<p dir="auto">When trying to run the test-suite on a fresh installation it breaks.<br>
Long story short, doctests require <code class="notranslate">PIL</code> and <code class="notranslate">test_lda_predict</code> fails as well.</p>
<p dir="auto">This has been done in a Linux machine. <a class="user-mention notranslate" data-hovercard-type="user" data-hovercard-url="/users/martinagvilas/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" href="https://github.com/martinagvilas">@martinagvilas</a> can you replicate in mac? thx.</p>
<p dir="auto">cc: <a class="user-mention notranslate" data-hovercard-type="user" data-hovercard-url="/users/lesteve/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" href="https://github.com/lesteve">@lesteve</a></p>
<p dir="auto">Here is what we did:</p>
<div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="$ conda remove --name test-sklearn --all
$ conda create --name test-sklearn -y
$ conda activate test-sklearn
$ conda install -c conda-forge scikit-learn cython pytest
$ cd ~
$ python -c &quot;import sklearn; print(sklearn.__version__)&quot;  # 0.21.3
$ conda remove --name test-sklearn scikit-learn --force-remove  # keep dependencies
$ conda list | grep sklearn
$ cd ~/code/scikit-learn
$ pip install -e .
$ pytest sklearn"><pre class="notranslate">$ conda remove --name test-sklearn --all
$ conda create --name test-sklearn -y
$ conda activate test-sklearn
$ conda install -c conda-forge scikit-learn cython pytest
$ <span class="pl-c1">cd</span> <span class="pl-k">~</span>
$ python -c <span class="pl-s"><span class="pl-pds">"</span>import sklearn; print(sklearn.__version__)<span class="pl-pds">"</span></span>  <span class="pl-c"><span class="pl-c">#</span> 0.21.3</span>
$ conda remove --name test-sklearn scikit-learn --force-remove  <span class="pl-c"><span class="pl-c">#</span> keep dependencies</span>
$ conda list <span class="pl-k">|</span> grep sklearn
$ <span class="pl-c1">cd</span> <span class="pl-k">~</span>/code/scikit-learn
$ pip install -e <span class="pl-c1">.</span>
$ pytest sklearn</pre></div>
<p dir="auto">Here is the full pytest output</p>
<details>
<div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="

========================= test session starts ==========================
platform linux -- Python 3.7.3, pytest-5.1.2, py-1.8.0, pluggy-0.12.0
rootdir: /home/sik/code/scikit-learn, inifile: setup.cfg
collected 13419 items / 2 skipped / 13417 selected                     

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=============================== FAILURES ===============================
______ [doctest] sklearn.feature_extraction.image.PatchExtractor _______
459         deterministic.
460         See :term:`Glossary &lt;random_state&gt;`.
461 
462 
463     Examples
464     --------
465     &gt;&gt;&gt; from sklearn.datasets import load_sample_images
466     &gt;&gt;&gt; from sklearn.feature_extraction import image
467     &gt;&gt;&gt; # Use the array data from the second image in this dataset:
468     &gt;&gt;&gt; X = load_sample_images().images[1]
UNEXPECTED EXCEPTION: ImportError('The Python Imaging Library (PIL) is required to load data from jpeg files. Please refer to https://pillow.readthedocs.io/en/stable/installation.html for installing PIL.')
Traceback (most recent call last):

  File &quot;/home/sik/miniconda3/envs/test-sklearn/lib/python3.7/doctest.py&quot;, line 1329, in __run
    compileflags, 1), test.globs)

  File &quot;&lt;doctest sklearn.feature_extraction.image.PatchExtractor[2]&gt;&quot;, line 1, in &lt;module&gt;

  File &quot;/home/sik/code/scikit-learn/sklearn/datasets/base.py&quot;, line 801, in load_sample_images
    images = [imread(filename) for filename in filenames]

  File &quot;/home/sik/code/scikit-learn/sklearn/datasets/base.py&quot;, line 801, in &lt;listcomp&gt;
    images = [imread(filename) for filename in filenames]

  File &quot;/home/sik/code/scikit-learn/sklearn/externals/_pilutil.py&quot;, line 204, in imread
    raise ImportError(PILLOW_ERROR_MESSAGE)

ImportError: The Python Imaging Library (PIL) is required to load data from jpeg files. Please refer to https://pillow.readthedocs.io/en/stable/installation.html for installing PIL.

/home/sik/code/scikit-learn/sklearn/feature_extraction/image.py:468: UnexpectedException
____ [doctest] sklearn.feature_extraction.image.extract_patches_2d _____
334         The collection of patches extracted from the image, where `n_patches`
335         is either `max_patches` or the total number of patches that can be
336         extracted.
337 
338     Examples
339     --------
340     &gt;&gt;&gt; from sklearn.datasets import load_sample_image
341     &gt;&gt;&gt; from sklearn.feature_extraction import image
342     &gt;&gt;&gt; # Use the array data from the first image in this dataset:
343     &gt;&gt;&gt; one_image = load_sample_image(&quot;china.jpg&quot;)
UNEXPECTED EXCEPTION: ImportError('The Python Imaging Library (PIL) is required to load data from jpeg files. Please refer to https://pillow.readthedocs.io/en/stable/installation.html for installing PIL.')
Traceback (most recent call last):

  File &quot;/home/sik/miniconda3/envs/test-sklearn/lib/python3.7/doctest.py&quot;, line 1329, in __run
    compileflags, 1), test.globs)

  File &quot;&lt;doctest sklearn.feature_extraction.image.extract_patches_2d[2]&gt;&quot;, line 1, in &lt;module&gt;

  File &quot;/home/sik/code/scikit-learn/sklearn/datasets/base.py&quot;, line 838, in load_sample_image
    images = load_sample_images()

  File &quot;/home/sik/code/scikit-learn/sklearn/datasets/base.py&quot;, line 801, in load_sample_images
    images = [imread(filename) for filename in filenames]

  File &quot;/home/sik/code/scikit-learn/sklearn/datasets/base.py&quot;, line 801, in &lt;listcomp&gt;
    images = [imread(filename) for filename in filenames]

  File &quot;/home/sik/code/scikit-learn/sklearn/externals/_pilutil.py&quot;, line 204, in imread
    raise ImportError(PILLOW_ERROR_MESSAGE)

ImportError: The Python Imaging Library (PIL) is required to load data from jpeg files. Please refer to https://pillow.readthedocs.io/en/stable/installation.html for installing PIL.

/home/sik/code/scikit-learn/sklearn/feature_extraction/image.py:343: UnexpectedException
___________________________ test_lda_predict ___________________________

    def test_lda_predict():
        # Test LDA classification.
        # This checks that LDA implements fit and predict and returns correct
        # values for simple toy data.
        for test_case in solver_shrinkage:
            solver, shrinkage = test_case
            clf = LinearDiscriminantAnalysis(solver=solver, shrinkage=shrinkage)
            y_pred = clf.fit(X, y).predict(X)
            assert_array_equal(y_pred, y, 'solver %s' % solver)
    
            # Assert that it works with 1D data
            y_pred1 = clf.fit(X1, y).predict(X1)
            assert_array_equal(y_pred1, y, 'solver %s' % solver)
    
            # Test probability estimates
            y_proba_pred1 = clf.predict_proba(X1)
            assert_array_equal((y_proba_pred1[:, 1] &gt; 0.5) + 1, y,
                               'solver %s' % solver)
            y_log_proba_pred1 = clf.predict_log_proba(X1)
            assert_array_almost_equal(np.exp(y_log_proba_pred1), y_proba_pred1,
&gt;                                     8, 'solver %s' % solver)
E           AssertionError: 
E           Arrays are not almost equal to 8 decimals
E           solver svd
E           Mismatch: 16.7%
E           Max absolute difference: 5.9604645e-08
E           Max relative difference: 4.7683716e-07
E            x: array([[9.9999994e-01, 1.1253517e-07],
E                  [9.9966466e-01, 3.3535002e-04],
E                  [9.9966466e-01, 3.3535002e-04],...
E            y: array([[9.99999881e-01, 1.12535155e-07],
E                  [9.99664664e-01, 3.35350138e-04],
E                  [9.99664664e-01, 3.35350138e-04],...

sklearn/tests/test_discriminant_analysis.py:80: AssertionError
======================= short test summary info ========================
SKIPPED [3] /home/sik/code/scikit-learn/sklearn/ensemble/_hist_gradient_boosting/tests/test_compare_lightgbm.py:17: could not import 'lightgbm': No module named 'lightgbm'
SKIPPED [1] /home/sik/miniconda3/envs/test-sklearn/lib/python3.7/site-packages/_pytest/unittest.py:238: This test is failing on the buildbot, but cannot reproduce. Temporarily disabling it until it can be reproduced and  fixed.
SKIPPED [1] /home/sik/code/scikit-learn/sklearn/compose/tests/test_column_transformer.py:132: could not import 'pandas': No module named 'pandas'
SKIPPED [2] /home/sik/code/scikit-learn/sklearn/compose/tests/test_column_transformer.py:263: could not import 'pandas': No module named 'pandas'
SKIPPED [1] /home/sik/code/scikit-learn/sklearn/compose/tests/test_column_transformer.py:467: could not import 'pandas': No module named 'pandas'
SKIPPED [1] /home/sik/code/scikit-learn/sklearn/compose/tests/test_column_transformer.py:538: could not import 'pandas': No module named 'pandas'
SKIPPED [9] /home/sik/code/scikit-learn/sklearn/compose/tests/test_column_transformer.py:812: could not import 'pandas': No module named 'pandas'
SKIPPED [1] /home/sik/code/scikit-learn/sklearn/compose/tests/test_column_transformer.py:1054: could not import 'pandas': No module named 'pandas'
SKIPPED [2] /home/sik/code/scikit-learn/sklearn/compose/tests/test_column_transformer.py:1091: could not import 'pandas': No module named 'pandas'
SKIPPED [1] /home/sik/code/scikit-learn/sklearn/compose/tests/test_column_transformer.py:1125: could not import 'pandas': No module named 'pandas'
SKIPPED [3] /home/sik/miniconda3/envs/test-sklearn/lib/python3.7/site-packages/_pytest/unittest.py:238: Download 20 newsgroups to run this test
SKIPPED [1] /home/sik/miniconda3/envs/test-sklearn/lib/python3.7/site-packages/_pytest/unittest.py:238: Covertype dataset can not be loaded.
SKIPPED [2] /home/sik/miniconda3/envs/test-sklearn/lib/python3.7/site-packages/_pytest/unittest.py:238: kddcup99 dataset can not be loaded.
SKIPPED [5] /home/sik/miniconda3/envs/test-sklearn/lib/python3.7/site-packages/_pytest/unittest.py:238: PIL not installed.
SKIPPED [1] sklearn/datasets/tests/test_olivetti_faces.py:20: Download Olivetti faces dataset to run this test
SKIPPED [1] /home/sik/code/scikit-learn/sklearn/datasets/tests/test_openml.py:289: could not import 'pandas': No module named 'pandas'
SKIPPED [1] /home/sik/code/scikit-learn/sklearn/datasets/tests/test_openml.py:329: could not import 'pandas': No module named 'pandas'
SKIPPED [1] /home/sik/code/scikit-learn/sklearn/datasets/tests/test_openml.py:348: could not import 'pandas': No module named 'pandas'
SKIPPED [1] /home/sik/code/scikit-learn/sklearn/datasets/tests/test_openml.py:389: could not import 'pandas': No module named 'pandas'
SKIPPED [1] /home/sik/code/scikit-learn/sklearn/datasets/tests/test_openml.py:426: could not import 'pandas': No module named 'pandas'
SKIPPED [1] /home/sik/code/scikit-learn/sklearn/datasets/tests/test_openml.py:478: could not import 'pandas': No module named 'pandas'
SKIPPED [1] /home/sik/code/scikit-learn/sklearn/datasets/tests/test_openml.py:490: could not import 'pandas': No module named 'pandas'
SKIPPED [1] /home/sik/code/scikit-learn/sklearn/datasets/tests/test_openml.py:518: could not import 'pandas': No module named 'pandas'
SKIPPED [1] /home/sik/code/scikit-learn/sklearn/datasets/tests/test_openml.py:557: could not import 'pandas': No module named 'pandas'
SKIPPED [1] /home/sik/code/scikit-learn/sklearn/datasets/tests/test_openml.py:595: could not import 'pandas': No module named 'pandas'
SKIPPED [1] /home/sik/code/scikit-learn/sklearn/datasets/tests/test_openml.py:633: could not import 'pandas': No module named 'pandas'
SKIPPED [1] /home/sik/miniconda3/envs/test-sklearn/lib/python3.7/site-packages/_pytest/unittest.py:238: Download RCV1 dataset to run this test.
SKIPPED [1] sklearn/datasets/tests/test_svmlight_format.py:188: testing the overflow of 32 bit sparse indexing requires a large amount of memory
SKIPPED [1] sklearn/decomposition/tests/test_sparse_pca.py:134: skipping mini_batch_fit_transform.
SKIPPED [34] /home/sik/code/scikit-learn/sklearn/conftest.py:18: could not import 'matplotlib': No module named 'matplotlib'
SKIPPED [2] /home/sik/code/scikit-learn/sklearn/impute/tests/test_impute.py:315: could not import 'pandas': No module named 'pandas'
SKIPPED [2] /home/sik/code/scikit-learn/sklearn/impute/tests/test_impute.py:428: could not import 'pandas': No module named 'pandas'
SKIPPED [2] /home/sik/code/scikit-learn/sklearn/inspection/tests/test_permutation_importance.py:52: could not import 'pandas': No module named 'pandas'
SKIPPED [1] /home/sik/code/scikit-learn/sklearn/inspection/tests/test_permutation_importance.py:114: could not import 'pandas': No module named 'pandas'
SKIPPED [2] /home/sik/code/scikit-learn/sklearn/linear_model/tests/test_logistic.py:1306: liblinear does not support multinomial logistic
SKIPPED [1] /home/sik/code/scikit-learn/sklearn/manifold/tests/test_spectral_embedding.py:166: could not import 'pyamg': No module named 'pyamg'
SKIPPED [1] /home/sik/code/scikit-learn/sklearn/manifold/tests/test_spectral_embedding.py:196: could not import 'pyamg': No module named 'pyamg'
SKIPPED [1] /home/sik/miniconda3/envs/test-sklearn/lib/python3.7/site-packages/_pytest/doctest.py:351: all tests skipped by +SKIP option
SKIPPED [4] /home/sik/code/scikit-learn/sklearn/preprocessing/tests/test_data.py:866: 'with_mean=True' cannot be used with sparse matrix.
SKIPPED [2] /home/sik/code/scikit-learn/sklearn/preprocessing/tests/test_data.py:1077: RobustScaler cannot center sparse matrix
SKIPPED [3] /home/sik/code/scikit-learn/sklearn/preprocessing/tests/test_encoders.py:127: could not import 'pandas': No module named 'pandas'
SKIPPED [2] /home/sik/code/scikit-learn/sklearn/preprocessing/tests/test_encoders.py:283: could not import 'pandas': No module named 'pandas'
SKIPPED [1] /home/sik/code/scikit-learn/sklearn/preprocessing/tests/test_encoders.py:378: could not import 'pandas': No module named 'pandas'
SKIPPED [4] /home/sik/code/scikit-learn/sklearn/preprocessing/tests/test_encoders.py:450: could not import 'pandas': No module named 'pandas'
SKIPPED [1] /home/sik/code/scikit-learn/sklearn/preprocessing/tests/test_encoders.py:584: could not import 'pandas': No module named 'pandas'
SKIPPED [1] /home/sik/code/scikit-learn/sklearn/preprocessing/tests/test_function_transformer.py:157: could not import 'pandas': No module named 'pandas'
SKIPPED [1] /home/sik/miniconda3/envs/test-sklearn/lib/python3.7/site-packages/_pytest/unittest.py:238: Can't instantiate estimator ColumnTransformer which requires parameters ['transformers']
SKIPPED [1] /home/sik/miniconda3/envs/test-sklearn/lib/python3.7/site-packages/_pytest/unittest.py:238: Can't instantiate estimator FeatureUnion which requires parameters ['transformer_list']
SKIPPED [1] /home/sik/miniconda3/envs/test-sklearn/lib/python3.7/site-packages/_pytest/unittest.py:238: Can't instantiate estimator GridSearchCV which requires parameters ['estimator', 'param_grid']
SKIPPED [1] /home/sik/miniconda3/envs/test-sklearn/lib/python3.7/site-packages/_pytest/unittest.py:238: Can't instantiate estimator Pipeline which requires parameters ['steps']
SKIPPED [1] /home/sik/miniconda3/envs/test-sklearn/lib/python3.7/site-packages/_pytest/unittest.py:238: Can't instantiate estimator RandomizedSearchCV which requires parameters ['estimator', 'param_distributions']
SKIPPED [1] /home/sik/miniconda3/envs/test-sklearn/lib/python3.7/site-packages/_pytest/unittest.py:238: Can't instantiate estimator SparseCoder which requires parameters ['dictionary']
SKIPPED [1] /home/sik/miniconda3/envs/test-sklearn/lib/python3.7/site-packages/_pytest/unittest.py:238: Can't instantiate estimator VotingClassifier which requires parameters ['estimators']
SKIPPED [1] /home/sik/miniconda3/envs/test-sklearn/lib/python3.7/site-packages/_pytest/unittest.py:238: Can't instantiate estimator VotingRegressor which requires parameters ['estimators']
SKIPPED [50] /home/sik/miniconda3/envs/test-sklearn/lib/python3.7/site-packages/_pytest/unittest.py:238: pandas is not installed: not testing for input of type pandas.Series to class weight.
SKIPPED [1] /home/sik/miniconda3/envs/test-sklearn/lib/python3.7/site-packages/_pytest/unittest.py:238: score_samples of BernoulliRBM is not invariant when applied to a subset.
SKIPPED [3] /home/sik/miniconda3/envs/test-sklearn/lib/python3.7/site-packages/_pytest/unittest.py:238: Skipping check_estimators_data_not_an_array for cross decomposition module as estimators are not deterministic.
SKIPPED [1] /home/sik/miniconda3/envs/test-sklearn/lib/python3.7/site-packages/_pytest/unittest.py:238: predict of DummyClassifier is not invariant when applied to a subset.
SKIPPED [1] /home/sik/miniconda3/envs/test-sklearn/lib/python3.7/site-packages/_pytest/unittest.py:238: transform of MiniBatchSparsePCA is not invariant when applied to a subset.
SKIPPED [1] /home/sik/miniconda3/envs/test-sklearn/lib/python3.7/site-packages/_pytest/unittest.py:238: Not testing NuSVC class weight as it is ignored.
SKIPPED [1] /home/sik/miniconda3/envs/test-sklearn/lib/python3.7/site-packages/_pytest/unittest.py:238: decision_function of NuSVC is not invariant when applied to a subset.
SKIPPED [1] /home/sik/miniconda3/envs/test-sklearn/lib/python3.7/site-packages/_pytest/unittest.py:238: transform of SparsePCA is not invariant when applied to a subset.
SKIPPED [2] /home/sik/miniconda3/envs/test-sklearn/lib/python3.7/site-packages/_pytest/unittest.py:238: numpydoc is required to test the docstrings
SKIPPED [1] /home/sik/code/scikit-learn/sklearn/utils/tests/test_multiclass.py:299: could not import 'pandas': No module named 'pandas'
SKIPPED [37] /home/sik/code/scikit-learn/sklearn/utils/tests/test_utils.py:239: could not import 'pandas': No module named 'pandas'
SKIPPED [39] /home/sik/code/scikit-learn/sklearn/utils/tests/test_utils.py:242: could not import 'pandas': No module named 'pandas'
SKIPPED [1] /home/sik/code/scikit-learn/sklearn/utils/tests/test_utils.py:423: could not import 'pandas': No module named 'pandas'
SKIPPED [1] /home/sik/code/scikit-learn/sklearn/utils/tests/test_utils.py:444: could not import 'pandas': No module named 'pandas'
SKIPPED [2] /home/sik/code/scikit-learn/sklearn/utils/tests/test_utils.py:466: could not import 'pandas': No module named 'pandas'
SKIPPED [1] /home/sik/miniconda3/envs/test-sklearn/lib/python3.7/site-packages/_pytest/unittest.py:238: Pandas not found
SKIPPED [1] /home/sik/code/scikit-learn/sklearn/utils/tests/test_validation.py:719: could not import 'pandas': No module named 'pandas'
SKIPPED [1] /home/sik/code/scikit-learn/sklearn/utils/tests/test_validation.py:732: could not import 'pandas': No module named 'pandas'
= 3 failed, 13147 passed, 265 skipped, 6 xfailed, 1572 warnings in 409.77s (0:06:49) =
 This problem is unconstrained.
RUNNING THE L-BFGS-B CODE

           * * *

Machine precision = 2.220D-16
 N =            3     M =           10

At X0         0 variables are exactly at the bounds

At iterate    0    f=  1.38629D+02    |proj g|=  6.27865D+01

           * * *

Tit   = total number of iterations
Tnf   = total number of function evaluations
Tnint = total number of segments explored during Cauchy searches
Skip  = number of BFGS updates skipped
Nact  = number of active bounds at final generalized Cauchy point
Projg = norm of the final projected gradient
F     = final function value

           * * *

   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F
    3      1      2      1     0     0   2.422D+01   9.713D+01
  F =   97.133816163368223     

STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 

 Cauchy                time 0.000E+00 seconds.
 Subspace minimization time 0.000E+00 seconds.
 Line search           time 0.000E+00 seconds.

 Total User time 0.000E+00 seconds.
"><pre class="notranslate">========================= <span class="pl-c1">test</span> session starts ==========================
platform linux -- Python 3.7.3, pytest-5.1.2, py-1.8.0, pluggy-0.12.0
rootdir: /home/sik/code/scikit-learn, inifile: setup.cfg
collected 13419 items / 2 skipped / 13417 selected                     

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=============================== FAILURES ===============================
______ [doctest] sklearn.feature_extraction.image.PatchExtractor _______
459         deterministic.
460         See :term:<span class="pl-s"><span class="pl-pds">`</span>Glossary <span class="pl-k">&lt;</span>random_state<span class="pl-k">&gt;</span><span class="pl-pds">`</span></span>.
461 
462 
463     Examples
464     --------
465     &gt;&gt;&gt; from sklearn.datasets import load_sample_images
466     &gt;&gt;&gt; from sklearn.feature_extraction import image
467     &gt;&gt;&gt; <span class="pl-c"><span class="pl-c">#</span> Use the array data from the second image in this dataset:</span>
468     &gt;&gt;&gt; X = <span class="pl-en">load_sample_images</span>().images[1]
UNEXPECTED EXCEPTION: ImportError(<span class="pl-s"><span class="pl-pds">'</span>The Python Imaging Library (PIL) is required to load data from jpeg files. Please refer to https://pillow.readthedocs.io/en/stable/installation.html for installing PIL.<span class="pl-pds">'</span></span>)
Traceback (most recent call last):

  File <span class="pl-s"><span class="pl-pds">"</span>/home/sik/miniconda3/envs/test-sklearn/lib/python3.7/doctest.py<span class="pl-pds">"</span></span>, line 1329, <span class="pl-k">in</span> __run
    compileflags, 1), test.globs)

  File <span class="pl-s"><span class="pl-pds">"</span>&lt;doctest sklearn.feature_extraction.image.PatchExtractor[2]&gt;<span class="pl-pds">"</span></span>, line 1, <span class="pl-k">in</span> <span class="pl-k">&lt;</span>module<span class="pl-k">&gt;</span>

  File <span class="pl-s"><span class="pl-pds">"</span>/home/sik/code/scikit-learn/sklearn/datasets/base.py<span class="pl-pds">"</span></span>, line 801, <span class="pl-k">in</span> load_sample_images
    images = [imread(filename) <span class="pl-k">for</span> <span class="pl-smi">filename</span> <span class="pl-k">in</span> filenames]

  File <span class="pl-s"><span class="pl-pds">"</span>/home/sik/code/scikit-learn/sklearn/datasets/base.py<span class="pl-pds">"</span></span>, line 801, <span class="pl-k">in</span> <span class="pl-k">&lt;</span>listcomp<span class="pl-k">&gt;</span>
    images = [imread(filename) <span class="pl-k">for</span> <span class="pl-smi">filename</span> <span class="pl-k">in</span> filenames]

  File <span class="pl-s"><span class="pl-pds">"</span>/home/sik/code/scikit-learn/sklearn/externals/_pilutil.py<span class="pl-pds">"</span></span>, line 204, <span class="pl-k">in</span> imread
    raise ImportError(PILLOW_ERROR_MESSAGE)

ImportError: The Python Imaging Library (PIL) is required to load data from jpeg files. Please refer to https://pillow.readthedocs.io/en/stable/installation.html <span class="pl-k">for</span> installing PIL.

/home/sik/code/scikit-learn/sklearn/feature_extraction/image.py:468: UnexpectedException
____ [doctest] sklearn.feature_extraction.image.extract_patches_2d _____
334         The collection of patches extracted from the image, where <span class="pl-s"><span class="pl-pds">`</span>n_patches<span class="pl-pds">`</span></span>
335         is either <span class="pl-s"><span class="pl-pds">`</span>max_patches<span class="pl-pds">`</span></span> or the total number of patches that can be
336         extracted.
337 
338     Examples
339     --------
340     &gt;&gt;&gt; from sklearn.datasets import load_sample_image
341     &gt;&gt;&gt; from sklearn.feature_extraction import image
342     &gt;&gt;&gt; <span class="pl-c"><span class="pl-c">#</span> Use the array data from the first image in this dataset:</span>
343     &gt;&gt;&gt; one_image = load_sample_image(<span class="pl-s"><span class="pl-pds">"</span>china.jpg<span class="pl-pds">"</span></span>)
UNEXPECTED EXCEPTION: ImportError(<span class="pl-s"><span class="pl-pds">'</span>The Python Imaging Library (PIL) is required to load data from jpeg files. Please refer to https://pillow.readthedocs.io/en/stable/installation.html for installing PIL.<span class="pl-pds">'</span></span>)
Traceback (most recent call last):

  File <span class="pl-s"><span class="pl-pds">"</span>/home/sik/miniconda3/envs/test-sklearn/lib/python3.7/doctest.py<span class="pl-pds">"</span></span>, line 1329, <span class="pl-k">in</span> __run
    compileflags, 1), test.globs)

  File <span class="pl-s"><span class="pl-pds">"</span>&lt;doctest sklearn.feature_extraction.image.extract_patches_2d[2]&gt;<span class="pl-pds">"</span></span>, line 1, <span class="pl-k">in</span> <span class="pl-k">&lt;</span>module<span class="pl-k">&gt;</span>

  File <span class="pl-s"><span class="pl-pds">"</span>/home/sik/code/scikit-learn/sklearn/datasets/base.py<span class="pl-pds">"</span></span>, line 838, <span class="pl-k">in</span> load_sample_image
    images = <span class="pl-en">load_sample_images</span>()

  File <span class="pl-s"><span class="pl-pds">"</span>/home/sik/code/scikit-learn/sklearn/datasets/base.py<span class="pl-pds">"</span></span>, line 801, <span class="pl-k">in</span> load_sample_images
    images = [imread(filename) <span class="pl-k">for</span> <span class="pl-smi">filename</span> <span class="pl-k">in</span> filenames]

  File <span class="pl-s"><span class="pl-pds">"</span>/home/sik/code/scikit-learn/sklearn/datasets/base.py<span class="pl-pds">"</span></span>, line 801, <span class="pl-k">in</span> <span class="pl-k">&lt;</span>listcomp<span class="pl-k">&gt;</span>
    images = [imread(filename) <span class="pl-k">for</span> <span class="pl-smi">filename</span> <span class="pl-k">in</span> filenames]

  File <span class="pl-s"><span class="pl-pds">"</span>/home/sik/code/scikit-learn/sklearn/externals/_pilutil.py<span class="pl-pds">"</span></span>, line 204, <span class="pl-k">in</span> imread
    raise ImportError(PILLOW_ERROR_MESSAGE)

ImportError: The Python Imaging Library (PIL) is required to load data from jpeg files. Please refer to https://pillow.readthedocs.io/en/stable/installation.html <span class="pl-k">for</span> installing PIL.

/home/sik/code/scikit-learn/sklearn/feature_extraction/image.py:343: UnexpectedException
___________________________ test_lda_predict ___________________________

    def <span class="pl-en">test_lda_predict</span>():
        <span class="pl-c"><span class="pl-c">#</span> Test LDA classification.</span>
        <span class="pl-c"><span class="pl-c">#</span> This checks that LDA implements fit and predict and returns correct</span>
        <span class="pl-c"><span class="pl-c">#</span> values for simple toy data.</span>
        <span class="pl-k">for</span> <span class="pl-smi">test_case</span> <span class="pl-k">in</span> solver_shrinkage:
            solver, shrinkage = test_case
            clf = LinearDiscriminantAnalysis(solver=solver, shrinkage=shrinkage)
            y_pred = clf.fit(X, y).predict(X)
            assert_array_equal(y_pred, y, <span class="pl-s"><span class="pl-pds">'</span>solver %s<span class="pl-pds">'</span></span> % solver)
    
            <span class="pl-c"><span class="pl-c">#</span> Assert that it works with 1D data</span>
            y_pred1 = clf.fit(X1, y).predict(X1)
            assert_array_equal(y_pred1, y, <span class="pl-s"><span class="pl-pds">'</span>solver %s<span class="pl-pds">'</span></span> % solver)
    
            <span class="pl-c"><span class="pl-c">#</span> Test probability estimates</span>
            y_proba_pred1 = clf.predict_proba(X1)
            assert_array_equal<span class="pl-s"><span class="pl-pds">((</span>y_proba_pred<span class="pl-c1">1</span>[<span class="pl-k">:,</span> <span class="pl-c1">1</span>] <span class="pl-k">&gt;</span> <span class="pl-c1">0</span>.<span class="pl-c1">5</span>) <span class="pl-k">+</span> <span class="pl-c1">1</span><span class="pl-k">,</span> y<span class="pl-k">,</span></span>
<span class="pl-s">                               'solver %s' % solver)</span>
<span class="pl-s">            y_log_proba_pred<span class="pl-c1">1</span> = clf.predict_log_proba(X<span class="pl-c1">1</span>)</span>
<span class="pl-s">            assert_array_almost_equal(np.exp(y_log_proba_pred<span class="pl-c1">1</span>), y_proba_pred<span class="pl-c1">1</span>,</span>
<span class="pl-s"><span class="pl-k">&gt;</span>                                     <span class="pl-c1">8</span><span class="pl-k">,</span> 'solver <span class="pl-k">%</span>s' <span class="pl-k">%</span> solver)</span>
<span class="pl-s">E           AssertionError: </span>
<span class="pl-s">E           Arrays are not almost equal to <span class="pl-c1">8</span> decimals</span>
<span class="pl-s">E           solver svd</span>
<span class="pl-s">E           Mismatch: <span class="pl-c1">16</span>.<span class="pl-c1">7</span>%</span>
<span class="pl-s">E           Max absolute difference: <span class="pl-c1">5</span>.<span class="pl-c1">9604645</span>e-<span class="pl-c1">08</span></span>
<span class="pl-s">E           Max relative difference: <span class="pl-c1">4</span>.<span class="pl-c1">7683716</span>e-<span class="pl-c1">07</span></span>
<span class="pl-s">E            x: array([[<span class="pl-c1">9</span>.<span class="pl-c1">9999994</span>e-<span class="pl-c1">01</span>, <span class="pl-c1">1</span>.<span class="pl-c1">1253517</span>e-<span class="pl-c1">07</span>],</span>
<span class="pl-s">E                  [<span class="pl-c1">9</span>.<span class="pl-c1">9966466</span>e-<span class="pl-c1">01</span>, <span class="pl-c1">3</span>.<span class="pl-c1">3535002</span>e-<span class="pl-c1">04</span>],</span>
<span class="pl-s">E                  [<span class="pl-c1">9</span>.<span class="pl-c1">9966466</span>e-<span class="pl-c1">01</span>, <span class="pl-c1">3</span>.<span class="pl-c1">3535002</span>e-<span class="pl-c1">04</span>],...</span>
<span class="pl-s">E            y: array([[<span class="pl-c1">9</span>.<span class="pl-c1">99999881</span>e-<span class="pl-c1">01</span>, <span class="pl-c1">1</span>.<span class="pl-c1">12535155</span>e-<span class="pl-c1">07</span>],</span>
<span class="pl-s">E                  [<span class="pl-c1">9</span>.<span class="pl-c1">99664664</span>e-<span class="pl-c1">01</span>, <span class="pl-c1">3</span>.<span class="pl-c1">35350138</span>e-<span class="pl-c1">04</span>],</span>
<span class="pl-s">E                  [<span class="pl-c1">9</span>.<span class="pl-c1">99664664</span>e-<span class="pl-c1">01</span>, <span class="pl-c1">3</span>.<span class="pl-c1">35350138</span>e-<span class="pl-c1">04</span>],...</span>
<span class="pl-s"></span>
<span class="pl-s">sklearn/tests/test_discriminant_analysis.py:<span class="pl-c1">80</span>: AssertionError</span>
<span class="pl-s"><span class="pl-k">=======================</span> short test summary info <span class="pl-k">========================</span></span>
<span class="pl-s">SKIPPED [<span class="pl-c1">3</span>] /home/sik/code/scikit-learn/sklearn/ensemble/_hist_gradient_boosting/tests/test_compare_lightgbm.py:<span class="pl-c1">17</span>: could not import 'lightgbm': No module named 'lightgbm'</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/miniconda<span class="pl-c1">3</span>/envs/test-sklearn/lib/python<span class="pl-c1">3</span>.<span class="pl-c1">7</span>/site-packages/_pytest/unittest.py:<span class="pl-c1">238</span>: This test is failing on the buildbot, but cannot reproduce. Temporarily disabling it until it can be reproduced and  fixed.</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/code/scikit-learn/sklearn/compose/tests/test_column_transformer.py:<span class="pl-c1">132</span>: could not import 'pandas': No module named 'pandas'</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">2</span>] /home/sik/code/scikit-learn/sklearn/compose/tests/test_column_transformer.py:<span class="pl-c1">263</span>: could not import 'pandas': No module named 'pandas'</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/code/scikit-learn/sklearn/compose/tests/test_column_transformer.py:<span class="pl-c1">467</span>: could not import 'pandas': No module named 'pandas'</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/code/scikit-learn/sklearn/compose/tests/test_column_transformer.py:<span class="pl-c1">538</span>: could not import 'pandas': No module named 'pandas'</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">9</span>] /home/sik/code/scikit-learn/sklearn/compose/tests/test_column_transformer.py:<span class="pl-c1">812</span>: could not import 'pandas': No module named 'pandas'</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/code/scikit-learn/sklearn/compose/tests/test_column_transformer.py:<span class="pl-c1">1054</span>: could not import 'pandas': No module named 'pandas'</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">2</span>] /home/sik/code/scikit-learn/sklearn/compose/tests/test_column_transformer.py:<span class="pl-c1">1091</span>: could not import 'pandas': No module named 'pandas'</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/code/scikit-learn/sklearn/compose/tests/test_column_transformer.py:<span class="pl-c1">1125</span>: could not import 'pandas': No module named 'pandas'</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">3</span>] /home/sik/miniconda<span class="pl-c1">3</span>/envs/test-sklearn/lib/python<span class="pl-c1">3</span>.<span class="pl-c1">7</span>/site-packages/_pytest/unittest.py:<span class="pl-c1">238</span>: Download <span class="pl-c1">20</span> newsgroups to run this test</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/miniconda<span class="pl-c1">3</span>/envs/test-sklearn/lib/python<span class="pl-c1">3</span>.<span class="pl-c1">7</span>/site-packages/_pytest/unittest.py:<span class="pl-c1">238</span>: Covertype dataset can not be loaded.</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">2</span>] /home/sik/miniconda<span class="pl-c1">3</span>/envs/test-sklearn/lib/python<span class="pl-c1">3</span>.<span class="pl-c1">7</span>/site-packages/_pytest/unittest.py:<span class="pl-c1">238</span>: kddcup<span class="pl-c1">99</span> dataset can not be loaded.</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">5</span>] /home/sik/miniconda<span class="pl-c1">3</span>/envs/test-sklearn/lib/python<span class="pl-c1">3</span>.<span class="pl-c1">7</span>/site-packages/_pytest/unittest.py:<span class="pl-c1">238</span>: PIL not installed.</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] sklearn/datasets/tests/test_olivetti_faces.py:<span class="pl-c1">20</span>: Download Olivetti faces dataset to run this test</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/code/scikit-learn/sklearn/datasets/tests/test_openml.py:<span class="pl-c1">289</span>: could not import 'pandas': No module named 'pandas'</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/code/scikit-learn/sklearn/datasets/tests/test_openml.py:<span class="pl-c1">329</span>: could not import 'pandas': No module named 'pandas'</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/code/scikit-learn/sklearn/datasets/tests/test_openml.py:<span class="pl-c1">348</span>: could not import 'pandas': No module named 'pandas'</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/code/scikit-learn/sklearn/datasets/tests/test_openml.py:<span class="pl-c1">389</span>: could not import 'pandas': No module named 'pandas'</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/code/scikit-learn/sklearn/datasets/tests/test_openml.py:<span class="pl-c1">426</span>: could not import 'pandas': No module named 'pandas'</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/code/scikit-learn/sklearn/datasets/tests/test_openml.py:<span class="pl-c1">478</span>: could not import 'pandas': No module named 'pandas'</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/code/scikit-learn/sklearn/datasets/tests/test_openml.py:<span class="pl-c1">490</span>: could not import 'pandas': No module named 'pandas'</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/code/scikit-learn/sklearn/datasets/tests/test_openml.py:<span class="pl-c1">518</span>: could not import 'pandas': No module named 'pandas'</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/code/scikit-learn/sklearn/datasets/tests/test_openml.py:<span class="pl-c1">557</span>: could not import 'pandas': No module named 'pandas'</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/code/scikit-learn/sklearn/datasets/tests/test_openml.py:<span class="pl-c1">595</span>: could not import 'pandas': No module named 'pandas'</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/code/scikit-learn/sklearn/datasets/tests/test_openml.py:<span class="pl-c1">633</span>: could not import 'pandas': No module named 'pandas'</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/miniconda<span class="pl-c1">3</span>/envs/test-sklearn/lib/python<span class="pl-c1">3</span>.<span class="pl-c1">7</span>/site-packages/_pytest/unittest.py:<span class="pl-c1">238</span>: Download RCV<span class="pl-c1">1</span> dataset to run this test.</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] sklearn/datasets/tests/test_svmlight_format.py:<span class="pl-c1">188</span>: testing the overflow of <span class="pl-c1">32</span> bit sparse indexing requires a large amount of memory</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] sklearn/decomposition/tests/test_sparse_pca.py:<span class="pl-c1">134</span>: skipping mini_batch_fit_transform.</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">34</span>] /home/sik/code/scikit-learn/sklearn/conftest.py:<span class="pl-c1">18</span>: could not import 'matplotlib': No module named 'matplotlib'</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">2</span>] /home/sik/code/scikit-learn/sklearn/impute/tests/test_impute.py:<span class="pl-c1">315</span>: could not import 'pandas': No module named 'pandas'</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">2</span>] /home/sik/code/scikit-learn/sklearn/impute/tests/test_impute.py:<span class="pl-c1">428</span>: could not import 'pandas': No module named 'pandas'</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">2</span>] /home/sik/code/scikit-learn/sklearn/inspection/tests/test_permutation_importance.py:<span class="pl-c1">52</span>: could not import 'pandas': No module named 'pandas'</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/code/scikit-learn/sklearn/inspection/tests/test_permutation_importance.py:<span class="pl-c1">114</span>: could not import 'pandas': No module named 'pandas'</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">2</span>] /home/sik/code/scikit-learn/sklearn/linear_model/tests/test_logistic.py:<span class="pl-c1">1306</span>: liblinear does not support multinomial logistic</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/code/scikit-learn/sklearn/manifold/tests/test_spectral_embedding.py:<span class="pl-c1">166</span>: could not import 'pyamg': No module named 'pyamg'</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/code/scikit-learn/sklearn/manifold/tests/test_spectral_embedding.py:<span class="pl-c1">196</span>: could not import 'pyamg': No module named 'pyamg'</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/miniconda<span class="pl-c1">3</span>/envs/test-sklearn/lib/python<span class="pl-c1">3</span>.<span class="pl-c1">7</span>/site-packages/_pytest/doctest.py:<span class="pl-c1">351</span>: all tests skipped by +SKIP option</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">4</span>] /home/sik/code/scikit-learn/sklearn/preprocessing/tests/test_data.py:<span class="pl-c1">866</span>: 'with_mean=True' cannot be used with sparse matrix.</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">2</span>] /home/sik/code/scikit-learn/sklearn/preprocessing/tests/test_data.py:<span class="pl-c1">1077</span>: RobustScaler cannot center sparse matrix</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">3</span>] /home/sik/code/scikit-learn/sklearn/preprocessing/tests/test_encoders.py:<span class="pl-c1">127</span>: could not import 'pandas': No module named 'pandas'</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">2</span>] /home/sik/code/scikit-learn/sklearn/preprocessing/tests/test_encoders.py:<span class="pl-c1">283</span>: could not import 'pandas': No module named 'pandas'</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/code/scikit-learn/sklearn/preprocessing/tests/test_encoders.py:<span class="pl-c1">378</span>: could not import 'pandas': No module named 'pandas'</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">4</span>] /home/sik/code/scikit-learn/sklearn/preprocessing/tests/test_encoders.py:<span class="pl-c1">450</span>: could not import 'pandas': No module named 'pandas'</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/code/scikit-learn/sklearn/preprocessing/tests/test_encoders.py:<span class="pl-c1">584</span>: could not import 'pandas': No module named 'pandas'</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/code/scikit-learn/sklearn/preprocessing/tests/test_function_transformer.py:<span class="pl-c1">157</span>: could not import 'pandas': No module named 'pandas'</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/miniconda<span class="pl-c1">3</span>/envs/test-sklearn/lib/python<span class="pl-c1">3</span>.<span class="pl-c1">7</span>/site-packages/_pytest/unittest.py:<span class="pl-c1">238</span>: Can't instantiate estimator ColumnTransformer which requires parameters ['transformers']</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/miniconda<span class="pl-c1">3</span>/envs/test-sklearn/lib/python<span class="pl-c1">3</span>.<span class="pl-c1">7</span>/site-packages/_pytest/unittest.py:<span class="pl-c1">238</span>: Can't instantiate estimator FeatureUnion which requires parameters ['transformer_list']</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/miniconda<span class="pl-c1">3</span>/envs/test-sklearn/lib/python<span class="pl-c1">3</span>.<span class="pl-c1">7</span>/site-packages/_pytest/unittest.py:<span class="pl-c1">238</span>: Can't instantiate estimator GridSearchCV which requires parameters ['estimator', 'param_grid']</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/miniconda<span class="pl-c1">3</span>/envs/test-sklearn/lib/python<span class="pl-c1">3</span>.<span class="pl-c1">7</span>/site-packages/_pytest/unittest.py:<span class="pl-c1">238</span>: Can't instantiate estimator Pipeline which requires parameters ['steps']</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/miniconda<span class="pl-c1">3</span>/envs/test-sklearn/lib/python<span class="pl-c1">3</span>.<span class="pl-c1">7</span>/site-packages/_pytest/unittest.py:<span class="pl-c1">238</span>: Can't instantiate estimator RandomizedSearchCV which requires parameters ['estimator', 'param_distributions']</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/miniconda<span class="pl-c1">3</span>/envs/test-sklearn/lib/python<span class="pl-c1">3</span>.<span class="pl-c1">7</span>/site-packages/_pytest/unittest.py:<span class="pl-c1">238</span>: Can't instantiate estimator SparseCoder which requires parameters ['dictionary']</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/miniconda<span class="pl-c1">3</span>/envs/test-sklearn/lib/python<span class="pl-c1">3</span>.<span class="pl-c1">7</span>/site-packages/_pytest/unittest.py:<span class="pl-c1">238</span>: Can't instantiate estimator VotingClassifier which requires parameters ['estimators']</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/miniconda<span class="pl-c1">3</span>/envs/test-sklearn/lib/python<span class="pl-c1">3</span>.<span class="pl-c1">7</span>/site-packages/_pytest/unittest.py:<span class="pl-c1">238</span>: Can't instantiate estimator VotingRegressor which requires parameters ['estimators']</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">50</span>] /home/sik/miniconda<span class="pl-c1">3</span>/envs/test-sklearn/lib/python<span class="pl-c1">3</span>.<span class="pl-c1">7</span>/site-packages/_pytest/unittest.py:<span class="pl-c1">238</span>: pandas is not installed: not testing for input of type pandas.Series to class weight.</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/miniconda<span class="pl-c1">3</span>/envs/test-sklearn/lib/python<span class="pl-c1">3</span>.<span class="pl-c1">7</span>/site-packages/_pytest/unittest.py:<span class="pl-c1">238</span>: score_samples of BernoulliRBM is not invariant when applied to a subset.</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">3</span>] /home/sik/miniconda<span class="pl-c1">3</span>/envs/test-sklearn/lib/python<span class="pl-c1">3</span>.<span class="pl-c1">7</span>/site-packages/_pytest/unittest.py:<span class="pl-c1">238</span>: Skipping check_estimators_data_not_an_array for cross decomposition module as estimators are not deterministic.</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/miniconda<span class="pl-c1">3</span>/envs/test-sklearn/lib/python<span class="pl-c1">3</span>.<span class="pl-c1">7</span>/site-packages/_pytest/unittest.py:<span class="pl-c1">238</span>: predict of DummyClassifier is not invariant when applied to a subset.</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/miniconda<span class="pl-c1">3</span>/envs/test-sklearn/lib/python<span class="pl-c1">3</span>.<span class="pl-c1">7</span>/site-packages/_pytest/unittest.py:<span class="pl-c1">238</span>: transform of MiniBatchSparsePCA is not invariant when applied to a subset.</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/miniconda<span class="pl-c1">3</span>/envs/test-sklearn/lib/python<span class="pl-c1">3</span>.<span class="pl-c1">7</span>/site-packages/_pytest/unittest.py:<span class="pl-c1">238</span>: Not testing NuSVC class weight as it is ignored.</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/miniconda<span class="pl-c1">3</span>/envs/test-sklearn/lib/python<span class="pl-c1">3</span>.<span class="pl-c1">7</span>/site-packages/_pytest/unittest.py:<span class="pl-c1">238</span>: decision_function of NuSVC is not invariant when applied to a subset.</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/miniconda<span class="pl-c1">3</span>/envs/test-sklearn/lib/python<span class="pl-c1">3</span>.<span class="pl-c1">7</span>/site-packages/_pytest/unittest.py:<span class="pl-c1">238</span>: transform of SparsePCA is not invariant when applied to a subset.</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">2</span>] /home/sik/miniconda<span class="pl-c1">3</span>/envs/test-sklearn/lib/python<span class="pl-c1">3</span>.<span class="pl-c1">7</span>/site-packages/_pytest/unittest.py:<span class="pl-c1">238</span>: numpydoc is required to test the docstrings</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/code/scikit-learn/sklearn/utils/tests/test_multiclass.py:<span class="pl-c1">299</span>: could not import 'pandas': No module named 'pandas'</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">37</span>] /home/sik/code/scikit-learn/sklearn/utils/tests/test_utils.py:<span class="pl-c1">239</span>: could not import 'pandas': No module named 'pandas'</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">39</span>] /home/sik/code/scikit-learn/sklearn/utils/tests/test_utils.py:<span class="pl-c1">242</span>: could not import 'pandas': No module named 'pandas'</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/code/scikit-learn/sklearn/utils/tests/test_utils.py:<span class="pl-c1">423</span>: could not import 'pandas': No module named 'pandas'</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/code/scikit-learn/sklearn/utils/tests/test_utils.py:<span class="pl-c1">444</span>: could not import 'pandas': No module named 'pandas'</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">2</span>] /home/sik/code/scikit-learn/sklearn/utils/tests/test_utils.py:<span class="pl-c1">466</span>: could not import 'pandas': No module named 'pandas'</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/miniconda<span class="pl-c1">3</span>/envs/test-sklearn/lib/python<span class="pl-c1">3</span>.<span class="pl-c1">7</span>/site-packages/_pytest/unittest.py:<span class="pl-c1">238</span>: Pandas not found</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/code/scikit-learn/sklearn/utils/tests/test_validation.py:<span class="pl-c1">719</span>: could not import 'pandas': No module named 'pandas'</span>
<span class="pl-s">SKIPPED [<span class="pl-c1">1</span>] /home/sik/code/scikit-learn/sklearn/utils/tests/test_validation.py:<span class="pl-c1">732</span>: could not import 'pandas': No module named 'pandas'</span>
<span class="pl-s">= <span class="pl-c1">3</span> failed, <span class="pl-c1">13147</span> passed, <span class="pl-c1">265</span> skipped, <span class="pl-c1">6</span> xfailed, <span class="pl-c1">1572</span> warnings in <span class="pl-c1">409</span>.<span class="pl-c1">77</span>s (<span class="pl-c1">0</span>:<span class="pl-c1">06</span>:<span class="pl-c1">49</span>) =</span>
<span class="pl-s"> This problem is unconstrained.</span>
<span class="pl-s">RUNNING THE L-BFGS-B CODE</span>
<span class="pl-s"></span>
<span class="pl-s">           * * *</span>
<span class="pl-s"></span>
<span class="pl-s">Machine precision = <span class="pl-c1">2</span>.<span class="pl-c1">220</span>D-<span class="pl-c1">16</span></span>
<span class="pl-s"> N =            <span class="pl-c1">3</span>     M =           <span class="pl-c1">10</span></span>
<span class="pl-s"></span>
<span class="pl-s">At X<span class="pl-c1">0</span>         <span class="pl-c1">0</span> variables are exactly at the bounds</span>
<span class="pl-s"></span>
<span class="pl-s">At iterate    <span class="pl-c1">0</span>    f=  <span class="pl-c1">1</span>.<span class="pl-c1">38629</span>D+<span class="pl-c1">02</span>    |proj g|=  <span class="pl-c1">6</span>.<span class="pl-c1">27865</span>D+<span class="pl-c1">01</span></span>
<span class="pl-s"></span>
<span class="pl-s">           * * *</span>
<span class="pl-s"></span>
<span class="pl-s">Tit   = total number of iterations</span>
<span class="pl-s">Tnf   = total number of function evaluations</span>
<span class="pl-s">Tnint = total number of segments explored during Cauchy searches</span>
<span class="pl-s">Skip  = number of BFGS updates skipped</span>
<span class="pl-s">Nact  = number of active bounds at final generalized Cauchy point</span>
<span class="pl-s">Projg = norm of the final projected gradient</span>
<span class="pl-s">F     = final function value</span>
<span class="pl-s"></span>
<span class="pl-s">           * * *</span>
<span class="pl-s"></span>
<span class="pl-s">   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F</span>
<span class="pl-s">    <span class="pl-c1">3</span>      <span class="pl-c1">1</span>      <span class="pl-c1">2</span>      <span class="pl-c1">1</span>     <span class="pl-c1">0</span>     <span class="pl-c1">0</span>   <span class="pl-c1">2</span>.<span class="pl-c1">422</span>D+<span class="pl-c1">01</span>   <span class="pl-c1">9</span>.<span class="pl-c1">713</span>D+<span class="pl-c1">01</span></span>
<span class="pl-s">  F =   <span class="pl-c1">97</span>.<span class="pl-c1">133816163368223</span>     </span>
<span class="pl-s"></span>
<span class="pl-s">STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 </span>
<span class="pl-s"></span>
<span class="pl-s"> Cauchy                time <span class="pl-c1">0</span>.<span class="pl-c1">000</span>E+<span class="pl-c1">00</span> seconds.</span>
<span class="pl-s"> Subspace minimization time <span class="pl-c1">0</span>.<span class="pl-c1">000</span>E+<span class="pl-c1">00</span> seconds.</span>
<span class="pl-s"> Line search           time <span class="pl-c1">0</span>.<span class="pl-c1">000</span>E+<span class="pl-c1">00</span> seconds.</span>
<span class="pl-s"></span>
<span class="pl-s"> Total User time <span class="pl-c1">0</span>.<span class="pl-c1">000</span>E+<span class="pl-c1">00</span> seconds.</span>
<span class="pl-s"></span></pre></div>
</details>
<p dir="auto">Here is the output of <code class="notranslate">conda list</code></p>
<details>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="# packages in environment at /home/sik/miniconda3/envs/test-sklearn:
#
# Name                    Version                   Build  Channel
_libgcc_mutex             0.1                        main  
atomicwrites              1.3.0                      py_0    conda-forge
attrs                     19.1.0                     py_0    conda-forge
bzip2                     1.0.8                h516909a_0    conda-forge
ca-certificates           2019.6.16            hecc5488_0    conda-forge
certifi                   2019.6.16                py37_1    conda-forge
cython                    0.29.13          py37he1b5a44_0    conda-forge
importlib_metadata        0.20                     py37_0    conda-forge
joblib                    0.13.2                   pypi_0    pypi
libblas                   3.8.0               12_openblas    conda-forge
libcblas                  3.8.0               12_openblas    conda-forge
libffi                    3.2.1             he1b5a44_1006    conda-forge
libgcc-ng                 9.1.0                hdf63c60_0  
libgfortran-ng            7.3.0                hdf63c60_0  
liblapack                 3.8.0               12_openblas    conda-forge
libopenblas               0.3.7                h6e990d7_1    conda-forge
libstdcxx-ng              9.1.0                hdf63c60_0  
more-itertools            7.2.0                      py_0    conda-forge
ncurses                   6.1               hf484d3e_1002    conda-forge
numpy                     1.17.1           py37h95a1406_0    conda-forge
openssl                   1.1.1c               h516909a_0    conda-forge
packaging                 19.0                       py_0    conda-forge
pip                       19.2.3                   py37_0    conda-forge
pluggy                    0.12.0                     py_0    conda-forge
py                        1.8.0                      py_0    conda-forge
pyparsing                 2.4.2                      py_0    conda-forge
pytest                    5.1.2                    py37_0    conda-forge
python                    3.7.3                h33d41f4_1    conda-forge
readline                  8.0                  hf8c457e_0    conda-forge
scikit-learn              0.22.dev0                 dev_0    &lt;develop&gt;
scipy                     1.3.1            py37h921218d_2    conda-forge
setuptools                41.2.0                   py37_0    conda-forge
six                       1.12.0                py37_1000    conda-forge
sqlite                    3.29.0               hcee41ef_1    conda-forge
tk                        8.6.9             hed695b0_1002    conda-forge
wcwidth                   0.1.7                      py_1    conda-forge
wheel                     0.33.6                   py37_0    conda-forge
xz                        5.2.4             h14c3975_1001    conda-forge
zipp                      0.6.0                      py_0    conda-forge
zlib                      1.2.11            h516909a_1005    conda-forge"><pre class="notranslate"><code class="notranslate"># packages in environment at /home/sik/miniconda3/envs/test-sklearn:
#
# Name                    Version                   Build  Channel
_libgcc_mutex             0.1                        main  
atomicwrites              1.3.0                      py_0    conda-forge
attrs                     19.1.0                     py_0    conda-forge
bzip2                     1.0.8                h516909a_0    conda-forge
ca-certificates           2019.6.16            hecc5488_0    conda-forge
certifi                   2019.6.16                py37_1    conda-forge
cython                    0.29.13          py37he1b5a44_0    conda-forge
importlib_metadata        0.20                     py37_0    conda-forge
joblib                    0.13.2                   pypi_0    pypi
libblas                   3.8.0               12_openblas    conda-forge
libcblas                  3.8.0               12_openblas    conda-forge
libffi                    3.2.1             he1b5a44_1006    conda-forge
libgcc-ng                 9.1.0                hdf63c60_0  
libgfortran-ng            7.3.0                hdf63c60_0  
liblapack                 3.8.0               12_openblas    conda-forge
libopenblas               0.3.7                h6e990d7_1    conda-forge
libstdcxx-ng              9.1.0                hdf63c60_0  
more-itertools            7.2.0                      py_0    conda-forge
ncurses                   6.1               hf484d3e_1002    conda-forge
numpy                     1.17.1           py37h95a1406_0    conda-forge
openssl                   1.1.1c               h516909a_0    conda-forge
packaging                 19.0                       py_0    conda-forge
pip                       19.2.3                   py37_0    conda-forge
pluggy                    0.12.0                     py_0    conda-forge
py                        1.8.0                      py_0    conda-forge
pyparsing                 2.4.2                      py_0    conda-forge
pytest                    5.1.2                    py37_0    conda-forge
python                    3.7.3                h33d41f4_1    conda-forge
readline                  8.0                  hf8c457e_0    conda-forge
scikit-learn              0.22.dev0                 dev_0    &lt;develop&gt;
scipy                     1.3.1            py37h921218d_2    conda-forge
setuptools                41.2.0                   py37_0    conda-forge
six                       1.12.0                py37_1000    conda-forge
sqlite                    3.29.0               hcee41ef_1    conda-forge
tk                        8.6.9             hed695b0_1002    conda-forge
wcwidth                   0.1.7                      py_1    conda-forge
wheel                     0.33.6                   py37_0    conda-forge
xz                        5.2.4             h14c3975_1001    conda-forge
zipp                      0.6.0                      py_0    conda-forge
zlib                      1.2.11            h516909a_1005    conda-forge
</code></pre></div>
</details>
<p dir="auto">I haven't followed the openmp building in detail but it looks like on OS X and windows building are now failing by default?<br>
That's pretty annoying for sprinting with a bigger group and also can raise the frustration level of new contributors who are likely to be on these OSs</p>