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TST: remove ignorewarning and introduce specific filterwarning in SAG #11606

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Merged
merged 2 commits into from
Jul 19, 2018
Merged

TST: remove ignorewarning and introduce specific filterwarning in SAG #11606

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45c95e4
TST: remove ignorewarning and introduce specific filterwarning in SAG
glemaitre Jul 17, 2018
486f288
Merge remote-tracking branch 'origin/master' into fix_master
glemaitre Jul 19, 2018
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21 changes: 9 additions & 12 deletions sklearn/linear_model/tests/test_sag.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,6 +4,7 @@
# License: BSD 3 clause

import math
import pytest
import numpy as np
import scipy.sparse as sp

Expand All @@ -20,7 +21,6 @@
from sklearn.utils.testing import assert_allclose
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_raise_message
from sklearn.utils.testing import ignore_warnings
from sklearn.utils import compute_class_weight
from sklearn.utils import check_random_state
from sklearn.preprocessing import LabelEncoder, LabelBinarizer
Expand Down Expand Up @@ -231,7 +231,6 @@ def get_step_size(X, alpha, fit_intercept, classification=True):
return 1.0 / (np.max(np.sum(X * X, axis=1)) + fit_intercept + alpha)


@ignore_warnings
def test_classifier_matching():
n_samples = 20
X, y = make_blobs(n_samples=n_samples, centers=2, random_state=0,
Expand Down Expand Up @@ -301,7 +300,7 @@ def test_regressor_matching():
assert_allclose(intercept2, clf.intercept_)


@ignore_warnings
@pytest.mark.filterwarnings('ignore:The max_iter was reached')
def test_sag_pobj_matches_logistic_regression():
"""tests if the sag pobj matches log reg"""
n_samples = 100
Expand Down Expand Up @@ -331,7 +330,7 @@ def test_sag_pobj_matches_logistic_regression():
assert_array_almost_equal(pobj3, pobj1, decimal=4)


@ignore_warnings
@pytest.mark.filterwarnings('ignore:The max_iter was reached')
def test_sag_pobj_matches_ridge_regression():
"""tests if the sag pobj matches ridge reg"""
n_samples = 100
Expand Down Expand Up @@ -363,7 +362,7 @@ def test_sag_pobj_matches_ridge_regression():
assert_array_almost_equal(pobj3, pobj2, decimal=4)


@ignore_warnings
@pytest.mark.filterwarnings('ignore:The max_iter was reached')
def test_sag_regressor_computed_correctly():
"""tests if the sag regressor is computed correctly"""
alpha = .1
Expand Down Expand Up @@ -407,7 +406,6 @@ def test_sag_regressor_computed_correctly():
# assert_almost_equal(clf2.intercept_, spintercept2, decimal=1)'''


@ignore_warnings
def test_get_auto_step_size():
X = np.array([[1, 2, 3], [2, 3, 4], [2, 3, 2]], dtype=np.float64)
alpha = 1.2
Expand Down Expand Up @@ -452,7 +450,7 @@ def test_get_auto_step_size():
max_squared_sum_, alpha, "wrong", fit_intercept)


@ignore_warnings
@pytest.mark.filterwarnings('ignore:The max_iter was reached')
def test_sag_regressor():
"""tests if the sag regressor performs well"""
xmin, xmax = -5, 5
Expand Down Expand Up @@ -491,7 +489,7 @@ def test_sag_regressor():
assert_greater(score2, 0.5)


@ignore_warnings
@pytest.mark.filterwarnings('ignore:The max_iter was reached')
def test_sag_classifier_computed_correctly():
"""tests if the binary classifier is computed correctly"""
alpha = .1
Expand Down Expand Up @@ -534,7 +532,7 @@ def test_sag_classifier_computed_correctly():
assert_almost_equal(clf2.intercept_, spintercept2, decimal=1)


@ignore_warnings
@pytest.mark.filterwarnings('ignore:The max_iter was reached')
def test_sag_multiclass_computed_correctly():
"""tests if the multiclass classifier is computed correctly"""
alpha = .1
Expand Down Expand Up @@ -593,7 +591,6 @@ def test_sag_multiclass_computed_correctly():
assert_almost_equal(clf2.intercept_[i], intercept2[i], decimal=1)


@ignore_warnings
def test_classifier_results():
"""tests if classifier results match target"""
alpha = .1
Expand All @@ -618,7 +615,7 @@ def test_classifier_results():
assert_almost_equal(pred2, y, decimal=12)


@ignore_warnings
@pytest.mark.filterwarnings('ignore:The max_iter was reached')
def test_binary_classifier_class_weight():
"""tests binary classifier with classweights for each class"""
alpha = .1
Expand Down Expand Up @@ -668,7 +665,7 @@ def test_binary_classifier_class_weight():
assert_almost_equal(clf2.intercept_, spintercept2, decimal=1)


@ignore_warnings
@pytest.mark.filterwarnings('ignore:The max_iter was reached')
def test_multiclass_classifier_class_weight():
"""tests multiclass with classweights for each class"""
alpha = .1
Expand Down