def _beginning_callback(self, iterations, epochs, batch_size, shuffle, data_summary, test_data_cols):
    confusion_matrix = None
    pbar = None
    self.pandas_full_data = pd.DataFrame()
    if (self.verbose == 1):
        pbar = tqdm(total=iterations)
    if self.output_config['confusion_matrix']:
        confusion_matrix = []
        for mdl in self.models:
            confusion_matrix.append({'name': mdl.name, 'matrix': pd.DataFrame()})
    if self.output_config['summary_of_run']:
        self._save_summary_of_run(self._run_summary(iterations, epochs, batch_size, shuffle), data_summary)
    test_results = None
    if (test_data_cols is not None):
        test_results = pd.DataFrame(columns=test_data_cols)
    return (confusion_matrix, pbar, test_results)
def _beginning_callback(self, iterations, epochs, batch_size, shuffle, data_summary, test_data_cols):
    confusion_matrix = None
    pbar = None
    self.pandas_full_data = pd.DataFrame()
    if (self.verbose == 1):
        pbar = tqdm(total=iterations)
    if self.output_config['confusion_matrix']:
        confusion_matrix = []
        for mdl in self.models:
            confusion_matrix.append({'name': mdl.name, 'matrix': pd.DataFrame()})
    if self.output_config['summary_of_run']:
        self._save_summary_of_run(self._run_summary(iterations, epochs, batch_size, shuffle), data_summary)
    test_results = None
    if (test_data_cols is not None):
        test_results = pd.DataFrame(columns=test_data_cols)
    return (confusion_matrix, pbar, test_results)