explosion/spaCy
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Merge branch 'develop' of https://github.com/explosion/spaCy into develop

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This commit is contained in:
Matthew Honnibal 2017-09-04 20:02:53 -05:00
commit 1b65115bc2
11 changed files with 49 additions and 23 deletions
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5
.travis.yml
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@ -14,8 +14,7 @@ os:
env:
- VIA=compile LC_ALL=en_US.ascii
- VIA=compile
# - VIA=sdist
#- VIA=pypi_nightly
install:
- "./travis.sh"
@ -23,7 +22,7 @@ install:
script:
- "pip install pytest pytest-timeout"
- if [[ "${VIA}" == "compile" ]]; then python -m pytest --tb=native spacy; fi
- if [[ "${VIA}" == "pypi" ]]; then python -m pytest --tb=native `python -c "import os.path; import spacy; print(os.path.abspath(ospath.dirname(spacy.__file__)))"`; fi
- if [[ "${VIA}" == "pypi_nightly" ]]; then python -m pytest --tb=native --models --en `python -c "import os.path; import spacy; print(os.path.abspath(os.path.dirname(spacy.__file__)))"`; fi
- if [[ "${VIA}" == "sdist" ]]; then python -m pytest --tb=native `python -c "import os.path; import spacy; print(os.path.abspath(os.path.dirname(spacy.__file__)))"`; fi
notifications:

12
spacy/_ml.py
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@ -212,12 +212,14 @@ class PrecomputableMaxouts(Model):
def drop_layer(layer, factor=2.):
def drop_layer_fwd(X, drop=0.):
drop *= factor
mask = layer.ops.get_dropout_mask((1,), drop)
if mask is None or mask > 0:
if drop <= 0.:
return layer.begin_update(X, drop=drop)
else:
return X, lambda dX, sgd=None: dX
coinflip = layer.ops.xp.random.random()
if (coinflip / factor) >= drop:
return layer.begin_update(X, drop=drop)
else:
return X, lambda dX, sgd=None: dX
model = wrap(drop_layer_fwd, layer)
model.predict = layer
@ -362,6 +364,8 @@ def get_token_vectors(tokens_attrs_vectors, drop=0.):
def backward(d_output, sgd=None):
return (tokens, d_output)
return vectors, backward
def fine_tune(embedding, combine=None):
if combine is not None:
raise NotImplementedError(

2
spacy/about.py
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@ -3,7 +3,7 @@
# https://github.com/pypa/warehouse/blob/master/warehouse/__about__.py
__title__ = 'spacy-nightly'
__version__ = '2.0.0a12'
__version__ = '2.0.0a13'
__summary__ = 'Industrial-strength Natural Language Processing (NLP) with Python and Cython'
__uri__ = 'https://spacy.io'
__author__ = 'Explosion AI'

3
spacy/lang/en/morph_rules.py
View File

@ -59,7 +59,8 @@ MORPH_RULES = {
"VBP": {
"are": {LEMMA: "be", "VerbForm": "Fin", "Tense": "Pres", "Mood": "Ind"},
"'re": {LEMMA: "be", "VerbForm": "Fin", "Tense": "Pres", "Mood": "Ind"}
"'re": {LEMMA: "be", "VerbForm": "Fin", "Tense": "Pres", "Mood": "Ind"},
"am": {LEMMA: "be", "VerbForm": "Fin", "Person": "One", "Tense": "Pres", "Mood": "Ind"},
},
"VBD": {

5
spacy/lemmatizer.py
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@ -44,6 +44,11 @@ class Lemmatizer(object):
return True
elif univ_pos == 'verb' and morphology.get('VerbForm') == 'inf':
return True
# This maps 'VBP' to base form -- probably just need 'IS_BASE'
# morphology
elif univ_pos == 'verb' and (morphology.get('VerbForm') == 'fin' and \
morphology.get('Tense') == 'pres'):
return True
elif univ_pos == 'adj' and morphology.get('Degree') == 'pos':
return True
elif VerbForm_inf in morphology:

8
spacy/pipeline.pyx
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@ -142,7 +142,7 @@ class BaseThincComponent(object):
deserialize = OrderedDict((
('cfg', lambda b: self.cfg.update(ujson.loads(b))),
('model', lambda b: self.model.from_bytes(b)),
('model', load_model),
('vocab', lambda b: self.vocab.from_bytes(b))
))
util.from_bytes(bytes_data, deserialize, exclude)
@ -417,7 +417,8 @@ class NeuralTagger(BaseThincComponent):
def from_bytes(self, bytes_data, **exclude):
def load_model(b):
if self.model is True:
token_vector_width = util.env_opt('token_vector_width', 128)
token_vector_width = util.env_opt('token_vector_width',
self.cfg.get('token_vector_width', 128))
self.model = self.Model(self.vocab.morphology.n_tags, token_vector_width)
self.model.from_bytes(b)
@ -451,7 +452,8 @@ class NeuralTagger(BaseThincComponent):
def from_disk(self, path, **exclude):
def load_model(p):
if self.model is True:
token_vector_width = util.env_opt('token_vector_width', 128)
token_vector_width = util.env_opt('token_vector_width',
self.cfg.get('token_vector_width', 128))
self.model = self.Model(self.vocab.morphology.n_tags, token_vector_width)
self.model.from_bytes(p.open('rb').read())

13
spacy/syntax/nn_parser.pyx
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@ -393,7 +393,8 @@ cdef class Parser:
tokvecs = self.model[0].ops.flatten(tokvecses)
if USE_FINE_TUNE:
tokvecs = self.model[0].ops.flatten(self.model[0]((docs, tokvecses)))
# TODO: This is incorrect! Unhack when training next model
tokvecs += self.model[0].ops.flatten(self.model[0]((docs, tokvecses)))
nr_state = len(docs)
nr_class = self.moves.n_moves
@ -531,8 +532,8 @@ cdef class Parser:
docs = [docs]
golds = [golds]
if USE_FINE_TUNE:
tokvecs, bp_my_tokvecs = self.model[0].begin_update(docs_tokvecs, drop=drop)
tokvecs = self.model[0].ops.flatten(tokvecs)
my_tokvecs, bp_my_tokvecs = self.model[0].begin_update(docs_tokvecs, drop=drop)
tokvecs += self.model[0].ops.flatten(my_tokvecs)
cuda_stream = get_cuda_stream()
@ -605,8 +606,8 @@ cdef class Parser:
assert min(lengths) >= 1
tokvecs = self.model[0].ops.flatten(tokvecs)
if USE_FINE_TUNE:
tokvecs, bp_my_tokvecs = self.model[0].begin_update(docs_tokvecs, drop=drop)
tokvecs = self.model[0].ops.flatten(tokvecs)
my_tokvecs, bp_my_tokvecs = self.model[0].begin_update(docs_tokvecs, drop=drop)
tokvecs += self.model[0].ops.flatten(my_tokvecs)
states = self.moves.init_batch(docs)
for gold in golds:
@ -705,7 +706,7 @@ cdef class Parser:
lower, stream, drop=dropout)
return state2vec, upper
nr_feature = 8
nr_feature = 13
def get_token_ids(self, states):
cdef StateClass state

2
spacy/tests/conftest.py
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@ -13,7 +13,7 @@ from .. import util
_languages = ['bn', 'da', 'de', 'en', 'es', 'fi', 'fr', 'he', 'hu', 'id',
'it', 'nb', 'nl', 'pl', 'pt', 'sv', 'xx']
_models = {'en': ['en_depent_web_sm', 'en_core_web_md'],
_models = {'en': ['en_core_web_sm'],
'de': ['de_core_news_md'],
'fr': ['fr_depvec_web_lg'],
'xx': ['xx_ent_web_md']}

16
spacy/tests/lang/en/test_lemmatizer.py
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@ -2,12 +2,18 @@
from __future__ import unicode_literals
import pytest
from ....tokens.doc import Doc
@pytest.fixture
def en_lemmatizer(EN):
return EN.Defaults.create_lemmatizer()
@pytest.mark.models('en')
def test_doc_lemmatization(EN):
doc = Doc(EN.vocab, words=['bleed'])
doc[0].tag_ = 'VBP'
assert doc[0].lemma_ == 'bleed'
@pytest.mark.models('en')
@pytest.mark.parametrize('text,lemmas', [("aardwolves", ["aardwolf"]),
@ -19,6 +25,16 @@ def test_en_lemmatizer_noun_lemmas(en_lemmatizer, text, lemmas):
assert en_lemmatizer.noun(text) == set(lemmas)
@pytest.mark.models('en')
@pytest.mark.parametrize('text,lemmas', [("bleed", ["bleed"]),
("feed", ["feed"]),
("need", ["need"]),
("ring", ["ring"]),
("axes", ["axis", "axe", "ax"])])
def test_en_lemmatizer_noun_lemmas(en_lemmatizer, text, lemmas):
assert en_lemmatizer.noun(text) == set(lemmas)
@pytest.mark.xfail
@pytest.mark.models('en')
def test_en_lemmatizer_base_forms(en_lemmatizer):

1
spacy/tests/lang/en/test_tagger.py
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@ -25,7 +25,6 @@ def test_tag_names(EN):
doc = EN(text, disable=['parser'])
assert type(doc[2].pos) == int
assert isinstance(doc[2].pos_, six.text_type)
assert type(doc[2].dep) == int
assert isinstance(doc[2].dep_, six.text_type)
assert doc[2].tag_ == u'NNS'

5
travis.sh
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@ -2,9 +2,8 @@
if [ "${VIA}" == "pypi" ]; then
rm -rf *
pip install spacy
python -m spacy.en.download
python -m spacy.de.download
pip install spacy-nightly
python -m spacy download en
fi
if [[ "${VIA}" == "sdist" && "${TRAVIS_PULL_REQUEST}" == "false" ]]; then