def test_normalizer_l1():
rng = np.random.RandomState(0)
X_dense = rng.randn(4, 5)
X_sparse_unpruned = sp.csr_matrix(X_dense)
# set the row number 3 to zero
X_dense[3, :] = 0.0
# set the row number 3 to zero without pruning (can happen in real life)
indptr_3 = X_sparse_unpruned.indptr[3]
indptr_4 = X_sparse_unpruned.indptr[4]
X_sparse_unpruned.data[indptr_3:indptr_4] = 0.0
# build the pruned variant using the regular constructor
X_sparse_pruned = sp.csr_matrix(X_dense)
# check inputs that support the no-copy optim
for X in (X_dense, X_sparse_pruned, X_sparse_unpruned):
normalizer = Normalizer(norm='l1', copy=True)
X_norm = normalizer.transform(X)
assert X_norm is not X
X_norm1 = toarray(X_norm)
normalizer = Normalizer(norm='l1', copy=False)
X_norm = normalizer.transform(X)
assert X_norm is X
X_norm2 = toarray(X_norm)
for X_norm in (X_norm1, X_norm2):
row_sums = np.abs(X_norm).sum(axis=1)
for i in range(3):
assert_almost_equal(row_sums[i], 1.0)
assert_almost_equal(row_sums[3], 0.0)
# check input for which copy=False won't prevent a copy
for init in (sp.coo_matrix, sp.csc_matrix, sp.lil_matrix):
X = init(X_dense)
X_norm = normalizer = Normalizer(norm='l2', copy=False).transform(X)
assert X_norm is not X
assert isinstance(X_norm, sp.csr_matrix)
X_norm = toarray(X_norm)
for i in xrange(3):
assert_almost_equal(row_sums[i], 1.0)
assert_almost_equal(la.norm(X_norm[3]), 0.0)
def test_normalizer_l1():
np.random.seed(0)
X_orig = np.random.randn(4, 5)
X_orig[3, :] = 0.0
# check inputs that support the no-copy optim
for init in (np.array, sp.csr_matrix):
X = init(X_orig.copy())
normalizer = Normalizer(norm='l1', copy=True)
X_norm = normalizer.transform(X)
assert X_norm is not X
X_norm1 = toarray(X_norm)
normalizer = Normalizer(norm='l1', copy=False)
X_norm = normalizer.transform(X)
assert X_norm is X
X_norm2 = toarray(X_norm)
for X_norm in (X_norm1, X_norm2):
row_sums = np.abs(X_norm).sum(axis=1)
for i in range(3):
assert_almost_equal(row_sums[i], 1.0)
assert_almost_equal(row_sums[3], 0.0)
# check input for which copy=False won't prevent a copy
for init in (sp.coo_matrix, sp.csc_matrix, sp.lil_matrix):
X = init(X_orig.copy())
X_norm = normalizer = Normalizer(norm='l2', copy=False).transform(X)
assert X_norm is not X
assert isinstance(X_norm, sp.csr_matrix)
X_norm = toarray(X_norm)
for i in xrange(3):
assert_almost_equal(row_sums[i], 1.0)
assert_almost_equal(la.norm(X_norm[3]), 0.0)
def test_normalizer_l1():
np.random.seed(0)
X_orig = np.random.randn(4, 5)
X_orig[3, :] = 0.0
# check inputs that support the no-copy optim
for init in (np.array, sp.csr_matrix):
X = init(X_orig.copy())
normalizer = Normalizer(norm='l1', copy=True)
X_norm = normalizer.transform(X)
assert X_norm is not X
X_norm1 = toarray(X_norm)
normalizer = Normalizer(norm='l1', copy=False)
X_norm = normalizer.transform(X)
assert X_norm is X
X_norm2 = toarray(X_norm)
for X_norm in (X_norm1, X_norm2):
row_sums = np.abs(X_norm).sum(axis=1)
for i in range(3):
assert_almost_equal(row_sums[i], 1.0)
assert_almost_equal(row_sums[3], 0.0)
# check input for which copy=False won't prevent a copy
for init in (sp.coo_matrix, sp.csc_matrix, sp.lil_matrix):
X = init(X_orig.copy())
X_norm = normalizer = Normalizer(norm='l2', copy=False).transform(X)
assert X_norm is not X
assert isinstance(X_norm, sp.csr_matrix)
X_norm = toarray(X_norm)
for i in xrange(3):
assert_almost_equal(row_sums[i], 1.0)
assert_almost_equal(la.norm(X_norm[3]), 0.0)
