def _test_features(config, feature_extractor_creator, filename, write_features):
feature_extractor = feature_extractor_creator(config)
passage = load_passage(filename, annotate=feature_extractor_creator.annotated)
textutil.annotate(passage, as_array=True, as_extra=False, vocab=config.vocab())
config.set_format(passage.extra.get("format") or "ucca")
oracle = Oracle(passage)
state = State(passage)
actions = Actions()
for key, param in feature_extractor.params.items():
if not param.numeric:
param.dropout = 0
feature_extractor.init_param(key)
features = [feature_extractor.init_features(state)]
while True:
extract_features(feature_extractor, state, features)
action = min(oracle.get_actions(state, actions).values(), key=str)
state.transition(action)
if state.need_label:
extract_features(feature_extractor, state, features)
label, _ = oracle.get_label(state, action)
state.label_node(label)
if state.finished:
break
features = ["%s %s\n" % i for f in features if f for i in (sorted(f.items()) + [("", "")])]
compare_file = os.path.join("test_files", "features", "-".join((basename(filename), str(feature_extractor_creator)))
+ ".txt")
if write_features:
with open(compare_file, "w", encoding="utf-8") as f:
f.writelines(features)
with open(compare_file, encoding="utf-8") as f:
assert f.readlines() == features, compare_file
def init(self):
self.config.set_format(self.in_format)
WIKIFIER.enabled = self.config.args.wikification
self.state = State(self.passage)
# Passage is considered labeled if there are any edges or node labels in it
edges, node_labels = map(any, zip(*[(n.outgoing, n.attrib.get(LABEL_ATTRIB))
for n in self.passage.layer(layer1.LAYER_ID).all]))
self.oracle = Oracle(self.passage) if self.training or self.config.args.verify or (
(self.config.args.verbose > 1 or self.config.args.use_gold_node_labels or self.config.args.action_stats)
and (edges or node_labels)) else None
for model in self.models:
model.init_model(self.config.format, lang=self.lang if self.config.args.multilingual else None)
if ClassifierProperty.require_init_features in model.classifier_properties:
model.init_features(self.state, self.training)
def test_oracle(self):
for passage in self.load_passages():
oracle = Oracle(passage)
state = State(passage)
actions_taken = []
while True:
actions = oracle.get_actions(state)
action = next(iter(actions))
state.transition(action)
actions_taken.append("%s\n" % action)
if state.finished:
break
with open("test_files/standard3.oracle_actions.txt") as f:
self.assertSequenceEqual(actions_taken, f.readlines())
def gen_actions(passage):
oracle = Oracle(passage)
state = State(passage)
actions = Actions()
while True:
action = min(oracle.get_actions(state, actions).values(), key=str)
state.transition(action)
s = str(action)
if state.need_label:
label, _ = oracle.get_label(state, action)
state.label_node(label)
s += " " + str(label)
yield s
if state.finished:
break
def test_oracle(self):
for passage in self.load_passages():
oracle = Oracle(passage)
state = State(passage)
actions = Actions()
actions_taken = []
while True:
action = min(oracle.get_actions(state, actions).values(), key=str)
state.transition(action)
actions_taken.append("%s\n" % action)
if state.finished:
break
# with open("test_files/standard3.oracle_actions.txt", "w") as f:
# f.writelines(actions_taken)
with open("test_files/standard3.oracle_actions.txt") as f:
self.assertSequenceEqual(actions_taken, f.readlines())
def gen_actions(passage, feature_extractor):
global envTrainingData, allLabels, allTypes, allActions
oracle = Oracle(passage)
state = State(passage)
actions = Actions()
while True:
acts = oracle.get_actions(state, actions).values()
type_label_maps = {
a.type: a.tag
for a in acts
} # There should be no duplicate types with different tags since there is only one golden tree
obs = feature_extractor.extract_features(state)['numeric']
for index in [7, 9, 11, 14, 15, 16, 17, 17, 18, 22]:
del obs[index]
for act in allActions:
cur_type = act['type']
cur_has_label = act['hasLabel']
cur_label = act['label']
# TODO: Double consider the reward mechanism.
# Encourage the agent to produce less mistake VS encourage it to produce more correctness:
# The latter will encourage an episode to go on endlessly, while the former encourage it to end as soon as possible.
# For now, choose to be neutral: 100% correct = 0.5 reward; 100% wrong = -0.5 reward
r = -0.5
if cur_type in list(
type_label_maps.keys()): # If action type matches
r += 0.5
if cur_has_label and cur_label == type_label_maps[
cur_type] or not cur_has_label: # If action has no label or label matches
r += 0.5
actNum = allActions.index(act)
trainingData = {'obs': obs, 'act': actNum, 'r': r}
envTrainingData.append(trainingData)
action = min(acts, key=str)
state.transition(action)
s = str(action)
# if state.need_label:
# label, _ = oracle.get_label(state, action)
# state.label_node(label)
# s += " " + str(label)
yield s
if state.finished:
break
def parse(self, passages, mode=ParseMode.test, evaluate=False):
"""
Parse given passages
:param passages: iterable of passages to parse
:param mode: ParseMode value.
If train, use oracle to train on given passages.
Otherwise, just parse with classifier.
:param evaluate: whether to evaluate parsed passages with respect to given ones.
Only possible when given passages are annotated.
:return: generator of parsed passages (or in train mode, the original ones),
or, if evaluate=True, of pairs of (Passage, Scores).
"""
assert mode in ParseMode, "Invalid parse mode: %s" % mode
train = (mode is ParseMode.train)
if not train and not self.trained:
list(self.train())
passage_word = "sentence" if self.args.sentences else \
"paragraph" if self.args.paragraphs else \
"passage"
self.total_actions = 0
self.total_correct_actions = 0
total_duration = 0
total_tokens = 0
passage_index = 0
if not hasattr(passages, "__iter__"): # Single passage given
passages = (passages, )
for passage_index, passage in enumerate(passages):
labeled = any(n.outgoing or n.attrib.get(LABEL_ATTRIB)
for n in passage.layer(layer1.LAYER_ID).all)
assert not train or labeled, "Cannot train on unannotated passage: %s" % passage.ID
assert not evaluate or labeled, "Cannot evaluate on unannotated passage: %s" % passage.ID
print("%s %-7s" % (passage_word, passage.ID),
end=Config().line_end,
flush=True)
started = time.time()
self.action_count = self.correct_action_count = self.label_count = self.correct_label_count = 0
textutil.annotate(passage, verbose=self.args.verbose >
1) # tag POS and parse dependencies
self.state = State(passage)
self.state_hash_history = set()
self.oracle = Oracle(passage) if train or (
self.args.verbose or Config().args.use_gold_node_labels
) and labeled or self.args.verify else None
failed = False
if ClassifierProperty.require_init_features in self.model.model.get_classifier_properties(
):
self.model.init_features(self.state, train)
try:
self.parse_passage(
train) # This is where the actual parsing takes place
except ParserException as e:
if train:
raise
Config().log("%s %s: %s" % (passage_word, passage.ID, e))
failed = True
guessed = self.state.create_passage(
verify=self.args.verify
) if not train or self.args.verify else passage
duration = time.time() - started
total_duration += duration
num_tokens = len(
set(self.state.terminals).difference(self.state.buffer))
total_tokens += num_tokens
if self.oracle: # We have an oracle to verify by
if not failed and self.args.verify:
self.verify_passage(guessed, passage, train)
if self.action_count:
accuracy_str = "%d%% (%d/%d)" % (
100 * self.correct_action_count / self.action_count,
self.correct_action_count, self.action_count)
if self.label_count:
accuracy_str += " %d%% (%d/%d)" % (
100 * self.correct_label_count / self.label_count,
self.correct_label_count, self.label_count)
print("%-30s" % accuracy_str, end=Config().line_end)
print("%0.3fs" % duration, end="")
print("%-15s" % (" (failed)" if failed else " (%d tokens/s)" %
(num_tokens / duration)),
end="")
print(Config().line_end, end="")
if self.oracle:
print(Config().line_end, flush=True)
self.model.model.finished_item(train)
self.total_correct_actions += self.correct_action_count
self.total_actions += self.action_count
self.total_correct_labels += self.correct_label_count
self.total_labels += self.label_count
if train and self.args.save_every and (
passage_index + 1) % self.args.save_every == 0:
self.eval_and_save()
self.eval_index += 1
yield (guessed, self.evaluate_passage(
guessed, passage)) if evaluate else guessed
if passages:
print("Parsed %d %ss" % (passage_index + 1, passage_word))
if self.oracle and self.total_actions:
accuracy_str = "%d%% correct actions (%d/%d)" % (
100 * self.total_correct_actions / self.total_actions,
self.total_correct_actions, self.total_actions)
if self.total_labels:
accuracy_str += ", %d%% correct labels (%d/%d)" % (
100 * self.total_correct_labels / self.total_labels,
self.total_correct_labels, self.total_labels)
print("Overall %s on %s" % (accuracy_str, mode.name))
if total_duration:
print(
"Total time: %.3fs (average time/%s: %.3fs, average tokens/s: %d)"
% (total_duration, passage_word, total_duration /
(passage_index + 1), total_tokens / total_duration),
flush=True)