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With numpy master, warnings during test (using NumpyTester) are treated as errors. This should encouraging us to get rid of these warnings during the tests (for instance by capturing them):
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ERROR: sklearn.cluster.tests.test_k_means.test_kmeans_dtype
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Traceback (most recent call last):
File "/usr/lib/pymodules/python2.7/nose/case.py", line 187, in runTest
self.test(*self.arg)
File "/home/varoquau/dev/scikit-learn/sklearn/cluster/tests/test_k_means.py", line 51, in test_kmeans_dtype
assert_array_equal(km.labels_, km.predict(X))
File "/home/varoquau/dev/scikit-learn/sklearn/cluster/k_means_.py", line 785, in predict
X = self._check_test_data(X)
File "/home/varoquau/dev/scikit-learn/sklearn/cluster/k_means_.py", line 716, in _check_test_data
RuntimeWarning, stacklevel=2)
RuntimeWarning: Got data type uint8, converted to float to avoid overflows
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ERROR: sklearn.cluster.tests.test_k_means.test_minibatch_init_with_large_k
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Traceback (most recent call last):
File "/usr/lib/pymodules/python2.7/nose/case.py", line 187, in runTest
self.test(*self.arg)
File "/home/varoquau/dev/scikit-learn/sklearn/cluster/tests/test_k_means.py", line 243, in test_minibatch_init_with_large_k
mb_k_means.fit(X)
File "/home/varoquau/dev/scikit-learn/sklearn/cluster/k_means_.py", line 1110, in fit
init_size=init_size)
File "/home/varoquau/dev/scikit-learn/sklearn/cluster/k_means_.py", line 556, in _init_centroids
RuntimeWarning, stacklevel=2)
RuntimeWarning: init_size=10 should be larger than k=20. Setting it to 3*k
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ERROR: sklearn.feature_extraction.tests.test_text.test_tfidf_no_smoothing
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Traceback (most recent call last):
File "/usr/lib/pymodules/python2.7/nose/case.py", line 187, in runTest
self.test(*self.arg)
File "/home/varoquau/dev/scikit-learn/sklearn/feature_extraction/tests/test_text.py", line 264, in test_tfidf_no_smoothing
tfidf = tr.fit_transform(X).toarray()
File "/home/varoquau/dev/scikit-learn/sklearn/base.py", line 360, in fit_transform
return self.fit(X, **fit_params).transform(X)
File "/home/varoquau/dev/scikit-learn/sklearn/feature_extraction/text.py", line 580, in fit
self.idf_ = np.log(float(n_samples) / df) + 1.0
RuntimeWarning: divide by zero encountered in divide
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ERROR: Compute the Adjusted Mutual Information and test against known values
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Traceback (most recent call last):
File "/usr/lib/pymodules/python2.7/nose/case.py", line 187, in runTest
self.test(*self.arg)
File "/home/varoquau/dev/scikit-learn/sklearn/metrics/cluster/tests/test_supervised.py", line 158, in test_adjusted_mutual_info_score
ami = adjusted_mutual_info_score(a110, b110)
File "/home/varoquau/dev/scikit-learn/sklearn/metrics/cluster/supervised.py", line 628, in adjusted_mutual_info_score
emi = expected_mutual_information(contingency, n_samples)
File "/home/varoquau/dev/scikit-learn/sklearn/metrics/cluster/supervised.py", line 678, in expected_mutual_information
emi += (term1[nij] * term2 * term3)
RuntimeWarning: underflow encountered in double_scalars
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ERROR: sklearn.svm.tests.test_svm.test_linearsvc_deepcopy
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Traceback (most recent call last):
File "/usr/lib/pymodules/python2.7/nose/case.py", line 187, in runTest
self.test(*self.arg)
File "/home/varoquau/dev/scikit-learn/sklearn/svm/tests/test_svm.py", line 608, in test_linearsvc_deepcopy
copy.deepcopy(clf).predict(rng.rand(2))
File "/home/varoquau/dev/scikit-learn/sklearn/svm/base.py", line 703, in predict
X = self._validate_for_predict(X)
File "/home/varoquau/dev/scikit-learn/sklearn/svm/base.py", line 761, in _validate_for_predict
stacklevel=3)
RuntimeWarning: Coefficients are the fortran-contiguous. Copying them.
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ERROR: sklearn.tests.test_cross_validation.test_shuffle_split_warnings
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Traceback (most recent call last):
File "/usr/lib/pymodules/python2.7/nose/case.py", line 187, in runTest
self.test(*self.arg)
File "/home/varoquau/dev/scikit-learn/sklearn/tests/test_cross_validation.py", line 173, in test_shuffle_split_warnings
cval.ShuffleSplit(10, 3, test_fraction=0.1)
File "/home/varoquau/dev/scikit-learn/sklearn/cross_validation.py", line 767, in __init__
DeprecationWarning, stacklevel=2)
DeprecationWarning: test_fraction is deprecated in 0.11 and scheduled for removal in 0.12, use test_size instead
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ERROR: sklearn.tests.test_multiclass.test_ecoc_fit_predict
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Traceback (most recent call last):
File "/usr/lib/pymodules/python2.7/nose/case.py", line 187, in runTest
self.test(*self.arg)
File "/home/varoquau/dev/scikit-learn/sklearn/tests/test_multiclass.py", line 196, in test_ecoc_fit_predict
ecoc.fit(iris.data, iris.target).predict(iris.data)
File "/home/varoquau/dev/scikit-learn/sklearn/multiclass.py", line 431, in fit
fit_ecoc(self.estimator, X, y, self.code_size, self.random_state)
File "/home/varoquau/dev/scikit-learn/sklearn/multiclass.py", line 337, in fit_ecoc
for i in range(Y.shape[1])]
File "/home/varoquau/dev/scikit-learn/sklearn/multiclass.py", line 33, in _fit_binary
estimator.fit(X, y)
File "/home/varoquau/dev/scikit-learn/sklearn/naive_bayes.py", line 269, in fit
self.class_log_prior_ = np.log(y_freq) - np.log(y_freq.sum())
RuntimeWarning: divide by zero encountered in log
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The text was updated successfully, but these errors were encountered:
With numpy master, warnings during test (using NumpyTester) are treated as errors. This should encouraging us to get rid of these warnings during the tests (for instance by capturing them):
====================================================================== ERROR: sklearn.cluster.tests.test_k_means.test_kmeans_dtype ---------------------------------------------------------------------- Traceback (most recent call last): File "/usr/lib/pymodules/python2.7/nose/case.py", line 187, in runTest self.test(*self.arg) File "/home/varoquau/dev/scikit-learn/sklearn/cluster/tests/test_k_means.py", line 51, in test_kmeans_dtype assert_array_equal(km.labels_, km.predict(X)) File "/home/varoquau/dev/scikit-learn/sklearn/cluster/k_means_.py", line 785, in predict X = self._check_test_data(X) File "/home/varoquau/dev/scikit-learn/sklearn/cluster/k_means_.py", line 716, in _check_test_data RuntimeWarning, stacklevel=2) RuntimeWarning: Got data type uint8, converted to float to avoid overflows ====================================================================== ERROR: sklearn.cluster.tests.test_k_means.test_minibatch_init_with_large_k ---------------------------------------------------------------------- Traceback (most recent call last): File "/usr/lib/pymodules/python2.7/nose/case.py", line 187, in runTest self.test(*self.arg) File "/home/varoquau/dev/scikit-learn/sklearn/cluster/tests/test_k_means.py", line 243, in test_minibatch_init_with_large_k mb_k_means.fit(X) File "/home/varoquau/dev/scikit-learn/sklearn/cluster/k_means_.py", line 1110, in fit init_size=init_size) File "/home/varoquau/dev/scikit-learn/sklearn/cluster/k_means_.py", line 556, in _init_centroids RuntimeWarning, stacklevel=2) RuntimeWarning: init_size=10 should be larger than k=20. Setting it to 3*k ====================================================================== ERROR: sklearn.feature_extraction.tests.test_text.test_tfidf_no_smoothing ---------------------------------------------------------------------- Traceback (most recent call last): File "/usr/lib/pymodules/python2.7/nose/case.py", line 187, in runTest self.test(*self.arg) File "/home/varoquau/dev/scikit-learn/sklearn/feature_extraction/tests/test_text.py", line 264, in test_tfidf_no_smoothing tfidf = tr.fit_transform(X).toarray() File "/home/varoquau/dev/scikit-learn/sklearn/base.py", line 360, in fit_transform return self.fit(X, **fit_params).transform(X) File "/home/varoquau/dev/scikit-learn/sklearn/feature_extraction/text.py", line 580, in fit self.idf_ = np.log(float(n_samples) / df) + 1.0 RuntimeWarning: divide by zero encountered in divide ====================================================================== ERROR: Compute the Adjusted Mutual Information and test against known values ---------------------------------------------------------------------- Traceback (most recent call last): File "/usr/lib/pymodules/python2.7/nose/case.py", line 187, in runTest self.test(*self.arg) File "/home/varoquau/dev/scikit-learn/sklearn/metrics/cluster/tests/test_supervised.py", line 158, in test_adjusted_mutual_info_score ami = adjusted_mutual_info_score(a110, b110) File "/home/varoquau/dev/scikit-learn/sklearn/metrics/cluster/supervised.py", line 628, in adjusted_mutual_info_score emi = expected_mutual_information(contingency, n_samples) File "/home/varoquau/dev/scikit-learn/sklearn/metrics/cluster/supervised.py", line 678, in expected_mutual_information emi += (term1[nij] * term2 * term3) RuntimeWarning: underflow encountered in double_scalars ====================================================================== ERROR: sklearn.svm.tests.test_svm.test_linearsvc_deepcopy ---------------------------------------------------------------------- Traceback (most recent call last): File "/usr/lib/pymodules/python2.7/nose/case.py", line 187, in runTest self.test(*self.arg) File "/home/varoquau/dev/scikit-learn/sklearn/svm/tests/test_svm.py", line 608, in test_linearsvc_deepcopy copy.deepcopy(clf).predict(rng.rand(2)) File "/home/varoquau/dev/scikit-learn/sklearn/svm/base.py", line 703, in predict X = self._validate_for_predict(X) File "/home/varoquau/dev/scikit-learn/sklearn/svm/base.py", line 761, in _validate_for_predict stacklevel=3) RuntimeWarning: Coefficients are the fortran-contiguous. Copying them. ====================================================================== ERROR: sklearn.tests.test_cross_validation.test_shuffle_split_warnings ---------------------------------------------------------------------- Traceback (most recent call last): File "/usr/lib/pymodules/python2.7/nose/case.py", line 187, in runTest self.test(*self.arg) File "/home/varoquau/dev/scikit-learn/sklearn/tests/test_cross_validation.py", line 173, in test_shuffle_split_warnings cval.ShuffleSplit(10, 3, test_fraction=0.1) File "/home/varoquau/dev/scikit-learn/sklearn/cross_validation.py", line 767, in __init__ DeprecationWarning, stacklevel=2) DeprecationWarning: test_fraction is deprecated in 0.11 and scheduled for removal in 0.12, use test_size instead ====================================================================== ERROR: sklearn.tests.test_multiclass.test_ecoc_fit_predict ---------------------------------------------------------------------- Traceback (most recent call last): File "/usr/lib/pymodules/python2.7/nose/case.py", line 187, in runTest self.test(*self.arg) File "/home/varoquau/dev/scikit-learn/sklearn/tests/test_multiclass.py", line 196, in test_ecoc_fit_predict ecoc.fit(iris.data, iris.target).predict(iris.data) File "/home/varoquau/dev/scikit-learn/sklearn/multiclass.py", line 431, in fit fit_ecoc(self.estimator, X, y, self.code_size, self.random_state) File "/home/varoquau/dev/scikit-learn/sklearn/multiclass.py", line 337, in fit_ecoc for i in range(Y.shape[1])] File "/home/varoquau/dev/scikit-learn/sklearn/multiclass.py", line 33, in _fit_binary estimator.fit(X, y) File "/home/varoquau/dev/scikit-learn/sklearn/naive_bayes.py", line 269, in fit self.class_log_prior_ = np.log(y_freq) - np.log(y_freq.sum()) RuntimeWarning: divide by zero encountered in log ----------------------------------------------------------------------The text was updated successfully, but these errors were encountered: