class_docstr: Fresh Pipeline with RotatIoN forest Classifier.
  
  This classifier simply transforms the input data using the TSFresh [1]_
  transformer with comprehensive features and builds a RotationForest estimator using
  the transformed data.
  
  Parameters
  ----------
  default_fc_parameters : str, default="comprehensive"
      Set of TSFresh features to be extracted, options are "minimal", "efficient" or
      "comprehensive".
  n_estimators : int, default=200
      Number of estimators for the RotationForest ensemble.
  verbose : int, default=0
      Level of output printed to the console (for information only)
  n_jobs : int, default=1
      The number of jobs to run in parallel for both `fit` and `predict`.
      ``-1`` means using all processors.
  chunksize : int or None, default=None
      Number of series processed in each parallel TSFresh job, should be optimised
      for efficient parallelisation.
  random_state : int or None, default=None
      Seed for random, integer.
  
  Attributes
  ----------
  n_classes_ : int
      Number of classes. Extracted from the data.
  classes_ : ndarray of shape (n_classes_)
      Holds the label for each class.
  
  See Also
  --------
  TSFreshFeatureExtractor, TSFreshClassifier, RotationForest
  
  References
  ----------
  .. [1] Christ, Maximilian, et al. "Time series feature extraction on basis of
      scalable hypothesis tests (tsfresh–a python package)." Neurocomputing 307
      (2018): 72-77.
      https://www.sciencedirect.com/science/article/pii/S0925231218304843
class_docstr_tok: ["Fresh","Pipeline","with","RotatIoN","forest","Classifier",".","This","classifier","simply","transforms","the","input","data","using","the","TSFresh","[","1","]","_","transformer","with","comprehensive","features","and","builds","a","RotationForest","estimator","using","the","transformed","data",".","Parameters","--","--","--","--","--","default_fc_parameters",":","str",",","default=","''","comprehensive","''","Set","of","TSFresh","features","to","be","extracted",",","options","are","``","minimal","''",",","``","efficient","''","or","``","comprehensive","''",".","n_estimators",":","int",",","default=200","Number","of","estimators","for","the","RotationForest","ensemble",".","verbose",":","int",",","default=0","Level","of","output","printed","to","the","console","(","for","information","only",")","n_jobs",":","int",",","default=1","The","number","of","jobs","to","run","in","parallel","for","both","`","fit","`","and","`","predict","`",".","``","-1","``","means","using","all","processors",".","chunksize",":","int","or","None",",","default=None","Number","of","series","processed","in","each","parallel","TSFresh","job",",","should","be","optimised","for","efficient","parallelisation",".","random_state",":","int","or","None",",","default=None","Seed","for","random",",","integer",".","Attributes","--","--","--","--","--","n_classes_",":","int","Number","of","classes",".","Extracted","from","the","data",".","classes_",":","ndarray","of","shape","(","n_classes_",")","Holds","the","label","for","each","class",".","See","Also","--","--","--","--","TSFreshFeatureExtractor",",","TSFreshClassifier",",","RotationForest","References","--","--","--","--","--","..","[","1","]","Christ",",","Maximilian",",","et","al",".","``","Time","series","feature","extraction","on","basis","of","scalable","hypothesis","tests","(","tsfresh\u2013a","python","package",")",".","''","Neurocomputing","307","(","2018",")",":","72-77.","https",":","\/\/www.sciencedirect.com\/science\/article\/pii\/S0925231218304843"]
code: def _get_train_probs(self, X, y) -> np.ndarray:
      self.check_is_fitted()
      X, y = check_X_y(X, y, coerce_to_numpy=True)
  
      n_instances, n_dims, series_length = X.shape
  
      if (
          n_instances != self.n_instances_
          or n_dims != self.n_dims_
          or series_length != self.series_length_
      ):
          raise ValueError(
              "n_instances, n_dims, series_length mismatch. X should be "
              "the same as the training data used in fit for generating train "
              "probabilities."
          )
  
      if not self.save_transformed_data:
          raise ValueError(
              "Currently only works with saved transform data from fit."
          )
  
      return self._rotf._get_train_probs(self.transformed_data_, y)
  
file_path: sktime/sktime/classification/feature_based/_fresh_prince.py
imports: import numpy
  from sktime.classification.base import BaseClassifier
  from sktime.classification.sklearn import RotationForest
  from sktime.transformations.panel.tsfresh import TSFreshFeatureExtractor
  from sktime.utils.validation.panel import check_X_y
label_desc: Use image node_id 5 for calling the FreshPRINCE obj's underlying member method code with example usage: obj._get_train_probs(X, y) and returns: self