TabularPredictor.load_data_internal

TabularPredictor.load_data_internal(data='train', return_X=True, return_y=True)

Loads the internal data representation used during model training. Individual AutoGluon models like the neural network may apply additional feature transformations that are not reflected in this method. This method only applies universal transforms employed by all AutoGluon models. Warning, the internal representation may:

Have different features compared to the original data. Have different row counts compared to the original data. Have indices which do not align with the original data. Have label values which differ from those in the original data.

Internal data representations should NOT be combined with the original data, in most cases this is not possible.

Parameters:

• data (str, default = 'train') –

  The data to load. Valid values are:

  ’train’:

  Load the training data used during model training. This is a transformed and augmented version of the train_data passed in fit().

  ’val’:

  Load the validation data used during model training. This is a transformed and augmented version of the tuning_data passed in fit(). If tuning_data=None was set in fit(), then tuning_data is an automatically generated validation set created by splitting train_data. Warning: Will raise an exception if called by a bagged predictor, as bagged predictors have no validation data.

• return_X (bool, default = True) – Whether to return the internal data features If set to False, then the first element in the returned tuple will be None.

• return_y (bool, default = True) – Whether to return the internal data labels If set to False, then the second element in the returned tuple will be None.

Return type:

Tuple of (pd.DataFrame, pd.Series) corresponding to the internal data features and internal data labels, respectively.