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Intel Python: sklearn test output

sergio_r_
Novice
‎04-16-2017 02:42 PM
959 Views

 

Just for your information, below is the output of:

nosetests -v sklearn

Sergio
Enhance your #MachineLearning and #BigData skills via #Python #SciPy:
1) https://www.packtpub.com/big-data-and-business-intelligence/numerical-and-scientific-computing-scipy-video
2) https://www.packtpub.com/big-data-and-business-intelligence/learning-scipy-numerical-and-scientific-computing-second-edition

======================================================================
ERROR: /home/intel/intelpython3/lib/python3.5/site-packages/sklearn/tests/test_c
ommon.py.test_non_meta_estimators:check_estimators_pickle(LinearRegression)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/intel/intelpython3/lib/python3.5/site-packages/nose/case.py", line
 198, in runTest
    self.test(*self.arg)
  File "/home/intel/intelpython3/lib/python3.5/site-packages/sklearn/utils/testi
ng.py", line 830, in __call__
    return self.check(*args, **kwargs)
  File "/home/intel/intelpython3/lib/python3.5/site-packages/sklearn/utils/testi
ng.py", line 355, in wrapper
    return fn(*args, **kwargs)
  File "/home/intel/intelpython3/lib/python3.5/site-packages/sklearn/utils/estim
ator_checks.py", line 889, in check_estimators_pickle
    pickled_estimator = pickle.dumps(estimator)
TypeError: can't pickle SwigPyObject objects

======================================================================
ERROR: /home/intel/intelpython3/lib/python3.5/site-packages/sklearn/tests/test_c
ommon.py.test_non_meta_estimators:check_fit2d_predict1d(LinearRegression)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/intel/intelpython3/lib/python3.5/site-packages/nose/case.py", line
 198, in runTest
    self.test(*self.arg)
  File "/home/intel/intelpython3/lib/python3.5/site-packages/sklearn/utils/testi
ng.py", line 830, in __call__
    return self.check(*args, **kwargs)
  File "/home/intel/intelpython3/lib/python3.5/site-packages/sklearn/utils/estim
ator_checks.py", line 479, in check_fit2d_predict1d
    getattr(estimator, method), X[0])
  File "/home/intel/intelpython3/lib/python3.5/site-packages/sklearn/utils/testi
ng.py", line 186, in assert_warns
    result = func(*args, **kw)
  File "/home/intel/intelpython3/lib/python3.5/site-packages/sklearn/daal4sklear
n/linear.py", line 156, in predict
    return daal_predict(self, X)
  File "/home/intel/intelpython3/lib/python3.5/site-packages/sklearn/daal4sklear
n/linear.py", line 56, in daal_predict
    predictionResult = algorithm.compute()
  File "/home/intel/intelpython3/lib/python3.5/site-packages/daal/algorithms/lin
ear_regression/prediction.py", line 253, in compute
    return _prediction9.Batch_Float64DefaultDense_compute(self)
SystemError: Number of columns in numeric table is incorrect
Details:
Argument name: beta


======================================================================
ERROR: /home/intel/intelpython3/lib/python3.5/site-packages/sklearn/tests/test_c
ommon.py.test_non_meta_estimators:check_estimators_pickle(RANSACRegressor)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/intel/intelpython3/lib/python3.5/site-packages/nose/case.py", line
 198, in runTest
    self.test(*self.arg)
  File "/home/intel/intelpython3/lib/python3.5/site-packages/sklearn/utils/testi
ng.py", line 830, in __call__
    return self.check(*args, **kwargs)
  File "/home/intel/intelpython3/lib/python3.5/site-packages/sklearn/utils/testi
ng.py", line 355, in wrapper
    return fn(*args, **kwargs)
  File "/home/intel/intelpython3/lib/python3.5/site-packages/sklearn/utils/estim
ator_checks.py", line 889, in check_estimators_pickle
    pickled_estimator = pickle.dumps(estimator)
TypeError: can't pickle SwigPyObject objects

======================================================================
ERROR: /home/intel/intelpython3/lib/python3.5/site-packages/sklearn/tests/test_c
ommon.py.test_non_meta_estimators:check_supervised_y_2d(RANSACRegressor)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/intel/intelpython3/lib/python3.5/site-packages/nose/case.py", line
 198, in runTest
    self.test(*self.arg)
  File "/home/intel/intelpython3/lib/python3.5/site-packages/sklearn/utils/testi
ng.py", line 830, in __call__
    return self.check(*args, **kwargs)
  File "/home/intel/intelpython3/lib/python3.5/site-packages/sklearn/utils/testi
ng.py", line 355, in wrapper
    return fn(*args, **kwargs)
  File "/home/intel/intelpython3/lib/python3.5/site-packages/sklearn/utils/estim
ator_checks.py", line 1151, in check_supervised_y_2d
    estimator.fit(X, y[:, np.newaxis])
  File "/home/intel/intelpython3/lib/python3.5/site-packages/sklearn/linear_mode
l/ransac.py", line 363, in fit
    self.residual_threshold))
ValueError: No inliers found, possible cause is setting residual_threshold (None
) too low.

======================================================================
ERROR: /home/intel/intelpython3/lib/python3.5/site-packages/sklearn/tests/test_c
ommon.py.test_non_meta_estimators:check_fit2d_predict1d(RANSACRegressor)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/intel/intelpython3/lib/python3.5/site-packages/nose/case.py", line
 198, in runTest
    self.test(*self.arg)
  File "/home/intel/intelpython3/lib/python3.5/site-packages/sklearn/utils/testi
ng.py", line 830, in __call__
    return self.check(*args, **kwargs)
  File "/home/intel/intelpython3/lib/python3.5/site-packages/sklearn/utils/estim
ator_checks.py", line 479, in check_fit2d_predict1d
    getattr(estimator, method), X[0])
  File "/home/intel/intelpython3/lib/python3.5/site-packages/sklearn/utils/testi
ng.py", line 186, in assert_warns
    result = func(*args, **kw)
  File "/home/intel/intelpython3/lib/python3.5/site-packages/sklearn/linear_mode
l/ransac.py", line 428, in predict
    return self.estimator_.predict(X)
  File "/home/intel/intelpython3/lib/python3.5/site-packages/sklearn/daal4sklear
n/linear.py", line 156, in predict
    return daal_predict(self, X)
  File "/home/intel/intelpython3/lib/python3.5/site-packages/sklearn/daal4sklear
n/linear.py", line 56, in daal_predict
    predictionResult = algorithm.compute()
  File "/home/intel/intelpython3/lib/python3.5/site-packages/daal/algorithms/lin
ear_regression/prediction.py", line 253, in compute
    return _prediction9.Batch_Float64DefaultDense_compute(self)
SystemError: Number of columns in numeric table is incorrect
Details:
Argument name: beta


======================================================================
ERROR: /home/intel/intelpython3/lib/python3.5/site-packages/sklearn/tests/test_c
ommon.py.test_non_meta_estimators:check_estimators_pickle(Ridge)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/intel/intelpython3/lib/python3.5/site-packages/nose/case.py", line
 198, in runTest
    self.test(*self.arg)
  File "/home/intel/intelpython3/lib/python3.5/site-packages/sklearn/utils/testi
ng.py", line 830, in __call__
    return self.check(*args, **kwargs)
  File "/home/intel/intelpython3/lib/python3.5/site-packages/sklearn/utils/testi
ng.py", line 355, in wrapper
    return fn(*args, **kwargs)
  File "/home/intel/intelpython3/lib/python3.5/site-packages/sklearn/utils/estim
ator_checks.py", line 889, in check_estimators_pickle
    pickled_estimator = pickle.dumps(estimator)
TypeError: can't pickle SwigPyObject objects

======================================================================
FAIL: sklearn.cluster.tests.test_bicluster.test_project_and_cluster
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/intel/intelpython3/lib/python3.5/site-packages/nose/case.py", line
 198, in runTest
    self.test(*self.arg)
  File "/home/intel/intelpython3/lib/python3.5/site-packages/sklearn/cluster/tes
ts/test_bicluster.py", line 207, in test_project_and_cluster
    assert_array_equal(labels, [0, 0, 1, 1])
  File "/home/intel/intelpython3/lib/python3.5/site-packages/numpy/testing/utils
.py", line 813, in assert_array_equal
    verbose=verbose, header='Arrays are not equal')
  File "/home/intel/intelpython3/lib/python3.5/site-packages/numpy/testing/utils
.py", line 739, in assert_array_compare
    raise AssertionError(msg)
AssertionError:
Arrays are not equal

(mismatch 100.0%)
 x: array([1, 1, 0, 0], dtype=int32)
 y: array([0, 0, 1, 1])

======================================================================
FAIL: sklearn.cluster.tests.test_k_means.test_k_means_non_collapsed
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/intel/intelpython3/lib/python3.5/site-packages/nose/case.py", line
 198, in runTest
    self.test(*self.arg)
  File "/home/intel/intelpython3/lib/python3.5/site-packages/sklearn/cluster/tes
ts/test_k_means.py", line 552, in test_k_means_non_collapsed
    assert_equal(len(np.unique(km.labels_)), 3)
AssertionError: 1 != 3

======================================================================
FAIL: sklearn.linear_model.tests.test_ridge.test_dense_sparse(<function _test_ri
dge_loo at 0x7fb59bd21840>,)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/intel/intelpython3/lib/python3.5/site-packages/nose/case.py", line
 198, in runTest
    self.test(*self.arg)
  File "/home/intel/intelpython3/lib/python3.5/site-packages/sklearn/linear_mode
l/tests/test_ridge.py", line 454, in check_dense_sparse
    ret_dense = test_func(DENSE_FILTER)
  File "/home/intel/intelpython3/lib/python3.5/site-packages/sklearn/linear_mode
l/tests/test_ridge.py", line 329, in _test_ridge_loo
    assert_almost_equal(errors, errors2)
  File "/home/intel/intelpython3/lib/python3.5/site-packages/numpy/testing/utils
.py", line 523, in assert_almost_equal
    return assert_array_almost_equal(actual, desired, decimal, err_msg)
  File "/home/intel/intelpython3/lib/python3.5/site-packages/numpy/testing/utils
.py", line 918, in assert_array_almost_equal
    precision=decimal)
  File "/home/intel/intelpython3/lib/python3.5/site-packages/numpy/testing/utils
.py", line 739, in assert_array_compare
    raise AssertionError(msg)
AssertionError:
Arrays are not almost equal to 7 decimals

(mismatch 100.0%)
 x: array([  1.8295168e+04,   5.6704414e+02,   1.1782287e+03,   5.2810053e+03,
         3.8443835e+02,   8.0439421e+03,   2.3982367e+03,   3.3362619e+03,
         1.1783452e+01,   5.1091672e+03,   1.3139571e+04,   1.5552388e+01,...
 y: array([  1.8505354e+04,   6.0452467e+02,   1.1257434e+03,   5.1693552e+03,
         4.1556914e+02,   8.1834151e+03,   2.4742189e+03,   3.4259267e+03,
         1.7722638e+01,   4.9988370e+03,   1.3318625e+04,   1.0045693e+01,...

======================================================================
FAIL: sklearn.manifold.tests.test_spectral_embedding.test_pipeline_spectral_clus
tering
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/intel/intelpython3/lib/python3.5/site-packages/nose/case.py", line
 198, in runTest
    self.test(*self.arg)
  File "/home/intel/intelpython3/lib/python3.5/site-packages/sklearn/manifold/te
sts/test_spectral_embedding.py", line 198, in test_pipeline_spectral_clustering
    true_labels), 1.0, 2)
  File "/home/intel/intelpython3/lib/python3.5/site-packages/numpy/testing/utils
.py", line 918, in assert_array_almost_equal
    precision=decimal)
  File "/home/intel/intelpython3/lib/python3.5/site-packages/numpy/testing/utils
.py", line 739, in assert_array_compare
    raise AssertionError(msg)
AssertionError:
Arrays are not almost equal to 2 decimals

(mismatch 100.0%)
 x: array(0.5866188259941206)
 y: array(1.0)

======================================================================
FAIL: sklearn.mixture.tests.test_dpgmm.test_class_weights
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/intel/intelpython3/lib/python3.5/site-packages/nose/case.py", line
 198, in runTest
    self.test(*self.arg)
  File "/home/intel/intelpython3/lib/python3.5/site-packages/sklearn/utils/testi
ng.py", line 355, in wrapper
    return fn(*args, **kwargs)
  File "/home/intel/intelpython3/lib/python3.5/site-packages/sklearn/mixture/tes
ts/test_dpgmm.py", line 41, in test_class_weights
    assert_array_less(dpgmm.weights_[~active], .05)
  File "/home/intel/intelpython3/lib/python3.5/site-packages/numpy/testing/utils
.py", line 990, in assert_array_less
    header='Arrays are not less-ordered')
  File "/home/intel/intelpython3/lib/python3.5/site-packages/numpy/testing/utils
.py", line 739, in assert_array_compare
    raise AssertionError(msg)
AssertionError:
Arrays are not less-ordered

(mismatch 14.285714285714292%)
 x: array([ 0.227048,  0.035354,  0.034998,  0.03491 ,  0.034875,  0.034874,
        0.034887])
 y: array(0.05)

======================================================================
FAIL: sklearn.model_selection.tests.test_validation.test_cross_val_score_with_sc
ore_func_regression
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/intel/intelpython3/lib/python3.5/site-packages/nose/case.py", line
 198, in runTest
    self.test(*self.arg)
  File "/home/intel/intelpython3/lib/python3.5/site-packages/sklearn/model_selec
tion/tests/test_validation.py", line 418, in test_cross_val_score_with_score_fun
c_regression
    assert_array_almost_equal(neg_mse_scores, expected_neg_mse, 2)
  File "/home/intel/intelpython3/lib/python3.5/site-packages/numpy/testing/utils
.py", line 918, in assert_array_almost_equal
    precision=decimal)
  File "/home/intel/intelpython3/lib/python3.5/site-packages/numpy/testing/utils
.py", line 739, in assert_array_compare
    raise AssertionError(msg)
AssertionError:
Arrays are not almost equal to 2 decimals

(mismatch 100.0%)
 x: array([ -760.52,  -514.85,  -265.83,  -275.24, -1647.  ])
 y: array([ -763.07,  -553.16,  -274.38,  -273.26, -1681.99])

======================================================================
FAIL: /home/intel/intelpython3/lib/python3.5/site-packages/sklearn/tests/test_co
mmon.py.test_non_meta_estimators:check_estimators_nan_inf(LinearRegression)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/intel/intelpython3/lib/python3.5/site-packages/nose/case.py", line
 198, in runTest
    self.test(*self.arg)
  File "/home/intel/intelpython3/lib/python3.5/site-packages/sklearn/utils/testi
ng.py", line 830, in __call__
    return self.check(*args, **kwargs)
  File "/home/intel/intelpython3/lib/python3.5/site-packages/sklearn/utils/estim
ator_checks.py", line 840, in check_estimators_nan_inf
    raise AssertionError(error_string_predict, Estimator)
AssertionError: ("Estimator doesn't check for NaN and inf in predict.", <class '
sklearn.linear_model.base.LinearRegression'>)

======================================================================
FAIL: /home/intel/intelpython3/lib/python3.5/site-packages/sklearn/tests/test_co
mmon.py.test_non_meta_estimators:check_estimators_nan_inf(RANSACRegressor)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/intel/intelpython3/lib/python3.5/site-packages/nose/case.py", line
 198, in runTest
    self.test(*self.arg)
  File "/home/intel/intelpython3/lib/python3.5/site-packages/sklearn/utils/testi
ng.py", line 830, in __call__
    return self.check(*args, **kwargs)
  File "/home/intel/intelpython3/lib/python3.5/site-packages/sklearn/utils/estim
ator_checks.py", line 840, in check_estimators_nan_inf
    raise AssertionError(error_string_predict, Estimator)
AssertionError: ("Estimator doesn't check for NaN and inf in predict.", <class '
sklearn.linear_model.ransac.RANSACRegressor'>)

======================================================================
FAIL: sklearn.tests.test_cross_validation.test_cross_val_score_with_score_func_r
egression
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/intel/intelpython3/lib/python3.5/site-packages/nose/case.py", line
 198, in runTest
    self.test(*self.arg)
  File "/home/intel/intelpython3/lib/python3.5/site-packages/sklearn/tests/test_
cross_validation.py", line 918, in test_cross_val_score_with_score_func_regressi
on
    assert_array_almost_equal(neg_mse_scores, expected_neg_mse, 2)
  File "/home/intel/intelpython3/lib/python3.5/site-packages/numpy/testing/utils
.py", line 918, in assert_array_almost_equal
    precision=decimal)
  File "/home/intel/intelpython3/lib/python3.5/site-packages/numpy/testing/utils
.py", line 739, in assert_array_compare
    raise AssertionError(msg)
AssertionError:
Arrays are not almost equal to 2 decimals

(mismatch 100.0%)
 x: array([ -760.52,  -514.85,  -265.83,  -275.24, -1647.  ])
 y: array([ -763.07,  -553.16,  -274.38,  -273.26, -1681.99])

----------------------------------------------------------------------
Ran 7166 tests in 203.755s

FAILED (SKIP=14, errors=6, failures=9)

0 Kudos

Link Copied

2 Replies
gaston-hillar
Valued Contributor I
‎04-17-2017 08:02 PM
959 Views

Hi Sergio,

I assume you executed the tests on Linux or on macOS. Instead of providing relevant information for your thread, it seems you focused on promoting your video and your book published by Packt. I've published ten books with Packt and I never needed to SPAM in forums to get attention. In fact, it is not a good practice at all. You will be flagged as SPAM and probably, your threads or posts won't be considered as serious threads or posts in the communities or forums. Take a look at the threads opened in these forums and you will notice that most professionals provide a detailed description of the problem.

I assume you didn't open this new thread just to SPAM and that you wanted to share the results of executing the tests with Intel Distribution for Python.

Let me share the results of running the same tests on Intel Distribution for Python 3.5.2 on Windows 10 64-bits:

======================================================================
ERROR: C:\IntelPython35\lib\site-packages\sklearn\tests\test_common.py.test_non_meta_estimators:check_estimators_pickle(LinearRegression)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\IntelPython35\lib\site-packages\nose\case.py", line 198, in runTest
    self.test(*self.arg)
  File "C:\IntelPython35\lib\site-packages\sklearn\utils\testing.py", line 830, in __call__
    return self.check(*args, **kwargs)
  File "C:\IntelPython35\lib\site-packages\sklearn\utils\testing.py", line 355, in wrapper
    return fn(*args, **kwargs)
  File "C:\IntelPython35\lib\site-packages\sklearn\utils\estimator_checks.py", line 889, in check_estimators_pickle
    pickled_estimator = pickle.dumps(estimator)
TypeError: can't pickle SwigPyObject objects

======================================================================
ERROR: C:\IntelPython35\lib\site-packages\sklearn\tests\test_common.py.test_non_meta_estimators:check_fit2d_predict1d(LinearRegression)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\IntelPython35\lib\site-packages\nose\case.py", line 198, in runTest
    self.test(*self.arg)
  File "C:\IntelPython35\lib\site-packages\sklearn\utils\testing.py", line 830, in __call__
    return self.check(*args, **kwargs)
  File "C:\IntelPython35\lib\site-packages\sklearn\utils\estimator_checks.py", line 479, in check_fit2d_predict1d
    getattr(estimator, method), X[0])
  File "C:\IntelPython35\lib\site-packages\sklearn\utils\testing.py", line 186, in assert_warns
    result = func(*args, **kw)
  File "C:\IntelPython35\lib\site-packages\sklearn\daal4sklearn\linear.py", line 155, in predict
    return daal_predict(self, X)
  File "C:\IntelPython35\lib\site-packages\sklearn\daal4sklearn\linear.py", line 55, in daal_predict
    predictionResult = algorithm.compute()
  File "C:\IntelPython35\lib\site-packages\daal\algorithms\linear_regression\prediction.py", line 252, in compute
    return _prediction5.Batch_Float64DefaultDense_compute(self)
SystemError: Number of columns in numeric table is incorrect
Details:
Argument name: beta


======================================================================
ERROR: C:\IntelPython35\lib\site-packages\sklearn\tests\test_common.py.test_non_meta_estimators:check_estimators_pickle(RANSACRegressor)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\IntelPython35\lib\site-packages\nose\case.py", line 198, in runTest
    self.test(*self.arg)
  File "C:\IntelPython35\lib\site-packages\sklearn\utils\testing.py", line 830, in __call__
    return self.check(*args, **kwargs)
  File "C:\IntelPython35\lib\site-packages\sklearn\utils\testing.py", line 355, in wrapper
    return fn(*args, **kwargs)
  File "C:\IntelPython35\lib\site-packages\sklearn\utils\estimator_checks.py", line 889, in check_estimators_pickle
    pickled_estimator = pickle.dumps(estimator)
TypeError: can't pickle SwigPyObject objects

======================================================================
ERROR: C:\IntelPython35\lib\site-packages\sklearn\tests\test_common.py.test_non_meta_estimators:check_supervised_y_2d(RANSACRegressor)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\IntelPython35\lib\site-packages\nose\case.py", line 198, in runTest
    self.test(*self.arg)
  File "C:\IntelPython35\lib\site-packages\sklearn\utils\testing.py", line 830, in __call__
    return self.check(*args, **kwargs)
  File "C:\IntelPython35\lib\site-packages\sklearn\utils\testing.py", line 355, in wrapper
    return fn(*args, **kwargs)
  File "C:\IntelPython35\lib\site-packages\sklearn\utils\estimator_checks.py", line 1151, in check_supervised_y_2d
    estimator.fit(X, y[:, np.newaxis])
  File "C:\IntelPython35\lib\site-packages\sklearn\linear_model\ransac.py", line 363, in fit
    self.residual_threshold))
ValueError: No inliers found, possible cause is setting residual_threshold (None) too low.

======================================================================
ERROR: C:\IntelPython35\lib\site-packages\sklearn\tests\test_common.py.test_non_meta_estimators:check_fit2d_predict1d(RANSACRegressor)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\IntelPython35\lib\site-packages\nose\case.py", line 198, in runTest
    self.test(*self.arg)
  File "C:\IntelPython35\lib\site-packages\sklearn\utils\testing.py", line 830, in __call__
    return self.check(*args, **kwargs)
  File "C:\IntelPython35\lib\site-packages\sklearn\utils\estimator_checks.py", line 479, in check_fit2d_predict1d
    getattr(estimator, method), X[0])
  File "C:\IntelPython35\lib\site-packages\sklearn\utils\testing.py", line 186, in assert_warns
    result = func(*args, **kw)
  File "C:\IntelPython35\lib\site-packages\sklearn\linear_model\ransac.py", line 428, in predict
    return self.estimator_.predict(X)
  File "C:\IntelPython35\lib\site-packages\sklearn\daal4sklearn\linear.py", line 155, in predict
    return daal_predict(self, X)
  File "C:\IntelPython35\lib\site-packages\sklearn\daal4sklearn\linear.py", line 55, in daal_predict
    predictionResult = algorithm.compute()
  File "C:\IntelPython35\lib\site-packages\daal\algorithms\linear_regression\prediction.py", line 252, in compute
    return _prediction5.Batch_Float64DefaultDense_compute(self)
SystemError: Number of columns in numeric table is incorrect
Details:
Argument name: beta


======================================================================
ERROR: C:\IntelPython35\lib\site-packages\sklearn\tests\test_common.py.test_non_meta_estimators:check_estimators_pickle(Ridge)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\IntelPython35\lib\site-packages\nose\case.py", line 198, in runTest
    self.test(*self.arg)
  File "C:\IntelPython35\lib\site-packages\sklearn\utils\testing.py", line 830, in __call__
    return self.check(*args, **kwargs)
  File "C:\IntelPython35\lib\site-packages\sklearn\utils\testing.py", line 355, in wrapper
    return fn(*args, **kwargs)
  File "C:\IntelPython35\lib\site-packages\sklearn\utils\estimator_checks.py", line 889, in check_estimators_pickle
    pickled_estimator = pickle.dumps(estimator)
TypeError: can't pickle SwigPyObject objects

======================================================================
FAIL: sklearn.decomposition.tests.test_fastica.test_fastica_simple
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\IntelPython35\lib\site-packages\nose\case.py", line 198, in runTest
    self.test(*self.arg)
  File "C:\IntelPython35\lib\site-packages\sklearn\decomposition\tests\test_fastica.py", line 110, in test_fastica_simple
    assert_almost_equal(np.dot(s1_, s1) / n_samples, 1, decimal=2)
  File "C:\Users\gaston\AppData\Roaming\Python\Python35\site-packages\numpy\testing\utils.py", line 589, in assert_almost_equal
    raise AssertionError(_build_err_msg())
AssertionError:
Arrays are not almost equal to 2 decimals
 ACTUAL: 0.0065581346415391354
 DESIRED: 1

======================================================================
FAIL: sklearn.linear_model.tests.test_ridge.test_dense_sparse(<function _test_ridge_loo at 0x0000022A0B59FC80>,)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\IntelPython35\lib\site-packages\nose\case.py", line 198, in runTest
    self.test(*self.arg)
  File "C:\IntelPython35\lib\site-packages\sklearn\linear_model\tests\test_ridge.py", line 454, in check_dense_sparse
    ret_dense = test_func(DENSE_FILTER)
  File "C:\IntelPython35\lib\site-packages\sklearn\linear_model\tests\test_ridge.py", line 329, in _test_ridge_loo
    assert_almost_equal(errors, errors2)
  File "C:\Users\gaston\AppData\Roaming\Python\Python35\site-packages\numpy\testing\utils.py", line 573, in assert_almost_equal
    return assert_array_almost_equal(actual, desired, decimal, err_msg)
  File "C:\Users\gaston\AppData\Roaming\Python\Python35\site-packages\numpy\testing\utils.py", line 979, in assert_array_almost_equal
    precision=decimal)
  File "C:\Users\gaston\AppData\Roaming\Python\Python35\site-packages\numpy\testing\utils.py", line 796, in assert_array_compare
    raise AssertionError(msg)
AssertionError:
Arrays are not almost equal to 7 decimals

(mismatch 100.0%)
 x: array([  1.8295168e+04,   5.6704414e+02,   1.1782287e+03,   5.2810053e+03,
         3.8443835e+02,   8.0439421e+03,   2.3982367e+03,   3.3362619e+03,
         1.1783452e+01,   5.1091672e+03,   1.3139571e+04,   1.5552388e+01,...
 y: array([  1.8505354e+04,   6.0452467e+02,   1.1257434e+03,   5.1693552e+03,
         4.1556914e+02,   8.1834151e+03,   2.4742189e+03,   3.4259267e+03,
         1.7722638e+01,   4.9988370e+03,   1.3318625e+04,   1.0045693e+01,...

======================================================================
FAIL: sklearn.model_selection.tests.test_validation.test_cross_val_score_with_score_func_regression
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\IntelPython35\lib\site-packages\nose\case.py", line 198, in runTest
    self.test(*self.arg)
  File "C:\IntelPython35\lib\site-packages\sklearn\model_selection\tests\test_validation.py", line 418, in test_cross_val_score_with_score_func_regression
    assert_array_almost_equal(neg_mse_scores, expected_neg_mse, 2)
  File "C:\Users\gaston\AppData\Roaming\Python\Python35\site-packages\numpy\testing\utils.py", line 979, in assert_array_almost_equal
    precision=decimal)
  File "C:\Users\gaston\AppData\Roaming\Python\Python35\site-packages\numpy\testing\utils.py", line 796, in assert_array_compare
    raise AssertionError(msg)
AssertionError:
Arrays are not almost equal to 2 decimals

(mismatch 100.0%)
 x: array([ -760.52,  -514.85,  -265.83,  -275.24, -1647.  ])
 y: array([ -763.07,  -553.16,  -274.38,  -273.26, -1681.99])

======================================================================
FAIL: C:\IntelPython35\lib\site-packages\sklearn\tests\test_common.py.test_non_meta_estimators:check_estimators_nan_inf(LinearRegression)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\IntelPython35\lib\site-packages\nose\case.py", line 198, in runTest
    self.test(*self.arg)
  File "C:\IntelPython35\lib\site-packages\sklearn\utils\testing.py", line 830, in __call__
    return self.check(*args, **kwargs)
  File "C:\IntelPython35\lib\site-packages\sklearn\utils\estimator_checks.py", line 840, in check_estimators_nan_inf
    raise AssertionError(error_string_predict, Estimator)
AssertionError: ("Estimator doesn't check for NaN and inf in predict.", <class 'sklearn.linear_model.base.LinearRegression'>)

======================================================================
FAIL: C:\IntelPython35\lib\site-packages\sklearn\tests\test_common.py.test_non_meta_estimators:check_estimators_nan_inf(RANSACRegressor)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\IntelPython35\lib\site-packages\nose\case.py", line 198, in runTest
    self.test(*self.arg)
  File "C:\IntelPython35\lib\site-packages\sklearn\utils\testing.py", line 830, in __call__
    return self.check(*args, **kwargs)
  File "C:\IntelPython35\lib\site-packages\sklearn\utils\estimator_checks.py", line 840, in check_estimators_nan_inf
    raise AssertionError(error_string_predict, Estimator)
AssertionError: ("Estimator doesn't check for NaN and inf in predict.", <class 'sklearn.linear_model.ransac.RANSACRegressor'>)

======================================================================
FAIL: sklearn.tests.test_cross_validation.test_cross_val_score_with_score_func_regression
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\IntelPython35\lib\site-packages\nose\case.py", line 198, in runTest
    self.test(*self.arg)
  File "C:\IntelPython35\lib\site-packages\sklearn\tests\test_cross_validation.py", line 918, in test_cross_val_score_with_score_func_regression
    assert_array_almost_equal(neg_mse_scores, expected_neg_mse, 2)
  File "C:\Users\gaston\AppData\Roaming\Python\Python35\site-packages\numpy\testing\utils.py", line 979, in assert_array_almost_equal
    precision=decimal)
  File "C:\Users\gaston\AppData\Roaming\Python\Python35\site-packages\numpy\testing\utils.py", line 796, in assert_array_compare
    raise AssertionError(msg)
AssertionError:
Arrays are not almost equal to 2 decimals

(mismatch 100.0%)
 x: array([ -760.52,  -514.85,  -265.83,  -275.24, -1647.  ])
 y: array([ -763.07,  -553.16,  -274.38,  -273.26, -1681.99])

----------------------------------------------------------------------
Ran 7159 tests in 250.648s

FAILED (SKIP=15, errors=6, failures=6)

 

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gaston-hillar
Valued Contributor I
‎04-19-2017 04:19 PM
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This thread continued in the following thread: https://software.intel.com/en-us/forums/intel-distribution-for-python/topic/731351

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