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[MRG-1] ENH: Allow float32 to pass through with copy #5932

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[MRG-1] ENH: Allow float32 to pass through with copy #5932

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f1b7a11
TST: Regression test for 32-bit input
jseabold Nov 29, 2015
6a4fa62
ENH: Allow float32 to pass through with copy
jseabold Nov 28, 2015
fe6b225
DOC: Clarify doc for copy behavior
jseabold Nov 29, 2015
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14 changes: 10 additions & 4 deletions sklearn/utils/sparsefuncs_fast.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -278,7 +278,9 @@ def inplace_csr_row_normalize_l1(X):
cdef unsigned int n_samples = X.shape[0]
cdef unsigned int n_features = X.shape[1]

cdef np.ndarray[DOUBLE, ndim=1] X_data = X.data
# might copy
cdef np.ndarray[DOUBLE, ndim=1] X_data = np.asarray(X.data,
dtype=np.float64)
cdef np.ndarray[int, ndim=1] X_indices = X.indices
cdef np.ndarray[int, ndim=1] X_indptr = X.indptr

Expand Down Expand Up @@ -313,7 +315,9 @@ def inplace_csr_row_normalize_l2(X):
cdef unsigned int n_samples = X.shape[0]
cdef unsigned int n_features = X.shape[1]

cdef np.ndarray[DOUBLE, ndim=1] X_data = X.data
# might copy
cdef np.ndarray[DOUBLE, ndim=1] X_data = np.asarray(X.data,
dtype=np.float64)
cdef np.ndarray[int, ndim=1] X_indices = X.indices
cdef np.ndarray[int, ndim=1] X_indptr = X.indptr

Expand Down Expand Up @@ -364,7 +368,7 @@ def assign_rows_csr(X,
"""Densify selected rows of a CSR matrix into a preallocated array.

Like out[out_rows] = X[X_rows].toarray() but without copying.
Only supported for dtype=np.float64.
No-copy only supported for dtype=np.float64.

Parameters
----------
Expand All @@ -378,7 +382,9 @@ def assign_rows_csr(X,
# but int is what scipy.sparse uses.
int i, ind, j
np.npy_intp rX
np.ndarray[DOUBLE, ndim=1] data = X.data
# might copy
np.ndarray[DOUBLE, ndim=1] data = np.asarray(X.data,
dtype=np.float64)
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This change to assign_rows_csr is not tested by the new regression test.

np.ndarray[int, ndim=1] indices = X.indices, indptr = X.indptr

if X_rows.shape[0] != out_rows.shape[0]:
Expand Down
11 changes: 10 additions & 1 deletion sklearn/utils/tests/test_sparsefuncs.py
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Expand Up @@ -14,7 +14,9 @@
inplace_swap_row, inplace_swap_column,
min_max_axis,
count_nonzero, csc_median_axis_0)
from sklearn.utils.sparsefuncs_fast import assign_rows_csr
from sklearn.utils.sparsefuncs_fast import (assign_rows_csr,
inplace_csr_row_normalize_l1,
inplace_csr_row_normalize_l2)
from sklearn.utils.testing import assert_raises


Expand Down Expand Up @@ -478,3 +480,10 @@ def test_csc_row_median():

# Test that it raises an Error for non-csc matrices.
assert_raises(TypeError, csc_median_axis_0, sp.csr_matrix(X))


def test_inplace_normalize():
# regression tests for passing 32-bit floating point
X = sp.rand(10, 5, dtype=np.float32, density=.5).tocsr()
inplace_csr_row_normalize_l1(X)
inplace_csr_row_normalize_l2(X)
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This does not check that X has been properly normalized, and in practice it cannot be the case as the data is array which is actually normalized is a copy of the input X.data when the dtype is not np.float64.