3205   /home/lesteve/dev/scikit-learn/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
    120   /home/lesteve/miniconda3/lib/python3.6/site-packages/scipy/special/_logsumexp.py:110: RuntimeWarning: underflow encountered in exp
    100   /home/lesteve/dev/scikit-learn/sklearn/preprocessing/tests/test_data.py:485: RuntimeWarning: underflow encountered in nextafter
     98   /home/lesteve/dev/scikit-learn/sklearn/gaussian_process/kernels.py:1206: RuntimeWarning: underflow encountered in exp
     66   /home/lesteve/dev/scikit-learn/sklearn/mixture/dpgmm.py:55: RuntimeWarning: underflow encountered in exp
     64   /home/lesteve/dev/scikit-learn/sklearn/utils/deprecation.py:77: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.
     59   /home/lesteve/dev/scikit-learn/sklearn/linear_model/stochastic_gradient.py:130: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.
     55   /home/lesteve/dev/scikit-learn/sklearn/utils/deprecation.py:77: DeprecationWarning: Function constant is deprecated; The function constant of regression_models is deprecated in version 0.19.1 and will be removed in 0.22.
     48   /home/lesteve/dev/scikit-learn/sklearn/utils/deprecation.py:77: DeprecationWarning: Function squared_exponential is deprecated; The function squared_exponential of correlation_models is deprecated in version 0.19.1 and will be removed in 0.22.
     39   /home/lesteve/dev/scikit-learn/sklearn/neural_network/multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
     37   /home/lesteve/dev/scikit-learn/sklearn/tree/tree.py:864: RuntimeWarning: divide by zero encountered in log
     36   /home/lesteve/dev/scikit-learn/sklearn/linear_model/base.py:340: RuntimeWarning: overflow encountered in exp
     32   /home/lesteve/dev/scikit-learn/sklearn/svm/classes.py:1089: DeprecationWarning: The random_state parameter is deprecated and will be removed in version 0.22.
     31   /home/lesteve/dev/scikit-learn/sklearn/utils/deprecation.py:58: DeprecationWarning: Class GaussianProcess is deprecated; GaussianProcess was deprecated in version 0.18 and will be removed in 0.20. Use the GaussianProcessRegressor instead.
     30   /home/lesteve/dev/scikit-learn/sklearn/random_projection.py:378: DataDimensionalityWarning: The number of components is higher than the number of features: n_features < n_components (3 < 32).The dimensionality of the problem will not be reduced.
     29   /home/lesteve/dev/scikit-learn/sklearn/utils/deprecation.py:77: DeprecationWarning: Function l1_cross_distances is deprecated; l1_cross_distances was deprecated in version 0.18 and will be removed in 0.20.
     28   /home/lesteve/dev/scikit-learn/sklearn/naive_bayes.py:104: RuntimeWarning: underflow encountered in exp
     28   /home/lesteve/dev/scikit-learn/sklearn/linear_model/coordinate_descent.py:1783: ConvergenceWarning: Objective did not converge, you might want to increase the number of iterations
     26   /home/lesteve/dev/scikit-learn/sklearn/metrics/pairwise.py:844: RuntimeWarning: underflow encountered in exp
     26   /home/lesteve/dev/scikit-learn/sklearn/gaussian_process/kernels.py:756: RuntimeWarning: underflow encountered in multiply
     25   /home/lesteve/dev/scikit-learn/sklearn/gaussian_process/kernels.py:1224: RuntimeWarning: underflow encountered in multiply
     24   /home/lesteve/dev/scikit-learn/sklearn/neighbors/tests/test_kde.py:38: RuntimeWarning: underflow encountered in exp
     24   /home/lesteve/dev/scikit-learn/sklearn/mixture/gmm.py:343: RuntimeWarning: underflow encountered in exp
     23   /home/lesteve/dev/scikit-learn/sklearn/gaussian_process/kernels.py:755: RuntimeWarning: underflow encountered in multiply
     22   /home/lesteve/dev/scikit-learn/sklearn/naive_bayes.py:468: RuntimeWarning: divide by zero encountered in log
     22   /home/lesteve/dev/scikit-learn/sklearn/linear_model/stochastic_gradient.py:907: RuntimeWarning: divide by zero encountered in log
     22   /home/lesteve/dev/scikit-learn/sklearn/gaussian_process/kernels.py:1216: RuntimeWarning: underflow encountered in exp
     20   /home/lesteve/dev/scikit-learn/sklearn/kernel_approximation.py:470: UserWarning: n_components > n_samples. This is not possible.
     16   /home/lesteve/dev/scikit-learn/sklearn/ensemble/bagging.py:750: RuntimeWarning: divide by zero encountered in log
     15   /home/lesteve/dev/scikit-learn/sklearn/random_projection.py:378: DataDimensionalityWarning: The number of components is higher than the number of features: n_features < n_components (2 < 32).The dimensionality of the problem will not be reduced.
     14   /home/lesteve/dev/scikit-learn/sklearn/linear_model/theil_sen.py:128: ConvergenceWarning: Maximum number of iterations 5 reached in spatial median for TheilSen regressor.
     14   /home/lesteve/dev/scikit-learn/sklearn/gaussian_process/gpc.py:398: RuntimeWarning: underflow encountered in multiply
     14   /home/lesteve/dev/scikit-learn/sklearn/decomposition/fastica_.py:118: UserWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.
     11   /home/lesteve/dev/scikit-learn/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True
     11   /home/lesteve/dev/scikit-learn/sklearn/gaussian_process/gpc.py:397: RuntimeWarning: underflow encountered in multiply
     10   /home/lesteve/dev/scikit-learn/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split1_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True
     10   /home/lesteve/dev/scikit-learn/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split0_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True
     10   /home/lesteve/dev/scikit-learn/sklearn/linear_model/sag.py:326: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
     10   /home/lesteve/dev/scikit-learn/sklearn/cross_decomposition/pls_.py:288: UserWarning: Y residual constant at iteration 1
      9   /home/lesteve/miniconda3/lib/python3.6/site-packages/numpy/core/numeric.py:2524: RuntimeWarning: underflow encountered in multiply
      9   /home/lesteve/dev/scikit-learn/sklearn/utils/deprecation.py:77: DeprecationWarning: Function linear is deprecated; The function linear of regression_models is deprecated in version 0.19.1 and will be removed in 0.22.
      9   /home/lesteve/dev/scikit-learn/sklearn/utils/deprecation.py:77: DeprecationWarning: Function absolute_exponential is deprecated; The function absolute_exponential of correlation_models is deprecated in version 0.19.1 and will be removed in 0.22.
      9   /home/lesteve/dev/scikit-learn/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True
      9   /home/lesteve/dev/scikit-learn/sklearn/tree/tree.py:868: RuntimeWarning: divide by zero encountered in log
      9   /home/lesteve/dev/scikit-learn/sklearn/cluster/birch.py:629: UserWarning: Number of subclusters found (2) by Birch is less than (3). Decrease the threshold.
      8   /home/lesteve/dev/scikit-learn/sklearn/neighbors/tests/test_kde.py:36: RuntimeWarning: underflow encountered in exp
      8   /home/lesteve/dev/scikit-learn/sklearn/gaussian_process/kernels.py:1230: RuntimeWarning: underflow encountered in multiply
      8   /home/lesteve/dev/scikit-learn/sklearn/gaussian_process/gpc.py:355: RuntimeWarning: underflow encountered in multiply
      7   /home/lesteve/dev/scikit-learn/sklearn/utils/deprecation.py:77: DeprecationWarning: Function quadratic is deprecated; The function quadratic of regression_models is deprecated in version 0.19.1 and will be removed in 0.22.
      7   /home/lesteve/dev/scikit-learn/sklearn/utils/deprecation.py:77: DeprecationWarning: Function linear is deprecated; The function linear of correlation_models is deprecated in version 0.19.1 and will be removed in 0.22.
      7   /home/lesteve/dev/scikit-learn/sklearn/utils/deprecation.py:77: DeprecationWarning: Function cubic is deprecated; The function cubic of correlation_models is deprecated in version 0.19.1 and will be removed in 0.22.
      7   /home/lesteve/dev/scikit-learn/sklearn/utils/deprecation.py:58: DeprecationWarning: Class RandomizedLasso is deprecated; The class RandomizedLasso is deprecated in 0.19 and will be removed in 0.21.
      7   /home/lesteve/dev/scikit-learn/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('train_r2'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True
      7   /home/lesteve/dev/scikit-learn/sklearn/semi_supervised/label_propagation.py:289: ConvergenceWarning: max_iter=5 was reached without convergence.
      7   /home/lesteve/dev/scikit-learn/sklearn/random_projection.py:378: DataDimensionalityWarning: The number of components is higher than the number of features: n_features < n_components (10 < 32).The dimensionality of the problem will not be reduced.
      7   /home/lesteve/dev/scikit-learn/sklearn/linear_model/omp.py:385: RuntimeWarning:  Orthogonal matching pursuit ended prematurely due to linear
      6   /home/lesteve/dev/scikit-learn/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split2_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True
      6   /home/lesteve/dev/scikit-learn/sklearn/tree/tree.py:282: DeprecationWarning: The min_impurity_split parameter is deprecated and will be removed in version 0.21. Use the min_impurity_decrease parameter instead.
      6   /home/lesteve/dev/scikit-learn/sklearn/manifold/spectral_embedding_.py:235: UserWarning: Graph is not fully connected, spectral embedding may not work as expected.
      6   /home/lesteve/dev/scikit-learn/sklearn/decomposition/online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.
      5   /home/lesteve/miniconda3/lib/python3.6/site-packages/scipy/misc/pilutil.py:165: DeprecationWarning: `fromimage` is deprecated!
      5   /home/lesteve/dev/scikit-learn/sklearn/random_projection.py:378: DataDimensionalityWarning: The number of components is higher than the number of features: n_features < n_components (4 < 32).The dimensionality of the problem will not be reduced.
      5   /home/lesteve/dev/scikit-learn/sklearn/model_selection/_validation.py:768: RuntimeWarning: Number of classes in training fold (8) does not match total number of classes (10). Results may not be appropriate for your use case. To fix this, use a cross-validation technique resulting in properly stratified folds
      4   /home/lesteve/miniconda3/lib/python3.6/site-packages/scipy/stats/kde.py:222: RuntimeWarning: underflow encountered in exp
      4   /home/lesteve/miniconda3/lib/python3.6/site-packages/numpy/lib/function_base.py:1142: RuntimeWarning: underflow encountered in multiply
      4   /home/lesteve/dev/scikit-learn/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('train_neg_mean_squared_error'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True
      4   /home/lesteve/dev/scikit-learn/sklearn/semi_supervised/label_propagation.py:403: RuntimeWarning: underflow encountered in true_divide
      4   /home/lesteve/dev/scikit-learn/sklearn/neighbors/nearest_centroid.py:141: UserWarning: Averaging for metrics other than euclidean and manhattan not supported. The average is set to be the mean.
      4   /home/lesteve/dev/scikit-learn/sklearn/gaussian_process/gpc.py:354: RuntimeWarning: underflow encountered in multiply
      4   /home/lesteve/dev/scikit-learn/sklearn/cluster/hierarchical.py:702: DeprecationWarning: Agglomerative "pooling_func" parameter is not used. It has been deprecated in version 0.20 and will beremoved in 0.22
      3   /home/lesteve/miniconda3/lib/python3.6/site-packages/scipy/sparse/csgraph/_laplacian.py:121: RuntimeWarning: underflow encountered in true_divide
      3   /home/lesteve/miniconda3/lib/python3.6/site-packages/scipy/misc/pilutil.py:380: DeprecationWarning: `bytescale` is deprecated!
      3   /home/lesteve/miniconda3/lib/python3.6/site-packages/numpy/testing/utils.py:761: DeprecationWarning: elementwise == comparison failed; this will raise an error in the future.
      3   /home/lesteve/miniconda3/lib/python3.6/site-packages/numpy/core/_methods.py:80: RuntimeWarning: invalid value encountered in double_scalars
      3   /home/lesteve/dev/scikit-learn/sklearn/model_selection/_validation.py:768: RuntimeWarning: Number of classes in training fold (7) does not match total number of classes (10). Results may not be appropriate for your use case. To fix this, use a cross-validation technique resulting in properly stratified folds
      3   /home/lesteve/dev/scikit-learn/sklearn/datasets/base.py:762: DeprecationWarning: `imread` is deprecated!
      2   /home/lesteve/miniconda3/lib/python3.6/site-packages/scipy/sparse/csgraph/_laplacian.py:122: RuntimeWarning: underflow encountered in true_divide
      2   /home/lesteve/miniconda3/lib/python3.6/site-packages/scipy/misc/pilutil.py:565: DeprecationWarning: `fromimage` is deprecated!
      2   /home/lesteve/miniconda3/lib/python3.6/site-packages/scipy/misc/pilutil.py:554: DeprecationWarning: `toimage` is deprecated!
      2   /home/lesteve/miniconda3/lib/python3.6/site-packages/numpy/lib/function_base.py:4016: RuntimeWarning: Invalid value encountered in median for 1 results
      2   /home/lesteve/miniconda3/lib/python3.6/site-packages/numpy/core/fromnumeric.py:2909: RuntimeWarning: Mean of empty slice.
      2   /home/lesteve/miniconda3/lib/python3.6/importlib/_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__
      2   /home/lesteve/dev/scikit-learn/sklearn/utils/validation.py:305: UserWarning: Can't check dok sparse matrix for nan or inf.
      2   /home/lesteve/dev/scikit-learn/sklearn/utils/deprecation.py:77: DeprecationWarning: Function distribute_covar_matrix_to_match_covariance_type is deprecated; The function distribute_covar_matrix_to_match_covariance_typeis deprecated in 0.18 and will be removed in 0.20.
      2   /home/lesteve/dev/scikit-learn/sklearn/utils/deprecation.py:77: DeprecationWarning: Function digamma is deprecated; The function digamma is deprecated in 0.18 and will be removed in 0.20. Use scipy.special.digamma instead.
      2   /home/lesteve/dev/scikit-learn/sklearn/utils/deprecation.py:58: DeprecationWarning: Class VBGMM is deprecated; The `VBGMM` class is not working correctly and it's better to use `sklearn.mixture.BayesianGaussianMixture` class with parameter `weight_concentration_prior_type='dirichlet_distribution'` instead. VBGMM is deprecated in 0.18 and will be removed in 0.20.
      2   /home/lesteve/dev/scikit-learn/sklearn/utils/deprecation.py:58: DeprecationWarning: Class RandomizedPCA is deprecated; RandomizedPCA was deprecated in 0.18 and will be removed in 0.20. Use PCA(svd_solver='randomized') instead. The new implementation DOES NOT store whiten ``components_``. Apply transform to get them.
      2   /home/lesteve/dev/scikit-learn/sklearn/utils/deprecation.py:58: DeprecationWarning: Class RandomizedLogisticRegression is deprecated; The class RandomizedLogisticRegression is deprecated in 0.19 and will be removed in 0.21.
      2   /home/lesteve/dev/scikit-learn/sklearn/utils/deprecation.py:58: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.
      2   /home/lesteve/dev/scikit-learn/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True
      2   /home/lesteve/dev/scikit-learn/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split4_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True
      2   /home/lesteve/dev/scikit-learn/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split3_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True
      2   /home/lesteve/dev/scikit-learn/sklearn/semi_supervised/label_propagation.py:285: RuntimeWarning: underflow encountered in multiply
      2   /home/lesteve/dev/scikit-learn/sklearn/semi_supervised/label_propagation.py:278: RuntimeWarning: underflow encountered in true_divide
      2   /home/lesteve/dev/scikit-learn/sklearn/neighbors/lof.py:180: UserWarning: n_neighbors (20) is greater than the total number of samples (9). n_neighbors will be set to (n_samples - 1) for estimation.
      2   /home/lesteve/dev/scikit-learn/sklearn/neighbors/lof.py:180: UserWarning: n_neighbors (20) is greater than the total number of samples (10). n_neighbors will be set to (n_samples - 1) for estimation.
      2   /home/lesteve/dev/scikit-learn/sklearn/neighbors/approximate.py:220: DeprecationWarning: LSHForest has poor performance and has been deprecated in 0.19. It will be removed in version 0.21.
      2   /home/lesteve/dev/scikit-learn/sklearn/mixture/gaussian_mixture.py:667: RuntimeWarning: underflow encountered in exp
      2   /home/lesteve/dev/scikit-learn/sklearn/linear_model/stochastic_gradient.py:130: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDRegressor'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.
      2   /home/lesteve/dev/scikit-learn/sklearn/linear_model/stochastic_gradient.py:130: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.passive_aggressive.PassiveAggressiveRegressor'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.
      2   /home/lesteve/dev/scikit-learn/sklearn/linear_model/stochastic_gradient.py:130: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.passive_aggressive.PassiveAggressiveClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.
      2   /home/lesteve/dev/scikit-learn/sklearn/linear_model/base.py:340: RuntimeWarning: underflow encountered in exp
      2   /home/lesteve/dev/scikit-learn/sklearn/gaussian_process/kernels.py:758: RuntimeWarning: underflow encountered in multiply
      2   /home/lesteve/dev/scikit-learn/sklearn/ensemble/voting_classifier.py:302: DeprecationWarning: 'flatten_transform' default value will be changed to True in 0.21.To silence this warning you may explicitly set flatten_transform=False.
      2   /home/lesteve/dev/scikit-learn/sklearn/discriminant_analysis.py:683: UserWarning: Variables are collinear
      2   /home/lesteve/dev/scikit-learn/sklearn/decomposition/factor_analysis.py:228: ConvergenceWarning: FactorAnalysis did not converge. You might want to increase the number of iterations.
      2   /home/lesteve/dev/scikit-learn/sklearn/datasets/samples_generator.py:1050: RuntimeWarning: underflow encountered in exp
      2   /home/lesteve/dev/scikit-learn/sklearn/datasets/lfw.py:193: DeprecationWarning: `imresize` is deprecated!
      2   /home/lesteve/dev/scikit-learn/sklearn/datasets/lfw.py:184: DeprecationWarning: `imread` is deprecated!
      2   /home/lesteve/dev/scikit-learn/sklearn/cluster/birch.py:629: UserWarning: Number of subclusters found (1) by Birch is less than (3). Decrease the threshold.
      1   /home/lesteve/miniconda3/lib/python3.6/site-packages/scipy/misc/pilutil.py:217: DeprecationWarning: `toimage` is deprecated!
      1   /home/lesteve/miniconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
      1   /home/lesteve/dev/scikit-learn/sklearn/utils/validation.py:508: DataConversionWarning: Data with input dtype float64 was converted to bool by check_pairwise_arrays.
      1   /home/lesteve/dev/scikit-learn/sklearn/utils/validation.py:401: DeprecationWarning: Passing 'None' to parameter 'accept_sparse' in methods check_array and check_X_y is deprecated in version 0.19 and will be removed in 0.21. Use 'accept_sparse=False'  instead.
      1   /home/lesteve/dev/scikit-learn/sklearn/utils/sparsefuncs.py:421: RuntimeWarning: invalid value encountered in less
      1   /home/lesteve/dev/scikit-learn/sklearn/utils/random.py:191: RuntimeWarning: invalid value encountered in true_divide
      1   /home/lesteve/dev/scikit-learn/sklearn/utils/deprecation.py:77: DeprecationWarning: Function logsumexp is deprecated; sklearn.utils.extmath.logsumexp was deprecated in version 0.19 and will be removed in 0.21. Use scipy.misc.logsumexp instead.
      1   /home/lesteve/dev/scikit-learn/sklearn/utils/deprecation.py:77: DeprecationWarning: Function log_normalize is deprecated; The function log_normalize is deprecated in 0.18 and will be removed in 0.20.
      1   /home/lesteve/dev/scikit-learn/sklearn/utils/deprecation.py:77: DeprecationWarning: Function grid_scores is deprecated; Attribute grid_scores was deprecated in version 0.19 and will be removed in 0.21. Use ``grid_scores_`` instead
      1   /home/lesteve/dev/scikit-learn/sklearn/utils/deprecation.py:77: DeprecationWarning: Function gammaln is deprecated; The function gammaln is deprecated in 0.18 and will be removed in 0.20. Use scipy.special.gammaln instead.
      1 /home/lesteve/dev/scikit-learn/sklearn/utils/deprecation.py:58: DeprecationWarning: Class VBGMM is deprecated; The `VBGMM` class is not working correctly and it's better to use `sklearn.mixture.BayesianGaussianMixture` class with parameter `weight_concentration_prior_type='dirichlet_distribution'` instead. VBGMM is deprecated in 0.18 and will be removed in 0.20.
      1 /home/lesteve/dev/scikit-learn/sklearn/utils/deprecation.py:58: DeprecationWarning: Class RandomizedPCA is deprecated; RandomizedPCA was deprecated in 0.18 and will be removed in 0.20. Use PCA(svd_solver='randomized') instead. The new implementation DOES NOT store whiten ``components_``. Apply transform to get them.
      1 /home/lesteve/dev/scikit-learn/sklearn/utils/deprecation.py:58: DeprecationWarning: Class RandomizedLogisticRegression is deprecated; The class RandomizedLogisticRegression is deprecated in 0.19 and will be removed in 0.21.
      1 /home/lesteve/dev/scikit-learn/sklearn/utils/deprecation.py:58: DeprecationWarning: Class RandomizedLasso is deprecated; The class RandomizedLasso is deprecated in 0.19 and will be removed in 0.21.
      1 /home/lesteve/dev/scikit-learn/sklearn/utils/deprecation.py:58: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.
      1 /home/lesteve/dev/scikit-learn/sklearn/utils/deprecation.py:58: DeprecationWarning: Class GaussianProcess is deprecated; GaussianProcess was deprecated in version 0.18 and will be removed in 0.20. Use the GaussianProcessRegressor instead.
      1 /home/lesteve/dev/scikit-learn/sklearn/utils/deprecation.py:58: DeprecationWarning: Class DPGMM is deprecated; The `DPGMM` class is not working correctly and it's better to use `sklearn.mixture.BayesianGaussianMixture` class with parameter `weight_concentration_prior_type='dirichlet_process'` instead. DPGMM is deprecated in 0.18 and will be removed in 0.20.
      1   /home/lesteve/dev/scikit-learn/sklearn/utils/deprecation.py:58: DeprecationWarning: Class DPGMM is deprecated; The `DPGMM` class is not working correctly and it's better to use `sklearn.mixture.BayesianGaussianMixture` class with parameter `weight_concentration_prior_type='dirichlet_process'` instead. DPGMM is deprecated in 0.18 and will be removed in 0.20.
      1 /home/lesteve/dev/scikit-learn/sklearn/neighbors/tests/test_kd_tree.py:98: RuntimeWarning: underflow encountered in exp
      1   /home/lesteve/dev/scikit-learn/sklearn/neighbors/tests/test_kd_tree.py:153: RuntimeWarning: underflow encountered in exp
      1 /home/lesteve/dev/scikit-learn/sklearn/neighbors/tests/test_kde.py:17: RuntimeWarning: underflow encountered in multiply
      1 /home/lesteve/dev/scikit-learn/sklearn/neighbors/tests/test_kde.py:17: RuntimeWarning: underflow encountered in exp
      1   /home/lesteve/dev/scikit-learn/sklearn/neighbors/tests/test_ball_tree.py:201: RuntimeWarning: underflow encountered in exp
      1 /home/lesteve/dev/scikit-learn/sklearn/neighbors/tests/test_ball_tree.py:145: RuntimeWarning: underflow encountered in exp
      1   /home/lesteve/dev/scikit-learn/sklearn/neighbors/lof.py:180: UserWarning: n_neighbors (500) is greater than the total number of samples (150). n_neighbors will be set to (n_samples - 1) for estimation.
      1   /home/lesteve/dev/scikit-learn/sklearn/neighbors/approximate.py:291: UserWarning: Number of candidates is not sufficient to retrieve 5 neighbors with min_hash_match = 31. Candidates are filled up uniformly from unselected indices.
      1   /home/lesteve/dev/scikit-learn/sklearn/neighbors/approximate.py:291: UserWarning: Number of candidates is not sufficient to retrieve 3 neighbors with min_hash_match = 32. Candidates are filled up uniformly from unselected indices.
      1 /home/lesteve/dev/scikit-learn/sklearn/neighbors/approximate.py:220: DeprecationWarning: LSHForest has poor performance and has been deprecated in 0.19. It will be removed in version 0.21.
      1   /home/lesteve/dev/scikit-learn/sklearn/model_selection/_validation.py:768: RuntimeWarning: Number of classes in training fold (1) does not match total number of classes (2). Results may not be appropriate for your use case. To fix this, use a cross-validation technique resulting in properly stratified folds
      1   /home/lesteve/dev/scikit-learn/sklearn/model_selection/_split.py:2026: FutureWarning: From version 0.21, test_size will always complement train_size unless both are specified.
      1   /home/lesteve/dev/scikit-learn/sklearn/model_selection/_search.py:588: RuntimeWarning: Ignoring fit_params passed as a constructor argument in favor of keyword arguments to the "fit" method.
      1   /home/lesteve/dev/scikit-learn/sklearn/model_selection/_search.py:584: DeprecationWarning: "fit_params" as a constructor argument was deprecated in version 0.19 and will be removed in version 0.21. Pass fit parameters to the "fit" method instead.
      1   /home/lesteve/dev/scikit-learn/sklearn/mixture/gaussian_mixture.py:167: RuntimeWarning: underflow encountered in multiply
      1   /home/lesteve/dev/scikit-learn/sklearn/metrics/ranking.py:102: DeprecationWarning: The 'reorder' parameter has been deprecated in version 0.20 and will be removed in 0.22. It is recommended not to set 'reorder' and ensure that x is monotonic increasing or monotonic decreasing.
      1   /home/lesteve/dev/scikit-learn/sklearn/metrics/classification.py:1139: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
      1   /home/lesteve/dev/scikit-learn/sklearn/linear_model/stochastic_gradient.py:130: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.perceptron.Perceptron'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.
      1   /home/lesteve/dev/scikit-learn/sklearn/linear_model/ridge.py:319: UserWarning: In Ridge, only 'sag' solver can currently fit the intercept when X is sparse. Solver has been automatically changed into 'sag'.
      1   /home/lesteve/dev/scikit-learn/sklearn/linear_model/omp.py:374: RuntimeWarning:  Orthogonal matching pursuit ended prematurely due to linear
      1   /home/lesteve/dev/scikit-learn/sklearn/gaussian_process/kernels.py:1362: RuntimeWarning: invalid value encountered in true_divide
      1   /home/lesteve/dev/scikit-learn/sklearn/gaussian_process/correlation_models.py:102: RuntimeWarning: underflow encountered in exp
      1   /home/lesteve/dev/scikit-learn/sklearn/discriminant_analysis.py:402: RuntimeWarning: invalid value encountered in true_divide
      1   /home/lesteve/dev/scikit-learn/sklearn/decomposition/tests/test_online_lda.py:375: RuntimeWarning: underflow encountered in exp
      1   /home/lesteve/dev/scikit-learn/sklearn/decomposition/online_lda.py:780: RuntimeWarning: divide by zero encountered in double_scalars
      1   /home/lesteve/dev/scikit-learn/sklearn/decomposition/nmf.py:788: RuntimeWarning: underflow encountered in multiply
      1   /home/lesteve/dev/scikit-learn/sklearn/decomposition/nmf.py:778: RuntimeWarning: underflow encountered in multiply
      1   /home/lesteve/dev/scikit-learn/sklearn/decomposition/nmf.py:165: RuntimeWarning: underflow encountered in multiply
      1   /home/lesteve/dev/scikit-learn/sklearn/decomposition/incremental_pca.py:279: RuntimeWarning: Mean of empty slice.
      1   /home/lesteve/dev/scikit-learn/sklearn/datasets/tests/test_lfw.py:73: DeprecationWarning: `imsave` is deprecated!
      1   /home/lesteve/dev/scikit-learn/sklearn/cross_validation.py:1686: FitFailedWarning: Classifier fit failed. The score on this train-test partition for these parameters will be set to 0.000000. Details: 
      1   /home/lesteve/dev/scikit-learn/sklearn/covariance/robust_covariance.py:622: UserWarning: The covariance matrix associated to your dataset is not full rank
      1   /home/lesteve/dev/scikit-learn/sklearn/covariance/graph_lasso_.py:261: ConvergenceWarning: graph_lasso: did not converge after 5 iteration: dual gap: -5.139e-04
      1   /home/lesteve/dev/scikit-learn/sklearn/covariance/graph_lasso_.py:261: ConvergenceWarning: graph_lasso: did not converge after 5 iteration: dual gap: -4.624e-01
      1   /home/lesteve/dev/scikit-learn/sklearn/covariance/graph_lasso_.py:261: ConvergenceWarning: graph_lasso: did not converge after 5 iteration: dual gap: -4.620e-01
      1   /home/lesteve/dev/scikit-learn/sklearn/covariance/graph_lasso_.py:261: ConvergenceWarning: graph_lasso: did not converge after 5 iteration: dual gap: 2.346e+00
      1   /home/lesteve/dev/scikit-learn/sklearn/cluster/hierarchical.py:426: UserWarning: the number of connected components of the connectivity matrix is 2 > 1. Completing it to avoid stopping the tree early.
      1   /home/lesteve/dev/scikit-learn/sklearn/cluster/dbscan_.py:141: SyntaxWarning: Parameter p is found in metric_params. The corresponding parameter from __init__ is ignored.
      1   /home/lesteve/dev/scikit-learn/sklearn/cluster/affinity_propagation_.py:391: UserWarning: This model does not have any cluster centers because affinity propagation did not converge. Labeling every sample as '-1'.
      1   /home/lesteve/dev/scikit-learn/sklearn/cluster/affinity_propagation_.py:224: ConvergenceWarning: Affinity propagation did not converge, this model will not have any cluster centers.