See <https://jenkins.shiningpanda.com/scikit-learn/job/python-2.7-numpy-1.5.1-scipy-0.10.0/162/changes>
Changes:
[olivier.grisel] FIX: make feature extraction work with the new py3k string API too
------------------------------------------
[...truncated 448 lines...]
compile options: '-I/home/slave/virtualenvs/cpython-2.7.2/lib/python2.7/site-packages/numpy/core/include -I/home/slave/virtualenvs/cpython-2.7.2/lib/python2.7/site-packages/numpy/core/include -I/sp/lib/python/cpython-2.7.2/include/python2.7 -c'
gcc: sklearn/linear_model/sgd_fast_sparse.c
/home/slave/virtualenvs/cpython-2.7.2/lib/python2.7/site-packages/numpy/core/include/numpy/__multiarray_api.h:1187: warning: ‘_import_array’ defined but not used
/home/slave/virtualenvs/cpython-2.7.2/lib/python2.7/site-packages/numpy/core/include/numpy/__ufunc_api.h:196: warning: ‘_import_umath’ defined but not used
gcc -pthread -shared -L/opt/local/lib -L/usr/local/lib -L/usr/lib -L/lib build/temp.linux-x86_64-2.7/sklearn/linear_model/sgd_fast_sparse.o -Lbuild/temp.linux-x86_64-2.7 -o sklearn/linear_model/sgd_fast_sparse.so
building 'sklearn.linear_model.sparse.cd_fast_sparse' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -I/tmp/13173940678/Python-2.7.2/Modules/expat -I/opt/local/include -I/usr/local/include -I/usr/include -I/include -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC
creating build/temp.linux-x86_64-2.7/sklearn/linear_model/sparse
creating build/temp.linux-x86_64-2.7/sklearn/linear_model/sparse/src
compile options: '-I/home/slave/virtualenvs/cpython-2.7.2/lib/python2.7/site-packages/numpy/core/include -I/home/slave/virtualenvs/cpython-2.7.2/lib/python2.7/site-packages/numpy/core/include -I/sp/lib/python/cpython-2.7.2/include/python2.7 -c'
gcc: sklearn/linear_model/sparse/src/cd_fast_sparse.c
/home/slave/virtualenvs/cpython-2.7.2/lib/python2.7/site-packages/numpy/core/include/numpy/__multiarray_api.h:1187: warning: ‘_import_array’ defined but not used
/home/slave/virtualenvs/cpython-2.7.2/lib/python2.7/site-packages/numpy/core/include/numpy/__ufunc_api.h:196: warning: ‘_import_umath’ defined but not used
gcc -pthread -shared -L/opt/local/lib -L/usr/local/lib -L/usr/lib -L/lib build/temp.linux-x86_64-2.7/sklearn/linear_model/sparse/src/cd_fast_sparse.o -Lbuild/temp.linux-x86_64-2.7 -o sklearn/linear_model/sparse/cd_fast_sparse.so
running scons
nosetests -s --with-doctest --doctest-tests --doctest-extension=rst \
--doctest-fixtures=_fixture doc/ doc/modules/
<https://jenkins.shiningpanda.com/scikit-learn/job/python-2.7-numpy-1.5.1-scipy-0.10.0/ws/sklearn/svm/classes.py>:176: FutureWarning: SVM: scale_C will be True by default in scikit-learn 0.11
cache_size, scale_C)
............<https://jenkins.shiningpanda.com/scikit-learn/job/python-2.7-numpy-1.5.1-scipy-0.10.0/ws/sklearn/svm/classes.py>:359: FutureWarning: SVM: scale_C will be True by default in scikit-learn 0.11
cache_size, scale_C)
..
----------------------------------------------------------------------
Ran 14 tests in 21.963s
OK
nosetests -s --with-coverage --cover-html --cover-html-dir=coverage \
--cover-package=sklearn sklearn
<https://jenkins.shiningpanda.com/scikit-learn/job/python-2.7-numpy-1.5.1-scipy-0.10.0/ws/sklearn/cross_val.py>:2: UserWarning: sklearn.cross_val namespace is deprecated in version 0.9 and will be removed in version 0.11. Please use sklearn.cross_validation instead.
warnings.warn('sklearn.cross_val namespace is deprecated in version 0.9'
<https://jenkins.shiningpanda.com/scikit-learn/job/python-2.7-numpy-1.5.1-scipy-0.10.0/ws/sklearn/feature_extraction/tests/test_text.py>:283: SyntaxWarning: assertion is always true, perhaps remove parentheses?
assert(vectorizer.inverse_transform(transformed_data),
<https://jenkins.shiningpanda.com/scikit-learn/job/python-2.7-numpy-1.5.1-scipy-0.10.0/ws/sklearn/hmm.py>:24: UserWarning: sklearn.hmm is orphaned, undocumented and has known numerical stability issues. If nobody volunteers to write documentation and make it more stable, this module will be removed in version 0.11.
warnings.warn('sklearn.hmm is orphaned, undocumented and has known numerical'
...........<https://jenkins.shiningpanda.com/scikit-learn/job/python-2.7-numpy-1.5.1-scipy-0.10.0/ws/sklearn/neighbors/base.py>:23: UserWarning: kneighbors: neighbor k+1 and neighbor k have the same distance: results will be dependent on data order.
warnings.warn(msg)
................................<https://jenkins.shiningpanda.com/scikit-learn/job/python-2.7-numpy-1.5.1-scipy-0.10.0/ws/sklearn/cluster/spectral.py>:77: UserWarning: pyamg not available, using scipy.sparse
warnings.warn('pyamg not available, using scipy.sparse')
.S.....................SS.S...............................<https://jenkins.shiningpanda.com/scikit-learn/job/python-2.7-numpy-1.5.1-scipy-0.10.0/ws/sklearn/decomposition/dict_learning.py>:262: UserWarning: Please note: the interface of sparse_encode has changed: It now follows the dictionary learning API and it also handles parallelization. Please read the docstring for more information.
warnings.warn("Please note: the interface of sparse_encode has changed: "
.........S.........................<https://jenkins.shiningpanda.com/scikit-learn/job/python-2.7-numpy-1.5.1-scipy-0.10.0/ws/sklearn/decomposition/nmf.py>:237: UserWarning: Iteration limit reached in nls subproblem.
warnings.warn("Iteration limit reached in nls subproblem.")
...................<https://jenkins.shiningpanda.com/scikit-learn/job/python-2.7-numpy-1.5.1-scipy-0.10.0/ws/sklearn/decomposition/sparse_pca.py>:147: RuntimeWarning: invalid value encountered in divide
U /= np.sqrt((U ** 2).sum(axis=0))
.....S.....<https://jenkins.shiningpanda.com/scikit-learn/job/python-2.7-numpy-1.5.1-scipy-0.10.0/ws/sklearn/ensemble/forest.py>:198: RuntimeWarning: divide by zero encountered in log
return np.log(self.predict_proba(X))
.....................EEE.........<https://jenkins.shiningpanda.com/scikit-learn/job/python-2.7-numpy-1.5.1-scipy-0.10.0/ws/sklearn/svm/classes.py>:359: FutureWarning: SVM: scale_C will be True by default in scikit-learn 0.11
cache_size, scale_C)
..................<https://jenkins.shiningpanda.com/scikit-learn/job/python-2.7-numpy-1.5.1-scipy-0.10.0/ws/sklearn/svm/classes.py>:176: FutureWarning: SVM: scale_C will be True by default in scikit-learn 0.11
cache_size, scale_C)
...................<https://jenkins.shiningpanda.com/scikit-learn/job/python-2.7-numpy-1.5.1-scipy-0.10.0/ws/sklearn/linear_model/least_angle.py>:224: RuntimeWarning: invalid value encountered in divide
z = -coefs[n_iter, active] / least_squares
..................S.....<https://jenkins.shiningpanda.com/scikit-learn/job/python-2.7-numpy-1.5.1-scipy-0.10.0/ws/sklearn/linear_model/coordinate_descent.py>:172: UserWarning: Objective did not converge, you might want to increase the number of iterations
warnings.warn('Objective did not converge, you might want'
...............................<https://jenkins.shiningpanda.com/scikit-learn/job/python-2.7-numpy-1.5.1-scipy-0.10.0/ws/sklearn/linear_model/omp.py>:170: UserWarning: Orthogonal matching pursuit ended prematurely due to linear
dependence in the dictionary. The requested precision might not have been met.
warn(premature)
............................................................................................................................................................<https://jenkins.shiningpanda.com/scikit-learn/job/python-2.7-numpy-1.5.1-scipy-0.10.0/ws/sklearn/mixture/gmm.py>:665: RuntimeWarning: underflow encountered in multiply
avg_cv = np.dot(post * obs.T, obs) / (post.sum() +
.......................................................................<https://jenkins.shiningpanda.com/scikit-learn/job/python-2.7-numpy-1.5.1-scipy-0.10.0/ws/sklearn/svm/classes.py>:264: FutureWarning: SVM: scale_C will be True by default in scikit-learn 0.11
cache_size, scale_C=None)
.<https://jenkins.shiningpanda.com/scikit-learn/job/python-2.7-numpy-1.5.1-scipy-0.10.0/ws/sklearn/svm/classes.py>:480: FutureWarning: SVM: scale_C will be True by default in scikit-learn 0.11
cache_size, scale_C=scale_C)
...<https://jenkins.shiningpanda.com/scikit-learn/job/python-2.7-numpy-1.5.1-scipy-0.10.0/ws/sklearn/svm/sparse/base.py>:23: FutureWarning: SVM: scale_C will be True by default in scikit-learn 0.11
scale_C)
.....................................<https://jenkins.shiningpanda.com/scikit-learn/job/python-2.7-numpy-1.5.1-scipy-0.10.0/ws/sklearn/svm/classes.py>:570: FutureWarning: SVM: scale_C will be True by default in scikit-learn 0.11
False, cache_size, scale_C=None)
.......................<https://jenkins.shiningpanda.com/scikit-learn/job/python-2.7-numpy-1.5.1-scipy-0.10.0/ws/sklearn/tree/tree.py>:614: RuntimeWarning: divide by zero encountered in log
return np.log(self.predict_proba(X))
............................................................................................................................................................
======================================================================
ERROR: sklearn.feature_extraction.tests.test_text.test_word_analyzer_unigrams
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/slave/virtualenvs/cpython-2.7.2/lib/python2.7/site-packages/nose/case.py", line 197, in runTest
self.test(*self.arg)
File "<https://jenkins.shiningpanda.com/scikit-learn/job/python-2.7-numpy-1.5.1-scipy-0.10.0/ws/sklearn/feature_extraction/tests/test_text.py",> line 98, in test_word_analyzer_unigrams
assert_equal(wa.analyze(text), expected)
File "<https://jenkins.shiningpanda.com/scikit-learn/job/python-2.7-numpy-1.5.1-scipy-0.10.0/ws/sklearn/feature_extraction/text.py",> line 153, in analyze
text_document = text_document.decode(self.charset, 'ignore')
File "/home/slave/virtualenvs/cpython-2.7.2/lib/python2.7/encodings/utf_8.py", line 16, in decode
return codecs.utf_8_decode(input, errors, True)
UnicodeEncodeError: 'ascii' codec can't encode character u'\xe9' in position 9: ordinal not in range(128)
======================================================================
ERROR: sklearn.feature_extraction.tests.test_text.test_word_analyzer_unigrams_and_bigrams
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/slave/virtualenvs/cpython-2.7.2/lib/python2.7/site-packages/nose/case.py", line 197, in runTest
self.test(*self.arg)
File "<https://jenkins.shiningpanda.com/scikit-learn/job/python-2.7-numpy-1.5.1-scipy-0.10.0/ws/sklearn/feature_extraction/tests/test_text.py",> line 119, in test_word_analyzer_unigrams_and_bigrams
assert_equal(wa.analyze(text), expected)
File "<https://jenkins.shiningpanda.com/scikit-learn/job/python-2.7-numpy-1.5.1-scipy-0.10.0/ws/sklearn/feature_extraction/text.py",> line 153, in analyze
text_document = text_document.decode(self.charset, 'ignore')
File "/home/slave/virtualenvs/cpython-2.7.2/lib/python2.7/encodings/utf_8.py", line 16, in decode
return codecs.utf_8_decode(input, errors, True)
UnicodeEncodeError: 'ascii' codec can't encode character u'\xe9' in position 9: ordinal not in range(128)
======================================================================
ERROR: sklearn.feature_extraction.tests.test_text.test_char_ngram_analyzer
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/slave/virtualenvs/cpython-2.7.2/lib/python2.7/site-packages/nose/case.py", line 197, in runTest
self.test(*self.arg)
File "<https://jenkins.shiningpanda.com/scikit-learn/job/python-2.7-numpy-1.5.1-scipy-0.10.0/ws/sklearn/feature_extraction/tests/test_text.py",> line 127, in test_char_ngram_analyzer
assert_equal(cnga.analyze(text)[:5], expected)
File "<https://jenkins.shiningpanda.com/scikit-learn/job/python-2.7-numpy-1.5.1-scipy-0.10.0/ws/sklearn/feature_extraction/text.py",> line 205, in analyze
text_document = text_document.decode(self.charset, 'ignore')
File "/home/slave/virtualenvs/cpython-2.7.2/lib/python2.7/encodings/utf_8.py", line 16, in decode
return codecs.utf_8_decode(input, errors, True)
UnicodeEncodeError: 'ascii' codec can't encode character u'\xe9' in position 9: ordinal not in range(128)
Name Stmts Miss Cover Missing
-------------------------------------------------------------------------------
sklearn 13 3 77% 29, 35-36
sklearn.ball_tree 3 0 100%
sklearn.base 119 5 96% 40, 55, 61, 109, 223
sklearn.check_build 16 11 31% 17-32
sklearn.cluster 6 0 100%
sklearn.cluster._feature_agglomeration 21 2 90% 60, 66
sklearn.cluster.affinity_propagation_ 75 8 89% 60, 63, 123, 126-127, 148-150
sklearn.cluster.dbscan_ 48 0 100%
sklearn.cluster.hierarchical 119 4 97% 71, 78, 90, 308
sklearn.cluster.k_means_ 335 24 93% 92, 235-239, 251, 270, 279, 582, 589, 759-764, 769-772, 786, 890-892, 919-924, 937, 948, 974, 1007, 1026, 1029
sklearn.cluster.mean_shift_ 77 6 92% 99, 102, 119, 153-155
sklearn.cluster.spectral 63 13 79% 68, 104, 108-122
sklearn.covariance 5 0 100%
sklearn.covariance.empirical_covariance_ 60 5 92% 59-60, 145, 218, 252
sklearn.covariance.graph_lasso_ 199 18 91% 132, 154, 170-175, 200, 311, 337, 343, 346, 438-439, 483, 502-504, 506-508
sklearn.covariance.outlier_detection 38 6 84% 79-82, 103, 110
sklearn.covariance.robust_covariance 184 12 93% 123, 128-130, 136, 217, 305-306, 314, 515-516, 605, 612
sklearn.covariance.shrunk_covariance_ 80 8 90% 176-180, 320-324
sklearn.cross_val 3 0 100%
sklearn.cross_validation 266 2 99% 613, 755
sklearn.datasets 41 1 98% 53
sklearn.datasets.base 134 49 63% 138-175, 288-290, 436-453, 486-494
sklearn.datasets.lfw 153 132 14% 52-54, 65-104, 112-162, 177-207, 259-275, 292-329, 337, 397-424, 433
sklearn.datasets.mlcomp 46 40 13% 10-12, 55-102
sklearn.datasets.mldata 62 4 94% 108, 126-128
sklearn.datasets.olivetti_faces 35 23 34% 81-108
sklearn.datasets.samples_generator 244 4 98% 973-976
sklearn.datasets.species_distributions 69 53 23% 65-78, 94-104, 121-131, 206-253
sklearn.datasets.svmlight_format 26 3 88% 131, 165-166
sklearn.datasets.twenty_newsgroups 119 84 29% 73-96, 136-142, 146, 151-193, 223-268, 277
sklearn.decomposition 6 0 100%
sklearn.decomposition.dict_learning 300 40 87% 101, 109, 112-113, 177, 296, 346-347, 349, 447, 456, 484, 489-490, 492, 515, 517, 520, 609, 619, 636, 643, 656-657, 659-660, 686, 690, 692, 696-697, 833, 1140-1155
sklearn.decomposition.fastica_ 131 14 89% 206, 234-243, 251-252, 276-278, 377
sklearn.decomposition.kernel_pca 86 5 94% 115, 130, 137, 167, 250
sklearn.decomposition.nmf 188 6 97% 107, 353, 381-382, 453, 496
sklearn.decomposition.pca 139 4 97% 45, 56-57, 313
sklearn.decomposition.sparse_pca 51 0 100%
sklearn.ensemble 5 0 100%
sklearn.ensemble.base 22 1 95% 65
sklearn.ensemble.forest 86 4 95% 105, 172-175
sklearn.externals 1 0 100%
sklearn.externals.joblib 10 0 100%
sklearn.externals.joblib.disk 51 37 27% 20-31, 37-46, 57, 81-107
sklearn.externals.joblib.format_stack 238 214 10% 35-36, 47-70, 74, 90-96, 106-118, 122-154, 158-183, 188-329, 344-397, 403-438
sklearn.externals.joblib.func_inspect 105 72 31% 40-45, 50, 74, 78-82, 87-97, 100, 104, 109-110, 113-115, 118-120, 150, 155, 158-232
sklearn.externals.joblib.hashing 49 10 80% 18-19, 46-49, 82-86, 95, 122
sklearn.externals.joblib.logger 72 38 47% 28, 41, 63, 67, 77, 92-120, 132-151
sklearn.externals.joblib.memory 231 65 72% 20-21, 29-34, 67, 139, 142, 153, 173-176, 178-185, 192, 257-292, 297-305, 342-343, 351, 353, 372, 392-393, 401-402, 417, 466, 504, 512, 523-525, 536-538, 545, 556
sklearn.externals.joblib.my_exceptions 42 10 76% 16, 20, 23, 38-39, 43, 53, 57, 70-71
sklearn.externals.joblib.numpy_pickle 132 44 67% 19-20, 60-61, 79-82, 98-101, 106-107, 125, 134, 139-142, 154-158, 163-165, 200, 220-239, 280-281, 285-290, 303, 305
sklearn.externals.joblib.parallel 166 64 61% 16-17, 26-27, 39-41, 63-74, 89, 118, 120, 125-143, 311-312, 319-333, 347-373, 377, 380, 397-403
sklearn.feature_extraction 2 0 100%
sklearn.feature_extraction.image 129 0 100%
sklearn.feature_extraction.text 195 10 95% 103, 112-117, 150, 202, 488, 512
sklearn.feature_selection 11 0 100%
sklearn.feature_selection.rfe 80 1 99% 128
sklearn.feature_selection.univariate_selection 139 3 98% 150, 261, 297
sklearn.gaussian_process 4 0 100%
sklearn.gaussian_process.correlation_models 78 28 64% 48, 53, 91, 96, 129-148, 178-185, 221, 227, 271, 277
sklearn.gaussian_process.gaussian_process 325 109 66% 23-24, 274-277, 283, 291, 293, 297, 302, 317, 322-334, 346-354, 399, 432-440, 463-493, 535, 549-553, 564-565, 583-590, 647-649, 655, 704-706, 712-761, 774, 790, 796, 807, 810, 815-818, 829, 833
sklearn.gaussian_process.regression_models 19 0 100%
sklearn.grid_search 141 17 88% 70-73, 98-99, 104-105, 116-121, 123, 125-128, 357, 386-387
sklearn.hmm 403 11 97% 234-235, 253-254, 493, 496, 674, 686, 737-738, 979
sklearn.kernel_approximation 68 4 94% 164, 211, 217, 236
sklearn.lda 93 14 85% 63-67, 95, 97, 105, 112, 119, 128, 130-131, 148
sklearn.linear_model 9 0 100%
sklearn.linear_model.base 226 13 94% 172, 175, 192, 199-200, 205, 262, 367, 391, 394, 451, 525, 552
sklearn.linear_model.bayes 124 8 94% 175-181, 207, 421
sklearn.linear_model.coordinate_descent 156 13 92% 134, 136, 435, 437, 441, 466-471, 537-539, 545
sklearn.linear_model.least_angle 271 11 96% 126, 194, 295, 386, 390, 598-601, 939, 989
sklearn.linear_model.logistic 14 0 100%
sklearn.linear_model.omp 194 14 93% 85-86, 177-178, 275, 279, 288, 374, 377, 382, 517, 526, 534, 555
sklearn.linear_model.ridge 169 9 95% 31, 43, 46, 95-98, 378-379
sklearn.linear_model.sparse 3 0 100%
sklearn.linear_model.sparse.base 11 7 36% 15-26
sklearn.linear_model.sparse.coordinate_descent 45 3 93% 47, 75, 100
sklearn.linear_model.sparse.logistic 16 1 94% 119
sklearn.linear_model.sparse.stochastic_gradient 44 0 100%
sklearn.linear_model.stochastic_gradient 31 0 100%
sklearn.manifold 2 0 100%
sklearn.manifold.isomap 44 0 100%
sklearn.manifold.locally_linear 214 114 47% 139, 155-157, 168, 257, 260, 269, 272, 275, 292-476
sklearn.metrics 11 0 100%
sklearn.metrics.cluster 11 0 100%
sklearn.metrics.cluster.supervised 137 1 99% 79
sklearn.metrics.cluster.unsupervised 26 1 96% 81
sklearn.metrics.metrics 200 21 90% 569-590, 618
sklearn.metrics.pairwise 139 14 90% 82, 84, 158, 179, 232, 376, 473, 512, 565-567, 579, 584, 587
sklearn.mixture 4 0 100%
sklearn.mixture.dpgmm 376 18 95% 231, 265, 281-282, 389-392, 491-492, 535, 714, 721-722, 766-769
sklearn.mixture.gmm 265 22 92% 386-387, 462, 471, 483, 575, 599, 601, 604, 607, 613, 616, 621, 635, 674, 678-682, 699, 701, 703
sklearn.multiclass 109 3 97% 50, 156, 390
sklearn.naive_bayes 105 7 93% 224-228, 234-236, 405
sklearn.neighbors 6 0 100%
sklearn.neighbors.base 209 8 96% 53, 63, 110, 114, 138, 237, 412, 425
sklearn.neighbors.classification 49 4 92% 316, 344-346
sklearn.neighbors.graph 10 0 100%
sklearn.neighbors.regression 42 3 93% 320, 349-352
sklearn.neighbors.unsupervised 7 0 100%
sklearn.pipeline 70 10 86% 165-168, 171-176
sklearn.pls 193 55 72% 59-60, 92-93, 214, 216, 218, 225, 229, 231, 234, 260, 267, 305, 345-351, 381-383, 769-770, 774-806, 810-816
sklearn.preprocessing 240 15 94% 41, 100, 104-105, 200-201, 233-234, 264-265, 313, 322, 339, 620, 643
sklearn.qda 75 8 89% 84, 86, 101, 108, 112, 125, 129, 133
sklearn.svm 3 0 100%
sklearn.svm.base 208 13 94% 13-14, 53, 80, 84, 88, 284, 289, 292, 344, 387, 434, 493
sklearn.svm.bounds 35 0 100%
sklearn.svm.classes 26 0 100%
sklearn.svm.sparse 2 0 100%
sklearn.svm.sparse.base 78 5 94% 74, 174, 178, 223, 300
sklearn.svm.sparse.classes 22 2 91% 161, 166
sklearn.tree 5 0 100%
sklearn.tree.tree 214 6 97% 124, 126, 213, 287, 346, 349
sklearn.utils 82 0 100%
sklearn.utils._csgraph 21 4 81% 65-66, 69, 74
sklearn.utils.arpack 622 338 46% 280-281, 297-299, 306, 311, 314, 318-319, 358-368, 428, 430, 432, 438-449, 452, 454, 462-496, 499, 502, 508, 515, 535, 541-542, 544-546, 552, 554, 560-563, 577, 585, 628, 630, 632, 637-678, 681, 684, 690, 705, 717, 727, 733-734, 736, 738, 744-747, 770, 793-802, 809-833, 840-841, 847, 851, 858-874, 879, 885-886, 905, 918-919, 922, 932-944, 947-952, 962-983, 986, 989, 992-997, 1002, 1008, 1015-1045, 1183, 1185-1190, 1195, 1201, 1204, 1213-1252, 1423-1440, 1443, 1445-1450, 1455, 1462, 1470-1480, 1484, 1494-1495, 1499-1529, 1563-1599
sklearn.utils.bench 3 2 33% 15-17
sklearn.utils.extmath 94 13 86% 30-36, 56, 64-68, 292
sklearn.utils.fixes 87 47 46% 19-36, 45-79, 83, 93, 114, 119, 134, 147-148
sklearn.utils.graph 74 4 95% 51, 68, 76, 86
sklearn.utils.sparsetools 1 0 100%
sklearn.utils.sparsetools.csgraph 61 34 44% 17-19, 29, 33-34, 38-50, 54, 58-63, 67-71, 77-80
sklearn.utils.validation 81 4 95% 121, 142-143, 166
-------------------------------------------------------------------------------
TOTAL 12033 2200 82%
----------------------------------------------------------------------
Ran 737 tests in 85.283s
FAILED (SKIP=7, errors=3)
make: *** [test-coverage] Error 1
Build step 'Custom Python Builder' marked build as failure
Archiving artifacts
Skipping Cobertura coverage report as build was not UNSTABLE or better ...
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