def worker(fold, n_users, n_items, dataset_dir):
traFilePath = dataset_dir + 'ratings__' + str(fold + 1) + '_tra.txt'
trasR = lil_matrix(
matBinarize(loadSparseR(n_users, n_items, traFilePath),
binarize_threshold))
print(
dataset_dir.split('/')[-2] + '@%d:' % (fold + 1), trasR.shape,
trasR.nnz, '%.2f' % (trasR.nnz / float(trasR.shape[0])))
tstFilePath = dataset_dir + 'ratings__' + str(fold + 1) + '_tst.txt'
tstsR = lil_matrix(
matBinarize(loadSparseR(n_users, n_items, tstFilePath),
binarize_threshold))
sampler = Sampler(trasR, gsize, negSample, batch_size)
gbprmf = GBPRMF(n_users, n_items, topN, rho, gsize, split_method,
eval_metrics, reg, n_factors, batch_size)
scores = gbprmf.train(fold + 1, trasR, tstsR, sampler)
print(
dataset_dir.split('/')[-2] + '@%d:' % (fold + 1), 'gsize=', gsize,
'rho=', rho, 'reg=', reg)
print(
dataset_dir.split('/')[-2] + '@%d:' % (fold + 1),
','.join(['%s' % eval_metric for eval_metric in eval_metrics]) +
'@%d=' % (topN) + ','.join(['%.6f' % (score) for score in scores]))
gbprmf.close()
return scores
def worker(fold, n_users, n_items, dataset_dir):
traFilePath = dataset_dir + 'ratings__' + str(fold + 1) + '_tra.txt'
trasR = lil_matrix(
matBinarize(loadSparseR(n_users, n_items, traFilePath),
binarize_threshold))
print(
dataset_dir.split('/')[-2] + '@%d:' % (fold + 1), trasR.shape,
trasR.nnz, '%.2f' % (trasR.nnz / float(trasR.shape[0])))
tstFilePath = dataset_dir + 'ratings__' + str(fold + 1) + '_tst.txt'
tstsR = lil_matrix(
matBinarize(loadSparseR(n_users, n_items, tstFilePath),
binarize_threshold))
sampler = Sampler(trasR, n_neg=negSample, batch_size=batch_size)
cml = CML(n_users, n_items, topN, split_method, eval_metrics, reg_cov,
margin, use_rank_weight, clip_norm, n_factors, batch_size)
scores = cml.train(fold + 1, trasR, tstsR, sampler)
print(
dataset_dir.split('/')[-2] + '@%d:' % (fold + 1),
','.join(['%s' % eval_metric for eval_metric in eval_metrics]) +
'@%d=' % (topN) + ','.join(['%.6f' % (score) for score in scores]))
cml.close()
return scores
def worker(fold, n_users, n_items, dataset_dir):
traFilePath = dataset_dir + 'ratings__' + str(fold + 1) + '_tra.txt'
trasR = lil_matrix(
matBinarize(loadSparseR(n_users, n_items, traFilePath),
binarize_threshold))
print(
dataset_dir.split('/')[-2] + '@%d:' % (fold + 1), trasR.shape,
trasR.nnz, '%.2f' % (trasR.nnz / float(trasR.shape[0])))
tstFilePath = dataset_dir + 'ratings__' + str(fold + 1) + '_tst.txt'
tstsR = lil_matrix(
matBinarize(loadSparseR(n_users, n_items, tstFilePath),
binarize_threshold))
prigp = PRIGP(n_users, n_items, topK, topN, split_method, eval_metrics,
alpha, reg, n_factors, batch_size)
scores = prigp.train(fold + 1, trasR, tstsR)
print('topK=', topK, 'alpha=', alpha, 'reg=', reg)
print(
dataset_dir.split('/')[-2] + '@%d:' % (fold + 1),
','.join(['%s' % eval_metric for eval_metric in eval_metrics]) +
'@%d=' % (topN) + ','.join(['%.6f' % (score) for score in scores]))
prigp.close()
return scores
def worker(fold, n_users, n_items, dataset_dir):
traFilePath = dataset_dir + 'ratings__' + str(fold + 1) + '_tra.txt'
trasR = lil_matrix(
matBinarize(loadSparseR(n_users, n_items, traFilePath),
binarize_threshold))
print(
dataset_dir.split('/')[-2] + '@%d:' % (fold + 1), trasR.shape,
trasR.nnz, '%.2f' % (trasR.nnz / float(trasR.shape[0])))
tstFilePath = dataset_dir + 'ratings__' + str(fold + 1) + '_tst.txt'
tstsR = lil_matrix(
matBinarize(loadSparseR(n_users, n_items, tstFilePath),
binarize_threshold))
uicf = UserItemCF(n_users, n_items, topK, theta, topN, split_method,
eval_metrics)
scores = uicf.train(fold + 1, trasR, tstsR)
print(
dataset_dir.split('/')[-2] + '@%d:' % (fold + 1),
','.join(['%s' % eval_metric for eval_metric in eval_metrics]) +
'@%d=' % (topN) + ','.join(['%.6f' % (score) for score in scores]))
return scores
def worker(fold, n_users, n_items, dataset_dir):
traFilePath = dataset_dir + 'ratings__' + str(fold + 1) + '_tra.txt'
trasR = lil_matrix(matBinarize(loadSparseR(n_users, n_items, traFilePath), binarize_threshold))
print(dataset_dir.split('/')[-2] + '@%d:' % (fold + 1), trasR.shape, trasR.nnz,
'%.2f' % (trasR.nnz / float(trasR.shape[0])))
tstFilePath = dataset_dir + 'ratings__' + str(fold + 1) + '_tst.txt'
tstsR = lil_matrix(matBinarize(loadSparseR(n_users, n_items, tstFilePath), binarize_threshold))
sampler = Sampler(trasR, negRatio, batch_size)
wrmf = WRMF(n_users, n_items, topN, split_method, eval_metrics, weight, reg, n_factors, batch_size)
scores = wrmf.train(fold+1, trasR, tstsR, sampler)
print(dataset_dir.split('/')[-2] + '@%d:' % (fold + 1),'weight=',weight,'reg=',reg)
print('fold=%d:' % (fold+1), ','.join(['%s' % eval_metric for eval_metric in eval_metrics]), '=',
','.join(['%.6f' % (score) for score in scores]))
return scores
