def testNormalizeExecution(self):
raw_dense = np.random.rand(10, 10)
raw_sparse = sps.random(10, 10, density=0.4, format='csr')
for chunk_size in [10, 6, (10, 6), (6, 10)]:
for raw, x in [
(raw_dense, mt.tensor(raw_dense, chunk_size=chunk_size)),
(raw_sparse, mt.tensor(raw_sparse, chunk_size=chunk_size))
]:
for norm in ['l1', 'l2', 'max']:
for axis in (0, 1):
for use_sklearn in [True, False]:
n = normalize(x,
norm=norm,
axis=axis,
return_norm=False)
n.op._use_sklearn = use_sklearn
result = self.executor.execute_tensor(
n, concat=True)[0]
expected = sk_normalize(raw,
norm=norm,
axis=axis,
return_norm=False)
if sps.issparse(expected):
expected = expected.A
np.testing.assert_almost_equal(
np.asarray(result), expected)
raw_dense = np.random.rand(10, 10)
raw_sparse = sps.random(10, 10, density=0.4, format='csr')
# test copy and return_normalize
for axis in (0, 1):
for chunk_size in (10, 6, (6, 10)):
for raw in (raw_dense, raw_sparse):
x = mt.tensor(raw, chunk_size=chunk_size)
n = normalize(x, axis=axis, copy=False, return_norm=True)
results = self.executor.execute_tensors(n)
raw_copy = raw.copy()
try:
expects = sk_normalize(raw_copy,
axis=axis,
copy=False,
return_norm=True)
except NotImplementedError:
continue
if sps.issparse(expects[0]):
expected = expects[0].A
else:
expected = expects[0]
np.testing.assert_almost_equal(np.asarray(results[0]),
expected)
np.testing.assert_almost_equal(results[1], expects[1])
def testNormalizeOp(self):
with self.assertRaises(ValueError):
normalize(mt.random.random(10, 3), norm='unknown')
with self.assertRaises(ValueError):
normalize(mt.random.random(10, 3), axis=-1)
with self.assertRaises(ValueError):
normalize(mt.random.rand(10, 3, 3))
def test_normalize_op():
with pytest.raises(ValueError):
normalize(mt.random.random(10, 3), norm='unknown')
with pytest.raises(ValueError):
normalize(mt.random.random(10, 3), axis=-1)
with pytest.raises(ValueError):
normalize(mt.random.rand(10, 3, 3))