default=500,
help="How often (in terms of number of data points) to evaluate on dev."
)
return parser
if __name__ == '__main__':
args = return_parser().parse_args()
data = loader.read_data_sets(
args.datadir, folders=['train', 'test', 'dev', 'user', 'item'])
data.train.labels['ratings'] = loader.center(data.train.labels['ratings'],
axis=None)
data.dev.labels['ratings'] = loader.center(data.dev.labels['ratings'],
axis=None)
x = tree_model.tree(data,
args.config,
initrange=args.initrange,
kfactors=args.kfactors,
lamb=args.lamb,
mb=args.mb,
learnrate=args.learnrate,
verbose=args.verbose,
maxbadcount=args.maxbadcount,
epochs=args.epochs,
random_seed=args.random_seed,
eval_rate=args.eval_rate)
#print stuff here to file.
parser.add_argument("random_seed", metavar="RANDOM_SEED", type=int,
help="For reproducible results.")
parser.add_argument("eval_rate", metavar="EVAL_RATE", type=int,
help="How often (in terms of number of data points) to evaluate on dev.")
return parser
if __name__ == '__main__':
args = return_parser().parse_args()
data = loader.read_data_sets(args.datadir, folders=['train', 'test', 'dev', 'user', 'item'])
data.train.labels['ratings'] = loader.center(data.train.labels['ratings'], axis=None)
data.dev.labels['ratings'] = loader.center(data.dev.labels['ratings'], axis=None)
data.user.features['age'] = loader.center(data.user.features['age'], axis=None)
data.item.features['year'] = loader.center(data.item.features['year'], axis=None)
data.user.features['age'] = loader.maxnormalize(data.user.features['age'])
data.item.features['year'] = loader.maxnormalize(data.item.features['year'])
x = tree_model.tree(data, args.config,data, args.config,
initrange=args.initrange,
kfactors=args.kfactors,
lamb =args.lamb,
mb=args.mb,
learnrate=args.learnrate,
verbose=args.verbose,
maxbadcount=args.maxbadcount,
epochs=args.epochs,
random_seed=args.random_seed,
eval_rate=args.eval_rate)
#print stuff here to file.
if __name__ == '__main__':
args = return_parser().parse_args()
data = loader.read_data_sets(args.datadir,
folders=['train', 'dev', 'user', 'item'])
data.train.labels['ratings'] = loader.center(data.train.labels['ratings'])
data.dev.labels['ratings'] = loader.center(data.dev.labels['ratings'])
data.user.features['age'] = loader.center(data.user.features['age'],
axis=None)
data.item.features['year'] = loader.center(data.item.features['year'],
axis=None)
data.user.features['age'] = loader.maxnormalize(data.user.features['age'])
data.item.features['year'] = loader.maxnormalize(
data.item.features['year'])
data.show()
print('=================mfmodel============================')
x = mfmodel.mf(data, 'mf.config', epochs=1)
print('=================treemodel============================')
x2 = tree_model.tree(data, 'tree.config', epochs=1)
print('=================dssmmodel============================')
x3 = dssm_model.dssm(data, 'dssm.config', epochs=1)
print('=================dnnconcat============================')
x4 = dnn_concat_model.dnn_concat(data, 'dnn_concat.config', epochs=1)
print('=================mult_dnn_concat============================')
x5 = dnn_concat_model.dnn_concat(data, 'dnn_mult_concat.config', epochs=1)
print('=================dsadd============================')
x5 = dsaddmodel.dsadd(data, 'dssm.config', epochs=1)
print('=================dssmrestricted============================')
x6 = dssm_restricted_model(data, 'dssm.config', epochs=1)