def train_test(model=None, train_examples=[], test_examples=[], metrics=standard_metrics(), training_metric=SemanticsAccuracyMetric(), seed=None, print_examples=False): # print_grammar(model.grammar) # print print('%d training examples, %d test examples' % (len(train_examples), len(test_examples))) # 'Before' test model.weights = defaultdict(float) # no weights evaluate_model(model=model, examples=train_examples, examples_label='train', metrics=metrics, print_examples=print_examples) evaluate_model(model=model, examples=test_examples, examples_label='test', metrics=metrics, print_examples=print_examples) # Train model = latent_sgd(model, train_examples, training_metric=training_metric, seed=seed) # 'After' test evaluate_model(model=model, examples=train_examples, examples_label='train', metrics=metrics, print_examples=print_examples) evaluate_model(model=model, examples=test_examples, examples_label='test', metrics=metrics, print_examples=print_examples)
def interact(domain, example_input=None, T=10): import readline model = domain.model() model = latent_sgd(model=model, examples=domain.train_examples(), training_metric=domain.training_metric(), T=T) print('\nHello! Enter a query%s:' % (', such as "%s"' % example_input if example_input else '')) while True: try: input = input('>>> ') except EOFError: print('\nBye!') return example = Example(input=input) parses = model.parse_input(input) if parses: print_parses(example, parses) else: print('No parse!')
def interact(domain, example_input=None, T=10): import readline model = domain.model() model = latent_sgd(model=model, examples=domain.train_examples(), training_metric=domain.training_metric(), T=T) print('\nHello! Enter a query%s:' % (', such as "%s"' % example_input if example_input else '')) while True: try: query = input('>>> ') except EOFError: print('\nBye!') return example = Example(input=query) parses = model.parse_input(query) if parses: print_parses(example, parses) else: print('No parse!')