def test_action_chooser_d2_3(): torch.manual_seed(1) act_chooser = FFActionChooser(NUM_FEATURES * TEST_EMBEDDING_DIM) dummy_feats = [ ag.Variable(torch.randn(1, TEST_EMBEDDING_DIM)) for _ in range(NUM_FEATURES) ] out = act_chooser(dummy_feats) out_list = out.view(-1).data.tolist() true_out = [-1.24434566, -0.83229464, -1.28438509] check_tensor_correctness([(out_list, true_out)])
def test_predict_after_train_d3_1(): global test_sent, gold, word_to_ix, vocab torch.manual_seed(1) feat_extract = SimpleFeatureExtractor() word_embed = VanillaWordEmbedding(word_to_ix, TEST_EMBEDDING_DIM) act_chooser = FFActionChooser(TEST_EMBEDDING_DIM * NUM_FEATURES) combiner = FFCombiner(TEST_EMBEDDING_DIM) parser = TransitionParser(feat_extract, word_embed, act_chooser, combiner) # Train for i in range(75): train([(test_sent[:-1], gold)], parser, optim.SGD(parser.parameters(), lr=0.01), verbose=False) # predict pred = parser.predict(test_sent[:-1]) gold_graph = dependency_graph_from_oracle(test_sent[:-1], gold) assert pred == gold_graph
def test_parse_logic_d3_1(): global test_sent, gold, word_to_ix, vocab torch.manual_seed(1) feat_extract = SimpleFeatureExtractor() word_embed = VanillaWordEmbedding(word_to_ix, TEST_EMBEDDING_DIM) act_chooser = FFActionChooser(TEST_EMBEDDING_DIM * NUM_FEATURES) combiner = FFCombiner(TEST_EMBEDDING_DIM) parser = TransitionParser(feat_extract, word_embed, act_chooser, combiner) output, dep_graph, actions_done = parser(test_sent[:-1], gold) assert len(output) == 16 # Made the right number of decisions # check one of the outputs checked_out = output[9].view(-1).data.tolist() true_out = [-1.2444578409194946, -1.3128550052642822, -0.8145193457603455] check_tensor_correctness([(true_out, checked_out)]) true_dep_graph = dependency_graph_from_oracle(test_sent, gold) assert true_dep_graph == dep_graph assert actions_done == [0, 1, 0, 1, 0, 0, 1, 2, 0, 0, 0, 1, 2, 2, 2, 0]