8000 Merge pull request #4826 from rvraghav93/exceptions · scikit-learn/scikit-learn@d4e9d79 · GitHub
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Merge pull request #4826 from rvraghav93/exceptions
[MRG + 1] move custom error/warning classes into sklearn.exceptions (and move `deprecated` away from `utils.__init__.py`)
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‎doc/developers/performance.rst

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@@ -105,7 +105,7 @@ silently dispatched to ``numpy.dot``. If you want to be sure when the original
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activate the related warning::
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>>> import warnings
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>>> from sklearn.utils.validation import NonBLASDotWarning
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>>> from sklearn.exceptions import NonBLASDotWarning
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>>> warnings.simplefilter('always', NonBLASDotWarning) # doctest: +SKIP
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.. _profiling-python-code:

‎doc/developers/utilities.rst

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- :class:`deprecated`: Decorator to mark a function or class as deprecated.
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- :class:`ConvergenceWarning`: Custom warning to catch convergence problems.
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Used in ``sklearn.covariance.graph_lasso``.
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- :class:`sklearn.exceptions.ConvergenceWarning`: Custom warning to catch
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convergence problems. Used in ``sklearn.covariance.graph_lasso``.

‎doc/modules/classes.rst

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@@ -378,6 +378,31 @@ partial dependence
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ensemble.partial_dependence.plot_partial_dependence
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.. _exceptions_ref:
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:mod:`sklea F42D rn.exceptions`: Exceptions and warnings
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==================================================
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.. automodule:: sklearn.exceptions
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:no-members:
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:no-inherited-members:
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.. currentmodule:: sklearn
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.. autosummary::
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:toctree: generated/
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:template: class_without_init.rst
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exceptions.NotFittedError
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exceptions.ChangedBehaviorWarning
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exceptions.ConvergenceWarning
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exceptions.DataConversionWarning
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exceptions.DataDimensionalityWarning
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exceptions.EfficiencyWarning
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exceptions.FitFailedWarning
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exceptions.NonBLASDotWarning
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exceptions.UndefinedMetricWarning
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.. _feature_extraction_ref:
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:mod:`sklearn.feature_extraction`: Feature Extraction

‎doc/templates/class_without_init.rst

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:mod:`{{module}}`.{{objname}}
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{{ underline }}==============
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.. currentmodule:: {{ module }}
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.. autoclass:: {{ objname }}
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.. include:: {{module}}.{{objname}}.examples
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.. raw:: html
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<div class="clearer"></div>

‎examples/linear_model/plot_sparse_recovery.py

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from sklearn.metrics import auc, precision_recall_curve
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from sklearn.ensemble import ExtraTreesRegressor
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from sklearn.utils.extmath import pinvh
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from sklearn.utils import ConvergenceWarning
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from sklearn.exceptions import ConvergenceWarning
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def mutual_incoherence(X_relevant, X_irelevant):

‎sklearn/__init__.py

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__all__ = ['calibration', 'cluster', 'covariance', 'cross_decomposition',
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'cross_validation', 'datasets', 'decomposition', 'dummy',
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'ensemble', 'externals', 'feature_extraction',
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'ensemble', 'exceptions', 'externals', 'feature_extraction',
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'feature_selection', 'gaussian_process', 'grid_search',
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'isotonic', 'kernel_approximation', 'kernel_ridge',
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'lda', 'learning_curve',

‎sklearn/base.py

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from scipy import sparse
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from .externals import six
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from .utils.fixes import signature
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from .utils.deprecation import deprecated
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from .exceptions import ChangedBehaviorWarning as ChangedBehaviorWarning_
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class ChangedBehaviorWarning(UserWarning):
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class ChangedBehaviorWarning(ChangedBehaviorWarning_):
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pass
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ChangedBehaviorWarning = deprecated("ChangedBehaviorWarning has been moved "
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"into the sklearn.exceptions module. "
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"It will not be available here from "
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"version 0.19")(ChangedBehaviorWarning)
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##############################################################################
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def clone(estimator, safe=True):

‎sklearn/cluster/birch.py

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from ..externals.six.moves import xrange
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from ..utils import check_array
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from ..utils.extmath import row_norms, safe_sparse_dot
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from ..utils.validation import NotFittedError, check_is_fitted
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from ..utils.validation import check_is_fitted
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from ..exceptions import NotFittedError
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from .hierarchical import AgglomerativeClustering
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‎sklearn/cluster/tests/test_k_means.py

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from sklearn.utils.testing import assert_raise_message
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from sklearn.utils.validation import DataConversionWarning
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from sklearn.utils.extmath import row_norms
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from sklearn.metrics.cluster import v_measure_score
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from sklearn.cluster import KMeans, k_means
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from sklearn.cluster.k_means_ import _mini_batch_step
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from sklearn.datasets.samples_generator import make_blobs
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from sklearn.externals.six.moves import cStringIO as StringIO
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from sklearn.exceptions import DataConversionWarning
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# non centered, sparse centers to check the

‎sklearn/covariance/graph_lasso_.py

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from .empirical_covariance_ import (empirical_covariance, EmpiricalCovariance,
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log_likelihood)
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from ..utils import ConvergenceWarning
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from ..exceptions import ConvergenceWarning
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from ..utils.extmath import pinvh
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from ..utils.validation import check_random_state, check_array
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from ..linear_model import lars_path

‎sklearn/covariance/tests/test_robust_covariance.py

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from sklearn.utils.testing import assert_almost_equal
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from sklearn.utils.testing import assert_array_almost_equal
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from sklearn.utils.testing import assert_raises, assert_warns
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from sklearn.utils.testing import assert_raises
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from sklearn.utils.testing import assert_raise_message
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f 10000 rom sklearn.utils.validation import NotFittedError
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from sklearn.exceptions import NotFittedError
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from sklearn import datasets
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from sklearn.covariance import empirical_covariance, MinCovDet, \

‎sklearn/cross_validation.py

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from .metrics.scorer import check_scoring
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from .utils.fixes import bincount
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from .gaussian_process.kernels import Kernel as GPKernel
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from .exceptions import FitFailedWarning
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__all__ = ['KFold',
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'LabelKFold',
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return np.array(scores)[:, 0]
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class FitFailedWarning(RuntimeWarning):
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pass
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def _fit_and_score(estimator, X, y, scorer, train, test, verbose,
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parameters, fit_params, return_train_score=False,
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return_parameters=False, error_score='raise'):

‎sklearn/decomposition/factor_analysis.py

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from ..utils import check_array, check_random_state
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from ..utils.extmath import fast_logdet, fast_dot, randomized_svd, squared_norm
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from ..utils.validation import check_is_fitted
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from ..utils import ConvergenceWarning
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from ..exceptions import ConvergenceWarning
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class FactorAnalysis(BaseEstimator, TransformerMixin):

‎sklearn/decomposition/kernel_pca.py

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from scipy import linalg
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from ..utils.arpack import eigsh
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from ..utils.validation import check_is_fitted, NotFittedError
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from ..utils.validation import check_is_fitted
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from ..exceptions import NotFittedError
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from ..base import BaseEstimator, TransformerMixin
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from ..preprocessing import KernelCenterer
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from ..metrics.pairwise import pairwise_kernels

‎sklearn/decomposition/nmf.py

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from ..utils.extmath import fast_dot
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from ..utils.validation import check_is_fitted, check_non_negative
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from ..utils import deprecated
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from ..utils import ConvergenceWarning
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from ..exceptions import ConvergenceWarning
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from .cdnmf_fast import _update_cdnmf_fast
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‎sklearn/decomposition/online_lda.py

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from ..base import BaseEstimator, TransformerMixin
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from ..utils import (check_random_state, check_array,
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gen_batches, gen_even_slices, _get_n_jobs)
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from ..utils.validation import NotFittedError, check_non_negative
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from ..utils.validation import check_non_negative
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from ..utils.extmath import logsumexp
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from ..externals.joblib import Parallel, delayed
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from ..externals.six.moves import xrange
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from ..exceptions import NotFittedError
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from ._online_lda import (mean_change, _dirichlet_expectation_1d,
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_dirichlet_expectation_2d)

‎sklearn/decomposition/tests/test_factor_analysis.py

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from sklearn.utils.testing import assert_raises
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from sklearn.utils.testing import assert_almost_equal
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from sklearn.utils.testing import assert_array_almost_equal
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from sklearn.utils import ConvergenceWarning
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from sklearn.exceptions import ConvergenceWarning
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from sklearn.decomposition import FactorAnalysis
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‎sklearn/decomposition/tests/test_online_lda.py

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from sklearn.utils.testing import assert_raises_regexp
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from sklearn.utils.testing import if_safe_multiprocessing_with_blas
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from sklearn.utils.validation import NotFittedError
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from sklearn.exceptions import NotFittedError
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from sklearn.externals.six.moves import xrange
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‎sklearn/ensemble/forest.py

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ExtraTreeClassifier, ExtraTreeRegressor)
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from ..tree._tree import DTYPE, DOUBLE
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from ..utils import check_random_state, check_array, compute_sample_weight
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from ..utils.validation import DataConversionWarning, NotFittedError
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from ..exceptions import DataConversionWarning, NotFittedError
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from .base import BaseEnsemble, _partition_estimators
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from ..utils.fixes import bincount
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from ..utils.multiclass import check_classification_targets

‎sklearn/ensemble/gradient_boosting.py

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from ..utils.fixes import bincount
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from ..utils.stats import _weighted_percentile
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from ..utils.validation import check_is_fitted
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from ..utils.validation import NotFittedError
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from ..utils.multiclass import check_classification_targets
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from ..exceptions import NotFittedError
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class QuantileEstimator(BaseEstimator):

‎sklearn/ensemble/tests/test_gradient_boosting.py

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from sklearn.utils.testing import assert_raises
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from sklearn.utils.testing import assert_true
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from sklearn.utils.testing import assert_warns
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from sklearn.utils.testing import ignore_warnings
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from sklearn.utils.validation import DataConversionWarning
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from sklearn.utils.validation import NotFittedError
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from sklearn.exceptions import DataConversionWarning
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from sklearn.exceptions import NotFittedError
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# toy sample
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X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]]

‎sklearn/exceptions.py

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"""
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The :mod:`sklearn.exceptions` module includes all custom warnings and error
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classes used across scikit-learn.
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"""
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__all__ = ['NotFittedError',
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'ChangedBehaviorWarning',
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'ConvergenceWarning',
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'DataConversionWarning',
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'DataDimensionalityWarning',
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'EfficiencyWarning',
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'FitFailedWarning',
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'NonBLASDotWarning',
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'UndefinedMetricWarning']
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class NotFittedError(ValueError, AttributeError):
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"""Exception class to raise if estimator is used before fitting.
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This class inherits from both ValueError and AttributeError to help with
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exception handling and backward compatibility.
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Examples
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--------
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>>> from sklearn.svm import LinearSVC
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>>> from sklearn.exceptions import NotFittedError
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>>> try:
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... LinearSVC().predict([[1, 2], [2, 3], [3, 4]])
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... except NotFittedError as e:
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... print(repr(e))
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... # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
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NotFittedError('This LinearSVC instance is not fitted yet',)
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"""
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class ChangedBehaviorWarning(UserWarning):
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"""Warning class used to notify the user of any change in the behavior."""
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class ConvergenceWarning(UserWarning):
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"""Custom warning to capture convergence problems"""
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class DataConversionWarning(UserWarning):
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"""Warning used to notify implicit data conversions happening in the code.
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This warning occurs when some input data needs to be converted or
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interpreted in a way that may not match the user's expectations.
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For example, this warning may occur when the the user
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- passes an integer array to a function which expects float input and
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will convert the input
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- requests a non-copying operation, but a copy is required to meet the
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implementation's data-type expectations;
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- passes an input whose shape can be interpreted ambiguously.
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"""
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class DataDimensionalityWarning(UserWarning):
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"""Custom warning to notify potential issues with data dimensionality.
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For example, in random projection, this warning is raised when the
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number of components, which quantifes the dimensionality of the target
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projection space, is higher than the number of features, which quantifies
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the dimensionality of the original source space, to imply that the
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dimensionality of the problem will not be reduced.
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"""
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class EfficiencyWarning(UserWarning):
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"""Warning used to notify the user of inefficient computation.
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This warning notifies the user that the efficiency may not be optimal due
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to some reason which may be included as a part of the warning message.
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This may be subclassed into a more specific Warning class.
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"""
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class FitFailedWarning(RuntimeWarning):
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"""Warning class used if there is an error while fitting the estimator.
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This Warning is used in meta estimators GridSearchCV and RandomizedSearchCV
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and the cross-validation helper function cross_val_score to warn when there
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is an error while fitting the estimator.
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Examples
87+
--------
88+
>>> from sklearn.grid_search import GridSearchCV
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>>> from sklearn.svm import LinearSVC
90+
>>> from sklearn.exceptions import FitFailedWarning
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>>> import warnings
92+
>>> warnings.simplefilter('always', FitFailedWarning)
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>>> gs = GridSearchCV(LinearSVC(), {'C': [-1, -2]}, error_score=0)
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>>> X, y = [[1, 2], [3, 4], [5, 6], [7, 8], [8, 9]], [0, 0, 0, 1, 1]
95+
>>> with warnings.catch_warnings(record=True) as w:
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... try:
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... gs.fit(X, y) # This will raise a ValueError since C is < 0
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... except ValueError:
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... pass
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... print(repr(w[-1].message))
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... # doctest: +NORMALIZE_WHITESPACE
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FitFailedWarning("Classifier fit failed. The score on this train-test
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partition for these parameters will be set to 0.000000. Details:
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\\nValueError('Penalty term must be positive; got (C=-2)',)",)
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"""
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class NonBLASDotWarning(EfficiencyWarning):
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"""Warning used when the dot operation does not use BLAS.
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This warning is used to notify the user that BLAS was not used for dot
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operation and hence the efficiency may be affected.
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"""
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class UndefinedMetricWarning(UserWarning):
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pass

‎sklearn/feature_selection/from_model.py

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import numpy as np
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from .base import SelectorMixin
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from ..base import (TransformerMixin, BaseEstimator, clone,
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MetaEstimatorMixin)
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from ..base import TransformerMixin, BaseEstimator, clone
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from ..externals import six
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from ..utils import safe_mask, check_array, deprecated
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from ..utils.validation import NotFittedError, check_is_fitted
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from ..utils.validation import check_is_fitted
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from ..exceptions import NotFittedError
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def _get_feature_importances(estimator):

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