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[MRG+1] Raise warning in scikit-learn/sklearn/linear_model/cd_fast.pyx for cases when the main loop exits without reaching the desired tolerance #11754

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Merged
merged 8 commits into from Feb 27, 2019
Merged

[MRG+1] Raise warning in scikit-learn/sklearn/linear_model/cd_fast.pyx for cases when the main loop exits without reaching the desired tolerance #11754

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5d273ad
Add convergence warning to enet_coordinate_descent solvers
brentfagan Aug 1, 2018
711733b
Change UserWarning to ConvergenceWarning in enet_coordinate_descent
brentfagan Aug 1, 2018
9f9b6ce
Add tests to check ConvergenceWarning in enet solvers
brentfagan Aug 5, 2018
ce020f1
Fix style
brentfagan Aug 5, 2018
95c6726
Add duality gap and tolerance to warning
brentfagan Aug 6, 2018
9718eb5
Make data smaller and lower max_iter
brentfagan Aug 6, 2018
4cfe7c5
use parametrize
agramfort Feb 25, 2019
dc7e3ee
lint
agramfort Feb 26, 2019
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29 changes: 29 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

from ..utils._cython_blas cimport (_axpy, _dot, _asum, _ger, _gemv, _nrm2,
_copy, _scal)
Expand Down Expand Up @@ -246,6 +247,14 @@ def enet_coordinate_descent(floating[::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."
" Duality gap: {}, tolerance: {}".format(gap, tol),
ConvergenceWarning)

return w, gap, tol, n_iter + 1


Expand Down Expand Up @@ -456,6 +465,13 @@ def sparse_enet_coordinate_descent(floating [::1] w,
# 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."
" Duality gap: {}, tolerance: {}".format(gap, tol),
ConvergenceWarning)

return w, gap, tol, n_iter + 1


Expand Down Expand Up @@ -604,6 +620,13 @@ def enet_coordinate_descent_gram(floating[::1] w,
# return if we reached desired tolerance
break


with gil:
warnings.warn("Objective did not converge."
" You might want to increase the number of iterations."
" Duality gap: {}, tolerance: {}".format(gap, tol),
ConvergenceWarning)

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


Expand Down Expand Up @@ -794,5 +817,11 @@ def enet_coordinate_descent_multi_task(floating[::1, :] W, floating l1_reg,
if gap < tol:
# return if we reached desired tolerance
break
else:
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after this warning message was printed, the for-loop goes on if max_iter is not reached, right?
And if max_iter is reached before the condition in 767 happens then it won't converge but never warn?

with gil:
warnings.warn("Objective did not converge."
" You might want to increase the number of iterations."
" Duality gap: {}, tolerance: {}".format(gap, tol),
ConvergenceWarning)

return np.asarray(W), gap, tol, n_iter + 1
13 changes: 0 additions & 13 deletions sklearn/linear_model/coordinate_descent.py
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Expand Up @@ -23,7 +23,6 @@
from ..utils.fixes import _joblib_parallel_args
from ..utils.validation import check_is_fitted
from ..utils.validation import column_or_1d
from ..exceptions import ConvergenceWarning

from . import cd_fast

Expand Down Expand Up @@ -481,13 +480,6 @@ def enet_path(X, y, l1_ratio=0.5, eps=1e-3, n_alphas=100, alphas=None,
coefs[..., i] = coef_
dual_gaps[i] = dual_gap_
n_iters.append(n_iter_)
if dual_gap_ > eps_:
warnings.warn('Objective did not converge.' +
' You might want' +
' to increase the number of iterations.' +
' Fitting data with very small alpha' +
' may cause precision problems.',
ConvergenceWarning)

if verbose:
if verbose > 2:
Expand Down Expand Up @@ -1812,11 +1804,6 @@ def fit(self, X, y):

self._set_intercept(X_offset, y_offset, X_scale)

if self.dual_gap_ > self.eps_:
warnings.warn('Objective did not converge, you might want'
' to increase the number of iterations',
ConvergenceWarning)

# return self for chaining fit and predict calls
return self

Expand Down
17 changes: 17 additions & 0 deletions sklearn/linear_model/tests/test_coordinate_descent.py
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Expand Up @@ -828,3 +828,20 @@ def test_warm_start_multitask_lasso():
clf2 = MultiTaskLasso(alpha=0.1, max_iter=10)
ignore_warnings(clf2.fit)(X, Y)
assert_array_almost_equal(clf2.coef_, clf.coef_)


@pytest.mark.parametrize('klass, n_classes, kwargs',
[(Lasso, 1, dict(precompute=True)),
(Lasso, 1, dict(precompute=False)),
(MultiTaskLasso, 2, dict()),
(MultiTaskLasso, 2, dict())])
def test_enet_coordinate_descent(klass, n_classes, kwargs):
"""Test that a warning is issued if model does not converge"""
clf = klass(max_iter=2, **kwargs)
n_samples = 5
n_features = 2
X = np.ones((n_samples, n_features)) * 1e50
y = np.ones((n_samples, n_classes))
if klass == Lasso:
y = y.ravel()
assert_warns(ConvergenceWarning, clf.fit, X, y)
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to test this use tiny data and set max_iter to a very tiny number. it will make testing faster.

besides this is already done in the estimator eg:

https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/linear_model/coordinate_descent.py#L486

why is it not enough for you? bug?

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For the cases identified in #10813, the estimator raises no warning. Essentially, there are numerical issues causing the duality gap and tolerance to be equal to zero - as such the warning won't be raised in the estimator.

12 changes: 12 additions & 0 deletions sklearn/linear_model/tests/test_sparse_coordinate_descent.py
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Expand Up @@ -8,6 +8,8 @@

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 @@ -290,3 +292,13 @@ 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(max_iter=2)
n_samples = 5
n_features = 2
X = sp.csc_matrix((n_samples, n_features)) * 1e50
y = np.ones(n_samples)
assert_warns(ConvergenceWarning, clf.fit, X, y)