def test_feature_extraction_d2_2():
global test_sent, gold, word_to_ix, vocab
torch.manual_seed(1)
feat_extractor = SimpleFeatureExtractor()
embedder = VanillaWordEmbedding(word_to_ix, TEST_EMBEDDING_DIM)
combiner = DummyCombiner()
embeds = embedder(test_sent)
state = ParserState(test_sent, embeds, combiner)
state.shift()
feats = feat_extractor.get_features(state)
feats_list = make_list(feats)
true = ([
-1.0276086330413818, -0.563052773475647, -0.8922905325889587,
-0.05825017765164375
], [
-0.4211951494216919, -0.510699987411499, -1.5726652145385742,
-0.12324775755405426
], [
3.586989402770996, -1.8312901258468628, 1.5987002849578857,
-1.277006983757019
])
pairs = zip(feats_list, true)
check_tensor_correctness(pairs)
def dependency_graph_from_oracle(sentence, actions):
"""
Take a sentence and a sequence of actions
and return a set of dependency edges for further evaluation
"""
stack = ParserState(sentence, [None]*len(sentence), DummyCombiner())
dependency_graph = set()
for act in [ Actions.action_to_ix[a] for a in actions ]:
if act == Actions.SHIFT:
stack.shift()
elif act == Actions.ARC_L:
dependency_graph.add(stack.arc_left())
elif act == Actions.ARC_R:
dependency_graph.add(stack.arc_right())
return dependency_graph
def dependency_graph_from_oracle(sentence, actions):
"""
Take a sentence and a sequence of shift-reduce actions and a sentence
and return a set of dependency edges for further evaluation
"""
stack = ParserState(sentence, [None]*len(sentence), DummyCombiner())
dependency_graph = set()
for act in [ Actions.action_to_ix[a] for a in actions ]:
if act == Actions.SHIFT:
stack.shift()
elif act == Actions.REDUCE_L:
dependency_graph.add(stack.reduce_left())
elif act == Actions.REDUCE_R:
dependency_graph.add(stack.reduce_right())
dependency_graph.add(DepGraphEdge((ROOT_TOK, -1), (stack.stack[-1].headword, stack.stack[-1].headword_pos)))
return dependency_graph
def test_feature_extraction_d2_2():
global test_sent, gold, word_to_ix, vocab
torch.manual_seed(1)
feat_extractor = SimpleFeatureExtractor()
embedder = VanillaWordEmbedding(word_to_ix, TEST_EMBEDDING_DIM)
combiner = DummyCombiner()
embeds = embedder(test_sent)
state = ParserState(test_sent, embeds, combiner)
state.shift()
feats = feat_extractor.get_features(state)
feats_list = make_list(feats)
true = ([-1.0276086330413818, -0.563052773475647, -0.8922905325889587, -0.05825017765164375],
[-0.4211951494216919, -0.510699987411499, -1.5726652145385742, -0.12324775755405426],
[3.586989402770996, -1.8312901258468628, 1.5987002849578857, -1.277006983757019])
pairs = zip(feats_list, true)
check_tensor_correctness(pairs)
def test_feature_extraction_d2_2():
""" 0.5 point(s) """
global test_sent, gold, word_to_ix, vocab
torch.manual_seed(1)
feat_extractor = SimpleFeatureExtractor()
embedder = VanillaWordEmbeddingLookup(word_to_ix, TEST_EMBEDDING_DIM)
combiner = DummyCombiner()
embeds = embedder(test_sent)
state = ParserState(test_sent, embeds, combiner)
state.shift()
state.shift()
feats = feat_extractor.get_features(state)
feats_list = make_list(feats)
true = ([ -1.8661, 1.4146, -1.8781, -0.4674 ], [ -0.9596, 0.5489, -0.9901, -0.3826 ], [ 0.5237, 0.0004, -1.2039, 3.5283 ])
pairs = zip(feats_list, true)
check_tensor_correctness(pairs)
def dependency_graph_from_oracle(sentence, actions):
"""
Take a sentence and a sequence of actions
and return a set of dependency edges for further evaluation
"""
stack = ParserState(sentence, [None] * len(sentence), DummyCombiner())
dependency_graph = set()
for act in [Actions.action_to_ix[a] for a in actions]:
if act == Actions.SHIFT:
stack.shift()
elif act == Actions.ARC_L:
dependency_graph.add(stack.arc_left())
elif act == Actions.ARC_R:
dependency_graph.add(stack.arc_right())
return dependency_graph
def dependency_graph_from_oracle(sentence, actions):
"""
Take a sentence and a sequence of shift-reduce actions and a sentence
and return a set of dependency edges for further evaluation
"""
stack = ParserState(sentence, [None] * len(sentence), DummyCombiner())
dependency_graph = set()
for act in [Actions.action_to_ix[a] for a in actions]:
if act == Actions.SHIFT:
stack.shift()
elif act == Actions.REDUCE_L:
dependency_graph.add(stack.reduce_left())
elif act == Actions.REDUCE_R:
dependency_graph.add(stack.reduce_right())
dependency_graph.add(
DepGraphEdge((ROOT_TOK, -1),
(stack.stack[-1].headword, stack.stack[-1].headword_pos)))
return dependency_graph
def make_dummy_parser_state(sentence):
dummy_embeds = [w + "-EMBEDDING"
for w in sentence] + [END_OF_INPUT_TOK + "-EMBEDDING"]
return ParserState(sentence + [END_OF_INPUT_TOK], dummy_embeds,
DummyCombiner())