def test_mkl_mabt():
for i in range(50):
A = lktu.rand_csr(20, 10, nnz=50)
B = lktu.rand_csr(5, 10, nnz=20)
As = mkl_ops.SparseM.from_csr(A)
Bs = mkl_ops.SparseM.from_csr(B)
Ch = mkl_ops._lk_mkl_spmabt(As.ptr, Bs.ptr)
C = mkl_ops._to_csr(Ch)
C = lm.CSR(N=C)
assert C.nrows == 20
assert C.ncols == 5
Csp = A.to_scipy() @ B.to_scipy().T
Cspa = Csp.toarray()
Ca = C.to_scipy().toarray()
assert Ca == approx(Cspa)
def test_unit_norm():
for n in range(50):
csr = rand_csr()
spm = csr.to_scipy().copy()
m2 = csr.normalize_rows('unit')
assert len(m2) == 100
for i in range(csr.nrows):
vs = csr.row_vs(i)
if len(vs) > 0:
assert np.linalg.norm(vs) == approx(1.0)
assert vs * m2[i] == approx(
spm.getrow(i).toarray()[0, csr.row_cs(i)])
def test_mean_center():
for n in range(50):
csr = rand_csr()
spm = csr.to_scipy().copy()
m2 = csr.normalize_rows('center')
assert len(m2) == 100
for i in range(csr.nrows):
vs = csr.row_vs(i)
if len(vs) > 0:
assert np.mean(vs) == approx(0.0)
assert vs + m2[i] == approx(
spm.getrow(i).toarray()[0, csr.row_cs(i)])
def test_filter():
csr = rand_csr()
csrf = csr.filter_nnzs(csr.values > 0)
assert all(csrf.values > 0)
assert csrf.nnz <= csr.nnz
for i in range(csr.nrows):
spo, epo = csr.row_extent(i)
spf, epf = csrf.row_extent(i)
assert epf - spf <= epo - spo
d1 = csr.to_scipy().toarray()
df = csrf.to_scipy().toarray()
d1[d1 < 0] = 0
assert df == approx(d1)
def test_csr_pickle(values):
csr = rand_csr(100, 50, 1000, values=values)
assert csr.nrows == 100
assert csr.ncols == 50
assert csr.nnz == 1000
data = pickle.dumps(csr)
csr2 = pickle.loads(data)
assert csr2.nrows == csr.nrows
assert csr2.ncols == csr.ncols
assert csr2.nnz == csr.nnz
assert all(csr2.rowptrs == csr.rowptrs)
assert all(csr2.colinds == csr.colinds)
if values:
assert all(csr2.values == csr.values)
else:
assert csr2.values is None
def test_csr64_pickle(values):
csr = rand_csr(100, 50, 1000, values=values)
csr = lm.CSR(csr.nrows, csr.ncols, csr.nnz, csr.rowptrs.astype(np.int64),
csr.colinds, csr.values)
assert csr.nrows == 100
assert csr.ncols == 50
assert csr.nnz == 1000
data = pickle.dumps(csr)
csr2 = pickle.loads(data)
assert csr2.nrows == csr.nrows
assert csr2.ncols == csr.ncols
assert csr2.nnz == csr.nnz
assert all(csr2.rowptrs == csr.rowptrs)
assert csr2.rowptrs.dtype == np.int64
assert all(csr2.colinds == csr.colinds)
if values:
assert all(csr2.values == csr.values)
else:
assert csr2.values is None