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[MRG] Add sparse efficiency warning to randomized_svd (fixes randomized_svd is slow for dok_matrix and lil_matrix #11262) #11264

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Merged
merged 5 commits into from
Jun 17, 2018
Merged

[MRG] Add sparse efficiency warning to randomized_svd (fixes randomized_svd is slow for dok_matrix and lil_matrix #11262) #11264

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cd69c1a
Add sparse efficiency warning to randomized_svd
scottgigante Jun 14, 2018
52d0fc9
compact sparseefficiencywarning code to address comment from @rth
scottgigante Jun 14, 2018
c36ec92
test for sparseefficiencywarning as suggested by @rth
scottgigante Jun 14, 2018
aaa489c
move warning to start of randomized_svd function
scottgigante Jun 17, 2018
2442b6f
make test_randomized_svd_sparse_warnings run faster
scottgigante Jun 17, 2018
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21 changes: 13 additions & 8 deletions sklearn/utils/extmath.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,8 +15,7 @@
import warnings

import numpy as np
from scipy import linalg
from scipy.sparse import issparse, csr_matrix
from scipy import linalg, sparse

from . import check_random_state, deprecated
from .fixes import np_version
Expand Down Expand Up @@ -60,9 +59,9 @@ def row_norms(X, squared=False):

Performs no input validation.
"""
if issparse(X):
if not isinstance(X, csr_matrix):
X = csr_matrix(X)
if sparse.issparse(X):
if not isinstance(X, sparse.csr_matrix):
X = sparse.csr_matrix(X)
norms = csr_row_norms(X)
else:
norms = np.einsum('ij,ij->i', X, X)
Expand Down Expand Up @@ -131,7 +130,7 @@ def safe_sparse_dot(a, b, dense_output=False):
dot_product : array or sparse matrix
sparse if ``a`` or ``b`` is sparse and ``dense_output=False``.
"""
if issparse(a) or issparse(b):
if sparse.issparse(a) or sparse.issparse(b):
ret = a * b
if dense_output and hasattr(ret, "toarray"):
ret = ret.toarray()
Expand Down Expand Up @@ -307,6 +306,12 @@ def randomized_svd(M, n_components, n_oversamples=10, n_iter='auto',
analysis
A. Szlam et al. 2014
"""
if isinstance(M, (sparse.lil_matrix, sparse.dok_matrix)):
warnings.warn("Calculating SVD of a {} is expensive. "
"csr_matrix is more efficient.".format(
type(M).__name__),
sparse.SparseEfficiencyWarning)

random_state = check_random_state(random_state)
n_random = n_components + n_oversamples
n_samples, n_features = M.shape
Expand Down Expand Up @@ -620,7 +625,7 @@ def safe_min(X):
Adapated from http://stackoverflow.com/q/13426580

"""
if issparse(X):
if sparse.issparse(X):
if len(X.data) == 0:
return 0
m = X.data.min()
Expand All @@ -633,7 +638,7 @@ def make_nonnegative(X, min_value=0):
"""Ensure `X.min()` >= `min_value`."""
min_ = safe_min(X)
if min_ < min_value:
if issparse(X):
if sparse.issparse(X):
raise ValueError("Cannot make the data matrix"
" nonnegative because it is sparse."
" Adding a value to every entry would"
Expand Down
15 changes: 15 additions & 0 deletions sklearn/utils/tests/test_extmath.py
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Original file line number Diff line number Diff line change
Expand Up @@ -365,6 +365,21 @@ def test_randomized_svd_power_iteration_normalizer():
assert_greater(15, np.abs(error_2 - error))


def test_randomized_svd_sparse_warnings():
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Can you figure out a way to make the test less time-consuming (e.g., smaller matrix?). Currently, it's among the most time-consuming tests (See Travis log).

# randomized_svd throws a warning for lil and dok matrix
rng = np.random.RandomState(42)
X = make_low_rank_matrix(50, 20, effective_rank=10, random_state=rng)
n_components = 5
for cls in (sparse.lil_matrix, sparse.dok_matrix):
X = cls(X)
assert_warns_message(
sparse.SparseEfficiencyWarning,
"Calculating SVD of a {} is expensive. "
"csr_matrix is more efficient.".format(cls.__name__),
randomized_svd, X, n_components, n_iter=1,
power_iteration_normalizer='none')


def test_svd_flip():
# Check that svd_flip works in both situations, and reconstructs input.
rs = np.random.RandomState(1999)
Expand Down