class_docstr: ABC for LibSVM-based classifiers.
class_docstr_tok: ["ABC","for","LibSVM-based","classifiers","."]
code: def _validate_targets(self, y):
      y_ = column_or_1d(y, warn=True)
      check_classification_targets(y)
      cls, y = np.unique(y_, return_inverse=True)
      self.class_weight_ = compute_class_weight(
          self.class_weight, cls, y_
      )
      if len(cls) < 2:
          raise ValueError(
              "The number of classes has to be greater than one; got %d"
              % len(cls)
          )
  
      self.classes_ = cls
  
      return np.asarray(y, dtype=np.float64, order="C")
  
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 2 for calling the BaseSVC obj's underlying member method code with example usage: obj._validate_targets(y) and returns: np