class_docstr: Abstract base class for early time series classifiers.
  
  The base classifier specifies the methods and method signatures that all
  early classifiers have to implement. Attributes with an underscore suffix are set in
  the method fit.
  
  Parameters
  ----------
  classes_            : ndarray of class labels, possibly strings
  n_classes_          : integer, number of classes (length of classes_)
  fit_time_           : integer, time (in milliseconds) for fit to run.
  _class_dictionary   : dictionary mapping classes_ onto integers 0...n_classes_-1.
  _threads_to_use     : number of threads to use in fit as determined by n_jobs.
  state_info          : An array containing the state info for each decision in X.
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docstr: Scores predicted labels against ground truth labels on X.
  
  Parameters
  ----------
  X : 3D np.array (any number of dimensions, equal length series)
          of shape [n_instances, n_dimensions, series_length]
      or 2D np.array (univariate, equal length series)
          of shape [n_instances, series_length]
      or pd.DataFrame with each column a dimension, each cell a pd.Series
          (any number of dimensions, equal or unequal length series)
      or of any other supported Panel mtype
          for list of mtypes, see datatypes.SCITYPE_REGISTER
          for specifications, see examples/AA_datatypes_and_datasets.ipynb
  y : 1D np.ndarray of int, of shape [n_instances] - class labels (ground truth)
      indices correspond to instance indices in X
  
  Returns
  -------
  Tuple of floats, harmonic mean, accuracy and earliness scores of predict(X) vs y
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code: def score(self, X, y) -> Tuple[float, float, float]:
      """Scores predicted labels against ground truth labels on X.
  
      Parameters
      ----------
      X : 3D np.array (any number of dimensions, equal length series)
              of shape [n_instances, n_dimensions, series_length]
          or 2D np.array (univariate, equal length series)
              of shape [n_instances, series_length]
          or pd.DataFrame with each column a dimension, each cell a pd.Series
              (any number of dimensions, equal or unequal length series)
          or of any other supported Panel mtype
              for list of mtypes, see datatypes.SCITYPE_REGISTER
              for specifications, see examples/AA_datatypes_and_datasets.ipynb
      y : 1D np.ndarray of int, of shape [n_instances] - class labels (ground truth)
          indices correspond to instance indices in X
  
      Returns
      -------
      Tuple of floats, harmonic mean, accuracy and earliness scores of predict(X) vs y
      """
      self.check_is_fitted()
  
      # boilerplate input checks for predict-like methods
      X = self._check_convert_X_for_predict(X)
  
      return self._score(X, y)
  
imports: from abc import ABC, abstractmethod
  from typing import Tuple
  import numpy
  from sktime.base import BaseEstimator
  from sktime.classification import BaseClassifier
label_desc: Use image node_id 7 for calling the BaseEarlyClassifier obj's underlying member method code with example usage: obj.score(X, y) and returns: self