The bagging tests are failing under old version of numpy / python. Can someone have a look at it?
---------- Forwarded message ---------- From: <ad...@shiningpanda.com> Date: 2013/9/11 Subject: Build failed in Jenkins: python-2.6-numpy-1.3.0-scipy-0.7.2 #2167 To: olivier.gri...@ensta.org, scikit-learn-comm...@lists.sourceforge.net, g.lou...@gmail.com, larsm...@gmail.com See <https://jenkins.shiningpanda-ci.com/scikit-learn/job/python-2.6-numpy-1.3.0-scipy-0.7.2/2167/changes> Changes: [larsmans] DOC copyedit DBSCAN implementation notes [larsmans] DOC reference for k-means++ in clustering narrative ------------------------------------------ [...truncated 3082 lines...] sklearn.tests.test_pipeline.test_pipeline_fit_transform ... ok sklearn.tests.test_pipeline.test_feature_union_weights ... ok sklearn.tests.test_pipeline.test_feature_union_feature_names ... ok QDA classification. ... ok sklearn.tests.test_qda.test_qda_priors ... ok sklearn.tests.test_qda.test_qda_store_covariances ... ok sklearn.tests.test_qda.test_qda_regularization ... ok sklearn.tests.test_random_projection.test_invalid_jl_domain ... ok sklearn.tests.test_random_projection.test_input_size_jl_min_dim ... ok Check basic properties of random matrix generation ... ok Check some statical properties of Gaussian random matrix ... ok Check some statical properties of sparse random matrix ... ok sklearn.tests.test_random_projection.test_sparse_random_projection_transformer_invalid_density ... ok sklearn.tests.test_random_projection.test_random_projection_transformer_invalid_input ... ok sklearn.tests.test_random_projection.test_try_to_transform_before_fit ... ok sklearn.tests.test_random_projection.test_too_many_samples_to_find_a_safe_embedding ... ok sklearn.tests.test_random_projection.test_random_projection_embedding_quality ... ok sklearn.tests.test_random_projection.test_SparseRandomProjection_output_representation ... ok sklearn.tests.test_random_projection.test_correct_RandomProjection_dimensions_embedding ... ok sklearn.tests.test_random_projection.test_warning_n_components_greater_than_n_features ... ok ====================================================================== ERROR: Predict probabilities. ---------------------------------------------------------------------- Traceback (most recent call last): File "/home/slave/virtualenvs/cpython-2.6/lib/python2.6/site-packages/nose/case.py", line 197, in runTest self.test(*self.arg) File "<https://jenkins.shiningpanda-ci.com/scikit-learn/job/python-2.6-numpy-1.3.0-scipy-0.7.2/ws/sklearn/ensemble/tests/test_bagging.py";,> line 163, in test_probability np.exp(ensemble.predict_log_proba(X_test))) File "/home/slave/virtualenvs/cpython-2.6/lib/python2.6/site-packages/numpy/testing/utils.py", line 537, in assert_array_almost_equal header='Arrays are not almost equal') File "/home/slave/virtualenvs/cpython-2.6/lib/python2.6/site-packages/numpy/testing/utils.py", line 399, in assert_array_compare raise ValueError(msg) ValueError: Arrays are not almost equal x: array([[ 0. , 0. , 1. ], [ 0. , 0. , 1. ], [ 0. , 0.1, 0.9],... y: array([[ NaN, NaN, 1. ], [ NaN, NaN, 1. ], [ NaN, NaN, 0.9],... Name Stmts Miss Cover Missing ----------------------------------------------------------------------------------- sklearn 26 3 88% 27, 35-38 sklearn.__check_build 18 3 83% 24, 45-46 sklearn.__check_build.setup 9 2 78% 17-18 sklearn._build_utils 17 4 76% 18, 22, 27-28 sklearn.base 151 9 94% 58, 76, 84, 364-365, 369-370, 379-380 sklearn.cluster 8 0 100% sklearn.cluster._feature_agglomeration 16 1 94% 46 sklearn.cluster.affinity_propagation_ 96 6 94% 145-146, 167-169, 252 sklearn.cluster.bicluster 2 0 100% sklearn.cluster.bicluster.spectral 174 3 98% 147-149, 151 sklearn.cluster.bicluster.utils 20 0 100% sklearn.cluster.dbscan_ 73 0 100% sklearn.cluster.hierarchical 154 2 99% 109, 128 sklearn.cluster.k_means_ 379 6 98% 95, 360, 680, 953, 1234, 1237 sklearn.cluster.mean_shift_ 81 6 93% 101, 104, 121, 155-157 sklearn.cluster.setup 18 2 89% 40-41 sklearn.cluster.spectral 99 6 94% 140-142, 154, 418, 452 sklearn.covariance 6 0 100% sklearn.covariance.empirical_covariance_ 62 0 100% sklearn.covariance.graph_lasso_ 197 12 94% 145, 167, 183-187, 213, 334, 360, 366, 369, 517, 535-536 sklearn.covariance.outlier_detection 38 2 95% 71, 101 sklearn.covariance.robust_covariance 211 19 91% 131, 141, 146-148, 154, 243, 341-342, 350, 399-405, 589, 597-602, 670 sklearn.covariance.shrunk_covariance_ 127 5 96% 183, 185-189 sklearn.cross_decomposition 2 0 100% sklearn.cross_decomposition.cca_ 5 0 100% sklearn.cross_decomposition.pls_ 205 37 82% 66-67, 102-103, 233, 235, 237, 244, 248, 250, 253, 256, 290, 332, 372-378, 408-410, 434, 734, 737, 742, 755, 767-773, 792 sklearn.cross_validation 383 2 99% 94, 1217 sklearn.datasets 46 0 100% sklearn.datasets.base 143 18 87% 159-162, 165-169, 475-484, 518-525 sklearn.datasets.california_housing 37 24 35% 27-29, 64-101 sklearn.datasets.covtype 50 23 54% 19-20, 69-78, 84-93, 100-104 sklearn.datasets.lfw 157 135 14% 53-55, 66-105, 113-163, 178-208, 260-276, 294-334, 342, 402-429, 439 sklearn.datasets.mlcomp 47 40 15% 11-13, 56-103 sklearn.datasets.mldata 79 7 91% 15-19, 150-152 sklearn.datasets.olivetti_faces 41 26 37% 32-35, 89-116 sklearn.datasets.samples_generator 357 37 90% 117, 121, 124, 535, 584-604, 742, 1132-1135, 1287-1314 sklearn.datasets.setup 14 2 86% 21-22 sklearn.datasets.species_distributions 72 55 24% 46-48, 69-82, 98-108, 125-135, 210-257 sklearn.datasets.svmlight_format 96 7 93% 126-127, 238, 334, 339, 347-348 sklearn.datasets.twenty_newsgroups 149 110 26% 58, 76-97, 105-106, 119-121, 132-141, 196-202, 206, 211-265, 305-356 sklearn.decomposition 10 0 100% sklearn.decomposition.dict_learning 289 30 90% 87, 91-92, 305-306, 308, 417, 445, 450-451, 453, 476, 478, 481, 571, 581, 605, 647, 797, 936, 1088, 1117-1133 sklearn.decomposition.factor_analysis 110 0 100% sklearn.decomposition.fastica_ 169 12 93% 108, 250, 276-277, 304-306, 320, 328, 337, 522, 528 sklearn.decomposition.kernel_pca 82 2 98% 172, 258 sklearn.decomposition.nmf 206 8 96% 109, 263, 381, 401-403, 426, 547 sklearn.decomposition.pca 205 8 96% 54, 66-67, 284, 328, 479, 491, 666 sklearn.decomposition.sparse_pca 59 2 97% 107, 252 sklearn.decomposition.truncated_svd 52 5 90% 82-84, 147, 188-190 sklearn.dummy 110 0 100% sklearn.ensemble 18 0 100% sklearn.ensemble.bagging 234 12 95% 61, 85, 137, 164-169, 273, 544, 588, 608, 615, 642 sklearn.ensemble.base 37 2 95% 63, 90 sklearn.ensemble.forest 287 22 92% 78, 125, 142, 234, 255, 310, 372, 385, 479-480, 597-600, 751, 755, 884, 888, 1032, 1036, 1169, 1173, 1265, 1269 sklearn.ensemble.gradient_boosting 384 12 97% 54, 124, 189, 216, 369, 420, 528, 533, 637, 652, 740-741 sklearn.ensemble.partial_dependence 159 104 35% 54, 56, 237-388 sklearn.ensemble.setup 10 2 80% 16-17 sklearn.ensemble.weight_boosting 278 28 90% 107-111, 145, 155, 191, 228, 237-238, 388, 442-443, 504-505, 530-531, 594-595, 688-690, 799, 902, 952-953, 978, 980, 992 sklearn.externals 1 0 100% sklearn.externals.joblib 10 0 100% sklearn.externals.joblib._compat 4 2 50% 7-8 sklearn.externals.joblib.disk 51 11 78% 28, 84-88, 94, 103-107 sklearn.externals.joblib.format_stack 227 46 80% 34-35, 50, 55, 63, 67-70, 133-135, 146, 148, 168-173, 193-197, 201-205, 208, 215-224, 246-247, 282-286, 299-300, 323, 345-346, 363-367, 372-375, 405, 412 sklearn.externals.joblib.func_inspect 125 19 85% 47-54, 82, 107-111, 118-119, 127, 152-153, 199, 238, 242 sklearn.externals.joblib.hashing 91 18 80% 22, 54-55, 71, 88-99, 144, 155-159, 195 sklearn.externals.joblib.logger 73 10 86% 29, 42, 68, 78, 94, 99, 115, 121, 138-139 sklearn.externals.joblib.memory 239 20 92% 19-20, 58, 130, 159-160, 282, 294-295, 305, 358, 360, 380-381, 400-401, 411, 481, 527, 552 sklearn.externals.joblib.my_exceptions 42 3 93% 43, 70-71 sklearn.externals.joblib.numpy_pickle 170 20 88% 21-27, 86, 115, 122-125, 196-197, 243-246, 272-273, 290, 298 sklearn.externals.joblib.parallel 222 23 90% 19-20, 29-30, 39-41, 54, 104, 124, 324, 331-332, 399-401, 451, 457, 460, 472-474, 480, 509 sklearn.externals.setup 6 0 100% sklearn.externals.six 170 63 63% 34-40, 54-56, 92-94, 107-115, 190, 195-201, 205-213, 228-230, 234-237, 240, 245, 260, 272-284, 298-308, 319-320 sklearn.feature_extraction 5 0 100% sklearn.feature_extraction.dict_vectorizer 97 3 97% 237, 255-256 sklearn.feature_extraction.hashing 39 1 97% 102 sklearn.feature_extraction.image 148 0 100% sklearn.feature_extraction.setup 10 0 100% sklearn.feature_extraction.stop_words 1 0 100% sklearn.feature_extraction.text 372 19 95% 104-105, 232, 381-385, 387-391, 414, 419-420, 614-618, 630, 635, 681, 743, 806, 836, 974 sklearn.feature_selection 14 0 100% sklearn.feature_selection.base 34 1 97% 112 sklearn.feature_selection.from_model 46 8 83% 47, 53, 59, 63, 72, 90, 101-104 sklearn.feature_selection.rfe 102 7 93% 122, 131, 144, 151, 205, 208, 353 sklearn.feature_selection.selector_mixin 4 0 100% sklearn.feature_selection.univariate_selection 175 5 97% 246, 358, 364, 414, 569 sklearn.feature_selection.variance_threshold 21 0 100% sklearn.gaussian_process 5 0 100% sklearn.gaussian_process.correlation_models 78 28 64% 47, 52, 90, 95, 128-147, 177-184, 220, 226, 270, 276 sklearn.gaussian_process.gaussian_process 335 105 69% 21, 309, 318, 320, 324, 329, 344, 349, 355, 361, 373-381, 426, 459-467, 495-520, 582-586, 597-598, 604-610, 616-623, 680-682, 688, 722-724, 742-744, 750-799, 812, 828, 834, 845, 848, 853-856, 868, 871, 876 sklearn.gaussian_process.regression_models 19 0 100% sklearn.grid_search 210 5 98% 276, 328, 379, 431, 666 sklearn.hmm 454 35 92% 156, 311-312, 417, 452-454, 536, 539, 710, 719-726, 752, 764, 815-816, 946, 1010, 1014, 1018, 1023, 1032, 1118, 1175, 1182, 1203, 1214-1216 sklearn.isotonic 71 6 92% 57-60, 166-169, 229-232 sklearn.kernel_approximation 152 6 96% 246, 255, 440-441, 453, 484 sklearn.lda 93 16 83% 91-95, 120, 124, 131, 140, 142-143, 162, 210-213 sklearn.linear_model 14 0 100% sklearn.linear_model.base 147 4 97% 269, 299, 404-405 sklearn.linear_model.bayes 126 8 94% 172-178, 204, 411 sklearn.linear_model.coordinate_descent 334 37 89% 82, 344, 352, 358, 361-362, 376, 385, 431-432, 436-441, 551-552, 596, 860-861, 920, 923, 926-928, 932, 963, 1018-1020, 1230-1231, 1346-1347, 1387-1388, 1401, 1417, 1420 sklearn.linear_model.least_angle 363 18 95% 165-166, 285-286, 293-302, 403, 518, 787-790 sklearn.linear_model.logistic 11 0 100% sklearn.linear_model.omp 286 29 90% 101-102, 209-210, 311-314, 328, 337, 444, 447, 452, 598-601, 606-610, 615-619, 624-628, 669, 733-740, 751 sklearn.linear_model.passive_aggressive 25 0 100% sklearn.linear_model.perceptron 5 0 100% sklearn.linear_model.randomized_l1 174 9 95% 69, 100, 122, 135, 142, 585, 603, 612-613 sklearn.linear_model.ridge 346 34 90% 67, 73-84, 140-153, 232, 242, 261-263, 276, 282, 290-292, 644, 649, 670, 708-712, 724, 753, 825, 1022 sklearn.linear_model.setup 19 2 89% 41-42 sklearn.linear_model.stochastic_gradient 306 11 96% 134-135, 225, 307-310, 386-391, 840-843 sklearn.manifold 5 0 100% sklearn.manifold.isomap 46 0 100% sklearn.manifold.locally_linear 218 22 90% 45, 47, 147, 176, 271, 274, 283, 286, 289, 310, 333-334, 360, 384-389, 437, 482-483 sklearn.manifold.mds 102 21 79% 79-80, 98-107, 122, 124-128, 229-233, 345, 378, 385-388 sklearn.manifold.spectral_embedding_ 144 19 87% 197-200, 270-282, 306, 387, 411-414, 459 sklearn.metrics 9 0 100% sklearn.metrics.cluster 15 0 100% sklearn.metrics.cluster.bicluster 2 0 100% sklearn.metrics.cluster.bicluster.bicluster_metrics 28 0 100% sklearn.metrics.cluster.setup 14 2 86% 22-23 sklearn.metrics.cluster.supervised 110 0 100% sklearn.metrics.cluster.unsupervised 27 0 100% sklearn.metrics.metrics 374 1 99% 1880 sklearn.metrics.pairwise 233 5 98% 176, 268, 785, 891, 1052 sklearn.metrics.scorer 77 3 96% 41, 107, 144 sklearn.metrics.setup 13 2 85% 20-21 sklearn.mixture 5 0 100% sklearn.mixture.dpgmm 348 25 93% 211-216, 219, 222, 228, 256, 271, 372-375, 465, 470, 507, 667, 695, 702, 747-750 sklearn.mixture.gmm 258 30 88% 244, 261-268, 279, 305, 307, 309, 363-364, 425, 427, 446, 480, 572, 596, 625, 627, 630, 633, 643, 646, 651-654, 672 sklearn.multiclass 169 10 94% 53, 79, 126, 265, 277, 296-300, 561 sklearn.naive_bayes 159 2 99% 319-320 sklearn.neighbors 10 0 100% sklearn.neighbors.base 242 8 97% 88, 97, 122, 148, 224, 316, 495, 610 sklearn.neighbors.classification 96 0 100% sklearn.neighbors.graph 10 0 100% sklearn.neighbors.kde 67 0 100% sklearn.neighbors.nearest_centroid 51 3 94% 90, 99, 157 sklearn.neighbors.regression 48 0 100% sklearn.neighbors.setup 13 0 100% sklearn.neighbors.unsupervised 7 0 100% sklearn.neural_network 1 0 100% sklearn.neural_network.rbm 84 0 100% sklearn.pipeline 138 6 96% 81, 91, 188, 273, 335, 342 sklearn.pls 3 0 100% sklearn.preprocessing 17 0 100% sklearn.preprocessing._weights 12 0 100% sklearn.preprocessing.data 331 6 98% 62, 392-394, 430, 439, 456 sklearn.preprocessing.imputation 177 9 95% 90, 93-98, 170, 175, 373, 385 sklearn.preprocessing.label 98 3 97% 217, 408, 434 sklearn.qda 76 5 93% 104, 168-172 sklearn.random_projection 114 0 100% sklearn.semi_supervised 2 0 100% sklearn.semi_supervised.label_propagation 112 4 96% 130, 135, 171, 209 sklearn.setup 63 4 94% 75-77, 90-91 sklearn.svm 4 0 100% sklearn.svm.base 279 9 97% 135, 290, 314, 358, 444, 498, 554, 619, 659 sklearn.svm.bounds 35 0 100% sklearn.svm.classes 25 0 100% sklearn.svm.setup 26 4 85% 57-58, 86-87 sklearn.tree 6 0 100% sklearn.tree.export 51 13 75% 57, 67-72, 95, 122-126, 130, 136 sklearn.tree.setup 14 2 86% 23-24 sklearn.tree.tree 193 13 93% 123, 127, 210, 214, 322, 443, 447, 634, 638, 685, 689, 736, 740 sklearn.utils 121 3 98% 323, 381, 385 sklearn.utils.arpack 635 285 55% 308, 313, 316, 365-369, 430, 432, 434, 440-451, 454, 456, 464-498, 501, 504, 510, 517, 537, 543-544, 546-548, 554, 556, 565, 579, 587, 630, 632, 634, 639-680, 683, 686, 692, 707, 719, 729, 735-736, 738, 740, 746-749, 772, 795-804, 811-835, 842-843, 849, 853, 860-876, 881, 887-888, 907, 934-946, 949-954, 964-985, 988, 991, 994-999, 1010, 1024, 1033-1047, 1185, 1187-1191, 1196, 1202, 1205, 1214-1253, 1423-1440, 1443, 1445-1449, 1461, 1469-1479, 1483, 1493-1494, 1498-1528, 1563, 1568-1569, 1603, 1616 sklearn.utils.bench 3 0 100% sklearn.utils.class_weight 19 0 100% sklearn.utils.extmath 191 29 85% 50-53, 58, 67-70, 95-118, 126, 131, 264-266, 380, 443, 601, 613 sklearn.utils.fixes 127 24 81% 25, 50-51, 57, 61, 71, 86-88, 94, 109, 119, 138, 145, 156, 174-175, 185-187, 196-198, 208-211, 213 sklearn.utils.graph 75 5 93% 52, 66, 72, 112, 131 sklearn.utils.linear_assignment_ 106 5 95% 97-100, 249 sklearn.utils.multiclass 81 0 100% sklearn.utils.setup 27 2 93% 75-76 sklearn.utils.sparsetools 3 0 100% sklearn.utils.sparsetools._graph_validation 31 13 58% 18, 29, 31-36, 39-46, 53, 56 sklearn.utils.sparsetools.setup 11 2 82% 28-29 sklearn.utils.validation 127 2 98% 56, 205 ----------------------------------------------------------------------------------- TOTAL 18954 2096 89% ---------------------------------------------------------------------- Ran 2650 tests in 300.384s FAILED (SKIP=42, errors=1) [Parallel(n_jobs=1)]: Done 1 jobs | elapsed: 0.0s [Parallel(n_jobs=1)]: Done 9 out of 9 | elapsed: 0.0s finished <https://jenkins.shiningpanda-ci.com/scikit-learn/job/python-2.6-numpy-1.3.0-scipy-0.7.2/ws/sklearn/metrics/metrics.py>:1490: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 due to no predicted samples. 'precision', 'predicted', average) <https://jenkins.shiningpanda-ci.com/scikit-learn/job/python-2.6-numpy-1.3.0-scipy-0.7.2/ws/sklearn/hmm.py>:795: RuntimeWarning: underflow encountered in multiply stats['obs*obs.T'][c] += posteriors[t, c] * obsobsT <https://jenkins.shiningpanda-ci.com/scikit-learn/job/python-2.6-numpy-1.3.0-scipy-0.7.2/ws/sklearn/naive_bayes.py>:207: RuntimeWarning: divide by zero encountered in log self.class_log_prior_ = (np.log(self.class_count_) <https://jenkins.shiningpanda-ci.com/scikit-learn/job/python-2.6-numpy-1.3.0-scipy-0.7.2/ws/sklearn/decomposition/pca.py>:654: DeprecationWarning: Sparse matrix support is deprecated and will be dropped in 0.16. Use TruncatedSVD instead. DeprecationWarning) <https://jenkins.shiningpanda-ci.com/scikit-learn/job/python-2.6-numpy-1.3.0-scipy-0.7.2/ws/sklearn/qda.py>:151: RuntimeWarning: divide by zero encountered in log + np.log(self.priors_)) <https://jenkins.shiningpanda-ci.com/scikit-learn/job/python-2.6-numpy-1.3.0-scipy-0.7.2/ws/sklearn/qda.py>:125: UserWarning: Variables are collinear warnings.warn("Variables are collinear") <https://jenkins.shiningpanda-ci.com/scikit-learn/job/python-2.6-numpy-1.3.0-scipy-0.7.2/ws/sklearn/qda.py>:147: RuntimeWarning: divide by zero encountered in power X2 = np.dot(Xm, R * (S ** (-0.5))) <https://jenkins.shiningpanda-ci.com/scikit-learn/job/python-2.6-numpy-1.3.0-scipy-0.7.2/ws/sklearn/qda.py>:147: RuntimeWarning: invalid value encountered in multiply X2 = np.dot(Xm, R * (S ** (-0.5))) <https://jenkins.shiningpanda-ci.com/scikit-learn/job/python-2.6-numpy-1.3.0-scipy-0.7.2/ws/sklearn/qda.py>:150: RuntimeWarning: divide by zero encountered in log return (-0.5 * (norm2 + np.sum(np.log(self.scalings_), 1)) 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 ... -- Olivier http://twitter.com/ogrisel - http://github.com/ogrisel ------------------------------------------------------------------------------ How ServiceNow helps IT people transform IT departments: 1. Consolidate legacy IT systems to a single system of record for IT 2. Standardize and globalize service processes across IT 3. Implement zero-touch automation to replace manual, redundant tasks http://pubads.g.doubleclick.net/gampad/clk?id=51271111&iu=/4140/ostg.clktrk _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
