output.txt

/home/rth/src/scikit-learn/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.
  "This module will be removed in 0.20.", DeprecationWarning)
/home/rth/src/scikit-learn/sklearn/grid_search.py:42: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.
  DeprecationWarning)
/home/rth/src/scikit-learn/sklearn/learning_curve.py:22: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the functions are moved. This module will be removed in 0.20
  DeprecationWarning)
/home/rth/src/scikit-learn/sklearn/utils/validation.py:661: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
 *  ARDRegression.. fit [OK].. predict [fail] 
/home/rth/src/scikit-learn/sklearn/utils/validation.py:661: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
 *  AdaBoostClassifier.. fit [OK].. predict [OK] 
/home/rth/src/scikit-learn/sklearn/utils/validation.py:661: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
 *  AdaBoostRegressor.. fit [OK].. predict [OK] 
 *  AdditiveChi2Sampler.. fit [OK].. transform [fail] 
 *  AffinityPropagation.. fit [OK].. predict [OK] 
 *  AgglomerativeClustering.. fit [OK] 
/home/rth/src/scikit-learn/sklearn/utils/validation.py:661: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
 *  BaggingClassifier.. fit [OK].. predict [OK] 
/home/rth/src/scikit-learn/sklearn/utils/validation.py:661: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
 *  BaggingRegressor.. fit [OK].. predict [OK] 
 *  BayesianGaussianMixture.. fit [OK].. predict [OK] 
/home/rth/src/scikit-learn/sklearn/utils/validation.py:661: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
 *  BayesianRidge.. fit [OK].. predict [fail] 
/home/rth/src/scikit-learn/sklearn/utils/validation.py:661: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
 *  BernoulliNB.. fit [fail] .. predict [fail]
.. predict [fail] 
 *  BernoulliRBM.. fit [OK].. transform [OK] 
 *  Binarizer.. fit [OK].. transform [fail] 
 *  Birch.. fit [fail] .. predict [fail]
.. predict [fail].. transform [fail] 
 *  CCA.. fit [fail] .. predict [fail]
.. predict [fail].. transform [fail] 
/home/rth/src/scikit-learn/sklearn/utils/validation.py:661: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
 *  CalibratedClassifierCV.. fit [fail] .. predict [fail]
.. predict [fail] 
/home/rth/src/scikit-learn/sklearn/utils/validation.py:661: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
 *  ComplementNB.. fit [fail] .. predict [fail]
.. predict [fail] 
 *  DBSCAN.. fit [OK] 
/home/rth/src/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.
  warnings.warn(msg, category=DeprecationWarning)
/home/rth/src/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.
  warnings.warn(msg, category=DeprecationWarning)
/home/rth/src/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.
  warnings.warn(msg, category=DeprecationWarning)
/home/rth/src/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.
  warnings.warn(msg, category=DeprecationWarning)
/home/rth/src/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.
  warnings.warn(msg, category=DeprecationWarning)
/home/rth/src/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.
  warnings.warn(msg, category=DeprecationWarning)
 *  DPGMM.. fit [fail] .. predict [fail]
/home/rth/src/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.
  warnings.warn(msg, category=DeprecationWarning)
/home/rth/src/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.
  warnings.warn(msg, category=DeprecationWarning)
/home/rth/src/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.
  warnings.warn(msg, category=DeprecationWarning)
/home/rth/src/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.
  warnings.warn(msg, category=DeprecationWarning)
.. predict [fail] 
 *  DecisionTreeClassifier.. fit [OK].. predict [OK] 
 *  DecisionTreeRegressor.. fit [OK].. predict [OK] 
 *  DictionaryLearning.. fit [fail] .. predict [fail]
.. transform [fail] 
/home/rth/src/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.
  ConvergenceWarning)
 *  ElasticNet.. fit [OK].. predict [fail] 
/home/rth/src/scikit-learn/sklearn/linear_model/coordinate_descent.py:1096: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
 *  ElasticNetCV.. fit [OK].. predict [fail] 
 *  EmpiricalCovariance.. fit [fail] .. predict [fail]
 
 *  ExtraTreeClassifier.. fit [OK].. predict [OK] 
 *  ExtraTreeRegressor.. fit [OK].. predict [OK] 
/tmp/test.py:35: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  model.fit(X_train, y_train)
 *  ExtraTreesClassifier.. fit [OK].. predict [OK] 
/tmp/test.py:35: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  model.fit(X_train, y_train)
 *  ExtraTreesRegressor.. fit [OK].. predict [OK] 
 *  FactorAnalysis.. fit [OK].. transform [fail] 
 *  FastICA.. fit [OK].. transform [OK] 
 *  FeatureAgglomeration.. fit [fail] .. predict [fail]
.. transform [fail] 
 *  FunctionTransformer.. fit [OK].. transform [OK] 
/home/rth/src/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.
  warnings.warn(msg, category=DeprecationWarning)
/home/rth/src/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.
  warnings.warn(msg, category=DeprecationWarning)
/home/rth/src/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.
  warnings.warn(msg, category=DeprecationWarning)
/home/rth/src/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.
  warnings.warn(msg, category=DeprecationWarning)
/home/rth/src/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.
  warnings.warn(msg, category=DeprecationWarning)
/home/rth/src/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.
  warnings.warn(msg, category=DeprecationWarning)
 *  GMM.. fit [OK].. predict [fail] 
 *  GaussianMixture.. fit [OK].. predict [OK] 
/home/rth/src/scikit-learn/sklearn/utils/validation.py:661: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
 *  GaussianNB.. fit [fail] .. predict [fail]
.. predict [fail] 
/home/rth/src/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.
  warnings.warn(msg, category=DeprecationWarning)
 *  GaussianProcess.. fit [fail] .. predict [fail]
.. predict [fail] 
/home/rth/src/scikit-learn/sklearn/utils/validation.py:661: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
 *  GaussianProcessClassifier.. fit [fail] .. predict [fail]
.. predict [fail] 
 *  GaussianProcessRegressor.. fit [fail] .. predict [fail]
.. predict [fail] 
 *  GaussianRandomProjection.. fit [fail] .. predict [fail]
.. transform [fail] 
/home/rth/src/scikit-learn/sklearn/utils/validation.py:661: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
/home/rth/src/scikit-learn/sklearn/feature_selection/base.py:80: UserWarning: No features were selected: either the data is too noisy or the selection test too strict.
  UserWarning)
 *  GenericUnivariateSelect.. fit [OK].. transform [OK] 
/home/rth/src/scikit-learn/sklearn/utils/validation.py:661: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
 *  GradientBoostingClassifier.. fit [OK].. predict [OK] 
/home/rth/src/scikit-learn/sklearn/utils/validation.py:661: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
 *  GradientBoostingRegressor.. fit [OK].. predict [OK] 
 *  GraphLasso.. fit [fail] .. predict [fail]
 
 *  GraphLassoCV.. fit [fail] .. predict [fail]
 
/home/rth/src/scikit-learn/sklearn/utils/validation.py:661: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
 *  HuberRegressor.. fit [fail] .. predict [fail]
.. predict [fail] 
 *  Imputer.. fit [OK].. transform [OK] 
 *  IncrementalPCA.. fit [OK].. transform [fail] 
 *  IsolationForest.. fit [OK].. predict [OK] 
 *  Isomap.. fit [OK].. transform [OK] 
 *  KMeans.. fit [fail] .. predict [fail]
.. predict [fail].. transform [fail] 
/tmp/test.py:35: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  model.fit(X_train, y_train)
 *  KNeighborsClassifier.. fit [OK].. predict [OK] 
 *  KNeighborsRegressor.. fit [OK].. predict [OK] 
 *  KernelCenterer.. fit [OK].. transform [OK] 
 *  KernelDensity.. fit [OK] 
 *  KernelPCA.. fit [OK].. transform [OK] 
/home/rth/.miniconda3/envs/sklearn-env/lib/python3.6/site-packages/scipy/linalg/basic.py:223: RuntimeWarning: scipy.linalg.solve
Ill-conditioned matrix detected. Result is not guaranteed to be accurate.
Reciprocal condition number: 5.375757022533974e-18
  ' condition number: {}'.format(rcond), RuntimeWarning)
 *  KernelRidge.. fit [OK].. predict [OK] 
/home/rth/src/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.
  DeprecationWarning)
/home/rth/src/scikit-learn/sklearn/random_projection.py:379: DataDimensionalityWarning: The number of components is higher than the number of features: n_features < n_components (1 < 32).The dimensionality of the problem will not be reduced.
  DataDimensionalityWarning)
 *  LSHForest.. fit [fail] .. predict [fail]
 
/home/rth/src/scikit-learn/sklearn/utils/validation.py:661: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
/home/rth/src/scikit-learn/sklearn/semi_supervised/label_propagation.py:203: RuntimeWarning: invalid value encountered in true_divide
  probabilities /= normalizer
 *  LabelPropagation.. fit [OK].. predict [OK] 
/home/rth/src/scikit-learn/sklearn/utils/validation.py:661: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
/home/rth/src/scikit-learn/sklearn/semi_supervised/label_propagation.py:203: RuntimeWarning: invalid value encountered in true_divide
  probabilities /= normalizer
 *  LabelSpreading.. fit [OK].. predict [OK] 
 *  Lars.. fit [OK].. predict [fail] 
/home/rth/src/scikit-learn/sklearn/utils/validation.py:661: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
 *  LarsCV.. fit [OK].. predict [fail] 
/home/rth/src/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.
  ConvergenceWarning)
 *  Lasso.. fit [OK].. predict [fail] 
/home/rth/src/scikit-learn/sklearn/linear_model/coordinate_descent.py:1096: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
 *  LassoCV.. fit [OK].. predict [fail] 
 *  LassoLars.. fit [OK].. predict [fail] 
/home/rth/src/scikit-learn/sklearn/utils/validation.py:661: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
 *  LassoLarsCV.. fit [OK].. predict [fail] 
/home/rth/src/scikit-learn/sklearn/utils/validation.py:661: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
 *  LassoLarsIC.. fit [OK].. predict [fail] 
 *  LatentDirichletAllocation.. fit [fail] .. predict [fail]
.. transform [fail] 
 *  LedoitWolf.. fit [fail] .. predict [fail]
 
/home/rth/src/scikit-learn/sklearn/utils/validation.py:661: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
/home/rth/src/scikit-learn/sklearn/discriminant_analysis.py:442: UserWarning: The priors do not sum to 1. Renormalizing
  UserWarning)
 *  LinearDiscriminantAnalysis.. fit [fail] .. predict [fail]
.. predict [fail].. transform [fail] 
 *  LinearRegression.. fit [OK].. predict [fail] 
/home/rth/src/scikit-learn/sklearn/utils/validation.py:661: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
 *  LinearSVC.. fit [OK].. predict [fail] 
/home/rth/src/scikit-learn/sklearn/utils/validation.py:661: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
 *  LinearSVR.. fit [OK].. predict [fail] 
/home/rth/src/scikit-learn/sklearn/neighbors/lof.py:180: UserWarning: n_neighbors (20) is greater than the total number of samples (6). n_neighbors will be set to (n_samples - 1) for estimation.
  % (self.n_neighbors, n_samples))
 *  LocalOutlierFactor.. fit [OK] 
 *  LocallyLinearEmbedding.. fit [fail] .. predict [fail]
.. transform [fail] 
/home/rth/src/scikit-learn/sklearn/utils/validation.py:661: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
 *  LogisticRegression.. fit [OK].. predict [fail] 
/home/rth/src/scikit-learn/sklearn/utils/validation.py:661: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
/home/rth/src/scikit-learn/sklearn/model_selection/_split.py:605: Warning: The least populated class in y has only 1 members, which is too few. The minimum number of members in any class cannot be less than n_splits=3.
  % (min_groups, self.n_splits)), Warning)
 *  LogisticRegressionCV.. fit [OK].. predict [fail] 
 *  MDS.. fit [OK] 
/home/rth/src/scikit-learn/sklearn/neural_network/multilayer_perceptron.py:926: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
 *  MLPClassifier.. fit [fail] .. predict [fail]
.. predict [fail] 
/home/rth/src/scikit-learn/sklearn/neural_network/multilayer_perceptron.py:1327: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
 *  MLPRegressor.. fit [fail] .. predict [fail]
.. predict [fail] 
 *  MaxAbsScaler.. fit [OK].. transform [OK] 
 *  MeanShift.. fit [fail] .. predict [fail]
.. predict [fail] 
 *  MinCovDet.. fit [fail] .. predict [fail]
 
 *  MinMaxScaler.. fit [OK].. transform [OK] 
 *  MiniBatchDictionaryLearning.. fit [fail] .. predict [fail]
.. transform [fail] 
 *  MiniBatchKMeans.. fit [fail] .. predict [fail]
.. predict [fail].. transform [fail] 
 *  MiniBatchSparsePCA.. fit [fail] .. predict [fail]
.. transform [fail] 
/home/rth/src/scikit-learn/sklearn/linear_model/coordinate_descent.py:1783: ConvergenceWarning: Objective did not converge, you might want to increase the number of iterations
  ConvergenceWarning)
 *  MultiTaskElasticNet.. fit [OK].. predict [fail] 
 *  MultiTaskElasticNetCV.. fit [OK].. predict [fail] 
/home/rth/src/scikit-learn/sklearn/linear_model/coordinate_descent.py:1783: ConvergenceWarning: Objective did not converge, you might want to increase the number of iterations
  ConvergenceWarning)
 *  MultiTaskLasso.. fit [OK].. predict [fail] 
 *  MultiTaskLassoCV.. fit [OK].. predict [fail] 
/home/rth/src/scikit-learn/sklearn/utils/validation.py:661: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
 *  MultinomialNB.. fit [fail] .. predict [fail]
.. predict [fail] 
 *  NMF.. fit [OK].. transform [OK] 
/home/rth/src/scikit-learn/sklearn/utils/validation.py:661: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
 *  NearestCentroid.. fit [fail] .. predict [fail]
.. predict [OK] 
 *  NearestNeighbors.. fit [OK] 
 *  Normalizer.. fit [OK].. transform [OK] 
/home/rth/src/scikit-learn/sklearn/utils/validation.py:661: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
 *  NuSVC.. fit [OK].. predict [OK] 
/home/rth/src/scikit-learn/sklearn/utils/validation.py:661: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
 *  NuSVR.. fit [OK].. predict [OK] 
/home/rth/src/scikit-learn/sklearn/kernel_approximation.py:470: UserWarning: n_components > n_samples. This is not possible.
n_components was set to n_samples, which results in inefficient evaluation of the full kernel.
  warnings.warn("n_components > n_samples. This is not possible.\n"
 *  Nystroem.. fit [OK].. transform [OK] 
 *  OAS.. fit [fail] .. predict [fail]
 
 *  OneClassSVM.. fit [OK].. predict [OK] 
 *  OrthogonalMatchingPursuit.. fit [OK].. predict [fail] 
 *  OrthogonalMatchingPursuitCV.. fit [fail] .. predict [fail]
.. predict [fail] 
 *  PCA.. fit [OK].. transform [fail] 
 *  PLSCanonical.. fit [fail] .. predict [fail]
.. predict [fail].. transform [fail] 
 *  PLSRegression.. fit [fail] .. predict [fail]
.. predict [fail].. transform [fail] 
 *  PLSSVD.. fit [fail] .. predict [fail]
.. transform [fail] 
/home/rth/src/scikit-learn/sklearn/linear_model/stochastic_gradient.py:131: FutureWarning: max_iter and tol parameters have been added in 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.
  FutureWarning)
/home/rth/src/scikit-learn/sklearn/utils/validation.py:661: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
 *  PassiveAggressiveClassifier.. fit [OK].. predict [fail] 
/home/rth/src/scikit-learn/sklearn/linear_model/stochastic_gradient.py:131: FutureWarning: max_iter and tol parameters have been added in 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.
  FutureWarning)
/home/rth/src/scikit-learn/sklearn/utils/validation.py:661: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
 *  PassiveAggressiveRegressor.. fit [OK].. predict [fail] 
/home/rth/src/scikit-learn/sklearn/linear_model/stochastic_gradient.py:131: FutureWarning: max_iter and tol parameters have been added in 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.
  FutureWarning)
/home/rth/src/scikit-learn/sklearn/utils/validation.py:661: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
 *  Perceptron.. fit [OK].. predict [fail] 
/home/rth/.miniconda3/envs/sklearn-env/lib/python3.6/site-packages/scipy/stats/morestats.py:901: RuntimeWarning: divide by zero encountered in log
  llf -= N / 2.0 * np.log(np.sum((y - y_mean)**2. / N, axis=0))
/home/rth/.miniconda3/envs/sklearn-env/lib/python3.6/site-packages/scipy/optimize/optimize.py:1849: RuntimeWarning: invalid value encountered in double_scalars
  tmp1 = (x - w) * (fx - fv)
/home/rth/.miniconda3/envs/sklearn-env/lib/python3.6/site-packages/scipy/optimize/optimize.py:1850: RuntimeWarning: invalid value encountered in double_scalars
  tmp2 = (x - v) * (fx - fw)
 *  PowerTransformer.. fit [OK].. transform [OK] 
/home/rth/src/scikit-learn/sklearn/utils/validation.py:661: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
 *  QuadraticDiscriminantAnalysis.. fit [fail] .. predict [fail]
.. predict [fail] 
 *  QuantileTransformer.. fit [OK].. transform [OK] 
 *  RANSACRegressor.. fit [fail] .. predict [fail]
.. predict [fail] 
 *  RBFSampler.. fit [OK].. transform [fail] 
/tmp/test.py:35: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  model.fit(X_train, y_train)
 *  RadiusNeighborsClassifier.. fit [OK].. predict [fail] 
 *  RadiusNeighborsRegressor.. fit [OK].. predict [OK] 
/tmp/test.py:35: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  model.fit(X_train, y_train)
 *  RandomForestClassifier.. fit [OK].. predict [OK] 
/tmp/test.py:35: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  model.fit(X_train, y_train)
 *  RandomForestRegressor.. fit [OK].. predict [OK] 
/home/rth/src/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.
  warnings.warn(msg, category=DeprecationWarning)
/home/rth/src/scikit-learn/sklearn/utils/validation.py:661: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
/home/rth/src/scikit-learn/sklearn/feature_selection/base.py:80: UserWarning: No features were selected: either the data is too noisy or the selection test too strict.
  UserWarning)
 *  RandomizedLasso.. fit [OK].. transform [OK] 
/home/rth/src/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.
  warnings.warn(msg, category=DeprecationWarning)
/home/rth/src/scikit-learn/sklearn/utils/validation.py:661: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
/home/rth/src/scikit-learn/sklearn/feature_selection/base.py:80: UserWarning: No features were selected: either the data is too noisy or the selection test too strict.
  UserWarning)
 *  RandomizedLogisticRegression.. fit [OK].. transform [OK] 
/home/rth/src/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.
  warnings.warn(msg, category=DeprecationWarning)
 *  RandomizedPCA.. fit [OK].. transform [fail] 
 *  Ridge.. fit [OK].. predict [fail] 
 *  RidgeCV.. fit [OK].. predict [fail] 
/home/rth/src/scikit-learn/sklearn/linear_model/ridge.py:821: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
 *  RidgeClassifier.. fit [OK].. predict [fail] 
/home/rth/src/scikit-learn/sklearn/linear_model/ridge.py:1367: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
 *  RidgeClassifierCV.. fit [OK].. predict [fail] 
 *  RobustScaler.. fit [OK].. transform [OK] 
/home/rth/src/scikit-learn/sklearn/linear_model/stochastic_gradient.py:131: FutureWarning: max_iter and tol parameters have been added in 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.
  FutureWarning)
/home/rth/src/scikit-learn/sklearn/utils/validation.py:661: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
 *  SGDClassifier.. fit [OK].. predict [fail] 
/home/rth/src/scikit-learn/sklearn/linear_model/stochastic_gradient.py:131: FutureWarning: max_iter and tol parameters have been added in 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.
  FutureWarning)
/home/rth/src/scikit-learn/sklearn/utils/validation.py:661: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
 *  SGDRegressor.. fit [OK].. predict [fail] 
/home/rth/src/scikit-learn/sklearn/utils/validation.py:661: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
 *  SVC.. fit [OK].. predict [OK] 
/home/rth/src/scikit-learn/sklearn/utils/validation.py:661: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
 *  SVR.. fit [OK].. predict [OK] 
/home/rth/src/scikit-learn/sklearn/utils/validation.py:661: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
 *  SelectFdr.. fit [OK].. transform [OK] 
/home/rth/src/scikit-learn/sklearn/utils/validation.py:661: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
 *  SelectFpr.. fit [OK].. transform [OK] 
/home/rth/src/scikit-learn/sklearn/utils/validation.py:661: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
 *  SelectFwe.. fit [OK].. transform [OK] 
 *  SelectKBest.. fit [fail] .. predict [fail]
.. transform [fail] 
/home/rth/src/scikit-learn/sklearn/utils/validation.py:661: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
/home/rth/src/scikit-learn/sklearn/feature_selection/base.py:80: UserWarning: No features were selected: either the data is too noisy or the selection test too strict.
  UserWarning)
 *  SelectPercentile.. fit [OK].. transform [OK] 
 *  ShrunkCovariance.. fit [fail] .. predict [fail]
 
 *  SkewedChi2Sampler.. fit [OK].. transform [OK] 
 *  SparsePCA.. fit [fail] .. predict [fail]
.. transform [fail] 
 *  SparseRandomProjection.. fit [fail] .. predict [fail]
.. transform [fail] 
 *  SpectralBiclustering.. fit [fail] .. predict [fail]
 
/home/rth/src/scikit-learn/sklearn/manifold/spectral_embedding_.py:237: UserWarning: Graph is not fully connected, spectral embedding may not work as expected.
  warnings.warn("Graph is not fully connected, spectral embedding"
 *  SpectralClustering.. fit [fail] .. predict [fail]
 
 *  SpectralCoclustering.. fit [fail] .. predict [fail]
 
/home/rth/src/scikit-learn/sklearn/manifold/spectral_embedding_.py:237: UserWarning: Graph is not fully connected, spectral embedding may not work as expected.
  warnings.warn("Graph is not fully connected, spectral embedding"
 *  SpectralEmbedding.. fit [OK] 
 *  StandardScaler.. fit [OK].. transform [OK] 
 *  TSNE.. fit [OK] 
/home/rth/src/scikit-learn/sklearn/utils/validation.py:661: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
 *  TheilSenRegressor.. fit [OK].. predict [fail] 
 *  TransformedTargetRegressor.. fit [OK].. predict [fail] 
/home/rth/src/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.
  warnings.warn(msg, category=DeprecationWarning)
/home/rth/src/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.
  warnings.warn(msg, category=DeprecationWarning)
/home/rth/src/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.
  warnings.warn(msg, category=DeprecationWarning)
/home/rth/src/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.
  warnings.warn(msg, category=DeprecationWarning)
 *  VBGMM.. fit [fail] .. predict [fail]
/home/rth/src/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.
  warnings.warn(msg, category=DeprecationWarning)
/home/rth/src/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.
  warnings.warn(msg, category=DeprecationWarning)
.. predict [fail] 
 *  VarianceThreshold.. fit [OK].. transform [OK] 
================================================================================
Estimators don't produce an exception with a datatime input: 62/150

sklearn_datetime_input_validation.py

import pandas as pd

from sklearn.model_selection import train_test_split
from sklearn.utils.testing import all_estimators

data_array = [['11/18/2017', 453],
              ['11/19/2017', 453],
              ['11/20/2017', 453],
              ['11/21/2017', 201],
              ['12/11/2017', 202],
              ['12/13/2017', 2045],
              ['12/13/2017', 347],
              ['12/14/2017', 981]]

dataset = pd.DataFrame(data_array, columns=['date', 'y'])
X = pd.DataFrame(pd.to_datetime(dataset['date']))
y = dataset[['y']]

X_train, X_test, y_train, y_test = train_test_split(X, y)

n_total = 0
n_valid = 0

for idx, (estimator_name, Estimator) in enumerate(all_estimators()):
    is_valid = True
    if Estimator.__name__.startswith('_'):
        continue
    print(' * ', estimator_name, end='')
    model = Estimator()
    n_total += 1

    try:
        model.fit(X_train, y_train)
        print('.. fit [OK]', end='')
    except:
        print('.. fit [fail] .. predict [fail]')
        is_valid = False

    if hasattr(model, 'predict'):
        try:
            model.predict(X_test)
            print('.. predict [OK]', end='')
        except:
            print('.. predict [fail]', end='')
            is_valid = False

    if hasattr(model, 'transform'):
        try:
            model.transform(X_test)
            print('.. transform [OK]', end='')
        except:
            print('.. transform [fail]', end='')
            is_valid = False
    print(' ')

    if is_valid:
        n_valid += 1

print('='*80)
print("Estimators don't produce an exception with a datatime input: {}/{}"
      .format(n_valid, n_total))