class_docstr: Mixin class for all multivariate classifiers in pyts.
docstr: Return the mean accuracy on the given test data and labels.
  
  Parameters
  ----------
  X : array-like, shape = (n_samples, n_features, n_timestamps)
      Multivariate time series.
  
  y : array-like, shape = (n_samples,)
      True labels for `X`.
  
  sample_weight : None or array-like, shape = (n_samples,) (default = None)
      Sample weights.
  
  Returns
  -------
  score : float
      Mean accuracy of ``self.predict(X)`` with regards to `y`.
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code: def score(self, X, y, sample_weight=None):
      """
      Return the mean accuracy on the given test data and labels.
  
      Parameters
      ----------
      X : array-like, shape = (n_samples, n_features, n_timestamps)
          Multivariate time series.
  
      y : array-like, shape = (n_samples,)
          True labels for `X`.
  
      sample_weight : None or array-like, shape = (n_samples,) (default = None)
          Sample weights.
  
      Returns
      -------
      score : float
          Mean accuracy of ``self.predict(X)`` with regards to `y`.
  
      """  # noqa: E501
      return accuracy_score(
          y, self.predict(X), sample_weight=sample_weight
      )
  
file_path: pyts/pyts/base.py
imports: from sklearn.metrics import accuracy_score
label_desc: Use image node_id 1 for calling the MultivariateClassifierMixin obj's underlying member method code with example usage: obj.score(X, y, sample_weight) and returns: accuracy_score