| from data.loader import FileIO | |
| class SELFRec(object): | |
| def __init__(self, config): | |
| self.social_data = [] | |
| self.feature_data = [] | |
| self.config = config | |
| if config['model.type'] == 'sequential': | |
| self.training_data, self.test_data = FileIO.load_data_set(config['sequence.data'], config['model.type']) | |
| else: | |
| self.training_data = FileIO.load_data_set(config['training.set'], config['model.type']) | |
| self.test_data = FileIO.load_data_set(config['test.set'], config['model.type']) | |
| self.kwargs = {} | |
| if config.contain('social.data'): | |
| social_data = FileIO.load_social_data(self.config['social.data']) | |
| self.kwargs['social.data'] = social_data | |
| # if config.contains('feature.data'): | |
| # self.social_data = FileIO.loadFeature(config,self.config['feature.data']) | |
| print('Reading data and preprocessing...') | |
| def execute(self): | |
| # import the model module | |
| import_str = 'from model.'+ self.config['model.type'] +'.' + self.config['model.name'] + ' import ' + self.config['model.name'] | |
| exec(import_str) | |
| recommender = self.config['model.name'] + '(self.config,self.training_data,self.test_data,**self.kwargs)' | |
| eval(recommender).execute() | |