def test_readonly():
Document.add_property('some_property', 123)
nlp = stanza.Pipeline(dir=TEST_MODELS_DIR, lang='en')
doc = nlp(EN_DOC)
assert doc.some_property == 123
with pytest.raises(ValueError):
doc.some_property = 456
def __init__(self, data, word2idx, tolower=True):
super(GraphData, self).__init__()
g_p = utils.doc2graph(Document(data[config.pf]))
g_h = utils.doc2graph(Document(data[config.hf]))
self.edge_index_p = g_p.edge_index
self.edge_index_h = g_h.edge_index
#print(g_p.node_attr)
# care [ROOT] [UNK] should not get lower!!!
if tolower == True:
self.x_p = torch.tensor([
word2idx[w.lower() if w[0] != "[" or w[-1] != "]" else w]
for w in g_p.node_attr
],
dtype=torch.long)
self.x_h = torch.tensor([
word2idx[w.lower() if w[0] != "[" or w[-1] != "]" else w]
for w in g_h.node_attr
],
dtype=torch.long)
else:
print("not to lower")
self.x_p = torch.tensor([word2idx[w] for w in g_p.node_attr],
dtype=torch.long)
self.x_h = torch.tensor([word2idx[w] for w in g_h.node_attr],
dtype=torch.long)
label_onehot = torch.zeros([1, config.NUM_CLASSES])
label_onehot[0][data[config.lf]] = 1
#label_onehot = label_onehot.squeeze()
#print(label_onehot.size())
self.label = label_onehot.to(dtype=torch.float)
self.pid = data[config.idf]
def check_mwt(filename):
"""
Checks whether or not there are MWTs in the given conll file
"""
doc = Document(CoNLL.conll2dict(filename))
data = doc.get_mwt_expansions(False)
return len(data) > 0
def __init__(self, data, word2idx, tolower=True):
super(GraphData, self).__init__()
g_p = utils.doc2graph(Document(data[config.pf]))
g_h = utils.doc2graph(Document(data[config.hf]))
self.edge_index_p = g_p.edge_index
self.edge_index_h = g_h.edge_index
#print(g_p.node_attr)
# care [ROOT] [UNK] should not get lower!!!
if tolower == True:
self.x_p = torch.tensor([
word2idx[w.lower() if w[0] != "[" or w[-1] != "]" else w]
for w in g_p.node_attr
],
dtype=torch.long)
self.x_h = torch.tensor([
word2idx[w.lower() if w[0] != "[" or w[-1] != "]" else w]
for w in g_h.node_attr
],
dtype=torch.long)
else:
print("not to lower")
self.x_p = torch.tensor([word2idx[w] for w in g_p.node_attr],
dtype=torch.long)
self.x_h = torch.tensor([word2idx[w] for w in g_h.node_attr],
dtype=torch.long)
self.label = data[config.lf]
self.pid = data[config.idf]
def evaluate(args):
# file paths
system_pred_file = args['output_file']
gold_file = args['gold_file']
model_file = args['save_dir'] + '/' + args['save_name'] if args['save_name'] is not None \
else '{}/{}_mwt_expander.pt'.format(args['save_dir'], args['shorthand'])
# load model
use_cuda = args['cuda'] and not args['cpu']
trainer = Trainer(model_file=model_file, use_cuda=use_cuda)
loaded_args, vocab = trainer.args, trainer.vocab
for k in args:
if k.endswith('_dir') or k.endswith('_file') or k in ['shorthand']:
loaded_args[k] = args[k]
logger.debug('max_dec_len: %d' % loaded_args['max_dec_len'])
# load data
logger.debug("Loading data with batch size {}...".format(
args['batch_size']))
doc = Document(CoNLL.conll2dict(input_file=args['eval_file']))
batch = DataLoader(doc,
args['batch_size'],
loaded_args,
vocab=vocab,
evaluation=True)
if len(batch) > 0:
dict_preds = trainer.predict_dict(
batch.doc.get_mwt_expansions(evaluation=True))
# decide trainer type and run eval
if loaded_args['dict_only']:
preds = dict_preds
else:
logger.info("Running the seq2seq model...")
preds = []
for i, b in enumerate(batch):
preds += trainer.predict(b)
if loaded_args.get('ensemble_dict', False):
preds = trainer.ensemble(
batch.doc.get_mwt_expansions(evaluation=True), preds)
else:
# skip eval if dev data does not exist
preds = []
# write to file and score
doc = copy.deepcopy(batch.doc)
doc.set_mwt_expansions(preds)
CoNLL.dict2conll(doc.to_dict(), system_pred_file)
if gold_file is not None:
_, _, score = scorer.score(system_pred_file, gold_file)
logger.info("MWT expansion score: {} {:.2f}".format(
args['shorthand'], score * 100))
def test_dict_to_doc_and_doc_to_dict():
doc = Document(DICT)
dicts = doc.to_dict()
dicts_tupleid = []
for sentence in dicts:
items = []
for item in sentence:
item['id'] = item['id'] if isinstance(item['id'], tuple) else (item['id'], )
items.append(item)
dicts_tupleid.append(items)
assert dicts_tupleid == DICT
def _create_stanza_document(sentence_dicts: List[List[Dict[str, str]]],
document_text: str) -> Document:
stanza_document = Document(sentence_dicts, text=document_text)
contains_entities = False
for sentence_index, sentence_dict in enumerate(sentence_dicts):
first_token = sentence_dict[0]
sentence_sentiment = first_token["sentence_sentiment"]
if sentence_sentiment is not None:
stanza_document.sentences[
sentence_index].sentiment = sentence_sentiment
if "ner" in first_token:
contains_entities = True
if contains_entities:
stanza_document.build_ents()
return stanza_document
def process(self, doc):
"""
Run language detection on a string, a Document, or a list of either, route to language specific pipeline
"""
# only return a list if given a list
singleton_input = not isinstance(doc, list)
if singleton_input:
docs = [doc]
else:
docs = doc
if docs and isinstance(docs[0], str):
docs = [Document([], text=text) for text in docs]
# run language identification
docs_w_langid = self.lang_id_pipeline.process(docs)
# create language specific batches, store global idx with each doc
lang_batches = {}
for doc in docs_w_langid:
if doc.lang not in lang_batches:
lang_batches[doc.lang] = []
lang_batches[doc.lang].append(doc)
# run through each language, submit a batch to the language specific pipeline
for lang in lang_batches.keys():
self._update_pipeline_cache(lang)
self.pipeline_cache[lang](lang_batches[lang])
# only return a list if given a list
if singleton_input:
return docs_w_langid[0]
else:
return docs_w_langid
def _process_list(self, docs):
"""
Identify language of list of strings or Documents
"""
if len(docs) == 0:
# TO DO: what standard do we want for bad input, such as empty list?
# TO DO: more handling of bad input
return
if isinstance(docs[0], str):
docs = [Document([], text) for text in docs]
docs_by_length = {}
for doc in docs:
text = LangIDProcessor.clean_text(
doc.text) if self._clean_text else doc.text
doc_length = len(text)
if doc_length not in docs_by_length:
docs_by_length[doc_length] = []
docs_by_length[doc_length].append((doc, text))
for doc_length in docs_by_length:
inputs = [doc[1] for doc in docs_by_length[doc_length]]
predictions = self._id_langs(self._text_to_tensor(inputs))
for doc, lang in zip(docs_by_length[doc_length], predictions):
doc[0].lang = lang
return docs
def test_xpos_attribute():
doc = Document(TEST_ONE_SENTENCE)
response = semgrex.process_doc(
doc, "{tag:NNP}=source <=zzz {word:Opal}=target")
check_response(response, response_len=1, source_index=2, reln='compound')
response = semgrex.process_doc(
doc, "{pos:NNP}=source <=zzz {word:Opal}=target")
check_response(response, response_len=1, source_index=2, reln='compound')
def doc2graph(doc, word2idx=None):
"""
2020/8/4 18:30
input Stanza Document : doc
output PytorchGeoData : G
G = {
x: id tensor
edge_idx : edges size = (2, l-1)
edge_attr: (u, v, edge_type in str)
node_attr: text
}
"""
if isinstance(doc,
list): #convert to Doc first if is in dict form ([[dict]])
doc = Document(doc)
# add root token for each sentences
e = [[], []]
edge_info = []
node_info = []
prev_token_sum = 0
prev_root_id = 0
cur_root_id = 0
# get original dependency
for idx, sent in enumerate(doc.sentences):
sent.print_dependencies
# node info by index(add root at the beginning of every sentence)
cur_root_id = len(node_info)
node_info.append("[ROOT]")
for token in sent.tokens:
node_info.append(token.to_dict()[0]['text'])
# edge info by index of u in edge (u,v)
for dep in sent.dependencies:
id1 = prev_token_sum + int(dep[0].to_dict()["id"])
id2 = prev_token_sum + int(dep[2].to_dict()["id"])
e[0].append(id1)
e[1].append(id2)
edge_info.append((id1, id2, dep[1]))
prev_token_sum += len(sent.tokens) + 1
# add links between sentence roots
if (cur_root_id != 0):
id1 = prev_root_id
id2 = cur_root_id
e[0].append(id1)
e[1].append(id2)
edge_info.append((id1, id2, "bridge"))
prev_root_id = cur_root_id
# id to embeddings
# x = torch.tensor([ for token in node_attr])
# done building edges and nodes
if word2idx == None:
# print("x is not id now, node info is in node_attr as text")
x = torch.tensor(list(range(doc.num_tokens + len(doc.sentences))))
else:
x = torch.tensor([word2idx[token] for token in node_info])
e = torch.tensor(e)
G = Data(x=x, edge_index=e, edge_attr=edge_info, node_attr=node_info)
return G
def test_dict_to_doc_and_doc_to_dict():
"""
Test the conversion from raw dict to Document and back
This code path will first turn start_char|end_char into start_char & end_char fields in the Document
That version to a dict will have separate fields for each of those
Finally, the conversion from that dict to a list of conll entries should convert that back to misc
"""
doc = Document(DICT)
dicts = doc.to_dict()
dicts_tupleid = []
for sentence in dicts:
items = []
for item in sentence:
item['id'] = item['id'] if isinstance(item['id'], tuple) else (item['id'], )
items.append(item)
dicts_tupleid.append(items)
conll = CoNLL.convert_dict(DICT)
assert conll == CONLL
def __init__(self, data):
super(GraphData, self).__init__()
# graph(edge) info, does not care batch
g_p = utils.doc2graph(Document(data[config.pf]))
g_h = utils.doc2graph(Document(data[config.hf]))
self.edge_index_p = g_p.edge_index
self.edge_index_h = g_h.edge_index
self.edge_attr_p = g_p.edge_attr
self.edge_attr_h = g_h.edge_attr
# node info, care batch
self.node_attr_p = g_p.node_attr
self.node_attr_h = g_h.node_attr
# one-hot label (1*num_classes), direct in batch first form
label_onehot = torch.zeros([1, config.NUM_CLASSES])
label_onehot[0][data[config.lf]] = 1
self.label = label_onehot.to(dtype=torch.float)
# problem id, direct in batch first form
self.pid = data[config.idf]
def bulk_process(self, docs):
"""
Most processors operate on the sentence level, where each sentence is processed independently and processors can benefit
a lot from the ability to combine sentences from multiple documents for faster batched processing. This is a transparent
implementation that allows these processors to batch process a list of Documents as if they were from a single Document.
"""
if hasattr(self, '_variant'):
return self._variant.bulk_process(docs)
combined_sents = [sent for doc in docs for sent in doc.sentences]
combined_doc = Document([])
combined_doc.sentences = combined_sents
combined_doc.num_tokens = sum(doc.num_tokens for doc in docs)
combined_doc.num_words = sum(doc.num_words for doc in docs)
self.process(combined_doc) # annotations are attached to sentence objects
return docs
def test_bulk(pipeline):
NUM_DOCS = 10
raw_text = [FR_MWT_SENTENCE] * NUM_DOCS
raw_doc = [Document([], text=doccontent) for doccontent in raw_text]
result = pipeline(raw_doc)
assert len(result) == NUM_DOCS
for doc in result:
compare_ignoring_whitespace(str(doc), EXPECTED_RESULT)
assert len(doc.sentences) == 1
assert doc.num_words == 26
assert doc.num_tokens == 24
def lemmatize(lemmatizer, conllu, morphs):
def clean_final(text):
finals = {"פ":"ף","כ":"ך","מ":"ם","נ":"ן","צ":"ץ"}
if text[-1] in finals:
text = text[:-1] + finals[text[-1]]
return text
def post_process(word, pos, lemma, morph):
if word == lemma:
if word + "\t" + pos in lex:
if pos == "VERB" and "Fut" in morph:
lemma = lex[word + "\t" + pos]
if pos == "VERB" and "Pres" in morph:
lemma = lex[word + "\t" + pos]
if pos == "VERB" and "Part" in morph:
lemma = lex[word + "\t" + pos]
if pos in ["NOUN", "ADJ"] and "Plur" in morph:
lemma = lex[word + "\t" + pos]
else:
if "Plur" in morph and pos in ["NOUN", "ADJ"] and (
word.endswith("ים") or word.endswith("ות")):
lemma = lemma[:-2]
if word.endswith("ות"):
lemma += "ה"
lemma = clean_final(lemma)
return lemma
uposed = [[l.split("\t") for l in s.split("\n")] for s in conllu.strip().split("\n\n")]
dicts = CoNLL.convert_conll(uposed)
for sent in dicts:
for tok in sent:
tok["id"] = int(tok["id"][0])
doc = Document(dicts)
lemmatized = lemmatizer(doc)
output = []
counter = 0
for sent in lemmatized.sentences:
for tok in sent.tokens:
word = tok.words[0]
lemma = word.lemma
if lemmatizer.do_post_process:
lemma = post_process(word.text, word.upos, word.lemma, morphs[counter])
row = [str(word.id), word.text, lemma, word.upos, word.xpos, '_', str(word.head), "_", "_", "_"]
output.append("\t".join(row))
counter += 1
output.append("")
lemmatized = "\n".join(output)
lemmatized = get_col(lemmatized,2)
return lemmatized
def StanzaAPI(self,conllu):
d=[]
e=[]
for s in conllu.split("\n"):
if s=="" or s.startswith("#"):
if e!=[]:
d.append(list(e))
e=[]
else:
t=s.split("\t")
e.append({"id":int(t[0]),"text":t[1],"lemma":t[2],"upos":t[3],"xpos":t[4],"misc":t[9]})
from stanza.models.common.doc import Document
from stanza.utils.conll import CoNLL
return CoNLL.conll_as_string(CoNLL.convert_dict(self.model(Document(d)).to_dict()))
def prep_conllu(tb, file_path, overwrite):
out_file = out_dir.joinpath(file_path.name)
if out_file.exists() and not overwrite:
print(f"{out_file.name} exists; skipping")
return None
lang, tb, tb_kwargs = determine_treebank(tb)
if not lang:
shutil.copy(file_path, out_file)
return None
doc = Document(CoNLL.conll2dict(input_file=file_path))
nlp = stanza.Pipeline(lang=lang,
processors='tokenize,mwt,pos',
tokenize_pretokenized=True)
doc = nlp.processors['pos'].process(doc)
return doc
def _encode_parse(sen, field_names) -> Document:
"""
Converts from xtsv sentence to Stanza Document
:param sen: An xtsv sentence
:param field_names: Field names
:return: Stanza Document containing one sentence
"""
stanza_sentence = [{
'id': i,
'text': line[field_names['form']],
'lemma': line[field_names['lemma']],
'upos': line[field_names['upostag']],
'feats': line[field_names['feats']],
} for i, line in enumerate(sen, start=1)]
return Document([stanza_sentence])
def retag_trees(trees, pipeline, xpos=True):
"""
Retag all of the trees using the given processor
Returns a list of new trees
"""
sentences = []
for tree in trees:
tokens = [{TEXT: pt.children[0].label} for pt in tree.preterminals()]
sentences.append(tokens)
doc = Document(sentences)
doc = pipeline(doc)
if xpos:
tag_lists = [[x.xpos for x in sentence.words] for sentence in doc.sentences]
else:
tag_lists = [[x.upos for x in sentence.words] for sentence in doc.sentences]
new_trees = [replace_tags(tree, tags) for tree, tags in zip(trees, tag_lists)]
return new_trees
def doc(sentences_dict):
doc = Document(sentences_dict)
return doc
def test_two_semgrex():
doc = Document(TEST_ONE_SENTENCE)
response = semgrex.process_doc(doc, "{}=source >obj=zzz {}=target",
"{}=source >obj=zzz {}=target")
check_response(response, semgrex_len=2)
def test_two_sentences():
doc = Document(TEST_TWO_SENTENCES)
response = semgrex.process_doc(doc, "{}=source >obj=zzz {}=target")
check_response(response, response_len=2)
def test_lemma_attribute():
doc = Document(TEST_ONE_SENTENCE)
response = semgrex.process_doc(
doc, "{lemma:Mox}=source <=zzz {lemma:Opal}=target")
check_response(response, response_len=1, source_index=2, reln='compound')
out_file = out_dir.joinpath(file_path.name)
write_doc_to_file(doc, out_file)
else:
out_train_file = out_dir.joinpath(file_path.name)
dev_name = file_path.name.split("-")[0] + "-ud-dev.conllu"
out_dev_file = out_dir.joinpath(dev_name)
out_dev_file.touch()
sents = doc.to_dict()
permutations = np.random.permutation(np.arange(len(sents))).tolist()
divide = len(sents) * 7 // 8
train_sents = [sents[idx] for idx in permutations[:divide]]
dev_sents = [sents[idx] for idx in permutations[divide:]]
train_doc = Document(train_sents)
write_doc_to_file(train_doc, out_train_file)
dev_doc = Document(dev_sents)
write_doc_to_file(dev_doc, out_dev_file)
elif args.split == "test":
kwargs = {}
if args.case == "preprocess":
kwargs['processors'] = 'tokenize,mwt,pos,lemma'
if not args.lang_file:
txt_files = list(ud_root.glob("*/*_*.txt"))
assert len(txt_files) == 82, f"number of txt_files = {len(txt_files)}; should be 82"
else:
with open(args.lang_file) as fp:
tbs = fp.read().splitlines()
def test_ner_attribute():
doc = Document(TEST_ONE_SENTENCE)
response = semgrex.process_doc(
doc, "{cpos:PROPN}=source <=zzz {ner:GEM}=target")
check_response(response, response_len=1, source_index=2, reln='compound')
def train(args):
# load data
logger.debug('max_dec_len: %d' % args['max_dec_len'])
logger.debug("Loading data with batch size {}...".format(
args['batch_size']))
train_doc = Document(CoNLL.conll2dict(input_file=args['train_file']))
train_batch = DataLoader(train_doc,
args['batch_size'],
args,
evaluation=False)
vocab = train_batch.vocab
args['vocab_size'] = vocab.size
dev_doc = Document(CoNLL.conll2dict(input_file=args['eval_file']))
dev_batch = DataLoader(dev_doc,
args['batch_size'],
args,
vocab=vocab,
evaluation=True)
utils.ensure_dir(args['save_dir'])
model_file = args['save_dir'] + '/' + args['save_name'] if args['save_name'] is not None \
else '{}/{}_mwt_expander.pt'.format(args['save_dir'], args['shorthand'])
# pred and gold path
system_pred_file = args['output_file']
gold_file = args['gold_file']
# skip training if the language does not have training or dev data
if len(train_batch) == 0 or len(dev_batch) == 0:
logger.warning("Skip training because no data available...")
return
# train a dictionary-based MWT expander
trainer = Trainer(args=args, vocab=vocab, use_cuda=args['cuda'])
logger.info("Training dictionary-based MWT expander...")
trainer.train_dict(train_batch.doc.get_mwt_expansions(evaluation=False))
logger.info("Evaluating on dev set...")
dev_preds = trainer.predict_dict(
dev_batch.doc.get_mwt_expansions(evaluation=True))
doc = copy.deepcopy(dev_batch.doc)
doc.set_mwt_expansions(dev_preds)
CoNLL.dict2conll(doc.to_dict(), system_pred_file)
_, _, dev_f = scorer.score(system_pred_file, gold_file)
logger.info("Dev F1 = {:.2f}".format(dev_f * 100))
if args.get('dict_only', False):
# save dictionaries
trainer.save(model_file)
else:
# train a seq2seq model
logger.info("Training seq2seq-based MWT expander...")
global_step = 0
max_steps = len(train_batch) * args['num_epoch']
dev_score_history = []
best_dev_preds = []
current_lr = args['lr']
global_start_time = time.time()
format_str = '{}: step {}/{} (epoch {}/{}), loss = {:.6f} ({:.3f} sec/batch), lr: {:.6f}'
# start training
for epoch in range(1, args['num_epoch'] + 1):
train_loss = 0
for i, batch in enumerate(train_batch):
start_time = time.time()
global_step += 1
loss = trainer.update(batch, eval=False) # update step
train_loss += loss
if global_step % args['log_step'] == 0:
duration = time.time() - start_time
logger.info(format_str.format(datetime.now().strftime("%Y-%m-%d %H:%M:%S"), global_step,\
max_steps, epoch, args['num_epoch'], loss, duration, current_lr))
# eval on dev
logger.info("Evaluating on dev set...")
dev_preds = []
for i, batch in enumerate(dev_batch):
preds = trainer.predict(batch)
dev_preds += preds
if args.get('ensemble_dict', False) and args.get(
'ensemble_early_stop', False):
logger.info("[Ensembling dict with seq2seq model...]")
dev_preds = trainer.ensemble(
dev_batch.doc.get_mwt_expansions(evaluation=True),
dev_preds)
doc = copy.deepcopy(dev_batch.doc)
doc.set_mwt_expansions(dev_preds)
CoNLL.dict2conll(doc.to_dict(), system_pred_file)
_, _, dev_score = scorer.score(system_pred_file, gold_file)
train_loss = train_loss / train_batch.num_examples * args[
'batch_size'] # avg loss per batch
logger.info(
"epoch {}: train_loss = {:.6f}, dev_score = {:.4f}".format(
epoch, train_loss, dev_score))
# save best model
if epoch == 1 or dev_score > max(dev_score_history):
trainer.save(model_file)
logger.info("new best model saved.")
best_dev_preds = dev_preds
# lr schedule
if epoch > args['decay_epoch'] and dev_score <= dev_score_history[
-1]:
current_lr *= args['lr_decay']
trainer.change_lr(current_lr)
dev_score_history += [dev_score]
logger.info("Training ended with {} epochs.".format(epoch))
best_f, best_epoch = max(dev_score_history) * 100, np.argmax(
dev_score_history) + 1
logger.info("Best dev F1 = {:.2f}, at epoch = {}".format(
best_f, best_epoch))
# try ensembling with dict if necessary
if args.get('ensemble_dict', False):
logger.info("[Ensembling dict with seq2seq model...]")
dev_preds = trainer.ensemble(
dev_batch.doc.get_mwt_expansions(evaluation=True),
best_dev_preds)
doc = copy.deepcopy(dev_batch.doc)
doc.set_mwt_expansions(dev_preds)
CoNLL.dict2conll(doc.to_dict(), system_pred_file)
_, _, dev_score = scorer.score(system_pred_file, gold_file)
logger.info("Ensemble dev F1 = {:.2f}".format(dev_score * 100))
best_f = max(best_f, dev_score)
def process(self, text):
sentence = [{
'id': (i + 1, ),
'text': 'LOL'
} for i, tok in enumerate(text.split())]
return Document([sentence], text)
def __init__(self, gold, pred, verbose=False, group=False):
"""
Align golden and predicted tokens, and their tags. Create dictionaries of falsely predicted tags
:param gold: the gold conllu file
:param pred: the predicted conlly file
:param verbose: if true print information about token numbers
:param group: if true, put falsely predicted ufeats labels into a dictionary that contains all the labels it was
falsely assigned and the number of times each predicted label was found
"""
gold = C.load_conll(open(gold,
'r', encoding='utf8'))
gold_dic = C.convert_conll(gold) # returns a dictionary with all the column names
gold_doc = Document(gold_dic)
pred = C.load_conll(open(pred, 'r', encoding='utf8'))
pred_dic = C.convert_conll(pred) # returns a dictionary with all the column names
pred_doc = Document(pred_dic)
# get the tokens
self.gold_tokens = [j['text'] for i in gold_dic for j in i]
self.pred_tokens = [j['text'] for i in pred_dic for j in i]
# get upos tags
gold_tags = [j['upos'] for i in gold_dic for j in i]
pred_tags = [j['upos'] for i in pred_dic for j in i]
# get xpos tags
gold_xpos = [j['xpos'] for i in gold_dic for j in i]
pred_xpos = [j['xpos'] for i in pred_dic for j in i]
# get ufeats tag
gold_feats = list()
pred_feats = list()
for i in gold_dic:
for j in i:
if 'feats' in j:
gold_feats.append(j['feats'])
else:
gold_feats.append('_')
for i in pred_dic:
for j in i:
if 'feats' in j:
pred_feats.append(j['feats'])
else:
pred_feats.append('_')
if verbose:
print('Number of gold tokens:', len(self.gold_tokens), ', number of predicted tokens:', len(self.pred_tokens))
# align gold and predicted tokens
cost, a2b, b2a, a2b_multi, b2a_multi = align(self.gold_tokens, self.pred_tokens)
# align tokens and their POS tags separately
self.aligned = list() # tokens
self.aligned_pos = list() # upos
self.aligned_feats = list()
self.aligned_xpos = list()
for i in range(len(b2a)):
t = (self.gold_tokens[b2a[i]], self.pred_tokens[i])
self.aligned.append(t)
p = (gold_tags[b2a[i]], pred_tags[i])
self.aligned_pos.append(p)
f = (gold_feats[b2a[i]], pred_feats[i])
self.aligned_feats.append(f)
x = (gold_xpos[b2a[i]], pred_xpos[i])
self.aligned_xpos.append(x)
# align predicted tags to golden tags, not vice versa as before
gold_aligned = list()
for i in range(len(a2b)):
t = (self.gold_tokens[i], self.pred_tokens[a2b[i]])
gold_aligned.append(t)
overall = list()
for (a, b) in self.aligned:
if a == b:
overall.append((a, b))
if verbose:
print('Aligned tokens. GOLD:', len(gold_aligned), 'PREDICTED:', len(self.aligned), 'ALIGNED:', len(overall))
self.conf_tags = {} # falsely predicted upos tags
self.conf_tags_all = {} # all upos tags
self.incorrect_upos = 0 # number of incorrectly predicted upos tags
# how many times different tags cooccured in gold and pred files
i = 0
for (a, b) in self.aligned_pos:
if a != b:
self.incorrect_upos += 1
if (a, b) not in self.conf_tags:
self.conf_tags[(a, b)] = 1
else:
self.conf_tags[(a, b)] += 1
if (a, b) not in self.conf_tags_all:
self.conf_tags_all[(a, b)] = 1
else:
self.conf_tags_all[(a, b)] += 1
i += 1
self.conf_feats = {}
self.conf_feats_all = {}
self.incorrect_feats = 0
i = 0
for (a, b) in self.aligned_feats:
a = "|".join(sorted(feat for feat in a.split("|")
if feat.split("=", 1)[0] in UNIVERSAL_FEATURES))
b = "|".join(sorted(feat for feat in b.split("|")
if feat.split("=", 1)[0] in UNIVERSAL_FEATURES))
if a != b:
self.incorrect_feats += 1
# create a dictionary for each falsely predicted ufeats labels and group all its false predictions
if group:
if a not in self.conf_feats:
self.conf_feats[a] = dict()
self.conf_feats[a][b] = 1
else:
if b not in self.conf_feats[a]:
self.conf_feats[a][b] = 1
else:
self.conf_feats[a][b] += 1
else:
if (a, b) not in self.conf_feats:
self.conf_feats[(a, b)] = 1
else:
self.conf_feats[(a, b)] += 1
if (a, b) not in self.conf_feats_all:
self.conf_feats_all[(a, b)] = 1
else:
self.conf_feats_all[(a, b)] += 1
i += 1
self.conf_xpos = {}
self.incorrect_xpos = 0
i = 0
for (a, b) in self.aligned_xpos:
if a != b:
self.incorrect_xpos += 1
if (a, b) not in self.conf_xpos:
self.conf_xpos[(a, b)] = 1
else:
self.conf_xpos[(a, b)] += 1
i += 1
def predict_with_pos(self, doc_dict):
# fix potential memory leak
torch.cuda.empty_cache()
conllu_data = doc_dict["dep"]
xml_data = doc_dict["xml"]
conllu_data = re.sub(
r'\n[0-9]+-[^\n]+\n', '\n', conllu_data
) # Remove any super tokens in input, we'll add them at the end
# First parse - just get best deprel and heads
diaparsed = diaparse(self.nlp1, conllu_data)
doc = CoNLL.load_conll(io.StringIO(diaparsed))
# overwrite xpos with our ensemble xpos
doc_with_our_xpos = CoNLL.conll2dict(input_str=conllu_data)
replace_xpos(doc, doc_with_our_xpos)
doc = [["\t".join(l) for l in sent] for sent in doc]
doc = "\n\n".join(["\n".join(sent) for sent in doc])
# Second parse - postprocess based on:
# 1. auxiliary parser predictions trained on EWT for PP attachment disambiguation
ewt_parse = diaparse(self.aux_parser, conllu_data)
doc = add_second_deps(doc, ewt_parse)
# 2. sequence tagger deprel predictions using high quality POS tags and embeddings
doc = add_sequence_tagger_preds(self.sequence_tagger, doc)
# 3. postprocessing rules to adjudicate these predictions in a harmonized way
doc = depedit1.run_depedit(doc)
# Add upos
uposed = depedit2.run_depedit(doc)
uposed = [[l.split("\t") for l in s.split("\n")]
for s in uposed.strip().split("\n\n")]
dicts = CoNLL.convert_conll(uposed)
# Now add lemmas using Stanza based on pretagged predicted upos (converted from our predicted xpos)
for sent in dicts:
for tok in sent:
tok["id"] = int(tok["id"])
doc = Document(dicts)
lemmatized = self.nlp2(doc)
output = []
for sent in lemmatized.sentences:
for tok in sent.tokens:
word = tok.words[0]
row = [
str(word.id), word.text, word.lemma, word.upos, word.xpos,
'_',
str(word.head), word.deprel, "_", "_"
]
output.append("\t".join(row))
output.append("")
lemmatized = "\n".join(output)
# Postprocess implausible lemmas (VBG ending in -ed, VBN ending in -ing, etc., incorrect -e restoration...)
lemmatized = postprocess_lemmas(lemmatized)
# Fix punctuation
lemmatized = fix_punct(lemmatized)
if "<text id=" in xml_data:
docname = re.search(r'<text id="([^"]+)"', xml_data).group(1)
morphed_and_enhanced = depedit3.run_depedit(lemmatized,
sent_id=True,
sent_text=True,
docname=docname,
filename=docname)
else:
morphed_and_enhanced = depedit3.run_depedit(lemmatized,
sent_text=True)
if xml_data != "":
xmled = conllu2xml(morphed_and_enhanced, xml_data)
else:
xmled = ""
return {"dep": morphed_and_enhanced, "xml": xmled}
