class_docstr: Represent a dataset in UEA/UCR format on the hard-drive.
class_docstr_tok: ["Represent","a","dataset","in","UEA\/UCR","format","on","the","hard-drive","."]
docstr: Load dataset.
docstr_tok: ["Load","dataset","."]
code: def load(self):
      """Load dataset."""
      # load training and test set from separate files
  
      X_train, y_train = load_from_tsfile_to_dataframe(
          self._train_path, return_separate_X_and_y=True
      )
      X_test, y_test = load_from_tsfile_to_dataframe(
          self._test_path, return_separate_X_and_y=True
      )
  
      # combine into single dataframe
      data_train = pd.concat([X_train, pd.Series(y_train)], axis=1)
      data_test = pd.concat([X_test, pd.Series(y_test)], axis=1)
  
      # rename target variable
      data_train.rename(
          columns={data_train.columns[-1]: self._target_name},
          inplace=True,
      )
      data_test.rename(
          columns={data_test.columns[-1]: self._target_name},
          inplace=True,
      )
  
      # concatenate the two dataframes, keeping training and test split in
      # index, necessary for later optional CV
      data = pd.concat(
          [data_train, data_test], axis=0, keys=["train", "test"]
      )
  
      return data
  
file_path: sktime/sktime/benchmarking/data.py
imports: import os
  import pandas
  from sktime.benchmarking.base import BaseDataset, HDDBaseDataset
  from sktime.datasets import load_from_tsfile_to_dataframe
label_desc: Use image node_id 2 for calling the UEADataset obj's underlying member method code with example usage: obj.load() and returns: data