10000 [MRG] Fix Linear models take unreasonable longer time in certain data size. · Pull Request #11753 · scikit-learn/scikit-learn · GitHub
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[MRG] Fix Linear models take unreasonable longer time in certain data size. #11753

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[MRG] Fix Linear models take unreasonable longer time in certain data size. #11753

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50c96d8
Add convergence warning to enet_coordinate_descent solvers
brentfagan Aug 1, 2018
731df32
Change UserWarning to ConvergenceWarning in enet_coordinate_descent
brentfagan Aug 1, 2018
dd5ebd6
Add tests to check ConvergenceWarning in enet solvers
brentfagan Aug 5, 2018
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22 changes: 22 additions & 0 deletions sklearn/linear_model/cd_fast.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -15,6 +15,7 @@ cimport cython
from cpython cimport bool
from cython cimport floating
import warnings
from ..exceptions import ConvergenceWarning

ctypedef np.float64_t DOUBLE
ctypedef np.uint32_t UINT32_t
Expand Down Expand Up @@ -302,6 +303,12 @@ def enet_coordinate_descent(np.ndarray[floating, ndim=1] w,
if gap < tol:
# return if we reached desired tolerance
break
else:
with gil:
warnings.warn("Objective did not converge."
" You might want to increase the number of iterations.",
ConvergenceWarning)

return w, gap, tol, n_iter + 1


Expand Down Expand Up @@ -521,6 +528,11 @@ def sparse_enet_coordinate_descent(floating [:] w,
if gap < tol:
# return if we reached desired tolerance
break
else:
with gil:
warnings.warn("Objective did not converge."
" You might want to increase the number of iterations.",
ConvergenceWarning)

return w, gap, tol, n_iter + 1

Expand Down Expand Up @@ -675,6 +687,11 @@ def enet_coordinate_descent_gram(floating[:] w, floating alpha, floating beta,
if gap < tol:
# return if we reached desired tolerance
break
else:
with gil:
warnings.warn("Objective did not converge."
" You might want to increase the number of iterations.",
ConvergenceWarning)

return np.asarray(w), gap, tol, n_iter + 1

Expand Down Expand Up @@ -880,5 +897,10 @@ def enet_coordinate_descent_multi_task(floating[::1, :] W, floating l1_reg,
if gap < tol:
# return if we reached desired tolerance
break
else:
with gil:
warnings.warn("Objective did not converge."
" You might want to increase the number of iterations.",
ConvergenceWarning)

return np.asarray(W), gap, tol, n_iter + 1
30 changes: 30 additions & 0 deletions sklearn/linear_model/tests/test_coordinate_descent.py
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Expand Up @@ -818,3 +818,33 @@ def test_coef_shape_not_zero():
est_no_intercept = Lasso(fit_intercept=False)
est_no_intercept.fit(np.c_[np.ones(3)], np.ones(3))
assert est_no_intercept.coef_.shape == (1,)


def test_enet_coordinate_descent():
"""Test that a warning is issued if model does not converge"""
clf = Lasso()
n_samples = 15500
n_features = 500
X = np.ones([n_samples, n_features]) * 1e50
y = np.ones([n_samples])
assert_warns(ConvergenceWarning, clf.fit, X, y)


def test_enet_coordinate_descent_gram():
"""Test that a warning is issued if model does not converge"""
clf = Lasso(precompute=True)
n_samples = 15500
n_features = 500
X = np.ones([n_samples, n_features]) * 1e50
y = np.ones([n_samples])
assert_warns(ConvergenceWarning, clf.fit, X, y)

def test_enet_coordinate_descent_multi_task():
"""Test that a warning is issued if model does not converge"""
clf = MultiTaskLasso()
n_samples = 15500
n_features = 500
n_classes = 2
X = np.ones([n_samples, n_features]) * 1e50
y = np.ones([n_samples, n_classes])
assert_warns(ConvergenceWarning, clf.fit, X, y)
12 changes: 12 additions & 0 deletions sklearn/linear_model/tests/test_sparse_coordinate_descent.py
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Expand Up @@ -9,6 +9,8 @@

from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import ignore_warnings
from sklearn.utils.testing import assert_warns
from sklearn.exceptions import ConvergenceWarning

from sklearn.linear_model.coordinate_descent import (Lasso, ElasticNet,
LassoCV, ElasticNetCV)
Expand Down Expand Up @@ -291,3 +293,13 @@ def test_same_multiple_output_sparse_dense():
predict_sparse = l_sp.predict(sample_sparse)

assert_array_almost_equal(predict_sparse, predict_dense)


def test_sparse_enet_coordinate_descent():
"""Test that a warning is issued if model does not converge"""
clf = Lasso()
n_samples = 15500
n_features = 500
X = sp.csc_matrix((n_samples, n_features)) * 1e50
y = np.ones([n_samples])
assert_warns(ConvergenceWarning, clf.fit, X, y)
0