ebuild log

sci-libs:scikits_learn-0.18.2:20170801-110932.log (text/x-log), 559.67 KB, created by Крыськов Денис on 2017-08-01 12:35:49 UTC

[32;01m * [39;49;00mPackage:    sci-libs/scikits_learn-0.18.2
[32;01m * [39;49;00mRepository: gentoo
[32;01m * [39;49;00mMaintainer: [e-mail]
[32;01m * [39;49;00mUSE:        abi_x86_64 amd64 doc elibc_glibc examples kernel_linux python_targets_python2_7 userland_GNU
[32;01m * [39;49;00mFEATURES:   preserve-libs sandbox userpriv usersandbox
>>> Unpacking source...
>>> Unpacking scikit-learn-0.18.2.tar.gz to /tmp/portage/sci-libs/scikits_learn-0.18.2/work
>>> Source unpacked in /tmp/portage/sci-libs/scikits_learn-0.18.2/work
>>> Preparing source in /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2 ...
 [32;01m*[0m Applying scikits_learn-0.18.1-system-cblas.patch ...
[A[72C [34;01m[ [32;01mok[34;01m ][0m
>>> Source prepared.
>>> Configuring source in /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2 ...
>>> Source configured.
>>> Compiling source in /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2 ...
 [32;01m*[0m python2_7: running distutils-r1_run_phase python_compile
/usr/bin/python2.7 setup.py build config_fc --noopt --noarch
Partial import of sklearn during the build process.
blas_opt_info:
system_info:
  NOT AVAILABLE

/usr/lib64/python2.7/site-packages/numpy/distutils/system_info.py:552: UserWarning: 
    Atlas (http://math-atlas.sourceforge.net/) libraries not found.
    Directories to search for the libraries can be specified in the
    numpy/distutils/site.cfg file (section [atlas]) or by setting
    the ATLAS environment variable.
  self.calc_info()
blas_info:
customize UnixCCompiler
C compiler: x86_64-pc-linux-gnu-gcc -O2 -march=haswell -fPIC

creating /tmp/portage/sci-libs/scikits_learn-0.18.2/temp/tmpsTb_UG/tmp
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/temp/tmpsTb_UG/tmp/portage
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/temp/tmpsTb_UG/tmp/portage/sci-libs
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/temp/tmpsTb_UG/tmp/portage/sci-libs/scikits_learn-0.18.2
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/temp/tmpsTb_UG/tmp/portage/sci-libs/scikits_learn-0.18.2/temp
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/temp/tmpsTb_UG/tmp/portage/sci-libs/scikits_learn-0.18.2/temp/tmpsTb_UG
compile options: '-I -c'
x86_64-pc-linux-gnu-gcc: /tmp/portage/sci-libs/scikits_learn-0.18.2/temp/tmpsTb_UG/source.c
/tmp/portage/sci-libs/scikits_learn-0.18.2/temp/ccH15qQl.o: In function `main':
source.c:(.text.startup+0x9c): undefined reference to `cblas_ddot'
collect2: error: ld returned 1 exit status
/tmp/portage/sci-libs/scikits_learn-0.18.2/temp/ccH15qQl.o: In function `main':
source.c:(.text.startup+0x9c): undefined reference to `cblas_ddot'
collect2: error: ld returned 1 exit status
  FOUND:
    libraries = ['blas', 'cblas']
    library_dirs = ['/usr/lib64']
    include_dirs = ['']

  FOUND:
    libraries = ['blas', 'cblas']
    library_dirs = ['/usr/lib64']
    define_macros = [('NO_ATLAS_INFO', 1)]
    include_dirs = ['']

non-existing path in 'sklearn/cluster': '../src/cblas'
non-existing path in 'sklearn/manifold': '../src/cblas'
non-existing path in 'sklearn/metrics': '../src/cblas'
non-existing path in 'sklearn/svm': '../src/cblas'
non-existing path in 'sklearn/linear_model': '../src/cblas'
non-existing path in 'sklearn/linear_model': '../src/cblas'
non-existing path in 'sklearn/utils': '../src/cblas'
non-existing path in 'sklearn/utils': '../src/cblas'
running build
running config_cc
unifing config_cc, config, build_clib, build_ext, build commands --compiler options
running config_fc
unifing config_fc, config, build_clib, build_ext, build commands --fcompiler options
running build_src
build_src
building library "libsvm-skl" sources
building extension "sklearn.__check_build._check_build" sources
building extension "sklearn.cluster._dbscan_inner" sources
building extension "sklearn.cluster._hierarchical" sources
building extension "sklearn.cluster._k_means_elkan" sources
building extension "sklearn.cluster._k_means" sources
building extension "sklearn.datasets._svmlight_format" sources
building extension "sklearn.decomposition._online_lda" sources
building extension "sklearn.decomposition.cdnmf_fast" sources
building extension "sklearn.ensemble._gradient_boosting" sources
building extension "sklearn.feature_extraction._hashing" sources
building extension "sklearn.manifold._utils" sources
building extension "sklearn.manifold._barnes_hut_tsne" sources
building extension "sklearn.metrics.pairwise_fast" sources
building extension "sklearn.metrics/cluster.expected_mutual_info_fast" sources
building extension "sklearn.neighbors.ball_tree" sources
building extension "sklearn.neighbors.kd_tree" sources
building extension "sklearn.neighbors.dist_metrics" sources
building extension "sklearn.neighbors.typedefs" sources
building extension "sklearn.tree._tree" sources
building extension "sklearn.tree._splitter" sources
building extension "sklearn.tree._criterion" sources
building extension "sklearn.tree._utils" sources
building extension "sklearn.svm.libsvm" sources
building extension "sklearn.svm.liblinear" sources
building extension "sklearn.svm.libsvm_sparse" sources
building extension "sklearn._isotonic" sources
building extension "sklearn.linear_model.cd_fast" sources
building extension "sklearn.linear_model.sgd_fast" sources
building extension "sklearn.linear_model.sag_fast" sources
building extension "sklearn.utils.sparsetools._traversal" sources
building extension "sklearn.utils.sparsetools._graph_tools" sources
building extension "sklearn.utils.sparsefuncs_fast" sources
building extension "sklearn.utils.arrayfuncs" sources
building extension "sklearn.utils.murmurhash" sources
building extension "sklearn.utils.lgamma" sources
building extension "sklearn.utils.graph_shortest_path" sources
building extension "sklearn.utils.fast_dict" sources
building extension "sklearn.utils.seq_dataset" sources
building extension "sklearn.utils.weight_vector" sources
building extension "sklearn.utils._random" sources
building extension "sklearn.utils._logistic_sigmoid" sources
building data_files sources
build_src: building npy-pkg config files
running build_py
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn
copying sklearn/setup.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn
copying sklearn/random_projection.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn
copying sklearn/qda.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn
copying sklearn/pipeline.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn
copying sklearn/naive_bayes.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn
copying sklearn/multioutput.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn
copying sklearn/multiclass.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn
copying sklearn/learning_curve.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn
copying sklearn/lda.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn
copying sklearn/kernel_ridge.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn
copying sklearn/kernel_approximation.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn
copying sklearn/isotonic.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn
copying sklearn/grid_search.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn
copying sklearn/exceptions.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn
copying sklearn/dummy.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn
copying sklearn/discriminant_analysis.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn
copying sklearn/cross_validation.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn
copying sklearn/calibration.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn
copying sklearn/base.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn
copying sklearn/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/__check_build
copying sklearn/__check_build/setup.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/__check_build
copying sklearn/__check_build/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/__check_build
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/_build_utils
copying sklearn/_build_utils/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/_build_utils
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/covariance
copying sklearn/covariance/shrunk_covariance_.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/covariance
copying sklearn/covariance/robust_covariance.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/covariance
copying sklearn/covariance/outlier_detection.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/covariance
copying sklearn/covariance/graph_lasso_.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/covariance
copying sklearn/covariance/empirical_covariance_.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/covariance
copying sklearn/covariance/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/covariance
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/covariance/tests
copying sklearn/covariance/tests/test_graph_lasso.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/covariance/tests
copying sklearn/covariance/tests/test_robust_covariance.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/covariance/tests
copying sklearn/covariance/tests/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/covariance/tests
copying sklearn/covariance/tests/test_covariance.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/covariance/tests
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/cross_decomposition
copying sklearn/cross_decomposition/pls_.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/cross_decomposition
copying sklearn/cross_decomposition/cca_.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/cross_decomposition
copying sklearn/cross_decomposition/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/cross_decomposition
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/cross_decomposition/tests
copying sklearn/cross_decomposition/tests/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/cross_decomposition/tests
copying sklearn/cross_decomposition/tests/test_pls.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/cross_decomposition/tests
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/feature_selection
copying sklearn/feature_selection/variance_threshold.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/feature_selection
copying sklearn/feature_selection/univariate_selection.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/feature_selection
copying sklearn/feature_selection/rfe.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/feature_selection
copying sklearn/feature_selection/mutual_info_.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/feature_selection
copying sklearn/feature_selection/from_model.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/feature_selection
copying sklearn/feature_selection/base.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/feature_selection
copying sklearn/feature_selection/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/feature_selection
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/feature_selection/tests
copying sklearn/feature_selection/tests/test_chi2.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/feature_selection/tests
copying sklearn/feature_selection/tests/test_feature_select.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/feature_selection/tests
copying sklearn/feature_selection/tests/test_from_model.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/feature_selection/tests
copying sklearn/feature_selection/tests/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/feature_selection/tests
copying sklearn/feature_selection/tests/test_rfe.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/feature_selection/tests
copying sklearn/feature_selection/tests/test_base.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/feature_selection/tests
copying sklearn/feature_selection/tests/test_variance_threshold.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/feature_selection/tests
copying sklearn/feature_selection/tests/test_mutual_info.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/feature_selection/tests
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/gaussian_process
copying sklearn/gaussian_process/kernels.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/gaussian_process
copying sklearn/gaussian_process/regression_models.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/gaussian_process
copying sklearn/gaussian_process/gpr.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/gaussian_process
copying sklearn/gaussian_process/gpc.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/gaussian_process
copying sklearn/gaussian_process/gaussian_process.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/gaussian_process
copying sklearn/gaussian_process/correlation_models.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/gaussian_process
copying sklearn/gaussian_process/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/gaussian_process
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/gaussian_process/tests
copying sklearn/gaussian_process/tests/test_kernels.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/gaussian_process/tests
copying sklearn/gaussian_process/tests/test_gpc.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/gaussian_process/tests
copying sklearn/gaussian_process/tests/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/gaussian_process/tests
copying sklearn/gaussian_process/tests/test_gpr.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/gaussian_process/tests
copying sklearn/gaussian_process/tests/test_gaussian_process.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/gaussian_process/tests
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/mixture
copying sklearn/mixture/gmm.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/mixture
copying sklearn/mixture/gaussian_mixture.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/mixture
copying sklearn/mixture/dpgmm.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/mixture
copying sklearn/mixture/bayesian_mixture.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/mixture
copying sklearn/mixture/base.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/mixture
copying sklearn/mixture/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/mixture
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/mixture/tests
copying sklearn/mixture/tests/test_gaussian_mixture.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/mixture/tests
copying sklearn/mixture/tests/test_gmm.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/mixture/tests
copying sklearn/mixture/tests/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/mixture/tests
copying sklearn/mixture/tests/test_bayesian_mixture.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/mixture/tests
copying sklearn/mixture/tests/test_dpgmm.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/mixture/tests
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/model_selection
copying sklearn/model_selection/_validation.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/model_selection
copying sklearn/model_selection/_split.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/model_selection
copying sklearn/model_selection/_search.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/model_selection
copying sklearn/model_selection/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/model_selection
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/model_selection/tests
copying sklearn/model_selection/tests/test_search.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/model_selection/tests
copying sklearn/model_selection/tests/test_validation.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/model_selection/tests
copying sklearn/model_selection/tests/test_split.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/model_selection/tests
copying sklearn/model_selection/tests/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/model_selection/tests
copying sklearn/model_selection/tests/common.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/model_selection/tests
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/neural_network
copying sklearn/neural_network/rbm.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/neural_network
copying sklearn/neural_network/multilayer_perceptron.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/neural_network
copying sklearn/neural_network/_stochastic_optimizers.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/neural_network
copying sklearn/neural_network/_base.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/neural_network
copying sklearn/neural_network/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/neural_network
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/neural_network/tests
copying sklearn/neural_network/tests/test_mlp.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/neural_network/tests
copying sklearn/neural_network/tests/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/neural_network/tests
copying sklearn/neural_network/tests/test_rbm.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/neural_network/tests
copying sklearn/neural_network/tests/test_stochastic_optimizers.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/neural_network/tests
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copying sklearn/preprocessing/label.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/preprocessing
copying sklearn/preprocessing/imputation.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/preprocessing
copying sklearn/preprocessing/data.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/preprocessing
copying sklearn/preprocessing/_function_transformer.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/preprocessing
copying sklearn/preprocessing/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/preprocessing
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/preprocessing/tests
copying sklearn/preprocessing/tests/test_imputation.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/preprocessing/tests
copying sklearn/preprocessing/tests/test_label.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/preprocessing/tests
copying sklearn/preprocessing/tests/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/preprocessing/tests
copying sklearn/preprocessing/tests/test_function_transformer.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/preprocessing/tests
copying sklearn/preprocessing/tests/test_data.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/preprocessing/tests
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/semi_supervised
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copying sklearn/semi_supervised/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/semi_supervised
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/semi_supervised/tests
copying sklearn/semi_supervised/tests/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/semi_supervised/tests
copying sklearn/semi_supervised/tests/test_label_propagation.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/semi_supervised/tests
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copying sklearn/cluster/mean_shift_.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/cluster
copying sklearn/cluster/k_means_.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/cluster
copying sklearn/cluster/hierarchical.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/cluster
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copying sklearn/cluster/birch.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/cluster
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copying sklearn/cluster/affinity_propagation_.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/cluster
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creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/cluster/tests
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copying sklearn/cluster/tests/test_k_means.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/cluster/tests
copying sklearn/cluster/tests/test_mean_shift.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/cluster/tests
copying sklearn/cluster/tests/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/cluster/tests
copying sklearn/cluster/tests/test_bicluster.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/cluster/tests
copying sklearn/cluster/tests/common.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/cluster/tests
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copying sklearn/cluster/tests/test_dbscan.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/cluster/tests
copying sklearn/cluster/tests/test_spectral.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/cluster/tests
copying sklearn/cluster/tests/test_birch.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/cluster/tests
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/datasets
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copying sklearn/datasets/svmlight_format.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/datasets
copying sklearn/datasets/species_distributions.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/datasets
copying sklearn/datasets/setup.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/datasets
copying sklearn/datasets/samples_generator.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/datasets
copying sklearn/datasets/rcv1.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/datasets
copying sklearn/datasets/olivetti_faces.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/datasets
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copying sklearn/datasets/mlcomp.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/datasets
copying sklearn/datasets/lfw.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/datasets
copying sklearn/datasets/kddcup99.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/datasets
copying sklearn/datasets/covtype.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/datasets
copying sklearn/datasets/california_housing.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/datasets
copying sklearn/datasets/base.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/datasets
copying sklearn/datasets/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/datasets
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/datasets/tests
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copying sklearn/datasets/tests/test_samples_generator.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/datasets/tests
copying sklearn/datasets/tests/test_rcv1.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/datasets/tests
copying sklearn/datasets/tests/test_svmlight_format.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/datasets/tests
copying sklearn/datasets/tests/test_kddcup99.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/datasets/tests
copying sklearn/datasets/tests/test_lfw.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/datasets/tests
copying sklearn/datasets/tests/test_20news.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/datasets/tests
copying sklearn/datasets/tests/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/datasets/tests
copying sklearn/datasets/tests/test_base.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/datasets/tests
copying sklearn/datasets/tests/test_covtype.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/datasets/tests
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copying sklearn/decomposition/pca.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/decomposition
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copying sklearn/ensemble/base.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/ensemble
copying sklearn/ensemble/bagging.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/ensemble
copying sklearn/ensemble/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/ensemble
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/ensemble/tests
copying sklearn/ensemble/tests/test_voting_classifier.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/ensemble/tests
copying sklearn/ensemble/tests/test_iforest.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/ensemble/tests
copying sklearn/ensemble/tests/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/ensemble/tests
copying sklearn/ensemble/tests/test_weight_boosting.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/ensemble/tests
copying sklearn/ensemble/tests/test_base.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/ensemble/tests
copying sklearn/ensemble/tests/test_gradient_boosting_loss_functions.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/ensemble/tests
copying sklearn/ensemble/tests/test_forest.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/ensemble/tests
copying sklearn/ensemble/tests/test_partial_dependence.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/ensemble/tests
copying sklearn/ensemble/tests/test_bagging.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/ensemble/tests
copying sklearn/ensemble/tests/test_gradient_boosting.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/ensemble/tests
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/externals
copying sklearn/externals/test_externals_setup.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/externals
copying sklearn/externals/six.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/externals
copying sklearn/externals/setup.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/externals
copying sklearn/externals/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/externals
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/feature_extraction
copying sklearn/feature_extraction/text.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/feature_extraction
copying sklearn/feature_extraction/stop_words.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/feature_extraction
copying sklearn/feature_extraction/setup.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/feature_extraction
copying sklearn/feature_extraction/image.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/feature_extraction
copying sklearn/feature_extraction/hashing.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/feature_extraction
copying sklearn/feature_extraction/dict_vectorizer.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/feature_extraction
copying sklearn/feature_extraction/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/feature_extraction
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/feature_extraction/tests
copying sklearn/feature_extraction/tests/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/feature_extraction/tests
copying sklearn/feature_extraction/tests/test_text.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/feature_extraction/tests
copying sklearn/feature_extraction/tests/test_dict_vectorizer.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/feature_extraction/tests
copying sklearn/feature_extraction/tests/test_image.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/feature_extraction/tests
copying sklearn/feature_extraction/tests/test_feature_hasher.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/feature_extraction/tests
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/manifold
copying sklearn/manifold/t_sne.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/manifold
copying sklearn/manifold/spectral_embedding_.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/manifold
copying sklearn/manifold/setup.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/manifold
copying sklearn/manifold/mds.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/manifold
copying sklearn/manifold/locally_linear.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/manifold
copying sklearn/manifold/isomap.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/manifold
copying sklearn/manifold/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/manifold
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/manifold/tests
copying sklearn/manifold/tests/test_isomap.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/manifold/tests
copying sklearn/manifold/tests/test_locally_linear.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/manifold/tests
copying sklearn/manifold/tests/test_t_sne.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/manifold/tests
copying sklearn/manifold/tests/test_mds.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/manifold/tests
copying sklearn/manifold/tests/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/manifold/tests
copying sklearn/manifold/tests/test_spectral_embedding.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/manifold/tests
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/metrics
copying sklearn/metrics/setup.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/metrics
copying sklearn/metrics/scorer.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/metrics
copying sklearn/metrics/regression.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/metrics
copying sklearn/metrics/ranking.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/metrics
copying sklearn/metrics/pairwise.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/metrics
copying sklearn/metrics/classification.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/metrics
copying sklearn/metrics/base.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/metrics
copying sklearn/metrics/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/metrics
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/metrics/tests
copying sklearn/metrics/tests/test_regression.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/metrics/tests
copying sklearn/metrics/tests/test_ranking.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/metrics/tests
copying sklearn/metrics/tests/test_score_objects.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/metrics/tests
copying sklearn/metrics/tests/test_classification.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/metrics/tests
copying sklearn/metrics/tests/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/metrics/tests
copying sklearn/metrics/tests/test_pairwise.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/metrics/tests
copying sklearn/metrics/tests/test_common.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/metrics/tests
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/metrics/cluster
copying sklearn/metrics/cluster/supervised.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/metrics/cluster
copying sklearn/metrics/cluster/setup.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/metrics/cluster
copying sklearn/metrics/cluster/bicluster.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/metrics/cluster
copying sklearn/metrics/cluster/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/metrics/cluster
copying sklearn/metrics/cluster/unsupervised.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/metrics/cluster
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/metrics/cluster/tests
copying sklearn/metrics/cluster/tests/test_unsupervised.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/metrics/cluster/tests
copying sklearn/metrics/cluster/tests/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/metrics/cluster/tests
copying sklearn/metrics/cluster/tests/test_bicluster.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/metrics/cluster/tests
copying sklearn/metrics/cluster/tests/test_supervised.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/metrics/cluster/tests
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/neighbors
copying sklearn/neighbors/unsupervised.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/neighbors
copying sklearn/neighbors/setup.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/neighbors
copying sklearn/neighbors/regression.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/neighbors
copying sklearn/neighbors/nearest_centroid.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/neighbors
copying sklearn/neighbors/kde.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/neighbors
copying sklearn/neighbors/graph.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/neighbors
copying sklearn/neighbors/classification.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/neighbors
copying sklearn/neighbors/base.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/neighbors
copying sklearn/neighbors/approximate.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/neighbors
copying sklearn/neighbors/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/neighbors
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/neighbors/tests
copying sklearn/neighbors/tests/test_approximate.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/neighbors/tests
copying sklearn/neighbors/tests/test_dist_metrics.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/neighbors/tests
copying sklearn/neighbors/tests/test_nearest_centroid.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/neighbors/tests
copying sklearn/neighbors/tests/test_neighbors.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/neighbors/tests
copying sklearn/neighbors/tests/test_kde.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/neighbors/tests
copying sklearn/neighbors/tests/test_ball_tree.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/neighbors/tests
copying sklearn/neighbors/tests/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/neighbors/tests
copying sklearn/neighbors/tests/test_kd_tree.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/neighbors/tests
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copying sklearn/tree/setup.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/tree
copying sklearn/tree/export.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/tree
copying sklearn/tree/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/tree
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/tree/tests
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copying sklearn/tree/tests/test_tree.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/tree/tests
copying sklearn/tree/tests/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/tree/tests
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copying sklearn/svm/classes.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/svm
copying sklearn/svm/bounds.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/svm
copying sklearn/svm/base.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/svm
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copying sklearn/svm/tests/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/svm/tests
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copying sklearn/linear_model/stochastic_gradient.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/linear_model
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copying sklearn/linear_model/ridge.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/linear_model
copying sklearn/linear_model/ransac.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/linear_model
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copying sklearn/linear_model/perceptron.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/linear_model
copying sklearn/linear_model/passive_aggressive.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/linear_model
copying sklearn/linear_model/omp.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/linear_model
copying sklearn/linear_model/logistic.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/linear_model
copying sklearn/linear_model/least_angle.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/linear_model
copying sklearn/linear_model/huber.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/linear_model
copying sklearn/linear_model/coordinate_descent.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/linear_model
copying sklearn/linear_model/bayes.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/linear_model
copying sklearn/linear_model/base.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/linear_model
copying sklearn/linear_model/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/linear_model
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/linear_model/tests
copying sklearn/linear_model/tests/test_coordinate_descent.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/linear_model/tests
copying sklearn/linear_model/tests/test_ransac.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/linear_model/tests
copying sklearn/linear_model/tests/test_perceptron.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/linear_model/tests
copying sklearn/linear_model/tests/test_omp.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/linear_model/tests
copying sklearn/linear_model/tests/test_least_angle.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/linear_model/tests
copying sklearn/linear_model/tests/test_sag.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/linear_model/tests
copying sklearn/linear_model/tests/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/linear_model/tests
copying sklearn/linear_model/tests/test_sgd.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/linear_model/tests
copying sklearn/linear_model/tests/test_ridge.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/linear_model/tests
copying sklearn/linear_model/tests/test_base.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/linear_model/tests
copying sklearn/linear_model/tests/test_huber.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/linear_model/tests
copying sklearn/linear_model/tests/test_logistic.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/linear_model/tests
copying sklearn/linear_model/tests/test_theil_sen.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/linear_model/tests
copying sklearn/linear_model/tests/test_randomized_l1.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/linear_model/tests
copying sklearn/linear_model/tests/test_passive_aggressive.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/linear_model/tests
copying sklearn/linear_model/tests/test_sparse_coordinate_descent.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/linear_model/tests
copying sklearn/linear_model/tests/test_bayes.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/linear_model/tests
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils
copying sklearn/utils/fixes.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils
copying sklearn/utils/validation.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils
copying sklearn/utils/testing.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils
copying sklearn/utils/stats.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils
copying sklearn/utils/sparsefuncs.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils
copying sklearn/utils/setup.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils
copying sklearn/utils/random.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils
copying sklearn/utils/optimize.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils
copying sklearn/utils/multiclass.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils
copying sklearn/utils/mocking.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils
copying sklearn/utils/metaestimators.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils
copying sklearn/utils/linear_assignment_.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils
copying sklearn/utils/graph.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils
copying sklearn/utils/extmath.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils
copying sklearn/utils/estimator_checks.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils
copying sklearn/utils/deprecation.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils
copying sklearn/utils/class_weight.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils
copying sklearn/utils/bench.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils
copying sklearn/utils/arpack.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils
copying sklearn/utils/_scipy_sparse_lsqr_backport.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils
copying sklearn/utils/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils/sparsetools
copying sklearn/utils/sparsetools/setup.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils/sparsetools
copying sklearn/utils/sparsetools/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils/sparsetools
copying sklearn/utils/sparsetools/_graph_validation.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils/sparsetools
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils/sparsetools/tests
copying sklearn/utils/sparsetools/tests/test_traversal.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils/sparsetools/tests
copying sklearn/utils/sparsetools/tests/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils/sparsetools/tests
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils/tests
copying sklearn/utils/tests/test_fast_dict.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils/tests
copying sklearn/utils/tests/test_testing.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils/tests
copying sklearn/utils/tests/test_metaestimators.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils/tests
copying sklearn/utils/tests/test_validation.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils/tests
copying sklearn/utils/tests/test_multiclass.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils/tests
copying sklearn/utils/tests/test_stats.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils/tests
copying sklearn/utils/tests/test_murmurhash.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils/tests
copying sklearn/utils/tests/test_bench.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils/tests
copying sklearn/utils/tests/test_utils.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils/tests
copying sklearn/utils/tests/test_fixes.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils/tests
copying sklearn/utils/tests/test_optimize.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils/tests
copying sklearn/utils/tests/test_sparsefuncs.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils/tests
copying sklearn/utils/tests/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils/tests
copying sklearn/utils/tests/test_shortest_path.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils/tests
copying sklearn/utils/tests/test_linear_assignment.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils/tests
copying sklearn/utils/tests/test_graph.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils/tests
copying sklearn/utils/tests/test_seq_dataset.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils/tests
copying sklearn/utils/tests/test_extmath.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils/tests
copying sklearn/utils/tests/test_random.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils/tests
copying sklearn/utils/tests/test_estimator_checks.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils/tests
copying sklearn/utils/tests/test_class_weight.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils/tests
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/tests
copying sklearn/tests/test_random_projection.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/tests
copying sklearn/tests/test_pipeline.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/tests
copying sklearn/tests/test_naive_bayes.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/tests
copying sklearn/tests/test_multioutput.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/tests
copying sklearn/tests/test_multiclass.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/tests
copying sklearn/tests/test_metaestimators.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/tests
copying sklearn/tests/test_learning_curve.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/tests
copying sklearn/tests/test_kernel_ridge.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/tests
copying sklearn/tests/test_kernel_approximation.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/tests
copying sklearn/tests/test_isotonic.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/tests
copying sklearn/tests/test_init.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/tests
copying sklearn/tests/test_grid_search.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/tests
copying sklearn/tests/test_dummy.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/tests
copying sklearn/tests/test_discriminant_analysis.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/tests
copying sklearn/tests/test_cross_validation.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/tests
copying sklearn/tests/test_common.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/tests
copying sklearn/tests/test_check_build.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/tests
copying sklearn/tests/test_calibration.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/tests
copying sklearn/tests/test_base.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/tests
copying sklearn/tests/__init__.py -> /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/tests
warning: build_py: byte-compiling is disabled, skipping.

running build_clib
customize UnixCCompiler
customize UnixCCompiler using build_clib
building 'libsvm-skl' library
compiling C++ sources
C compiler: x86_64-pc-linux-gnu-g++ -O2 -march=haswell -fPIC

creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/svm
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/svm/src
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/svm/src/libsvm
compile options: '-I/usr/lib64/python2.7/site-packages/numpy/core/include -c'
x86_64-pc-linux-gnu-g++: sklearn/svm/src/libsvm/libsvm_template.cpp
x86_64-pc-linux-gnu-ar: adding 1 object files to /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/liblibsvm-skl.a
running build_ext
customize UnixCCompiler
customize UnixCCompiler using build_ext
customize UnixCCompiler
customize UnixCCompiler using build_ext
building 'sklearn.__check_build._check_build' extension
compiling C sources
C compiler: x86_64-pc-linux-gnu-gcc -O2 -march=haswell -fPIC

creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/__check_build
compile options: '-I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c'
x86_64-pc-linux-gnu-gcc: sklearn/__check_build/_check_build.c
x86_64-pc-linux-gnu-gcc -shared -Wl,-O1 -Wl,--as-needed -shared -O2 -march=haswell /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/__check_build/_check_build.o -L/usr/lib64 -L/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7 -lpython2.7 -o /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/__check_build/_check_build.so
building 'sklearn.cluster._dbscan_inner' extension
compiling C++ sources
C compiler: x86_64-pc-linux-gnu-g++ -O2 -march=haswell -fPIC

creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/cluster
compile options: '-I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c'
x86_64-pc-linux-gnu-g++: sklearn/cluster/_dbscan_inner.cpp
In file included from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1788:0,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from sklearn/cluster/_dbscan_inner.cpp:472:
/usr/lib64/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
 #warning "Using deprecated NumPy API, disable it by " \
  ^
x86_64-pc-linux-gnu-g++ -shared -Wl,-O1 -Wl,--as-needed -shared -O2 -march=haswell /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/cluster/_dbscan_inner.o -L/usr/lib64 -L/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7 -lpython2.7 -o /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/cluster/_dbscan_inner.so
building 'sklearn.cluster._hierarchical' extension
compiling C++ sources
C compiler: x86_64-pc-linux-gnu-g++ -O2 -march=haswell -fPIC

compile options: '-I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c'
x86_64-pc-linux-gnu-g++: sklearn/cluster/_hierarchical.cpp
In file included from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1788:0,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from sklearn/cluster/_hierarchical.cpp:468:
/usr/lib64/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
 #warning "Using deprecated NumPy API, disable it by " \
  ^
x86_64-pc-linux-gnu-g++ -shared -Wl,-O1 -Wl,--as-needed -shared -O2 -march=haswell /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/cluster/_hierarchical.o -L/usr/lib64 -L/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7 -lm -lpython2.7 -o /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/cluster/_hierarchical.so
building 'sklearn.cluster._k_means_elkan' extension
compiling C sources
C compiler: x86_64-pc-linux-gnu-gcc -O2 -march=haswell -fPIC

compile options: '-I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c'
x86_64-pc-linux-gnu-gcc: sklearn/cluster/_k_means_elkan.c
In file included from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1788:0,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from sklearn/cluster/_k_means_elkan.c:453:
/usr/lib64/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
 #warning "Using deprecated NumPy API, disable it by " \
  ^
x86_64-pc-linux-gnu-gcc -shared -Wl,-O1 -Wl,--as-needed -shared -O2 -march=haswell /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/cluster/_k_means_elkan.o -L/usr/lib64 -L/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7 -lm -lpython2.7 -o /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/cluster/_k_means_elkan.so
building 'sklearn.cluster._k_means' extension
compiling C sources
C compiler: x86_64-pc-linux-gnu-gcc -O2 -march=haswell -fPIC

compile options: '-DNO_ATLAS_INFO=1 -I../src/cblas -I/usr/lib64/python2.7/site-packages/numpy/core/include -Isklearn/cluster/ -I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c'
x86_64-pc-linux-gnu-gcc: sklearn/cluster/_k_means.c
In file included from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1788:0,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from sklearn/cluster/_k_means.c:475:
/usr/lib64/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
 #warning "Using deprecated NumPy API, disable it by " \
  ^
sklearn/cluster/_k_means.c: In function '__pyx_fuse_0__pyx_f_7sklearn_7cluster_8_k_means__assign_labels_array':
sklearn/cluster/_k_means.c:3619:15: warning: assignment from incompatible pointer type [-Wincompatible-pointer-types]
   __pyx_v_dot = cblas_sdot;
               ^
sklearn/cluster/_k_means.c: In function '__pyx_fuse_1__pyx_f_7sklearn_7cluster_8_k_means__assign_labels_array':
sklearn/cluster/_k_means.c:4358:15: warning: assignment from incompatible pointer type [-Wincompatible-pointer-types]
   __pyx_v_dot = cblas_ddot;
               ^
sklearn/cluster/_k_means.c: In function '__pyx_fuse_0__pyx_f_7sklearn_7cluster_8_k_means__assign_labels_csr':
sklearn/cluster/_k_means.c:5803:15: warning: assignment from incompatible pointer type [-Wincompatible-pointer-types]
   __pyx_v_dot = cblas_sdot;
               ^
sklearn/cluster/_k_means.c: In function '__pyx_fuse_1__pyx_f_7sklearn_7cluster_8_k_means__assign_labels_csr':
sklearn/cluster/_k_means.c:6599:15: warning: assignment from incompatible pointer type [-Wincompatible-pointer-types]
   __pyx_v_dot = cblas_ddot;
               ^
x86_64-pc-linux-gnu-gcc -shared -Wl,-O1 -Wl,--as-needed -shared -O2 -march=haswell /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/cluster/_k_means.o -L/usr/lib64 -L/usr/lib64 -L/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7 -lblas -lcblas -lm -lpython2.7 -o /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/cluster/_k_means.so
building 'sklearn.datasets._svmlight_format' extension
compiling C sources
C compiler: x86_64-pc-linux-gnu-gcc -O2 -march=haswell -fPIC

creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/datasets
compile options: '-I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c'
x86_64-pc-linux-gnu-gcc: sklearn/datasets/_svmlight_format.c
In file included from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1788:0,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from sklearn/datasets/_svmlight_format.c:451:
/usr/lib64/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
 #warning "Using deprecated NumPy API, disable it by " \
  ^
x86_64-pc-linux-gnu-gcc -shared -Wl,-O1 -Wl,--as-needed -shared -O2 -march=haswell /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/datasets/_svmlight_format.o -L/usr/lib64 -L/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7 -lpython2.7 -o /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/datasets/_svmlight_format.so
building 'sklearn.decomposition._online_lda' extension
compiling C sources
C compiler: x86_64-pc-linux-gnu-gcc -O2 -march=haswell -fPIC

creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/decomposition
compile options: '-I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c'
x86_64-pc-linux-gnu-gcc: sklearn/decomposition/_online_lda.c
In file included from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1788:0,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from sklearn/decomposition/_online_lda.c:454:
/usr/lib64/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
 #warning "Using deprecated NumPy API, disable it by " \
  ^
x86_64-pc-linux-gnu-gcc -shared -Wl,-O1 -Wl,--as-needed -shared -O2 -march=haswell /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/decomposition/_online_lda.o -L/usr/lib64 -L/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7 -lm -lpython2.7 -o /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/decomposition/_online_lda.so
building 'sklearn.decomposition.cdnmf_fast' extension
compiling C sources
C compiler: x86_64-pc-linux-gnu-gcc -O2 -march=haswell -fPIC

compile options: '-I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c'
x86_64-pc-linux-gnu-gcc: sklearn/decomposition/cdnmf_fast.c
x86_64-pc-linux-gnu-gcc -shared -Wl,-O1 -Wl,--as-needed -shared -O2 -march=haswell /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/decomposition/cdnmf_fast.o -L/usr/lib64 -L/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7 -lm -lpython2.7 -o /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/decomposition/cdnmf_fast.so
building 'sklearn.ensemble._gradient_boosting' extension
compiling C sources
C compiler: x86_64-pc-linux-gnu-gcc -O2 -march=haswell -fPIC

creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/ensemble
compile options: '-I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c'
x86_64-pc-linux-gnu-gcc: sklearn/ensemble/_gradient_boosting.c
In file included from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1788:0,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from sklearn/ensemble/_gradient_boosting.c:450:
/usr/lib64/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
 #warning "Using deprecated NumPy API, disable it by " \
  ^
x86_64-pc-linux-gnu-gcc -shared -Wl,-O1 -Wl,--as-needed -shared -O2 -march=haswell /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/ensemble/_gradient_boosting.o -L/usr/lib64 -L/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7 -lpython2.7 -o /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/ensemble/_gradient_boosting.so
building 'sklearn.feature_extraction._hashing' extension
compiling C sources
C compiler: x86_64-pc-linux-gnu-gcc -O2 -march=haswell -fPIC

creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/feature_extraction
compile options: '-I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c'
x86_64-pc-linux-gnu-gcc: sklearn/feature_extraction/_hashing.c
In file included from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1788:0,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from sklearn/feature_extraction/_hashing.c:454:
/usr/lib64/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
 #warning "Using deprecated NumPy API, disable it by " \
  ^
x86_64-pc-linux-gnu-gcc -shared -Wl,-O1 -Wl,--as-needed -shared -O2 -march=haswell /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/feature_extraction/_hashing.o -L/usr/lib64 -L/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7 -lm -lpython2.7 -o /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/feature_extraction/_hashing.so
building 'sklearn.manifold._utils' extension
compiling C sources
C compiler: x86_64-pc-linux-gnu-gcc -O2 -march=haswell -fPIC

creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/manifold
compile options: '-I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c'
extra options: '-O3'
x86_64-pc-linux-gnu-gcc: sklearn/manifold/_utils.c
In file included from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1788:0,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from sklearn/manifold/_utils.c:458:
/usr/lib64/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
 #warning "Using deprecated NumPy API, disable it by " \
  ^
x86_64-pc-linux-gnu-gcc -shared -Wl,-O1 -Wl,--as-needed -shared -O2 -march=haswell /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/manifold/_utils.o -L/usr/lib64 -L/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7 -lm -lpython2.7 -o /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/manifold/_utils.so
building 'sklearn.manifold._barnes_hut_tsne' extension
compiling C sources
C compiler: x86_64-pc-linux-gnu-gcc -O2 -march=haswell -fPIC

compile options: '-DNO_ATLAS_INFO=1 -I../src/cblas -I/usr/lib64/python2.7/site-packages/numpy/core/include -Isklearn/manifold/ -I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c'
extra options: '-O4'
x86_64-pc-linux-gnu-gcc: sklearn/manifold/_barnes_hut_tsne.c
In file included from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1788:0,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from sklearn/manifold/_barnes_hut_tsne.c:477:
/usr/lib64/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
 #warning "Using deprecated NumPy API, disable it by " \
  ^
sklearn/manifold/_barnes_hut_tsne.c: In function '__pyx_f_7sklearn_8manifold_16_barnes_hut_tsne_insert':
sklearn/manifold/_barnes_hut_tsne.c:3648:27: warning: format '%i' expects argument of type 'int', but argument 2 has type 'long int' [-Wformat=]
     printf(((char const *)"[t-SNE] [d=%i] Inserting pos %i [%f, %f] duplicate_count=%i into child %p\n"), __pyx_v_depth, __pyx_v_point_index, (__pyx_v_pos[0]), (__pyx_v_pos[1]), __pyx_v_duplicate_count, __pyx_v_root);
                           ^
sklearn/manifold/_barnes_hut_tsne.c:3648:27: warning: format '%i' expects argument of type 'int', but argument 3 has type 'long int' [-Wformat=]
sklearn/manifold/_barnes_hut_tsne.c:3648:27: warning: format '%i' expects argument of type 'int', but argument 6 has type 'long int' [-Wformat=]
sklearn/manifold/_barnes_hut_tsne.c:3861:29: warning: format '%i' expects argument of type 'int', but argument 2 has type 'long int' [-Wformat=]
       printf(((char const *)"[t-SNE] [d=%i] Inserting [%f, %f] into blank cell\n"), __pyx_v_depth, (__pyx_v_pos[0]), (__pyx_v_pos[1]));
                             ^
sklearn/manifold/_barnes_hut_tsne.c:3947:29: warning: format '%i' expects argument of type 'int', but argument 2 has type 'long int' [-Wformat=]
       printf(((char const *)"[t-SNE] [d=%i] Node %p is occupied or is a leaf.\n"), __pyx_v_depth, __pyx_v_root);
                             ^
sklearn/manifold/_barnes_hut_tsne.c:3956:29: warning: format '%i' expects argument of type 'int', but argument 2 has type 'long int' [-Wformat=]
       printf(((char const *)"[t-SNE] [d=%i] Node %p leaf = %i. Size %i\n"), __pyx_v_depth, __pyx_v_root, __pyx_v_root->is_leaf, __pyx_v_root->size);
                             ^
sklearn/manifold/_barnes_hut_tsne.c:3956:29: warning: format '%i' expects argument of type 'int', but argument 5 has type 'long int' [-Wformat=]
sklearn/manifold/_barnes_hut_tsne.c:4042:33: warning: format '%i' expects argument of type 'int', but argument 2 has type 'long int' [-Wformat=]
           printf(((char const *)"[t-SNE] Warning: [d=%i] Detected identical points. Returning. Leaf now has size %i\n"), __pyx_v_depth, __pyx_v_root->size);
                                 ^
sklearn/manifold/_barnes_hut_tsne.c:4042:33: warning: format '%i' expects argument of type 'int', but argument 3 has type 'long int' [-Wformat=]
sklearn/manifold/_barnes_hut_tsne.c:4107:31: warning: format '%i' expects argument of type 'int', but argument 2 has type 'long int' [-Wformat=]
         printf(((char const *)"[t-SNE] [d=%i] Subdividing this leaf node %p\n"), __pyx_v_depth, __pyx_v_root);
                               ^
sklearn/manifold/_barnes_hut_tsne.c:4171:31: warning: format '%i' expects argument of type 'int', but argument 2 has type 'long int' [-Wformat=]
         printf(((char const *)"[t-SNE] [d=%i] Relocating old point to node %p\n"), __pyx_v_depth, __pyx_v_child);
                               ^
sklearn/manifold/_barnes_hut_tsne.c:4216:29: warning: format '%i' expects argument of type 'int', but argument 2 has type 'long int' [-Wformat=]
       printf(((char const *)"[t-SNE] [d=%i] Selecting node for new point\n"), __pyx_v_depth);
                             ^
sklearn/manifold/_barnes_hut_tsne.c: In function '__pyx_f_7sklearn_8manifold_16_barnes_hut_tsne_insert_many':
sklearn/manifold/_barnes_hut_tsne.c:4409:29: warning: format '%i' expects argument of type 'int', but argument 2 has type 'long int' [-Wformat=]
       printf(((char const *)"[t-SNE] inserting point %i: [%f, %f]\n"), __pyx_v_i, (__pyx_v_row[0]), (__pyx_v_row[1]));
                             ^
sklearn/manifold/_barnes_hut_tsne.c: In function '__pyx_f_7sklearn_8manifold_16_barnes_hut_tsne_count_points':
sklearn/manifold/_barnes_hut_tsne.c:4997:29: warning: format '%i' expects argument of type 'int', but argument 2 has type 'long int' [-Wformat=]
       printf(((char const *)"[t-SNE] %i points in node %p\n"), __pyx_v_count, __pyx_v_root);
                             ^
sklearn/manifold/_barnes_hut_tsne.c:5108:31: warning: format '%d' expects argument of type 'int', but argument 2 has type 'long int' [-Wformat=]
         printf(((char const *)"[t-SNE] Child has size %d\n"), __pyx_v_child->size);
                               ^
sklearn/manifold/_barnes_hut_tsne.c:5211:27: warning: format '%i' expects argument of type 'int', but argument 2 has type 'long int' [-Wformat=]
     printf(((char const *)"[t-SNE] %i points in this node\n"), __pyx_v_count);
                           ^
sklearn/manifold/_barnes_hut_tsne.c: In function '__pyx_f_7sklearn_8manifold_16_barnes_hut_tsne_compute_gradient':
sklearn/manifold/_barnes_hut_tsne.c:5312:27: warning: format '%i' expects argument of type 'int', but argument 2 has type 'long int' [-Wformat=]
     printf(((char const *)"[t-SNE] Allocating %i elements in force arrays\n"), ((__pyx_v_n * __pyx_v_n_dimensions) * 2));
                           ^
sklearn/manifold/_barnes_hut_tsne.c: In function '__pyx_f_7sklearn_8manifold_16_barnes_hut_tsne_compute_gradient_negative':
sklearn/manifold/_barnes_hut_tsne.c:6346:27: warning: format '%i' expects argument of type 'int', but argument 2 has type 'long int' [-Wformat=]
     printf(((char const *)"[t-SNE] Tree: %i clock ticks | "), __pyx_v_dta);
                           ^
sklearn/manifold/_barnes_hut_tsne.c:6355:27: warning: format '%i' expects argument of type 'int', but argument 2 has type 'long int' [-Wformat=]
     printf(((char const *)"Force computation: %i clock ticks\n"), __pyx_v_dtb);
                           ^
sklearn/manifold/_barnes_hut_tsne.c: In function '__pyx_pf_7sklearn_8manifold_16_barnes_hut_tsne_2gradient':
sklearn/manifold/_barnes_hut_tsne.c:7992:27: warning: format '%i' expects argument of type 'int', but argument 2 has type 'Py_ssize_t {aka long int}' [-Wformat=]
     printf(((char const *)"[t-SNE] Inserting %i points\n"), (__pyx_v_pos_output.shape[0]));
                           ^
x86_64-pc-linux-gnu-gcc -shared -Wl,-O1 -Wl,--as-needed -shared -O2 -march=haswell /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/manifold/_barnes_hut_tsne.o -L/usr/lib64 -L/usr/lib64 -L/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7 -lblas -lcblas -lpython2.7 -o /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/manifold/_barnes_hut_tsne.so
building 'sklearn.metrics.pairwise_fast' extension
compiling C sources
C compiler: x86_64-pc-linux-gnu-gcc -O2 -march=haswell -fPIC

creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/metrics
compile options: '-DNO_ATLAS_INFO=1 -I../src/cblas -I/usr/lib64/python2.7/site-packages/numpy/core/include -Isklearn/metrics/ -I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c'
x86_64-pc-linux-gnu-gcc: sklearn/metrics/pairwise_fast.c
In file included from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1788:0,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from sklearn/metrics/pairwise_fast.c:474:
/usr/lib64/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
 #warning "Using deprecated NumPy API, disable it by " \
  ^
x86_64-pc-linux-gnu-gcc -shared -Wl,-O1 -Wl,--as-needed -shared -O2 -march=haswell /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/metrics/pairwise_fast.o -L/usr/lib64 -L/usr/lib64 -L/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7 -lblas -lcblas -lm -lpython2.7 -o /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/metrics/pairwise_fast.so
building 'sklearn.metrics/cluster.expected_mutual_info_fast' extension
compiling C sources
C compiler: x86_64-pc-linux-gnu-gcc -O2 -march=haswell -fPIC

creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/metrics/cluster
compile options: '-I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c'
x86_64-pc-linux-gnu-gcc: sklearn/metrics/cluster/expected_mutual_info_fast.c
In file included from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1788:0,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from sklearn/metrics/cluster/expected_mutual_info_fast.c:454:
/usr/lib64/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
 #warning "Using deprecated NumPy API, disable it by " \
  ^
x86_64-pc-linux-gnu-gcc -shared -Wl,-O1 -Wl,--as-needed -shared -O2 -march=haswell /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/metrics/cluster/expected_mutual_info_fast.o -L/usr/lib64 -L/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7 -lm -lpython2.7 -o /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/metrics/cluster/expected_mutual_info_fast.so
building 'sklearn.neighbors.ball_tree' extension
compiling C sources
C compiler: x86_64-pc-linux-gnu-gcc -O2 -march=haswell -fPIC

creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/neighbors
compile options: '-I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c'
x86_64-pc-linux-gnu-gcc: sklearn/neighbors/ball_tree.c
In file included from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1788:0,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from sklearn/neighbors/ball_tree.c:453:
/usr/lib64/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
 #warning "Using deprecated NumPy API, disable it by " \
  ^
x86_64-pc-linux-gnu-gcc -shared -Wl,-O1 -Wl,--as-needed -shared -O2 -march=haswell /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/neighbors/ball_tree.o -L/usr/lib64 -L/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7 -lm -lpython2.7 -o /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/neighbors/ball_tree.so
building 'sklearn.neighbors.kd_tree' extension
compiling C sources
C compiler: x86_64-pc-linux-gnu-gcc -O2 -march=haswell -fPIC

compile options: '-I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c'
x86_64-pc-linux-gnu-gcc: sklearn/neighbors/kd_tree.c
In file included from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1788:0,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from sklearn/neighbors/kd_tree.c:453:
/usr/lib64/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
 #warning "Using deprecated NumPy API, disable it by " \
  ^
x86_64-pc-linux-gnu-gcc -shared -Wl,-O1 -Wl,--as-needed -shared -O2 -march=haswell /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/neighbors/kd_tree.o -L/usr/lib64 -L/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7 -lm -lpython2.7 -o /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/neighbors/kd_tree.so
building 'sklearn.neighbors.dist_metrics' extension
compiling C sources
C compiler: x86_64-pc-linux-gnu-gcc -O2 -march=haswell -fPIC

compile options: '-I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/lib64/python2.7/site-packages/numpy/core/include/numpy -I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c'
x86_64-pc-linux-gnu-gcc: sklearn/neighbors/dist_metrics.c
In file included from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1788:0,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from sklearn/neighbors/dist_metrics.c:454:
/usr/lib64/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
 #warning "Using deprecated NumPy API, disable it by " \
  ^
sklearn/neighbors/dist_metrics.c: In function '__pyx_f_7sklearn_9neighbors_12dist_metrics_18SEuclideanDistance_dist':
sklearn/neighbors/dist_metrics.c:6715:85: warning: passing argument 1 of '__pyx_f_7sklearn_9neighbors_12dist_metrics_18SEuclideanDistance_rdist' from incompatible pointer type [-Wincompatible-pointer-types]
   __pyx_t_1 = __pyx_f_7sklearn_9neighbors_12dist_metrics_18SEuclideanDistance_rdist(((struct __pyx_obj_7sklearn_9neighbors_12dist_metrics_DistanceMetric *)__pyx_v_self), __pyx_v_x1, __pyx_v_x2, __pyx_v_size); if (unlikely(__pyx_t_1 == -1.0)) __PYX_ERR(1, 464, __pyx_L1_error)
                                                                                     ^
sklearn/neighbors/dist_metrics.c:6520:54: note: expected 'struct __pyx_obj_7sklearn_9neighbors_12dist_metrics_SEuclideanDistance *' but argument is of type 'struct __pyx_obj_7sklearn_9neighbors_12dist_metrics_DistanceMetric *'
 static __pyx_t_7sklearn_9neighbors_8typedefs_DTYPE_t __pyx_f_7sklearn_9neighbors_12dist_metrics_18SEuclideanDistance_rdist(struct __pyx_obj_7sklearn_9neighbors_12dist_metrics_SEuclideanDistance *__pyx_v_self, __pyx_t_7sklearn_9neighbors_8typedefs_DTYPE_t *__pyx_v_x1, __pyx_t_7sklearn_9neighbors_8typedefs_DTYPE_t *__pyx_v_x2, __pyx_t_7sklearn_9neighbors_8typedefs_ITYPE_t __pyx_v_size) {
                                                      ^
sklearn/neighbors/dist_metrics.c: In function '__pyx_f_7sklearn_9neighbors_12dist_metrics_17MinkowskiDistance_dist':
sklearn/neighbors/dist_metrics.c:7529:84: warning: passing argument 1 of '__pyx_f_7sklearn_9neighbors_12dist_metrics_17MinkowskiDistance_rdist' from incompatible pointer type [-Wincompatible-pointer-types]
   __pyx_t_1 = __pyx_f_7sklearn_9neighbors_12dist_metrics_17MinkowskiDistance_rdist(((struct __pyx_obj_7sklearn_9neighbors_12dist_metrics_DistanceMetric *)__pyx_v_self), __pyx_v_x1, __pyx_v_x2, __pyx_v_size); if (unlikely(__pyx_t_1 == -1.0)) __PYX_ERR(1, 553, __pyx_L1_error)
                                                                                    ^
sklearn/neighbors/dist_metrics.c:7450:54: note: expected 'struct __pyx_obj_7sklearn_9neighbors_12dist_metrics_MinkowskiDistance *' but argument is of type 'struct __pyx_obj_7sklearn_9neighbors_12dist_metrics_DistanceMetric *'
 static __pyx_t_7sklearn_9neighbors_8typedefs_DTYPE_t __pyx_f_7sklearn_9neighbors_12dist_metrics_17MinkowskiDistance_rdist(struct __pyx_obj_7sklearn_9neighbors_12dist_metrics_MinkowskiDistance *__pyx_v_self, __pyx_t_7sklearn_9neighbors_8typedefs_DTYPE_t *__pyx_v_x1, __pyx_t_7sklearn_9neighbors_8typedefs_DTYPE_t *__pyx_v_x2, __pyx_t_7sklearn_9neighbors_8typedefs_ITYPE_t __pyx_v_size) {
                                                      ^
sklearn/neighbors/dist_metrics.c: In function '__pyx_f_7sklearn_9neighbors_12dist_metrics_18WMinkowskiDistance_dist':
sklearn/neighbors/dist_metrics.c:8232:85: warning: passing argument 1 of '__pyx_f_7sklearn_9neighbors_12dist_metrics_18WMinkowskiDistance_rdist' from incompatible pointer type [-Wincompatible-pointer-types]
   __pyx_t_1 = __pyx_f_7sklearn_9neighbors_12dist_metrics_18WMinkowskiDistance_rdist(((struct __pyx_obj_7sklearn_9neighbors_12dist_metrics_DistanceMetric *)__pyx_v_self), __pyx_v_x1, __pyx_v_x2, __pyx_v_size); if (unlikely(__pyx_t_1 == -1.0)) __PYX_ERR(1, 612, __pyx_L1_error)
                                                                                     ^
sklearn/neighbors/dist_metrics.c:8047:54: note: expected 'struct __pyx_obj_7sklearn_9neighbors_12dist_metrics_WMinkowskiDistance *' but argument is of type 'struct __pyx_obj_7sklearn_9neighbors_12dist_metrics_DistanceMetric *'
 static __pyx_t_7sklearn_9neighbors_8typedefs_DTYPE_t __pyx_f_7sklearn_9neighbors_12dist_metrics_18WMinkowskiDistance_rdist(struct __pyx_obj_7sklearn_9neighbors_12dist_metrics_WMinkowskiDistance *__pyx_v_self, __pyx_t_7sklearn_9neighbors_8typedefs_DTYPE_t *__pyx_v_x1, __pyx_t_7sklearn_9neighbors_8typedefs_DTYPE_t *__pyx_v_x2, __pyx_t_7sklearn_9neighbors_8typedefs_ITYPE_t __pyx_v_size) {
                                                      ^
sklearn/neighbors/dist_metrics.c: In function '__pyx_f_7sklearn_9neighbors_12dist_metrics_19MahalanobisDistance_dist':
sklearn/neighbors/dist_metrics.c:9093:86: warning: passing argument 1 of '__pyx_f_7sklearn_9neighbors_12dist_metrics_19MahalanobisDistance_rdist' from incompatible pointer type [-Wincompatible-pointer-types]
   __pyx_t_1 = __pyx_f_7sklearn_9neighbors_12dist_metrics_19MahalanobisDistance_rdist(((struct __pyx_obj_7sklearn_9neighbors_12dist_metrics_DistanceMetric *)__pyx_v_self), __pyx_v_x1, __pyx_v_x2, __pyx_v_size); if (unlikely(__pyx_t_1 == -1.0)) __PYX_ERR(1, 685, __pyx_L1_error)
                                                                                      ^
sklearn/neighbors/dist_metrics.c:8853:54: note: expected 'struct __pyx_obj_7sklearn_9neighbors_12dist_metrics_MahalanobisDistance *' but argument is of type 'struct __pyx_obj_7sklearn_9neighbors_12dist_metrics_DistanceMetric *'
 static __pyx_t_7sklearn_9neighbors_8typedefs_DTYPE_t __pyx_f_7sklearn_9neighbors_12dist_metrics_19MahalanobisDistance_rdist(struct __pyx_obj_7sklearn_9neighbors_12dist_metrics_MahalanobisDistance *__pyx_v_self, __pyx_t_7sklearn_9neighbors_8typedefs_DTYPE_t *__pyx_v_x1, __pyx_t_7sklearn_9neighbors_8typedefs_DTYPE_t *__pyx_v_x2, __pyx_t_7sklearn_9neighbors_8typedefs_ITYPE_t __pyx_v_size) {
                                                      ^
x86_64-pc-linux-gnu-gcc -shared -Wl,-O1 -Wl,--as-needed -shared -O2 -march=haswell /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/neighbors/dist_metrics.o -L/usr/lib64 -L/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7 -lm -lpython2.7 -o /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/neighbors/dist_metrics.so
building 'sklearn.neighbors.typedefs' extension
compiling C sources
C compiler: x86_64-pc-linux-gnu-gcc -O2 -march=haswell -fPIC

compile options: '-I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c'
x86_64-pc-linux-gnu-gcc: sklearn/neighbors/typedefs.c
In file included from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1788:0,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from sklearn/neighbors/typedefs.c:453:
/usr/lib64/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
 #warning "Using deprecated NumPy API, disable it by " \
  ^
x86_64-pc-linux-gnu-gcc -shared -Wl,-O1 -Wl,--as-needed -shared -O2 -march=haswell /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/neighbors/typedefs.o -L/usr/lib64 -L/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7 -lm -lpython2.7 -o /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/neighbors/typedefs.so
building 'sklearn.tree._tree' extension
compiling C sources
C compiler: x86_64-pc-linux-gnu-gcc -O2 -march=haswell -fPIC

creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/tree
compile options: '-I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c'
extra options: '-O3'
x86_64-pc-linux-gnu-gcc: sklearn/tree/_tree.c
In file included from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1788:0,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from sklearn/tree/_tree.c:456:
/usr/lib64/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
 #warning "Using deprecated NumPy API, disable it by " \
  ^
sklearn/tree/_tree.c: In function '__pyx_f_7sklearn_4tree_5_tree_4Tree__get_node_ndarray':
sklearn/tree/_tree.c:13694:36: warning: passing argument 1 of '(PyObject * (*)(PyTypeObject *, PyArray_Descr *, int,  npy_intp *, npy_intp *, void *, int,  PyObject *))*(PyArray_API + 752u)' from incompatible pointer type [-Wincompatible-pointer-types]
   __pyx_t_2 = PyArray_NewFromDescr(((PyObject *)__pyx_ptype_5numpy_ndarray), ((PyArray_Descr *)__pyx_t_1), 1, __pyx_v_shape, __pyx_v_strides, ((void *)__pyx_v_self->nodes), NPY_DEFAULT, Py_None); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 1103, __pyx_L1_error)
                                    ^
sklearn/tree/_tree.c:13694:36: note: expected 'PyTypeObject * {aka struct _typeobject *}' but argument is of type 'PyObject * {aka struct _object *}'
x86_64-pc-linux-gnu-gcc -shared -Wl,-O1 -Wl,--as-needed -shared -O2 -march=haswell /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/tree/_tree.o -L/usr/lib64 -L/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7 -lm -lpython2.7 -o /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/tree/_tree.so
building 'sklearn.tree._splitter' extension
compiling C sources
C compiler: x86_64-pc-linux-gnu-gcc -O2 -march=haswell -fPIC

compile options: '-I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c'
extra options: '-O3'
x86_64-pc-linux-gnu-gcc: sklearn/tree/_splitter.c
In file included from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1788:0,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from sklearn/tree/_splitter.c:456:
/usr/lib64/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
 #warning "Using deprecated NumPy API, disable it by " \
  ^
x86_64-pc-linux-gnu-gcc -shared -Wl,-O1 -Wl,--as-needed -shared -O2 -march=haswell /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/tree/_splitter.o -L/usr/lib64 -L/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7 -lm -lpython2.7 -o /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/tree/_splitter.so
building 'sklearn.tree._criterion' extension
compiling C sources
C compiler: x86_64-pc-linux-gnu-gcc -O2 -march=haswell -fPIC

compile options: '-I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c'
extra options: '-O3'
x86_64-pc-linux-gnu-gcc: sklearn/tree/_criterion.c
In file included from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1788:0,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from sklearn/tree/_criterion.c:456:
/usr/lib64/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
 #warning "Using deprecated NumPy API, disable it by " \
  ^
x86_64-pc-linux-gnu-gcc -shared -Wl,-O1 -Wl,--as-needed -shared -O2 -march=haswell /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/tree/_criterion.o -L/usr/lib64 -L/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7 -lm -lpython2.7 -o /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/tree/_criterion.so
building 'sklearn.tree._utils' extension
compiling C sources
C compiler: x86_64-pc-linux-gnu-gcc -O2 -march=haswell -fPIC

compile options: '-I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c'
extra options: '-O3'
x86_64-pc-linux-gnu-gcc: sklearn/tree/_utils.c
In file included from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1788:0,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from sklearn/tree/_utils.c:456:
/usr/lib64/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
 #warning "Using deprecated NumPy API, disable it by " \
  ^
x86_64-pc-linux-gnu-gcc -shared -Wl,-O1 -Wl,--as-needed -shared -O2 -march=haswell /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/tree/_utils.o -L/usr/lib64 -L/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7 -lm -lpython2.7 -o /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/tree/_utils.so
building 'sklearn.svm.libsvm' extension
compiling C sources
C compiler: x86_64-pc-linux-gnu-gcc -O2 -march=haswell -fPIC

compile options: '-I/usr/lib64/python2.7/site-packages/numpy/core/include -Isklearn/svm/src/libsvm -I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c'
x86_64-pc-linux-gnu-gcc: sklearn/svm/libsvm.c
In file included from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1788:0,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from sklearn/svm/libsvm.c:458:
/usr/lib64/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
 #warning "Using deprecated NumPy API, disable it by " \
  ^
x86_64-pc-linux-gnu-g++ -shared -Wl,-O1 -Wl,--as-needed -shared -O2 -march=haswell /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/svm/libsvm.o -L/usr/lib64 -L/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7 -llibsvm-skl -lpython2.7 -o /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/svm/libsvm.so
building 'sklearn.svm.liblinear' extension
compiling C sources
C compiler: x86_64-pc-linux-gnu-gcc -O2 -march=haswell -fPIC

compile options: '-DNO_ATLAS_INFO=1 -I../src/cblas -I/usr/lib64/python2.7/site-packages/numpy/core/include -Isklearn/svm/ -I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c'
x86_64-pc-linux-gnu-gcc: sklearn/svm/liblinear.c
In file included from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1788:0,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from sklearn/svm/liblinear.c:476:
/usr/lib64/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
 #warning "Using deprecated NumPy API, disable it by " \
  ^
In file included from sklearn/svm/liblinear.c:479:0:
sklearn/svm/src/liblinear/liblinear_helper.c: In function 'set_problem':
sklearn/svm/src/liblinear/liblinear_helper.c:145:28: warning: assignment from incompatible pointer type [-Wincompatible-pointer-types]
     problem->sample_weight = sample_weight;
                            ^
sklearn/svm/src/liblinear/liblinear_helper.c: In function 'csr_set_problem':
sklearn/svm/src/liblinear/liblinear_helper.c:174:28: warning: assignment from incompatible pointer type [-Wincompatible-pointer-types]
     problem->sample_weight = sample_weight;
                            ^
compiling C++ sources
C compiler: x86_64-pc-linux-gnu-g++ -O2 -march=haswell -fPIC

creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/svm/src/liblinear
compile options: '-DNO_ATLAS_INFO=1 -I../src/cblas -I/usr/lib64/python2.7/site-packages/numpy/core/include -Isklearn/svm/ -I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c'
x86_64-pc-linux-gnu-g++: sklearn/svm/src/liblinear/tron.cpp
x86_64-pc-linux-gnu-g++: sklearn/svm/src/liblinear/linear.cpp
x86_64-pc-linux-gnu-g++ -shared -Wl,-O1 -Wl,--as-needed -shared -O2 -march=haswell /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/svm/liblinear.o /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/svm/src/liblinear/tron.o /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/svm/src/liblinear/linear.o -L/usr/lib64 -L/usr/lib64 -L/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7 -lblas -lcblas -lm -lpython2.7 -o /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/svm/liblinear.so
building 'sklearn.svm.libsvm_sparse' extension
compiling C sources
C compiler: x86_64-pc-linux-gnu-gcc -O2 -march=haswell -fPIC

compile options: '-I/usr/lib64/python2.7/site-packages/numpy/core/include -Isklearn/svm/src/libsvm -I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c'
x86_64-pc-linux-gnu-gcc: sklearn/svm/libsvm_sparse.c
In file included from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1788:0,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from sklearn/svm/libsvm_sparse.c:456:
/usr/lib64/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
 #warning "Using deprecated NumPy API, disable it by " \
  ^
x86_64-pc-linux-gnu-g++ -shared -Wl,-O1 -Wl,--as-needed -shared -O2 -march=haswell /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/svm/libsvm_sparse.o -L/usr/lib64 -L/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7 -llibsvm-skl -lpython2.7 -o /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/svm/libsvm_sparse.so
building 'sklearn._isotonic' extension
compiling C sources
C compiler: x86_64-pc-linux-gnu-gcc -O2 -march=haswell -fPIC

compile options: '-I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c'
x86_64-pc-linux-gnu-gcc: sklearn/_isotonic.c
In file included from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1788:0,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from sklearn/_isotonic.c:453:
/usr/lib64/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
 #warning "Using deprecated NumPy API, disable it by " \
  ^
x86_64-pc-linux-gnu-gcc -shared -Wl,-O1 -Wl,--as-needed -shared -O2 -march=haswell /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/_isotonic.o -L/usr/lib64 -L/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7 -lm -lpython2.7 -o /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/_isotonic.so
building 'sklearn.linear_model.cd_fast' extension
compiling C sources
C compiler: x86_64-pc-linux-gnu-gcc -O2 -march=haswell -fPIC

creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/linear_model
compile options: '-DNO_ATLAS_INFO=1 -I../src/cblas -I/usr/lib64/python2.7/site-packages/numpy/core/include -Isklearn/linear_model/ -I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c'
x86_64-pc-linux-gnu-gcc: sklearn/linear_model/cd_fast.c
In file included from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1788:0,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from sklearn/linear_model/cd_fast.c:475:
/usr/lib64/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
 #warning "Using deprecated NumPy API, disable it by " \
  ^
sklearn/linear_model/cd_fast.c: In function '__pyx_pf_7sklearn_12linear_model_7cd_fast_8enet_coordinate_descent':
sklearn/linear_model/cd_fast.c:4567:15: warning: assignment from incompatible pointer type [-Wincompatible-pointer-types]
   __pyx_v_dot = cblas_sdot;
               ^
sklearn/linear_model/cd_fast.c:4576:16: warning: assignment from incompatible pointer type [-Wincompatible-pointer-types]
   __pyx_v_axpy = cblas_saxpy;
                ^
sklearn/linear_model/cd_fast.c:4585:16: warning: assignment from incompatible pointer type [-Wincompatible-pointer-types]
   __pyx_v_asum = cblas_sasum;
                ^
sklearn/linear_model/cd_fast.c: In function '__pyx_pf_7sklearn_12linear_model_7cd_fast_10enet_coordinate_descent':
sklearn/linear_model/cd_fast.c:5982:15: warning: assignment from incompatible pointer type [-Wincompatible-pointer-types]
   __pyx_v_dot = cblas_ddot;
               ^
sklearn/linear_model/cd_fast.c:5991:16: warning: assignment from incompatible pointer type [-Wincompatible-pointer-types]
   __pyx_v_axpy = cblas_daxpy;
                ^
sklearn/linear_model/cd_fast.c:6000:16: warning: assignment from incompatible pointer type [-Wincompatible-pointer-types]
   __pyx_v_asum = cblas_dasum;
                ^
sklearn/linear_model/cd_fast.c: In function '__pyx_pf_7sklearn_12linear_model_7cd_fast_14sparse_enet_coordinate_descent':
sklearn/linear_model/cd_fast.c:8167:15: warning: assignment from incompatible pointer type [-Wincompatible-pointer-types]
   __pyx_v_dot = cblas_sdot;
               ^
sklearn/linear_model/cd_fast.c:8176:16: warning: assignment from incompatible pointer type [-Wincompatible-pointer-types]
   __pyx_v_asum = cblas_sasum;
                ^
sklearn/linear_model/cd_fast.c: In function '__pyx_pf_7sklearn_12linear_model_7cd_fast_16sparse_enet_coordinate_descent':
sklearn/linear_model/cd_fast.c:10117:15: warning: assignment from incompatible pointer type [-Wincompatible-pointer-types]
   __pyx_v_dot = cblas_ddot;
               ^
sklearn/linear_model/cd_fast.c:10126:16: warning: assignment from incompatible pointer type [-Wincompatible-pointer-types]
   __pyx_v_asum = cblas_dasum;
                ^
sklearn/linear_model/cd_fast.c: In function '__pyx_pf_7sklearn_12linear_model_7cd_fast_20enet_coordinate_descent_gram':
sklearn/linear_model/cd_fast.c:12557:15: warning: assignment from incompatible pointer type [-Wincompatible-pointer-types]
   __pyx_v_dot = cblas_sdot;
               ^
sklearn/linear_model/cd_fast.c:12566:16: warning: assignment from incompatible pointer type [-Wincompatible-pointer-types]
   __pyx_v_axpy = cblas_saxpy;
                ^
sklearn/linear_model/cd_fast.c:12575:16: warning: assignment from incompatible pointer type [-Wincompatible-pointer-types]
   __pyx_v_asum = cblas_sasum;
                ^
sklearn/linear_model/cd_fast.c: In function '__pyx_pf_7sklearn_12linear_model_7cd_fast_22enet_coordinate_descent_gram':
sklearn/linear_model/cd_fast.c:14060:15: warning: assignment from incompatible pointer type [-Wincompatible-pointer-types]
   __pyx_v_dot = cblas_ddot;
               ^
sklearn/linear_model/cd_fast.c:14069:16: warning: assignment from incompatible pointer type [-Wincompatible-pointer-types]
   __pyx_v_axpy = cblas_daxpy;
                ^
sklearn/linear_model/cd_fast.c:14078:16: warning: assignment from incompatible pointer type [-Wincompatible-pointer-types]
   __pyx_v_asum = cblas_dasum;
                ^
x86_64-pc-linux-gnu-gcc -shared -Wl,-O1 -Wl,--as-needed -shared -O2 -march=haswell /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/linear_model/cd_fast.o -L/usr/lib64 -L/usr/lib64 -L/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7 -lblas -lcblas -lm -lpython2.7 -o /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/linear_model/cd_fast.so
building 'sklearn.linear_model.sgd_fast' extension
compiling C sources
C compiler: x86_64-pc-linux-gnu-gcc -O2 -march=haswell -fPIC

compile options: '-DNO_ATLAS_INFO=1 -I../src/cblas -I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c'
x86_64-pc-linux-gnu-gcc: sklearn/linear_model/sgd_fast.c
In file included from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1788:0,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from sklearn/linear_model/sgd_fast.c:474:
/usr/lib64/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
 #warning "Using deprecated NumPy API, disable it by " \
  ^
x86_64-pc-linux-gnu-gcc -shared -Wl,-O1 -Wl,--as-needed -shared -O2 -march=haswell /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/linear_model/sgd_fast.o -L/usr/lib64 -L/usr/lib64 -L/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7 -lblas -lcblas -lm -lpython2.7 -o /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/linear_model/sgd_fast.so
building 'sklearn.linear_model.sag_fast' extension
compiling C sources
C compiler: x86_64-pc-linux-gnu-gcc -O2 -march=haswell -fPIC

compile options: '-I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c'
x86_64-pc-linux-gnu-gcc: sklearn/linear_model/sag_fast.c
In file included from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1788:0,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from sklearn/linear_model/sag_fast.c:451:
/usr/lib64/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
 #warning "Using deprecated NumPy API, disable it by " \
  ^
x86_64-pc-linux-gnu-gcc -shared -Wl,-O1 -Wl,--as-needed -shared -O2 -march=haswell /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/linear_model/sag_fast.o -L/usr/lib64 -L/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7 -lpython2.7 -o /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/linear_model/sag_fast.so
building 'sklearn.utils.sparsetools._traversal' extension
compiling C sources
C compiler: x86_64-pc-linux-gnu-gcc -O2 -march=haswell -fPIC

creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/utils
creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/utils/sparsetools
compile options: '-I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c'
x86_64-pc-linux-gnu-gcc: sklearn/utils/sparsetools/_traversal.c
In file included from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1788:0,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from sklearn/utils/sparsetools/_traversal.c:450:
/usr/lib64/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
 #warning "Using deprecated NumPy API, disable it by " \
  ^
x86_64-pc-linux-gnu-gcc -shared -Wl,-O1 -Wl,--as-needed -shared -O2 -march=haswell /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/utils/sparsetools/_traversal.o -L/usr/lib64 -L/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7 -lpython2.7 -o /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils/sparsetools/_traversal.so
building 'sklearn.utils.sparsetools._graph_tools' extension
compiling C sources
C compiler: x86_64-pc-linux-gnu-gcc -O2 -march=haswell -fPIC

compile options: '-I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c'
x86_64-pc-linux-gnu-gcc: sklearn/utils/sparsetools/_graph_tools.c
In file included from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1788:0,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from sklearn/utils/sparsetools/_graph_tools.c:450:
/usr/lib64/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
 #warning "Using deprecated NumPy API, disable it by " \
  ^
x86_64-pc-linux-gnu-gcc -shared -Wl,-O1 -Wl,--as-needed -shared -O2 -march=haswell /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/utils/sparsetools/_graph_tools.o -L/usr/lib64 -L/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7 -lpython2.7 -o /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils/sparsetools/_graph_tools.so
building 'sklearn.utils.sparsefuncs_fast' extension
compiling C sources
C compiler: x86_64-pc-linux-gnu-gcc -O2 -march=haswell -fPIC

compile options: '-I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c'
x86_64-pc-linux-gnu-gcc: sklearn/utils/sparsefuncs_fast.c
In file included from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1788:0,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from sklearn/utils/sparsefuncs_fast.c:448:
/usr/lib64/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
 #warning "Using deprecated NumPy API, disable it by " \
  ^
x86_64-pc-linux-gnu-gcc -shared -Wl,-O1 -Wl,--as-needed -shared -O2 -march=haswell /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/utils/sparsefuncs_fast.o -L/usr/lib64 -L/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7 -lm -lpython2.7 -o /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils/sparsefuncs_fast.so
building 'sklearn.utils.arrayfuncs' extension
compiling C sources
C compiler: x86_64-pc-linux-gnu-gcc -O2 -march=haswell -fPIC

compile options: '-DNO_ATLAS_INFO=1 -I../src/cblas -I/usr/lib64/python2.7/site-packages/numpy/core/include -Isklearn/utils/ -I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c'
x86_64-pc-linux-gnu-gcc: sklearn/utils/arrayfuncs.c
In file included from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1788:0,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from sklearn/utils/arrayfuncs.c:474:
/usr/lib64/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
 #warning "Using deprecated NumPy API, disable it by " \
  ^
x86_64-pc-linux-gnu-gcc -shared -Wl,-O1 -Wl,--as-needed -shared -O2 -march=haswell /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/utils/arrayfuncs.o -L/usr/lib64 -L/usr/lib64 -L/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7 -lblas -lcblas -lm -lpython2.7 -o /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils/arrayfuncs.so
building 'sklearn.utils.murmurhash' extension
compiling C sources
C compiler: x86_64-pc-linux-gnu-gcc -O2 -march=haswell -fPIC

compile options: '-Isklearn/utils/src -I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c'
x86_64-pc-linux-gnu-gcc: sklearn/utils/murmurhash.c
In file included from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1788:0,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from sklearn/utils/murmurhash.c:449:
/usr/lib64/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
 #warning "Using deprecated NumPy API, disable it by " \
  ^
compiling C++ sources
C compiler: x86_64-pc-linux-gnu-g++ -O2 -march=haswell -fPIC

creating /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/utils/src
compile options: '-Isklearn/utils/src -I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c'
x86_64-pc-linux-gnu-g++: sklearn/utils/src/MurmurHash3.cpp
x86_64-pc-linux-gnu-g++ -shared -Wl,-O1 -Wl,--as-needed -shared -O2 -march=haswell /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/utils/murmurhash.o /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/utils/src/MurmurHash3.o -L/usr/lib64 -L/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7 -lpython2.7 -o /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils/murmurhash.so
building 'sklearn.utils.lgamma' extension
compiling C sources
C compiler: x86_64-pc-linux-gnu-gcc -O2 -march=haswell -fPIC

compile options: '-Isklearn/utils/src -I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c'
x86_64-pc-linux-gnu-gcc: sklearn/utils/src/gamma.c
x86_64-pc-linux-gnu-gcc: sklearn/utils/lgamma.c
x86_64-pc-linux-gnu-gcc -shared -Wl,-O1 -Wl,--as-needed -shared -O2 -march=haswell /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/utils/lgamma.o /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/utils/src/gamma.o -L/usr/lib64 -L/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7 -lm -lpython2.7 -o /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils/lgamma.so
building 'sklearn.utils.graph_shortest_path' extension
compiling C sources
C compiler: x86_64-pc-linux-gnu-gcc -O2 -march=haswell -fPIC

compile options: '-I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c'
x86_64-pc-linux-gnu-gcc: sklearn/utils/graph_shortest_path.c
In file included from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1788:0,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from sklearn/utils/graph_shortest_path.c:450:
/usr/lib64/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
 #warning "Using deprecated NumPy API, disable it by " \
  ^
x86_64-pc-linux-gnu-gcc -shared -Wl,-O1 -Wl,--as-needed -shared -O2 -march=haswell /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/utils/graph_shortest_path.o -L/usr/lib64 -L/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7 -lpython2.7 -o /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils/graph_shortest_path.so
building 'sklearn.utils.fast_dict' extension
compiling C++ sources
C compiler: x86_64-pc-linux-gnu-g++ -O2 -march=haswell -fPIC

compile options: '-I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c'
x86_64-pc-linux-gnu-g++: sklearn/utils/fast_dict.cpp
In file included from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1788:0,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from sklearn/utils/fast_dict.cpp:474:
/usr/lib64/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
 #warning "Using deprecated NumPy API, disable it by " \
  ^
x86_64-pc-linux-gnu-g++ -shared -Wl,-O1 -Wl,--as-needed -shared -O2 -march=haswell /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/utils/fast_dict.o -L/usr/lib64 -L/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7 -lm -lpython2.7 -o /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils/fast_dict.so
building 'sklearn.utils.seq_dataset' extension
compiling C sources
C compiler: x86_64-pc-linux-gnu-gcc -O2 -march=haswell -fPIC

compile options: '-I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c'
x86_64-pc-linux-gnu-gcc: sklearn/utils/seq_dataset.c
In file included from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1788:0,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from sklearn/utils/seq_dataset.c:450:
/usr/lib64/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
 #warning "Using deprecated NumPy API, disable it by " \
  ^
x86_64-pc-linux-gnu-gcc -shared -Wl,-O1 -Wl,--as-needed -shared -O2 -march=haswell /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/utils/seq_dataset.o -L/usr/lib64 -L/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7 -lpython2.7 -o /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils/seq_dataset.so
building 'sklearn.utils.weight_vector' extension
compiling C sources
C compiler: x86_64-pc-linux-gnu-gcc -O2 -march=haswell -fPIC

compile options: '-DNO_ATLAS_INFO=1 -I../src/cblas -I/usr/lib64/python2.7/site-packages/numpy/core/include -Isklearn/utils/ -I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c'
x86_64-pc-linux-gnu-gcc: sklearn/utils/weight_vector.c
In file included from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1788:0,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from sklearn/utils/weight_vector.c:474:
/usr/lib64/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
 #warning "Using deprecated NumPy API, disable it by " \
  ^
x86_64-pc-linux-gnu-gcc -shared -Wl,-O1 -Wl,--as-needed -shared -O2 -march=haswell /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/utils/weight_vector.o -L/usr/lib64 -L/usr/lib64 -L/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7 -lblas -lcblas -lm -lpython2.7 -o /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils/weight_vector.so
building 'sklearn.utils._random' extension
compiling C sources
C compiler: x86_64-pc-linux-gnu-gcc -O2 -march=haswell -fPIC

compile options: '-I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c'
x86_64-pc-linux-gnu-gcc: sklearn/utils/_random.c
In file included from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1788:0,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from sklearn/utils/_random.c:453:
/usr/lib64/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
 #warning "Using deprecated NumPy API, disable it by " \
  ^
x86_64-pc-linux-gnu-gcc -shared -Wl,-O1 -Wl,--as-needed -shared -O2 -march=haswell /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/utils/_random.o -L/usr/lib64 -L/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7 -lm -lpython2.7 -o /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils/_random.so
building 'sklearn.utils._logistic_sigmoid' extension
compiling C sources
C compiler: x86_64-pc-linux-gnu-gcc -O2 -march=haswell -fPIC

compile options: '-I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c'
x86_64-pc-linux-gnu-gcc: sklearn/utils/_logistic_sigmoid.c
In file included from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1788:0,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
                 from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from sklearn/utils/_logistic_sigmoid.c:454:
/usr/lib64/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
 #warning "Using deprecated NumPy API, disable it by " \
  ^
x86_64-pc-linux-gnu-gcc -shared -Wl,-O1 -Wl,--as-needed -shared -O2 -march=haswell /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7/sklearn/utils/_logistic_sigmoid.o -L/usr/lib64 -L/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/temp.linux-x86_64-2.7 -lm -lpython2.7 -o /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/utils/_logistic_sigmoid.so
 [32;01m*[0m python2_7: running distutils-r1_run_phase python_compile_all
make -j4 html 
# These two lines make the build a bit more lengthy, and the
# the embedding of images more robust
rm -rf _build/html/_images
#rm -rf _build/doctrees/
sphinx-build -b html -d _build/doctrees   . _build/html/stable
Running Sphinx v1.4.4
making output directory...
fatal: Not a git repository (or any parent up to mount point /tmp)
Stopping at filesystem boundary (GIT_DISCOVERY_ACROSS_FILESYSTEM not set).
/usr/lib64/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')
Failed to execute git to get revision
loading pickled environment... not yet created
[autosummary] generating autosummary for: about.rst, data_transforms.rst, datasets/covtype.rst, datasets/index.rst, datasets/kddcup99.rst, datasets/labeled_faces.rst, datasets/mldata.rst, datasets/olivetti_faces.rst, datasets/rcv1.rst, datasets/twenty_newsgroups.rst, ..., tutorial/statistical_inference/index.rst, tutorial/statistical_inference/model_selection.rst, tutorial/statistical_inference/putting_together.rst, tutorial/statistical_inference/settings.rst, tutorial/statistical_inference/supervised_learning.rst, tutorial/statistical_inference/unsupervised_learning.rst, tutorial/text_analytics/working_with_text_data.rst, unsupervised_learning.rst, user_guide.rst, whats_new.rst
/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.
  "This module will be removed in 0.20.", DeprecationWarning)
/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/grid_search.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.
  DeprecationWarning)
/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/lda.py:6: DeprecationWarning: lda.LDA has been moved to discriminant_analysis.LinearDiscriminantAnalysis in 0.17 and will be removed in 0.19
  "in 0.17 and will be removed in 0.19", DeprecationWarning)
/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/learning_curve.py:23: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the functions are moved. This module will be removed in 0.20
  DeprecationWarning)
/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/qda.py:6: DeprecationWarning: qda.QDA has been moved to discriminant_analysis.QuadraticDiscriminantAnalysis in 0.17 and will be removed in 0.19.
  "in 0.17 and will be removed in 0.19.", DeprecationWarning)
[autosummary] generating autosummary for: /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/modules/generated/sklearn.base.BaseEstimator.rst, /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/modules/generated/sklearn.base.ClassifierMixin.rst, /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/modules/generated/sklearn.base.ClusterMixin.rst, /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/modules/generated/sklearn.base.RegressorMixin.rst, /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/modules/generated/sklearn.base.TransformerMixin.rst, /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/modules/generated/sklearn.base.clone.rst, /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/modules/generated/sklearn.calibration.CalibratedClassifierCV.rst, /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/modules/generated/sklearn.calibration.calibration_curve.rst, /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/modules/generated/sklearn.cluster.AffinityPropagation.rst, /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/modules/generated/sklearn.cluster.AgglomerativeClustering.rst, ..., /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/modules/generated/sklearn.svm.libsvm.predict_proba.rst, /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/modules/generated/sklearn.tree.DecisionTreeClassifier.rst, /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/modules/generated/sklearn.tree.DecisionTreeRegressor.rst, /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/modules/generated/sklearn.tree.ExtraTreeClassifier.rst, /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/modules/generated/sklearn.tree.ExtraTreeRegressor.rst, /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/modules/generated/sklearn.tree.export_graphviz.rst, /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/modules/generated/sklearn.utils.check_random_state.rst, /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/modules/generated/sklearn.utils.estimator_checks.check_estimator.rst, /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/modules/generated/sklearn.utils.resample.rst, /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/modules/generated/sklearn.utils.shuffle.rst
plotting code blocks in ../examples/plot_compare_reduction.py

/usr/lib64/python2.7/site-packages/scipy/linalg/basic.py:884: RuntimeWarning: internal gelsd driver lwork query error, required iwork dimension not returned. This is likely the result of LAPACK bug 0038, fixed in LAPACK 3.2.2 (released July 21, 2010). Falling back to 'gelss' driver.
  warnings.warn(mesg, RuntimeWarning)
/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/random_projection.py:376: DataDimensionalityWarning: The number of components is higher than the number of features: n_features < n_components (64 < 300).The dimensionality of the problem will not be reduced.
  DataDimensionalityWarning)
/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/random_projection.py:376: DataDimensionalityWarning: The number of components is higher than the number of features: n_features < n_components (64 < 1000).The dimensionality of the problem will not be reduced.
  DataDimensionalityWarning)
/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/random_projection.py:376: DataDimensionalityWarning: The number of components is higher than the number of features: n_features < n_components (64 < 10000).The dimensionality of the problem will not be reduced.
  DataDimensionalityWarning)
No handlers could be found for logger "sklearn.datasets.twenty_newsgroups"
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plotting code blocks in ../examples/plot_johnson_lindenstrauss_bound.py

Embedding 500 samples with dim 64 using various random projections
Projected 500 samples from 64 to 300 in 0.006s
Random matrix with size: 0.029MB
Mean distances rate: 1.00 (0.08)
Projected 500 samples from 64 to 1000 in 0.019s
Random matrix with size: 0.096MB
Mean distances rate: 1.00 (0.05)
Projected 500 samples from 64 to 10000 in 0.180s
Random matrix with size: 0.959MB
Mean distances rate: 1.01 (0.02)
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SVR complexity and bandwidth selected and model fitted in 0.644 s
KRR complexity and bandwidth selected and model fitted in 0.203 s
Support vector ratio: 0.310
SVR prediction for 100000 inputs in 0.064 s
KRR prediction for 100000 inputs in 0.263 s
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plotting code blocks in ../examples/plot_multioutput_face_completion.py

downloading Olivetti faces from http://cs.nyu.edu/~roweis/data/olivettifaces.mat to /tmp/portage/sci-libs/scikits_learn-0.18.2/homedir/scikit_learn_data
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../examples/plot_multioutput_face_completion.py is not compiling:
Traceback (most recent call last):
  File "/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/sphinxext/sphinx_gallery/gen_rst.py", line 467, in execute_script
    exec(code_block, example_globals)
  File "<string>", line 15, in <module>
  File "/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/datasets/olivetti_faces.py", line 116, in fetch_olivetti_faces
    fhandle = urlopen(DATA_URL)
  File "/usr/lib64/python2.7/urllib2.py", line 154, in urlopen
    return opener.open(url, data, timeout)
  File "/usr/lib64/python2.7/urllib2.py", line 429, in open
    response = self._open(req, data)
  File "/usr/lib64/python2.7/urllib2.py", line 447, in _open
    '_open', req)
  File "/usr/lib64/python2.7/urllib2.py", line 407, in _call_chain
    result = func(*args)
  File "/usr/lib64/python2.7/urllib2.py", line 1228, in http_open
    return self.do_open(httplib.HTTPConnection, req)
  File "/usr/lib64/python2.7/urllib2.py", line 1198, in do_open
    raise URLError(err)
URLError: <urlopen error [Errno 110] Connection timed out>

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plotting code blocks in ../examples/applications/plot_model_complexity_influence.py
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../examples/applications/plot_model_complexity_influence.py is not compiling:
Traceback (most recent call last):
  File "/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/sphinxext/sphinx_gallery/gen_rst.py", line 467, in execute_script
    exec(code_block, example_globals)
  File "<string>", line 2, in <module>
  File "<string>", line 13, in generate_data
  File "/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/datasets/twenty_newsgroups.py", line 338, in fetch_20newsgroups_vectorized
    remove=remove)
  File "/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/datasets/twenty_newsgroups.py", line 225, in fetch_20newsgroups
    cache_path=cache_path)
  File "/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/datasets/twenty_newsgroups.py", line 91, in download_20newsgroups
    opener = urlopen(URL)
  File "/usr/lib64/python2.7/urllib2.py", line 154, in urlopen
    return opener.open(url, data, timeout)
  File "/usr/lib64/python2.7/urllib2.py", line 429, in open
    response = self._open(req, data)
  File "/usr/lib64/python2.7/urllib2.py", line 447, in _open
    '_open', req)
  File "/usr/lib64/python2.7/urllib2.py", line 407, in _call_chain
    result = func(*args)
  File "/usr/lib64/python2.7/urllib2.py", line 1228, in http_open
    return self.do_open(httplib.HTTPConnection, req)
  File "/usr/lib64/python2.7/urllib2.py", line 1198, in do_open
    raise URLError(err)
URLError: <urlopen error [Errno 110] Connection timed out>

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plotting code blocks in ../examples/applications/plot_out_of_core_classification.py
downloading dataset (once and for all) into /tmp/portage/sci-libs/scikits_learn-0.18.2/homedir/scikit_learn_data/reuters
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../examples/applications/plot_out_of_core_classification.py is not compiling:
Traceback (most recent call last):
  File "/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/sphinxext/sphinx_gallery/gen_rst.py", line 467, in execute_script
    exec(code_block, example_globals)
  File "<string>", line 54, in <module>
  File "<string>", line 32, in get_minibatch
  File "<string>", line 115, in stream_reuters_documents
  File "/usr/lib64/python2.7/urllib.py", line 98, in urlretrieve
    return opener.retrieve(url, filename, reporthook, data)
  File "/usr/lib64/python2.7/urllib.py", line 245, in retrieve
    fp = self.open(url, data)
  File "/usr/lib64/python2.7/urllib.py", line 213, in open
    return getattr(self, name)(url)
  File "/usr/lib64/python2.7/urllib.py", line 350, in open_http
    h.endheaders(data)
  File "/usr/lib64/python2.7/httplib.py", line 1038, in endheaders
    self._send_output(message_body)
  File "/usr/lib64/python2.7/httplib.py", line 882, in _send_output
    self.send(msg)
  File "/usr/lib64/python2.7/httplib.py", line 844, in send
    self.connect()
  File "/usr/lib64/python2.7/httplib.py", line 821, in connect
    self.timeout, self.source_address)
  File "/usr/lib64/python2.7/socket.py", line 575, in create_connection
    raise err
IOError: [Errno socket error] [Errno 110] Connection timed out

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plotting code blocks in ../examples/applications/plot_out_of_core_classification.py
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../examples/applications/plot_out_of_core_classification.py is not compiling:
Traceback (most recent call last):
  File "/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/sphinxext/sphinx_gallery/gen_rst.py", line 467, in execute_script
    exec(code_block, example_globals)
  File "<string>", line 14, in <module>
NameError: name 'cls_stats' is not defined

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plotting code blocks in ../examples/applications/plot_outlier_detection_housing.py

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plotting code blocks in ../examples/applications/plot_prediction_latency.py
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plotting code blocks in ../examples/applications/plot_prediction_latency.py
Benchmarking SGDRegressor(alpha=0.01, average=False, epsilon=0.1, eta0=0.01,
       fit_intercept=True, l1_ratio=0.25, learning_rate='invscaling',
       loss='squared_loss', n_iter=5, penalty='elasticnet', power_t=0.25,
       random_state=None, shuffle=True, verbose=0, warm_start=False)
Benchmarking RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
           max_features='auto', max_leaf_nodes=None,
           min_impurity_split=1e-07, min_samples_leaf=1,
           min_samples_split=2, min_weight_fraction_leaf=0.0,
           n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
           verbose=0, warm_start=False)
Benchmarking SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
  kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
benchmarking with 100 features
benchmarking with 250 features
benchmarking with 500 features
example run in 3.40s
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plotting code blocks in ../examples/applications/plot_species_distribution_modeling.py

Downloading species data from http://www.cs.princeton.edu/~schapire/maxent/datasets/samples.zip to /tmp/portage/sci-libs/scikits_learn-0.18.2/homedir/scikit_learn_data
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../examples/applications/plot_species_distribution_modeling.py is not compiling:
Traceback (most recent call last):
  File "/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/sphinxext/sphinx_gallery/gen_rst.py", line 467, in execute_script
    exec(code_block, example_globals)
  File "<string>", line 170, in <module>
  File "<string>", line 65, in plot_species_distribution
  File "/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/datasets/species_distributions.py", line 227, in fetch_species_distributions
    X = np.load(BytesIO(urlopen(SAMPLES_URL).read()))
  File "/usr/lib64/python2.7/urllib2.py", line 154, in urlopen
    return opener.open(url, data, timeout)
  File "/usr/lib64/python2.7/urllib2.py", line 429, in open
    response = self._open(req, data)
  File "/usr/lib64/python2.7/urllib2.py", line 447, in _open
    '_open', req)
  File "/usr/lib64/python2.7/urllib2.py", line 407, in _call_chain
    result = func(*args)
  File "/usr/lib64/python2.7/urllib2.py", line 1228, in http_open
    return self.do_open(httplib.HTTPConnection, req)
  File "/usr/lib64/python2.7/urllib2.py", line 1198, in do_open
    raise URLError(err)
URLError: <urlopen error [Errno 110] Connection timed out>

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plotting code blocks in ../examples/applications/plot_stock_market.py

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plotting code blocks in ../examples/applications/plot_stock_market.py
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../examples/applications/plot_stock_market.py is not compiling:
Traceback (most recent call last):
  File "/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/sphinxext/sphinx_gallery/gen_rst.py", line 467, in execute_script
    exec(code_block, example_globals)
  File "<string>", line 105, in <module>
  File "<string>", line 28, in quotes_historical_google
  File "/usr/lib64/python2.7/urllib2.py", line 154, in urlopen
    return opener.open(url, data, timeout)
  File "/usr/lib64/python2.7/urllib2.py", line 429, in open
    response = self._open(req, data)
  File "/usr/lib64/python2.7/urllib2.py", line 447, in _open
    '_open', req)
  File "/usr/lib64/python2.7/urllib2.py", line 407, in _call_chain
    result = func(*args)
  File "/usr/lib64/python2.7/urllib2.py", line 1228, in http_open
    return self.do_open(httplib.HTTPConnection, req)
  File "/usr/lib64/python2.7/urllib2.py", line 1198, in do_open
    raise URLError(err)
URLError: <urlopen error [Errno 101] Network is unreachable>

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plotting code blocks in ../examples/applications/plot_stock_market.py
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../examples/applications/plot_stock_market.py is not compiling:
Traceback (most recent call last):
  File "/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/sphinxext/sphinx_gallery/gen_rst.py", line 467, in execute_script
    exec(code_block, example_globals)
  File "<string>", line 5, in <module>
NameError: name 'variation' is not defined

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plotting code blocks in ../examples/applications/plot_stock_market.py
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../examples/applications/plot_stock_market.py is not compiling:
Traceback (most recent call last):
  File "/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/sphinxext/sphinx_gallery/gen_rst.py", line 467, in execute_script
    exec(code_block, example_globals)
  File "<string>", line 2, in <module>
AttributeError: 'GraphLassoCV' object has no attribute 'covariance_'

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plotting code blocks in ../examples/applications/plot_stock_market.py
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../examples/applications/plot_stock_market.py is not compiling:
Traceback (most recent call last):
  File "/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/sphinxext/sphinx_gallery/gen_rst.py", line 467, in execute_script
    exec(code_block, example_globals)
  File "<string>", line 8, in <module>
NameError: name 'X' is not defined

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plotting code blocks in ../examples/applications/plot_stock_market.py
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../examples/applications/plot_stock_market.py is not compiling:
Traceback (most recent call last):
  File "/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/sphinxext/sphinx_gallery/gen_rst.py", line 467, in execute_script
    exec(code_block, example_globals)
  File "<string>", line 7, in <module>
AttributeError: 'GraphLassoCV' object has no attribute 'precision_'

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plotting code blocks in ../examples/applications/plot_tomography_l1_reconstruction.py

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plotting code blocks in ../examples/bicluster/plot_spectral_biclustering.py

consensus score: 1.0
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plotting code blocks in ../examples/bicluster/plot_spectral_coclustering.py

consensus score: 1.000
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plotting code blocks in ../examples/calibration/plot_calibration.py

Brier scores: (the smaller the better)
No calibration: 0.104
With isotonic calibration: 0.084
With sigmoid calibration: 0.109
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plotting code blocks in ../examples/calibration/plot_calibration.py
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plotting code blocks in ../examples/calibration/plot_calibration_curve.py

Logistic:
	Brier: 0.099
	Precision: 0.872
	Recall: 0.851
	F1: 0.862

Naive Bayes:
	Brier: 0.118
	Precision: 0.857
	Recall: 0.876
	F1: 0.867

Naive Bayes + Isotonic:
	Brier: 0.098
	Precision: 0.883
	Recall: 0.836
	F1: 0.859

Naive Bayes + Sigmoid:
	Brier: 0.109
	Precision: 0.861
	Recall: 0.871
	F1: 0.866

Logistic:
	Brier: 0.099
	Precision: 0.872
	Recall: 0.851
	F1: 0.862

SVC:
	Brier: 0.163
	Precision: 0.872
	Recall: 0.852
	F1: 0.862

SVC + Isotonic:
	Brier: 0.100
	Precision: 0.853
	Recall: 0.878
	F1/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
: 0.865

SVC + Sigmoid:
	Brier: 0.099
	Precision: 0.874
	Recall: 0.849
	F1: 0.861

 - time elapsed : 1.4 sec
plotting code blocks in ../examples/calibration/plot_calibration_multiclass.py

Log-loss of
 * uncalibrated classifier trained on 800 datapoints: 1.280 
 * classifier trained on 600 datapoints and calibrated on 200 datapoint: 0.534
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plotting code blocks in ../examples/calibration/plot_compare_calibration.py

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plotting code blocks in ../examples/calibration/plot_compare_calibration.py
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plotting code blocks in ../examples/classification/plot_classification_probability.py

classif_rate for GPC : 82.666667 
classif_rate for L2 logistic (OvR) : 76.666667 
classif_rate for L1 logistic : 79.333333 
classif_rate for Linear SVC : 82.000000 
classif_rate for L2 logistic (Multinomial) : 82.000000 
 - time elapsed : 3.3 sec
plotting code blocks in ../examples/classification/plot_classifier_comparison.py

 - time elapsed : 6.1 sec
plotting code blocks in ../examples/classification/plot_digits_classification.py

Classification report for classifier SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
  decision_function_shape=None, degree=3, gamma=0.001, kernel='rbf',
  max_iter=-1, probability=False, random_state=None, shrinking=True,
  tol=0.001, verbose=False):
             precision    recall  f1-score   support

          0       1.00      0.99      0.99        88
          1       0.99      0.97      0.98        91
          2       0.99      0.99      0.99        86
          3       0.98      0.87      0.92        91
          4       0.99      0.96      0.97        92
          5       0.95      0.97      0.96        91
          6       0.99      0.99      0.99        91
          7       0.96      0.99      0.97        89
          8       0.94      1.00      0.97        88
          9       0.93      0.98      0.95        92

avg / total       0.97      0.97      0.97       899


Confusion matrix:
[[87  0  0  0  1  0  0  0  0  0]
 [ 0 88  1  0  0  0  0  0  1  1]
 [ 0  0 85  1  0  0  0  0  0  0]
 [ 0  0  0 79  0  3  0  4  5  0]
 [ 0  0  0  0 88  0  0  0  0  4]
 [ 0  0  0  0  0 88  1  0  0  2]
 [ 0  1  0  0  0  0 90  0  0  0]
 [ 0  0  0  0  0  1  0 88  0  0]
 [ 0  0  0  0  0  0  0  0 88  0]
 [ 0  0  0  1  0  1  0  0  0 90]]
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plotting code blocks in ../examples/classification/plot_lda.py
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plotting code blocks in ../examples/classification/plot_lda_qda.py

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plotting code blocks in ../examples/classification/plot_lda_qda.py
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plotting code blocks in ../examples/classification/plot_lda_qda.py
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plotting code blocks in ../examples/classification/plot_lda_qda.py
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plotting code blocks in ../examples/classification/plot_lda_qda.py
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plotting code blocks in ../examples/cluster/plot_adjusted_for_chance_measures.py

Computing adjusted_rand_score for 10 values of n_clusters and n_samples=100
done in 0.025s
Computing v_measure_score for 10 values of n_clusters and n_samples=100
done in 0.034s
Computing adjusted_mutual_info_score for 10 values of n_clusters and n_samples=100
done in 0.240s
Computing mutual_info_score for 10 values of n_clusters and n_samples=100
done in 0.029s
Computing adjusted_rand_score for 10 values of n_clusters and n_samples=1000
done in 0.046s
Computing v_measure_score for 10 values of n_clusters and n_samples=1000
done in 0.046s
Computing adjusted_mutual_info_score for 10 values of n_clusters and n_samples=1000
done in 0.172s
Computing mutual_info_score for 10 values of n_clusters and n_samples=1000
done in 0.037s
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plotting code blocks in ../examples/cluster/plot_affinity_propagation.py

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plotting code blocks in ../examples/cluster/plot_affinity_propagation.py
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plotting code blocks in ../examples/cluster/plot_affinity_propagation.py
Estimated number of cluster/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/manifold/spectral_embedding_.py:229: UserWarning: Graph is not fully connected, spectral embedding may not work as expected.
  warnings.warn("Graph is not fully connected, spectral embedding"
/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/cluster/hierarchical.py:193: UserWarning: the number of connected components of the connectivity matrix is 2 > 1. Completing it to avoid stopping the tree early.
  connectivity, n_components = _fix_connectivity(X, connectivity)
/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/cluster/hierarchical.py:418: UserWarning: the number of connected components of the connectivity matrix is 2 > 1. Completing it to avoid stopping the tree early.
  connectivity, n_components = _fix_connectivity(X, connectivity)
/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/cluster/hierarchical.py:193: UserWarning: the number of connected components of the connectivity matrix is 3 > 1. Completing it to avoid stopping the tree early.
  connectivity, n_components = _fix_connectivity(X, connectivity)
/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/cluster/hierarchical.py:418: UserWarning: the number of connected components of the connectivity matrix is 3 > 1. Completing it to avoid stopping the tree early.
  connectivity, n_components = _fix_connectivity(X, connectivity)
s: 3
Homogeneity: 0.872
Completeness: 0.872
V-measure: 0.872
Adjusted Rand Index: 0.912
Adjusted Mutual Information: 0.871
Silhouette Coefficient: 0.753
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plotting code blocks in ../examples/cluster/plot_affinity_propagation.py
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plotting code blocks in ../examples/cluster/plot_agglomerative_clustering.py
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plotting code blocks in ../examples/cluster/plot_agglomerative_clustering_metrics.py
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plotting code blocks in ../examples/cluster/plot_birch_vs_minibatchkmeans.py

Birch without global clustering as the final step took 2.23 seconds
n_clusters : 158
Birch with global clustering as the final step took 2.22 seconds
n_clusters : 100
Time taken to run MiniBatchKMeans 2.19 seconds
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plotting code blocks in ../examples/cluster/plot_cluster_comparison.py

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plotting code blocks in ../examples/cluster/plot_cluster_iris.py

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plotting code blocks in ../examples/cluster/plot_color_quantization.py

________________________________________________________________________________
../examples/cluster/plot_color_quantization.py is not compiling:
Traceback (most recent call last):
  File "/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/sphinxext/sphinx_gallery/gen_rst.py", line 467, in execute_script
    exec(code_block, example_globals)
  File "<string>", line 24, in <module>
  File "/usr/lib64/python2.7/site-packages/PIL/Image.py", line 622, in __getattr__
    raise AttributeError(name)
AttributeError: __float__

________________________________________________________________________________
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plotting code blocks in ../examples/cluster/plot_dbscan.py

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plotting code blocks in ../examples/cluster/plot_dbscan.py
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plotting code blocks in ../examples/cluster/plot_dbscan.py
Estimated number of clusters: 3
Homogeneity: 0.953
Completeness: 0.883
V-measure: 0.917
Adjusted Rand Index: 0.952
Adjusted Mutual Information: 0.883
Silhouette Coefficient: 0.626
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plotting code blocks in ../examples/cluster/plot_dbscan.py
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plotting code blocks in ../examples/cluster/plot_dict_face_patches.py

downloading Olivetti faces from http://cs.nyu.edu/~roweis/data/olivettifaces.mat to /tmp/portage/sci-libs/scikits_learn-0.18.2/homedir/scikit_learn_data
________________________________________________________________________________
../examples/cluster/plot_dict_face_patches.py is not compiling:
Traceback (most recent call last):
  File "/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/sphinxext/sphinx_gallery/gen_rst.py", line 467, in execute_script
    exec(code_block, example_globals)
  File "<string>", line 13, in <module>
  File "/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/datasets/olivetti_faces.py", line 116, in fetch_olivetti_faces
    fhandle = urlopen(DATA_URL)
  File "/usr/lib64/python2.7/urllib2.py", line 154, in urlopen
    return opener.open(url, data, timeout)
  File "/usr/lib64/python2.7/urllib2.py", line 429, in open
    response = self._open(req, data)
  File "/usr/lib64/python2.7/urllib2.py", line 447, in _open
    '_open', req)
  File "/usr/lib64/python2.7/urllib2.py", line 407, in _call_chain
    result = func(*args)
  File "/usr/lib64/python2.7/urllib2.py", line 1228, in http_open
    return self.do_open(httplib.HTTPConnection, req)
  File "/usr/lib64/python2.7/urllib2.py", line 1198, in do_open
    raise URLError(err)
URLError: <urlopen error [Errno 110] Connection timed out>

________________________________________________________________________________
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plotting code blocks in ../examples/cluster/plot_dict_face_patches.py
Learning the dictionary... 
________________________________________________________________________________
../examples/cluster/plot_dict_face_patches.py is not compiling:
Traceback (most recent call last):
  File "/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/sphinxext/sphinx_gallery/gen_rst.py", line 467, in execute_script
    exec(code_block, example_globals)
  File "<string>", line 14, in <module>
NameError: name 'faces' is not defined

________________________________________________________________________________
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plotting code blocks in ../examples/cluster/plot_dict_face_patches.py
________________________________________________________________________________
../examples/cluster/plot_dict_face_patches.py is not compiling:
Traceback (most recent call last):
  File "/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/sphinxext/sphinx_gallery/gen_rst.py", line 467, in execute_script
    exec(code_block, example_globals)
  File "<string>", line 2, in <module>
AttributeError: 'MiniBatchKMeans' object has no attribute 'cluster_centers_'

________________________________________________________________________________
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plotting code blocks in ../examples/cluster/plot_digits_agglomeration.py

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plotting code blocks in ../examples/cluster/plot_digits_linkage.py

Computing embedding
Done.
ward : 6.62s
average : 6.70s
complete : 6.53s
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plotting code blocks in ../examples/cluster/plot_face_compress.py

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plotting code blocks in ../examples/cluster/plot_face_segmentation.py

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plotting code blocks in ../examples/cluster/plot_face_segmentation.py
Spectral clustering: kmeans, 3.92s
Spectral clustering: discretize, 3.31s
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plotting code blocks in ../examples/cluster/plot_face_ward_segmentation.py

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plotting code blocks in ../examples/cluster/plot_face_ward_segmentation.py
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plotting code blocks in ../examples/cluster/plot_face_ward_segmentation.py
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plotting code blocks in ../examples/cluster/plot_face_ward_segmentation.py
Compute structured hierarchical clustering...
Elapsed time:  0.336949110031
Number of pixels:  7752
Number of clusters:  15
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plotting code blocks in ../examples/cluster/plot_face_ward_segmentation.py
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plotting code blocks in ../examples/cluster/plot_feature_agglomeration_vs_univariate_selection.py

________________________________________________________________________________
../examples/cluster/plot_feature_agglomeration_vs_univariate_selection.py is not compiling:
Traceback (most recent call last):
  File "/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/sphinxext/sphinx_gallery/gen_rst.py", line 467, in execute_script
    exec(code_block, example_globals)
  File "<string>", line 19, in <module>
ImportError: No module named joblib

________________________________________________________________________________
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plotting code blocks in ../examples/cluster/plot_feature_agglomeration_vs_univariate_selection.py
 - time elapsed : 0.024 sec
plotting code blocks in ../examples/cluster/plot_feature_agglomeration_vs_univariate_selection.py
________________________________________________________________________________
../examples/cluster/plot_feature_agglomeration_vs_univariate_selection.py is not compiling:
Traceback (most recent call last):
  File "/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/sphinxext/sphinx_gallery/gen_rst.py", line 467, in execute_script
    exec(code_block, example_globals)
  File "<string>", line 1, in <module>
NameError: name 'KFold' is not defined

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plotting code blocks in ../examples/cluster/plot_feature_agglomeration_vs_univariate_selection.py
________________________________________________________________________________
../examples/cluster/plot_feature_agglomeration_vs_univariate_selection.py is not compiling:
Traceback (most recent call last):
  File "/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/sphinxext/sphinx_gallery/gen_rst.py", line 467, in execute_script
    exec(code_block, example_globals)
  File "<string>", line 7, in <module>
NameError: name 'coef_selection_' is not defined

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plotting code blocks in ../examples/cluster/plot_kmeans_assumptions.py

 - time elapsed : 0.21 sec
plotting code blocks in ../examples/cluster/plot_kmeans_digits.py

n_digits: 10, 	 n_samples 1797, 	 n_features 64
_______________________________________________________________________________
init    time  inertia    homo   compl  v-meas     ARI AMI  silhouette
k-means++   0.21s    69432   0.602   0.650   0.625   0.465   0.598    0.146
   random   0.16s    69694   0.669   0.710   0.689   0.553   0.666    0.147
PCA-based   0.03s    70804   0.671   0.698   0.684   0.561   0.668    0.118
_______________________________________________________________________________
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plotting code blocks in ../examples/cluster/plot_kmeans_digits.py
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plotting code blocks in ../examples/cluster/plot_kmeans_silhouette_analysis.py

For n_clusters = 2 The average silhouette_score is : 0.704978749608
For n_clusters = 3 The average silhouette_score is : 0.588200401213
For n_clusters = 4 The average silhouette_score is : 0.650518663273
For n_clusters = 5 The average silhouette_score is : 0.563764690262
For n_clusters = 6 The average silhouette_score is : 0.450466629437
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plotting code blocks in ../examples/cluster/plot_kmeans_stability_low_dim_dense.py

Evaluation of KMeans with k-means++ init
Evaluation of KMeans with random init
Evaluation of MiniBatchKMeans with k-means++ init
Evaluation of MiniBatchKMeans with random init
 - time elapsed : 1.8 sec
plotting code blocks in ../examples/cluster/plot_mean_shift.py

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plotting code blocks in ../examples/cluster/plot_mean_shift.py
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plotting code blocks in ../examples/cluster/plot_mean_shift.py
number of estimated clusters : 3
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plotting code blocks in ../examples/cluster/plot_mean_shift.py
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plotting code blocks in ../examples/cluster/plot_mini_batch_kmeans.py

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plotting code blocks in ../examples/cluster/plot_segmentation_toy.py

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plotting code blocks in ../examples/cluster/plot_ward_structured_vs_unstructured.py

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plotting code blocks in ../examples/cluster/plot_ward_structured_vs_unstructured.py
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plotting code blocks in ../examples/cluster/plot_ward_structured_vs_unstructured.py
Compute unstructured hierarchical clustering...
Elapsed time: 0.47s
Number of points: 1500
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plotting code blocks in ../examples/cluster/plot_ward_structured_vs_unstructured.py
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plotting code blocks in ../examples/cluster/plot_ward_structured_vs_unstructured.py
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plotting code blocks in ../examples/cluster/plot_ward_structured_vs_unstructured.py
Compute structured hierarchical clustering...
Elapsed time: 0.08s
Number of points: 1500
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plotting code blocks in ../examples/cluster/plot_ward_structured_vs_unstructured.py
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plotting code blocks in ../examples/cross_decomposition/plot_compare_cross_decomposition.py

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plotting code blocks in ../examples/cross_decomposition/plot_compare_cross_decomposition.py
Corr(X)
[[ 1.    0.52 -0.    0.  ]
 [ 0.52  1.    0.08  0.02]
 [-0.    0.08  1.    0.45]
 [ 0.    0.02  0.45  1.  ]]
Corr(Y)
[[ 1.    0.52 -0.02 -0.07]
 [ 0.52  1.   -0.    0.03]
 [-0.02 -0.    1.    0.52]
 [-0.07  0.03  0.52  1.  ]]
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plotting code blocks in ../examples/cross_decomposition/plot_compare_cross_decomposition.py
True B (such that: Y = XB + Err)
[[1 1 1]
 [2 2 2]
 [0 0 0]
 [0 0 0]
 [0 0 0]
 [0 0 0]
 [0 0 0]
 [0 0 0]
 [0 0 0]
 [0 0 0]]
Estimated B
[[ 1.   1.   1. ]
 [ 2.   2.   2.1]
 [-0.   0.   0. ]
 [ 0.   0.  -0. ]
 [ 0.   0.   0. ]
 [ 0.   0.  -0. ]
 [-0.   0.   0. ]
 [ 0.  -0.  -0. ]
 [ 0.  -0.  -0. ]
 [ 0.  -0.  -0.1]]
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plotting code blocks in ../examples/cross_decomposition/plot_compare_cross_decomposition.py
Estimated betas
[[ 1. ]
 [ 2. ]
 [ 0. ]
 [ 0. ]
 [-0. ]
 [ 0.1]
 [-0. ]
 [-0. ]
 [ 0.1]
 [-0. ]]
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plotting code blocks in ../examples/datasets/plot_random_multilabel_dataset.py

The data was generated from (random_state=100):
Class	P(C)	P(w0|C)	P(w1|C)
red	0.44	0.56	0.44
blue	0.22	0.01	0.99
yellow	0.34	0.47	0.53
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plotting code blocks in ../examples/decomposition/plot_faces_decomposition.py

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plotting code blocks in ../examples/decomposition/plot_faces_decomposition.py
downloading Olivetti faces from http://cs.nyu.edu/~roweis/data/olivettifaces.mat to /tmp/portage/sci-libs/scikits_learn-0.18.2/homedir/scikit_learn_data
________________________________________________________________________________
../examples/decomposition/plot_faces_decomposition.py is not compiling:
Traceback (most recent call last):
  File "/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/sphinxext/sphinx_gallery/gen_rst.py", line 467, in execute_script
    exec(code_block, example_globals)
  File "<string>", line 1, in <module>
  File "/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/datasets/olivetti_faces.py", line 116, in fetch_olivetti_faces
    fhandle = urlopen(DATA_URL)
  File "/usr/lib64/python2.7/urllib2.py", line 154, in urlopen
    return opener.open(url, data, timeout)
  File "/usr/lib64/python2.7/urllib2.py", line 429, in open
    response = self._open(req, data)
  File "/usr/lib64/python2.7/urllib2.py", line 447, in _open
    '_open', req)
  File "/usr/lib64/python2.7/urllib2.py", line 407, in _call_chain
    result = func(*args)
  File "/usr/lib64/python2.7/urllib2.py", line 1228, in http_open
    return self.do_open(httplib.HTTPConnection, req)
  File "/usr/lib64/python2.7/urllib2.py", line 1198, in do_open
    raise URLError(err)
URLError: <urlopen error [Errno 110] Connection timed out>

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plotting code blocks in ../examples/decomposition/plot_faces_decomposition.py
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plotting code blocks in ../examples/decomposition/plot_faces_decomposition.py
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plotting code blocks in ../examples/decomposition/plot_faces_decomposition.py
________________________________________________________________________________
../examples/decomposition/plot_faces_decomposition.py is not compiling:
Traceback (most recent call last):
  File "/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/sphinxext/sphinx_gallery/gen_rst.py", line 467, in execute_script
    exec(code_block, example_globals)
  File "<string>", line 2, in <module>
NameError: name 'faces_centered' is not defined

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plotting code blocks in ../examples/decomposition/plot_faces_decomposition.py
Extracting the top 6 Eigenfaces - PCA using randomized SVD...
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../examples/decomposition/plot_faces_decomposition.py is not compiling:
Traceback (most recent call last):
  File "/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/sphinxext/sphinx_gallery/gen_rst.py", line 467, in execute_script
    exec(code_block, example_globals)
  File "<string>", line 5, in <module>
NameError: name 'faces' is not defined

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plotting code blocks in ../examples/decomposition/plot_ica_blind_source_separation.py

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plotting code blocks in ../examples/decomposition/plot_image_denoising.py
Distorting image...
Extracting reference patches...
done in 0.04s.
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plotting code blocks in ../examples/decomposition/plot_image_denoising.py
Learning the dictionary...
done in 11.55s.
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plotting code blocks in ../examples/decomposition/plot_image_denoising.py
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plotting code blocks in ../examples/decomposition/plot_image_denoising.py
Extracting noisy patches... 
done in 0.01s.
Orthogonal Matching Pursuit
1 atom...
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Orthogonal Matching Pursuit
2 atoms...
done in 10.30s.
Least-angle regression
5 atoms...
done in 49.41s.
Thresholding
 alpha=0.1...
done in 1.60s.
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  ConvergenceWarning)
sed : 67 sec
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plotting code blocks in ../examples/decomposition/plot_pca_vs_fa_model_selection.py
best n_components by PCA CV = 10
best n_components by FactorAnalysis CV = 10
best n_components by PCA MLE = 10
best n_components by PCA CV = 40
best n_components by FactorAnalysis CV = 10
best n_components by PCA MLE = 38
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plotting code blocks in ../examples/decomposition/plot_pca_vs_lda.py

explained variance ratio (first two components): [ 0.92461621  0.05301557]
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plotting code blocks in ../examples/ensemble/plot_bias_variance.py

Tree: 0.0255 (error) = 0.0003 (bias^2)  + 0.0152 (var) + 0.0098 (noise)
Bagging(Tree): 0.0196 (error) = 0.0004 (bias^2)  + 0.0092 (var) + 0.0098 (noise)
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plotting code blocks in ../examples/ensemble/plot_forest_importances.py

Feature ranking:
1. feature 1 (0.295413)
2. feature 2 (0.208518)
3. feature 0 (0.177943)
4. feature 3 (0.047204)
5. feature 6 (0.046537)
6. feature 8 (0.045973)
7. feature 7 (0.045557)
8. feature 4 (0.044536)
9. feature 9 (0.044522)
10. feature 5 (0.043797)
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plotting code blocks in ../examples/ensemble/plot_forest_importances_faces.py

downloading Olivetti faces from http://cs.nyu.edu/~roweis/data/olivettifaces.mat to /tmp/portage/sci-libs/scikits_learn-0.18.2/homedir/scikit_learn_data
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../examples/ensemble/plot_forest_importances_faces.py is not compiling:
Traceback (most recent call last):
  File "/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/sphinxext/sphinx_gallery/gen_rst.py", line 467, in execute_script
    exec(code_block, example_globals)
  File "<string>", line 13, in <module>
  File "/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/datasets/olivetti_faces.py", line 116, in fetch_olivetti_faces
    fhandle = urlopen(DATA_URL)
  File "/usr/lib64/python2.7/urllib2.py", line 154, in urlopen
    return opener.open(url, data, timeout)
  File "/usr/lib64/python2.7/urllib2.py", line 429, in open
    response = self._open(req, data)
  File "/usr/lib64/python2.7/urllib2.py", line 447, in _open
    '_open', req)
  File "/usr/lib64/python2.7/urllib2.py", line 407, in _call_chain
    result = func(*args)
  File "/usr/lib64/python2.7/urllib2.py", line 1228, in http_open
    return self.do_open(httplib.HTTPConnection, req)
  File "/usr/lib64/python2.7/urllib2.py", line 1198, in do_open
    raise URLError(err)
URLError: <urlopen error [Errno 110] Connection timed out>

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plotting code blocks in ../examples/ensemble/plot_forest_iris.py

DecisionTree with features [0, 1] has a score of 0.926666666667
RandomForest with 30 estimators with features [0, 1] has a score of 0.926666666667
ExtraTrees with 30 estimators with features [0, 1] has a score of 0.926666666667
AdaBoost with 30 estimators with features [0, 1] has a score of 0.84
DecisionTree with features [0, 2] has a score of 0.993333333333
RandomForest with 30 estimators with features [0, 2] has a score of 0.993333333333
ExtraTrees with 30 estimators with features [0, 2] has a score of 0.993333333333
AdaBoost with 30 estimators with features [0, 2] has a score of 0.993333333333
DecisionTree with features [2, 3] has a score of 0.993333333333
RandomForest with 30 estimators with features [2, 3] has a score of 0.993333333333
ExtraTrees with 30 estimators with features [2, 3] has a score of 0.993333333333
AdaBoost with 30 estimators with features [2, 3] has a score of 0.993333333333
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plotting code blocks in ../examples/ensemble/plot_gradient_boosting_oob.py

Accuracy: 0.6820
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plotting code blocks in ../examples/ensemble/plot_gradient_boosting_regression.py
MSE: 6.6267
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plotting code blocks in ../examples/ensemble/plot_partial_dependence.py

downloading Cal. housing from http://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.tgz to /tmp/portage/sci-libs/scikits_learn-0.18.2/homedir/scikit_learn_data
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../examples/ensemble/plot_partial_dependence.py is not compiling:
Traceback (most recent call last):
  File "/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/sphinxext/sphinx_gallery/gen_rst.py", line 467, in execute_script
    exec(code_block, example_globals)
  File "<string>", line 68, in <module>
  File "<string>", line 17, in main
  File "/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/datasets/california_housing.py", line 93, in fetch_california_housing
    archive_fileobj = BytesIO(urlopen(DATA_URL).read())
  File "/usr/lib64/python2.7/urllib2.py", line 154, in urlopen
    return opener.open(url, data, timeout)
  File "/usr/lib64/python2.7/urllib2.py", line 429, in open
    response = self._open(req, data)
  File "/usr/lib64/python2.7/urllib2.py", line 447, in _open
    '_open', req)
  File "/usr/lib64/python2.7/urllib2.py", line 407, in _call_chain
    result = func(*args)
  File "/usr/lib64/python2.7/urllib2.py", line 1228, in http_open
    return self.do_open(httplib.HTTPConnection, req)
  File "/usr/lib64/python2.7/urllib2.py", line 1198, in do_open
    raise URLError(err)
URLError: <urlopen error [Errno 110] Connection timed out>

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plotting code /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/gaussian_process/gpc.py:391: RuntimeWarning: overflow encountered in exp
  pi = 1 / (1 + np.exp(-f))
blocks in ../examples/ensemble/plot_voting_probas.py

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plotting code blocks in ../examples/exercises/plot_cv_diabetes.py

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plotting code blocks in ../examples/exercises/plot_cv_diabetes.py
Answer to the bonus question: how much can you trust the selection of alpha?

Alpha parameters maximising the generalization score on different
subsets of the data:
[fold 0] alpha: 0.10405, score: 0.53573
[fold 1] alpha: 0.05968, score: 0.16278
[fold 2] alpha: 0.10405, score: 0.44437

Answer: Not very much since we obtained different alphas for different
subsets of the data and moreover, the scores for these alphas differ
quite substantially.
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plotting code blocks in ../examples/feature_selection/plot_permutation_test_for_classification.py
Classification score 0.513333333333 (pvalue : 0.00990099009901)
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plotting code blocks in ../examples/feature_selection/plot_rfe_digits.py

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plotting code blocks in ../examples/feature_selection/plot_rfe_with_cross_validation.py

Optimal number of features : 3
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plotting code blocks in ../examples/gaussian_process/plot_compare_gpr_krr.py

Time for KRR fitting: 4.534
Time for GPR fitting: 0.118
Time for KRR prediction: 0.044
Time for GPR prediction: 0.053
Time for GPR prediction with standard-deviation: 0.297
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plotting code blocks in ../examples/gaussian_process/plot_gpc.py

Log Marginal Likelihood (initial): -17.598
Log Marginal Likelihood (optimized): -3.875
Accuracy: 1.000 (initial) 1.000 (optimized)
Log-loss: 0.214 (initial) 0.319 (optimized)
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plotting code blocks in ../examples/gaussian_process/plot_gpc_iris.py

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plotting code blocks in ../examples/gaussian_process/plot_gpc_isoprobability.py

Learned kernel: 0.0256**2 * DotProduct(sigma_0=5.72) ** 2 
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plotting code blocks in ../examples/gaussian_process/plot_gpc_xor.py

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plotting code blocks in ../examples/gaussian_process/plot_gpr_co2.py

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../examples/gaussian_process/plot_gpr_co2.py is not compiling:
Traceback (most recent call last):
  File "/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/sphinxext/sphinx_gallery/gen_rst.py", line 467, in execute_script
    exec(code_block, example_globals)
  File "<string>", line 16, in <module>
  File "/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/datasets/mldata.py", line 142, in fetch_mldata
    mldata_url = urlopen(urlname)
  File "/usr/lib64/python2.7/urllib2.py", line 154, in urlopen
    return opener.open(url, data, timeout)
  File "/usr/lib64/python2.7/urllib2.py", line 429, in open
    response = self._op/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/gaussian_process/gpr.py:339: RuntimeWarning: covariance is not positive-semidefinite.
  y_samples = rng.multivariate_normal(y_mean, y_cov, n_samples).T
/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/gaussian_process/gpr.py:308: UserWarning: Predicted variances smaller than 0. Setting those variances to 0.
  warnings.warn("Predicted variances smaller than 0. "
/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/gaussian_process/kernels.py:288: RuntimeWarning: divide by zero encountered in log
  return np.log(np.vstack(bounds))
en(req, data)
  File "/usr/lib64/python2.7/urllib2.py", line 447, in _open
    '_open', req)
  File "/usr/lib64/python2.7/urllib2.py", line 407, in _call_chain
    result = func(*args)
  File "/usr/lib64/python2.7/urllib2.py", line 1228, in http_open
    return self.do_open(httplib.HTTPConnection, req)
  File "/usr/lib64/python2.7/urllib2.py", line 1198, in do_open
    raise URLError(err)
URLError: <urlopen error [Errno 110] Connection timed out>

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Lasso(alpha=0.1, copy_X=True, fit_intercept=True, max_iter=1000,
   normalize=False, positive=False, precompute=False, random_state=None,
   selection='cyclic', tol=0.0001, warm_start=False)
r^2 on test data : 0.384710
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plotting code blocks in ../examples/linear_model/plot_lasso_and_elasticnet.py
ElasticNet(alpha=0.1, copy_X=True, fit_intercept=True, l1_ratio=0.7,
      max_iter=1000, normalize=False, positive=False, precompute=False,
      random_state=None, selection='cyclic', tol=0.0001, warm_start=False)
r^2 on test data : 0.240176
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plotting code blocks in ../examples/linear_model/plot_lasso_coordinate_descent_path.py

Computing regularization path using the lasso...
Computing regularization path using the positive lasso...
Computing regularization path using the elastic net...
Computing regularization path using the positive elastic net...
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plotting code blocks in ../examples/linear_model/plot_lasso_lars.py

Computing regularization path using the LARS ...
./usr/lib/python-exec/python2.7/sphinx-build:17: RuntimeWarning: divide by zero encountered in log10
/usr/lib/python-exec/python2.7/sphinx-build:9: RuntimeWarning: divide by zero encountered in log10
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Computing regularization path using the coordinate descent lasso...
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Computing regularization path using the Lars lasso...
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plotting code blocks in ../examples/linear_model/plot_logistic.py

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plotting code blocks in ../examples/linear_model/plot_logistic_l1_l2_sparsity.py

C=100.00
Sparsity with L1 penalty: 6.25%
score with L1 penalty: 0.9104
Sparsity with L2 penalty: 4.69%
score with L2 penalty: 0.9098
C=1.00
Sparsity with L1 penalty: 10.94%
score with L1 penalty: 0.9104
Sparsity with L2 penalty: 4.69%
score with L2 penalty: 0.9093
C=0.01
Sparsity with L1 penalty: 85.94%
score with L1 penalty: 0.8614
Sparsity with L2 penalty: 4.69%
score with L2 penalty: 0.8915
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plotting code blocks in ../examples/linear_model/plot_logistic_multinomial.py

training score : 0.995 (multinomial)
training score : 0.976 (ovr)
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Computing regularization path ...
This took  0:00:00.026707
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Coefficients: 
 [ 938.23786125]
Mean squared error: 2548.07
Variance score: 0.47
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Estimated coefficients (true, normal, RANSAC):
82.1903908408 [ 54.17236387] [ 82.08533159]
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training SGD
training ASGD
training Perceptron
training Passive-Aggressive I
training Passive-Aggressive II
training SAG
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standard: 0.084 sec
ltsa: 0.44 sec
hessian: 0.25 sec
modified: 0.17 sec
Isomap: 0.47 sec
MDS: 2.2 sec
SpectralEmbedding: 0.096 sec
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plotting code blocks in ../examples/manifold/plot_lle_digits.py

Computing random projection
Computing PCA projection
Computing Linear Discriminant Analysis projection
Computing Isomap embedding
Done.
Computing LLE embedding
Done. Reconstruction error: 1.63544e-06
Computing modified LLE embedding
Done. Reconstruction error: 0.360818
Computing Hessian LLE embedding
Done. Reconstruction error: 0.212805
Computing LTSA embedding
Done. Reconstruction error: 0.212804
Computing MDS embedding
Done. Stress: 150446492.243191
Computing Totally Random Trees embedding
Computing Spectral embedding
Computing t-SNE embedding
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plotting code blocks in ../examples/manifold/plot_manifold_sphere.py

standard: 0.06 sec
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hessian: 0.18 sec
modified: 0.12 sec
ISO: 0.26 sec
MDS: 1.1 sec
Spectral Embedding: 0.06 sec
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plotting code blocks in ../examples/manifold/plot_swissroll.py

Computing LLE embedding
Done. Reconstruction error: 9.45494e-08
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plotting code blocks in ../examples/model_selection/plot_confusion_matrix.py

Confusion matrix, without normalization
[[13  0  0]
 [ 0 10  6]
 [ 0  0  9]]
Normalized confusion matrix
[[ 1.    0.    0.  ]
 [ 0.    0.62  0.38]
 [ 0.    0.    1.  ]]
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Average difference of 0.007523 with std. dev. of 0.007621.
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Optimal regularization parameter : 0.000335292414925
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Index size: 1000, exact: 0.001s, LSHF: 0.007s, speedup: 0.1, accuracy: 1.00 +/-0.00
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best bandwidth: 3.79269019073
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None 0.813333333333
0.2 0.82
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plotting code blocks in ../examples/neighbors/plot_species_kde.py
Downloading species data from http://www.cs.princeton.edu/~schapire/maxent/datasets/samples.zip to /tmp/portage/sci-libs/scikits_learn-0.18.2/homedir/scikit_learn_data
________________________________________________________________________________
../examples/neighbors/plot_species_kde.py is not compiling:
Traceback (most recent call last):
  File "/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/sphinxext/sphinx_gallery/gen_rst.py", line 467, in execute_script
    exec(code_block, example_globals)
  File "<string>", line 20, in <module>
  File "/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/datasets/species_distributions.py", line 227, in fetch_species_distributions
    X = np.load(BytesIO(urlopen(SAMPLES_URL).read()))
  File "/usr/lib64/python2.7/urllib2.py", line 154, in urlopen
    return opener.open(url, data, timeout)
  File "/usr/lib64/python2.7/urllib2.py", line 429, in open
    response = self._open(req, data)
  File "/usr/lib64/python2.7/urllib2.py", line 447, in _open
    '_open', req)
  File "/usr/lib64/python2.7/urllib2.py", line 407, in _call_chain
    result = func(*args)
  File "/usr/lib64/python2.7/urllib2.py", line 1228, in http_open
    return self.do_open(httplib.HTTPConnection, req)
  File "/usr/lib64/python2.7/urllib2.py", line 1198, in do_open
    raise URLError(err)
URLError: <urlopen error [Errno 110] Connection timed out>

________________________________________________________________________________
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plotting code blocks in ../examples/neural_networks/plot_mlp_alpha.py

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plotting code blocks in ../examples/neural_networks/plot_mlp_training_curves.py


learning on dataset iris
training: constant learning-rate
Training set score: 0.980000
Training set loss: 0.096922
training: constant with momentum
Training set score: 0.980000
Training set loss: 0.050260
training: constant with Nesterov's momentum
Training set score: 0.980000
Training set loss: 0.050277
training: inv-scaling learning-rate
Training set score: 0.360000
Training set loss: 0.979983
training: inv-scaling with momentum
Training set score: 0.860000
Training set loss: 0.504017
training: inv-scaling with Nesterov's momentum
Training set score: 0.860000
Training set loss: 0.504760
training: adam
Training set score: 0.980000
Training set loss: 0.046248

learning on dataset digits
training: constant learning-rate
Training set score: 0.956038
Training set loss: 0.243802
training: constant with momentum
Training set score: 0.992766
Training set loss: 0.041297
training: constant with Nesterov's momentum
Training set score: 0.993879
Training set loss: 0.042898
training: inv-scaling learning-rate
Training set score: 0.638843
Training set loss: 1.855465
training: inv-scaling with momentum
Training set score: 0.912632
Training set loss: 0.290584
training: inv-scaling with Nesterov's momentum
Training set score: 0.909293
Training set loss: 0.318387
training: adam
Training set score: 0.991653
Training set loss: 0.045934

learning on dataset circles
training: constant learning-rate
Training set score: 0.830000
Training set loss: 0.681498
training: constant with momentum
Training set score: 0.940000
Training set loss: 0.163712
training: constant with Nesterov's momentum
Training set score: 0.940000
Training set loss: 0.163012
training: inv-scaling learning-rate
Training set score: 0.500000
Training set loss: 0.692855
training: inv-scaling with momentum
Training set score: 0.510000
Training set loss: 0.688376
training: inv-scaling with Nesterov's momentum
Training set score: 0.500000
Training set loss: 0.688593
training: adam
Training set score: 0.930000
Training set loss: 0.159988

learning on dataset moons
training: constant learning-rate
Training set score: 0.850000
Training set loss: 0.342245
training: constant with momentum
Training set score: 0.850000
Training set loss: 0.345580
training: constant with Nesterov's momentum
Training set score: 0.850000
Training set loss: 0.336284
training: inv-scaling learning-rate
Training set score: 0.500000
Training set loss: 0.689729
training: inv-scaling with momentum
Training set score: 0.830000
Training set loss: 0.512595
training: inv-scaling with Nesterov's momentum
Training set score: 0.830000
Training set loss: 0.513034
training: adam
Training set score: 0.850000
Training set loss: 0.334243
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plotting code blocks in ../examples/neural_networks/plot_mnist_filters.py

________________________________________________________________________________
../examples/neural_networks/plot_mnist_filters.py is not compiling:
Traceback (most recent call last):
  File "/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/sphinxext/sphinx_gallery/gen_rst.py", line 467, in execute_script
    exec(code_block, example_globals)
  File "<string>", line 7, in <module>
  File "/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/datasets/mldata.py", line 142, in fetch_mldata
    mldata_url = urlopen(urlname)
  File "/usr/lib64/python2.7/urllib2.py", line 154, in urlopen
    return opener.open(url, data, timeout)
  File "/usr/lib64/python2.7/urllib2.py", line 429, in open
    response = self._open(req, data)
  File "/usr/lib64/python2.7/urllib2.py", line 447, in _open
    '_open', req)
  File "/usr/lib64/python2.7/urllib2.py", line 407, in _call_chain
    result = func(*args)
  File "/usr/lib64/python2.7/urllib2.py", line 1228, in http_open
    return self.do_open(httplib.HTTPConnection, req)
  File "/usr/lib64/python2.7/urllib2.py", line 1198, in do_open
    raise URLError(err)
URLError: <urlopen error [Errno 110] Connection timed out>

________________________________________________________________________________
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plotting code blocks in ../examples/neural_networks/plot_rbm_logistic_classification.py

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plotting code blocks in ../examples/neural_networks/plot_rbm_logistic_classification.py
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plotting code blocks in ../examples/neural_networks/plot_rbm_logistic_classification.py
[BernoulliRBM] Iteration 1, pseudo-likelihood = -25.39, time = 0.38s
[BernoulliRBM] Iteration 2, pseudo-likelihood = -23.77, time = 0.54s
[BernoulliRBM] Iteration 3, pseudo-likelihood = -22.94, time = 0.54s
[BernoulliRBM] Iteration 4, pseudo-likelihood = -21.91, time = 0.54s
[BernoulliRBM] Iteration 5, pseudo-likelihood = -21.69, time = 0.54s
[BernoulliRBM] Iteration 6, pseudo-likelihood = -21.06, time = 0.53s
[BernoulliRBM] Iteration 7, pseudo-likelihood = -20.89, time = 0.53s
[BernoulliRBM] Iteration 8, pseudo-likelihood = -20.64, time = 0.53s
[BernoulliRBM] Iteration 9, pseudo-likelihood = -20.36, time = 0.53s
[BernoulliRBM] Iteration 10, pseudo-likelihood = -20.09, time = 0.53s
[BernoulliRBM] Iteration 11, pseudo-likelihood = -20.08, time = 0.53s
[BernoulliRBM] Iteration 12, pseudo-likelihood = -19.82, time = 0.53s
[BernoulliRBM] Iteration 13, pseudo-likelihood = -19.64, time = 0.53s
[BernoulliRBM] Iteration 14, pseudo-likelihood = -19.61, time = 0.53s
[BernoulliRBM] Iteration 15, pseudo-likelihood = -19.57, time = 0.53s
[BernoulliRBM] Iteration 16, pseudo-likelihood = -19.41, time = 0.53s
[BernoulliRBM] Iteration 17, pseudo-likelihood = -19.30, time = 0.53s
[BernoulliRBM] Iteration 18, pseudo-likelihood = -19.25, time = 0.53s
[BernoulliRBM] Iteration 19, pseudo-likelihood = -19.27, time = 0.53s
[BernoulliRBM] Iteration 20, pseudo-likelihood = -19.01, time = 0.53s
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plotting code blocks in ../examples/neural_networks/plot_rbm_logistic_classification.py

Logistic regression using RBM features:
             precision    recall  f1-score   support

          0       0.99      0.99      0.99       174
          1       0.92      0.95      0.93       184
          2       0.95      0.98      0.97       166
          3       0.97      0.91      0.94       194
          4       0.97      0.95      0.96       186
          5       0.93      0.93      0.93       181
          6       0.98      0.97      0.97       207
          7       0.95      1.00      0.97       154
          8       0.90      0.88      0.89       182
          9       0.91      0.93      0.92       169

avg / total       0.95      0.95      0.95      1797


Logistic regression using raw pixel features:
             precision    recall  f1-score   support

          0       0.85      0.94      0.89       174
          1       0.57      0.55      0.56       184
          2       0.72      0.85      0.78       166
          3       0.76      0.74      0.75       194
          4       0.85      0.82      0.84       186
          5       0.74      0.75      /tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
0.75       181
          6       0.93      0.88      0.91       207
          7       0.86      0.90      0.88       154
          8       0.68      0.55      0.61       182
          9       0.71      0.74      0.72       169

avg / total       0.77      0.77      0.77      1797


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plotting code blocks in ../examples/preprocessing/plot_function_transformer.py
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plotting code blocks in ../examples/preprocessing/plot_robust_scaling.py

Testset accuracy using standard scaler: 0.545
Testset accuracy using robust scaler:   0.705
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plotting code blocks in ../examples/semi_supervised/plot_label_propagation_digits.py

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plotting code blocks in ../examples/semi_supervised/plot_label_propagation_digits.py
Label Spreading model: 30 labeled & 300 unlabeled points (330 total)
             precision    recall  f1-score   support

          0       1.00      1.00      1.00        23
          1       0.58      0.54      0.56        28
          2       0.96      0.93      0.95        29
          3       0.00      0.00      0.00        28
          4       0.91      0.80      0.85        25
          5       0.96      0.79      0.87        33
          6       0.97      0.97      0.97        36
          7       0.89      1.00      0.94        34
          8       0.48      0.83      0.61        29
          9       0.54      0.77      0.64        35

avg / total       0.73      0.77      0.74       300

Confusion matrix
[[23  0  0  0  0  0  0  0  0]
 [ 0 15  1  0  0  1  0 11  0]
 [ 0  0 27  0  0  0  2  0  0]
 [ 0  5  0 20  0  0  0  0  0]
 [ 0  0  0  0 26  0  0  1  6]
 [ 0  1  0  0  0 35  0  0  0]
 [ 0  0  0  0  0  0 34  0  0]
 [ 0  5  0  0  0  0  0 24  0]
 [ 0  0  0  2  1  0  2  3 27]]
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plotting code blocks in ../examples/semi_supervised/plot_label_propagation_digits.py
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plotting code blocks in ../examples/semi_supervised/plot_label_propagation_digits_active_learning.py

Iteration 0 ______________________________________________________________________
Label Spreading model: 10 labeled & 320 unlabeled (330 total)
             precision    recall  f1-score   support

          0       0.00      0.00      0.00        24
          1       0.49      0.90      0.63        29
          2       0.88      0.97      0.92        31
          3       0.00      0.00      0.00        28
          4       0.00      0.00      0.00        27
          5       0.89      0.49      0.63        35
          6       0.86      0.95      0.90        40
          7       0.75      0.92      0.83        36
          8       0.54      0.79      0.64        33
          9       0.41      0.86      0.56        37

avg / total       0.52      0.63      0.55       320

Confusion matrix
[[26  1  0  0  1  0  1]
 [ 1 30  0  0  0  0  0]
 [ 0  0 17  6  0  2 10]
 [ 2  0  0 38  0  0  0]
 [ 0  3  0  0 33  0  0]
 [ 7  0  0  0  0 26  0]
 [ 0  0  2  0  0  3 32]]
Iteration 1 ______________________________________________________________________
Label Spreading model: 15 labeled & 315 unlabeled (330 total)
             precision    recall  f1-score   support

          0       1.00      1.00      1.00        23
          1       0.61      0.59      0.60        29
          2       0.91      0.97      0.94        31
          3       1.00      0.56      0.71        27
          4       0.79      0.88      0.84        26
          5       0.89      0.46      0.60        35
          6       0.86      0.95      0.90        40
          7       0.97      0.92      0.94        36
          8       0.54      0.84      0.66        31
          9       0.70      0.81      0.75        37

avg / total       0.82      0.80      0.79       315

Confusion matrix
[[23  0  0  0  0  0  0  0  0  0]
 [ 0 17  1  0  2  0  0  1  7  1]
 [ 0  1 30  0  0  0  0  0  0  0]
 [ 0  0  0 15  0  0  0  0 10  2]
 [ 0  3  0  0 23  0  0  0  0  0]
 [ 0  0  0  0  1 16  6  0  2 10]
 [ 0  2  0  0  0  0 38  0  0  0]
 [ 0  0  2  0  1  0  0 33  0  0]
 [ 0  5  0  0  0  0  0  0 26  0]
 [ 0  0  0  0  2  2  0  0  3 30]]
Iteration 2 ______________________________________________________________________
Label Spreading model: 20 labeled & 310 unlabeled (330 total)
             precision    recall  f1-score   support

          0       1.00      1.00      1.00        23
          1       0.68      0.59      0.63        29
          2       0.91      0.97      0.94        31
          3       0.96      1.00      0.98        23
          4       0.81      1.00      0.89        25
          5       0.89      0.46      0.60        35
          6       0.86      0.95      0.90        40
          7       0.97      0.92      0.94        36
          8       0.68      0.84      0.75        31
          9       0.75      0.81      0.78        37

avg / total       0.85      0.84      0.83       310

Confusion matrix
[[23  0  0  0  0  0  0  0  0  0]
 [ 0 17  1  0  2  0  0  1  7  1]
 [ 0  1 30  0  0  0  0  0  0  0]
 [ 0  0  0 23  0  0  0  0  0  0]
 [ 0  0  0  0 25  0  0  0  0  0]
 [ 0  0  0  1  1 16  6  0  2  9]
 [ 0  2  0  0  0  0 38  0  0  0]
 [ 0  0  2  0  1  0  0 33  0  0]
 [ 0  5  0  0  0  0  0  0 26  0]
 [ 0  0  0  0  2  2  0  0  3 30]]
Iteration 3 ______________________________________________________________________
Label Spreading model: 25 labeled & 305 unlabeled (330 total)
             precision    recall  f1-score   support

          0       1.00      1.00      1.00        23
          1       0.70      0.85      0.77        27
          2       1.00      0.90      0.95        31
          3       1.00      1.00      1.00        23
          4       1.00      1.00      1.00        25
          5       0.96      0.74      0.83        34
          6       1.00      0.95      0.97        40
          7       0.90      1.00      0.95        35
          8       0.83      0.81      0.82        31
          9       0.75      0.83      0.79        36

avg / total       0.91      0.90      0.90       305

Confusion matrix
[[23  0  0  0  0  0  0  0  0  0]
 [ 0 23  0  0  0  0  0  0  4  0]
 [ 0  1 28  0  0  0  0  2  0  0]
 [ 0  0  0 23  0  0  0  0  0  0]
 [ 0  0  0  0 25  0  0  0  0  0]
 [ 0  0  0  0  0 25  0  0  0  9]
 [ 0  2  0  0  0  0 38  0  0  0]
 [ 0  0  0  0  0  0  0 35  0  0]
 [ 0  5  0  0  0  0  0  0 25  1]
 [ 0  2  0  0  0  1  0  2  1 30]]
Iteration 4 ______________________________________________________________________
Label Spreading model: 30 labeled & 300 unlabeled (330 total)
             precision    recall  f1-score   support

          0       1.00      1.00      1.00        23
          1       0.77      0.88      0.82        26
          2       1.00      0.90      0.95        31
          3       1.00      1.00      1.00        23
          4       1.00      1.00      1.00        25
          5       0.94      0.97      0.95        32
          6       1.00      0.97      0.99        39
          7       0.90      1.00      0.95        35
          8       0.89      0.81      0.85        31
          9       0.94      0.89      0.91        35

avg / total       0.94      0.94      0.94       300

Confusion matrix
[[23  0  0  0  0  0  0  0  0  0]
 [ 0 23  0  0  0  0  0  0  3  0]
 [ 0  1 28  0  0  0  0  2  0  0]
 [ 0  0  0 23  0  0  0  0  0  0]
 [ 0  0  0  0 25  0  0  0  0  0]
 [ 0  0  0  0  0 31  0  0  0  1]
 [ 0  1  0  0  0  0 38  0  0  0]
 [ 0  0  0  0  0  0  0 35  0  0]
 [ 0  5  0  0  0  0  0  0 25  1]
 [ 0  0  0  0  0  2  0  2  0 31]]
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The binary tree structure has 5 nodes and has the following tree structure:
node=0 test node: go to node 1 if X[:, 3] <= 0.800000011921s else to node 2.
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	node=2 test node: go to node 3 if X[:, 2] <= 4.94999980927s else to node 4.
		node=3 leaf node.
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Rules used to predict sample 0: 
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The following samples [0, 1] share the node [0 2] in the tree
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[u"precision_recall_fscore_support(y_true, y_pred, beta=1.0, labels=None, pos_label=1, average=None, warn_for=('precision', 'recall', 'f-score'), sample_weight=None)", u':module: sklearn.metrics', u'', u'', u'', u'Compute precision, recall, F-measure and support for each class', u'', u'The precision is the ratio ``tp / (tp + fp)`` where ``tp`` is the number of', u'true positives and ``fp`` the number of false positives. The precision is', u'intuitively the ability of the classifier not to label as positive a sample', u'that is negative.', u'', u'The recall is the ratio ``tp / (tp + fn)`` where ``tp`` is the number of', u'true positives and ``fn`` the number of false negatives. The recall is', u'intuitively the ability of the classifier to find all the positive samples.', u'', u'The F-beta score can be interpreted as a weighted harmonic mean of', u'the precision and recall, where an F-beta score reaches its best', u'value at 1 and worst score at 0.', u'', u'The F-beta score weights recall more than precision by a factor of', u'``beta``. ``beta == 1.0`` means recall and precision are equally important.', u'', u'The support is the number of occurrences of each class in ``y_true``.', u'', u'If ``pos_label is None`` and in binary classification, this function', u'returns the average precision, recall and F-measure if ``average``', u"is one of ``'micro'``, ``'macro'``, ``'weighted'`` or ``'samples'``.", u'', u'Read more in the :ref:`User Guide <precision_recall_f_measure_metrics>`.', u'', u':Parameters:', u'', u'    **y_true** : 1d array-like, or label indicator array / sparse matrix', u'', u'        Ground truth (correct) target values.', u'        ', u'', u'    **y_pred** : 1d array-like, or label indicator array / sparse matrix', u'', u'        Estimated targets as returned by a classifier.', u'        ', u'', u'    **beta** : float, 1.0 by default', u'', u'        The strength of recall versus precision in the F-score.', u'        ', u'', u'    **labels** : list, optional', u'', u"        The set of labels to include when ``average != 'binary'``, and their", u'        order if ``average is None``. Labels present in the data can be', u'        excluded, for example to calculate a multiclass average ignoring a', u'        majority negative class, while labels not present in the data will', u'        result in 0 components in a macro average. For multilabel targets,', u'        labels are column indices. By default, all labels in ``y_true`` and', u'        ``y_pred`` are used in sorted order.', u'        ', u'', u'    **pos_label** : str or int, 1 by default', u'', u"        The class to report if ``average='binary'`` and the data is binary.", u'        If the data are multiclass or multilabel, this will be ignored;', u"        setting ``labels=[pos_label]`` and ``average != 'binary'`` will report", u'        scores for that label only.', u'        ', u'', u"    **average** : string, [None (default), 'binary', 'micro', 'macro', 'samples',                        'weighted']", u'', u'        If ``None``, the scores for each class are returned. Otherwise, this', u'        determines the type of averaging performed on the data:', u'        ', u"        ``'binary'``:", u'            Only report results for the class specified by ``pos_label``.', u'            This is applicable only if targets (``y_{true,pred}``) are binary.', u"        ``'micro'``:", u'            Calculate metrics globally by counting the total true positives,', u'            false negatives and false positives.', u"        ``'macro'``:", u'            Calculate metrics for each label, and find their unweighted', u'            mean.  This does not take label imbalance into account.', u"        ``'weighted'``:", u'            Calculate metrics for each label, and find their average, weighted', u'            by support (the number of true instances for each label). This', u"            alters 'macro' to account for label imbalance; it can result in an", u'            F-score that is not between precision and recall.', u"        ``'samples'``:", u'            Calculate metrics for each instance, and find their average (only', u'            meaningful for multilabel classification where this differs from', u'            :func:`accuracy_score`).', u'        ', u'', u'    **warn_for** : tuple or set, for internal use', u'', u'        This determines which warnings will be made in the case that this', u'        function is being used to return only one of its metrics.', u'        ', u'', u'    **sample_weight** : array-like of shape = [n_samples], optional', u'', u'        Sample weights.', u'', u':Returns:', u'', u'    **precision** : float (if average is not None) or array of float, shape =        [n_unique_labels]', u'', u'        ', u'', u'    **recall** : float (if average is not None) or array of float, , shape =        [n_unique_labels]', u'', u'        ', u'', u'    **fbeta_score** : float (if average is not None) or array of float, shape =        [n_unique_labels]', u'', u'        ', u'', u'    **support** : int (if average is not None) or array of int, shape =        [n_unique_labels]', u'', u'        The number of occurrences of each label in ``y_true``.', u'', u'.. rubric:: References', u'', u'.. [R220] `Wikipedia entry for the Precision and recall', u'       <https://en.wikipedia.org/wiki/Precision_and_recall>`_', u'', u'.. [R221] `Wikipedia entry for the F1-score', u'       <https://en.wikipedia.org/wiki/F1_score>`_', u'', u'.. [R222] `Discriminative Methods for Multi-labeled Classification Advances', u'       in Knowledge Discovery and Data Mining (2004), pp. 22-30 by Shantanu', u'       Godbole, Sunita Sarawagi', u'       <http://www.godbole.net/shantanu/pubs/multilabelsvm-pakdd04.pdf>`', u'', u'.. only:: latex', u'', u'   [R220]_, [R221]_, [R222]_', u'', u'.. rubric:: Examples', u'', u'', u'>>> from sklearn.metrics import precision_recall_fscore_support', u">>> y_true = np.array(['cat', 'dog', 'pig', 'cat', 'dog', 'pig'])", u">>> y_pred = np.array(['cat', 'pig', 'dog', 'cat', 'cat', 'dog'])", u">>> precision_recall_fscore_support(y_true, y_pred, average='macro')", u'... # doctest: +ELLIPSIS', u'(0.22..., 0.33..., 0.26..., None)', u">>> precision_recall_fscore_support(y_true, y_pred, average='micro')", u'... # doctest: +ELLIPSIS', u'(0.33..., 0.33..., 0.33..., None)', u">>> precision_recall_fscore_support(y_true, y_pred, average='weighted')", u'... # doctest: +ELLIPSIS', u'(0.22..., 0.33..., 0.26..., None)', u'', u'It is possible to compute per-label precisions, recalls, F1-scores and', u'supports instead of averaging:', u'>>> precision_recall_fscore_support(y_true, y_pred, average=None,', u"... labels=['pig', 'dog', 'cat'])", u'... # doctest: +ELLIPSIS,+NORMALIZE_WHITESPACE', u'(array([ 0. ,  0. ,  0.66...]),', u' array([ 0.,  0.,  1.]),', u' array([ 0. ,  0. ,  0.8]),', u' array([2, 2, 2]))']:143: ERROR: Unexpected indentation.
/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/mixture/bayesian_mixture.py:docstring of sklearn.mixture.BayesianGaussianMixture:19: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/mixture/gaussian_mixture.py:docstring of sklearn.mixture.GaussianMixture:12: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2-python2_7/lib/sklearn/mixture/gaussian_mixture.py:docstring of sklearn.mixture.GaussianMixture:25: WARNING: Block quote ends without a blank line; unexpected unindent.
[u"GridSearchCV(estimator, param_grid, scoring=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', error_score='raise', return_train_score=True)", u':module: sklearn.model_selection', u'', u'', u'', u'Exhaustive search over specified parameter values for an estimator.', u'', u'Important members are fit, predict.', u'', u'GridSearchCV implements a "fit" and a "score" method.', u'It also implements "predict", "predict_proba", "decision_function",', u'"transform" and "inverse_transform" if they are implemented in the', u'estimator used.', u'', u'The parameters of the estimator used to apply these methods are optimized', u'by cross-validated grid-search over a parameter grid.', u'', u'Read more in the :ref:`User Guide <grid_search>`.', u'', u':Parameters:', u'', u'    **estimator** : estimator object.', u'', u'        This is assumed to implement the scikit-learn estimator interface.', u'        Either estimator needs to provide a ``score`` function,', u'        or ``scoring`` must be passed.', u'        ', u'', u'    **param_grid** : dict or list of dictionaries', u'', u'        Dictionary with parameters names (string) as keys and lists of', u'        parameter settings to try as values, or a list of such', u'        dictionaries, in which case the grids spanned by each dictionary', u'        in the list are explored. This enables searching over any sequence', u'        of parameter settings.', u'        ', u'', u'    **scoring** : string, callable or None, default=None', u'', u'        A string (see model evaluation documentation) or', u'        a scorer callable object / function with signature', u'        ``scorer(estimator, X, y)``.', u'        If ``None``, the ``score`` method of the estimator is used.', u'        ', u'', u'    **fit_params** : dict, optional', u'', u'        Parameters to pass to the fit method.', u'        ', u'', u'    **n_jobs** : int, default=1', u'', u'        Number of jobs to run in parallel.', u'        ', u'', u'    **pre_dispatch** : int, or string, optional', u'', u'        Controls the number of jobs that get dispatched during parallel', u'        execution. Reducing this number can be useful to avoid an', u'        explosion of memory consumption when more jobs get dispatched', u'        than CPUs can process. This parameter can be:', u'        ', u'            - None, in which case all the jobs are immediately', u'              created and spawned. Use this for lightweight and', u'              fast-running jobs, to avoid delays due to on-demand', u'              spawning of the jobs', u'        ', u'            - An int, giving the exact number of total jobs that are', u'              spawned', u'        ', u'            - A string, giving an expression as a function of n_jobs,', u"              as in '2*n_jobs'", u'        ', u'', u'    **iid** : boolean, default=True', u'', u'        If True, the data is assumed to be identically distributed across', u'        the folds, and the loss minimized is the total loss per sample,', u'        and not the mean loss across the folds.', u'        ', u'', u'    **cv** : int, cross-validation generator or an iterable, optional', u'', u'        Determines the cross-validation splitting strategy.', u'        Possible inputs for cv are:', u'          - None, to use the default 3-fold cross validation,', u'          - integer, to specify the number of folds in a `(Stratified)KFold`,', u'          - An object to be used as a cross-validation generator.', u'          - An iterable yielding train, test splits.', u'        ', u'        For integer/None inputs, if the estimator is a classifier and ``y`` is', u'        either binary or multiclass, :class:`StratifiedKFold` is used. In all', u'        other cases, :class:`KFold` is used.', u'        ', u'        Refer :ref:`User Guide <cross_validation>` for the various', u'        cross-validation strategies that can be used here.', u'        ', u'', u'    **refit** : boolean, default=True', u'', u'        Refit the best estimator with the entire dataset.', u'        If "False", it is impossible to make predictions using', u'        this GridSearchCV instance after fitting.', u'        ', u'', u'    **verbose** : integer', u'', u'        Controls the verbosity: the higher, the more messages.', u'        ', u'', u"    **error_score** : 'raise' (default) or numeric", u'', u'        Value to assign to the score if an error occurs in estimator fitting.', u"        If set to 'raise', the error is raised. If a numeric value is given,", u'        FitFailedWarning is raised. This parameter does not affect the refit', u'        step, which will always raise the error.', u'        ', u'', u'    **return_train_score** : boolean, default=True', u'', u"        If ``'False'``, the ``cv_results_`` attribute will not include training", u'        scores.', u'', u':Attributes:', u'', u'    **cv_results_** : dict of numpy (masked) ndarrays', u'', u'        A dict with keys as column headers and values as columns, that can be', u'        imported into a pandas ``DataFrame``.', u'        ', u'        For instance the below given table', u'        ', u'        +------------+-----------+------------+-----------------+---+---------+', u'        |param_kernel|param_gamma|param_degree|split0_test_score|...|rank_....|', u'        +============+===========+============+=================+===+=========+', u"        |  'poly'    |     --    |      2     |        0.8      |...|    2    |", u'        +------------+-----------+------------+-----------------+---+---------+', u"        |  'poly'    |     --    |      3     |        0.7      |...|    4    |", u'        +------------+-----------+------------+-----------------+---+---------+', u"        |  'rbf'     |     0.1   |     --     |        0.8      |...|    3    |", u'        +------------+-----------+------------+-----------------+---+---------+', u"        |  'rbf'     |     0.2   |     --     |        0.9      |...|    1    |", u'        +------------+-----------+------------+-----------------+---+---------+', u'        ', u'        will be represented by a ``cv_results_`` dict of::', u'        ', u'            {', u"            'param_kernel': masked_array(data = ['poly', 'poly', 'rbf', 'rbf'],", u'                                         mask = [False False False False]...)', u"            'param_gamma': masked_array(data = [-- -- 0.1 0.2],", u'                                        mask = [ True  True False False]...),', u"            'param_degree': masked_array(data = [2.0 3.0 -- --],", u'                                         mask = [False False  True  True]...),', u"            'split0_test_score'  : [0.8, 0.7, 0.8, 0.9],", u"            'split1_test_score'  : [0.82, 0.5, 0.7, 0.78],", u"            'mean_test_score'    : [0.81, 0.60, 0.75, 0.82],", u"            'std_test_score'     : [0.02, 0.01, 0.03, 0.03],", u"            'rank_test_score'    : [2, 4, 3, 1],", u"            'split0_train_score' : [0.8, 0.9, 0.7],", u"            'split1_train_score' : [0.82, 0.5, 0.7],", u"            'mean_train_score'   : [0.81, 0.7, 0.7],", u"            'std_train_score'    : [0.03, 0.03, 0.04],", u"            'mean_fit_time'      : [0.73, 0.63, 0.43, 0.49],", u"            'std_fit_time'       : [0.01, 0.02, 0.01, 0.01],", u"            'mean_score_time'    : [0.007, 0.06, 0.04, 0.04],", u"            'std_score_time'     : [0.001, 0.002, 0.003, 0.005],", u"            'params'             : [{'kernel': 'poly', 'degree': 2}, ...],", u'            }', u'        ', u"        NOTE that the key ``'params'`` is used to store a list of parameter", u'        settings dict for all the parameter candidates.', u'        ', u'        The ``mean_fit_time``, ``std_fit_time``, ``mean_score_time`` and', u'        ``std_score_time`` are all in seconds.', u'        ', u'', u'    **best_estimator_** : estimator', u'', u'        Estimator that was chosen by the search, i.e. estimator', u'        which gave highest score (or smallest loss if specified)', u'        on the left out data. Not available if refit=False.', u'        ', u'', u'    **best_score_** : float', u'', u'        Score of best_estimator on the left out data.', u'        ', u'', u'    **best_params_** : dict', u'', u'        Parameter setting that gave the best results on the hold out data.', u'        ', u'', u'    **best_index_** : int', u'', u'        The index (of the ``cv_results_`` arrays) which corresponds to the best', u'        candidate parameter setting.', u'        ', u"        The dict at ``search.cv_results_['params'][search.best_index_]`` gives", u'        the parameter setting for the best model, that gives the highest', u'        mean score (``search.best_score_``).', u'        ', u'', u'    **scorer_** : function', u'', u'        Scorer function used on the held out data to choose the best', u'        parameters for the model.', u'        ', u'', u'    **n_splits_** : int', u'', u'        The number of cross-validation splits (folds/iterations).', u'', u'.. seealso::', u'', u'    ', u'    :class:`ParameterGrid`', u'        generates all the combinations of a hyperparameter grid.', u'    ', u'    :func:`sklearn.model_selection.train_test_split`', u'        utility function to split the data into a development set usable for fitting a GridSearchCV instance and an evaluation set for its final evaluation.', u'    ', u'    :func:`sklearn.metrics.make_scorer`', u'        Make a scorer from a performance metric or loss function.', u'    ', u'.. rubric:: Notes', u'', u'', u'The parameters selected are those that maximize the score of the left out', u'data, unless an explicit score is passed in which case it is used instead.', u'', u'If `n_jobs` was set to a value higher than one, the data is copied for each', u'point in the grid (and not `n_jobs` times). This is done for efficiency', u'reasons if individual jobs take very little time, but may raise errors if', u'the dataset is large and not enough memory is available.  A workaround in', u'this case is to set `pre_dispatch`. Then, the memory is copied only', u'`pre_dispatch` many times. A reasonable value for `pre_dispatch` is `2 *', u'n_jobs`.', u'', u'.. rubric:: Examples', u'', u'', u'>>> from sklearn import svm, datasets', u'>>> from sklearn.model_selection import GridSearchCV', u'>>> iris = datasets.load_iris()', u">>> parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]}", u'>>> svr = svm.SVC()', u'>>> clf = GridSearchCV(svr, parameters)', u'>>> clf.fit(iris.data, iris.target)', u'...                             # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS', u'GridSearchCV(cv=None, error_score=...,', u'       estimator=SVC(C=1.0, cache_size=..., class_weight=..., coef0=...,', u'                     decision_function_shape=None, degree=..., gamma=...,', u"                     kernel='rbf', max_iter=-1, probability=False,", u'                     random_state=None, shrinking=True, tol=...,', u'                     verbose=False),', u'       fit_params={}, iid=..., n_jobs=1,', u'       param_grid=..., pre_dispatch=..., refit=..., return_train_score=...,', u'       scoring=..., verbose=...)', u'>>> sorted(clf.cv_results_.keys())', u'...                             # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS', u"['mean_fit_time', 'mean_score_time', 'mean_test_score',...", u" 'mean_train_score', 'param_C', 'param_kernel', 'params',...", u" 'rank_test_score', 'split0_test_score',...", u" 'split0_train_score', 'split1_test_score', 'split1_train_score',...", u" 'split2_test_score', 'split2_train_score',...", u" 'std_fit_time', 'std_score_time', 'std_test_score', 'std_train_score'...]", u'', u'.. rubric:: Methods', u'', u'.. autosummary::', u'', u'   decision_function', u'   fit', u'   get_params', u'   inverse_transform', u'   predict', u'   predict_log_proba', u'   predict_proba', u'   score', u'   set_params', u'   transform', u'.. automethod:: __init__', u'', u'.. py:method:: GridSearchCV.decision_function(*args, **kwargs)', u'   :module: sklearn.model_selection', u'', u'   ', u'   ', u'   Call decision_function on the estimator with the best found parameters.', u'   ', u'   Only available if ``refit=True`` and the underlying estimator supports', u'   ``decision_function``.', u'   ', u'   :Parameters:', u'   ', u'       **X** : indexable, length n_samples', u'   ', u'           Must fulfill the input assumptions of the', u'           underlying estimator.', u'', u'.. py:method:: GridSearchCV.fit(X, y=None, groups=None)', u'   :module: sklearn.model_selection', u'', u'   ', u'   ', u'   Run fit with all sets of parameters.', u'   ', u'   ', u'   :Parameters:', u'   ', u'       **X** : array-like, shape = [n_samples, n_features]', u'   ', u'           Training vector, where n_samples is the number of samples and', u'           n_features is the number of features.', u'           ', u'   ', u'       **y** : array-like, shape = [n_samples] or [n_samples, n_output], optional', u'   ', u'           Target relative to X for classification or regression;', u'           None for unsupervised learning.', u'           ', u'   ', u'       **groups** : array-like, with shape (n_samples,), optional', u'   ', u'           Group labels for the samples used while splitting the dataset into', u'           train/test set.', u'', u'.. py:method:: GridSearchCV.get_params(deep=True)', u'   :module: sklearn.model_selection', u'', u'   ', u'   ', u'   Get parameters for this estimator.', u'   ', u'   ', u'   :Parameters:', u'   ', u'       **deep** : boolean, optional', u'   ', u'           If True, will return the parameters for this estimator and', u'           contained subobjects that are estimators.', u'   ', u'   :Returns:', u'   ', u'       **params** : mapping of string to any', u'   ', u'           Parameter names mapped to their values.', u'', u'.. py:method:: GridSearchCV.inverse_transform(*args, **kwargs)', u'   :module: sklearn.model_selection', u'', u'   ', u'   ', u'   Call inverse_transform on the estimator with the best found params.', u'   ', u'   Only available if the underlying estimator implements', u'   ``inverse_transform`` and ``refit=True``.', u'   ', u'   :Parameters:', u'   ', u'       **Xt** : indexable, length n_samples', u'   ', u'           Must fulfill the input assumptions of the', u'           underlying estimator.', u'', u'.. py:method:: GridSearchCV.predict(*args, **kwargs)', u'   :module: sklearn.model_selection', u'', u'   ', u'   ', u'   Call predict on the estimator with the best found parameters.', u'   ', u'   Only available if ``refit=True`` and the underlying estimator supports', u'   ``predict``.', u'   ', u'   :Parameters:', u'   ', u'       **X** : indexable, length n_samples', u'   ', u'           Must fulfill the input assumptions of the', u'           underlying estimator.', u'', u'.. py:method:: GridSearchCV.predict_log_proba(*args, **kwargs)', u'   :module: sklearn.model_selection', u'', u'   ', u'   ', u'   Call predict_log_proba on the estimator with the best found parameters.', u'   ', u'   Only available if ``refit=True`` and the underlying estimator supports', u'   ``predict_log_proba``.', u'   ', u'   :Parameters:', u'   ', u'       **X** : indexable, length n_samples', u'   ', u'           Must fulfill the input assumptions of the', u'           underlying estimator.', u'', u'.. py:method:: GridSearchCV.predict_proba(*args, **kwargs)', u'   :module: sklearn.model_selection', u'', u'   ', u'   ', u'   Call predict_proba on the estimator with the best found parameters.', u'   ', u'   Only available if ``refit=True`` and the underlying estimator supports', u'   ``predict_proba``.', u'   ', u'   :Parameters:', u'   ', u'       **X** : indexable, length n_samples', u'   ', u'           Must fulfill the input assumptions of the', u'           underlying estimator.', u'', u'.. py:method:: GridSearchCV.score(X, y=None)', u'   :module: sklearn.model_selection', u'', u'   ', u'   ', u'   Returns the score on the given data, if the estimator has been refit.', u'   ', u'   This uses the score defined by ``scoring`` where provided, and the', u'   ``best_estimator_.score`` method otherwise.', u'   ', u'   :Parameters:', u'   ', u'       **X** : array-like, shape = [n_samples, n_features]', u'   ', u'           Input data, where n_samples is the number of samples and', u'           n_features is the number of features.', u'           ', u'   ', u'       **y** : array-like, shape = [n_samples] or [n_samples, n_output], optional', u'   ', u'           Target relative to X for classification or regression;', u'           None for unsupervised learning.', u'   ', u'   :Returns:', u'   ', u'       **score** : float', u'   ', u'           ', u'', u'.. py:method:: GridSearchCV.set_params(**params)', u'   :module: sklearn.model_selection', u'', u'   ', u'   ', u'   Set the parameters of this estimator.', u'   ', u'   The method works on simple estimators as well as on nested objects', u'   (such as pipelines). The latter have parameters of the form', u"   ``<component>__<parameter>`` so that it's possible to update each", u'   component of a nested object.', u'   ', u'   :Returns:', u'   ', u'       **self** : ', u'   ', u'           ', u'', u'.. py:method:: GridSearchCV.transform(*args, **kwargs)', u'   :module: sklearn.model_selection', u'', u'   ', u'   ', u'   Call transform on the estimator with the best found parameters.', u'   ', u'   Only available if the underlying estimator supports ``transform`` and', u'   ``refit=True``.', u'   ', u'   :Parameters:', u'   ', u'       **X** : indexable, length n_samples', u'   ', u'           Must fulfill the input assumptions of the', u'           underlying estimator.']:76: ERROR: Unexpected indentation.
[u"GridSearchCV(estimator, param_grid, scoring=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', error_score='raise', return_train_score=True)", u':module: sklearn.model_selection', u'', u'', u'', u'Exhaustive search over specified parameter values for an estimator.', u'', u'Important members are fit, predict.', u'', u'GridSearchCV implements a "fit" and a "score" method.', u'It also implements "predict", "predict_proba", "decision_function",', u'"transform" and "inverse_transform" if they are implemented in the', u'estimator used.', u'', u'The parameters of the estimator used to apply these methods are optimized', u'by cross-validated grid-search over a parameter grid.', u'', u'Read more in the :ref:`User Guide <grid_search>`.', u'', u':Parameters:', u'', u'    **estimator** : estimator object.', u'', u'        This is assumed to implement the scikit-learn estimator interface.', u'        Either estimator needs to provide a ``score`` function,', u'        or ``scoring`` must be passed.', u'        ', u'', u'    **param_grid** : dict or list of dictionaries', u'', u'        Dictionary with parameters names (string) as keys and lists of', u'        parameter settings to try as values, or a list of such', u'        dictionaries, in which case the grids spanned by each dictionary', u'        in the list are explored. This enables searching over any sequence', u'        of parameter settings.', u'        ', u'', u'    **scoring** : string, callable or None, default=None', u'', u'        A string (see model evaluation documentation) or', u'        a scorer callable object / function with signature', u'        ``scorer(estimator, X, y)``.', u'        If ``None``, the ``score`` method of the estimator is used.', u'        ', u'', u'    **fit_params** : dict, optional', u'', u'        Parameters to pass to the fit method.', u'        ', u'', u'    **n_jobs** : int, default=1', u'', u'        Number of jobs to run in parallel.', u'        ', u'', u'    **pre_dispatch** : int, or string, optional', u'', u'        Controls the number of jobs that get dispatched during parallel', u'        execution. Reducing this number can be useful to avoid an', u'        explosion of memory consumption when more jobs get dispatched', u'        than CPUs can process. This parameter can be:', u'        ', u'            - None, in which case all the jobs are immediately', u'              created and spawned. Use this for lightweight and', u'              fast-running jobs, to avoid delays due to on-demand', u'              spawning of the jobs', u'        ', u'            - An int, giving the exact number of total jobs that are', u'              spawned', u'        ', u'            - A string, giving an expression as a function of n_jobs,', u"              as in '2*n_jobs'", u'        ', u'', u'    **iid** : boolean, default=True', u'', u'        If True, the data is assumed to be identically distributed across', u'        the folds, and the loss minimized is the total loss per sample,', u'        and not the mean loss across the folds.', u'        ', u'', u'    **cv** : int, cross-validation generator or an iterable, optional', u'', u'        Determines the cross-validation splitting strategy.', u'        Possible inputs for cv are:', u'          - None, to use the default 3-fold cross validation,', u'          - integer, to specify the number of folds in a `(Stratified)KFold`,', u'          - An object to be used as a cross-validation generator.', u'          - An iterable yielding train, test splits.', u'        ', u'        For integer/None inputs, if the estimator is a classifier and ``y`` is', u'        either binary or multiclass, :class:`StratifiedKFold` is used. In all', u'        other cases, :class:`KFold` is used.', u'        ', u'        Refer :ref:`User Guide <cross_validation>` for the various', u'        cross-validation strategies that can be used here.', u'        ', u'', u'    **refit** : boolean, default=True', u'', u'        Refit the best estimator with the entire dataset.', u'        If "False", it is impossible to make predictions using', u'        this GridSearchCV instance after fitting.', u'        ', u'', u'    **verbose** : integer', u'', u'        Controls the verbosity: the higher, the more messages.', u'        ', u'', u"    **error_score** : 'raise' (default) or numeric", u'', u'        Value to assign to the score if an error occurs in estimator fitting.', u"        If set to 'raise', the error is raised. If a numeric value is given,", u'        FitFailedWarning is raised. This parameter does not affect the refit', u'        step, which will always raise the error.', u'        ', u'', u'    **return_train_score** : boolean, default=True', u'', u"        If ``'False'``, the ``cv_results_`` attribute will not include training", u'        scores.', u'', u':Attributes:', u'', u'    **cv_results_** : dict of numpy (masked) ndarrays', u'', u'        A dict with keys as column headers and values as columns, that can be', u'        imported into a pandas ``DataFrame``.', u'        ', u'        For instance the below given table', u'        ', u'        +------------+-----------+------------+-----------------+---+---------+', u'        |param_kernel|param_gamma|param_degree|split0_test_score|...|rank_....|', u'        +============+===========+============+=================+===+=========+', u"        |  'poly'    |     --    |      2     |        0.8      |...|    2    |", u'        +------------+-----------+------------+-----------------+---+---------+', u"        |  'poly'    |     --    |      3     |        0.7      |...|    4    |", u'        +------------+-----------+------------+-----------------+---+---------+', u"        |  'rbf'     |     0.1   |     --     |        0.8      |...|    3    |", u'        +------------+-----------+------------+-----------------+---+---------+', u"        |  'rbf'     |     0.2   |     --     |        0.9      |...|    1    |", u'        +------------+-----------+------------+-----------------+---+---------+', u'        ', u'        will be represented by a ``cv_results_`` dict of::', u'        ', u'            {', u"            'param_kernel': masked_array(data = ['poly', 'poly', 'rbf', 'rbf'],", u'                                         mask = [False False False False]...)', u"            'param_gamma': masked_array(data = [-- -- 0.1 0.2],", u'                                        mask = [ True  True False False]...),', u"            'param_degree': masked_array(data = [2.0 3.0 -- --],", u'                                         mask = [False False  True  True]...),', u"            'split0_test_score'  : [0.8, 0.7, 0.8, 0.9],", u"            'split1_test_score'  : [0.82, 0.5, 0.7, 0.78],", u"            'mean_test_score'    : [0.81, 0.60, 0.75, 0.82],", u"            'std_test_score'     : [0.02, 0.01, 0.03, 0.03],", u"            'rank_test_score'    : [2, 4, 3, 1],", u"            'split0_train_score' : [0.8, 0.9, 0.7],", u"            'split1_train_score' : [0.82, 0.5, 0.7],", u"            'mean_train_score'   : [0.81, 0.7, 0.7],", u"            'std_train_score'    : [0.03, 0.03, 0.04],", u"            'mean_fit_time'      : [0.73, 0.63, 0.43, 0.49],", u"            'std_fit_time'       : [0.01, 0.02, 0.01, 0.01],", u"            'mean_score_time'    : [0.007, 0.06, 0.04, 0.04],", u"            'std_score_time'     : [0.001, 0.002, 0.003, 0.005],", u"            'params'             : [{'kernel': 'poly', 'degree': 2}, ...],", u'            }', u'        ', u"        NOTE that the key ``'params'`` is used to store a list of parameter", u'        settings dict for all the parameter candidates.', u'        ', u'        The ``mean_fit_time``, ``std_fit_time``, ``mean_score_time`` and', u'        ``std_score_time`` are all in seconds.', u'        ', u'', u'    **best_estimator_** : estimator', u'', u'        Estimator that was chosen by the search, i.e. estimator', u'        which gave highest score (or smallest loss if specified)', u'        on the left out data. Not available if refit=False.', u'        ', u'', u'    **best_score_** : float', u'', u'        Score of best_estimator on the left out data.', u'        ', u'', u'    **best_params_** : dict', u'', u'        Parameter setting that gave the best results on the hold out data.', u'        ', u'', u'    **best_index_** : int', u'', u'        The index (of the ``cv_results_`` arrays) which corresponds to the best', u'        candidate parameter setting.', u'        ', u"        The dict at ``search.cv_results_['params'][search.best_index_]`` gives", u'        the parameter setting for the best model, that gives the highest', u'        mean score (``search.best_score_``).', u'        ', u'', u'    **scorer_** : function', u'', u'        Scorer function used on the held out data to choose the best', u'        parameters for the model.', u'        ', u'', u'    **n_splits_** : int', u'', u'        The number of cross-validation splits (folds/iterations).', u'', u'.. seealso::', u'', u'    ', u'    :class:`ParameterGrid`', u'        generates all the combinations of a hyperparameter grid.', u'    ', u'    :func:`sklearn.model_selection.train_test_split`', u'        utility function to split the data into a development set usable for fitting a GridSearchCV instance and an evaluation set for its final evaluation.', u'    ', u'    :func:`sklearn.metrics.make_scorer`', u'        Make a scorer from a performance metric or loss function.', u'    ', u'.. rubric:: Notes', u'', u'', u'The parameters selected are those that maximize the score of the left out', u'data, unless an explicit score is passed in which case it is used instead.', u'', u'If `n_jobs` was set to a value higher than one, the data is copied for each', u'point in the grid (and not `n_jobs` times). This is done for efficiency', u'reasons if individual jobs take very little time, but may raise errors if', u'the dataset is large and not enough memory is available.  A workaround in', u'this case is to set `pre_dispatch`. Then, the memory is copied only', u'`pre_dispatch` many times. A reasonable value for `pre_dispatch` is `2 *', u'n_jobs`.', u'', u'.. rubric:: Examples', u'', u'', u'>>> from sklearn import svm, datasets', u'>>> from sklearn.model_selection import GridSearchCV', u'>>> iris = datasets.load_iris()', u">>> parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]}", u'>>> svr = svm.SVC()', u'>>> clf = GridSearchCV(svr, parameters)', u'>>> clf.fit(iris.data, iris.target)', u'...                             # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS', u'GridSearchCV(cv=None, error_score=...,', u'       estimator=SVC(C=1.0, cache_size=..., class_weight=..., coef0=...,', u'                     decision_function_shape=None, degree=..., gamma=...,', u"                     kernel='rbf', max_iter=-1, probability=False,", u'                     random_state=None, shrinking=True, tol=...,', u'                     verbose=False),', u'       fit_params={}, iid=..., n_jobs=1,', u'       param_grid=..., pre_dispatch=..., refit=..., return_train_score=...,', u'       scoring=..., verbose=...)', u'>>> sorted(clf.cv_results_.keys())', u'...                             # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS', u"['mean_fit_time', 'mean_score_time', 'mean_test_score',...", u" 'mean_train_score', 'param_C', 'param_kernel', 'params',...", u" 'rank_test_score', 'split0_test_score',...", u" 'split0_train_score', 'split1_test_score', 'split1_train_score',...", u" 'split2_test_score', 'split2_train_score',...", u" 'std_fit_time', 'std_score_time', 'std_test_score', 'std_train_score'...]", u'', u'.. rubric:: Methods', u'', u'.. autosummary::', u'', u'   decision_function', u'   fit', u'   get_params', u'   inverse_transform', u'   predict', u'   predict_log_proba', u'   predict_proba', u'   score', u'   set_params', u'   transform', u'.. automethod:: __init__', u'', u'.. py:method:: GridSearchCV.decision_function(*args, **kwargs)', u'   :module: sklearn.model_selection', u'', u'   ', u'   ', u'   Call decision_function on the estimator with the best found parameters.', u'   ', u'   Only available if ``refit=True`` and the underlying estimator supports', u'   ``decision_function``.', u'   ', u'   :Parameters:', u'   ', u'       **X** : indexable, length n_samples', u'   ', u'           Must fulfill the input assumptions of the', u'           underlying estimator.', u'', u'.. py:method:: GridSearchCV.fit(X, y=None, groups=None)', u'   :module: sklearn.model_selection', u'', u'   ', u'   ', u'   Run fit with all sets of parameters.', u'   ', u'   ', u'   :Parameters:', u'   ', u'       **X** : array-like, shape = [n_samples, n_features]', u'   ', u'           Training vector, where n_samples is the number of samples and', u'           n_features is the number of features.', u'           ', u'   ', u'       **y** : array-like, shape = [n_samples] or [n_samples, n_output], optional', u'   ', u'           Target relative to X for classification or regression;', u'           None for unsupervised learning.', u'           ', u'   ', u'       **groups** : array-like, with shape (n_samples,), optional', u'   ', u'           Group labels for the samples used while splitting the dataset into', u'           train/test set.', u'', u'.. py:method:: GridSearchCV.get_params(deep=True)', u'   :module: sklearn.model_selection', u'', u'   ', u'   ', u'   Get parameters for this estimator.', u'   ', u'   ', u'   :Parameters:', u'   ', u'       **deep** : boolean, optional', u'   ', u'           If True, will return the parameters for this estimator and', u'           contained subobjects that are estimators.', u'   ', u'   :Returns:', u'   ', u'       **params** : mapping of string to any', u'   ', u'           Parameter names mapped to their values.', u'', u'.. py:method:: GridSearchCV.inverse_transform(*args, **kwargs)', u'   :module: sklearn.model_selection', u'', u'   ', u'   ', u'   Call inverse_transform on the estimator with the best found params.', u'   ', u'   Only available if the underlying estimator implements', u'   ``inverse_transform`` and ``refit=True``.', u'   ', u'   :Parameters:', u'   ', u'       **Xt** : indexable, length n_samples', u'   ', u'           Must fulfill the input assumptions of the', u'           underlying estimator.', u'', u'.. py:method:: GridSearchCV.predict(*args, **kwargs)', u'   :module: sklearn.model_selection', u'', u'   ', u'   ', u'   Call predict on the estimator with the best found parameters.', u'   ', u'   Only available if ``refit=True`` and the underlying estimator supports', u'   ``predict``.', u'   ', u'   :Parameters:', u'   ', u'       **X** : indexable, length n_samples', u'   ', u'           Must fulfill the input assumptions of the', u'           underlying estimator.', u'', u'.. py:method:: GridSearchCV.predict_log_proba(*args, **kwargs)', u'   :module: sklearn.model_selection', u'', u'   ', u'   ', u'   Call predict_log_proba on the estimator with the best found parameters.', u'   ', u'   Only available if ``refit=True`` and the underlying estimator supports', u'   ``predict_log_proba``.', u'   ', u'   :Parameters:', u'   ', u'       **X** : indexable, length n_samples', u'   ', u'           Must fulfill the input assumptions of the', u'           underlying estimator.', u'', u'.. py:method:: GridSearchCV.predict_proba(*args, **kwargs)', u'   :module: sklearn.model_selection', u'', u'   ', u'   ', u'   Call predict_proba on the estimator with the best found parameters.', u'   ', u'   Only available if ``refit=True`` and the underlying estimator supports', u'   ``predict_proba``.', u'   ', u'   :Parameters:', u'   ', u'       **X** : indexable, length n_samples', u'   ', u'           Must fulfill the input assumptions of the', u'           underlying estimator.', u'', u'.. py:method:: GridSearchCV.score(X, y=None)', u'   :module: sklearn.model_selection', u'', u'   ', u'   ', u'   Returns the score on the given data, if the estimator has been refit.', u'   ', u'   This uses the score defined by ``scoring`` where provided, and the', u'   ``best_estimator_.score`` method otherwise.', u'   ', u'   :Parameters:', u'   ', u'       **X** : array-like, shape = [n_samples, n_features]', u'   ', u'           Input data, where n_samples is the number of samples and', u'           n_features is the number of features.', u'           ', u'   ', u'       **y** : array-like, shape = [n_samples] or [n_samples, n_output], optional', u'   ', u'           Target relative to X for classification or regression;', u'           None for unsupervised learning.', u'   ', u'   :Returns:', u'   ', u'       **score** : float', u'   ', u'           ', u'', u'.. py:method:: GridSearchCV.set_params(**params)', u'   :module: sklearn.model_selection', u'', u'   ', u'   ', u'   Set the parameters of this estimator.', u'   ', u'   The method works on simple estimators as well as on nested objects', u'   (such as pipelines). The latter have parameters of the form', u"   ``<component>__<parameter>`` so that it's possible to update each", u'   component of a nested object.', u'   ', u'   :Returns:', u'   ', u'       **self** : ', u'   ', u'           ', u'', u'.. py:method:: GridSearchCV.transform(*args, **kwargs)', u'   :module: sklearn.model_selection', u'', u'   ', u'   ', u'   Call transform on the estimator with the best found parameters.', u'   ', u'   Only available if the underlying estimator supports ``transform`` and', u'   ``refit=True``.', u'   ', u'   :Parameters:', u'   ', u'       **X** : indexable, length n_samples', u'   ', u'           Must fulfill the input assumptions of the', u'           underlying estimator.']:120: ERROR: Unknown target name: "rank".
[u"RandomizedSearchCV(estimator, param_distributions, n_iter=10, scoring=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', random_state=None, error_score='raise', return_train_score=True)", u':module: sklearn.model_selection', u'', u'', u'', u'Randomized search on hyper parameters.', u'', u'RandomizedSearchCV implements a "fit" and a "score" method.', u'It also implements "predict", "predict_proba", "decision_function",', u'"transform" and "inverse_transform" if they are implemented in the', u'estimator used.', u'', u'The parameters of the estimator used to apply these methods are optimized', u'by cross-validated search over parameter settings.', u'', u'In contrast to GridSearchCV, not all parameter values are tried out, but', u'rather a fixed number of parameter settings is sampled from the specified', u'distributions. The number of parameter settings that are tried is', u'given by n_iter.', u'', u'If all parameters are presented as a list,', u'sampling without replacement is performed. If at least one parameter', u'is given as a distribution, sampling with replacement is used.', u'It is highly recommended to use continuous distributions for continuous', u'parameters.', u'', u'Read more in the :ref:`User Guide <randomized_parameter_search>`.', u'', u':Parameters:', u'', u'    **estimator** : estimator object.', u'', u'        A object of that type is instantiated for each grid point.', u'        This is assumed to implement the scikit-learn estimator interface.', u'        Either estimator needs to provide a ``score`` function,', u'        or ``scoring`` must be passed.', u'        ', u'', u'    **param_distributions** : dict', u'', u'        Dictionary with parameters names (string) as keys and distributions', u'        or lists of parameters to try. Distributions must provide a ``rvs``', u'        method for sampling (such as those from scipy.stats.distributions).', u'        If a list is given, it is sampled uniformly.', u'        ', u'', u'    **n_iter** : int, default=10', u'', u'        Number of parameter settings that are sampled. n_iter trades', u'        off runtime vs quality of the solution.', u'        ', u'', u'    **scoring** : string, callable or None, default=None', u'', u'        A string (see model evaluation documentation) or', u'        a scorer callable object / function with signature', u'        ``scorer(estimator, X, y)``.', u'        If ``None``, the ``score`` method of the estimator is used.', u'        ', u'', u'    **fit_params** : dict, optional', u'', u'        Parameters to pass to the fit method.', u'        ', u'', u'    **n_jobs** : int, default=1', u'', u'        Number of jobs to run in parallel.', u'        ', u'', u'    **pre_dispatch** : int, or string, optional', u'', u'        Controls the number of jobs that get dispatched during parallel', u'        execution. Reducing this number can be useful to avoid an', u'        explosion of memory consumption when more jobs get dispatched', u'        than CPUs can process. This parameter can be:', u'        ', u'            - None, in which case all the jobs are immediately', u'              created and spawned. Use this for lightweight and', u'              fast-running jobs, to avoid delays due to on-demand', u'              spawning of the jobs', u'        ', u'            - An int, giving the exact number of total jobs that are', u'              spawned', u'        ', u'            - A string, giving an expression as a function of n_jobs,', u"              as in '2*n_jobs'", u'        ', u'', u'    **iid** : boolean, default=True', u'', u'        If True, the data is assumed to be identically distributed across', u'        the folds, and the loss minimized is the total loss per sample,', u'        and not the mean loss across the folds.', u'        ', u'', u'    **cv** : int, cross-validation generator or an iterable, optional', u'', u'        Determines the cross-validation splitting strategy.', u'        Possible inputs for cv are:', u'          - None, to use the default 3-fold cross validation,', u'          - integer, to specify the number of folds in a `(Stratified)KFold`,', u'          - An object to be used as a cross-validation generator.', u'          - An iterable yielding train, test splits.', u'        ', u'        For integer/None inputs, if the estimator is a classifier and ``y`` is', u'        either binary or multiclass, :class:`StratifiedKFold` is used. In all', u'        other cases, :class:`KFold` is used.', u'        ', u'        Refer :ref:`User Guide <cross_validation>` for the various', u'        cross-validation strategies that can be used here.', u'        ', u'', u'    **refit** : boolean, default=True', u'', u'        Refit the best estimator with the entire dataset.', u'        If "False", it is impossible to make predictions using', u'        this RandomizedSearchCV instance after fitting.', u'        ', u'', u'    **verbose** : integer', u'', u'        Controls the verbosity: the higher, the more messages.', u'        ', u'', u'    **random_state** : int or RandomState', u'', u'        Pseudo random number generator state used for random uniform sampling', u'        from lists of possible values instead of scipy.stats distributions.', u'        ', u'', u"    **error_score** : 'raise' (default) or numeric", u'', u'        Value to assign to the score if an error occurs in estimator fitting.', u"        If set to 'raise', the error is raised. If a numeric value is given,", u'        FitFailedWarning is raised. This parameter does not affect the refit', u'        step, which will always raise the error.', u'        ', u'', u'    **return_train_score** : boolean, default=True', u'', u"        If ``'False'``, the ``cv_results_`` attribute will not include training", u'        scores.', u'', u':Attributes:', u'', u'    **cv_results_** : dict of numpy (masked) ndarrays', u'', u'        A dict with keys as column headers and values as columns, that can be', u'        imported into a pandas ``DataFrame``.', u'        ', u'        For instance the below given table', u'        ', u'        +--------------+-------------+-------------------+---+---------------+', u'        | param_kernel | param_gamma | split0_test_score |...|rank_test_score|', u'        +==============+=============+===================+===+===============+', u"        |    'rbf'     |     0.1     |        0.8        |...|       2       |", u'        +--------------+-------------+-------------------+---+---------------+', u"        |    'rbf'     |     0.2     |        0.9        |...|       1       |", u'        +--------------+-------------+-------------------+---+---------------+', u"        |    'rbf'     |     0.3     |        0.7        |...|       1       |", u'        +--------------+-------------+-------------------+---+---------------+', u'        ', u'        will be represented by a ``cv_results_`` dict of::', u'        ', u'            {', u"            'param_kernel' : masked_array(data = ['rbf', 'rbf', 'rbf'],", u'                                          mask = False),', u"            'param_gamma'  : masked_array(data = [0.1 0.2 0.3], mask = False),", u"            'split0_test_score'  : [0.8, 0.9, 0.7],", u"            'split1_test_score'  : [0.82, 0.5, 0.7],", u"            'mean_test_score'    : [0.81, 0.7, 0.7],", u"            'std_test_score'     : [0.02, 0.2, 0.],", u"            'rank_test_score'    : [3, 1, 1],", u"            'split0_train_score' : [0.8, 0.9, 0.7],", u"            'split1_train_score' : [0.82, 0.5, 0.7],", u"            'mean_train_score'   : [0.81, 0.7, 0.7],", u"            'std_train_score'    : [0.03, 0.03, 0.04],", u"            'mean_fit_time'      : [0.73, 0.63, 0.43, 0.49],", u"            'std_fit_time'       : [0.01, 0.02, 0.01, 0.01],", u"            'mean_score_time'    : [0.007, 0.06, 0.04, 0.04],", u"            'std_score_time'     : [0.001, 0.002, 0.003, 0.005],", u"            'params' : [{'kernel' : 'rbf', 'gamma' : 0.1}, ...],", u'            }', u'        ', u"        NOTE that the key ``'params'`` is used to store a list of parameter", u'        settings dict for all the parameter candidates.', u'        ', u'        The ``mean_fit_time``, ``std_fit_time``, ``mean_score_time`` and', u'        ``std_score_time`` are all in seconds.', u'        ', u'', u'    **best_estimator_** : estimator', u'', u'        Estimator that was chosen by the search, i.e. estimator', u'        which gave highest score (or smallest loss if specified)', u'        on the left out data. Not available if refit=False.', u'        ', u'', u'    **best_score_** : float', u'', u'        Score of best_estimator on the left out data.', u'        ', u'', u'    **best_params_** : dict', u'', u'        Parameter setting that gave the best results on the hold out data.', u'        ', u'', u'    **best_index_** : int', u'', u'        The index (of the ``cv_results_`` arrays) which corresponds to the best', u'        candidate parameter setting.', u'        ', u"        The dict at ``search.cv_results_['params'][search.best_index_]`` gives", u'        the parameter setting for the best model, that gives the highest', u'        mean score (``search.best_score_``).', u'        ', u'', u'    **scorer_** : function', u'', u'        Scorer function used on the held out data to choose the best', u'        parameters for the model.', u'        ', u'', u'    **n_splits_** : int', u'', u'        The number of cross-validation splits (folds/iterations).', u'', u'.. seealso::', u'', u'    ', u'    :class:`GridSearchCV`', u'        Does exhaustive search over a grid of parameters.', u'    ', u'    :class:`ParameterSampler`', u'        A generator over parameter settins, constructed from param_distributions.', u'    ', u'.. rubric:: Notes', u'', u'', u'The parameters selected are those that maximize the score of the held-out', u'data, according to the scoring parameter.', u'', u'If `n_jobs` was set to a value higher than one, the data is copied for each', u'parameter setting(and not `n_jobs` times). This is done for efficiency', u'reasons if individual jobs take very little time, but may raise errors if', u'the dataset is large and not enough memory is available.  A workaround in', u'this case is to set `pre_dispatch`. Then, the memory is copied only', u'`pre_dispatch` many times. A reasonable value for `pre_dispatch` is `2 *', u'n_jobs`.', u'', u'.. rubric:: Methods', u'', u'.. autosummary::', u'', u'   decision_function', u'   fit', u'   get_params', u'   inverse_transform', u'   predict', u'   predict_log_proba', u'   predict_proba', u'   score', u'   set_params', u'   transform', u'.. automethod:: __init__', u'', u'.. py:method:: RandomizedSearchCV.decision_function(*args, **kwargs)', u'   :module: sklearn.model_selection', u'', u'   ', u'   ', u'   Call decision_function on the estimator with the best found parameters.', u'   ', u'   Only available if ``refit=True`` and the underlying estimator supports', u'   ``decision_function``.', u'   ', u'   :Parameters:', u'   ', u'       **X** : indexable, length n_samples', u'   ', u'           Must fulfill the input assumptions of the', u'           underlying estimator.', u'', u'.. py:method:: RandomizedSearchCV.fit(X, y=None, groups=None)', u'   :module: sklearn.model_selection', u'', u'   ', u'   ', u'   Run fit on the estimator with randomly drawn parameters.', u'   ', u'   ', u'   :Parameters:', u'   ', u'       **X** : array-like, shape = [n_samples, n_features]', u'   ', u'           Training vector, where n_samples in the number of samples and', u'           n_features is the number of features.', u'           ', u'   ', u'       **y** : array-like, shape = [n_samples] or [n_samples, n_output], optional', u'   ', u'           Target relative to X for classification or regression;', u'           None for unsupervised learning.', u'           ', u'   ', u'       **groups** : array-like, with shape (n_samples,), optional', u'   ', u'           Group labels for the samples used while splitting the dataset into', u'           train/test set.', u'', u'.. py:method:: RandomizedSearchCV.get_params(deep=True)', u'   :module: sklearn.model_selection', u'', u'   ', u'   ', u'   Get parameters for this estimator.', u'   ', u'   ', u'   :Parameters:', u'   ', u'       **deep** : boolean, optional', u'   ', u'           If True, will return the parameters for this estimator and', u'           contained subobjects that are estimators.', u'   ', u'   :Returns:', u'   ', u'       **params** : mapping of string to any', u'   ', u'           Parameter names mapped to their values.', u'', u'.. py:method:: RandomizedSearchCV.inverse_transform(*args, **kwargs)', u'   :module: sklearn.model_selection', u'', u'   ', u'   ', u'   Call inverse_transform on the estimator with the best found params.', u'   ', u'   Only available if the underlying estimator implements', u'   ``inverse_transform`` and ``refit=True``.', u'   ', u'   :Parameters:', u'   ', u'       **Xt** : indexable, length n_samples', u'   ', u'           Must fulfill the input assumptions of the', u'           underlying estimator.', u'', u'.. py:method:: RandomizedSearchCV.predict(*args, **kwargs)', u'   :module: sklearn.model_selection', u'', u'   ', u'   ', u'   Call predict on the estimator with the best found parameters.', u'   ', u'   Only available if ``refit=True`` and the underlying estimator supports', u'   ``predict``.', u'   ', u'   :Parameters:', u'   ', u'       **X** : indexable, length n_samples', u'   ', u'           Must fulfill the input assumptions of the', u'           underlying estimator.', u'', u'.. py:method:: RandomizedSearchCV.predict_log_proba(*args, **kwargs)', u'   :module: sklearn.model_selection', u'', u'   ', u'   ', u'   Call predict_log_proba on the estimator with the best found parameters.', u'   ', u'   Only available if ``refit=True`` and the underlying estimator supports', u'   ``predict_log_proba``.', u'   ', u'   :Parameters:', u'   ', u'       **X** : indexable, length n_samples', u'   ', u'           Must fulfill the input assumptions of the', u'           underlying estimator.', u'', u'.. py:method:: RandomizedSearchCV.predict_proba(*args, **kwargs)', u'   :module: sklearn.model_selection', u'', u'   ', u'   ', u'   Call predict_proba on the estimator with the best found parameters.', u'   ', u'   Only available if ``refit=True`` and the underlying estimator supports', u'   ``predict_proba``.', u'   ', u'   :Parameters:', u'   ', u'       **X** : indexable, length n_samples', u'   ', u'           Must fulfill the input assumptions of the', u'           underlying estimator.', u'', u'.. py:method:: RandomizedSearchCV.score(X, y=None)', u'   :module: sklearn.model_selection', u'', u'   ', u'   ', u'   Returns the score on the given data, if the estimator has been refit.', u'   ', u'   This uses the score defined by ``scoring`` where provided, and the', u'   ``best_estimator_.score`` method otherwise.', u'   ', u'   :Parameters:', u'   ', u'       **X** : array-like, shape = [n_samples, n_features]', u'   ', u'           Input data, where n_samples is the number of samples and', u'           n_features is the number of features.', u'           ', u'   ', u'       **y** : array-like, shape = [n_samples] or [n_samples, n_output], optional', u'   ', u'           Target relative to X for classification or regression;', u'           None for unsupervised learning.', u'   ', u'   :Returns:', u'   ', u'       **score** : float', u'   ', u'           ', u'', u'.. py:method:: RandomizedSearchCV.set_params(**params)', u'   :module: sklearn.model_selection', u'', u'   ', u'   ', u'   Set the parameters of this estimator.', u'   ', u'   The method works on simple estimators as well as on nested objects', u'   (such as pipelines). The latter have parameters of the form', u"   ``<component>__<parameter>`` so that it's possible to update each", u'   component of a nested object.', u'   ', u'   :Returns:', u'   ', u'       **self** : ', u'   ', u'           ', u'', u'.. py:method:: RandomizedSearchCV.transform(*args, **kwargs)', u'   :module: sklearn.model_selection', u'', u'   ', u'   ', u'   Call transform on the estimator with the best found parameters.', u'   ', u'   Only available if the underlying estimator supports ``transform`` and', u'   ``refit=True``.', u'   ', u'   :Parameters:', u'   ', u'       **X** : indexable, length n_samples', u'   ', u'           Must fulfill the input assumptions of the', u'           underlying estimator.']:90: ERROR: Unexpected indentation.
[u'check_cv(cv=3, y=None, classifier=False)', u':module: sklearn.model_selection', u'', u'', u'', u'Input checker utility for building a cross-validator', u'', u'', u':Parameters:', u'', u'    **cv** : int, cross-validation generator or an iterable, optional', u'', u'        Determines the cross-validation splitting strategy.', u'        Possible inputs for cv are:', u'          - None, to use the default 3-fold cross-validation,', u'          - integer, to specify the number of folds.', u'          - An object to be used as a cross-validation generator.', u'          - An iterable yielding train/test splits.', u'        ', u'        For integer/None inputs, if classifier is True and ``y`` is either', u'        binary or multiclass, :class:`StratifiedKFold` is used. In all other', u'        cases, :class:`KFold` is used.', u'        ', u'        Refer :ref:`User Guide <cross_validation>` for the various', u'        cross-validation strategies that can be used here.', u'        ', u'', u'    **y** : array-like, optional', u'', u'        The target variable for supervised learning problems.', u'        ', u'', u'    **classifier** : boolean, optional, default False', u'', u'        Whether the task is a classification task, in which case', u'        stratified KFold will be used.', u'', u':Returns:', u'', u'    **checked_cv** : a cross-validator instance.', u'', u'        The return value is a cross-validator which generates the train/test', u'        splits via the ``split`` method.']:11: ERROR: Unexpected indentation.
[u"cross_val_predict(estimator, X, y=None, groups=None, cv=None, n_jobs=1, verbose=0, fit_params=None, pre_dispatch='2*n_jobs', method='predict')", u':module: sklearn.model_selection', u'', u'', u'', u'Generate cross-validated estimates for each input data point', u'', u'Read more in the :ref:`User Guide <cross_validation>`.', u'', u':Parameters:', u'', u"    **estimator** : estimator object implementing 'fit' and 'predict'", u'', u'        The object to use to fit the data.', u'        ', u'', u'    **X** : array-like', u'', u'        The data to fit. Can be, for example a list, or an array at least 2d.', u'        ', u'', u'    **y** : array-like, optional, default: None', u'', u'        The target variable to try to predict in the case of', u'        supervised learning.', u'        ', u'', u'    **groups** : array-like, with shape (n_samples,), optional', u'', u'        Group labels for the samples used while splitting the dataset into', u'        train/test set.', u'        ', u'', u'    **cv** : int, cross-validation generator or an iterable, optional', u'', u'        Determines the cross-validation splitting strategy.', u'        Possible inputs for cv are:', u'          - None, to use the default 3-fold cross validation,', u'          - integer, to specify the number of folds in a `(Stratified)KFold`,', u'          - An object to be used as a cross-validation generator.', u'          - An iterable yielding train, test splits.', u'        ', u'        For integer/None inputs, if the estimator is a classifier and ``y`` is', u'        either binary or multiclass, :class:`StratifiedKFold` is used. In all', u'        other cases, :class:`KFold` is used.', u'        ', u'        Refer :ref:`User Guide <cross_validation>` for the various', u'        cross-validation strategies that can be used here.', u'        ', u'', u'    **n_jobs** : integer, optional', u'', u'        The number of CPUs to use to do the computation. -1 means', u"        'all CPUs'.", u'        ', u'', u'    **verbose** : integer, optional', u'', u'        The verbosity level.', u'        ', u'', u'    **fit_params** : dict, optional', u'', u'        Parameters to pass to the fit method of the estimator.', u'        ', u'', u'    **pre_dispatch** : int, or string, optional', u'', u'        Controls the number of jobs that get dispatched during parallel', u'        execution. Reducing this number can be useful to avoid an', u'        explosion of memory consumption when more jobs get dispatched', u'        than CPUs can process. This parameter can be:', u'        ', u'            - None, in which case all the jobs are immediately', u'              created and spawned. Use this for lightweight and', u'              fast-running jobs, to avoid delays due to on-demand', u'              spawning of the jobs', u'        ', u'            - An int, giving the exact number of total jobs that are', u'              spawned', u'        ', u'            - A string, giving an expression as a function of n_jobs,', u"              as in '2*n_jobs'", u'        ', u'', u"    **method** : string, optional, default: 'predict'", u'', u'        Invokes the passed method name of the passed estimator.', u'', u':Returns:', u'', u'    **predictions** : ndarray', u'', u'        This is the result of calling ``method``', u'', u'.. rubric:: Examples', u'', u'', u'>>> from sklearn import datasets, linear_model', u'>>> from sklearn.model_selection import cross_val_predict', u'>>> diabetes = datasets.load_diabetes()', u'>>> X = diabetes.data[:150]', u'>>> y = diabetes.target[:150]', u'>>> lasso = linear_model.Lasso()', u'>>> y_pred = cross_val_predict(lasso, X, y)']:31: ERROR: Unexpected indentation.
[u"cross_val_score(estimator, X, y=None, groups=None, scoring=None, cv=None, n_jobs=1, verbose=0, fit_params=None, pre_dispatch='2*n_jobs')", u':module: sklearn.model_selection', u'', u'', u'', u'Evaluate a score by cross-validation', u'', u'Read more in the :ref:`User Guide <cross_validation>`.', u'', u':Parameters:', u'', u"    **estimator** : estimator object implementing 'fit'", u'', u'        The object to use to fit the data.', u'        ', u'', u'    **X** : array-like', u'', u'        The data to fit. Can be, for example a list, or an array at least 2d.', u'        ', u'', u'    **y** : array-like, optional, default: None', u'', u'        The target variable to try to predict in the case of', u'        supervised learning.', u'        ', u'', u'    **groups** : array-like, with shape (n_samples,), optional', u'', u'        Group labels for the samples used while splitting the dataset into', u'        train/test set.', u'        ', u'', u'    **scoring** : string, callable or None, optional, default: None', u'', u'        A string (see model evaluation documentation) or', u'        a scorer callable object / function with signature', u'        ``scorer(estimator, X, y)``.', u'        ', u'', u'    **cv** : int, cross-validation generator or an iterable, optional', u'', u'        Determines the cross-validation splitting strategy.', u'        Possible inputs for cv are:', u'          - None, to use the default 3-fold cross validation,', u'          - integer, to specify the number of folds in a `(Stratified)KFold`,', u'          - An object to be used as a cross-validation generator.', u'          - An iterable yielding train, test splits.', u'        ', u'        For integer/None inputs, if the estimator is a classifier and ``y`` is', u'        either binary or multiclass, :class:`StratifiedKFold` is used. In all', u'        other cases, :class:`KFold` is used.', u'        ', u'        Refer :ref:`User Guide <cross_validation>` for the various', u'        cross-validation strategies that can be used here.', u'        ', u'', u'    **n_jobs** : integer, optional', u'', u'        The number of CPUs to use to do the computation. -1 means', u"        'all CPUs'.", u'        ', u'', u'    **verbose** : integer, optional', u'', u'        The verbosity level.', u'        ', u'', u'    **fit_params** : dict, optional', u'', u'        Parameters to pass to the fit method of the estimator.', u'        ', u'', u'    **pre_dispatch** : int, or string, optional', u'', u'        Controls the number of jobs that get dispatched during parallel', u'        execution. Reducing this number can be useful to avoid an', u'        explosion of memory consumption when more jobs get dispatched', u'        than CPUs can process. This parameter can be:', u'        ', u'            - None, in which case all the jobs are immediately', u'              created and spawned. Use this for lightweight and', u'              fast-running jobs, to avoid delays due to on-demand', u'              spawning of the jobs', u'        ', u'            - An int, giving the exact number of total jobs that are', u'              spawned', u'        ', u'            - A string, giving an expression as a function of n_jobs,', u"              as in '2*n_jobs'", u'', u':Returns:', u'', u'    **scores** : array of float, shape=(len(list(cv)),)', u'', u'        Array of scores of the estimator for each run of the cross validation.', u'', u'.. seealso::', u'', u'    ', u'    :func:`sklearn.metrics.make_scorer`', u'        Make a scorer from a performance metric or loss function.', u'    ', u'.. rubric:: Examples', u'', u'', u'>>> from sklearn import datasets, linear_model', u'>>> from sklearn.model_selection import cross_val_score', u'>>> diabetes = datasets.load_diabetes()', u'>>> X = diabetes.data[:150]', u'>>> y = diabetes.target[:150]', u'>>> lasso = linear_model.Lasso()', u'>>> print(cross_val_score(lasso, X, y))  # doctest: +ELLIPSIS', u'[ 0.33150734  0.08022311  0.03531764]']:37: ERROR: Unexpected indentation.
[u"learning_curve(estimator, X, y, groups=None, train_sizes=array([ 0.1 ,  0.33,  0.55,  0.78,  1.  ]), cv=None, scoring=None, exploit_incremental_learning=False, n_jobs=1, pre_dispatch='all', verbose=0)", u':module: sklearn.model_selection', u'', u'', u'', u'Learning curve.', u'', u'Determines cross-validated training and test scores for different training', u'set sizes.', u'', u'A cross-validation generator splits the whole dataset k times in training', u'and test data. Subsets of the training set with varying sizes will be used', u'to train the estimator and a score for each training subset size and the', u'test set will be computed. Afterwards, the scores will be averaged over', u'all k runs for each training subset size.', u'', u'Read more in the :ref:`User Guide <learning_curve>`.', u'', u':Parameters:', u'', u'    **estimator** : object type that implements the "fit" and "predict" methods', u'', u'        An object of that type which is cloned for each validation.', u'        ', u'', u'    **X** : array-like, shape (n_samples, n_features)', u'', u'        Training vector, where n_samples is the number of samples and', u'        n_features is the number of features.', u'        ', u'', u'    **y** : array-like, shape (n_samples) or (n_samples, n_features), optional', u'', u'        Target relative to X for classification or regression;', u'        None for unsupervised learning.', u'        ', u'', u'    **groups** : array-like, with shape (n_samples,), optional', u'', u'        Group labels for the samples used while splitting the dataset into', u'        train/test set.', u'        ', u'', u'    **train_sizes** : array-like, shape (n_ticks,), dtype float or int', u'', u'        Relative or absolute numbers of training examples that will be used to', u'        generate the learning curve. If the dtype is float, it is regarded as a', u'        fraction of the maximum size of the training set (that is determined', u'        by the selected validation method), i.e. it has to be within (0, 1].', u'        Otherwise it is interpreted as absolute sizes of the training sets.', u'        Note that for classification the number of samples usually have to', u'        be big enough to contain at least one sample from each class.', u'        (default: np.linspace(0.1, 1.0, 5))', u'        ', u'', u'    **cv** : int, cross-validation generator or an iterable, optional', u'', u'        Determines the cross-validation splitting strategy.', u'        Possible inputs for cv are:', u'          - None, to use the default 3-fold cross validation,', u'          - integer, to specify the number of folds in a `(Stratified)KFold`,', u'          - An object to be used as a cross-validation generator.', u'          - An iterable yielding train, test splits.', u'        ', u'        For integer/None inputs, if the estimator is a classifier and ``y`` is', u'        either binary or multiclass, :class:`StratifiedKFold` is used. In all', u'        other cases, :class:`KFold` is used.', u'        ', u'        Refer :ref:`User Guide <cross_validation>` for the various', u'        cross-validation strategies that can be used here.', u'        ', u'', u'    **scoring** : string, callable or None, optional, default: None', u'', u'        A string (see model evaluation documentation) or', u'        a scorer callable object / function with signature', u'        ``scorer(estimator, X, y)``.', u'        ', u'', u'    **exploit_incremental_learning** : boolean, optional, default: False', u'', u'        If the estimator supports incremental learning, this will be', u'        used to speed up fitting for different training set sizes.', u'        ', u'', u'    **n_jobs** : integer, optional', u'', u'        Number of jobs to run in parallel (default 1).', u'        ', u'', u'    **pre_dispatch** : integer or string, optional', u'', u'        Number of predispatched jobs for parallel execution (default is', u'        all). The option can reduce the allocated memory. The string can', u"        be an expression like '2*n_jobs'.", u'        ', u'', u'    **verbose** : integer, optional', u'', u'        Controls the verbosity: the higher, the more messages.', u'', u':Returns:', u'', u'    **train_sizes_abs** : array, shape = (n_unique_ticks,), dtype int', u'', u'        Numbers of training examples that has been used to generate the', u'        learning curve. Note that the number of ticks might be less', u'        than n_ticks because duplicate entries will be removed.', u'        ', u'', u'    **train_scores** : array, shape (n_ticks, n_cv_folds)', u'', u'        Scores on training sets.', u'        ', u'', u'    **test_scores** : array, shape (n_ticks, n_cv_folds)', u'', u'        Scores on test set.', u'', u'.. rubric:: Notes', u'', u'', u'See :ref:`examples/model_selection/plot_learning_curve.py', u'<sphx_glr_auto_examples_model_selection_plot_learning_curve.py>`']:52: ERROR: Unexpected indentation.
[u'permutation_test_score(estimator, X, y, groups=None, cv=None, n_permutations=100, n_jobs=1, random_state=0, verbose=0, scoring=None)', u':module: sklearn.model_selection', u'', u'', u'', u'Evaluate the significance of a cross-validated score with permutations', u'', u'Read more in the :ref:`User Guide <cross_validation>`.', u'', u':Parameters:', u'', u"    **estimator** : estimator object implementing 'fit'", u'', u'        The object to use to fit the data.', u'        ', u'', u'    **X** : array-like of shape at least 2D', u'', u'        The data to fit.', u'        ', u'', u'    **y** : array-like', u'', u'        The target variable to try to predict in the case of', u'        supervised learning.', u'        ', u'', u'    **groups** : array-like, with shape (n_samples,), optional', u'', u'        Labels to constrain permutation within groups, i.e. ``y`` values', u'        are permuted among samples with the same group identifier.', u'        When not specified, ``y`` values are permuted among all samples.', u'        ', u'        When a grouped cross-validator is used, the group labels are', u'        also passed on to the ``split`` method of the cross-validator. The', u'        cross-validator uses them for grouping the samples  while splitting', u'        the dataset into train/test set.', u'        ', u'', u'    **scoring** : string, callable or None, optional, default: None', u'', u'        A string (see model evaluation documentation) or', u'        a scorer callable object / function with signature', u'        ``scorer(estimator, X, y)``.', u'        ', u'', u'    **cv** : int, cross-validation generator or an iterable, optional', u'', u'        Determines the cross-validation splitting strategy.', u'        Possible inputs for cv are:', u'          - None, to use the default 3-fold cross validation,', u'          - integer, to specify the number of folds in a `(Stratified)KFold`,', u'          - An object to be used as a cross-validation generator.', u'          - An iterable yielding train, test splits.', u'        ', u'        For integer/None inputs, if the estimator is a classifier and ``y`` is', u'        either binary or multiclass, :class:`StratifiedKFold` is used. In all', u'        other cases, :class:`KFold` is used.', u'        ', u'        Refer :ref:`User Guide <cross_validation>` for the various', u'        cross-validation strategies that can be used here.', u'        ', u'', u'    **n_permutations** : integer, optional', u'', u'        Number of times to permute ``y``.', u'        ', u'', u'    **n_jobs** : integer, optional', u'', u'        The number of CPUs to use to do the computation. -1 means', u"        'all CPUs'.", u'        ', u'', u'    **random_state** : RandomState or an int seed (0 by default)', u'', u'        A random number generator instance to define the state of the', u'        random permutations generator.', u'        ', u'', u'    **verbose** : integer, optional', u'', u'        The verbosity level.', u'', u':Returns:', u'', u'    **score** : float', u'', u'        The true score without permuting targets.', u'        ', u'', u'    **permutation_scores** : array, shape (n_permutations,)', u'', u'        The scores obtained for each permutations.', u'        ', u'', u'    **pvalue** : float', u'', u'        The returned value equals p-value if `scoring` returns bigger', u'        numbers for better scores (e.g., accuracy_score). If `scoring` is', u'        rather a loss function (i.e. when lower is better such as with', u'        `mean_squared_error`) then this is actually the complement of the', u'        p-value:  1 - p-value.', u'', u'.. rubric:: Notes', u'', u'', u'This function implements Test 1 in:', u'', u'    Ojala and Garriga. Permutation Tests for Studying Classifier', u'    Performance.  The Journal of Machine Learning Research (2010)', u'    vol. 11']:43: ERROR: Unexpected indentation.
[u"validation_curve(estimator, X, y, param_name, param_range, groups=None, cv=None, scoring=None, n_jobs=1, pre_dispatch='all', verbose=0)", u':module: sklearn.model_selection', u'', u'', u'', u'Validation curve.', u'', u'Determine training and test scores for varying parameter values.', u'', u'Compute scores for an estimator with different values of a specified', u'parameter. This is similar to grid search with one parameter. However, this', u'will also compute training scores and is merely a utility for plotting the', u'results.', u'', u'Read more in the :ref:`User Guide <learning_curve>`.', u'', u':Parameters:', u'', u'    **estimator** : object type that implements the "fit" and "predict" methods', u'', u'        An object of that type which is cloned for each validation.', u'        ', u'', u'    **X** : array-like, shape (n_samples, n_features)', u'', u'        Training vector, where n_samples is the number of samples and', u'        n_features is the number of features.', u'        ', u'', u'    **y** : array-like, shape (n_samples) or (n_samples, n_features), optional', u'', u'        Target relative to X for classification or regression;', u'        None for unsupervised learning.', u'        ', u'', u'    **param_name** : string', u'', u'        Name of the parameter that will be varied.', u'        ', u'', u'    **param_range** : array-like, shape (n_values,)', u'', u'        The values of the parameter that will be evaluated.', u'        ', u'', u'    **groups** : array-like, with shape (n_samples,), optional', u'', u'        Group labels for the samples used while splitting the dataset into', u'        train/test set.', u'        ', u'', u'    **cv** : int, cross-validation generator or an iterable, optional', u'', u'        Determines the cross-validation splitting strategy.', u'        Possible inputs for cv are:', u'          - None, to use the default 3-fold cross validation,', u'          - integer, to specify the number of folds in a `(Stratified)KFold`,', u'          - An object to be used as a cross-validation generator.', u'          - An iterable yielding train, test splits.', u'        ', u'        For integer/None inputs, if the estimator is a classifier and ``y`` is', u'        either binary or multiclass, :class:`StratifiedKFold` is used. In all', u'        other cases, :class:`KFold` is used.', u'        ', u'        Refer :ref:`User Guide <cross_validation>` for the various', u'        cross-validation strategies that can be used here.', u'        ', u'', u'    **scoring** : string, callable or None, optional, default: None', u'', u'        A string (see model evaluation documentation) or', u'        a scorer callable object / function with signature', u'        ``scorer(estimator, X, y)``.', u'        ', u'', u'    **n_jobs** : integer, optional', u'', u'        Number of jobs to run in parallel (default 1).', u'        ', u'', u'    **pre_dispatch** : integer or string, optional', u'', u'        Number of predispatched jobs for parallel execution (default is', u'        all). The option can reduce the allocated memory. The string can', u"        be an expression like '2*n_jobs'.", u'        ', u'', u'    **verbose** : integer, optional', u'', u'        Controls the verbosity: the higher, the more messages.', u'', u':Returns:', u'', u'    **train_scores** : array, shape (n_ticks, n_cv_folds)', u'', u'        Scores on training sets.', u'        ', u'', u'    **test_scores** : array, shape (n_ticks, n_cv_folds)', u'', u'        Scores on test set.', u'', u'.. rubric:: Notes', u'', u'', u'See :ref:`sphx_glr_auto_examples_model_selection_plot_validation_curve.py`']:47: ERROR: Unexpected indentation.
/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/modules/generated/sklearn.tree.export_graphviz.rst:6: WARNING: error while formatting arguments for sklearn.tree.export_graphviz:
/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/modules/linear_model.rst:1125: WARNING: Duplicate explicit target name: "f1".
/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/modules/linear_model.rst:1046: ERROR: Too many autonumbered footnote references: only 0 corresponding footnotes available.
/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/modules/linear_model.rst:1046: ERROR: Duplicate target name, cannot be used as a unique reference: "f1".
/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/modules/scaling_strategies.rst:112: WARNING: image file not readable: modules/../auto_examples/applications/images/sphx_glr_plot_out_of_core_classification_003.png
/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/modules/scaling_strategies.rst:118: WARNING: image file not readable: modules/../auto_examples/applications/images/sphx_glr_plot_out_of_core_classification_003.png
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/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/whats_new.rst:55: ERROR: Unexpected indentation.
/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/whats_new.rst:60: WARNING: Block quote ends without a blank line; unexpected unindent.
/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/whats_new.rst:81: ERROR: Unknown target name: "mohammed affan".
/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/whats_new.rst:148: ERROR: Unknown target name: "nelson liu".
/tmp/portage/sci-libs/scikits_learn-0.18.2/work/scikit-learn-0.18.2/doc/whats_new.rst:155: ERROR: Unknown target name: "ibraim ganiev".
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Exception occurred:
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