def export_final_train_data(self):
    data = pd.read_csv(self.MOOD_DATA_AFTER_OBJECT)
    train = data[['n_E', 'score', 'time_state', 'sentiments', 'type', 'vector']]
    train = train.loc[6:, :]
    self.mean_score = self.image_score_df[(self.image_score_df['score'] != (- 1))].mean()[0]
    train.score = train['score'].apply((lambda x: self.change_neg_image_score(x)))
    train.type = train['type'].map(self.label_dict_reverse)
    train.vector.fillna('[0 0 0 0 0 0 0 0 0 0 0 0 0]', inplace=True)
    train.vector = train.vector.apply((lambda x: self.change_vector_to_int(x)))
    train.sort_values(by='n_E', inplace=True, ascending=False)
    train.to_csv(self.FINAL_RESULT_TRAIN_DATA)
def export_final_train_data(self):
    data = pd.read_csv(self.MOOD_DATA_AFTER_OBJECT)
    train = data[['n_E', 'score', 'time_state', 'sentiments', 'type', 'vector']]
    train = train.loc[6:, :]
    self.mean_score = self.image_score_df[(self.image_score_df['score'] != (- 1))].mean()[0]
    train.score = train['score'].apply((lambda x: self.change_neg_image_score(x)))
    train.type = train['type'].map(self.label_dict_reverse)
    train.vector.fillna('[0 0 0 0 0 0 0 0 0 0 0 0 0]', inplace=True)
    train.vector = train.vector.apply((lambda x: self.change_vector_to_int(x)))
    train.sort_values(by='n_E', inplace=True, ascending=False)
    train.to_csv(self.FINAL_RESULT_TRAIN_DATA)