def __init__(self, model_file=None, model_type=None, beam=1):
self.args = Config().args
self.state = None # State object created at each parse
self.oracle = None # Oracle object created at each parse
self.action_count = self.correct_action_count = self.total_actions = self.total_correct_actions = 0
self.label_count = self.correct_label_count = self.total_labels = self.total_correct_labels = 0
self.model = Model(model_type, model_file)
self.update_only_on_error = \
ClassifierProperty.update_only_on_error in self.model.model.get_classifier_properties()
self.beam = beam # Currently unused
self.state_hash_history = None # For loop checking
# Used in verify_passage to optionally ignore a mismatch in linkage nodes:
self.ignore_node = None if self.args.linkage else lambda n: n.tag == layer1.NodeTags.Linkage
self.best_score = self.dev = self.iteration = self.eval_index = None
self.trained = False
def __call__(self, config):
config.args.vocab = self.vocab
config.args.word_vectors = self.wordvectors
config.args.omit_features = self.omit
return SparseFeatureExtractor(omit_features=self.omit) if self.name == SPARSE else DenseFeatureExtractor(
OrderedDict((p.name, p.create_from_config()) for p in Model(None, config=config).param_defs()),
indexed=self.indexed, node_dropout=0, omit_features=self.omit)
def __init__(self, model_file=None, model_type=None, beam=1):
self.state = None # State object created at each parse
self.oracle = None # Oracle object created at each parse
self.scores = None # NumPy array of action scores at each action
self.action_count = 0
self.correct_count = 0
self.total_actions = 0
self.total_correct = 0
self.model = Model(model_type, model_file, Actions().all)
self.beam = beam # Currently unused
self.state_hash_history = None # For loop checking
# Used in verify_passage to optionally ignore a mismatch in linkage nodes:
self.ignore_node = None if Config(
).args.linkage else lambda n: n.tag == layer1.NodeTags.Linkage
self.best_score = self.dev = self.iteration = self.eval_index = None
self.dev_scores = []
self.trained = False
def test_model(model_type, formats, test_passage, iterations, omit_features, config):
filename = "_".join(filter(None, [os.path.join("test_files", "models", "test"), model_type, omit_features]+formats))
remove_existing(filename)
config.update(dict(classifier=model_type, copy_shared=None, omit_features=omit_features))
finalized = model = Model(filename, config=config)
for i in range(iterations):
parse(formats, model, test_passage, train=True)
finalized = model.finalize(finished_epoch=True)
assert not getattr(finalized.feature_extractor, "node_dropout", 0), finalized.feature_extractor.node_dropout
parse(formats, model, test_passage, train=False)
finalized.save()
loaded = Model(filename, config=config)
loaded.load()
assert not getattr(loaded.feature_extractor, "node_dropout", 0), loaded.feature_extractor.node_dropout
for key, param in sorted(model.feature_extractor.params.items()):
loaded_param = loaded.feature_extractor.params[key]
assert param == loaded_param
assert_all_params_equal(finalized.all_params(), loaded.all_params(), decay=weight_decay(model))
def test_model(model_type, formats, test_passage, iterations, omit_features,
config):
filename = "_".join(
filter(None, [
os.path.join("test_files", "models", "test"), model_type,
omit_features
] + formats))
remove_existing(filename)
config.update(
dict(classifier=model_type,
copy_shared=None,
omit_features=omit_features))
finalized = model = Model(filename, config=config)
for i in range(iterations):
parse(formats, model, test_passage, train=True)
finalized = model.finalize(finished_epoch=True)
assert not getattr(finalized.feature_extractor, "node_dropout",
0), finalized.feature_extractor.node_dropout
parse(formats, model, test_passage, train=False)
finalized.save()
loaded = Model(filename, config=config)
loaded.load()
assert not getattr(loaded.feature_extractor, "node_dropout",
0), loaded.feature_extractor.node_dropout
for key, param in sorted(model.feature_extractor.params.items()):
loaded_param = loaded.feature_extractor.params[key]
assert param == loaded_param
assert_all_params_equal(finalized.all_params(),
loaded.all_params(),
decay=weight_decay(model))
def load_model(filename):
model = Model(filename=filename)
model.load()
return model
class Parser(object):
"""
Main class to implement transition-based UCCA parser
"""
def __init__(self, model_file=None, model_type=None, beam=1):
self.args = Config().args
self.state = None # State object created at each parse
self.oracle = None # Oracle object created at each parse
self.action_count = self.correct_action_count = self.total_actions = self.total_correct_actions = 0
self.label_count = self.correct_label_count = self.total_labels = self.total_correct_labels = 0
self.model = Model(model_type, model_file)
self.update_only_on_error = \
ClassifierProperty.update_only_on_error in self.model.model.get_classifier_properties()
self.beam = beam # Currently unused
self.state_hash_history = None # For loop checking
# Used in verify_passage to optionally ignore a mismatch in linkage nodes:
self.ignore_node = None if self.args.linkage else lambda n: n.tag == layer1.NodeTags.Linkage
self.best_score = self.dev = self.iteration = self.eval_index = None
self.trained = False
def train(self, passages=None, dev=None, iterations=1):
"""
Train parser on given passages
:param passages: iterable of passages to train on
:param dev: iterable of passages to tune on
:param iterations: number of iterations to perform
"""
self.trained = True
if passages:
if ClassifierProperty.trainable_after_saving in self.model.model.get_classifier_properties(
):
try:
self.model.load()
except FileNotFoundError:
print("not found, starting from untrained model.")
self.best_score = 0
self.dev = dev
for self.iteration in range(1, iterations + 1):
self.eval_index = 0
print("Training iteration %d of %d: " %
(self.iteration, iterations))
Config().random.shuffle(passages)
list(self.parse(passages, mode=ParseMode.train))
yield self.eval_and_save(self.iteration == iterations,
finished_epoch=True)
print("Trained %d iterations" % iterations)
if dev or not passages:
self.model.load()
def eval_and_save(self, last=False, finished_epoch=False):
scores = None
model = self.model
self.model = self.model.finalize(finished_epoch=finished_epoch)
if self.dev:
if not self.best_score:
self.model.save()
print("Evaluating on dev passages")
passage_scores = [
s for _, s in self.parse(
self.dev, mode=ParseMode.dev, evaluate=True)
]
scores = Scores(passage_scores)
average_score = scores.average_f1()
print("Average labeled F1 score on dev: %.3f" % average_score)
print_scores(scores,
self.args.devscores,
prefix_title="iteration",
prefix=[self.iteration] +
([self.eval_index] if self.args.save_every else []))
if average_score >= self.best_score:
print("Better than previous best score (%.3f)" %
self.best_score)
if self.best_score:
self.model.save()
self.best_score = average_score
else:
print("Not better than previous best score (%.3f)" %
self.best_score)
elif last or self.args.save_every is not None:
self.model.save()
if not last:
self.model = model # Restore non-finalized model
self.model.load_labels()
return scores
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)
def parse_passage(self, train):
"""
Internal method to parse a single passage
:param train: use oracle to train on given passages, or just parse with classifier?
"""
if self.args.verbose > 1:
print(" initial state: %s" % self.state)
while True:
if self.args.check_loops:
self.check_loop()
features = self.model.feature_extractor.extract_features(
self.state)
true_actions = self.get_true_actions(train)
action, predicted_action = self.choose_action(
features, train, true_actions)
try:
self.state.transition(action)
except AssertionError as e:
raise ParserException("Invalid transition: %s %s" %
(action, self.state)) from e
if self.args.verbose > 1:
if self.oracle:
print(" predicted: %-15s true: %-15s taken: %-15s %s" %
(predicted_action, "|".join(
map(str,
true_actions.values())), action, self.state))
else:
print(" action: %-15s %s" % (action, self.state))
if self.state.need_label: # Label action that requires a choice of label
true_label = self.get_true_label(action.orig_node)
label, predicted_label = self.choose_label(
features, train, true_label)
self.state.label_node(label)
if self.args.verbose > 1:
if self.oracle and not Config().args.use_gold_node_labels:
print(" predicted label: %-15s true label: %-15s" %
(predicted_label, true_label))
else:
print(" label: %-15s" % label)
self.model.model.finished_step(train)
if self.args.verbose > 1:
for line in self.state.log:
print(" " + line)
if self.state.finished:
return # action is Finish (or early update is triggered)
def get_true_actions(self, train):
true_actions = {}
if self.oracle:
try:
true_actions = self.oracle.get_actions(self.state,
self.model.actions,
create=train)
except (AttributeError, AssertionError) as e:
if train:
raise ParserException(
"Error in oracle during training") from e
return true_actions
def choose_action(self, features, train, true_actions):
scores = self.model.model.score(
features, axis=ACTION_AXIS) # Returns NumPy array
if self.args.verbose > 2:
print(" action scores: " +
",".join(("%s: %g" % x
for x in zip(self.model.actions.all, scores))))
try:
predicted_action = self.predict(scores, self.model.actions.all,
self.state.is_valid_action)
except StopIteration as e:
raise ParserException(
"No valid action available\n%s" %
(self.oracle.log if self.oracle else "")) from e
action = true_actions.get(predicted_action.id)
is_correct = (action is not None)
if is_correct:
self.correct_action_count += 1
else:
action = Config().random.choice(list(
true_actions.values())) if train else predicted_action
if train and not (is_correct and self.update_only_on_error):
best_action = self.predict(scores[list(true_actions.keys())],
list(true_actions.values()))
self.model.model.update(features,
axis=ACTION_AXIS,
pred=predicted_action.id,
true=best_action.id,
importance=self.args.swap_importance
if best_action.is_swap else 1)
if train and not is_correct and self.args.early_update:
self.state.finished = True
self.action_count += 1
return action, predicted_action
def get_true_label(self, node):
true_label = None
if self.oracle:
if node is not None:
true_label = node.attrib.get(LABEL_ATTRIB)
if true_label is not None:
true_label, _, _ = true_label.partition(LABEL_SEPARATOR)
if not self.state.is_valid_label(true_label):
raise ParserException("True label is invalid: %s %s" %
(true_label, self.state))
return true_label
def choose_label(self, features, train, true_label):
true_id = self.model.labels[
true_label] if self.oracle else None # Needs to happen before score()
if Config().args.use_gold_node_labels:
return true_label, true_label
scores = self.model.model.score(features, axis=LABEL_AXIS)
if self.args.verbose > 2:
print(" label scores: " +
",".join(("%s: %g" % x
for x in zip(self.model.labels.all, scores))))
label = predicted_label = self.predict(scores, self.model.labels.all,
self.state.is_valid_label)
if self.oracle:
is_correct = (label == true_label)
if is_correct:
self.correct_label_count += 1
if train and not (is_correct and self.update_only_on_error):
self.model.model.update(features,
axis=LABEL_AXIS,
pred=self.model.labels[label],
true=true_id)
label = true_label
self.label_count += 1
return label, predicted_label
def predict(self, scores, values, is_valid=None):
"""
Choose action/label based on classifier
Usually the best action/label is valid, so max is enough to choose it in O(n) time
Otherwise, sorts all the other scores to choose the best valid one in O(n lg n)
:return: valid action/label with maximum probability according to classifier
"""
return next(
filter(is_valid,
(values[i] for i in self.generate_descending(scores))))
@staticmethod
def generate_descending(scores):
yield scores.argmax()
yield from scores.argsort(
)[::
-1] # Contains the max, but otherwise items might be missed (different order)
def check_loop(self):
"""
Check if the current state has already occurred, indicating a loop
"""
h = hash(self.state)
assert h not in self.state_hash_history,\
"\n".join(["Transition loop", self.state.str("\n")] + [self.oracle.str("\n")] if self.oracle else ())
self.state_hash_history.add(h)
def evaluate_passage(self, guessed, ref):
converters = CONVERTERS.get(ref.extra.get(
"format")) # returns (input converter, output converter) tuple
score = EVALUATORS.get(ref.extra.get("format"), evaluation).evaluate(
guessed,
ref,
converter=converters
and converters[1], # converter output is list of lines
verbose=guessed and self.args.verbose > 2,
constructions=self.args.constructions)
print("F1=%.3f" % score.average_f1(), flush=True)
return score
def verify_passage(self, guessed, ref, show_diff):
"""
Compare predicted passage to true passage and raise an exception if they differ
:param ref: true passage
:param guessed: predicted passage to compare
:param show_diff: if passages differ, show the difference between them?
Depends on guessed having the original node IDs annotated in the "remarks" field for each node
"""
assert ref.equals(guessed, ignore_node=self.ignore_node), \
"Failed to produce true passage" + (diffutil.diff_passages(ref, guessed) if show_diff else "")
class Parser(object):
"""
Main class to implement transition-based UCCA parser
"""
def __init__(self, model_file=None, model_type=None, beam=1):
self.state = None # State object created at each parse
self.oracle = None # Oracle object created at each parse
self.scores = None # NumPy array of action scores at each action
self.action_count = 0
self.correct_count = 0
self.total_actions = 0
self.total_correct = 0
self.model = Model(model_type, model_file, Actions().all)
self.beam = beam # Currently unused
self.state_hash_history = None # For loop checking
# Used in verify_passage to optionally ignore a mismatch in linkage nodes:
self.ignore_node = None if Config(
).args.linkage else lambda n: n.tag == layer1.NodeTags.Linkage
self.best_score = self.dev = self.iteration = self.eval_index = None
self.dev_scores = []
self.trained = False
def train(self, passages=None, dev=None, iterations=1):
"""
Train parser on given passages
:param passages: iterable of passages to train on
:param dev: iterable of passages to tune on
:param iterations: number of iterations to perform
:return: trained model
"""
self.trained = True
if passages:
if ClassifierProperty.trainable_after_saving in self.model.model.get_classifier_properties(
):
try:
self.model.load()
except FileNotFoundError:
print("not found, starting from untrained model.")
self.best_score = 0
self.dev = dev
if Config().args.devscores:
with open(Config().args.devscores, "w") as f:
print(",".join(["iteration"] +
evaluation.Scores.field_titles(
Config().args.constructions)),
file=f)
for self.iteration in range(1, iterations + 1):
self.eval_index = 0
print("Training iteration %d of %d: " %
(self.iteration, iterations))
list(self.parse(passages, mode=ParseMode.train))
self.eval_and_save(self.iteration == iterations,
finished_epoch=True)
Config().random.shuffle(passages)
print("Trained %d iterations" % iterations)
if dev or not passages:
self.model.load()
def eval_and_save(self, last=False, finished_epoch=False):
model = self.model
self.model = self.model.finalize(finished_epoch=finished_epoch)
if self.dev:
print("Evaluating on dev passages")
scores = [
s for _, s in self.parse(
self.dev, mode=ParseMode.dev, evaluate=True)
]
scores = evaluation.Scores.aggregate(scores)
self.dev_scores.append(scores)
score = scores.average_f1()
print("Average labeled F1 score on dev: %.3f" % score)
if Config().args.devscores:
prefix = [self.iteration]
if Config().args.save_every:
prefix.append(self.eval_index)
with open(Config().args.devscores, "a") as f:
print(",".join([".".join(map(str, prefix))] +
scores.fields()),
file=f)
if score >= self.best_score:
print("Better than previous best score (%.3f)" %
self.best_score)
self.best_score = score
self.model.save()
else:
print("Not better than previous best score (%.3f)" %
self.best_score)
elif last or Config().args.save_every is not None:
self.model.save()
if not last:
self.model = model # Restore non-finalized model
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:
self.train()
passage_word = "sentence" if Config().args.sentences else \
"paragraph" if Config().args.paragraphs else \
"passage"
self.total_actions = 0
self.total_correct = 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):
l0 = passage.layer(layer0.LAYER_ID)
num_tokens = len(l0.all)
l1 = passage.layer(layer1.LAYER_ID)
labeled = len(l1.all) > 1
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 = 0
self.correct_count = 0
textutil.annotate(passage, verbose=Config().args.verbose
) # tag POS and parse dependencies
self.state = State(passage)
self.state_hash_history = set()
self.oracle = Oracle(passage) if train 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
predicted_passage = self.state.create_passage(assert_proper=Config().args.verify) \
if not train or Config().args.verify else passage
duration = time.time() - started
total_duration += duration
num_tokens -= len(self.state.buffer)
total_tokens += num_tokens
if train: # We have an oracle to verify by
if not failed and Config().args.verify:
self.verify_passage(passage, predicted_passage, train)
if self.action_count:
print("%-16s" %
("%d%% (%d/%d)" %
(100 * self.correct_count / self.action_count,
self.correct_count, self.action_count)),
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 train:
print(Config().line_end, flush=True)
self.model.model.finished_item(train)
self.total_correct += self.correct_count
self.total_actions += self.action_count
if train and Config().args.save_every and (
passage_index + 1) % Config().args.save_every == 0:
self.eval_and_save()
self.eval_index += 1
yield (predicted_passage,
evaluate_passage(
predicted_passage,
passage)) if evaluate else predicted_passage
if passages:
print("Parsed %d %ss" % (passage_index + 1, passage_word))
if self.oracle and self.total_actions:
print("Overall %d%% correct transitions (%d/%d) on %s" %
(100 * self.total_correct / self.total_actions,
self.total_correct, self.total_actions, mode.name))
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)
def parse_passage(self, train):
"""
Internal method to parse a single passage
:param train: use oracle to train on given passages, or just parse with classifier?
"""
if Config().args.verbose:
print(" initial state: %s" % self.state)
while True:
if Config().args.check_loops:
self.check_loop(print_oracle=train)
true_actions = []
if self.oracle is not None:
try:
true_actions = self.oracle.get_actions(self.state)
except (AttributeError, AssertionError) as e:
if train:
raise ParserException(
"Error in oracle during training") from e
features = self.model.feature_extractor.extract_features(
self.state)
predicted_action = self.predict_action(
features, true_actions) # sets self.scores
action = predicted_action
correct_action = False
if not true_actions:
true_actions = "?"
elif predicted_action in true_actions:
self.correct_count += 1
correct_action = True
elif train:
action = Config().random.choice(true_actions)
if train and not (correct_action
and ClassifierProperty.update_only_on_error
in self.model.model.get_classifier_properties()):
best_true_action = true_actions[0] if len(true_actions) == 1 else \
true_actions[self.scores[[a.id for a in true_actions]].argmax()]
self.model.model.update(
features, predicted_action.id, best_true_action.id,
Config().args.swap_importance
if best_true_action.is_swap else 1)
self.action_count += 1
self.model.model.finished_step(train)
try:
self.state.transition(action)
except AssertionError as e:
raise ParserException("Invalid transition (%s): %s" %
(action, e)) from e
if Config().args.verbose:
if self.oracle is None:
print(" action: %-15s %s" % (action, self.state))
else:
print(" predicted: %-15s true: %-15s taken: %-15s %s" %
(predicted_action, "|".join(map(
str, true_actions)), action, self.state))
for line in self.state.log:
print(" " + line)
if self.state.finished or train and not correct_action and Config(
).args.early_update:
return # action is FINISH
def check_loop(self, print_oracle):
"""
Check if the current state has already occurred, indicating a loop
:param print_oracle: whether to print the oracle in case of an assertion error
"""
h = hash(self.state)
assert h not in self.state_hash_history,\
"\n".join(["Transition loop", self.state.str("\n")] +
[self.oracle.str("\n")] if print_oracle else ())
self.state_hash_history.add(h)
def predict_action(self, features, true_actions):
"""
Choose action based on classifier
:param features: extracted feature values
:param true_actions: from the oracle, to copy orig_node if the same action is selected
:return: valid action with maximum probability according to classifier
"""
self.scores = self.model.model.score(features) # Returns a NumPy array
if Config().args.verbose >= 2:
print(" scores: " + " ".join(("%g" % s for s in self.scores)))
best_action = self.select_action(self.scores.argmax(), true_actions)
if self.state.is_valid(best_action):
return best_action
# Usually the best action is valid, so max is enough to choose it in O(n) time
# Otherwise, sort all the other scores to choose the best valid one in O(n lg n)
sorted_ids = self.scores.argsort()[::-1]
actions = (self.select_action(i, true_actions) for i in sorted_ids)
try:
return next(a for a in actions if self.state.is_valid(a))
except StopIteration as e:
raise ParserException(
"No valid actions available\n" +
("True actions: %s" % true_actions if true_actions else self.
oracle.log if self.oracle is not None else "") +
"\nReturned actions: %s" %
[self.select_action(i)
for i in sorted_ids] + "\nScores: %s" % self.scores) from e
@staticmethod
def select_action(i, true_actions=()):
"""
Find action with the given ID in true actions (if exists) or in all actions
:param i: ID to lookup
:param true_actions: preferred set of actions to look in first
:return: Action with id=i
"""
try:
return next(a for a in true_actions if a.id == i)
except StopIteration:
return Actions().all[i]
def verify_passage(self, passage, predicted_passage, show_diff):
"""
Compare predicted passage to true passage and die if they differ
:param passage: true passage
:param predicted_passage: predicted passage to compare
:param show_diff: if passages differ, show the difference between them?
Depends on predicted_passage having the original node IDs annotated
in the "remarks" field for each node.
"""
assert passage.equals(predicted_passage, ignore_node=self.ignore_node),\
"Failed to produce true passage" + \
(diffutil.diff_passages(
passage, predicted_passage) if show_diff else "")