def get_amr(tokens, actions, entity_rules):
# play state machine to get AMR
state_machine = AMRStateMachine(tokens, entity_rules=entity_rules)
for action in actions:
# FIXME: It is unclear that this will be allways the right option
# manual exploration of dev yielded 4 cases and it works for the 4
if action == "<unk>":
action = f'PRED({state_machine.get_top_of_stack()[0].lower()})'
state_machine.applyAction(action)
# sanity check: foce close
if not state_machine.is_closed:
alert_str = yellow_font('Machine not closed!')
print(alert_str)
state_machine.CLOSE()
# TODO: Probably waisting ressources here
amr_str = state_machine.amr.toJAMRString()
return AMR.get_amr_line(amr_str.split('\n'))
def runOracle(self,
gold_amrs,
propbank_args=None,
out_oracle=None,
out_amr=None,
out_sentences=None,
out_actions=None,
out_rule_stats=None,
add_unaligned=0,
no_whitespace_in_actions=False,
multitask_words=None,
copy_lemma_action=False,
addnode_count_cutoff=None):
print_log("oracle", "Parsing data")
# deep copy of gold AMRs
self.gold_amrs = [gold_amr.copy() for gold_amr in gold_amrs]
# print about inconsistencies in annotations
alert_inconsistencies(self.gold_amrs)
# open all files (if paths provided) and get writers to them
oracle_write = writer(out_oracle)
amr_write = writer(out_amr)
sentence_write = writer(out_sentences, add_return=True)
actions_write = writer(out_actions, add_return=True)
# This will store overall stats
self.stats = {
'possible_predicates': Counter(),
'action_vocabulary': Counter(),
'addnode_counts': Counter()
}
# unaligned tokens
included_unaligned = [
'-',
'and',
'multi-sentence',
'person',
'cause-01',
'you',
'more',
'imperative',
'1',
'thing',
]
# initialize spacy lemmatizer out of the sentence loop for speed
spacy_lemmatizer = None
if copy_lemma_action:
spacy_lemmatizer = get_spacy_lemmatizer()
# Loop over golf AMRs
for sent_idx, gold_amr in tqdm(enumerate(self.gold_amrs),
desc=f'computing oracle',
total=len(self.gold_amrs)):
if self.verbose:
print("New Sentence " + str(sent_idx) + "\n\n\n")
# TODO: Describe what is this pre-processing
gold_amr = preprocess_amr(gold_amr, add_unaligned,
included_unaligned)
# Initialize state machine
tr = AMRStateMachine(gold_amr.tokens,
verbose=self.verbose,
add_unaligned=add_unaligned,
spacy_lemmatizer=spacy_lemmatizer,
entity_rules=self.entity_rules)
self.transitions.append(tr)
self.amrs.append(tr.amr)
# Loop over potential actions
while tr.buffer or tr.stack:
if self.tryMerge(tr, tr.amr, gold_amr):
action = 'MERGE'
elif self.tryEntity(tr, tr.amr, gold_amr):
action = f'ADDNODE({self.entity_type})'
elif self.tryDependent(tr, tr.amr, gold_amr):
edge = self.new_edge[1:] \
if self.new_edge.startswith(':') else self.new_edge
action = f'DEPENDENT({self.new_node},{edge})'
self.dep_id = None
elif self.tryConfirm(tr, tr.amr, gold_amr):
# if --copy-lemma-action check if lemma or first sense
# equal node name. Use corresponding action
if copy_lemma_action:
lemma, _ = tr.get_top_of_stack(lemma=True)
if copy_lemma_action and lemma == self.new_node:
action = 'COPY_LEMMA'
elif copy_lemma_action and f'{lemma}-01' == self.new_node:
action = 'COPY_SENSE01'
else:
action = f'PRED({self.new_node})'
else:
action = f'PRED({self.new_node})'
elif self.tryIntroduce(tr, tr.amr, gold_amr):
action = 'INTRODUCE'
elif self.tryLA(tr, tr.amr, gold_amr):
if self.new_edge == 'root':
action = f'LA({self.new_edge})'
else:
action = f'LA({self.new_edge[1:]})'
elif self.tryRA(tr, tr.amr, gold_amr):
if self.new_edge == 'root':
action = f'RA({self.new_edge})'
else:
action = f'RA({self.new_edge[1:]})'
elif self.tryReduce(tr, tr.amr, gold_amr):
action = 'REDUCE'
elif self.trySWAP(tr, tr.amr, gold_amr):
action = 'UNSHIFT'
elif tr.buffer:
action = 'SHIFT'
else:
tr.stack = []
tr.buffer = []
break
# Store stats
# get token(s) at the top of the stack
token, merged_tokens = tr.get_top_of_stack()
action_label = action.split('(')[0]
# check action has not invalid chars and normalize
# TODO: --no-whitespace-in-actions being deprecated
if no_whitespace_in_actions and action_label == 'PRED':
assert '_' not in action, \
"--no-whitespace-in-actions prohibits use of _ in actions"
if ' ' in action_label:
action = action.replace(' ', '_')
# Add prediction ot top of the buffer
if action == 'SHIFT' and multitask_words is not None:
action = label_shift(tr, multitask_words)
# APPLY ACTION
tr.applyAction(action)
# Close machine
tr.CLOSE(training=True,
gold_amr=gold_amr,
use_addnonde_rules=use_addnode_rules)
# update files
if out_oracle:
# to avoid printing
oracle_write(str(tr))
# JAMR format AMR
amr_write(tr.amr.toJAMRString())
# Tokens and actions
# extra tag to be reduced at start
tokens = tr.amr.tokens
actions = tr.actions
# Update action count
self.stats['action_vocabulary'].update(actions)
del gold_amr.nodes[-1]
addnode_actions = [a for a in actions if a.startswith('ADDNODE')]
self.stats['addnode_counts'].update(addnode_actions)
# separator
if no_whitespace_in_actions:
sep = " "
else:
sep = "\t"
tokens = sep.join(tokens)
actions = sep.join(actions)
# Write
sentence_write(tokens)
actions_write(actions)
print_log("oracle", "Done")
# close files if open
oracle_write()
amr_write()
sentence_write()
actions_write()
self.labelsO2idx = {'<pad>': 0}
self.labelsA2idx = {'<pad>': 0}
self.pred2idx = {'<pad>': 0}
self.action2idx = {'<pad>': 0}
for tr in self.transitions:
for a in tr.actions:
a = AMRStateMachine.readAction(a)[0]
self.action2idx.setdefault(a, len(self.action2idx))
for p in tr.predicates:
self.pred2idx.setdefault(p, len(self.pred2idx))
for l in tr.labels:
self.labelsO2idx.setdefault(l, len(self.labelsO2idx))
for l in tr.labelsA:
self.labelsA2idx.setdefault(l, len(self.labelsA2idx))
self.stats["action2idx"] = self.action2idx
self.stats["pred2idx"] = self.pred2idx
self.stats["labelsO2idx"] = self.labelsO2idx
self.stats["labelsA2idx"] = self.labelsA2idx
# Compute the word dictionary
self.char2idx = {'<unk>': 0}
self.word2idx = {'<unk>': 0, '<eof>': 1, '<ROOT>': 2, '<unaligned>': 3}
self.node2idx = {}
self.word_counter = Counter()
for amr in self.gold_amrs:
for tok in amr.tokens:
self.word_counter[tok] += 1
self.word2idx.setdefault(tok, len(self.word2idx))
for ch in tok:
self.char2idx.setdefault(ch, len(self.char2idx))
for n in amr.nodes:
self.node2idx.setdefault(amr.nodes[n], len(self.node2idx))
self.stats["char2idx"] = self.char2idx
self.stats["word2idx"] = self.word2idx
self.stats["node2idx"] = self.node2idx
self.stats["word_counter"] = self.word_counter
self.stats['possible_predicates'] = self.possiblePredicates
if addnode_count_cutoff:
self.stats['addnode_blacklist'] = [
a for a, c in self.stats['addnode_counts'].items()
if c <= addnode_count_cutoff
]
num_addnode_blackl = len(self.stats['addnode_blacklist'])
num_addnode = len(self.stats['addnode_counts'])
print(f'{num_addnode_blackl}/{num_addnode} blacklisted ADDNODES')
del self.stats['addnode_counts']
# State machine stats for this senetnce
if out_rule_stats:
with open(out_rule_stats, 'w') as fid:
fid.write(json.dumps(self.stats))