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.
type=int, help="For reproducible results.") 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']) data.dev.labels['ratings'] = loader.center(data.dev.labels['ratings']) data.user.features['age'] = loader.center(data.user.features['age']) data.item.features['year'] = loader.center(data.item.features['year']) data.user.features['age'] = loader.maxnormalize(data.user.features['age']) data.item.features['year'] = loader.maxnormalize( data.item.features['year']) x = dsaddmodel.dsadd(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)
type=str, help="Name of experiment (for resolving results path).") 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']) data.dev.labels['ratings'] = loader.center(data.dev.labels['ratings']) data.user.features['age'] = loader.center(data.user.features['age']) #data.item.features['year'] = loader.center(data.item.features['year']) data.user.features['age'] = loader.maxnormalize(data.user.features['age']) #data.item.features['year'] = loader.maxnormalize(data.item.features['year']) x = dssm_model.dssm(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.
help="Loss file for spearmint_condor $lossfn argument.") parser.add_argument("expname", metavar="EXPNAME", type=str, help="Name of experiment (for resolving results path).") 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']) data.dev.labels['ratings'] = loader.center(data.dev.labels['ratings']) data.user.features['age'] = loader.center(data.user.features['age']) #data.item.features['year'] = loader.center(data.item.features['year']) data.user.features['age'] = loader.maxnormalize(data.user.features['age']) #data.item.features['year'] = loader.maxnormalize(data.item.features['year']) x = dssm_model.dssm(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. lfile = str(args.lossfile)
def test_max_sparse_test_axis1(): assert np.array_equal(loader.maxnormalize( y, axis=1), [[0.0, 0.0, 1.0], [.5, 1, .5], [1.0 / 3.0, 1.0, 0.0]])
def test_max_sparse_test_axis0(): assert np.array_equal( loader.maxnormalize(y, axis=0), [[0.0, 0.0, 1.0], [1.0, 2.0 / 3.0, 1.0 / 3.0], [1.0, 1.0, 0.0]])