def __init__(self, src_dict, tgt_dict, machine_type, machine_rules=None,
entity_rules=None):
# Get all actions indexed by prefix
self.action_indexer = get_action_indexer(tgt_dict.symbols)
# Load rule stats if provided
if machine_rules is not None:
assert os.path.isfile(machine_rules)
rule_stats = read_rule_stats(machine_rules)
# self.state_machine = StateMachine(folder)
self.get_new_state_machine = machine_generator(
rule_stats['possible_predicates'],
entity_rules=entity_rules
)
else:
assert machine_type != 'AMR', \
"AMR machine expects --machine-rules"
rule_stats = None
self.get_new_state_machine = machine_generator(None)
# store some variables
self.src_dict = src_dict
self.tgt_dict = tgt_dict
self.machine_type = machine_type
def main():
# Argument handling
args = argument_parser()
# Read
# Load AMR (replace some unicode characters)
if args.in_amr:
corpus = read_amr(args.in_amr, unicode_fixes=True)
amrs = corpus.amrs
# Load tokens
if args.in_tokens:
sentences = read_tokenized_sentences(args.in_tokens, separator='\t')
# Load actions i.e. oracle
if args.in_actions:
actions = read_tokenized_sentences(args.in_actions, separator='\t')
# Load scored actions i.e. mined oracle
if args.in_scored_actions:
scored_actions = read_action_scores(args.in_scored_actions)
# measure performance
print_score_action_stats(scored_actions)
# Load rule stats
if args.in_rule_stats:
rule_stats = read_rule_stats(args.in_rule_stats)
# Modify
# merge --in-actions and --in-scored-actions and store in --out-actions
if args.merge_mined:
# sanity checks
assert args.in_tokens, "--merge-mined requires --in-tokens"
assert args.in_actions, "--merge-mined requires --in-actions"
assert args.in_rule_stats, "--merge-mined requires --in-rule-stats"
assert args.out_rule_stats, "--merge-mined requires --out-rule-stats"
if args.in_actions:
assert len(actions) == len(scored_actions)
print(f'Merging {args.out_actions} and {args.in_scored_actions}')
# actions
actions = merge_actions(actions, scored_actions)
# fix actions split by whitespace arguments
if args.fix_actions:
actions = fix_actions_split_by_spaces(actions)
# merge rules
if args.merge_mined:
out_rule_stats = merge_rules(sentences, actions, rule_stats, entity_rules=args.entity_rules)
print(f'Merging {args.out_rule_stats} and {args.in_rule_stats}')
# Write
# actions
if args.out_actions:
dirname = os.path.dirname(args.out_actions)
if dirname:
os.makedirs(dirname, exist_ok=True)
write_tokenized_sentences(
args.out_actions,
actions,
separator='\t'
)
print(f'Wrote {args.out_actions}')
# rule stats
if args.out_rule_stats:
write_rule_stats(args.out_rule_stats, out_rule_stats)
print(f'Wrote {args.out_rule_stats}')
# AMR
if args.out_amr:
with open(args.out_amr, 'w') as fid:
for amr in amrs:
fid.write(amr.toJAMRString())
def make_binary_stack(args, target_vocab, input_prefix, output_prefix, eos_idx, pad_idx, mask_predicates=False, allow_unk=False, tokenize=None):
assert tokenize
# involved files
# for debug
input_senteces = input_prefix + '.en'
input_actions = input_prefix + '.actions'
# The AMR state machine allways expects rules
if args.machine_type == 'AMR':
assert args.machine_rules and os.path.isfile(args.machine_rules), \
f'Missing {args.machine_rules}'
# Read rules
train_rule_stats = read_rule_stats(args.machine_rules)
actions_by_stack_rules = train_rule_stats['possible_predicates']
else:
actions_by_stack_rules = None
action_indexer = get_action_indexer(target_vocab.symbols)
# initialize indices for each of variables
# memory (stack, buffer, dead) (position in memory)
stack_buffer_names = ['memory', 'memory_pos']
# FIXME: These values are hard-coded elsewhere in code
state_indices = [3, 4, 5]
assert eos_idx not in state_indices, "Can not reuse EOS index"
assert pad_idx not in state_indices, "Can not reuse PAD index"
indexed_data = {}
for name in stack_buffer_names:
indexed_data[name] = indexed_dataset.make_builder(
dataset_dest_file(args, output_prefix, name, "bin"),
impl=args.dataset_impl,
)
if mask_predicates:
# mask of target predictions
masks_path = dataset_dest_file(args, output_prefix, 'target_masks', "bin")
indexed_target_masks = indexed_dataset.make_builder(
masks_path,
impl=args.dataset_impl,
)
# active indices
active_logits_path = dataset_dest_file(args, output_prefix, 'active_logits', "bin")
indexed_active_logits = indexed_dataset.make_builder(
active_logits_path,
impl=args.dataset_impl,
)
# Returns function that generates initialized state machines given
# sentence
get_new_state_machine = machine_generator(actions_by_stack_rules, entity_rules=args.entity_rules)
num_sents = 0
missing_actions = Counter()
with open(input_actions, 'r') as fid_act, \
open(input_senteces, 'r') as fid_sent:
# Loop over sentences
for sentence in tqdm(fid_sent):
# Get actions, tokens
sent_tokens = tokenize(sentence)
sent_actions = tokenize(fid_act.readline())
# intialize state machine batch for size 1
state_machine = get_new_state_machine(
sent_tokens,
machine_type=args.machine_type
)
# collect target and source masks
sent_data = {}
for name in stack_buffer_names:
sent_data[name] = []
shape = (len(sent_actions), len(target_vocab.symbols))
logits_mask = np.zeros(shape)
active_logits = set()
for action_idx, gold_action in enumerate(sent_actions):
# active logits for this action
if mask_predicates:
# Get total valid actions by expanding base ones
valid_actions, invalid_actions = state_machine.get_valid_actions()
valid_action_idx = (
action_indexer(valid_actions)
- action_indexer(invalid_actions)
)
# if action is missing add it and count it
if gold_action in target_vocab.symbols:
gold_action_index = target_vocab.symbols.index(gold_action)
else:
gold_action_index = target_vocab.symbols.index('<unk>')
if gold_action_index not in valid_action_idx:
valid_action_idx.add(gold_action_index)
missing_actions.update([gold_action])
# if length 1 add pad to avoid deltas during learning
if len(valid_action_idx) == 1:
valid_action_idx.add(pad_idx)
# append number of nodes to regain matrix
logits_mask[action_idx, list(valid_action_idx)] = 1
active_logits |= valid_action_idx
# stack and buffer
memory, memory_pos = get_word_states(
state_machine,
sent_tokens,
indices=state_indices
)
# word states
sent_data['memory'].append(torch.Tensor(memory))
# note we use position 0 for reduced words
sent_data['memory_pos'].append(
torch.Tensor(memory_pos)
)
# Update machine
state_machine.applyAction(gold_action)
for name in stack_buffer_names:
# note that data needs to be stores as a 1d array
indexed_data[name].add_item(
torch.stack(sent_data[name]).view(-1)
)
# valid nodes
if mask_predicates:
active_logits = list(active_logits)
# reduce size to active items
logits_mask = logits_mask[:, active_logits]
indexed_target_masks.add_item(
torch.Tensor(logits_mask).view(-1)
)
# active indices
indexed_active_logits.add_item(torch.Tensor(
active_logits
))
# update number of sents
num_sents += 1
if not num_sents % 100:
print("\r%d sentences" % num_sents, end = '')
print("")
# close indexed data files
for name in stack_buffer_names:
output_file_idx = dataset_dest_file(args, output_prefix, name, "idx")
indexed_data[name].finalize(output_file_idx)
# close valid action mask
if mask_predicates:
target_mask_idx = dataset_dest_file(args, output_prefix, 'target_masks', "idx")
indexed_target_masks.finalize(target_mask_idx)
# active indices
active_logits_idx = dataset_dest_file(args, output_prefix, 'active_logits', "idx")
indexed_active_logits.finalize(active_logits_idx)
# inform about mssing actions
if missing_actions:
print(yellow_font("There were missing actions"))
print(missing_actions)
def main():
# Argument handling
args = argument_parser()
# read rules
train_rule_stats = read_rule_stats(args.in_rule_stats)
assert 'action_vocabulary' in train_rule_stats
assert 'possible_predicates' in train_rule_stats
action_list = list(sorted(train_rule_stats['action_vocabulary'].keys()))
# get all actions indexec by action root
action_by_basic = defaultdict(list)
for action in train_rule_stats['action_vocabulary'].keys():
key = action.split('(')[0]
action_by_basic[key].append(action)
# open file for reading if provided
write_out_states = h5_writer(args.out_word_states)
write_out_valid_actions = h5_writer(args.out_valid_actions)
# Read content
# TODO: Point to LDC data
sentences = readlines(args.in_sentences)
actions = readlines(args.in_actions)
assert len(sentences) == len(actions)
# initialize spacy lemmatizer out of the sentence loop for speed
spacy_lemmatizer = get_spacy_lemmatizer()
sent_idx = -1
stats = {
'missing_pred_count': Counter(),
'missing_action_count': Counter(),
'fan_out_count': Counter()
}
for sent_tokens, sent_actions in tqdm(
zip(sentences, actions),
desc='extracting oracle masks',
total=len(actions)
):
# keep count of sentence index
sent_idx += 1
if args.offset and sent_idx < args.offset:
continue
# Initialize state machine
amr_state_machine = AMRStateMachine(
sent_tokens,
spacy_lemmatizer=spacy_lemmatizer
)
# process each action
word_states_sent = []
valid_actions_sent = []
for raw_action in sent_actions:
# Store states BEFORE ACTION
# state of each word (buffer B, stack S, reduced X)
word_states = get_word_states(amr_state_machine, sent_tokens)
# Get actions valid for this state
valid_actions = get_valid_actions(
action_list,
amr_state_machine,
train_rule_stats,
action_by_basic,
raw_action,
stats
)
# update info
word_states_sent.append(word_states)
valid_actions_sent.append(valid_actions)
# Update machine
amr_state_machine.applyAction(raw_action)
# Write states for this sentence
write_out_states(word_states_sent)
write_out_valid_actions(valid_actions_sent)
# inform usre about missing predicates
for miss in ['missing_pred_count', 'missing_action_count']:
num_missing = len(stats[miss])
if num_missing:
alert_str = f'{num_missing} {miss} rule_stats'
print(yellow_font(alert_str))
# inform user about fan-out stats
mode_fan_out = stats['fan_out_count'].most_common(1)[0][0]
max_fan_out = max(stats['fan_out_count'].keys())
alert_str = f'num_actions mode: {mode_fan_out} max: {max_fan_out}'
print(alert_str)
# Close file
write_out_states()
write_out_valid_actions()
def main():
# Argument handling
args = argument_parser()
# Get data
sentences = read_tokenized_sentences(args.in_sentences,
separator=args.separator)
# Initialize logger/printer
logger = Logger(step_by_step=args.step_by_step,
clear_print=args.clear_print,
pause_time=args.pause_time,
verbose=args.verbose)
# generate rules to restrict action space by stack content
if args.action_rules_from_stats:
rule_stats = read_rule_stats(args.action_rules_from_stats)
actions_by_stack_rules = rule_stats['possible_predicates']
for token, counter in rule_stats['possible_predicates'].items():
actions_by_stack_rules[token] = Counter(counter)
else:
actions_by_stack_rules = None
# Fake parser built from actions
actions = read_tokenized_sentences(args.in_actions,
separator=args.separator)
assert len(sentences) == len(actions)
parsing_model = FakeAMRParser(
from_sent_act_pairs=zip(sentences, actions),
machine_type=args.machine_type,
logger=logger,
actions_by_stack_rules=actions_by_stack_rules,
no_whitespace_in_actions=args.no_whitespace_in_actions,
entity_rules=args.entity_rules)
# Get output AMR writer
if args.out_amr:
amr_write = writer(args.out_amr)
if args.out_bio_tags:
bio_write = writer(args.out_bio_tags)
# Loop over sentences
for sent_idx, tokens in tqdm(enumerate(sentences), desc='parsing'):
# fast-forward until desired sentence number
if args.offset and sent_idx < args.offset:
continue
# parse
# NOTE: To simulate the real endpoint, input provided as a string of
# whitespace separated tokens
machine, bio_tags = parsing_model.parse_sentence(" ".join(tokens))
# sanity check annotations
dupes = get_duplicate_edges(machine.amr)
if args.sanity_check and any(dupes):
msg = yellow_font('WARNING:')
print(f'{msg} duplicated edges in sent {sent_idx}', end=' ')
print(dict(dupes))
print(' '.join(machine.tokens))
# store output AMR
if args.out_bio_tags:
tag_str = '\n'.join([f'{to} {ta}' for to, ta in bio_tags])
tag_str += '\n\n'
bio_write(tag_str)
if args.out_amr:
try:
amr_write(machine.amr.toJAMRString())
except InvalidAMRError as exception:
print(f'\nFailed at sentence {sent_idx}\n')
raise exception
if (getattr(parsing_model, "rule_violation")
and parsing_model.rule_violation):
print(yellow_font("There were one or more action rule violations"))
print(parsing_model.rule_violation)
if args.action_rules_from_stats:
print("Predict rules had following statistics")
print(parsing_model.pred_counts)
# close output writers
if args.out_amr:
amr_write()
if args.out_bio_tags:
bio_write()
