help="comma-separated item IDs for loved items")
parser.add_argument('--like',
type=csv,
help="comma-separated item IDs for liked items")
parser.add_argument('--neutral',
type=csv,
help="comma-separated item IDs for neutral items")
parser.add_argument('--dislike',
type=csv,
help="comma-separated item IDs for disliked items")
parser.add_argument('--hate',
type=csv,
help="comma-separated item IDs for hated items")
args = parser.parse_args()
model = LatentFactorsModel(args.model_path)
tastes = {}
if args.love is not None:
for item in args.love:
tastes[item] = 1.0
if args.like is not None:
for item in args.like:
tastes[item] = 0.75
if args.neutral is not None:
for item in args.neutral:
tastes[item] = 0.5
if args.dislike is not None:
for item in args.dislike:
tastes[item] = 0.25
if args.hate is not None:
'--max-features',
type=int,
help="""the maximum number of features to use in cross-validation
(ignored if sampling size is not defined)""")
parser.add_argument(
'-o',
'--output',
type=str,
help="output CSV filename, to store validation scores (MAE)")
args = parser.parse_args()
if not os.access(args.ratings_path, os.R_OK):
print("'%s' is not readable." % args.ratings_path)
sys.exit(0)
model = LatentFactorsModel()
if args.delimiter is not None:
model.set_training_csv_delimiter(args.delimiter)
if args.rank is not None:
model.set_training_rank(args.rank)
k = 10
if args.folds is not None:
k = args.folds
feature_sampling = None
if args.feature_sampling_interval is not None:
feature_sampling_interval = args.feature_sampling_interval
