def get_system(name, args, schema, model_path=None, timed=False):
lexicon = Lexicon.from_pickle(args.lexicon)
templates = Templates.from_pickle(args.templates)
if name == 'rulebased':
templates = Templates.from_pickle(args.templates)
generator = Generator(templates)
manager = Manager.from_pickle(args.policy)
return RulebasedSystem(lexicon, generator, manager, timed)
elif name == 'cmd':
return CmdSystem()
else:
raise ValueError('Unknown system %s' % name)
nargs='*',
help='JSON transcripts to extract templates')
parser.add_argument('--max-examples', default=-1, type=int)
parser.add_argument('--templates', help='Path to load templates')
parser.add_argument('--reviews', help='Path to load templates')
parser.add_argument('--templates-output', help='Path to save templates')
parser.add_argument('--model', help='Path to load model')
parser.add_argument('--model-output',
help='Path to save the dialogue manager model')
args = parser.parse_args()
examples = read_examples(args.transcripts, args.max_examples, Scenario)
parsed_dialogues = []
templates = Templates()
lexicon = Lexicon.from_pickle(args.lexicon)
for example in examples:
utterances = parse_example(example, lexicon, templates)
parsed_dialogues.append(utterances)
# Train n-gram model
sequences = []
for d in parsed_dialogues:
sequences.append([u.lf.intent for u in d])
manager = Manager.from_train(sequences)
manager.save(args.model_output)
if args.reviews:
print 'read reviews from', args.reviews
templates.read_reviews(args.reviews)
