def read_ratings(self, file_name): if self.raw_ratings is None: Dataset.read_ratings(self, file_name) else: return self.raw_ratings
[stdout.write(' %.4f' % p) for p in propensities] stdout.write('\n') with open(propensity_file) as fin: line = fin.readline() propensities = np.asarray([float(f) for f in line.split()]) propensities /= propensities.sum() # [stdout.write('%.4f ' % p) for p in propensities] # stdout.write('\n') weights = 1.0 / propensities # [stdout.write('%.4f ' % w) for w in weights] # stdout.write('\n') reader = Reader(line_format='user item rating', sep='\t') data = Dataset(reader=reader, rating_scale=rating_scale) raw_trainset = data.read_ratings(train_file) raw_testset = data.read_ratings(test_file) trainset = data.construct_trainset(raw_trainset) testset = data.construct_testset(raw_testset) #### default n_factors_opt = [ 100, ] n_epochs_opt = [ 20, ] biased_opt = [ True, ] reg_all_opt = [