,0] = A[0,:].T
assert_array_equal(A[0,:].A.T, B[:-1,0].A)
def test_slice_assignment(self):
B = self.spmatrix((4,3))
expected = array([[10,0,0],
[0,0,6],
[0,14,0],
[0,0,0]])
block = [[1,0],[0,4]]
with suppress_warnings() as sup:
sup.filter(SparseEfficiencyWarning,
"Changing the sparsity structure of a cs[cr]_matrix is expensive")
B[0,0] = 5
B[1,2] = 3
B[2,1] = 7
B[:,:] = B+B
assert_array_equal(B.todense(),expected)
B[:2,:2] = csc_matrix(array(block))
assert_array_equal(B.todense()[:2,:2],block)
def test_sparsity_modifying_assignment(self):
B = self.spmatrix((4,3))
with suppress_warnings() as sup:
sup.filter(SparseEfficiencyWarning,
"Changing the sparsity structure of a cs[cr]_matrix is expensive")
B[0,0] = 5
B[1,2] = 3
B[2,1] = 7
B[3,0] = 10
B[:3] = csr_matrix(np.eye(3))
expected = array([[1,0,0],[0,1,0],[0,0,1],[10,0,0]])
assert_array_equal(B.toarray(), expected)
def test_set_slice(self):
A = self.spmatrix((5,10))
B = matrix(zeros((5,10), float))
s_ = np.s_
slices = [s_[:2], s_[1:2], s_[3:], s_[3::2],
s_[8:3:-1], s_[4::-2], s_[:5:-1],
0, 1, s_[:], s_[1:5], -1, -2, -5,
array(-1), np.int8(-3)]
with suppress_warnings() as sup:
sup.filter(SparseEfficiencyWarning,
"Changing the sparsity structure of a cs[cr]_matrix is expensive")
for j, a in enumerate(slices):
A[a] = j
B[a] = j
assert_array_equal(A.todense(), B, repr(a))
for i, a in enumerate(slices):
for j, b in enumerate(slices):
A[a,b] = 10*i + 1000*(j+1)
B[a,b] = 10*i + 1000*(j+1)
assert_array_equal(A.todense(), B, repr((a, b)))
A[0, 1:10:2] = xrange(1,10,2)
B[0, 1:10:2] = xrange(1,10,2)
assert_array_equal(A.todense(), B)
A[1:5:2,0] = np.array(range(1,5,2))[:,None]
B[1:5:2,0] = np.array(range(1,5,2))[:,None]
assert_array_equal(A.todense(), B)
# The next commands should raise exceptions
assert_raises(ValueError, A.__setitem__, (0, 0), list(range(100)))
assert_raises(ValueError, A.__setitem__, (0, 0), arange(100))
assert_raises(ValueError, A.__setitem__, (0, slice(None)),
list(range(100)))
assert_raises(ValueError, A.__setitem__, (slice(None), 1),
list(range(100)))
assert_raises(ValueError, A.__setitem__, (slice(None), 1), A.copy())
assert_raises(ValueError, A.__setitem__,
([[1, 2, 3], [0, 3, 4]], [1, 2, 3]), [1, 2, 3, 4])
assert_raises(ValueError, A.__setitem__,
([[1, 2, 3], [0, 3, 4], [4, 1, 3]],
[[1, 2, 4], [0, 1, 3]]), [2, 3, 4])
class _TestFancyIndexing(object):
"""Tests fancy indexing features. The tests for any matrix formats
that implement these features should derive from this class.
"""
def test_bad_index(self):
A = self.spmatrix(np.zeros([5, 5]))
assert_raises((IndexError, ValueError, TypeError), A.__getitem__, "foo")
assert_raises((IndexError, ValueError, TypeError), A.__getitem__, (2, "foo"))
assert_raises((IndexError, ValueError), A.__getitem__,
([1, 2, 3], [1, 2, 3, 4]))
def test_fancy_indexing(self):
B = asmatrix(arange(50).reshape(5,10))
A = self.spmatrix(B)
# [i]
assert_equal(A[[1,3]].todense(), B[[1,3]])
# [i,[1,2]]
assert_equal(A[3,[1,3]].todense(), B[3,[1,3]])
assert_equal(A[-1,[2,-5]].todense(),B[-1,[2,-5]])
assert_equal(A[array(-1),[2,-5]].todense(),B[-1,[2,-5]])
assert_equal(A[-1,array([2,-5])].todense(),B[-1,[2,-5]])
assert_equal(A[array(-1),array([2,-5])].todense(),B[-1,[2,-5]])
# [1:2,[1,2]]
assert_equal(A[:,[2,8,3,-1]].todense(),B[:,[2,8,3,-1]])
assert_equal(A[3:4,[9]].todense(), B[3:4,[9]])
assert_equal(A[1:4,[-1,-5]].todense(), B[1:4,[-1,-5]])
assert_equal(A[1:4,array([-1,-5])].todense(), B[1:4,[-1,-5]])
# [[1,2],j]
assert_equal(A[[1,3],3].todense(), B[[1,3],3])
assert_equal(A[[2,-5],-4].todense(), B[[2,-5],-4])
assert_equal(A[array([2,-5]),-4].todense(), B[[2,-5],-4])
assert_equal(A[[2,-5],array(-4)].todense(), B[[2,-5],-4])
assert_equal(A[array([2,-5]),array(-4)].todense(), B[[2,-5],-4])
# [[1,2],1:2]
assert_equal(A[[1,3],:].todense(), B[[1,3],:])
assert_equal(A[[2,-5],8:-1].todense(),B[[2,-5],8:-1])
assert_equal(A[array([2,-5]),8:-1].todense(),B[[2,-5],8:-1])
# [[1,2],[1,2]]
assert_equal(todense(A[[1,3],[2,4]]), B[[1,3],[2,4]])
assert_equal(todense(A[[-1,-3],[2,-4]]), B[[-1,-3],[2,-4]])
assert_equal(todense(A[array([-1,-3]),[2,-4]]), B[[-1,-3],[2,-4]])
assert_equal(todense(A[[-1,-3],array([2,-4])]), B[[-1,-3],[2,-4]])
assert_equal(todense(A[array([-1,-3]),array([2,-4])]), B[[-1,-3],[2,-4]])
# [[[1],[2]],[1,2]]
assert_equal(A[[[1],[3]],[2,4]].todense(), B[[[1],[3]],[2,4]])
assert_equal(A[[[-1],[-3],[-2]],[2,-4]].todense(),B[[[-1],[-3],[-2]],[2,-4]])
assert_equal(A[array([[-1],[-3],[-2]]),[2,-4]].todense(),B[[[-1],[-3],[-2]],[2,-4]])
assert_equal(A[[[-1],[-3],[-2]],array([2,-4])].todense(),B[[[-1],[-3],[-2]],[2,-4]])
assert_equal(A[array([[-1],[-3],[-2]]),array([2,-4])].todense(),B[[[-1],[-3],[-2]],[2,-4]])
# [[1,2]]
assert_equal(A[[1,3]].todense(), B[[1,3]])
assert_equal(A[[-1,-3]].todense(),B[[-1,-3]])
assert_equal(A[array([-1,-3])].todense(),B[[-1,-3]])
# [[1,2],:][:,[1,2]]
assert_equal(A[[1,3],:][:,[2,4]].todense(), B[[1,3],:][:,[2,4]])
assert_equal(A[[-1,-3],:][:,[2,-4]].todense(), B[[-1,-3],:][:,[2,-4]])
assert_equal(A[array([-1,-3]),:][:,array([2,-4])].todense(), B[[-1,-3],:][:,[2,-4]])
# [:,[1,2]][[1,2],:]
assert_equal(A[:,[1,3]][[2,4],:].todense(), B[:,[1,3]][[2,4],:])
assert_equal(A[:,[-1,-3]][[2,-4],:].todense(), B[:,[-1,-3]][[2,-4],:])
assert_equal(A[:,array([-1,-3])][array([2,-4]),:].todense(), B[:,[-1,-3]][[2,-4],:])
# Check bug reported by Robert Cimrman:
# http://thread.gmane.org/gmane.comp.python.scientific.devel/7986 (dead link)
s = slice(int8(2),int8(4),None)
assert_equal(A[s,:].todense(), B[2:4,:])
assert_equal(A[:,s].todense(), B[:,2:4])
# Regression for gh-4917: index with tuple of 2D arrays
i = np.array([[1]], dtype=int)
assert_equal(A[i,i].todense(), B[i,i])
# Regression for gh-4917: index with tuple of empty nested lists
assert_equal(A[[[]], [[]]].todense(), B[[[]], [[]]])
def test_fancy_indexing_randomized(self):
np.random.seed(1234) # make runs repeatable
NUM_SAMPLES = 50
M = 6
N = 4
D = np.asmatrix(np.random.rand(M,N))
D = np.multiply(D, D > 0.5)
I = np.random.randint(-M + 1, M, size=NUM_SAMPLES)
J = np.random.randint(-N + 1, N, size=NUM_SAMPLES)
S = self.spmatrix(D)
SIJ = S[I,J]
if isspmatrix(SIJ):
SIJ = SIJ.todense()
assert_equal(SIJ, D[I,J])
I_bad = I + M
J_bad = J - N
assert_raises(IndexError, S.__getitem__, (I_bad,J))
assert_raises(IndexError, S.__getitem__, (I,J_bad))
def test_fancy_indexing_boolean(self):
np.random.seed(1234) # make runs repeatable
B = asmatrix(arange(50).reshape(5,10))
A = self.spmatrix(B)
I = np.array(np.random.randint(0, 2, size=5), dtype=bool)
J = np.array(np.random.randint(0, 2, size=10), dtype=bool)
X = np.array(np.random.randint(0, 2, size=(5, 10)), dtype=bool)
assert_equal(todense(A[I]), B[I])
assert_equal(todense(A[:,J]), B[:, J])
assert_equal(todense(A[X]), B[X])
assert_equal(todense(A[B > 9]), B[B > 9])
I = np.array([True, False, True, True, False])
J = np.array([False, True, True, False, True,
False, False, False, False, False])
assert_equal(todense(A[I, J]), B[I, J])
Z1 = np.zeros((6, 11), dtype=bool)
Z2 = np.zeros((6, 11), dtype=bool)
Z2[0,-1] = True
Z3 = np.zeros((6, 11), dtype=bool)
Z3[-1,0] = True
assert_equal(A[Z1], np.array([]))
assert_raises(IndexError, A.__getitem__, Z2)
assert_raises(IndexError, A.__getitem__, Z3)
assert_raises((IndexError, ValueError), A.__getitem__, (X, 1))
def test_fancy_indexing_sparse_boolean(self):
np.random.seed(1234) # make runs repeatable
B = asmatrix(arange(50).reshape(5,10))
A = self.spmatrix(B)
X = np.array(np.random.randint(0, 2, size=(5, 10)), dtype=bool)
Xsp = csr_matrix(X)
assert_equal(todense(A[Xsp]), B[X])
assert_equal(todense(A[A > 9]), B[B > 9])
Z = np.array(np.random.randint(0, 2, size=(5, 11)), dtype=bool)
Y = np.array(np.random.randint(0, 2, size=(6, 10)), dtype=bool)
Zsp = csr_matrix(Z)
Ysp = csr_matrix(Y)
assert_raises(IndexError, A.__getitem__, Zsp)
assert_raises(IndexError, A.__getitem__, Ysp)
assert_raises((IndexError, ValueError), A.__getitem__, (Xsp, 1))
def test_fancy_indexing_regression_3087(self):
mat = self.spmatrix(array([[1, 0, 0], [0,1,0], [1,0,0]]))
desired_cols = np.ravel(mat.sum(0)) > 0
assert_equal(mat[:, desired_cols].A, [[1, 0], [0, 1], [1, 0]])
def test_fancy_indexing_seq_assign(self):
mat = self.spmatrix(array([[1, 0], [0, 1]]))
assert_raises(ValueError, mat.__setitem__, (0, 0), np.array([1,2]))
def test_fancy_indexing_empty(self):
B = asmatrix(arange(50).reshape(5,10))
B[1,:] = 0
B[:,2] = 0
B[3,6] = 0
A = self.spmatrix(B)
K = np.array([False, False, False, False, False])
assert_equal(todense(A[K]), B[K])
K = np.array([], dtype=int)
assert_equal(todense(A[K]), B[K])
assert_equal(todense(A[K,K]), B[K,K])
J = np.array([0, 1, 2, 3, 4], dtype=int)[:,None]
assert_equal(todense(A[K,J]), B[K,J])
assert_equal(todense(A[J,K]), B[J,K])
@contextlib.contextmanager
def check_remains_sorted(X):
"""Checks that sorted indices property is retained through an operation
"""
if not hasattr(X, 'has_sorted_indices') or not X.has_sorted_indices:
yield
return
yield
indices = X.indices.copy()
X.has_sorted_indices = False
X.sort_indices()
assert_array_equal(indices, X.indices,
'Expected sorted indices, found unsorted')
class _TestFancyIndexingAssign(object):
def test_bad_index_assign(self):
A = self.spmatrix(np.zeros([5, 5]))
assert_raises((IndexError, ValueError, TypeError), A.__setitem__, "foo", 2)
assert_raises((IndexError, ValueError, TypeError), A.__setitem__, (2, "foo"), 5)
def test_fancy_indexing_set(self):
n, m = (5, 10)
def _test_set_slice(i, j):
A = self.spmatrix((n, m))
B = asmatrix(np.zeros((n, m)))
with suppress_warnings() as sup:
sup.filter(SparseEfficiencyWarning,
"Changing the sparsity structure of a cs[cr]_matrix is expensive")
B[i, j] = 1
with check_remains_sorted(A):
A[i, j] = 1
assert_array_almost_equal(A.todense(), B)
# [1:2,1:2]
for i, j in [((2, 3, 4), slice(None, 10, 4)),
(np.arange(3), slice(5, -2)),
(slice(2, 5), slice(5, -2))]:
_test_set_slice(i, j)
for i, j in [(np.arange(3), np.arange(3)), ((0, 3, 4), (1, 2, 4))]:
_test_set_slice(i, j)
def test_fancy_assignment_dtypes(self):
def check(dtype):
A = self.spmatrix((5, 5), dtype=dtype)
with suppress_warnings() as sup:
sup.filter(SparseEfficiencyWarning,
"Changing the sparsity structure of a cs[cr]_matrix is expensive")
A[[0,1],[0,1]] = dtype.type(1)
assert_equal(A.sum(), dtype.type(1)*2)
A[0:2,0:2] = dtype.type(1.0)
assert_equal(A.sum(), dtype.type(1)*4)
A[2,2] = dtype.type(1.0)
assert_equal(A.sum(), dtype.type(1)*4 + dtype.type(1))
for dtype in supported_dtypes:
check(np.dtype(dtype))
def test_sequence_assignment(self):
A = self.spmatrix((4,3))
B = self.spmatrix(eye(3,4))
i0 = [0,1,2]
i1 = (0,1,2)
i2 = array(i0)
with suppress_warnings() as sup:
sup.filter(SparseEfficiencyWarning,
"Changing the sparsity structure of a cs[cr]_matrix is expensive")
with check_remains_sorted(A):
A[0,i0] = B[i0,0].T
A[1,i1] = B[i1,1].T
A[2,i2] = B[i2,2].T
assert_array_equal(A.todense(),B.T.todense())
# column slice
A = self.spmatrix((2,3))
with check_remains_sorted(A):
A[1,1:3] = [10,20]
assert_array_equal(A.todense(), [[0,0,0],[0,10,20]])
# row slice
A = self.spmatrix((3,2))
with check_remains_sorted(A):
A[1:3,1] = [[10],[20]]
assert_array_equal(A.todense(), [[0,0],[0,10],[0,20]])
# both slices
A = self.spmatrix((3,3))
B = asmatrix(np.zeros((3,3)))
with check_remains_sorted(A):
for C in [A, B]:
C[[0,1,2], [0,1,2]] = [4,5,6]
assert_array_equal(A.toarray(), B)
# both slices (2)
A = self.spmatrix((4, 3))
with check_remains_sorted(A):
A[(1, 2, 3), (0, 1, 2)] = [1, 2, 3]
assert_almost_equal(A.sum(), 6)
B = asmatrix(np.zeros((4, 3)))
B[(1, 2, 3), (0, 1, 2)] = [1, 2, 3]
assert_array_equal(A.todense(), B)
def test_fancy_assign_empty(self):
B = asmatrix(arange(50).reshape(5,10))
B[1,:] = 0
B[:,2] = 0
B[3,6] = 0
A = self.spmatrix(B)
K = np.array([False, False, False, False, False])
A[K] = 42
assert_equal(todense(A), B)
K = np.array([], dtype=int)
A[K] = 42
assert_equal(todense(A), B)
A[K,K] = 42
assert_equal(todense(A), B)
J = np.array([0, 1, 2, 3, 4], dtype=int)[:,None]
A[K,J] = 42
assert_equal(todense(A), B)
A[J,K] = 42
assert_equal(todense(A), B)
class _TestFancyMultidim(object):
def test_fancy_indexing_ndarray(self):
sets = [
(np.array([[1], [2], [3]]), np.array([3, 4, 2])),
(np.array([[1], [2], [3]]), np.array([[3, 4, 2]])),
(np.array([[1, 2, 3]]), np.array([[3], [4], [2]])),
(np.array([1, 2, 3]), np.array([[3], [4], [2]])),
(np.array([[1, 2, 3], [3, 4, 2]]),
np.array([[5, 6, 3], [2, 3, 1]]))
]
# These inputs generate 3-D outputs
# (np.array([[[1], [2], [3]], [[3], [4], [2]]]),
# np.array([[[5], [6], [3]], [[2], [3], [1]]])),
for I, J in sets:
np.random.seed(1234)
D = np.asmatrix(np.random.rand(5, 7))
S = self.spmatrix(D)
SIJ = S[I,J]
if isspmatrix(SIJ):
SIJ = SIJ.todense()
assert_equal(SIJ, D[I,J])
I_bad = I + 5
J_bad = J + 7
assert_raises(IndexError, S.__getitem__, (I_bad,J))
assert_raises(IndexError, S.__getitem__, (I,J_bad))
# This would generate 3-D arrays -- not supported
assert_raises(IndexError, S.__getitem__, ([I, I], slice(None)))
assert_raises(IndexError, S.__getitem__, (slice(None), [J, J]))
class _TestFancyMultidimAssign(object):
def test_fancy_assign_ndarray(self):
np.random.seed(1234)
D = np.asmatrix(np.random.rand(5, 7))
S = self.spmatrix(D)
X = np.random.rand(2, 3)
I = np.array([[1, 2, 3], [3, 4, 2]])
J = np.array([[5, 6, 3], [2, 3, 1]])
with check_remains_sorted(S):
S[I,J] = X
D[I,J] = X
assert_equal(S.todense(), D)
I_bad = I + 5
J_bad = J + 7
C = [1, 2, 3]
with check_remains_sorted(S):
S[I,J] = C
D[I,J] = C
assert_equal(S.todense(), D)
with check_remains_sorted(S):
S[I,J] = 3
D[I,J] = 3
assert_equal(S.todense(), D)
assert_raises(IndexError, S.__setitem__, (I_bad,J), C)
assert_raises(IndexError, S.__setitem__, (I,J_bad), C)
def test_fancy_indexing_multidim_set(self):
n, m = (5, 10)
def _test_set_slice(i, j):
A = self.spmatrix((n, m))
with check_remains_sorted(A), suppress_warnings() as sup:
sup.filter(SparseEfficiencyWarning,
"Changing the sparsity structure of a cs[cr]_matrix is expensive")
A[i, j] = 1
B = asmatrix(np.zeros((n, m)))
B[i, j] = 1
assert_array_almost_equal(A.todense(), B)
# [[[1, 2], [1, 2]], [1, 2]]
for i, j in [(np.array([[1, 2], [1, 3]]), [1, 3]),
(np.array([0, 4]), [[0, 3], [1, 2]]),
([[1, 2, 3], [0, 2, 4]], [[0, 4, 3], [4, 1, 2]])]:
_test_set_slice(i, j)
def test_fancy_assign_list(self):
np.random.seed(1234)
D = np.asmatrix(np.random.rand(5, 7))
S = self.spmatrix(D)
X = np.random.rand(2, 3)
I = [[1, 2, 3], [3, 4, 2]]
J = [[5, 6, 3], [2, 3, 1]]
S[I,J] = X
D[I,J] = X
assert_equal(S.todense(), D)
I_bad = [[ii + 5 for ii in i] for i in I]
J_bad = [[jj + 7 for jj in j] for j in J]
C = [1, 2, 3]
S[I,J] = C
D[I,J] = C
assert_equal(S.todense(), D)
S[I,J] = 3
D[I,J] = 3
assert_equal(S.todense(), D)
assert_raises(IndexError, S.__setitem__, (I_bad,J), C)
assert_raises(IndexError, S.__setitem__, (I,J_bad), C)
def test_fancy_assign_slice(self):
np.random.seed(1234)
D = np.asmatrix(np.random.rand(5, 7))
S = self.spmatrix(D)
I = [[1, 2, 3], [3, 4, 2]]
J = [[5, 6, 3], [2, 3, 1]]
I_bad = [[ii + 5 for ii in i] for i in I]
J_bad = [[jj + 7 for jj in j] for j in J]
C = [1, 2, 3, 4, 5, 6, 7]
assert_raises(IndexError, S.__setitem__, (I_bad, slice(None)), C)
assert_raises(IndexError, S.__setitem__, (slice(None), J_bad), C)
class _TestArithmetic(object):
"""
Test real/complex arithmetic
"""
def __arith_init(self):
# these can be represented exactly in FP (so arithmetic should be exact)
self.__A = matrix([[-1.5, 6.5, 0, 2.25, 0, 0],
[3.125, -7.875, 0.625, 0, 0, 0],
[0, 0, -0.125, 1.0, 0, 0],
[0, 0, 8.375, 0, 0, 0]],'float64')
self.__B = matrix([[0.375, 0, 0, 0, -5, 2.5],
[14.25, -3.75, 0, 0, -0.125, 0],
[0, 7.25, 0, 0, 0, 0],
[18.5, -0.0625, 0, 0, 0, 0]],'complex128')
self.__B.imag = matrix([[1.25, 0, 0, 0, 6, -3.875],
[2.25, 4.125, 0, 0, 0, 2.75],
[0, 4.125, 0, 0, 0, 0],
[-0.0625, 0, 0, 0, 0, 0]],'float64')
# fractions are all x/16ths
assert_array_equal((self.__A*16).astype('int32'),16*self.__A)
assert_array_equal((self.__B.real*16).astype('int32'),16*self.__B.real)
assert_array_equal((self.__B.imag*16).astype('int32'),16*self.__B.imag)
self.__Asp = self.spmatrix(self.__A)
self.__Bsp = self.spmatrix(self.__B)
def test_add_sub(self):
self.__arith_init()
# basic tests
assert_array_equal((self.__Asp+self.__Bsp).todense(),self.__A+self.__B)
# check conversions
for x in supported_dtypes:
A = self.__A.astype(x)
Asp = self.spmatrix(A)
for y in supported_dtypes:
if not np.issubdtype(y, np.complexfloating):
B = self.__B.real.astype(y)
else:
B = self.__B.astype(y)
Bsp = self.spmatrix(B)
# addition
D1 = A + B
S1 = Asp + Bsp
assert_equal(S1.dtype,D1.dtype)
assert_array_equal(S1.todense(),D1)
assert_array_equal(Asp + B,D1) # check sparse + dense
assert_array_equal(A + Bsp,D1) # check dense + sparse
# subtraction
if (np.dtype('bool') in [x, y]) and (
NumpyVersion(np.__version__) >= '1.9.0.dev'):
# boolean array subtraction deprecated in 1.9.0
continue
D1 = A - B
S1 = Asp - Bsp
assert_equal(S1.dtype,D1.dtype)
assert_array_equal(S1.todense(),D1)
assert_array_equal(Asp - B,D1) # check sparse - dense
assert_array_equal(A - Bsp,D1) # check dense - sparse
def test_mu(self):
self.__arith_init()
# basic tests
assert_array_equal((self.__Asp*self.__Bsp.T).todense(),self.__A*self.__B.T)
for x in supported_dtypes:
A = self.__A.astype(x)
Asp = self.spmatrix(A)
for y in supported_dtypes:
if np.issubdtype(y, np.complexfloating):
B = self.__B.astype(y)
else:
B = self.__B.real.astype(y)
Bsp = self.spmatrix(B)
D1 = A * B.T
S1 = Asp * Bsp.T
assert_allclose(S1.todense(), D1,
atol=1e-14*abs(D1).max())
assert_equal(S1.dtype,D1.dtype)
class _TestMinMax(object):
def test_minmax(self):
for dtype in [np.float32, np.float64, np.int32, np.int64, np.complex128]:
D = np.arange(20, dtype=dtype).reshape(5,4)
X = self.spmatrix(D)
assert_equal(X.min(), 0)
assert_equal(X.max(), 19)
assert_equal(X.min().dtype, dtype)
assert_equal(X.max().dtype, dtype)
D *= -1
X = self.spmatrix(D)
assert_equal(X.min(), -19)
assert_equal(X.max(), 0)
D += 5
X = self.spmatrix(D)
assert_equal(X.min(), -14)
assert_equal(X.max(), 5)
# try a fully dense matrix
X = self.spmatrix(np.arange(1, 10).reshape(3, 3))
assert_equal(X.min(), 1)
assert_equal(X.min().dtype, X.dtype)
X = -X
assert_equal(X.max(), -1)
# and a fully sparse matrix
Z = self.spmatrix(np.zeros(1))
assert_equal(Z.min(), 0)
assert_equal(Z.max(), 0)
assert_equal(Z.max().dtype, Z.dtype)
# another test
D = np.arange(20, dtype=float).reshape(5,4)
D[0:2, :] = 0
X = self.spmatrix(D)
assert_equal(X.min(), 0)
assert_equal(X.max(), 19)
# zero-size matrices
for D in [np.zeros((0, 0)), np.zeros((0, 10)), np.zeros((10, 0))]:
X = self.spmatrix(D)
assert_raises(ValueError, X.min)
assert_raises(ValueError, X.max)
def test_minmax_axis(self):
D = np.matrix(np.arange(50).reshape(5,10))
# completely empty rows, leaving some completely full:
D[1, :] = 0
# empty at end for reduceat:
D[:, 9] = 0
# partial rows/cols:
D[3, 3] = 0
# entries on either side of 0:
D[2, 2] = -1
X = self.spmatrix(D)
axes = [-2, -1, 0, 1]
for axis in axes:
assert_array_equal(X.max(axis=axis).A, D.max(axis=axis).A)
assert_array_equal(X.min(axis=axis).A, D.min(axis=axis).A)
# full matrix
D = np.matrix(np.arange(1, 51).reshape(10, 5))
X = self.spmatrix(D)
for axis in axes:
assert_array_equal(X.max(axis=axis).A, D.max(axis=axis).A)
assert_array_equal(X.min(axis=axis).A, D.min(axis=axis).A)
# empty matrix
D = np.matrix(np.zeros((10, 5)))
X = self.spmatrix(D)
for axis in axes:
assert_array_equal(X.max(axis=axis).A, D.max(axis=axis).A)
assert_array_equal(X.min(axis=axis).A, D.min(axis=axis).A)
axes_even = [0, -2]
axes_odd = [1, -1]
# zero-size matrices
D = np.zeros((0, 10))
X = self.spmatrix(D)
for axis in axes_even:
assert_raises(ValueError, X.min, axis=axis)
assert_raises(ValueError, X.max, axis=axis)
for axis in axes_odd:
assert_array_equal(np.zeros((0, 1)), X.min(axis=axis).A)
assert_array_equal(np.zeros((0, 1)), X.max(axis=axis).A)
D = np.zeros((10, 0))
X = self.spmatrix(D)
for axis in axes_odd:
assert_raises(ValueError, X.min, axis=axis)
assert_raises(ValueError, X.max, axis=axis)
for axis in axes_even:
assert_array_equal(np.zeros((1, 0)), X.min(axis=axis).A)
assert_array_equal(np.zeros((1, 0)), X.max(axis=axis).A)
def test_minmax_invalid_params(self):
dat = np.matrix([[0, 1, 2],
[3, -4, 5],
[-6, 7, 9]])
datsp = self.spmatrix(dat)
for fname in ('min', 'max'):
func = getattr(datsp, fname)
assert_raises(ValueError, func, axis=3)
assert_raises(TypeError, func, axis=(0, 1))
assert_raises(TypeError, func, axis=1.5)
assert_raises(ValueError, func, axis=1, out=1)
def test_numpy_minmax(self):
# See gh-5987
# xref gh-7460 in 'numpy'
from scipy.sparse import data
dat = np.matrix([[0, 1, 2],
[3, -4, 5],
[-6, 7, 9]])
datsp = self.spmatrix(dat)
# We are only testing sparse matrices who have
# implemented 'min' and 'max' because they are
# the ones with the compatibility issues with
# the 'numpy' implementation.
if isinstance(datsp, data._minmax_mixin):
assert_array_equal(np.min(datsp), np.min(dat))
assert_array_equal(np.max(datsp), np.max(dat))
def test_argmax(self):
D1 = np.array([
[-1, 5, 2, 3],
[0, 0, -1, -2],
[-1, -2, -3, -4],
[1, 2, 3, 4],
[1, 2, 0, 0],
])
D2 = D1.transpose()
for D in [D1, D2]:
mat = csr_matrix(D)
assert_equal(mat.argmax(), np.argmax(D))
assert_equal(mat.argmin(), np.argmin(D))
assert_equal(mat.argmax(axis=0),
np.asmatrix(np.argmax(D, axis=0)))
assert_equal(mat.argmin(axis=0),
np.asmatrix(np.argmin(D, axis=0)))
assert_equal(mat.argmax(axis=1),
np.asmatrix(np.argmax(D, axis=1).reshape(-1, 1)))
assert_equal(mat.argmin(axis=1),
np.asmatrix(np.argmin(D, axis=1).reshape(-1, 1)))
D1 = np.empty((0, 5))
D2 = np.empty((5, 0))
for axis in [None, 0]:
mat = self.spmatrix(D1)
assert_raises(ValueError, mat.argmax, axis=axis)
assert_raises(ValueError, mat.argmin, axis=axis)
for axis in [None, 1]:
mat = self.spmatrix(D2)
assert_raises(ValueError, mat.argmax, axis=axis)
assert_raises(ValueError, mat.argmin, axis=axis)
class _TestGetNnzAxis(object):
def test_getnnz_axis(self):
dat = np.matrix([[0, 2],
[3, 5],
[-6, 9]])
bool_dat = dat.astype(bool).A
datsp = self.spmatrix(dat)
accepted_return_dtypes = (np.int32, np.int64)
assert_array_equal(bool_dat.sum(axis=None), datsp.getnnz(axis=None))
assert_array_equal(bool_dat.sum(), datsp.getnnz())
assert_array_equal(bool_dat.sum(axis=0), datsp.getnnz(axis=0))
assert_in(datsp.getnnz(axis=0).dtype, accepted_return_dtypes)
assert_array_equal(bool_dat.sum(axis=1), datsp.getnnz(axis=1))
assert_in(datsp.getnnz(axis=1).dtype, accepted_return_dtypes)
assert_array_equal(bool_dat.sum(axis=-2), datsp.getnnz(axis=-2))
assert_in(datsp.getnnz(axis=-2).dtype, accepted_return_dtypes)
assert_array_equal(bool_dat.sum(axis=-1), datsp.getnnz(axis=-1))
assert_in(datsp.getnnz(axis=-1).dtype, accepted_return_dtypes)
assert_raises(ValueError, datsp.getnnz, axis=2)
#------------------------------------------------------------------------------
# Tailored base class for generic tests
#------------------------------------------------------------------------------
def _possibly_unimplemented(cls, require=True):
"""
Construct a class that either runs tests as usual (require=True),
or each method skips if it encounters a common error.
"""
if require:
return cls
else:
def wrap(fc):
@functools.wraps(fc)
def wrapper(*a, **kw):
try:
return fc(*a, **kw)
except (NotImplementedError, TypeError, ValueError,
IndexError, AttributeError):
raise pytest.skip("feature not implemented")
return wrapper
new_dict = dict(cls.__dict__)
for name, func in cls.__dict__.items():
if name.startswith('test_'):
new_dict[name] = wrap(func)
return type(cls.__name__ + "NotImplemented",
cls.__bases__,
new_dict)
def sparse_test_class(getset=True, slicing=True, slicing_assign=True,
fancy_indexing=True, fancy_assign=True,
fancy_multidim_indexing=True, fancy_multidim_assign=True,
minmax=True, nnz_axis=True):
"""
Construct a base class, optionally converting some of the tests in
the suite to check that the feature is not implemented.
"""
bases = (_TestCommon,
_possibly_unimplemented(_TestGetSet, getset),
_TestSolve,
_TestInplaceArithmetic,
_TestArithmetic,
_possibly_unimplemented(_TestSlicing, slicing),
_possibly_unimplemented(_TestSlicingAssign, slicing_assign),
_possibly_unimplemented(_TestFancyIndexing, fancy_indexing),
_possibly_unimplemented(_TestFancyIndexingAssign,
fancy_assign),
_possibly_unimplemented(_TestFancyMultidim,
fancy_indexing and fancy_multidim_indexing),
_possibly_unimplemented(_TestFancyMultidimAssign,
fancy_multidim_assign and fancy_assign),
_possibly_unimplemented(_TestMinMax, minmax),
_possibly_unimplemented(_TestGetNnzAxis, nnz_axis))
# check that test names do not clash
names = {}
for cls in bases:
for name in cls.__dict__:
if not name.startswith('test_'):
continue
old_cls = names.get(name)
if old_cls is not None:
raise ValueError("Test class %s overloads test %s defined in %s" % (
cls.__name__, name, old_cls.__name__))
names[name] = cls
return type("TestBase", bases, {})
#------------------------------------------------------------------------------
# Matrix class based tests
#------------------------------------------------------------------------------
class TestCSR(sparse_test_class()):
@classmethod
def spmatrix(cls, *args, **kwargs):
with suppress_warnings() as sup:
sup.filter(SparseEfficiencyWarning,
"Changing the sparsity structure of a csr_matrix is expensive")
return csr_matrix(*args, **kwargs)
math_dtypes = [np.bool_, np.int_, np.float_, np.complex_]
def test_constructor1(self):
b = matrix([[0,4,0],
[3,0,0],
[0,2,0]],'d')
bsp = csr_matrix(b)
assert_array_almost_equal(bsp.data,[4,3,2])
assert_array_equal(bsp.indices,[1,0,1])
assert_array_equal(bsp.indptr,[0,1,2,3])
assert_equal(bsp.getnnz(),3)
assert_equal(bsp.getformat(),'csr')
assert_array_equal(bsp.todense(),b)
def test_constructor2(self):
b = zeros((6,6),'d')
b[3,4] = 5
bsp = csr_matrix(b)
assert_array_almost_equal(bsp.data,[5])
assert_array_equal(bsp.indices,[4])
assert_array_equal(bsp.indptr,[0,0,0,0,1,1,1])
assert_array_almost_equal(bsp.todense(),b)
def test_constructor3(self):
b = matrix([[1,0],
[0,2],
[3,0]],'d')
bsp = csr_matrix(b)
assert_array_almost_equal(bsp.data,[1,2,3])
assert_array_equal(bsp.indices,[0,1,0])
assert_array_equal(bsp.indptr,[0,1,2,3])
assert_array_almost_equal(bsp.todense(),b)
### currently disabled
## def test_constructor4(self):
## """try using int64 indices"""
## data = arange( 6 ) + 1
## col = array( [1, 2, 1, 0, 0, 2], dtype='int64' )
## ptr = array( [0, 2, 4, 6], dtype='int64' )
##
## a = csr_matrix( (data, col, ptr), shape = (3,3) )
##
## b = matrix([[0,1,2],
## [4,3,0],
## [5,0,6]],'d')
##
## assert_equal(a.indptr.dtype,numpy.dtype('int64'))
## assert_equal(a.indices.dtype,numpy.dtype('int64'))
## assert_array_equal(a.todense(),b)
def test_constructor4(self):
# using (data, ij) format
row = array([2, 3, 1, 3, 0, 1, 3, 0, 2, 1, 2])
col = array([0, 1, 0, 0, 1, 1, 2, 2, 2, 2, 1])
data = array([6., 10., 3., 9., 1., 4.,
11., 2., 8., 5., 7.])
ij = vstack((row,col))
csr = csr_matrix((data,ij),(4,3))
assert_array_equal(arange(12).reshape(4,3),csr.todense())
def test_constructor5(self):
# infer dimensions from arrays
indptr = array([0,1,3,3])
indices = array([0,5,1,2])
data = array([1,2,3,4])
csr = csr_matrix((data, indices, indptr))
assert_array_equal(csr.shape,(3,6))
def test_constructor6(self):
# infer dimensions and dtype from lists
indptr = [0, 1, 3, 3]
indices = [0, 5, 1, 2]
data = [1, 2, 3, 4]
csr = csr_matrix((data, indices, indptr))
assert_array_equal(csr.shape, (3,6))
assert_(np.issubdtype(csr.dtype, np.signedinteger))
def test_sort_indices(self):
data = arange(5)
indices = array([7, 2, 1, 5, 4])
indptr = array([0, 3, 5])
asp = csr_matrix((data, indices, indptr), shape=(2,10))
bsp = asp.copy()
asp.sort_indices()
assert_array_equal(asp.indices,[1, 2, 7, 4, 5])
assert_array_equal(asp.todense(),bsp.todense())
def test_eliminate_zeros(self):
data = array([1, 0, 0, 0, 2, 0, 3, 0])
indices = array([1, 2, 3, 4, 5, 6, 7, 8])
indptr = array([0, 3, 8])
asp = csr_matrix((data, indices, indptr), shape=(2,10))
bsp = asp.copy()
asp.eliminate_zeros()
assert_array_equal(asp.nnz, 3)
assert_array_equal(asp.data,[1, 2, 3])
assert_array_equal(asp.todense(),bsp.todense())
def test_ufuncs(self):
X = csr_matrix(np.arange(20).reshape(4, 5) / 20.)
for f in ["sin", "tan", "arcsin", "arctan", "sinh", "tanh",
"arcsinh", "arctanh", "rint", "sign", "expm1", "log1p",
"deg2rad", "rad2deg", "floor", "ceil", "trunc", "sqrt"]:
assert_equal(hasattr(csr_matrix, f), True)
X2 = getattr(X, f)()
assert_equal(X.shape, X2.shape)
assert_array_equal(X.indices, X2.indices)
assert_array_equal(X.indptr, X2.indptr)
assert_array_equal(X2.toarray(), getattr(np, f)(X.toarray()))
def test_unsorted_arithmetic(self):
data = arange(5)
indices = array([7, 2, 1, 5, 4])
indptr = array([0, 3, 5])
asp = csr_matrix((data, indices, indptr), shape=(2,10))
data = arange(6)
indices = array([8, 1, 5, 7, 2, 4])
indptr = array([0, 2, 6])
bsp = csr_matrix((data, indices, indptr), shape=(2,10))
assert_equal((asp + bsp).todense(), asp.todense() + bsp.todense())
def test_fancy_indexing_broadcast(self):
# broadcasting indexing mode is supported
I = np.array([[1], [2], [3]])
J = np.array([3, 4, 2])
np.random.seed(1234)
D = np.asmatrix(np.random.rand(5, 7))
S = self.spmatrix(D)
SIJ = S[I,J]
if isspmatrix(SIJ):
SIJ = SIJ.todense()
assert_equal(SIJ, D[I,J])
def test_has_sorted_indices(self):
"Ensure has_sorted_indices memoizes sorted state for sort_indices"
sorted_inds = np.array([0, 1])
unsorted_inds = np.array([1, 0])
data = np.array([1, 1])
indptr = np.array([0, 2])
M = csr_matrix((data, sorted_inds, indptr)).copy()
assert_equal(True, M.has_sorted_indices)
M = csr_matrix((data, unsorted_inds, indptr)).copy()
assert_equal(False, M.has_sorted_indices)
# set by sorting
M.sort_indices()
assert_equal(True, M.has_sorted_indices)
assert_array_equal(M.indices, sorted_inds)
M = csr_matrix((data, unsorted_inds, indptr)).copy()
# set manually (although underlyingly unsorted)
M.has_sorted_indices = True
assert_equal(True, M.has_sorted_indices)
assert_array_equal(M.indices, unsorted_inds)
# ensure sort bypassed when has_sorted_indices == True
M.sort_indices()
assert_array_equal(M.indices, unsorted_inds)
def test_has_canonical_format(self):
"Ensure has_canonical_format memoizes state for sum_duplicates"
M = csr_matrix((np.array([2]), np.array([0]), np.array([0, 1])))
assert_equal(True, M.has_canonical_format)
indices = np.array([0, 0]) # contains duplicate
data = np.array([1, 1])
indptr = np.array([0, 2])
M = csr_matrix((data, indices, indptr)).copy()
assert_equal(False, M.has_canonical_format)
# set by deduplicating
M.sum_duplicates()
assert_equal(True, M.has_canonical_format)
assert_equal(1, len(M.indices))
M = csr_matrix((data, indices, indptr)).copy()
# set manually (although underlyingly duplicated)
M.has_canonical_format = True
assert_equal(True, M.has_canonical_format)
assert_equal(2, len(M.indices)) # unaffected content
# ensure deduplication bypassed when has_canonical_format == True
M.sum_duplicates()
assert_equal(2, len(M.indices)) # unaffected content
def test_scalar_idx_dtype(self):
# Check that index dtype takes into account all parameters
# passed to sparsetools, including the scalar ones
indptr = np.zeros(2, dtype=np.int32)
indices = np.zeros(0, dtype=np.int32)
vals = np.zeros(0)
a = csr_matrix((vals, indices, indptr), shape=(1, 2**31-1))
b = csr_matrix((vals, indices, indptr), shape=(1, 2**31))
ij = np.zeros((2, 0), dtype=np.int32)
c = csr_matrix((vals, ij), shape=(1, 2**31-1))
d = csr_matrix((vals, ij), shape=(1, 2**31))
e = csr_matrix((1, 2**31-1))
f = csr_matrix((1, 2**31))
assert_equal(a.indptr.dtype, np.int32)
assert_equal(b.indptr.dtype, np.int64)
assert_equal(c.indptr.dtype, np.int32)
assert_equal(d.indptr.dtype, np.int64)
assert_equal(e.indptr.dtype, np.int32)
assert_equal(f.indptr.dtype, np.int64)
# These shouldn't fail
for x in [a, b, c, d, e, f]:
x + x
TestCSR.init_class()
class TestCSC(sparse_test_class()):
@classmethod
def spmatrix(cls, *args, **kwargs):
with suppress_warnings() as sup:
sup.filter(SparseEfficiencyWarning,
"Changing the sparsity structure of a csc_matrix is expensive")
return csc_matrix(*args, **kwargs)
math_dtypes = [np.bool_, np.int_, np.float_, np.complex_]
def test_constructor1(self):
b = matrix([[1,0,0,0],[0,0,1,0],[0,2,0,3]],'d')
bsp = csc_matrix(b)
assert_array_almost_equal(bsp.data,[1,2,1,3])
assert_array_equal(bsp.indices,[0,2,1,2])
assert_array_equal(bsp.indptr,[0,1,2,3,4])
assert_equal(bsp.getnnz(),4)
assert_equal(bsp.shape,b.shape)
assert_equal(bsp.getformat(),'csc')
def test_constructor2(self):
b = zeros((6,6),'d')
b[2,4] = 5
bsp = csc_matrix(b)
assert_array_almost_equal(bsp.data,[5])
assert_array_equal(bsp.indices,[2])
assert_array_equal(bsp.indptr,[0,0,0,0,0,1,1])
def test_constructor3(self):
b = matrix([[1,0],[0,0],[0,2]],'d')
bsp = csc_matrix(b)
assert_array_almost_equal(bsp.data,[1,2])
assert_array_equal(bsp.indices,[0,2])
assert_array_equal(bsp.indptr,[0,1,2])
def test_constructor4(self):
# using (data, ij) format
row = array([2, 3, 1, 3, 0, 1, 3, 0, 2, 1, 2])
col = array([0, 1, 0, 0, 1, 1, 2, 2, 2, 2, 1])
data = array([6., 10., 3., 9., 1., 4.,
11., 2., 8., 5., 7.])
ij = vstack((row,col))
csc = csc_matrix((data,ij),(4,3))
assert_array_equal(arange(12).reshape(4,3),csc.todense())
def test_constructor5(self):
# infer dimensions from arrays
indptr = array([0,1,3,3])
indices = array([0,5,1,2])
data = array([1,2,3,4])
csc = csc_matrix((data, indices, indptr))
assert_array_equal(csc.shape,(6,3))
def test_constructor6(self):
# infer dimensions and dtype from lists
indptr = [0, 1, 3, 3]
indices = [0, 5, 1, 2]
data = [1, 2, 3, 4]
csc = csc_matrix((data, indices, indptr))
assert_array_equal(csc.shape,(6,3))
assert_(np.issubdtype(csc.dtype, np.signedinteger))
def test_eliminate_zeros(self):
data = array([1, 0, 0, 0, 2, 0, 3, 0])
indices = array([1, 2, 3, 4, 5, 6, 7, 8])
indptr = array([0, 3, 8])
asp = csc_matrix((data, indices, indptr), shape=(10,2))
bsp = asp.copy()
asp.eliminate_zeros()
assert_array_equal(asp.nnz, 3)
assert_array_equal(asp.data,[1, 2, 3])
assert_array_equal(asp.todense(),bsp.todense())
def test_sort_indices(self):
data = arange(5)
row = array([7, 2, 1, 5, 4])
ptr = [0, 3, 5]
asp = csc_matrix((data, row, ptr), shape=(10,2))
bsp = asp.copy()
asp.sort_indices()
assert_array_equal(asp.indices,[1, 2, 7, 4, 5])
assert_array_equal(asp.todense(),bsp.todense())
def test_ufuncs(self):
X = csc_matrix(np.arange(21).reshape(7, 3) / 21.)
for f in ["sin", "tan", "arcsin", "arctan", "sinh", "tanh",
"arcsinh", "arctanh", "rint", "sign", "expm1", "log1p",
"deg2rad", "rad2deg", "floor", "ceil", "trunc", "sqrt"]:
assert_equal(hasattr(csr_matrix, f), True)
X2 = getattr(X, f)()
assert_equal(X.shape, X2.shape)
assert_array_equal(X.indices, X2.indices)
assert_array_equal(X.indptr, X2.indptr)
assert_array_equal(X2.toarray(), getattr(np, f)(X.toarray()))
def test_unsorted_arithmetic(self):
data = arange(5)
indices = array([7, 2, 1, 5, 4])
indptr = array([0, 3, 5])
asp = csc_matrix((data, indices, indptr), shape=(10,2))
data = arange(6)
indices = array([8, 1, 5, 7, 2, 4])
indptr = array([0, 2, 6])
bsp = csc_matrix((data, indices, indptr), shape=(10,2))
assert_equal((asp + bsp).todense(), asp.todense() + bsp.todense())
def test_fancy_indexing_broadcast(self):
# broadcasting indexing mode is supported
I = np.array([[1], [2], [3]])
J = np.array([3, 4, 2])
np.random.seed(1234)
D = np.asmatrix(np.random.rand(5, 7))
S = self.spmatrix(D)
SIJ = S[I,J]
if isspmatrix(SIJ):
SIJ = SIJ.todense()
assert_equal(SIJ, D[I,J])
def test_scalar_idx_dtype(self):
# Check that index dtype takes into account all parameters
# passed to sparsetools, including the scalar ones
indptr = np.zeros(2, dtype=np.int32)
indices = np.zeros(0, dtype=np.int32)
vals = np.zeros(0)
a = csc_matrix((vals, indices, indptr), shape=(2**31-1, 1))
b = csc_matrix((vals, indices, indptr), shape=(2**31, 1))
ij = np.zeros((2, 0), dtype=np.int32)
c = csc_matrix((vals, ij), shape=(2**31-1, 1))
d = csc_matrix((vals, ij), shape=(2**31, 1))
e = csr_matrix((1, 2**31-1))
f = csr_matrix((1, 2**31))
assert_equal(a.indptr.dtype, np.int32)
assert_equal(b.indptr.dtype, np.int64)
assert_equal(c.indptr.dtype, np.int32)
assert_equal(d.indptr.dtype, np.int64)
assert_equal(e.indptr.dtype, np.int32)
assert_equal(f.indptr.dtype, np.int64)
# These shouldn't fail
for x in [a, b, c, d, e, f]:
x + x
TestCSC.init_class()
class TestDOK(sparse_test_class(minmax=False, nnz_axis=False)):
spmatrix = dok_matrix
math_dtypes = [np.int_, np.float_, np.complex_]
def test_mult(self):
A = dok_matrix((10,10))
A[0,3] = 10
A[5,6] = 20
D = A*A.T
E = A*A.H
assert_array_equal(D.A, E.A)
def test_add_nonzero(self):
A = self.spmatrix((3,2))
A[0,1] = -10
A[2,0] = 20
A = A + 10
B = matrix([[10, 0], [10, 10], [30, 10]])
assert_array_equal(A.todense(), B)
A = A + 1j
B = B + 1j
assert_array_equal(A.todense(), B)
def test_dok_divide_scalar(self):
A = self.spmatrix((3,2))
A[0,1] = -10
A[2,0] = 20
assert_array_equal((A/1j).todense(), A.todense()/1j)
assert_array_equal((A/9).todense(), A.todense()/9)
def test_convert(self):
# Test provided by Andrew Straw. Fails in SciPy <= r1477.
(m, n) = (6, 7)
a = dok_matrix((m, n))
# set a few elements, but none in the last column
a[2,1] = 1
a[0,2] = 2
a[3,1] = 3
a[1,5] = 4
a[4,3] = 5
a[4,2] = 6
# assert that the last column is all zeros
assert_array_equal(a.toarray()[:,n-1], zeros(m,))
# make sure it still works for CSC format
csc = a.tocsc()
assert_array_equal(csc.toarray()[:,n-1], zeros(m,))
# now test CSR
(m, n) = (n, m)
b = a.transpose()
assert_equal(b.shape, (m, n))
# assert that the last row is all zeros
assert_array_equal(b.toarray()[m-1,:], zeros(n,))
# make sure it still works for CSR format
csr = b.tocsr()
assert_array_equal(csr.toarray()[m-1,:], zeros(n,))
def test_ctor(self):
# Empty ctor
assert_raises(TypeError, dok_matrix)
# Dense ctor
b = matrix([[1,0,0,0],[0,0,1,0],[0,2,0,3]],'d')
A = dok_matrix(b)
assert_equal(b.dtype, A.dtype)
assert_equal(A.todense(), b)
# Sparse ctor
c = csr_matrix(b)
assert_equal(A.todense(), c.todense())
data = [[0, 1, 2], [3, 0, 0]]
d = dok_matrix(data, dtype=np.float32)
assert_equal(d.dtype, np.float32)
da = d.toarray()
assert_equal(da.dtype, np.float32)
assert_array_equal(da, data)
def test_ticket1160(self):
# Regression test for ticket #1160.
a = dok_matrix((3,3))
a[0,0] = 0
# This assert would fail, because the above assignment would
# incorrectly call __set_item__ even though the value was 0.
assert_((0,0) not in a.keys(), "Unexpected entry (0,0) in keys")
# Slice assignments were also affected.
b = dok_matrix((3,3))
b[:,0] = 0
assert_(len(b.keys()) == 0, "Unexpected entries in keys")
TestDOK.init_class()
class TestLIL(sparse_test_class(minmax=False)):
spmatrix = lil_matrix
math_dtypes = [np.int_, np.float_, np.complex_]
def test_dot(self):
A = matrix(zeros((10,10)))
A[0,3] = 10
A[5,6] = 20
B = lil_matrix((10,10))
B[0,3] = 10
B[5,6] = 20
assert_array_equal(A * A.T, (B * B.T).todense())
assert_array_equal(A * A.H, (B * B.H).todense())
def test_scalar_mul(self):
x = lil_matrix((3,3))
x[0,0] = 2
x = x*2
assert_equal(x[0,0],4)
x = x*0
assert_equal(x[0,0],0)
def test_inplace_ops(self):
A = lil_matrix([[0,2,3],[4,0,6]])
B = lil_matrix([[0,1,0],[0,2,3]])
data = {'add': (B,A + B),
'sub': (B,A - B),
'mul': (3,A * 3)}
for op,(other,expected) in data.items():
result = A.copy()
getattr(result, '__i%s__' % op)(other)
assert_array_equal(result.todense(), expected.todense())
# Ticket 1604.
A = lil_matrix((1,3), dtype=np.dtype('float64'))
B = array([0.1,0.1,0.1])
A[0,:] += B
assert_array_equal(A[0,:].toarray().squeeze(), B)
def test_lil_iteration(self):
row_data = [[1,2,3],[4,5,6]]
B = lil_matrix(array(row_data))
for r,row in enumerate(B):
assert_array_equal(row.todense(),array(row_data[r],ndmin=2))
def test_lil_from_csr(self):
# Tests whether a lil_matrix can be constructed from a
# csr_matrix.
B = lil_matrix((10,10))
B[0,3] = 10
B[5,6] = 20
B[8,3] = 30
B[3,8] = 40
B[8,9] = 50
C = B.tocsr()
D = lil_matrix(C)
assert_array_equal(C.A, D.A)
def test_fancy_indexing_lil(self):
M = asmatrix(arange(25).reshape(5,5))
A = lil_matrix(M)
assert_equal(A[array([1,2,3]),2:3].todense(), M[array([1,2,3]),2:3])
def test_point_wise_multiply(self):
l = lil_matrix((4,3))
l[0,0] = 1
l[1,1] = 2
l[2,2] = 3
l[3,1] = 4
m = lil_matrix((4,3))
m[0,0] = 1
m[0,1] = 2
m[2,2] = 3
m[3,1] = 4
m[3,2] = 4
assert_array_equal(l.multiply(m).todense(),
m.multiply(l).todense())
assert_array_equal(l.multiply(m).todense(),
[[1,0,0],
[0,0,0],
[0,0,9],
[0,16,0]])
def test_lil_multiply_removal(self):
# Ticket #1427.
a = lil_matrix(np.ones((3,3)))
a *= 2.
a[0, :] = 0
TestLIL.init_class()
class TestCOO(sparse_test_class(getset=False,
slicing=False, slicing_assign=False,
fancy_indexing=False, fancy_assign=False)):
spmatrix = coo_matrix
math_dtypes = [np.int_, np.float_, np.complex_]
def test_constructor1(self):
# unsorted triplet format
row = array([2, 3, 1, 3, 0, 1, 3, 0, 2, 1, 2])
col = array([0, 1, 0, 0, 1, 1, 2, 2, 2, 2, 1])
data = array([6., 10., 3., 9., 1., 4.,
11., 2., 8., 5., 7.])
coo = coo_matrix((data,(row,col)),(4,3))
assert_array_equal(arange(12).reshape(4,3),coo.todense())
def test_constructor2(self):
# unsorted triplet format with duplicates (which are summed)
row = array([0,1,2,2,2,2,0,0,2,2])
col = array([0,2,0,2,1,1,1,0,0,2])
data = array([2,9,-4,5,7,0,-1,2,1,-5])
coo = coo_matrix((data,(row,col)),(3,3))
mat = matrix([[4,-1,0],[0,0,9],[-3,7,0]])
assert_array_equal(mat,coo.todense())
def test_constructor3(self):
# empty matrix
coo = coo_matrix((4,3))
assert_array_equal(coo.shape,(4,3))
assert_array_equal(coo.row,[])
assert_array_equal(coo.col,[])
assert_array_equal(coo.data,[])
assert_array_equal(coo.todense(),zeros((4,3)))
def test_constructor4(self):
# from dense matrix
mat = array([[0,1,0,0],
[7,0,3,0],
[0,4,0,0]])
coo = coo_matrix(mat)
assert_array_equal(coo.todense(),mat)
# upgrade rank 1 arrays to row matrix
mat = array([0,1,0,0])
coo = coo_matrix(mat)
assert_array_equal(coo.todense(),mat.reshape(1,-1))
@pytest.mark.xfail(run=False, reason='COO does not have a __getitem__')
def test_iterator(self):
pass
def test_todia_all_zeros(self):
zeros = [[0, 0]]
dia = coo_matrix(zeros).todia()
assert_array_equal(dia.A, zeros)
def test_sum_duplicates(self):
coo = coo_matrix((4,3))
coo.sum_duplicates()
coo = coo_matrix(([1,2], ([1,0], [1,0])))
coo.sum_duplicates()
assert_array_equal(coo.A, [[2,0],[0,1]])
coo = coo_matrix(([1,2], ([1,1], [1,1])))
coo.sum_duplicates()
assert_array_equal(coo.A, [[0,0],[0,3]])
assert_array_equal(coo.row, [1])
assert_array_equal(coo.col, [1])
assert_array_equal(coo.data, [3])
def test_todok_duplicates(self):
coo = coo_matrix(([1,1,1,1], ([0,2,2,0], [0,1,1,0])))
dok = coo.todok()
assert_array_equal(dok.A, coo.A)
def test_eliminate_zeros(self):
data = array([1, 0, 0, 0, 2, 0, 3, 0])
row = array([0, 0, 0, 1, 1, 1, 1, 1])
col = array([1, 2, 3, 4, 5, 6, 7, 8])
asp = coo_matrix((data, (row, col)), shape=(2,10))
bsp = asp.copy()
asp.eliminate_zeros()
assert_((asp.data != 0).all())
assert_array_equal(asp.A, bsp.A)
def test_reshape_copy(self):
arr = [[0, 10, 0, 0], [0, 0, 0, 0], [0, 20, 30, 40]]
new_shape = (2, 6)
x = coo_matrix(arr)
y = x.reshape(new_shape)
assert_(y.data is x.data)
y = x.reshape(new_shape, copy=False)
assert_(y.data is x.data)
y = x.reshape(new_shape, copy=True)
assert_(not np.may_share_memory(y.data, x.data))
def test_large_dimensions_reshape(self):
# Test that reshape is immune to integer overflow when number of elements
# exceeds 2^31-1
mat1 = coo_matrix(([1], ([3000000], [1000])), (3000001, 1001))
mat2 = coo_matrix(([1], ([1000], [3000000])), (1001, 3000001))
# assert_array_equal is slow for big matrices because it expects dense
# Using __ne__ and nnz instead
assert_((mat1.reshape((1001, 3000001), order='C') != mat2).nnz == 0)
assert_((mat2.reshape((3000001, 1001), order='F') != mat1).nnz == 0)
TestCOO.init_class()
class TestDIA(sparse_test_class(getset=False, slicing=False, slicing_assign=False,
fancy_indexing=False, fancy_assign=False,
minmax=False, nnz_axis=False)):
spmatrix = dia_matrix
math_dtypes = [np.int_, np.float_, np.complex_]
def test_constructor1(self):
D = matrix([[1, 0, 3, 0],
[1, 2, 0, 4],
[0, 2, 3, 0],
[0, 0, 3, 4]])
data = np.array([[1,2,3,4]]).repeat(3,axis=0)
offsets = np.array([0,-1,2])
assert_equal(dia_matrix((data,offsets), shape=(4,4)).todense(), D)
@pytest.mark.xfail(run=False, reason='DIA does not have a __getitem__')
def test_iterator(self):
pass
@with_64bit_maxval_limit(3)
def test_setdiag_dtype(self):
m = dia_matrix(np.eye(3))
assert_equal(m.offsets.dtype, np.int32)
m.setdiag((3,), k=2)
assert_equal(m.offsets.dtype, np.int32)
m = dia_matrix(np.eye(4))
assert_equal(m.offsets.dtype, np.int64)
m.setdiag((3,), k=3)
assert_equal(m.offsets.dtype, np.int64)
@pytest.mark.skip(reason='DIA stores extra zeros')
def test_getnnz_axis(self):
pass
TestDIA.init_class()
class TestBSR(sparse_test_class(getset=False,
slicing=False, slicing_assign=False,
fancy_indexing=False, fancy_assign=False,
nnz_axis=False)):
spmatrix = bsr_matrix
math_dtypes = [np.int_, np.float_, np.complex_]
def test_constructor1(self):
# check native BSR format constructor
indptr = array([0,2,2,4])
indices = array([0,2,2,3])
data = zeros((4,2,3))
data[0] = array([[0, 1, 2],
[3, 0, 5]])
data[1] = array([[0, 2, 4],
[6, 0, 10]])
data[2] = array([[0, 4, 8],
[12, 0, 20]])
data[3] = array([[0, 5, 10],
[15, 0, 25]])
A = kron([[1,0,2,0],[0,0,0,0],[0,0,4,5]], [[0,1,2],[3,0,5]])
Asp = bsr_matrix((data,indices,indptr),shape=(6,12))
assert_equal(Asp.todense(),A)
# infer shape from arrays
Asp = bsr_matrix((data,indices,indptr))
assert_equal(Asp.todense(),A)
def test_constructor2(self):
# construct from dense
# test zero mats
for shape in [(1,1), (5,1), (1,10), (10,4), (3,7), (2,1)]:
A = zeros(shape)
assert_equal(bsr_matrix(A).todense(),A)
A = zeros((4,6))
assert_equal(bsr_matrix(A,blocksize=(2,2)).todense(),A)
assert_equal(bsr_matrix(A,blocksize=(2,3)).todense(),A)
A = kron([[1,0,2,0],[0,0,0,0],[0,0,4,5]], [[0,1,2],[3,0,5]])
assert_equal(bsr_matrix(A).todense(),A)
assert_equal(bsr_matrix(A,shape=(6,12)).todense(),A)
assert_equal(bsr_matrix(A,blocksize=(1,1)).todense(),A)
assert_equal(bsr_matrix(A,blocksize=(2,3)).todense(),A)
assert_equal(bsr_matrix(A,blocksize=(2,6)).todense(),A)
assert_equal(bsr_matrix(A,blocksize=(2,12)).todense(),A)
assert_equal(bsr_matrix(A,blocksize=(3,12)).todense(),A)
assert_equal(bsr_matrix(A,blocksize=(6,12)).todense(),A)
A = kron([[1,0,2,0],[0,1,0,0],[0,0,0,0]], [[0,1,2],[3,0,5]])
assert_equal(bsr_matrix(A,blocksize=(2,3)).todense(),A)
def test_constructor3(self):
# construct from coo-like (data,(row,col)) format
arg = ([1,2,3], ([0,1,1], [0,0,1]))
A = array([[1,0],[2,3]])
assert_equal(bsr_matrix(arg, blocksize=(2,2)).todense(), A)
def test_constructor4(self):
# regression test for gh-6292: bsr_matrix((data, indices, indptr)) was
# trying to compare an int to a None
n = 8
data = np.ones((n, n, 1), dtype=np.int8)
indptr = np.array([0, n], dtype=np.int32)
indices = np.arange(n, dtype=np.int32)
bsr_matrix((data, indices, indptr), blocksize=(n, 1), copy=False)
def test_bsr_tocsr(self):
# check native conversion from BSR to CSR
indptr = array([0, 2, 2, 4])
indices = array([0, 2, 2, 3])
data = zeros((4, 2, 3))
data[0] = array([[0, 1, 2],
[3, 0, 5]])
data[1] = array([[0, 2, 4],
[6, 0, 10]])
data[2] = array([[0, 4, 8],
[12, 0, 20]])
data[3] = array([[0, 5, 10],
[15, 0, 25]])
A = kron([[1, 0, 2, 0], [0, 0, 0, 0], [0, 0, 4, 5]],
[[0, 1, 2], [3, 0, 5]])
Absr = bsr_matrix((data, indices, indptr), shape=(6, 12))
Acsr = Absr.tocsr()
Acsr_via_coo = Absr.tocoo().tocsr()
assert_equal(Acsr.todense(), A)
assert_equal(Acsr.todense(), Acsr_via_coo.todense())
def test_eliminate_zeros(self):
data = kron([1, 0, 0, 0, 2, 0, 3, 0], [[1,1],[1,1]]).T
data = data.reshape(-1,2,2)
indices = array([1, 2, 3, 4, 5, 6, 7, 8])
indptr = array([0, 3, 8])
asp = bsr_matrix((data, indices, indptr), shape=(4,20))
bsp = asp.copy()
asp.eliminate_zeros()
assert_array_equal(asp.nnz, 3*4)
assert_array_equal(asp.todense(),bsp.todense())
def test_bsr_matvec(self):
A = bsr_matrix(arange(2*3*4*5).reshape(2*4,3*5), blocksize=(4,5))
x = arange(A.shape[1]).reshape(-1,1)
assert_equal(A*x, A.todense()*x)
def test_bsr_matvecs(self):
A = bsr_matrix(arange(2*3*4*5).reshape(2*4,3*5), blocksize=(4,5))
x = arange(A.shape[1]*6).reshape(-1,6)
assert_equal(A*x, A.todense()*x)
@pytest.mark.xfail(run=False, reason='BSR does not have a __getitem__')
def test_iterator(self):
pass
@pytest.mark.xfail(run=False, reason='BSR does not have a __setitem__')
def test_setdiag(self):
pass
def test_resize_blocked(self):
# test resize() with non-(1,1) blocksize
D = np.array([[1, 0, 3, 4],
[2, 0, 0, 0],
[3, 0, 0, 0]])
S = self.spmatrix(D, blocksize=(1, 2))
assert_(S.resize((3, 2)) is None)
assert_array_equal(S.A, [[1, 0],
[2, 0],
[3, 0]])
S.resize((2, 2))
assert_array_equal(S.A, [[1, 0],
[2, 0]])
S.resize((3, 2))
assert_array_equal(S.A, [[1, 0],
[2, 0],
[0, 0]])
S.resize((3, 4))
assert_array_equal(S.A, [[1, 0, 0, 0],
[2, 0, 0, 0],
[0, 0, 0, 0]])
assert_raises(ValueError, S.resize, (2, 3))
@pytest.mark.xfail(run=False, reason='BSR does not have a __setitem__')
def test_setdiag_comprehensive(self):
pass
def test_scalar_idx_dtype(self):
# Check that index dtype takes into account all parameters
# passed to sparsetools, including the scalar ones
indptr = np.zeros(2, dtype=np.int32)
indices = np.zeros(0, dtype=np.int32)
vals = np.zeros((0, 1, 1))
a = bsr_matrix((vals, indices, indptr), shape=(1, 2**31-1))
b = bsr_matrix((vals, indices, indptr), shape=(1, 2**31))
c = bsr_matrix((1, 2**31-1))
d = bsr_matrix((1, 2**31))
assert_equal(a.indptr.dtype, np.int32)
assert_equal(b.indptr.dtype, np.int64)
assert_equal(c.indptr.dtype, np.int32)
assert_equal(d.indptr.dtype, np.int64)
try:
vals2 = np.zeros((0, 1, 2**31-1))
vals3 = np.zeros((0, 1, 2**31))
e = bsr_matrix((vals2, indices, indptr), shape=(1, 2**31-1))
f = bsr_matrix((vals3, indices, indptr), shape=(1, 2**31))
assert_equal(e.indptr.dtype, np.int32)
assert_equal(f.indptr.dtype, np.int64)
except (MemoryError, ValueError):
# May fail on 32-bit Python
e = 0
f = 0
# These shouldn't fail
for x in [a, b, c, d, e, f]:
x + x
TestBSR.init_class()
#------------------------------------------------------------------------------
# Tests for non-canonical representations (with duplicates, unsorted indices)
#------------------------------------------------------------------------------
def _same_sum_duplicate(data, *inds, **kwargs):
"""Duplicates entries to produce the same matrix"""
indptr = kwargs.pop('indptr', None)
if np.issubdtype(data.dtype, np.bool_) or \
np.issubdtype(data.dtype, np.unsignedinteger):
if indptr is None:
return (data,) + inds
else:
return (data,) + inds + (indptr,)
zeros_pos = (data == 0).nonzero()
# duplicate data
data = data.repeat(2, axis=0)
data[::2] -= 1
data[1::2] = 1
# don't spoil all explicit zeros
if zeros_pos[0].size > 0:
pos = tuple(p[0] for p in zeros_pos)
pos1 = (2*pos[0],) + pos[1:]
pos2 = (2*pos[0]+1,) + pos[1:]
data[pos1] = 0
data[pos2] = 0
inds = tuple(indices.repeat(2) for indices in inds)
if indptr is None:
return (data,) + inds
else:
return (data,) + inds + (indptr * 2,)
class _NonCanonicalMixin(object):
def spmatrix(self, D, sorted_indices=False, **kwargs):
"""Replace D with a non-canonical equivalent: containing
duplicate elements and explicit zeros"""
construct = super(_NonCanonicalMixin, self).spmatrix
M = construct(D, **kwargs)
zero_pos = (M.A == 0).nonzero()
has_zeros = (zero_pos[0].size > 0)
if has_zeros:
k = zero_pos[0].size//2
with suppress_warnings() as sup:
sup.filter(SparseEfficiencyWarning,
"Changing the sparsity structure of a cs[cr]_matrix is expensive")
M = self._insert_explicit_zero(M, zero_pos[0][k], zero_pos[1][k])
arg1 = self._arg1_for_noncanonical(M, sorted_indices)
if 'shape' not in kwargs:
kwargs['shape'] = M.shape
NC = construct(arg1, **kwargs)
# check that result is valid
if NC.dtype in [np.float32, np.complex64]:
# For single-precision floats, the differences between M and NC
# that are introduced by the extra operations involved in the
# construction of NC necessitate a more lenient tolerance level
# than the default.
rtol = 1e-05
else:
rtol = 1e-07
assert_allclose(NC.A, M.A, rtol=rtol)
# check that at least one explicit zero
if has_zeros:
assert_((NC.data == 0).any())
# TODO check that NC has duplicates (which are not explicit zeros)
return NC
@pytest.mark.skip(reason='bool(matrix) counts explicit zeros')
def test_bool(self):
pass
@pytest.mark.skip(reason='getnnz-axis counts explicit zeros')
def test_getnnz_axis(self):
pass
@pytest.mark.skip(reason='nnz counts explicit zeros')
def test_empty(self):
pass
class _NonCanonicalCompressedMixin(_NonCanonicalMixin):
def _arg1_for_noncanonical(self, M, sorted_indices=False):
"""Return non-canonical constructor arg1 equivalent to M"""
data, indices, indptr = _same_sum_duplicate(M.data, M.indices,
indptr=M.indptr)
if not sorted_indices:
for start, stop in izip(indptr, indptr[1:]):
indices[start:stop] = indices[start:stop][::-1].copy()
data[start:stop] = data[start:stop][::-1].copy()
return data, indices, indptr
def _insert_explicit_zero(self, M, i, j):
M[i,j] = 0
return M
class _NonCanonicalCSMixin(_NonCanonicalCompressedMixin):
def test_getelement(self):
def check(dtype, sorted_indices):
D = array([[1,0,0],
[4,3,0],
[0,2,0],
[0,0,0]], dtype=dtype)
A = self.spmatrix(D, sorted_indices=sorted_indices)
M,N = D.shape
for i in range(-M, M):
for j in range(-N, N):
assert_equal(A[i,j], D[i,j])
for ij in [(0,3),(-1,3),(4,0),(4,3),(4,-1), (1, 2, 3)]:
assert_raises((IndexError, TypeError), A.__getitem__, ij)
for dtype in supported_dtypes:
for sorted_indices in [False, True]:
check(np.dtype(dtype), sorted_indices)
def test_setitem_sparse(self):
D = np.eye(3)
A = self.spmatrix(D)
B = self.spmatrix([[1,2,3]])
D[1,:] = B.toarray()
with suppress_warnings() as sup:
sup.filter(SparseEfficiencyWarning,
"Changing the sparsity structure of a cs[cr]_matrix is expensive")
A[1,:] = B
assert_array_equal(A.toarray(), D)
D[:,2] = B.toarray().ravel()
with suppress_warnings() as sup:
sup.filter(SparseEfficiencyWarning,
"Changing the sparsity structure of a cs[cr]_matrix is expensive")
A[:,2] = B.T
assert_array_equal(A.toarray(), D)
@pytest.mark.xfail(run=False, reason='inverse broken with non-canonical matrix')
def test_inv(self):
pass
@pytest.mark.xfail(run=False, reason='solve broken with non-canonical matrix')
def test_solve(self):
pass
class TestCSRNonCanonical(_NonCanonicalCSMixin, TestCSR):
pass
class TestCSCNonCanonical(_NonCanonicalCSMixin, TestCSC):
pass
class TestBSRNonCanonical(_NonCanonicalCompressedMixin, TestBSR):
def _insert_explicit_zero(self, M, i, j):
x = M.tocsr()
x[i,j] = 0
return x.tobsr(blocksize=M.blocksize)
@pytest.mark.xfail(run=False, reason='diagonal broken with non-canonical BSR')
def test_diagonal(self):
pass
@pytest.mark.xfail(run=False, reason='expm broken with non-canonical BSR')
def test_expm(self):
pass
class TestCOONonCanonical(_NonCanonicalMixin, TestCOO):
def _arg1_for_noncanonical(self, M, sorted_indices=None):
"""Return non-canonical constructor arg1 equivalent to M"""
data, row, col = _same_sum_duplicate(M.data, M.row, M.col)
return data, (row, col)
def _insert_explicit_zero(self, M, i, j):
M.data = np.r_[M.data.dtype.type(0), M.data]
M.row = np.r_[M.row.dtype.type(i), M.row]
M.col = np.r_[M.col.dtype.type(j), M.col]
return M
def test_setdiag_noncanonical(self):
m = self.spmatrix(np.eye(3))
m.sum_duplicates()
m.setdiag([3, 2], k=1)
m.sum_duplicates()
assert_(np.all(np.diff(m.col) >= 0))
def cases_64bit():
TEST_CLASSES = [TestBSR, TestCOO, TestCSC, TestCSR, TestDIA,
# lil/dok->other conversion operations have get_index_dtype
TestDOK, TestLIL
]
# The following features are missing, so skip the tests:
SKIP_TESTS = {
'test_expm': 'expm for 64-bit indices not available',
'test_inv': 'linsolve for 64-bit indices not available',
'test_solve': 'linsolve for 64-bit indices not available',
'test_scalar_idx_dtype': 'test implemented in base class',
'test_large_dimensions_reshape': 'test actually requires 64-bit to work',
}
for cls in TEST_CLASSES:
for method_name in sorted(dir(cls)):
method = getattr(cls, method_name)
if (method_name.startswith('test_') and
not getattr(method, 'slow', False)):
marks = []
msg = SKIP_TESTS.get(method_name)
if bool(msg):
marks += [pytest.mark.skip(reason=msg)]
if LooseVersion(pytest.__version__) >= LooseVersion("3.6.0"):
markers = getattr(method, 'pytestmark', [])
for mark in markers:
if mark.name in ('skipif', 'skip', 'xfail', 'xslow'):
marks.append(mark)
else:
for mname in ['skipif', 'skip', 'xfail', 'xslow']:
if hasattr(method, mname):
marks += [getattr(method, mname)]
yield pytest.param(cls, method_name, marks=marks)
class Test64Bit(object):
MAT_CLASSES = [bsr_matrix, coo_matrix, csc_matrix, csr_matrix, dia_matrix]
def _create_some_matrix(self, mat_cls, m, n):
return mat_cls(np.random.rand(m, n))
def _compare_index_dtype(self, m, dtype):
dtype = np.dtype(dtype)
if isinstance(m, csc_matrix) or isinstance(m, csr_matrix) \
or isinstance(m, bsr_matrix):
return (m.indices.dtype == dtype) and (m.indptr.dtype == dtype)
elif isinstance(m, coo_matrix):
return (m.row.dtype == dtype) and (m.col.dtype == dtype)
elif isinstance(m, dia_matrix):
return (m.offsets.dtype == dtype)
else:
raise ValueError("matrix %r has no integer indices" % (m,))
def test_decorator_maxval_limit(self):
# Test that the with_64bit_maxval_limit decorator works
@with_64bit_maxval_limit(maxval_limit=10)
def check(mat_cls):
m = mat_cls(np.random.rand(10, 1))
assert_(self._compare_index_dtype(m, np.int32))
m = mat_cls(np.random.rand(11, 1))
assert_(self._compare_index_dtype(m, np.int64))
for mat_cls in self.MAT_CLASSES:
check(mat_cls)
def test_decorator_maxval_random(self):
# Test that the with_64bit_maxval_limit decorator works (2)
@with_64bit_maxval_limit(random=True)
def check(mat_cls):
seen_32 = False
seen_64 = False
for k in range(100):
m = self._create_some_matrix(mat_cls, 9, 9)
seen_32 = seen_32 or self._compare_index_dtype(m, np.int32)
seen_64 = seen_64 or self._compare_index_dtype(m, np.int64)
if seen_32 and seen_64:
break
else:
raise AssertionError("both 32 and 64 bit indices not seen")
for mat_cls in self.MAT_CLASSES:
check(mat_cls)
def _check_resiliency(self, cls, method_name, **kw):
# Resiliency test, to check that sparse matrices deal reasonably
# with varying index data types.
@with_64bit_maxval_limit(**kw)
def check(cls, method_name):
instance = cls()
if hasattr(instance, 'setup_method'):
instance.setup_method()
try:
getattr(instance, method_name)()
finally:
if hasattr(instance, 'teardown_method'):
instance.teardown_method()
check(cls, method_name)
@pytest.mark.parametrize('cls,method_name', cases_64bit())
def test_resiliency_limit_10(self, cls, method_name):
self._check_resiliency(cls, method_name, maxval_limit=10)
@pytest.mark.parametrize('cls,method_name', cases_64bit())
def test_resiliency_random(self, cls, method_name):
# bsr_matrix.eliminate_zeros relies on csr_matrix constructor
# not making copies of index arrays --- this is not
# necessarily true when we pick the index data type randomly
self._check_resiliency(cls, method_name, random=True)
@pytest.mark.parametrize('cls,method_name', cases_64bit())
def test_resiliency_all_32(self, cls, method_name):
self._check_resiliency(cls, method_name, fixed_dtype=np.int32)
@pytest.mark.parametrize('cls,method_name', cases_64bit())
def test_resiliency_all_64(self, cls, method_name):
self._check_resiliency(cls, method_name, fixed_dtype=np.int64)
@pytest.mark.parametrize('cls,method_name', cases_64bit())
def test_no_64(self, cls, method_name):
self._check_resiliency(cls, method_name, assert_32bit=True)
def test_downcast_intp(self):
# Check that bincount and ufunc.reduceat intp downcasts are
# dealt with. The point here is to trigger points in the code
# that can fail on 32-bit systems when using 64-bit indices,
# due to use of functions that only work with intp-size
# indices.
@with_64bit_maxval_limit(fixed_dtype=np.int64,
downcast_maxval=1)
def check_limited():
# These involve indices larger than `downcast_maxval`
a = csc_matrix([[1, 2], [3, 4], [5, 6]])
assert_raises(AssertionError, a.getnnz, axis=1)
assert_raises(AssertionError, a.sum, axis=0)
a = csr_matrix([[1, 2, 3], [3, 4, 6]])
assert_raises(AssertionError, a.getnnz, axis=0)
a = coo_matrix([[1, 2, 3], [3, 4, 5]])
assert_raises(AssertionError, a.getnnz, axis=0)
@with_64bit_maxval_limit(fixed_dtype=np.int64)
def check_unlimited():
# These involve indices larger than `downcast_maxval`
a = csc_matrix([[1, 2], [3, 4], [5, 6]])
a.getnnz(axis=1)
a.sum(axis=0)
a = csr_matrix([[1, 2, 3], [3, 4, 6]])
a.getnnz(axis=0)
a = coo_matrix([[1, 2, 3], [3, 4, 5]])
a.getnnz(axis=0)
check_limited()
check_unlimited()