def test_combiner_d2_4():
torch.manual_seed(1)
combiner = FFCombiner(6)
head_feat = ag.Variable(torch.randn(1, 6))
modifier_feat = ag.Variable(torch.randn(1, 6))
combined = combiner(head_feat, modifier_feat)
combined_list = combined.view(-1).data.tolist()
true_out = [-0.1194517, 0.31343767, 0.29966655, 0.26423377, -0.09193783, -0.56594414]
check_tensor_correctness([(combined_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]