def test_factories_merge_noun_chunks(doc):
assert len(doc) == 7
nlp = Language()
merge_noun_chunks = nlp.create_pipe("merge_noun_chunks")
merge_noun_chunks(doc)
assert len(doc) == 6
assert doc[2].text == "New York"
def test_factories_merge_ents(doc):
assert len(doc) == 7
assert len(list(doc.ents)) == 1
nlp = Language()
merge_entities = nlp.create_pipe("merge_entities")
merge_entities(doc)
assert len(doc) == 6
assert len(list(doc.ents)) == 1
assert doc[2].text == "New York"
def test_list_of_docs_pickles_efficiently():
nlp = Language()
for i in range(10000):
_ = nlp.vocab[unicode_(i)] # noqa: F841
one_pickled = pickle.dumps(nlp("0"), -1)
docs = list(nlp.pipe(unicode_(i) for i in range(100)))
many_pickled = pickle.dumps(docs, -1)
assert len(many_pickled) < (len(one_pickled) * 2)
many_unpickled = pickle.loads(many_pickled)
assert many_unpickled[0].text == "0"
assert many_unpickled[-1].text == "99"
assert len(many_unpickled) == 100
def test_issue1654():
nlp = Language(Vocab())
assert not nlp.pipeline
nlp.add_pipe(lambda doc: doc, name="1")
nlp.add_pipe(lambda doc: doc, name="2", after="1")
nlp.add_pipe(lambda doc: doc, name="3", after="2")
assert nlp.pipe_names == ["1", "2", "3"]
nlp2 = Language(Vocab())
assert not nlp2.pipeline
nlp2.add_pipe(lambda doc: doc, name="3")
nlp2.add_pipe(lambda doc: doc, name="2", before="3")
nlp2.add_pipe(lambda doc: doc, name="1", before="2")
assert nlp2.pipe_names == ["1", "2", "3"]
def test_issue1915():
cfg = {"hidden_depth": 2} # should error out
nlp = Language()
nlp.add_pipe(nlp.create_pipe("ner"))
nlp.get_pipe("ner").add_label("answer")
with pytest.raises(ValueError):
nlp.begin_training(**cfg)
def test_issue1967(label):
nlp = Language()
config = {}
ner = nlp.create_pipe("ner", config=config)
example = Example.from_dict(
Doc(ner.vocab, words=["word"]),
{
"ids": [0],
"words": ["word"],
"tags": ["tag"],
"heads": [0],
"deps": ["dep"],
"entities": [label],
},
)
assert "JOB-NAME" in ner.moves.get_actions(examples=[example])[1]
def test_textcat_learns_multilabel():
random.seed(5)
numpy.random.seed(5)
docs = []
nlp = Language()
letters = ["a", "b", "c"]
for w1 in letters:
for w2 in letters:
cats = {letter: float(w2 == letter) for letter in letters}
docs.append((Doc(nlp.vocab,
words=["d"] * 3 + [w1, w2] + ["d"] * 3), cats))
random.shuffle(docs)
textcat = TextCategorizer(nlp.vocab, width=8)
for letter in letters:
textcat.add_label(letter)
optimizer = textcat.initialize(lambda: [])
for i in range(30):
losses = {}
examples = [
Example.from_dict(doc, {"cats": cats}) for doc, cat in docs
]
textcat.update(examples, sgd=optimizer, losses=losses)
random.shuffle(docs)
for w1 in letters:
for w2 in letters:
doc = Doc(nlp.vocab, words=["d"] * 3 + [w1, w2] + ["d"] * 3)
truth = {letter: w2 == letter for letter in letters}
textcat(doc)
for cat, score in doc.cats.items():
if not truth[cat]:
assert score < 0.5
else:
assert score > 0.5
def _extract_doc_matches(
self,
lang: Language,
doc: Doc,
keywords: Sequence[str],
scores: Sequence[float],
) -> Dict[str, DocMatch]:
'''
extract and format info for all keywords in a given document.
attr: str the spacy token attribute to use to match in the sentence search
'''
matcher = PhraseMatcher(lang.vocab, attr='LOWER')
patterns = [lang.make_doc(str(kw)) for kw in keywords]
matcher.add("Keywords", patterns)
sents = self._extract_sentence_matches(doc,
keywords,
matcher,
attr='LOWER')
matches: Dict[str, DocMatch] = {
kw: DocMatch(doc, kw, score, sents[kw])
for kw, score in zip(keywords, scores)
}
return matches
def test_pipe_factories_config_excludes_nlp():
"""Test that the extra values we temporarily add to component config
blocks/functions are removed and not copied around.
"""
name = "test_pipe_factories_config_excludes_nlp"
func = lambda nlp, name: lambda doc: doc
Language.factory(name, func=func)
config = {
"nlp": {"lang": "en", "pipeline": [name]},
"components": {name: {"factory": name}},
}
nlp = English.from_config(config)
assert nlp.pipe_names == [name]
pipe_cfg = nlp.get_pipe_config(name)
pipe_cfg == {"factory": name}
assert nlp._pipe_configs[name] == {"factory": name}
def test_user_data_from_disk():
nlp = Language()
doc = nlp("Hello")
doc.user_data[(0, 1)] = False
b = doc.to_bytes()
doc2 = doc.__class__(doc.vocab).from_bytes(b)
assert doc2.user_data[(0, 1)] is False
def corpus(nlp: Language):
for original_example in original_examples:
doc = nlp.make_doc(original_example[0])
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=UserWarning)
spacy_example = Example.from_dict(doc, original_example[1])
yield spacy_example
def test_user_data_unpickles():
nlp = Language()
doc = nlp("Hello")
doc.user_data[(0, 1)] = False
b = pickle.dumps(doc)
doc2 = pickle.loads(b)
assert doc2.user_data[(0, 1)] is False
def get_smiles_language():
"""
Get SMILES language.
Returns:
a spacy.language.Language representing SMILES.
"""
valid_values = list(filter(lambda k: k != PADDING_ATOM, ATOM_MAPPING.keys()))
vocabulary = Vocab(strings=valid_values)
def make_doc(smiles):
"""
Make a SMILES document.
Arguments:
smiles (str): a SMILES representing a molecule.
Returns:
a spacy.tokens.Doc representing the molecule.
"""
if len(smiles) == 0:
tokens = np.random.choice(valid_values)
else:
tokens = [
token
for token in ATOM_REGEX.split(smiles)
if token
][:MAX_LENGTH]
return Doc(vocabulary, words=tokens, spaces=[False]*len(tokens))
return Language(vocabulary, make_doc)
def test_add_pipe(nlp: Language):
"""
Works as a pipeline component and can be disabled.
"""
# given
nlp.add_pipe("topicrank", last=True)
# works as a pipeline component
# when
text = "linear constraints over the"
doc = nlp(text)
phrases = [p.text for p in doc._.phrases]
# then
assert len(doc._.phrases) > 0
assert any(map(lambda x: "constraints" in x, phrases))
# identifies phrases not in noun chunks
# when
text = """\
everything you need to know about student loan interest rates, variable \
and fixed rates, capitalization, amortization, student loan refinancing \
and more.\
"""
doc = nlp(text)
phrases = [p.text for p in doc._.phrases]
# then
assert len(doc._.phrases) >= 2
# resolves Py 3.5 dict KeyError
# when
text = "linear constraints over the set of natural numbers"
doc = nlp(text)
phrases = [p.text for p in doc._.phrases]
# then
assert any(map(lambda x: "constraints" in x, phrases))
# pipeline can be disabled
# when
with nlp.select_pipes(disable=["topicrank"]):
doc = nlp(text)
# then
assert len(doc._.phrases) == 0
def test_ner_warns_no_lookups():
nlp = Language()
nlp.vocab.lookups = Lookups()
assert not len(nlp.vocab.lookups)
ner = nlp.create_pipe("ner")
nlp.add_pipe(ner)
with pytest.warns(UserWarning):
nlp.begin_training()
nlp.vocab.lookups.add_table("lexeme_norm")
nlp.vocab.lookups.get_table("lexeme_norm")["a"] = "A"
with pytest.warns(None) as record:
nlp.begin_training()
assert not record.list
def from_dir(cls, tag_map, model_dir):
vocab = Vocab(tag_map=tag_map, get_lex_attr=Language.default_lex_attrs())
tokenizer = Tokenizer(vocab, {}, None, None, None)
tagger = Tagger.blank(vocab, TAGGER_TEMPLATES)
cfg = Config.read(path.join(model_dir, 'deps'), 'config')
parser = Parser.from_dir(path.join(model_dir, 'deps'), vocab.strings, ArcEager)
return cls(vocab, tokenizer, tagger, parser)
def matcher(nlp: Language, ) -> FuzzyMatcher:
"""Fuzzy matcher with patterns added."""
animals = ["Heifer", "chicken"]
sounds = ["mooo"]
names = ["Steven"]
matcher = FuzzyMatcher(nlp.vocab)
matcher.add(
"ANIMAL",
[nlp.make_doc(animal) for animal in animals],
kwargs=[{
"ignore_case": False
}, {}],
)
matcher.add("SOUND", [nlp.make_doc(sound) for sound in sounds])
matcher.add("NAME", [nlp.make_doc(name) for name in names],
on_match=add_name_ent)
return matcher
def vocab():
vocab = Vocab(Language.default_lex_attrs())
lex = vocab['dog']
assert vocab[vocab.strings['dog']].orth_ == 'dog'
lex = vocab['the']
lex = vocab['quick']
lex = vocab['jumped']
return vocab
def test__scan_doc_returns_matches_over_min_r1(
searcher: FuzzySearcher, nlp: Language, scan_example: Doc
) -> None:
"""It returns all spans of len(query) in doc if ratio >= min_r1."""
query = nlp.make_doc("Shirley")
assert searcher._scan_doc(
scan_example, query, fuzzy_func="simple", min_r1=30, ignore_case=True
) == {4: 86}
def test_issue999(train_data):
"""Test that adding entities and resuming training works passably OK.
There are two issues here:
1) We have to readd labels. This isn't very nice.
2) There's no way to set the learning rate for the weight update, so we
end up out-of-scale, causing it to learn too fast.
"""
TRAIN_DATA = [
["hey", []],
["howdy", []],
["hey there", []],
["hello", []],
["hi", []],
["i'm looking for a place to eat", []],
["i'm looking for a place in the north of town", [[31, 36, "LOCATION"]]],
["show me chinese restaurants", [[8, 15, "CUISINE"]]],
["show me chines restaurants", [[8, 14, "CUISINE"]]],
]
nlp = Language()
ner = nlp.create_pipe("ner")
nlp.add_pipe(ner)
for _, offsets in TRAIN_DATA:
for start, end, label in offsets:
ner.add_label(label)
nlp.begin_training()
ner.model.learn_rate = 0.001
for itn in range(100):
random.shuffle(TRAIN_DATA)
for raw_text, entity_offsets in TRAIN_DATA:
nlp.update([raw_text], [{"entities": entity_offsets}])
with make_tempdir() as model_dir:
nlp.to_disk(model_dir)
nlp2 = Language().from_disk(model_dir)
for raw_text, entity_offsets in TRAIN_DATA:
doc = nlp2(raw_text)
ents = {(ent.start_char, ent.end_char): ent.label_ for ent in doc.ents}
for start, end, label in entity_offsets:
if (start, end) in ents:
assert ents[(start, end)] == label
break
else:
if entity_offsets:
raise Exception(ents)
def test__adjust_left_right_positions_finds_better_match(
searcher: FuzzySearcher, nlp: Language
) -> None:
"""It optimizes the initial match to find a better match."""
doc = nlp.make_doc("Patient was prescribed Zithromax tablets.")
query = nlp.make_doc("zithromax tablet")
match_values = {0: 30, 2: 50, 3: 97, 4: 50}
assert searcher._adjust_left_right_positions(
doc,
query,
match_values,
pos=3,
fuzzy_func="simple",
min_r2=70,
ignore_case=True,
flex=2,
) == (3, 5, 97)
def test__adjust_left_right_positions_with_no_flex(
searcher: FuzzySearcher, nlp: Language
) -> None:
"""It returns the intial match when flex value = 0."""
doc = nlp.make_doc("Patient was prescribed Zithroma tablets.")
query = nlp.make_doc("zithromax")
match_values = {3: 94}
assert searcher._adjust_left_right_positions(
doc,
query,
match_values,
pos=3,
fuzzy_func="simple",
min_r2=70,
ignore_case=True,
flex=0,
) == (3, 4, 94)
def test__scan_doc_returns_all_matches_with_no_min_r1(
searcher: FuzzySearcher, nlp: Language, scan_example: Doc
) -> None:
"""It returns all spans of len(query) in doc if min_r1 = 0."""
query = nlp.make_doc("Shirley")
assert searcher._scan_doc(
scan_example, query, fuzzy_func="simple", min_r1=0, ignore_case=True
) == {0: 0, 1: 0, 2: 18, 3: 22, 4: 86}
def main(use_gpu=False, nb_epoch=50):
if use_gpu:
Model.ops = CupyOps()
Model.Ops = CupyOps
train, test = datasets.imdb()
print("Load data")
train_X, train_y = zip(*train)
test_X, test_y = zip(*test)
train_y = to_categorical(train_y, nb_classes=2)
test_y = to_categorical(test_y, nb_classes=2)
nlp = Language()
dev_X = train_X[-1000:]
dev_y = train_y[-1000:]
train_X = train_X[:-1000]
train_y = train_y[:-1000]
print("Parse data")
train_X = [nlp.make_doc(x) for x in train_X]
dev_X = [nlp.make_doc(x) for x in dev_X]
model = build_model(2, 1)
print("Begin training")
with model.begin_training(train_X, train_y, L2=1e-6) as (trainer, optimizer):
epoch_loss = [0.0]
def report_progress():
with model.use_params(optimizer.averages):
print(epoch_loss[-1], model.evaluate(dev_X, dev_y), trainer.dropout)
epoch_loss.append(0.0)
trainer.each_epoch.append(report_progress)
trainer.nb_epoch = nb_epoch
trainer.dropout = 0.0
trainer.batch_size = 128
trainer.dropout_decay = 0.0
for X, y in trainer.iterate(train_X[:1000], train_y[:1000]):
yh, backprop = model.begin_update(X, drop=trainer.dropout)
loss = ((yh - y) ** 2.0).sum() / y.shape[0]
backprop((yh - y) / y.shape[0], optimizer)
epoch_loss[-1] += loss
with model.use_params(optimizer.averages):
print("Avg dev.: %.3f" % model.evaluate(dev_X, dev_y))
with open("out.pickle", "wb") as file_:
pickle.dump(model, file_, -1)
def test_simple_train():
nlp = Language()
nlp.add_pipe(nlp.create_pipe("textcat"))
nlp.get_pipe("textcat").add_label("answer")
nlp.begin_training()
for i in range(5):
for text, answer in [
("aaaa", 1.0),
("bbbb", 0),
("aa", 1.0),
("bbbbbbbbb", 0.0),
("aaaaaa", 1),
]:
nlp.update([text], [{"cats": {"answer": answer}}])
doc = nlp("aaa")
assert "answer" in doc.cats
assert doc.cats["answer"] >= 0.5
def test_pickle_single_doc():
nlp = Language()
doc = nlp("pickle roundtrip")
doc._context = 3
data = pickle.dumps(doc, 1)
doc2 = pickle.loads(data)
assert doc2.text == "pickle roundtrip"
assert doc2._context == 3
def test_serialize_language_exclude(meta_data):
name = "name-in-fixture"
nlp = Language(meta=meta_data)
assert nlp.meta["name"] == name
new_nlp = Language().from_bytes(nlp.to_bytes())
assert new_nlp.meta["name"] == name
new_nlp = Language().from_bytes(nlp.to_bytes(), exclude=["meta"])
assert not new_nlp.meta["name"] == name
new_nlp = Language().from_bytes(nlp.to_bytes(exclude=["meta"]))
assert not new_nlp.meta["name"] == name
def loadWordFeatures(dom, loc="wordfeats", loadpickle=False, savepickle=False):
if loadpickle:
# we load saved word features from our pickle file
# the word features were generated as below
with open(os.path.join(dirname, 'data', 'wordfeatures.pkl'),
'rb') as f:
featdict = pickle.load(f)
return featdict[dom]
nlp = Language().from_disk(loc)
taskwords = {
'household': [[' '], ['Pick and Place glass'],
['Pick and Place plastic can'], ['Pick and Place lemon'],
['Pick and Place plastic bottle'],
['Pick and Place apple'], ['Pick and Place plastic cup'],
['Navigate while avoiding moving people'],
['Navigate to the main room door'],
['Navigate while following a person'],
['Navigate to the dining table'],
['Navigate while avoiding obstacles'],
['Navigate to the living room']],
'driving': [
[' '],
['Parking backwards cars and people around, misaligned'],
['Parking backwards empty lot, misaligned'],
['Parking backwards cars and people around, aligned'],
['Parking forwards empty lot, aligned'],
['Parking forwards cars and people around, misaligned'],
['Parking forwards empty lot, misaligned'],
['Navigating lane merge with other moving vehicles'],
['Navigating lane merge on a clear road'],
['Navigating traffic-circle with other moving vehicles'],
['Navigating traffic-circle on a clear road'],
['Navigating T-junction with other moving vehicles'],
['Navigating T-junction on a clear road'],
]
}
featdict = {}
for d, task_word_list in taskwords.items():
wordfeatures = []
for i in range(len(task_word_list)):
print(task_word_list[i][0])
wordfeatures.append(nlp(task_word_list[i][0]).vector)
wordfeatures = np.array(wordfeatures)
featdict[d] = wordfeatures
wordfeatures = featdict[dom]
# save the data
if savepickle:
with open(os.path.join(dirname, 'data', 'wordfeatures.pkl'),
'wb') as f:
pickle.dump(featdict, f, protocol=pickle.HIGHEST_PROTOCOL)
return wordfeatures
def test_beam_parse():
nlp = Language()
nlp.add_pipe(DependencyParser(nlp.vocab), name="parser")
nlp.parser.add_label("nsubj")
nlp.parser.begin_training([], token_vector_width=8, hidden_width=8)
doc = nlp.make_doc("Australia is a country")
nlp.parser(doc, beam_width=2)
def test_tagger_warns_no_lemma_lookups():
nlp = Language()
nlp.vocab.lookups = Lookups()
assert not len(nlp.vocab.lookups)
tagger = nlp.create_pipe("tagger")
with pytest.warns(UserWarning):
tagger.begin_training()
nlp.add_pipe(tagger)
with pytest.warns(UserWarning):
nlp.begin_training()
nlp.vocab.lookups.add_table("lemma_lookup")
with pytest.warns(None) as record:
nlp.begin_training()
assert not record.list
def test_pipe_class_component_config():
name = "test_class_component_config"
@Language.factory(name)
class Component:
def __init__(
self, nlp: Language, name: str, value1: StrictInt, value2: StrictStr
):
self.nlp = nlp
self.value1 = value1
self.value2 = value2
self.is_base = True
def __call__(self, doc: Doc) -> Doc:
return doc
@English.factory(name)
class ComponentEN:
def __init__(
self, nlp: Language, name: str, value1: StrictInt, value2: StrictStr
):
self.nlp = nlp
self.value1 = value1
self.value2 = value2
self.is_base = False
def __call__(self, doc: Doc) -> Doc:
return doc
nlp = Language()
with pytest.raises(ConfigValidationError): # no config provided
nlp.add_pipe(name)
with pytest.raises(ConfigValidationError): # invalid config
nlp.add_pipe(name, config={"value1": "10", "value2": "hello"})
nlp.add_pipe(name, config={"value1": 10, "value2": "hello"})
pipe = nlp.get_pipe(name)
assert isinstance(pipe.nlp, Language)
assert pipe.value1 == 10
assert pipe.value2 == "hello"
assert pipe.is_base is True
nlp_en = English()
with pytest.raises(ConfigValidationError): # invalid config
nlp_en.add_pipe(name, config={"value1": "10", "value2": "hello"})
nlp_en.add_pipe(name, config={"value1": 10, "value2": "hello"})
pipe = nlp_en.get_pipe(name)
assert isinstance(pipe.nlp, English)
assert pipe.value1 == 10
assert pipe.value2 == "hello"
assert pipe.is_base is False
def test_implicit_label():
nlp = Language()
nlp.add_pipe("tagger")
train_examples = []
for t in TRAIN_DATA:
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
nlp.initialize(get_examples=lambda: train_examples)
def normalize_np(np: List[Token], nlp_md: Language) -> str:
"""
Function to normalize a noun phrase
"""
normalized_np = " ".join([
token.lemma_ for token in nlp_md.tokenizer(np)
if token.text not in NLTK_STOPWORDS
])
return normalized_np
def test_language_source_and_vectors(nlp2):
nlp = Language(Vocab())
textcat = nlp.add_pipe("textcat")
for label in ("POSITIVE", "NEGATIVE"):
textcat.add_label(label)
nlp.initialize()
long_string = "thisisalongstring"
assert long_string not in nlp.vocab.strings
assert long_string not in nlp2.vocab.strings
nlp.vocab.strings.add(long_string)
assert nlp.vocab.vectors.to_bytes() != nlp2.vocab.vectors.to_bytes()
vectors_bytes = nlp.vocab.vectors.to_bytes()
with pytest.warns(UserWarning):
nlp2.add_pipe("textcat", name="textcat2", source=nlp)
# strings should be added
assert long_string in nlp2.vocab.strings
# vectors should remain unmodified
assert nlp.vocab.vectors.to_bytes() == vectors_bytes
def test_language_pipe_error_handler_pipe(en_vocab, n_process):
"""Test the error handling of a component's pipe method"""
Language.component("my_perhaps_sentences", func=perhaps_set_sentences)
Language.component("assert_sents_error", func=assert_sents_error)
ops = get_current_ops()
if isinstance(ops, NumpyOps) or n_process < 2:
texts = [f"{str(i)} is enough. Done" for i in range(100)]
nlp = English()
nlp.add_pipe("my_perhaps_sentences")
nlp.add_pipe("assert_sents_error")
nlp.initialize()
with pytest.raises(ValueError):
# assert_sents_error requires sentence boundaries, will throw an error otherwise
docs = list(nlp.pipe(texts, n_process=n_process, batch_size=10))
nlp.set_error_handler(ignore_error)
docs = list(nlp.pipe(texts, n_process=n_process, batch_size=10))
# we lose/ignore the failing 4,40-49 docs
assert len(docs) == 89
def build_pipeline(disable: list = []):
"""
Function that creates the pipeline for the creation of (sentence, reference) tuples.
Returns:
- nlp: spaCy pipeline instance
"""
nlp = load_spacy_model("de_core_news_sm")
# Matching section references using ReferenceMatcher class
Language.component("reference_matcher", func=match_reference)
nlp.add_pipe("sentencizer")
nlp.add_pipe("reference_matcher", before="tagger")
nlp.disable_pipes(*disable)
print("\nActivated pipes:")
print(nlp.pipe_names)
return nlp
def test_entity_ruler_existing_bytes_old_format_safe(patterns, en_vocab):
nlp = Language(vocab=en_vocab)
ruler = EntityRuler(nlp, patterns=patterns, overwrite_ents=True)
bytes_old_style = srsly.msgpack_dumps(ruler.patterns)
new_ruler = EntityRuler(nlp)
new_ruler = new_ruler.from_bytes(bytes_old_style)
assert len(new_ruler) == len(ruler)
assert new_ruler.patterns == ruler.patterns
assert new_ruler.overwrite is not ruler.overwrite
def lang():
vector_data = {k: np.random.normal(0, 1, (2,)) for k in ["red", "blue", "cat", "dog", "green", "purple"]}
vector_data['cat'] += 10
vector_data['dog'] += 10
vocab = Vocab(strings=vector_data.keys())
for word, vector in vector_data.items():
vocab.set_vector(word, vector)
nlp = Language(vocab=vocab)
return SpacyLanguage(nlp)
def register_benepar_component_factory():
# Starting with spaCy 3.0, nlp.add_pipe no longer directly accepts
# BeneparComponent instances. We must instead register a component factory.
import spacy
if spacy.__version__.startswith("2"):
return
from spacy.language import Language
Language.factory(
"benepar",
default_config={
"subbatch_max_tokens": 500,
"disable_tagger": False,
},
func=create_benepar_component,
)
def create_pipeline(nlp: Language, cfg: omegaconf.DictConfig) -> List[Pipe]:
if not isinstance(cfg, omegaconf.DictConfig):
cfg = OmegaConf.create(cfg)
pipes = []
for name, pipe_config in cfg.items():
pipe_config = OmegaConf.to_container(pipe_config or OmegaConf.create({}))
pipes.append(nlp.create_pipe(name, config=pipe_config or dict()))
return pipes
def main(train_loc, dev_loc, model_dir, tag_map_loc):
with open(tag_map_loc) as file_:
tag_map = json.loads(file_.read())
train_sents = list(read_conllx(train_loc))
train_sents = PseudoProjectivity.preprocess_training_data(train_sents)
actions = ArcEager.get_actions(gold_parses=train_sents)
features = get_templates('basic')
model_dir = pathlib.Path(model_dir)
with (model_dir / 'deps' / 'config.json').open('w') as file_:
json.dump({'pseudoprojective': True, 'labels': actions, 'features': features}, file_)
vocab = Vocab(lex_attr_getters=Language.Defaults.lex_attr_getters, tag_map=tag_map)
# Populate vocab
for _, doc_sents in train_sents:
for (ids, words, tags, heads, deps, ner), _ in doc_sents:
for word in words:
_ = vocab[word]
for dep in deps:
_ = vocab[dep]
for tag in tags:
_ = vocab[tag]
for tag in tags:
assert tag in tag_map, repr(tag)
tagger = Tagger(vocab, tag_map=tag_map)
parser = DependencyParser(vocab, actions=actions, features=features)
for itn in range(15):
for _, doc_sents in train_sents:
for (ids, words, tags, heads, deps, ner), _ in doc_sents:
doc = Doc(vocab, words=words)
gold = GoldParse(doc, tags=tags, heads=heads, deps=deps)
tagger(doc)
parser.update(doc, gold)
doc = Doc(vocab, words=words)
tagger.update(doc, gold)
random.shuffle(train_sents)
scorer = score_model(vocab, tagger, parser, read_conllx(dev_loc))
print('%d:\t%.3f\t%.3f' % (itn, scorer.uas, scorer.tags_acc))
nlp = Language(vocab=vocab, tagger=tagger, parser=parser)
nlp.end_training(model_dir)
scorer = score_model(vocab, tagger, parser, read_conllx(dev_loc))
print('%d:\t%.3f\t%.3f\t%.3f' % (itn, scorer.uas, scorer.las, scorer.tags_acc))
def test_serialize_with_custom_tokenizer():
"""Test that serialization with custom tokenizer works without token_match.
See: https://support.prodi.gy/t/how-to-save-a-custom-tokenizer/661/2
"""
prefix_re = re.compile(r"""1/|2/|:[0-9][0-9][A-K]:|:[0-9][0-9]:""")
suffix_re = re.compile(r"""""")
infix_re = re.compile(r"""[~]""")
def custom_tokenizer(nlp):
return Tokenizer(
nlp.vocab,
{},
prefix_search=prefix_re.search,
suffix_search=suffix_re.search,
infix_finditer=infix_re.finditer,
)
nlp = Language()
nlp.tokenizer = custom_tokenizer(nlp)
with make_tempdir() as d:
nlp.to_disk(d)
def test_beam_parse():
nlp = Language()
nlp.add_pipe(DependencyParser(nlp.vocab), name="parser")
nlp.parser.add_label("nsubj")
nlp.parser.begin_training([], token_vector_width=8, hidden_width=8)
doc = nlp.make_doc("Australia is a country")
nlp.parser(doc, beam_width=2)
def test_serialize_language_exclude(meta_data):
name = "name-in-fixture"
nlp = Language(meta=meta_data)
assert nlp.meta["name"] == name
new_nlp = Language().from_bytes(nlp.to_bytes())
assert nlp.meta["name"] == name
new_nlp = Language().from_bytes(nlp.to_bytes(), exclude=["meta"])
assert not new_nlp.meta["name"] == name
new_nlp = Language().from_bytes(nlp.to_bytes(exclude=["meta"]))
assert not new_nlp.meta["name"] == name
with pytest.raises(ValueError):
nlp.to_bytes(meta=False)
with pytest.raises(ValueError):
Language().from_bytes(nlp.to_bytes(), meta=False)
def test_serialize_language_meta_disk(meta_data):
language = Language(meta=meta_data)
with make_tempdir() as d:
language.to_disk(d)
new_language = Language().from_disk(d)
assert new_language.meta == language.meta
