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[MRG] FIX row_norms return dtype same as input #12423

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[MRG] FIX row_norms return dtype same as input #12423

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a0dd9d8
fix csr row norms dtype
jeremiedbb Oct 19, 2018
298b4ca
add test
jeremiedbb Oct 19, 2018
1e0e1e4
typo
jeremiedbb Oct 19, 2018
46b5ea3
fix norms def
jeremiedbb Oct 19, 2018
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21 changes: 11 additions & 10 deletions sklearn/utils/sparsefuncs_fast.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -25,35 +25,36 @@ ctypedef fused integral:

ctypedef np.float64_t DOUBLE


def csr_row_norms(X):
"""L2 norm of each row in CSR matrix X."""
if X.dtype not in [np.float32, np.float64]:
X = X.astype(np.float64)
return _csr_row_norms(X.data, X.shape, X.indices, X.indptr)

norms = np.zeros(X.shape[0], dtype=X.data.dtype)
_csr_row_norms(X.data, X.shape, X.indices, X.indptr, norms)

return norms


def _csr_row_norms(np.ndarray[floating, ndim=1, mode="c"] X_data,
shape,
np.ndarray[integral, ndim=1, mode="c"] X_indices,
np.ndarray[integral, ndim=1, mode="c"] X_indptr):
np.ndarray[integral, ndim=1, mode="c"] X_indptr,
floating[::1] norms):
cdef:
unsigned long long n_samples = shape[0]
unsigned long long n_features = shape[1]
np.ndarray[DOUBLE, ndim=1, mode="c"] norms

np.npy_intp i, j

unsigned long long i
integral j
double sum_

norms = np.zeros(n_samples, dtype=np.float64)

for i in range(n_samples):
sum_ = 0.0
for j in range(X_indptr[i], X_indptr[i + 1]):
sum_ += X_data[j] * X_data[j]
norms[i] = sum_

return norms


def csr_mean_variance_axis0(X):
"""Compute mean and variance along axis 0 on a CSR matrix
Expand Down
14 changes: 13 additions & 1 deletion sklearn/utils/tests/test_sparsefuncs.py
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Expand Up @@ -18,7 +18,8 @@
count_nonzero, csc_median_axis_0)
from sklearn.utils.sparsefuncs_fast import (assign_rows_csr,
inplace_csr_row_normalize_l1,
inplace_csr_row_normalize_l2)
inplace_csr_row_normalize_l2,
csr_row_norms)
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_allclose

Expand Down Expand Up @@ -512,3 +513,14 @@ def test_inplace_normalize():
if inplace_csr_row_normalize is inplace_csr_row_normalize_l2:
X_csr.data **= 2
assert_array_almost_equal(np.abs(X_csr).sum(axis=1), ones)


@pytest.mark.parametrize("dtype", [np.float32, np.float64])
def test_csr_row_norms(dtype):
# checks that csr_row_norms returns the same output as
# scipy.sparse.linalg.norm, and that the dype is the same as X.
X = sp.random(100, 10, format='csr', dtype=dtype)
scipy_norms = sp.linalg.norm(X, axis=1)**2
norms = csr_row_norms(X)
assert norms.dtype.type is dtype
assert_array_almost_equal(norms, scipy_norms)