def predict(model: models.Model, sentences: List[Sentence],
parsing_system: ParsingSystem,
vocabulary: Vocabulary) -> List[DependencyTree]:
"""
Predicts the dependency tree for a given sentence by greedy decoding.
We generate a initial configuration (features) for ``sentence`` using
``parsing_system`` and ``vocabulary``. Then we apply the ``model`` to predict
what's the best transition for this configuration and apply this transition
(greedily) with ``parsing_system`` to get the next configuration. We do
this till the terminal configuration is reached.
"""
predicted_trees = []
num_transitions = parsing_system.num_transitions()
for sentence in tqdm(sentences):
configuration = parsing_system.initial_configuration(sentence)
while not parsing_system.is_terminal(configuration):
features = get_configuration_features(configuration, vocabulary)
features = np.array(features).reshape((1, -1))
logits = model(features)["logits"].numpy()
opt_score = -float('inf')
opt_trans = ""
for j in range(num_transitions):
if (logits[0, j] > opt_score and parsing_system.can_apply(
configuration, parsing_system.transitions[j])):
opt_score = logits[0, j]
opt_trans = parsing_system.transitions[j]
configuration = parsing_system.apply(configuration, opt_trans)
predicted_trees.append(configuration.tree)
return predicted_trees
def evaluate(sentences: List[Sentence], parsing_system: ParsingSystem,
predicted_trees: List[DependencyTree],
label_trees: List[DependencyTree]) -> str:
"""
Predict the dependency trees and evaluate them comparing with gold trees.
"""
return parsing_system.evaluate(sentences, predicted_trees, label_trees)
def generate_training_instances(parsing_system: ParsingSystem,
sentences: List[List[str]],
vocabulary: Vocabulary,
trees: List[DependencyTree]) -> List[Dict]:
"""
Generates training instances of configuration and transition labels
from the sentences and the corresponding dependency trees.
"""
num_transitions = parsing_system.num_transitions()
instances: Dict[str, List] = []
for i in tqdm(range(len(sentences))):
if trees[i].is_projective():
c = parsing_system.initial_configuration(sentences[i])
while not parsing_system.is_terminal(c):
oracle = parsing_system.get_oracle(c, trees[i])
feature = get_configuration_features(c, vocabulary)
label = []
for j in range(num_transitions):
t = parsing_system.transitions[j]
if t == oracle:
label.append(1.)
elif parsing_system.can_apply(c, t):
label.append(0.)
else:
label.append(-1.)
if 1.0 not in label:
print(i, label)
instances.append({"input": feature, "label": label})
c = parsing_system.apply(c, oracle)
return instances
print("Reading training data")
train_sentences, train_trees = read_conll_data(args.train_data_file_path)
print("Reading validation data")
validation_sentences, validation_trees = read_conll_data(
args.validation_data_file_path)
vocabulary = Vocabulary(train_sentences, train_trees)
sorted_labels = [
item[0] for item in sorted(vocabulary.label_token_to_id.items(),
key=lambda e: e[1])
]
non_null_sorted_labels = sorted_labels[1:]
parsing_system = ParsingSystem(non_null_sorted_labels)
# Generating training instances takes ~20 minutes everytime. So once you finalize the
# feature generation and want to try different configs for experiments, you can use caching.
if args.use_cached_data:
print("Loading cached training instances")
cache_processed_data_path = args.train_data_file_path.replace(
"conll", "jsonl")
if not os.path.exists(cache_processed_data_path):
raise Exception(
f"You asked to use cached data but {cache_processed_data_path} "
f"is not available.")
with open(cache_processed_data_path, "r") as file:
train_instances = [
json.loads(line) for line in tqdm(file.readlines())
if line.strip()
type=str,
help=
'serialization directory of the trained model. Used only for vocab.')
parser.add_argument('gold_data_path',
type=str,
help='gold data file path.')
parser.add_argument('prediction_data_path',
type=str,
help='predictions data file path.')
args = parser.parse_args()
print("Reading data")
sentences, label_trees = read_conll_data(args.gold_data_path)
_, predicted_trees = read_conll_data(args.prediction_data_path)
print("Reading vocabulary")
vocabulary_path = os.path.join(args.load_serialization_dir, "vocab.pickle")
vocabulary = Vocabulary.load(vocabulary_path)
sorted_labels = [
item[0] for item in sorted(vocabulary.label_token_to_id.items(),
key=lambda e: e[1])
]
non_null_sorted_labels = sorted_labels[1:]
parsing_system = ParsingSystem(non_null_sorted_labels)
print("Evaluating")
report = evaluate(sentences, parsing_system, predicted_trees, label_trees)
print(report)
