class_docstr: ABC for LibSVM-based classifiers.
class_docstr_tok: ["ABC","for","LibSVM-based","classifiers","."]
docstr: Compute log probabilities of possible outcomes for samples in X.
  
  The model need to have probability information computed at training
  time: fit with attribute `probability` set to True.
  
  Parameters
  ----------
  X : array-like, shape (n_samples, n_features)
      For kernel="precomputed", the expected shape of X is
      [n_samples_test, n_samples_train]
  
  Returns
  -------
  T : array-like, shape (n_samples, n_classes)
      Returns the log-probabilities of the sample for each class in
      the model. The columns correspond to the classes in sorted
      order, as they appear in the attribute `classes_`.
  
  Notes
  -----
  The probability model is created using cross validation, so
  the results can be slightly different than those obtained by
  predict. Also, it will produce meaningless results on very small
  datasets.
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code: def predict_log_proba(self):
      """Compute log probabilities of possible outcomes for samples in X.
  
      The model need to have probability information computed at training
      time: fit with attribute `probability` set to True.
  
      Parameters
      ----------
      X : array-like, shape (n_samples, n_features)
          For kernel="precomputed", the expected shape of X is
          [n_samples_test, n_samples_train]
  
      Returns
      -------
      T : array-like, shape (n_samples, n_classes)
          Returns the log-probabilities of the sample for each class in
          the model. The columns correspond to the classes in sorted
          order, as they appear in the attribute `classes_`.
  
      Notes
      -----
      The probability model is created using cross validation, so
      the results can be slightly different than those obtained by
      predict. Also, it will produce meaningless results on very small
      datasets.
      """
      self._check_proba()
      return self._predict_log_proba
  
imports: from __future__ import print_function
  import numpy
  import scipy.sparse
  import warnings
  from abc import ABCMeta, abstractmethod
  from .None import libsvm, liblinear
  from .None import libsvm_sparse
  from ..base import BaseEstimator, ClassifierMixin
  from ..preprocessing import LabelEncoder
  from ..utils.multiclass import _ovr_decision_function
  from ..utils import check_array, check_consistent_length, check_random_state
  from ..utils import column_or_1d, check_X_y
  from ..utils import compute_class_weight, deprecated
  from ..utils.extmath import safe_sparse_dot
  from ..utils.validation import check_is_fitted
  from ..utils.multiclass import check_classification_targets
  from ..externals import six
  from ..exceptions import ChangedBehaviorWarning
  from ..exceptions import ConvergenceWarning
  from ..exceptions import NotFittedError
label_desc: Use image node_id 8 for calling the BaseSVC obj's underlying member method code with example usage: obj.predict_log_proba() and returns: self