explosion/spaCy
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15
CONTRIBUTING.md
View File

@ -87,7 +87,16 @@ Code should loosely follow [pep8](https://www.python.org/dev/peps/pep-0008/). Re
### Python conventions
All Python code must be written in an **intersection of Python 2 and Python 3**. This is easy in Cython, but somewhat ugly in Python. We could use some extra utilities for this. Please pay particular attention to code that serialises json objects.
All Python code must be written in an **intersection of Python 2 and Python 3**. This is easy in Cython, but somewhat ugly in Python. Logic that deals with Python or platform compatibility should only live in [`spacy.compat`](spacy/compat.py). To distinguish them from the builtin functions, replacement functions are suffixed with an undersocre, for example `unicode_`. If you need to access the user's version or platform information, for example to show more specific error messages, you can use the `is_config()` helper function.
```python
from .compat import unicode_, json_dumps, is_config
compatible_unicode = unicode_('hello world')
compatible_json = json_dumps({'key': 'value'})
if is_config(windows=True, python2=True):
print("You are using Python 2 on Windows.")
```
Code that interacts with the file-system should accept objects that follow the `pathlib.Path` API, without assuming that the object inherits from `pathlib.Path`. If the function is user-facing and takes a path as an argument, it should check whether the path is provided as a string. Strings should be converted to `pathlib.Path` objects.
@ -95,6 +104,8 @@ At the time of writing (v1.7), spaCy's serialization and deserialization functio
Although spaCy uses a lot of classes, inheritance is viewed with some suspicion — it's seen as a mechanism of last resort. You should discuss plans to extend the class hierarchy before implementing.
We have a number of conventions around variable naming that are still being documented, and aren't 100% strict. A general policy is that instances of the class `Doc` should by default be called `doc`, `Token` `token`, `Lexeme` `lex`, `Vocab` `vocab` and `Language` `nlp`. You should avoid naming variables that are of other types these names. For instance, don't name a text string `doc` — you should usually call this `text`. Two general code style preferences further help with naming. First, lean away from introducing temporary variables, as these clutter your namespace. This is one reason why comprehension expressions are often preferred. Second, keep your functions shortish, so that can work in a smaller scope. Of course, this is a question of trade-offs.
### Cython conventions
spaCy's core data structures are implemented as [Cython](http://cython.org/) `cdef` classes. Memory is managed through the `cymem.cymem.Pool` class, which allows you to allocate memory which will be freed when the `Pool` object is garbage collected. This means you usually don't have to worry about freeing memory. You just have to decide which Python object owns the memory, and make it own the `Pool`. When that object goes out of scope, the memory will be freed. You do have to take care that no pointers outlive the object that owns them — but this is generally quite easy.
@ -126,7 +137,7 @@ cdef int c_total(const int* int_array, int length) nogil:
return total
```
If this is confusing, consider that the compiler couldn't deal with `for item in int_array:` — there's no length attached to a raw pointer, so how could we figure out where to stop? The length is provided in the slice notation as a solution to this. Note that we don't have to declare the type of `item` in the code above -- the compiler can easily infer it. This gives us tidy code that looks quite like Python, but is exactly as fast as C — because we've made sure the compilation to C is trivial.
If this is confusing, consider that the compiler couldn't deal with `for item in int_array:` — there's no length attached to a raw pointer, so how could we figure out where to stop? The length is provided in the slice notation as a solution to this. Note that we don't have to declare the type of `item` in the code above — the compiler can easily infer it. This gives us tidy code that looks quite like Python, but is exactly as fast as C — because we've made sure the compilation to C is trivial.
Your functions cannot be declared `nogil` if they need to create Python objects or call Python functions. This is perfectly okay — you shouldn't torture your code just to get `nogil` functions. However, if your function isn't `nogil`, you should compile your module with `cython -a --cplus my_module.pyx` and open the resulting `my_module.html` file in a browser. This will let you see how Cython is compiling your code. Calls into the Python run-time will be in bright yellow. This lets you easily see whether Cython is able to correctly type your code, or whether there are unexpected problems.

4
README.rst
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@ -10,7 +10,7 @@ open-source software, released under the MIT license.
📊 **Help us improve the library!** `Take the spaCy user survey <https://survey.spacy.io>`_.
💫 **Version 1.7 out now!** `Read the release notes here. <https://github.com/explosion/spaCy/releases/>`_
💫 **Version 1.8 out now!** `Read the release notes here. <https://github.com/explosion/spaCy/releases/>`_
.. image:: https://img.shields.io/travis/explosion/spaCy/master.svg?style=flat-square
:target: https://travis-ci.org/explosion/spaCy
@ -320,6 +320,7 @@ and ``--model`` are optional and enable additional tests:
=========== ============== ===========
Version Date Description
=========== ============== ===========
`v1.8.0`_ ``2017-04-16`` Better NER training, saving and loading
`v1.7.5`_ ``2017-04-07`` Bug fixes and new CLI commands
`v1.7.3`_ ``2017-03-26`` Alpha support for Hebrew, new CLI commands and bug fixes
`v1.7.2`_ ``2017-03-20`` Small fixes to beam parser and model linking
@ -350,6 +351,7 @@ Version Date Description
`v0.93`_ ``2015-09-22`` Bug fixes to word vectors
=========== ============== ===========
.. _v1.8.0: https://github.com/explosion/spaCy/releases/tag/v1.8.0
.. _v1.7.5: https://github.com/explosion/spaCy/releases/tag/v1.7.5
.. _v1.7.3: https://github.com/explosion/spaCy/releases/tag/v1.7.3
.. _v1.7.2: https://github.com/explosion/spaCy/releases/tag/v1.7.2

11
examples/pos_tag.py
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@ -1,7 +1,8 @@
'''Print part-of-speech tagged, true-cased, (very roughly) sentence-separated
"""
Print part-of-speech tagged, true-cased, (very roughly) sentence-separated
text, with each "sentence" on a newline, and spaces between tokens. Supports
multi-processing.
'''
"""
from __future__ import print_function, unicode_literals, division
import io
import bz2
@ -22,14 +23,14 @@ def parallelize(func, iterator, n_jobs, extra):
def iter_texts_from_json_bz2(loc):
'''
"""
Iterator of unicode strings, one per document (here, a comment).
Expects a a path to a BZ2 file, which should be new-line delimited JSON. The
document text should be in a string field titled 'body'.
This is the data format of the Reddit comments corpus.
'''
"""
with bz2.BZ2File(loc) as file_:
for i, line in enumerate(file_):
yield ujson.loads(line)['body']
@ -80,7 +81,7 @@ def is_sent_begin(word):
def main(in_loc, out_dir, n_workers=4, batch_size=100000):
if not path.exists(out_dir):
path.join(out_dir)
texts = partition(batch_size, iter_texts(in_loc))
texts = partition(batch_size, iter_texts_from_json_bz2(in_loc))
parallelize(transform_texts, enumerate(texts), n_workers, [out_dir])

45
examples/train_ner_standalone.py → examples/training/train_ner_standalone.py
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@ -1,3 +1,4 @@
#!/usr/bin/env python
'''Example of training a named entity recognition system from scratch using spaCy
This example is written to be self-contained and reasonably transparent.
@ -81,7 +82,7 @@ def load_vocab(path):
def init_ner_model(vocab, features=None):
if features is None:
features = tuple(EntityRecognizer.feature_templates)
return BeamEntityRecognizer(vocab, features=features)
return EntityRecognizer(vocab, features=features)
def save_ner_model(model, path):
@ -99,7 +100,7 @@ def save_ner_model(model, path):
def load_ner_model(vocab, path):
return BeamEntityRecognizer.load(path, vocab)
return EntityRecognizer.load(path, vocab)
class Pipeline(object):
@ -110,18 +111,21 @@ class Pipeline(object):
raise IOError("Cannot load pipeline from %s\nDoes not exist" % path)
if not path.is_dir():
raise IOError("Cannot load pipeline from %s\nNot a directory" % path)
vocab = load_vocab(path / 'vocab')
vocab = load_vocab(path)
tokenizer = Tokenizer(vocab, {}, None, None, None)
ner_model = load_ner_model(vocab, path / 'ner')
return cls(vocab, tokenizer, ner_model)
def __init__(self, vocab=None, tokenizer=None, ner_model=None):
def __init__(self, vocab=None, tokenizer=None, entity=None):
if vocab is None:
self.vocab = init_vocab()
vocab = init_vocab()
if tokenizer is None:
tokenizer = Tokenizer(vocab, {}, None, None, None)
if ner_model is None:
self.entity = init_ner_model(self.vocab)
if entity is None:
entity = init_ner_model(self.vocab)
self.vocab = vocab
self.tokenizer = tokenizer
self.entity = entity
self.pipeline = [self.entity]
def __call__(self, input_):
@ -173,7 +177,7 @@ class Pipeline(object):
save_ner_model(self.entity, path / 'ner')
def train(nlp, train_examples, dev_examples, nr_epoch=5):
def train(nlp, train_examples, dev_examples, ctx, nr_epoch=5):
next_epoch = train_examples
print("Iter", "Loss", "P", "R", "F")
for i in range(nr_epoch):
@ -186,14 +190,17 @@ def train(nlp, train_examples, dev_examples, nr_epoch=5):
next_epoch.append((input_, annot))
random.shuffle(next_epoch)
scores = nlp.evaluate(dev_examples)
precision = '%.2f' % scores['ents_p']
recall = '%.2f' % scores['ents_r']
f_measure = '%.2f' % scores['ents_f']
print(i, int(loss), precision, recall, f_measure)
report_scores(i, loss, scores)
nlp.average_weights()
scores = nlp.evaluate(dev_examples)
print("After averaging")
print(scores['ents_p'], scores['ents_r'], scores['ents_f'])
report_scores(channels, i+1, loss, scores)
def report_scores(i, loss, scores):
precision = '%.2f' % scores['ents_p']
recall = '%.2f' % scores['ents_r']
f_measure = '%.2f' % scores['ents_f']
print('%d %s %s %s' % (int(loss), precision, recall, f_measure))
def read_examples(path):
@ -221,15 +228,17 @@ def read_examples(path):
train_loc=("Path to your training data", "positional", None, Path),
dev_loc=("Path to your development data", "positional", None, Path),
)
def main(model_dir, train_loc, dev_loc, nr_epoch=10):
def main(model_dir=Path('/home/matt/repos/spaCy/spacy/data/de-1.0.0'),
train_loc=None, dev_loc=None, nr_epoch=30):
train_examples = read_examples(train_loc)
dev_examples = read_examples(dev_loc)
nlp = Pipeline()
nlp = Pipeline.load(model_dir)
train(nlp, train_examples, list(dev_examples), nr_epoch)
train(nlp, train_examples, list(dev_examples), ctx, nr_epoch)
nlp.save(model_dir)
if __name__ == '__main__':
plac.call(main)
main()

103
examples/training/train_new_entity_type.py Normal file
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@ -0,0 +1,103 @@
#!/usr/bin/env python
"""
Example of training an additional entity type
This script shows how to add a new entity type to an existing pre-trained NER
model. To keep the example short and simple, only four sentences are provided
as examples. In practice, you'll need many more — a few hundred would be a
good start. You will also likely need to mix in examples of other entity
types, which might be obtained by running the entity recognizer over unlabelled
sentences, and adding their annotations to the training set.
The actual training is performed by looping over the examples, and calling
`nlp.entity.update()`. The `update()` method steps through the words of the
input. At each word, it makes a prediction. It then consults the annotations
provided on the GoldParse instance, to see whether it was right. If it was
wrong, it adjusts its weights so that the correct action will score higher
next time.
After training your model, you can save it to a directory. We recommend
wrapping models as Python packages, for ease of deployment.
For more details, see the documentation:
* Training the Named Entity Recognizer: https://spacy.io/docs/usage/train-ner
* Saving and loading models: https://spacy.io/docs/usage/saving-loading
Developed for: spaCy 1.7.6
Last tested for: spaCy 1.7.6
"""
# coding: utf8
from __future__ import unicode_literals, print_function
import random
from pathlib import Path
import spacy
from spacy.gold import GoldParse
from spacy.tagger import Tagger
def train_ner(nlp, train_data, output_dir):
# Add new words to vocab
for raw_text, _ in train_data:
doc = nlp.make_doc(raw_text)
for word in doc:
_ = nlp.vocab[word.orth]
for itn in range(20):
random.shuffle(train_data)
for raw_text, entity_offsets in train_data:
gold = GoldParse(doc, entities=entity_offsets)
doc = nlp.make_doc(raw_text)
nlp.tagger(doc)
loss = nlp.entity.update(doc, gold)
nlp.end_training()
if output_dir:
if not output_dir.exists():
output_dir.mkdir()
nlp.save_to_directory(output_dir)
def main(model_name, output_directory=None):
print("Loading initial model", model_name)
nlp = spacy.load(model_name)
if output_directory is not None:
output_directory = Path(output_directory)
train_data = [
(
"Horses are too tall and they pretend to care about your feelings",
[(0, 6, 'ANIMAL')],
),
(
"horses are too tall and they pretend to care about your feelings",
[(0, 6, 'ANIMAL')]
),
(
"horses pretend to care about your feelings",
[(0, 6, 'ANIMAL')]
),
(
"they pretend to care about your feelings, those horses",
[(48, 54, 'ANIMAL')]
)
]
nlp.entity.add_label('ANIMAL')
train_ner(nlp, train_data, output_directory)
# Test that the entity is recognized
doc = nlp('Do you like horses?')
for ent in doc.ents:
print(ent.label_, ent.text)
if output_directory:
print("Loading from", output_directory)
nlp2 = spacy.load('en', path=output_directory)
nlp2.entity.add_label('ANIMAL')
doc2 = nlp2('Do you like horses?')
for ent in doc2.ents:
print(ent.label_, ent.text)
if __name__ == '__main__':
import plac
plac.call(main)

2
fabfile.py vendored
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@ -14,7 +14,7 @@ VENV_DIR = path.join(PWD, ENV)
def env(lang='python2.7'):
if path.exists(VENV_DIR):
local('rm -rf {env}'.format(env=VENV_DIR))
local('virtualenv -p {lang} {env}'.format(lang=lang, env=VENV_DIR))
local('python -m virtualenv -p {lang} {env}'.format(lang=lang, env=VENV_DIR))
def install():

1
requirements.txt
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@ -11,3 +11,4 @@ ujson>=1.35
dill>=0.2,<0.3
requests>=2.13.0,<3.0.0
regex==2017.4.5
pytest>=3.0.6,<4.0.0

67
spacy/__init__.py
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@ -1,53 +1,40 @@
# coding: utf8
from __future__ import unicode_literals, print_function
from __future__ import unicode_literals
import json
from pathlib import Path
from .util import set_lang_class, get_lang_class, parse_package_meta
from . import util
from .deprecated import resolve_model_name
from .cli import info
from . import en
from . import de
from . import zh
from . import es
from . import it
from . import hu
from . import fr
from . import pt
from . import nl
from . import sv
from . import fi
from . import bn
from . import he
from .about import *
from . import en, de, zh, es, it, hu, fr, pt, nl, sv, fi, bn, he
set_lang_class(en.English.lang, en.English)
set_lang_class(de.German.lang, de.German)
set_lang_class(es.Spanish.lang, es.Spanish)
set_lang_class(pt.Portuguese.lang, pt.Portuguese)
set_lang_class(fr.French.lang, fr.French)
set_lang_class(it.Italian.lang, it.Italian)
set_lang_class(hu.Hungarian.lang, hu.Hungarian)
set_lang_class(zh.Chinese.lang, zh.Chinese)
set_lang_class(nl.Dutch.lang, nl.Dutch)
set_lang_class(sv.Swedish.lang, sv.Swedish)
set_lang_class(fi.Finnish.lang, fi.Finnish)
set_lang_class(bn.Bengali.lang, bn.Bengali)
set_lang_class(he.Hebrew.lang, he.Hebrew)
_languages = (en.English, de.German, es.Spanish, pt.Portuguese, fr.French,
it.Italian, hu.Hungarian, zh.Chinese, nl.Dutch, sv.Swedish,
fi.Finnish, bn.Bengali, he.Hebrew)
for _lang in _languages:
util.set_lang_class(_lang.lang, _lang)
def load(name, **overrides):
data_path = overrides.get('path', util.get_data_path())
model_name = resolve_model_name(name)
meta = parse_package_meta(data_path, model_name, require=False)
if overrides.get('path') in (None, False, True):
data_path = util.get_data_path()
model_name = resolve_model_name(name)
model_path = data_path / model_name
if not model_path.exists():
lang_name = util.get_lang_class(name).lang
model_path = None
util.print_msg(
"Only loading the '{}' tokenizer.".format(lang_name),
title="Warning: no model found for '{}'".format(name))
else:
model_path = util.ensure_path(overrides['path'])
data_path = model_path.parent
model_name = ''
meta = util.parse_package_meta(data_path, model_name, require=False)
lang = meta['lang'] if meta and 'lang' in meta else name
cls = get_lang_class(lang)
cls = util.get_lang_class(lang)
overrides['meta'] = meta
model_path = Path(data_path / model_name)
if model_path.exists():
overrides['path'] = model_path
overrides['path'] = model_path
return cls(**overrides)

17
spacy/__main__.py
View File

@ -14,8 +14,9 @@ from spacy.cli import convert as cli_convert
class CLI(object):
"""Command-line interface for spaCy"""
"""
Command-line interface for spaCy
"""
commands = ('download', 'link', 'info', 'package', 'train', 'model', 'convert')
@plac.annotations(
@ -29,7 +30,6 @@ class CLI(object):
can be shortcut, model name or, if --direct flag is set, full model name
with version.
"""
cli_download(model, direct)
@ -44,7 +44,6 @@ class CLI(object):
either the name of a pip package, or the local path to the model data
directory. Linking models allows loading them via spacy.load(link_name).
"""
cli_link(origin, link_name, force)
@ -58,23 +57,22 @@ class CLI(object):
speficied as an argument, print model information. Flag --markdown
prints details in Markdown for easy copy-pasting to GitHub issues.
"""
cli_info(model, markdown)
@plac.annotations(
input_dir=("directory with model data", "positional", None, str),
output_dir=("output parent directory", "positional", None, str),
meta=("path to meta.json", "option", "m", str),
force=("force overwriting of existing folder in output directory", "flag", "f", bool)
)
def package(self, input_dir, output_dir, force=False):
def package(self, input_dir, output_dir, meta=None, force=False):
"""
Generate Python package for model data, including meta and required
installation files. A new directory will be created in the specified
output directory, and model data will be copied over.
"""
cli_package(input_dir, output_dir, force)
cli_package(input_dir, output_dir, meta, force)
@plac.annotations(
@ -93,7 +91,6 @@ class CLI(object):
"""
Train a model. Expects data in spaCy's JSON format.
"""
cli_train(lang, output_dir, train_data, dev_data, n_iter, not no_tagger,
not no_parser, not no_ner, parser_L1)
@ -108,7 +105,6 @@ class CLI(object):
"""
Initialize a new model and its data directory.
"""
cli_model(lang, model_dir, freqs_data, clusters_data, vectors_data)
@plac.annotations(
@ -122,7 +118,6 @@ class CLI(object):
Convert files into JSON format for use with train command and other
experiment management functions.
"""
cli_convert(input_file, output_dir, n_sents, morphology)

2
spacy/about.py
View File

@ -3,7 +3,7 @@
# https://github.com/pypa/warehouse/blob/master/warehouse/__about__.py
__title__ = 'spacy'
__version__ = '1.7.5'
__version__ = '1.8.0'
__summary__ = 'Industrial-strength Natural Language Processing (NLP) with Python and Cython'
__uri__ = 'https://spacy.io'
__author__ = 'Matthew Honnibal'

9
spacy/attrs.pyx
View File

@ -1,3 +1,7 @@
# coding: utf8
from __future__ import unicode_literals
IDS = {
"": NULL_ATTR,
"IS_ALPHA": IS_ALPHA,
@ -92,7 +96,8 @@ NAMES = [key for key, value in sorted(IDS.items(), key=lambda item: item[1])]
def intify_attrs(stringy_attrs, strings_map=None, _do_deprecated=False):
'''Normalize a dictionary of attributes, converting them to ints.
"""
Normalize a dictionary of attributes, converting them to ints.
Arguments:
stringy_attrs (dict):
@ -105,7 +110,7 @@ def intify_attrs(stringy_attrs, strings_map=None, _do_deprecated=False):
inty_attrs (dict):
Attributes dictionary with keys and optionally values converted to
ints.
'''
"""
inty_attrs = {}
if _do_deprecated:
if 'F' in stringy_attrs:

3
spacy/cfile.pyx
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@ -1,3 +1,6 @@
# coding: utf8
from __future__ import unicode_literals
from libc.stdio cimport fopen, fclose, fread, fwrite
from libc.string cimport memcpy

5
spacy/cli/convert.py
View File

@ -1,8 +1,7 @@
# coding: utf8
from __future__ import unicode_literals, division, print_function
from __future__ import unicode_literals
import io
from pathlib import Path, PurePosixPath
from pathlib import Path
from .converters import conllu2json
from .. import util

10
spacy/cli/converters/conllu2json.py
View File

@ -1,13 +1,14 @@
# coding: utf8
from __future__ import unicode_literals, division, print_function
from __future__ import unicode_literals
import json
from ...gold import read_json_file, merge_sents
from ...compat import json_dumps
from ... import util
def conllu2json(input_path, output_path, n_sents=10, use_morphology=False):
"""Convert conllu files into JSON format for use with train cli.
"""
Convert conllu files into JSON format for use with train cli.
use_morphology parameter enables appending morphology to tags, which is
useful for languages such as Spanish, where UD tags are not so rich.
"""
@ -29,7 +30,8 @@ def conllu2json(input_path, output_path, n_sents=10, use_morphology=False):
output_filename = input_path.parts[-1].replace(".conllu", ".json")
output_file = output_path / output_filename
json.dump(docs, output_file.open('w', encoding='utf-8'), indent=2)
with output_file.open('w', encoding='utf-8') as f:
f.write(json_dumps(docs))
util.print_msg("Created {} documents".format(len(docs)),
title="Generated output file {}".format(output_file))

1
spacy/cli/download.py
View File

@ -1,7 +1,6 @@
# coding: utf8
from __future__ import unicode_literals
import pip
import requests
import os
import subprocess

17
spacy/cli/info.py
View File

@ -4,6 +4,7 @@ from __future__ import unicode_literals
import platform
from pathlib import Path
from ..compat import unicode_
from .. import about
from .. import util
@ -13,12 +14,11 @@ def info(model=None, markdown=False):
data = util.parse_package_meta(util.get_data_path(), model, require=True)
model_path = Path(__file__).parent / util.get_data_path() / model
if model_path.resolve() != model_path:
data['link'] = str(model_path)
data['source'] = str(model_path.resolve())
data['link'] = unicode_(model_path)
data['source'] = unicode_(model_path.resolve())
else:
data['source'] = str(model_path)
data['source'] = unicode_(model_path)
print_info(data, "model " + model, markdown)
else:
data = get_spacy_data()
print_info(data, "spaCy", markdown)
@ -26,10 +26,8 @@ def info(model=None, markdown=False):
def print_info(data, title, markdown):
title = "Info about {title}".format(title=title)
if markdown:
util.print_markdown(data, title=title)
else:
util.print_table(data, title=title)
@ -37,7 +35,7 @@ def print_info(data, title, markdown):
def get_spacy_data():
return {
'spaCy version': about.__version__,
'Location': str(Path(__file__).parent.parent),
'Location': unicode_(Path(__file__).parent.parent),
'Platform': platform.platform(),
'Python version': platform.python_version(),
'Installed models': ', '.join(list_models())
@ -49,5 +47,6 @@ def list_models():
# won't show up in list, but it seems worth it
exclude = ['cache', 'pycache', '__pycache__']
data_path = util.get_data_path()
models = [f.parts[-1] for f in data_path.iterdir() if f.is_dir()]
return [m for m in models if m not in exclude]
if data_path:
models = [f.parts[-1] for f in data_path.iterdir() if f.is_dir()]
return [m for m in models if m not in exclude]

26
spacy/cli/link.py
View File

@ -4,6 +4,7 @@ from __future__ import unicode_literals
import pip
from pathlib import Path
import importlib
from ..compat import unicode_, symlink_to
from .. import util
@ -20,7 +21,6 @@ def link_package(package_name, link_name, force=False):
# Python's installation and import rules are very complicated.
pkg = importlib.import_module(package_name)
package_path = Path(pkg.__file__).parent.parent
meta = get_meta(package_path, package_name)
model_name = package_name + '-' + meta['version']
model_path = package_path / package_name / model_name
@ -29,7 +29,7 @@ def link_package(package_name, link_name, force=False):
def symlink(model_path, link_name, force):
model_path = Path(model_path)
if not Path(model_path).exists():
if not model_path.exists():
util.sys_exit(
"The data should be located in {p}".format(p=model_path),
title="Can't locate model data")
@ -43,13 +43,21 @@ def symlink(model_path, link_name, force):
elif link_path.exists():
link_path.unlink()
# Add workaround for Python 2 on Windows (see issue #909)
if util.is_python2() and util.is_windows():
import subprocess
command = ['mklink', '/d', link_path, model_path]
subprocess.call(command, shell=True)
else:
link_path.symlink_to(model_path)
try:
symlink_to(link_path, model_path)
except:
# This is quite dirty, but just making sure other errors are caught so
# users at least see a proper message.
util.print_msg(
"Creating a symlink in spacy/data failed. Make sure you have the "
"required permissions and try re-running the command as admin, or "
"use a virtualenv to install spaCy in a user directory, instead of "
"doing a system installation.",
"You can still import the model as a Python package and call its "
"load() method, or create the symlink manually:",
"{a} --> {b}".format(a=unicode_(model_path), b=unicode_(link_path)),
title="Error: Couldn't link model to '{l}'".format(l=link_name))
raise
util.print_msg(
"{a} --> {b}".format(a=model_path.as_posix(), b=link_path.as_posix()),

2
spacy/cli/model.py
View File

@ -95,7 +95,7 @@ def read_clusters(clusters_path):
return clusters
def populate_vocab(vocab, clusters, probs, oov_probs):
def populate_vocab(vocab, clusters, probs, oov_prob):
# Ensure probs has entries for all words seen during clustering.
for word in clusters:
if word not in probs:

46
spacy/cli/package.py
View File

@ -1,57 +1,67 @@
# coding: utf8
from __future__ import unicode_literals
import json
import shutil
import requests
from pathlib import Path
from .. import about
from ..compat import unicode_, json_dumps
from .. import util
def package(input_dir, output_dir, force):
def package(input_dir, output_dir, meta_path, force):
input_path = Path(input_dir)
output_path = Path(output_dir)
check_dirs(input_path, output_path)
meta_path = util.ensure_path(meta_path)
check_dirs(input_path, output_path, meta_path)
template_setup = get_template('setup.py')
template_manifest = get_template('MANIFEST.in')
template_init = get_template('en_model_name/__init__.py')
meta = generate_meta()
meta_path = meta_path or input_path / 'meta.json'
if meta_path.is_file():
util.print_msg(unicode_(meta_path), title="Reading meta.json from file")
meta = util.read_json(meta_path)
else:
meta = generate_meta()
validate_meta(meta, ['lang', 'name', 'version'])
model_name = meta['lang'] + '_' + meta['name']
model_name_v = model_name + '-' + meta['version']
main_path = output_path / model_name_v
package_path = main_path / model_name
create_dirs(package_path, force)
shutil.copytree(input_path.as_posix(), (package_path / model_name_v).as_posix())
create_file(main_path / 'meta.json', json.dumps(meta, indent=2))
shutil.copytree(unicode_(input_path), unicode_(package_path / model_name_v))
create_file(main_path / 'meta.json', json_dumps(meta))
create_file(main_path / 'setup.py', template_setup)
create_file(main_path / 'MANIFEST.in', template_manifest)
create_file(package_path / '__init__.py', template_init)
util.print_msg(
main_path.as_posix(),
unicode_(main_path),
"To build the package, run `python setup.py sdist` in that directory.",
title="Successfully created package {p}".format(p=model_name_v))
def check_dirs(input_path, output_path):
def check_dirs(input_path, output_path, meta_path):
if not input_path.exists():
util.sys_exit(input_path.as_poisx(), title="Model directory not found")
util.sys_exit(unicode_(input_path.as_poisx), title="Model directory not found")
if not output_path.exists():
util.sys_exit(output_path.as_posix(), title="Output directory not found")
util.sys_exit(unicode_(output_path), title="Output directory not found")
if meta_path and not meta_path.exists():
util.sys_exit(unicode_(meta_path), title="meta.json not found")
def create_dirs(package_path, force):
if package_path.exists():
if force:
shutil.rmtree(package_path.as_posix())
shutil.rmtree(unicode_(package_path))
else:
util.sys_exit(package_path.as_posix(),
"Please delete the directory and try again.",
util.sys_exit(unicode_(package_path),
"Please delete the directory and try again, or use the --force "
"flag to overwrite existing directories.",
title="Package directory already exists")
Path.mkdir(package_path, parents=True)
@ -81,6 +91,14 @@ def generate_meta():
return meta
def validate_meta(meta, keys):
for key in keys:
if key not in meta or meta[key] == '':
util.sys_exit(
"This setting is required to build your package.",
title='No "{k}" setting found in meta.json'.format(k=key))
def get_template(filepath):
url = 'https://raw.githubusercontent.com/explosion/spacy-dev-resources/master/templates/model/'
r = requests.get(url + filepath)

16
spacy/cli/train.py
View File

@ -2,11 +2,9 @@
from __future__ import unicode_literals, division, print_function
import json
from pathlib import Path
from ..util import ensure_path
from ..scorer import Scorer
from ..tagger import Tagger
from ..syntax.parser import Parser
from ..gold import GoldParse, merge_sents
from ..gold import read_json_file as read_gold_json
from .. import util
@ -14,9 +12,9 @@ from .. import util
def train(language, output_dir, train_data, dev_data, n_iter, tagger, parser, ner,
parser_L1):
output_path = Path(output_dir)
train_path = Path(train_data)
dev_path = Path(dev_data)
output_path = ensure_path(output_dir)
train_path = ensure_path(train_data)
dev_path = ensure_path(dev_data)
check_dirs(output_path, train_path, dev_path)
lang = util.get_lang_class(language)
@ -45,7 +43,7 @@ def train(language, output_dir, train_data, dev_data, n_iter, tagger, parser, ne
def train_config(config):
config_path = Path(config)
config_path = ensure_path(config)
if not config_path.is_file():
util.sys_exit(config_path.as_posix(), title="Config file not found")
config = json.load(config_path)
@ -59,8 +57,8 @@ def train_model(Language, train_data, dev_data, output_path, tagger_cfg, parser_
entity_cfg, n_iter):
print("Itn.\tN weight\tN feats\tUAS\tNER F.\tTag %\tToken %")
with Language.train(output_path, train_data, tagger_cfg, parser_cfg, entity_cfg) as trainer:
loss = 0
with Language.train(output_path, train_data,
pos=tagger_cfg, deps=parser_cfg, ner=entity_cfg) as trainer:
for itn, epoch in enumerate(trainer.epochs(n_iter, augment_data=None)):
for doc, gold in epoch:
trainer.update(doc, gold)

54
spacy/compat.py Normal file
View File

@ -0,0 +1,54 @@
# coding: utf8
from __future__ import unicode_literals
import six
import sys
import ujson
try:
import cPickle as pickle
except ImportError:
import pickle
try:
import copy_reg
except ImportError:
import copyreg as copy_reg
is_python2 = six.PY2
is_python3 = six.PY3
is_windows = sys.platform.startswith('win')
is_linux = sys.platform.startswith('linux')
is_osx = sys.platform == 'darwin'
if is_python2:
bytes_ = str
unicode_ = unicode
basestring_ = basestring
input_ = raw_input
json_dumps = lambda data: ujson.dumps(data, indent=2).decode('utf8')
elif is_python3:
bytes_ = bytes
unicode_ = str
basestring_ = str
input_ = input
json_dumps = lambda data: ujson.dumps(data, indent=2)
def symlink_to(orig, dest):
if is_python2 and is_windows:
import subprocess
subprocess.call(['mklink', '/d', unicode(orig), unicode(dest)], shell=True)
else:
orig.symlink_to(dest)
def is_config(python2=None, python3=None, windows=None, linux=None, osx=None):
return ((python2 == None or python2 == is_python2) and
(python3 == None or python3 == is_python3) and
(windows == None or windows == is_windows) and
(linux == None or linux == is_linux) and
(osx == None or osx == is_osx))

59
spacy/deprecated.py
View File

@ -1,16 +1,14 @@
# coding: utf8
from __future__ import unicode_literals
from pathlib import Path
from . import about
from . import util
from .cli import download
from .cli import link
try:
basestring
except NameError:
basestring = str
def read_lang_data(package):
tokenization = package.load_json(('tokenizer', 'specials.json'))
with package.open(('tokenizer', 'prefix.txt'), default=None) as file_:
@ -36,7 +34,8 @@ def align_tokens(ref, indices): # Deprecated, surely?
def detokenize(token_rules, words): # Deprecated?
"""To align with treebanks, return a list of "chunks", where a chunk is a
"""
To align with treebanks, return a list of "chunks", where a chunk is a
sequence of tokens that are separated by whitespace in actual strings. Each
chunk should be a tuple of token indices, e.g.
@ -57,10 +56,30 @@ def detokenize(token_rules, words): # Deprecated?
return positions
def fix_glove_vectors_loading(overrides):
"""Special-case hack for loading the GloVe vectors, to support deprecated
<1.0 stuff. Phase this out once the data is fixed."""
def match_best_version(target_name, target_version, path):
path = util.ensure_path(path)
if path is None or not path.exists():
return None
matches = []
for data_name in path.iterdir():
name, version = split_data_name(data_name.parts[-1])
if name == target_name:
matches.append((tuple(float(v) for v in version.split('.')), data_name))
if matches:
return Path(max(matches)[1])
else:
return None
def split_data_name(name):
return name.split('-', 1) if '-' in name else (name, '')
def fix_glove_vectors_loading(overrides):
"""
Special-case hack for loading the GloVe vectors, to support deprecated
<1.0 stuff. Phase this out once the data is fixed.
"""
if 'data_dir' in overrides and 'path' not in overrides:
raise ValueError("The argument 'data_dir' has been renamed to 'path'")
if overrides.get('path') is False:
@ -68,18 +87,16 @@ def fix_glove_vectors_loading(overrides):
if overrides.get('path') in (None, True):
data_path = util.get_data_path()
else:
path = overrides['path']
if isinstance(path, basestring):
path = Path(path)
path = util.ensure_path(overrides['path'])
data_path = path.parent
vec_path = None
if 'add_vectors' not in overrides:
if 'vectors' in overrides:
vec_path = util.match_best_version(overrides['vectors'], None, data_path)
vec_path = match_best_version(overrides['vectors'], None, data_path)
if vec_path is None:
return overrides
else:
vec_path = util.match_best_version('en_glove_cc_300_1m_vectors', None, data_path)
vec_path = match_best_version('en_glove_cc_300_1m_vectors', None, data_path)
if vec_path is not None:
vec_path = vec_path / 'vocab' / 'vec.bin'
if vec_path is not None:
@ -88,13 +105,13 @@ def fix_glove_vectors_loading(overrides):
def resolve_model_name(name):
"""If spaCy is loaded with 'de', check if symlink already exists. If
not, user have upgraded from older version and have old models installed.
"""
If spaCy is loaded with 'de', check if symlink already exists. If
not, user may have upgraded from older version and have old models installed.
Check if old model directory exists and if so, return that instead and create
shortcut link. If English model is found and no shortcut exists, raise error
and tell user to install new model.
"""
if name == 'en' or name == 'de':
versions = ['1.0.0', '1.1.0']
data_path = Path(util.get_data_path())
@ -117,9 +134,11 @@ def resolve_model_name(name):
class ModelDownload():
"""Replace download modules within en and de with deprecation warning and
"""
Replace download modules within en and de with deprecation warning and
download default language model (using shortcut). Use classmethods to allow
importing ModelDownload as download and calling download.en() etc."""
importing ModelDownload as download and calling download.en() etc.
"""
@classmethod
def load(self, lang):

6
spacy/en/__init__.py
View File

@ -11,12 +11,6 @@ from ..deprecated import fix_glove_vectors_loading
from .language_data import *
try:
basestring
except NameError:
basestring = str
class English(Language):
lang = 'en'

36
spacy/gold.pyx
View File

@ -1,15 +1,13 @@
# cython: profile=True
# coding: utf8
from __future__ import unicode_literals, print_function
import io
import json
import re
import os
from os import path
import ujson as json
import ujson
from .syntax import nonproj
from .util import ensure_path
def tags_to_entities(tags):
@ -141,12 +139,13 @@ def _min_edit_path(cand_words, gold_words):
def read_json_file(loc, docs_filter=None):
if path.isdir(loc):
for filename in os.listdir(loc):
yield from read_json_file(path.join(loc, filename))
loc = ensure_path(loc)
if loc.is_dir():
for filename in loc.iterdir():
yield from read_json_file(loc / filename)
else:
with io.open(loc, 'r', encoding='utf8') as file_:
docs = json.load(file_)
with loc.open('r', encoding='utf8') as file_:
docs = ujson.load(file_)
for doc in docs:
if docs_filter is not None and not docs_filter(doc):
continue
@ -220,7 +219,8 @@ cdef class GoldParse:
def __init__(self, doc, annot_tuples=None, words=None, tags=None, heads=None,
deps=None, entities=None, make_projective=False):
"""Create a GoldParse.
"""
Create a GoldParse.
Arguments:
doc (Doc):
@ -302,7 +302,8 @@ cdef class GoldParse:
self.heads = proj_heads
def __len__(self):
"""Get the number of gold-standard tokens.
"""
Get the number of gold-standard tokens.
Returns (int): The number of gold-standard tokens.
"""
@ -310,13 +311,16 @@ cdef class GoldParse:
@property
def is_projective(self):
"""Whether the provided syntactic annotations form a projective dependency
tree."""
"""
Whether the provided syntactic annotations form a projective dependency
tree.
"""
return not nonproj.is_nonproj_tree(self.heads)
def biluo_tags_from_offsets(doc, entities):
'''Encode labelled spans into per-token tags, using the Begin/In/Last/Unit/Out
"""
Encode labelled spans into per-token tags, using the Begin/In/Last/Unit/Out
scheme (biluo).
Arguments:
@ -347,7 +351,7 @@ def biluo_tags_from_offsets(doc, entities):
tags = biluo_tags_from_offsets(doc, entities)
assert tags == ['O', 'O', 'U-LOC', 'O']
'''
"""
starts = {token.idx: token.i for token in doc}
ends = {token.idx+len(token): token.i for token in doc}
biluo = ['-' for _ in doc]

202
spacy/language.py
View File

@ -1,39 +1,25 @@
from __future__ import absolute_import
from __future__ import unicode_literals
import pathlib
# coding: utf8
from __future__ import absolute_import, unicode_literals
from contextlib import contextmanager
import shutil
import ujson
try:
basestring
except NameError:
basestring = str
try:
unicode
except NameError:
unicode = str
from .tokenizer import Tokenizer
from .vocab import Vocab
from .tagger import Tagger
from .matcher import Matcher
from . import attrs
from . import orth
from . import util
from . import language_data
from .lemmatizer import Lemmatizer
from .train import Trainer
from .attrs import TAG, DEP, ENT_IOB, ENT_TYPE, HEAD, PROB, LANG, IS_STOP
from .syntax.parser import get_templates
from .syntax.nonproj import PseudoProjectivity
from .pipeline import DependencyParser, EntityRecognizer
from .syntax.arc_eager import ArcEager
from .syntax.ner import BiluoPushDown
from .compat import json_dumps
from .attrs import IS_STOP
from . import attrs
from . import orth
from . import util
from . import language_data
class BaseDefaults(object):
@ -150,25 +136,15 @@ class BaseDefaults(object):
return pipeline
token_match = language_data.TOKEN_MATCH
prefixes = tuple(language_data.TOKENIZER_PREFIXES)
suffixes = tuple(language_data.TOKENIZER_SUFFIXES)
infixes = tuple(language_data.TOKENIZER_INFIXES)
tag_map = dict(language_data.TAG_MAP)
tokenizer_exceptions = {}
parser_features = get_templates('parser')
entity_features = get_templates('ner')
tagger_features = Tagger.feature_templates # TODO -- fix this
stop_words = set()
lemma_rules = {}
lemma_exc = {}
lemma_index = {}
@ -202,53 +178,46 @@ class BaseDefaults(object):
class Language(object):
'''A text-processing pipeline. Usually you'll load this once per process, and
"""
A text-processing pipeline. Usually you'll load this once per process, and
pass the instance around your program.
'''
"""
Defaults = BaseDefaults
lang = None
@classmethod
@contextmanager
def train(cls, path, gold_tuples, *configs):
if isinstance(path, basestring):
path = pathlib.Path(path)
tagger_cfg, parser_cfg, entity_cfg = configs
dep_model_dir = path / 'deps'
ner_model_dir = path / 'ner'
pos_model_dir = path / 'pos'
if dep_model_dir.exists():
shutil.rmtree(str(dep_model_dir))
if ner_model_dir.exists():
shutil.rmtree(str(ner_model_dir))
if pos_model_dir.exists():
shutil.rmtree(str(pos_model_dir))
dep_model_dir.mkdir()
ner_model_dir.mkdir()
pos_model_dir.mkdir()
def setup_directory(cls, path, **configs):
"""
Initialise a model directory.
"""
for name, config in configs.items():
directory = path / name
if directory.exists():
shutil.rmtree(str(directory))
directory.mkdir()
with (directory / 'config.json').open('wb') as file_:
data = json_dumps(config)
file_.write(data)
if not (path / 'vocab').exists():
(path / 'vocab').mkdir()
if parser_cfg['pseudoprojective']:
@classmethod
@contextmanager
def train(cls, path, gold_tuples, **configs):
parser_cfg = configs.get('deps', {})
if parser_cfg.get('pseudoprojective'):
# preprocess training data here before ArcEager.get_labels() is called
gold_tuples = PseudoProjectivity.preprocess_training_data(gold_tuples)
parser_cfg['actions'] = ArcEager.get_actions(gold_parses=gold_tuples)
entity_cfg['actions'] = BiluoPushDown.get_actions(gold_parses=gold_tuples)
for subdir in ('deps', 'ner', 'pos'):
if subdir not in configs:
configs[subdir] = {}
if parser_cfg:
configs['deps']['actions'] = ArcEager.get_actions(gold_parses=gold_tuples)
if 'ner' in configs:
configs['ner']['actions'] = BiluoPushDown.get_actions(gold_parses=gold_tuples)
with (dep_model_dir / 'config.json').open('wb') as file_:
data = ujson.dumps(parser_cfg)
if isinstance(data, unicode):
data = data.encode('utf8')
file_.write(data)
with (ner_model_dir / 'config.json').open('wb') as file_:
data = ujson.dumps(entity_cfg)
if isinstance(data, unicode):
data = data.encode('utf8')
file_.write(data)
with (pos_model_dir / 'config.json').open('wb') as file_:
data = ujson.dumps(tagger_cfg)
if isinstance(data, unicode):
data = data.encode('utf8')
file_.write(data)
cls.setup_directory(path, **configs)
self = cls(
path=path,
@ -269,14 +238,22 @@ class Language(object):
self.entity = self.Defaults.create_entity(self)
self.pipeline = self.Defaults.create_pipeline(self)
yield Trainer(self, gold_tuples)
self.end_training(path=path)
self.end_training()
self.save_to_directory(path)
def __init__(self, **overrides):
"""
Create or load the pipeline.
Arguments:
**overrides: Keyword arguments indicating which defaults to override.
Returns:
Language: The newly constructed object.
"""
if 'data_dir' in overrides and 'path' not in overrides:
raise ValueError("The argument 'data_dir' has been renamed to 'path'")
path = overrides.get('path', True)
if isinstance(path, basestring):
path = pathlib.Path(path)
path = util.ensure_path(overrides.get('path', True))
if path is True:
path = util.get_data_path() / self.lang
if not path.exists() and 'path' not in overrides:
@ -322,11 +299,12 @@ class Language(object):
self.pipeline = [self.tagger, self.parser, self.matcher, self.entity]
def __call__(self, text, tag=True, parse=True, entity=True):
"""Apply the pipeline to some text. The text can span multiple sentences,
"""
Apply the pipeline to some text. The text can span multiple sentences,
and can contain arbtrary whitespace. Alignment into the original string
is preserved.
Args:
Argsuments:
text (unicode): The text to be processed.
Returns:
@ -352,7 +330,8 @@ class Language(object):
return doc
def pipe(self, texts, tag=True, parse=True, entity=True, n_threads=2, batch_size=1000):
'''Process texts as a stream, and yield Doc objects in order.
"""
Process texts as a stream, and yield Doc objects in order.
Supports GIL-free multi-threading.
@ -361,7 +340,7 @@ class Language(object):
tag (bool)
parse (bool)
entity (bool)
'''
"""
skip = {self.tagger: not tag, self.parser: not parse, self.entity: not entity}
stream = (self.make_doc(text) for text in texts)
for proc in self.pipeline:
@ -373,51 +352,42 @@ class Language(object):
for doc in stream:
yield doc
def end_training(self, path=None):
if path is None:
path = self.path
elif isinstance(path, basestring):
path = pathlib.Path(path)
def save_to_directory(self, path):
"""
Save the Vocab, StringStore and pipeline to a directory.
if self.tagger:
self.tagger.model.end_training()
self.tagger.model.dump(str(path / 'pos' / 'model'))
if self.parser:
self.parser.model.end_training()
self.parser.model.dump(str(path / 'deps' / 'model'))
if self.entity:
self.entity.model.end_training()
self.entity.model.dump(str(path / 'ner' / 'model'))
Arguments:
path (string or pathlib path): Path to save the model.
"""
configs = {
'pos': self.tagger.cfg if self.tagger else {},
'deps': self.parser.cfg if self.parser else {},
'ner': self.entity.cfg if self.entity else {},
}
path = util.ensure_path(path)
self.setup_directory(path, **configs)
strings_loc = path / 'vocab' / 'strings.json'
with strings_loc.open('w', encoding='utf8') as file_:
self.vocab.strings.dump(file_)
self.vocab.dump(path / 'vocab' / 'lexemes.bin')
# TODO: Word vectors?
if self.tagger:
tagger_freqs = list(self.tagger.freqs[TAG].items())
else:
tagger_freqs = []
self.tagger.model.dump(str(path / 'pos' / 'model'))
if self.parser:
dep_freqs = list(self.parser.moves.freqs[DEP].items())
head_freqs = list(self.parser.moves.freqs[HEAD].items())
else:
dep_freqs = []
head_freqs = []
self.parser.model.dump(str(path / 'deps' / 'model'))
if self.entity:
entity_iob_freqs = list(self.entity.moves.freqs[ENT_IOB].items())
entity_type_freqs = list(self.entity.moves.freqs[ENT_TYPE].items())
else:
entity_iob_freqs = []
entity_type_freqs = []
with (path / 'vocab' / 'serializer.json').open('wb') as file_:
data = ujson.dumps([
(TAG, tagger_freqs),
(DEP, dep_freqs),
(ENT_IOB, entity_iob_freqs),
(ENT_TYPE, entity_type_freqs),
(HEAD, head_freqs)
])
if isinstance(data, unicode):
data = data.encode('utf8')
file_.write(data)
self.entity.model.dump(str(path / 'ner' / 'model'))
def end_training(self, path=None):
if self.tagger:
self.tagger.model.end_training()
if self.parser:
self.parser.model.end_training()
if self.entity:
self.entity.model.end_training()
# NB: This is slightly different from before --- we no longer default
# to taking nlp.path
if path is not None:
self.save_to_directory(path)

17
spacy/lemmatizer.py
View File

@ -1,13 +1,8 @@
from __future__ import unicode_literals, print_function
import codecs
import pathlib
import ujson as json
# coding: utf8
from __future__ import unicode_literals
from .symbols import POS, NOUN, VERB, ADJ, PUNCT
from .symbols import VerbForm_inf, VerbForm_none
from .symbols import Number_sing
from .symbols import Degree_pos
from .symbols import VerbForm_inf, VerbForm_none, Number_sing, Degree_pos
class Lemmatizer(object):
@ -38,8 +33,10 @@ class Lemmatizer(object):
return lemmas
def is_base_form(self, univ_pos, morphology=None):
'''Check whether we're dealing with an uninflected paradigm, so we can
avoid lemmatization entirely.'''
"""
Check whether we're dealing with an uninflected paradigm, so we can
avoid lemmatization entirely.
"""
morphology = {} if morphology is None else morphology
others = [key for key in morphology if key not in (POS, 'number', 'pos', 'verbform')]
true_morph_key = morphology.get('morph', 0)

25
spacy/lexeme.pyx
View File

@ -1,4 +1,7 @@
# cython: embedsignature=True
# coding: utf8
from __future__ import unicode_literals, print_function
from libc.math cimport sqrt
from cpython.ref cimport Py_INCREF
from cymem.cymem cimport Pool
@ -9,14 +12,11 @@ from cython.view cimport array as cvarray
cimport numpy as np
np.import_array()
from libc.string cimport memset
import numpy
from .orth cimport word_shape
from .typedefs cimport attr_t, flags_t
import numpy
from .attrs cimport IS_ALPHA, IS_ASCII, IS_DIGIT, IS_LOWER, IS_PUNCT, IS_SPACE
from .attrs cimport IS_TITLE, IS_UPPER, LIKE_URL, LIKE_NUM, LIKE_EMAIL, IS_STOP
from .attrs cimport IS_BRACKET
@ -30,13 +30,15 @@ memset(&EMPTY_LEXEME, 0, sizeof(LexemeC))
cdef class Lexeme:
"""An entry in the vocabulary. A Lexeme has no string context --- it's a
"""
An entry in the vocabulary. A Lexeme has no string context --- it's a
word-type, as opposed to a word token. It therefore has no part-of-speech
tag, dependency parse, or lemma (lemmatization depends on the part-of-speech
tag).
"""
def __init__(self, Vocab vocab, int orth):
"""Create a Lexeme object.
"""
Create a Lexeme object.
Arguments:
vocab (Vocab): The parent vocabulary
@ -80,7 +82,8 @@ cdef class Lexeme:
return self.c.orth
def set_flag(self, attr_id_t flag_id, bint value):
"""Change the value of a boolean flag.
"""
Change the value of a boolean flag.
Arguments:
flag_id (int): The attribute ID of the flag to set.
@ -89,7 +92,8 @@ cdef class Lexeme:
Lexeme.c_set_flag(self.c, flag_id, value)
def check_flag(self, attr_id_t flag_id):
"""Check the value of a boolean flag.
"""
Check the value of a boolean flag.
Arguments:
flag_id (int): The attribute ID of the flag to query.
@ -98,7 +102,8 @@ cdef class Lexeme:
return True if Lexeme.c_check_flag(self.c, flag_id) else False
def similarity(self, other):
'''Compute a semantic similarity estimate. Defaults to cosine over vectors.
"""
Compute a semantic similarity estimate. Defaults to cosine over vectors.
Arguments:
other:
@ -106,7 +111,7 @@ cdef class Lexeme:
Token and Lexeme objects.
Returns:
score (float): A scalar similarity score. Higher is more similar.
'''
"""
if self.vector_norm == 0 or other.vector_norm == 0:
return 0.0
return numpy.dot(self.vector, other.vector) / (self.vector_norm * other.vector_norm)

39
spacy/matcher.pyx
View File

@ -1,7 +1,10 @@
# cython: profile=True
# cython: infer_types=True
# coding: utf8
from __future__ import unicode_literals
import ujson
from .typedefs cimport attr_t
from .typedefs cimport hash_t
from .attrs cimport attr_id_t
@ -52,12 +55,6 @@ from .attrs import FLAG36 as L9_ENT
from .attrs import FLAG35 as L10_ENT
try:
import ujson as json
except ImportError:
import json
cpdef enum quantifier_t:
_META
ONE
@ -164,7 +161,7 @@ def _convert_strings(token_specs, string_store):
def merge_phrase(matcher, doc, i, matches):
'''Callback to merge a phrase on match'''
ent_id, label, start, end = matches[i]
span = doc[start : end]
span = doc[start : end]
span.merge(ent_type=label, ent_id=ent_id)
@ -180,7 +177,8 @@ cdef class Matcher:
@classmethod
def load(cls, path, vocab):
'''Load the matcher and patterns from a file path.
"""
Load the matcher and patterns from a file path.
Arguments:
path (Path):
@ -189,16 +187,17 @@ cdef class Matcher:
The vocabulary that the documents to match over will refer to.
Returns:
Matcher: The newly constructed object.
'''
"""
if (path / 'gazetteer.json').exists():
with (path / 'gazetteer.json').open('r', encoding='utf8') as file_:
patterns = json.load(file_)
patterns = ujson.load(file_)
else:
patterns = {}
return cls(vocab, patterns)
def __init__(self, vocab, patterns={}):
"""Create the Matcher.
"""
Create the Matcher.
Arguments:
vocab (Vocab):
@ -227,7 +226,8 @@ cdef class Matcher:
def add_entity(self, entity_key, attrs=None, if_exists='raise',
acceptor=None, on_match=None):
"""Add an entity to the matcher.
"""
Add an entity to the matcher.
Arguments:
entity_key (unicode or int):
@ -264,7 +264,8 @@ cdef class Matcher:
self._callbacks[entity_key] = on_match
def add_pattern(self, entity_key, token_specs, label=""):
"""Add a pattern to the matcher.
"""
Add a pattern to the matcher.
Arguments:
entity_key (unicode or int):
@ -307,7 +308,8 @@ cdef class Matcher:
return entity_key
def has_entity(self, entity_key):
"""Check whether the matcher has an entity.
"""
Check whether the matcher has an entity.
Arguments:
entity_key (string or int): The entity key to check.
@ -318,7 +320,8 @@ cdef class Matcher:
return entity_key in self._entities
def get_entity(self, entity_key):
"""Retrieve the attributes stored for an entity.
"""
Retrieve the attributes stored for an entity.
Arguments:
entity_key (unicode or int): The entity to retrieve.
@ -332,7 +335,8 @@ cdef class Matcher:
return None
def __call__(self, Doc doc, acceptor=None):
"""Find all token sequences matching the supplied patterns on the Doc.
"""
Find all token sequences matching the supplied patterns on the Doc.
Arguments:
doc (Doc):
@ -445,7 +449,8 @@ cdef class Matcher:
return matches
def pipe(self, docs, batch_size=1000, n_threads=2):
"""Match a stream of documents, yielding them in turn.
"""
Match a stream of documents, yielding them in turn.
Arguments:
docs: A stream of documents.

15
spacy/morphology.pyx
View File

@ -1,13 +1,9 @@
# cython: infer_types
# coding: utf8
from __future__ import unicode_literals
from libc.string cimport memset
try:
import ujson as json
except ImportError:
import json
from .parts_of_speech cimport ADJ, VERB, NOUN, PUNCT
from .attrs cimport POS, IS_SPACE
from .parts_of_speech import IDS as POS_IDS
@ -16,7 +12,9 @@ from .attrs import LEMMA, intify_attrs
def _normalize_props(props):
'''Transform deprecated string keys to correct names.'''
"""
Transform deprecated string keys to correct names.
"""
out = {}
for key, value in props.items():
if key == POS:
@ -98,13 +96,14 @@ cdef class Morphology:
flags[0] &= ~(one << flag_id)
def add_special_case(self, unicode tag_str, unicode orth_str, attrs, force=False):
'''Add a special-case rule to the morphological analyser. Tokens whose
"""
Add a special-case rule to the morphological analyser. Tokens whose
tag and orth match the rule will receive the specified properties.
Arguments:
tag (unicode): The part-of-speech tag to key the exception.
orth (unicode): The word-form to key the exception.
'''
"""
tag = self.strings[tag_str]
tag_id = self.reverse_index[tag]
orth = self.strings[orth_str]

8
spacy/multi_words.py
View File

@ -1,8 +0,0 @@
class RegexMerger(object):
def __init__(self, regexes):
self.regexes = regexes
def __call__(self, tokens):
for tag, entity_type, regex in self.regexes:
for m in regex.finditer(tokens.string):
tokens.merge(m.start(), m.end(), tag, m.group(), entity_type)

3
spacy/orth.pyx
View File

@ -1,6 +1,7 @@
# coding: utf8
# cython: infer_types=True
# coding: utf8
from __future__ import unicode_literals
import unicodedata
import re

1
spacy/parts_of_speech.pyx
View File

@ -1,3 +1,4 @@
# coding: utf8
from __future__ import unicode_literals

39
spacy/pipeline.pyx
View File

@ -1,3 +1,6 @@
# coding: utf8
from __future__ import unicode_literals
from .syntax.parser cimport Parser
from .syntax.beam_parser cimport BeamParser
from .syntax.ner cimport BiluoPushDown
@ -11,44 +14,40 @@ from .attrs import DEP, ENT_TYPE
cdef class EntityRecognizer(Parser):
"""Annotate named entities on Doc objects."""
"""
Annotate named entities on Doc objects.
"""
TransitionSystem = BiluoPushDown
feature_templates = get_feature_templates('ner')
def add_label(self, label):
for action in self.moves.action_types:
self.moves.add_action(action, label)
if 'actions' in self.cfg:
self.cfg['actions'].setdefault(action,
{}).setdefault(label, True)
Parser.add_label(self, label)
if isinstance(label, basestring):
label = self.vocab.strings[label]
# Set label into serializer. Super hacky :(
for attr, freqs in self.vocab.serializer_freqs:
if attr == ENT_TYPE and label not in freqs:
freqs.append([label, 1])
# Super hacky :(
self.vocab._serializer = None
cdef class BeamEntityRecognizer(BeamParser):
"""Annotate named entities on Doc objects."""
"""
Annotate named entities on Doc objects.
"""
TransitionSystem = BiluoPushDown
feature_templates = get_feature_templates('ner')
def add_label(self, label):
for action in self.moves.action_types:
self.moves.add_action(action, label)
if 'actions' in self.cfg:
self.cfg['actions'].setdefault(action,
{}).setdefault(label, True)
Parser.add_label(self, label)
if isinstance(label, basestring):
label = self.vocab.strings[label]
# Set label into serializer. Super hacky :(
for attr, freqs in self.vocab.serializer_freqs:
if attr == ENT_TYPE and label not in freqs:
freqs.append([label, 1])
# Super hacky :(
self.vocab._serializer = None
@ -58,11 +57,7 @@ cdef class DependencyParser(Parser):
feature_templates = get_feature_templates('basic')
def add_label(self, label):
for action in self.moves.action_types:
self.moves.add_action(action, label)
if 'actions' in self.cfg:
self.cfg['actions'].setdefault(action,
{}).setdefault(label, True)
Parser.add_label(self, label)
if isinstance(label, basestring):
label = self.vocab.strings[label]
for attr, freqs in self.vocab.serializer_freqs:
@ -78,11 +73,7 @@ cdef class BeamDependencyParser(BeamParser):
feature_templates = get_feature_templates('basic')
def add_label(self, label):
for action in self.moves.action_types:
self.moves.add_action(action, label)
if 'actions' in self.cfg:
self.cfg['actions'].setdefault(action,
{}).setdefault(label, True)
Parser.add_label(self, label)
if isinstance(label, basestring):
label = self.vocab.strings[label]
for attr, freqs in self.vocab.serializer_freqs:

9
spacy/scorer.py
View File

@ -1,12 +1,13 @@
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
# coding: utf8
from __future__ import division, print_function, unicode_literals
from .gold import tags_to_entities
class PRFScore(object):
"""A precision / recall / F score"""
"""
A precision / recall / F score
"""
def __init__(self):
self.tp = 0
self.fp = 0

36
spacy/strings.pyx
View File

@ -1,12 +1,11 @@
# cython: infer_types=True
# coding: utf8
from __future__ import unicode_literals, absolute_import
cimport cython
from libc.string cimport memcpy
from libc.stdint cimport uint64_t, uint32_t
from murmurhash.mrmr cimport hash64, hash32
from preshed.maps cimport map_iter, key_t
from .typedefs cimport hash_t
@ -73,13 +72,16 @@ cdef Utf8Str _allocate(Pool mem, const unsigned char* chars, uint32_t length) ex
cdef class StringStore:
'''Map strings to and from integer IDs.'''
"""
Map strings to and from integer IDs.
"""
def __init__(self, strings=None, freeze=False):
'''Create the StringStore.
"""
Create the StringStore.
Arguments:
strings: A sequence of unicode strings to add to the store.
'''
"""
self.mem = Pool()
self._map = PreshMap()
self._oov = PreshMap()
@ -104,7 +106,8 @@ cdef class StringStore:
return (StringStore, (list(self),))
def __len__(self):
"""The number of strings in the store.
"""
The number of strings in the store.
Returns:
int The number of strings in the store.
@ -112,8 +115,9 @@ cdef class StringStore:
return self.size-1
def __getitem__(self, object string_or_id):
"""Retrieve a string from a given integer ID, or vice versa.
"""
Retrieve a string from a given integer ID, or vice versa.
Arguments:
string_or_id (bytes or unicode or int):
The value to encode.
@ -149,17 +153,18 @@ cdef class StringStore:
raise TypeError(type(string_or_id))
utf8str = self._intern_utf8(byte_string, len(byte_string))
if utf8str is NULL:
# TODO: We need to use 32 bit here, for compatibility with the
# TODO: We need to use 32 bit here, for compatibility with the
# vocabulary values. This makes birthday paradox probabilities
# pretty bad.
# We could also get unlucky here, and hash into a value that
# collides with the 'real' strings.
# collides with the 'real' strings.
return hash32_utf8(byte_string, len(byte_string))
else:
return utf8str - self.c
def __contains__(self, unicode string not None):
"""Check whether a string is in the store.
"""
Check whether a string is in the store.
Arguments:
string (unicode): The string to check.
@ -172,7 +177,8 @@ cdef class StringStore:
return self._map.get(key) is not NULL
def __iter__(self):
"""Iterate over the strings in the store, in order.
"""
Iterate over the strings in the store, in order.
Yields: unicode A string in the store.
"""
@ -230,7 +236,8 @@ cdef class StringStore:
return &self.c[self.size-1]
def dump(self, file_):
"""Save the strings to a JSON file.
"""
Save the strings to a JSON file.
Arguments:
file_ (buffer): The file to save the strings.
@ -244,7 +251,8 @@ cdef class StringStore:
file_.write(string_data)
def load(self, file_):
"""Load the strings from a JSON file.
"""
Load the strings from a JSON file.
Arguments:
file_ (buffer): The file from which to load the strings.

1
spacy/symbols.pyx
View File

@ -1,3 +1,4 @@
# coding: utf8
from __future__ import unicode_literals
IDS = {

8
spacy/syntax/_parse_features.pyx
View File

@ -7,17 +7,17 @@ out of "context") is in features/extractor.pyx
The atomic feature names are listed in a big enum, so that the feature tuples
can refer to them.
"""
from libc.string cimport memset
# coding: utf-8
from __future__ import unicode_literals
from libc.string cimport memset
from itertools import combinations
from cymem.cymem cimport Pool
from ..structs cimport TokenC
from .stateclass cimport StateClass
from ._state cimport StateC
from cymem.cymem cimport Pool
cdef inline void fill_token(atom_t* context, const TokenC* token) nogil:
if token is NULL:

46
spacy/syntax/arc_eager.pyx
View File

@ -1,29 +1,26 @@
# cython: profile=True
# cython: cdivision=True
# cython: infer_types=True
# coding: utf-8
from __future__ import unicode_literals
from cpython.ref cimport PyObject, Py_INCREF, Py_XDECREF
import ctypes
import os
from ..structs cimport TokenC
from libc.stdint cimport uint32_t
from libc.string cimport memcpy
from cymem.cymem cimport Pool
from .stateclass cimport StateClass
from ._state cimport StateC, is_space_token
from .nonproj import PseudoProjectivity
from .nonproj import is_nonproj_tree
from .transition_system cimport do_func_t, get_cost_func_t
from .transition_system cimport move_cost_func_t, label_cost_func_t
from ..gold cimport GoldParse
from ..gold cimport GoldParseC
from ..attrs cimport TAG, HEAD, DEP, ENT_IOB, ENT_TYPE, IS_SPACE
from ..lexeme cimport Lexeme
from libc.stdint cimport uint32_t
from libc.string cimport memcpy
from cymem.cymem cimport Pool
from .stateclass cimport StateClass
from ._state cimport StateC, is_space_token
from .nonproj import PseudoProjectivity
from .nonproj import is_nonproj_tree
from ..structs cimport TokenC
DEF NON_MONOTONIC = True
@ -317,17 +314,20 @@ cdef class ArcEager(TransitionSystem):
def get_actions(cls, **kwargs):
actions = kwargs.get('actions',
{
SHIFT: {'': True},
REDUCE: {'': True},
RIGHT: {},
LEFT: {},
BREAK: {'ROOT': True}})
SHIFT: [''],
REDUCE: [''],
RIGHT: [],
LEFT: [],
BREAK: ['ROOT']})
seen_actions = set()
for label in kwargs.get('left_labels', []):
if label.upper() != 'ROOT':
actions[LEFT][label] = True
if (LEFT, label) not in seen_actions:
actions[LEFT].append(label)
for label in kwargs.get('right_labels', []):
if label.upper() != 'ROOT':
actions[RIGHT][label] = True
if (RIGHT, label) not in seen_actions:
actions[RIGHT].append(label)
for raw_text, sents in kwargs.get('gold_parses', []):
for (ids, words, tags, heads, labels, iob), ctnts in sents:
@ -336,9 +336,11 @@ cdef class ArcEager(TransitionSystem):
label = 'ROOT'
if label != 'ROOT':
if head < child:
actions[RIGHT][label] = True
if (RIGHT, label) not in seen_actions:
actions[RIGHT].append(label)
elif head > child:
actions[LEFT][label] = True
if (LEFT, label) not in seen_actions:
actions[LEFT].append(label)
return actions
property action_types:

31
spacy/syntax/beam_parser.pyx
View File

@ -1,50 +1,34 @@
"""
MALT-style dependency parser
"""
# cython: profile=True
# cython: experimental_cpp_class_def=True
# cython: cdivision=True
# cython: infer_types=True
"""
MALT-style dependency parser
"""
from __future__ import unicode_literals
# coding: utf-8
from __future__ import unicode_literals, print_function
cimport cython
from cpython.ref cimport PyObject, Py_INCREF, Py_XDECREF
from libc.stdint cimport uint32_t, uint64_t
from libc.string cimport memset, memcpy
from libc.stdlib cimport rand
from libc.math cimport log, exp, isnan, isinf
import random
import os.path
from os import path
import shutil
import json
import math
from cymem.cymem cimport Pool, Address
from murmurhash.mrmr cimport real_hash64 as hash64
from thinc.typedefs cimport weight_t, class_t, feat_t, atom_t, hash_t
from util import Config
from thinc.linear.features cimport ConjunctionExtracter
from thinc.structs cimport FeatureC, ExampleC
from thinc.extra.search cimport Beam
from thinc.extra.search cimport MaxViolation
from thinc.extra.search cimport Beam, MaxViolation
from thinc.extra.eg cimport Example
from thinc.extra.mb cimport Minibatch
from ..structs cimport TokenC
from ..tokens.doc cimport Doc
from ..strings cimport StringStore
from .transition_system cimport TransitionSystem, Transition
from ..gold cimport GoldParse
from . import _parse_features
from ._parse_features cimport CONTEXT_SIZE
from ._parse_features cimport fill_context
@ -266,4 +250,3 @@ def is_gold(StateClass state, GoldParse gold, StringStore strings):
id_, word, tag, head, dep, ner = gold.orig_annot[gold.cand_to_gold[i]]
truth.add((id_, head, dep))
return truth == predicted

11
spacy/syntax/iterators.pyx
View File

@ -1,9 +1,14 @@
from spacy.parts_of_speech cimport NOUN, PROPN, PRON
# coding: utf-8
from __future__ import unicode_literals
from ..parts_of_speech cimport NOUN, PROPN, PRON
def english_noun_chunks(obj):
'''Detect base noun phrases from a dependency parse.
Works on both Doc and Span.'''
"""
Detect base noun phrases from a dependency parse.
Works on both Doc and Span.
"""
labels = ['nsubj', 'dobj', 'nsubjpass', 'pcomp', 'pobj',
'attr', 'ROOT', 'root']
doc = obj.doc # Ensure works on both Doc and Span.

55
spacy/syntax/ner.pyx
View File

@ -1,17 +1,16 @@
# coding: utf-8
from __future__ import unicode_literals
from .transition_system cimport Transition
from .transition_system cimport do_func_t
from ..structs cimport TokenC, Entity
from thinc.typedefs cimport weight_t
from ..gold cimport GoldParseC
from ..gold cimport GoldParse
from ..attrs cimport ENT_TYPE, ENT_IOB
from .stateclass cimport StateClass
from ._state cimport StateC
from .transition_system cimport Transition
from .transition_system cimport do_func_t
from ..structs cimport TokenC, Entity
from ..gold cimport GoldParseC
from ..gold cimport GoldParse
from ..attrs cimport ENT_TYPE, ENT_IOB
cdef enum:
@ -21,6 +20,7 @@ cdef enum:
LAST
UNIT
OUT
ISNT
N_MOVES
@ -31,6 +31,7 @@ MOVE_NAMES[IN] = 'I'
MOVE_NAMES[LAST] = 'L'
MOVE_NAMES[UNIT] = 'U'
MOVE_NAMES[OUT] = 'O'
MOVE_NAMES[ISNT] = 'x'
cdef do_func_t[N_MOVES] do_funcs
@ -54,16 +55,20 @@ cdef class BiluoPushDown(TransitionSystem):
def get_actions(cls, **kwargs):
actions = kwargs.get('actions',
{
MISSING: {'': True},
BEGIN: {},
IN: {},
LAST: {},
UNIT: {},
OUT: {'': True}
MISSING: [''],
BEGIN: [],
IN: [],
LAST: [],
UNIT: [],
OUT: ['']
})
seen_entities = set()
for entity_type in kwargs.get('entity_types', []):
if entity_type in seen_entities:
continue
seen_entities.add(entity_type)
for action in (BEGIN, IN, LAST, UNIT):
actions[action][entity_type] = True
actions[action].append(entity_type)
moves = ('M', 'B', 'I', 'L', 'U')
for raw_text, sents in kwargs.get('gold_parses', []):
for (ids, words, tags, heads, labels, biluo), _ in sents:
@ -72,8 +77,10 @@ cdef class BiluoPushDown(TransitionSystem):
if ner_tag.count('-') != 1:
raise ValueError(ner_tag)
_, label = ner_tag.split('-')
for move_str in ('B', 'I', 'L', 'U'):
actions[moves.index(move_str)][label] = True
if label not in seen_entities:
seen_entities.add(label)
for move_str in ('B', 'I', 'L', 'U'):
actions[moves.index(move_str)].append(label)
return actions
property action_types:
@ -111,11 +118,17 @@ cdef class BiluoPushDown(TransitionSystem):
label = 0
elif '-' in name:
move_str, label_str = name.split('-', 1)
# Hacky way to denote 'not this entity'
if label_str.startswith('!'):
label_str = label_str[1:]
move_str = 'x'
label = self.strings[label_str]
else:
move_str = name
label = 0
move = MOVE_NAMES.index(move_str)
if move == ISNT:
return Transition(clas=0, move=ISNT, label=label, score=0)
for i in range(self.n_moves):
if self.c[i].move == move and self.c[i].label == label:
return self.c[i]
@ -225,6 +238,9 @@ cdef class Begin:
elif g_act == BEGIN:
# B, Gold B --> Label match
return label != g_tag
# Support partial supervision in the form of "not this label"
elif g_act == ISNT:
return label == g_tag
else:
# B, Gold I --> False (P)
# B, Gold L --> False (P)
@ -359,6 +375,9 @@ cdef class Unit:
elif g_act == UNIT:
# U, Gold U --> True iff tag match
return label != g_tag
# Support partial supervision in the form of "not this label"
elif g_act == ISNT:
return label == g_tag
else:
# U, Gold B --> False
# U, Gold I --> False
@ -388,7 +407,7 @@ cdef class Out:
cdef int g_act = gold.ner[s.B(0)].move
cdef int g_tag = gold.ner[s.B(0)].label
if g_act == MISSING:
if g_act == MISSING or g_act == ISNT:
return 0
elif g_act == BEGIN:
# O, Gold B --> False

5
spacy/syntax/nonproj.pyx
View File

@ -1,8 +1,9 @@
# coding: utf-8
from __future__ import unicode_literals
from copy import copy
from ..tokens.doc cimport Doc
from spacy.attrs import DEP, HEAD
from ..attrs import DEP, HEAD
def ancestors(tokenid, heads):
@ -201,5 +202,3 @@ class PseudoProjectivity:
filtered_sents.append(((ids,words,tags,heads,filtered_labels,iob), ctnts))
filtered.append((raw_text, filtered_sents))
return filtered

124
spacy/syntax/parser.pyx
View File

@ -1,56 +1,44 @@
# cython: infer_types=True
"""
MALT-style dependency parser
"""
# coding: utf-8
# cython: infer_types=True
from __future__ import unicode_literals
from collections import Counter
import ujson
cimport cython
cimport cython.parallel
from cpython.ref cimport PyObject, Py_INCREF, Py_XDECREF
from cpython.exc cimport PyErr_CheckSignals
from libc.stdint cimport uint32_t, uint64_t
from libc.string cimport memset, memcpy
from libc.stdlib cimport malloc, calloc, free
import os.path
from collections import Counter
from os import path
import shutil
import json
import sys
from .nonproj import PseudoProjectivity
from cymem.cymem cimport Pool, Address
from murmurhash.mrmr cimport hash64
from thinc.typedefs cimport weight_t, class_t, feat_t, atom_t, hash_t
from thinc.linear.avgtron cimport AveragedPerceptron
from thinc.linalg cimport VecVec
from thinc.structs cimport SparseArrayC
from thinc.structs cimport SparseArrayC, FeatureC, ExampleC
from thinc.extra.eg cimport Example
from cymem.cymem cimport Pool, Address
from murmurhash.mrmr cimport hash64
from preshed.maps cimport MapStruct
from preshed.maps cimport map_get
from thinc.structs cimport FeatureC
from thinc.structs cimport ExampleC
from thinc.extra.eg cimport Example
from util import Config
from ..structs cimport TokenC
from ..tokens.doc cimport Doc
from ..strings cimport StringStore
from .transition_system import OracleError
from .transition_system cimport TransitionSystem, Transition
from ..gold cimport GoldParse
from . import _parse_features
from ._parse_features cimport CONTEXT_SIZE
from ._parse_features cimport fill_context
from .stateclass cimport StateClass
from ._state cimport StateC
from .nonproj import PseudoProjectivity
from .transition_system import OracleError
from .transition_system cimport TransitionSystem, Transition
from ..structs cimport TokenC
from ..tokens.doc cimport Doc
from ..strings cimport StringStore
from ..gold cimport GoldParse
USE_FTRL = True
DEBUG = False
@ -80,7 +68,9 @@ cdef class ParserModel(AveragedPerceptron):
return nr_feat
def update(self, Example eg, itn=0):
'''Does regression on negative cost. Sort of cute?'''
"""
Does regression on negative cost. Sort of cute?
"""
self.time += 1
cdef int best = arg_max_if_gold(eg.c.scores, eg.c.costs, eg.c.nr_class)
cdef int guess = eg.guess
@ -132,10 +122,13 @@ cdef class ParserModel(AveragedPerceptron):
cdef class Parser:
"""Base class of the DependencyParser and EntityRecognizer."""
"""
Base class of the DependencyParser and EntityRecognizer.
"""
@classmethod
def load(cls, path, Vocab vocab, TransitionSystem=None, require=False, **cfg):
"""Load the statistical model from the supplied path.
"""
Load the statistical model from the supplied path.
Arguments:
path (Path):
@ -148,10 +141,16 @@ cdef class Parser:
The newly constructed object.
"""
with (path / 'config.json').open() as file_:
cfg = json.load(file_)
cfg = ujson.load(file_)
# TODO: remove this shim when we don't have to support older data
if 'labels' in cfg and 'actions' not in cfg:
cfg['actions'] = cfg.pop('labels')
# TODO: remove this shim when we don't have to support older data
for action_name, labels in dict(cfg['actions']).items():
# We need this to be sorted
if isinstance(labels, dict):
labels = list(sorted(labels.keys()))
cfg['actions'][action_name] = labels
self = cls(vocab, TransitionSystem=TransitionSystem, model=None, **cfg)
if (path / 'model').exists():
self.model.load(str(path / 'model'))
@ -161,7 +160,8 @@ cdef class Parser:
return self
def __init__(self, Vocab vocab, TransitionSystem=None, ParserModel model=None, **cfg):
"""Create a Parser.
"""
Create a Parser.
Arguments:
vocab (Vocab):
@ -186,12 +186,18 @@ cdef class Parser:
self.model.learn_rate = cfg.get('learn_rate', 0.001)
self.cfg = cfg
# TODO: This is a pretty hacky fix to the problem of adding more
# labels. The issue is they come in out of order, if labels are
# added during training
for label in cfg.get('extra_labels', []):
self.add_label(label)
def __reduce__(self):
return (Parser, (self.vocab, self.moves, self.model), None, None)
def __call__(self, Doc tokens):
"""Apply the entity recognizer, setting the annotations onto the Doc object.
"""
Apply the entity recognizer, setting the annotations onto the Doc object.
Arguments:
doc (Doc): The document to be processed.
@ -208,7 +214,8 @@ cdef class Parser:
self.moves.finalize_doc(tokens)
def pipe(self, stream, int batch_size=1000, int n_threads=2):
"""Process a stream of documents.
"""
Process a stream of documents.
Arguments:
stream: The sequence of documents to process.
@ -296,7 +303,8 @@ cdef class Parser:
return 0
def update(self, Doc tokens, GoldParse gold, itn=0):
"""Update the statistical model.
"""
Update the statistical model.
Arguments:
doc (Doc):
@ -334,15 +342,17 @@ cdef class Parser:
self.moves.finalize_state(stcls.c)
return loss
def step_through(self, Doc doc):
"""Set up a stepwise state, to introspect and control the transition sequence.
def step_through(self, Doc doc, GoldParse gold=None):
"""
Set up a stepwise state, to introspect and control the transition sequence.
Arguments:
doc (Doc): The document to step through.
gold (GoldParse): Optional gold parse
Returns (StepwiseState):
A state object, to step through the annotation process.
"""
return StepwiseState(self, doc)
return StepwiseState(self, doc, gold=gold)
def from_transition_sequence(self, Doc doc, sequence):
"""Control the annotations on a document by specifying a transition sequence
@ -360,18 +370,28 @@ cdef class Parser:
def add_label(self, label):
# Doesn't set label into serializer -- subclasses override it to do that.
for action in self.moves.action_types:
self.moves.add_action(action, label)
added = self.moves.add_action(action, label)
if added:
# Important that the labels be stored as a list! We need the
# order, or the model goes out of synch
self.cfg.setdefault('extra_labels', []).append(label)
cdef class StepwiseState:
cdef readonly StateClass stcls
cdef readonly Example eg
cdef readonly Doc doc
cdef readonly GoldParse gold
cdef readonly Parser parser
def __init__(self, Parser parser, Doc doc):
def __init__(self, Parser parser, Doc doc, GoldParse gold=None):
self.parser = parser
self.doc = doc
if gold is not None:
self.gold = gold
self.parser.moves.preprocess_gold(self.gold)
else:
self.gold = GoldParse(doc)
self.stcls = StateClass.init(doc.c, doc.length)
self.parser.moves.initialize_state(self.stcls.c)
self.eg = Example(
@ -406,6 +426,24 @@ cdef class StepwiseState:
return [self.doc.vocab.strings[self.stcls.c._sent[i].dep]
for i in range(self.stcls.c.length)]
@property
def costs(self):
"""
Find the action-costs for the current state.
"""
if not self.gold:
raise ValueError("Can't set costs: No GoldParse provided")
self.parser.moves.set_costs(self.eg.c.is_valid, self.eg.c.costs,
self.stcls, self.gold)
costs = {}
for i in range(self.parser.moves.n_moves):
if not self.eg.c.is_valid[i]:
continue
transition = self.parser.moves.c[i]
name = self.parser.moves.move_name(transition.move, transition.label)
costs[name] = self.eg.c.costs[i]
return costs
def predict(self):
self.eg.reset()
self.eg.c.nr_feat = self.parser.model.set_featuresC(self.eg.c.atoms, self.eg.c.features,

8
spacy/syntax/stateclass.pyx
View File

@ -1,5 +1,9 @@
# coding: utf-8
from __future__ import unicode_literals
from libc.string cimport memcpy, memset
from libc.stdint cimport uint32_t
from ..vocab cimport EMPTY_LEXEME
from ..structs cimport Entity
from ..lexeme cimport Lexeme
@ -28,6 +32,6 @@ cdef class StateClass:
top = words[self.S(0)] + '_%d' % self.S_(0).head
second = words[self.S(1)] + '_%d' % self.S_(1).head
third = words[self.S(2)] + '_%d' % self.S_(2).head
n0 = words[self.B(0)]
n1 = words[self.B(1)]
n0 = words[self.B(0)]
n1 = words[self.B(1)]
return ' '.join((third, second, top, '|', n0, n1))

7
spacy/syntax/transition_system.pyx
View File

@ -1,4 +1,8 @@
# cython: infer_types=True
# coding: utf-8
from __future__ import unicode_literals
from cpython.ref cimport PyObject, Py_INCREF, Py_XDECREF
from cymem.cymem cimport Pool
from thinc.typedefs cimport weight_t
from collections import defaultdict
@ -6,7 +10,6 @@ from collections import defaultdict
from ..structs cimport TokenC
from .stateclass cimport StateClass
from ..attrs cimport TAG, HEAD, DEP, ENT_TYPE, ENT_IOB
from cpython.ref cimport PyObject, Py_INCREF, Py_XDECREF
cdef weight_t MIN_SCORE = -90000
@ -32,7 +35,7 @@ cdef class TransitionSystem:
self.c = <Transition*>self.mem.alloc(self._size, sizeof(Transition))
for action, label_strs in sorted(labels_by_action.items()):
for label_str in sorted(label_strs):
for label_str in label_strs:
self.add_action(int(action), label_str)
self.root_label = self.strings['ROOT']
self.freqs = {} if _freqs is None else _freqs

18
spacy/syntax/util.py
View File

@ -1,18 +0,0 @@
from os import path
import json
class Config(object):
def __init__(self, **kwargs):
for key, value in kwargs.items():
setattr(self, key, value)
def get(self, attr, default=None):
return self.__dict__.get(attr, default)
@classmethod
def write(cls, model_dir, name, **kwargs):
open(path.join(model_dir, '%s.json' % name), 'w').write(json.dumps(kwargs))
@classmethod
def read(cls, model_dir, name):
return cls(**json.load(open(path.join(model_dir, '%s.json' % name))))

31
spacy/tagger.pyx
View File

@ -1,5 +1,7 @@
import json
import pathlib
# coding: utf8
from __future__ import unicode_literals
import ujson
from collections import defaultdict
from cymem.cymem cimport Pool
@ -12,8 +14,8 @@ from thinc.linalg cimport VecVec
from .tokens.doc cimport Doc
from .attrs cimport TAG
from .gold cimport GoldParse
from .attrs cimport *
from . import util
cpdef enum:
@ -106,10 +108,13 @@ cdef inline void _fill_from_token(atom_t* context, const TokenC* t) nogil:
cdef class Tagger:
"""Annotate part-of-speech tags on Doc objects."""
"""
Annotate part-of-speech tags on Doc objects.
"""
@classmethod
def load(cls, path, vocab, require=False):
"""Load the statistical model from the supplied path.
"""
Load the statistical model from the supplied path.
Arguments:
path (Path):
@ -123,10 +128,10 @@ cdef class Tagger:
"""
# TODO: Change this to expect config.json when we don't have to
# support old data.
path = path if not isinstance(path, basestring) else pathlib.Path(path)
path = util.ensure_path(path)
if (path / 'templates.json').exists():
with (path / 'templates.json').open('r', encoding='utf8') as file_:
templates = json.load(file_)
templates = ujson.load(file_)
elif require:
raise IOError(
"Required file %s/templates.json not found when loading Tagger" % str(path))
@ -142,7 +147,8 @@ cdef class Tagger:
return self
def __init__(self, Vocab vocab, TaggerModel model=None, **cfg):
"""Create a Tagger.
"""
Create a Tagger.
Arguments:
vocab (Vocab):
@ -180,7 +186,8 @@ cdef class Tagger:
tokens._py_tokens = [None] * tokens.length
def __call__(self, Doc tokens):
"""Apply the tagger, setting the POS tags onto the Doc object.
"""
Apply the tagger, setting the POS tags onto the Doc object.
Arguments:
doc (Doc): The tokens to be tagged.
@ -208,7 +215,8 @@ cdef class Tagger:
tokens._py_tokens = [None] * tokens.length
def pipe(self, stream, batch_size=1000, n_threads=2):
"""Tag a stream of documents.
"""
Tag a stream of documents.
Arguments:
stream: The sequence of documents to tag.
@ -225,7 +233,8 @@ cdef class Tagger:
yield doc
def update(self, Doc tokens, GoldParse gold, itn=0):
"""Update the statistical model, with tags supplied for the given document.
"""
Update the statistical model, with tags supplied for the given document.
Arguments:
doc (Doc):

24
spacy/tests/he/test_tokenizer.py
View File

@ -3,15 +3,21 @@ from __future__ import unicode_literals
import pytest
ABBREVIATION_TESTS = [
('פייתון היא שפת תכנות דינמית', ['פייתון', 'היא', 'שפת', 'תכנות', 'דינמית'])
]
TESTCASES = ABBREVIATION_TESTS
@pytest.mark.parametrize('text,expected_tokens', TESTCASES)
def test_tokenizer_handles_testcases(he_tokenizer, text, expected_tokens):
@pytest.mark.parametrize('text,expected_tokens',
[('פייתון היא שפת תכנות דינמית', ['פייתון', 'היא', 'שפת', 'תכנות', 'דינמית'])])
def test_tokenizer_handles_abbreviation(he_tokenizer, text, expected_tokens):
tokens = he_tokenizer(text)
token_list = [token.text for token in tokens if not token.is_space]
assert expected_tokens == token_list
assert expected_tokens == token_list
@pytest.mark.parametrize('text,expected_tokens', [
pytest.mark.xfail(('עקבת אחריו בכל רחבי המדינה.', ['עקבת', 'אחריו', 'בכל', 'רחבי', 'המדינה', '.'])),
('עקבת אחריו בכל רחבי המדינה?', ['עקבת', 'אחריו', 'בכל', 'רחבי', 'המדינה', '?']),
('עקבת אחריו בכל רחבי המדינה!', ['עקבת', 'אחריו', 'בכל', 'רחבי', 'המדינה', '!']),
('עקבת אחריו בכל רחבי המדינה..', ['עקבת', 'אחריו', 'בכל', 'רחבי', 'המדינה', '..']),
('עקבת אחריו בכל רחבי המדינה...', ['עקבת', 'אחריו', 'בכל', 'רחבי', 'המדינה', '...'])])
def test_tokenizer_handles_punct(he_tokenizer, text, expected_tokens):
tokens = he_tokenizer(text)
assert expected_tokens == [token.text for token in tokens]

1
spacy/tests/tagger/test_lemmatizer.py
View File

@ -16,6 +16,7 @@ def test_tagger_lemmatizer_noun_lemmas(lemmatizer, text, lemmas):
assert lemmatizer.noun(text) == set(lemmas)
@pytest.mark.xfail
@pytest.mark.models
def test_tagger_lemmatizer_base_forms(lemmatizer):
if lemmatizer is None:

7
spacy/tests/tokenizer/test_tokenizer.py
View File

@ -3,9 +3,8 @@ from __future__ import unicode_literals
from ...vocab import Vocab
from ...tokenizer import Tokenizer
from ...util import utf8open
from ... import util
from os import path
import pytest
@ -75,8 +74,8 @@ Phasellus tincidunt, augue quis porta finibus, massa sapien consectetur augue, n
@pytest.mark.parametrize('file_name', ["sun.txt"])
def test_tokenizer_handle_text_from_file(tokenizer, file_name):
loc = path.join(path.dirname(__file__), file_name)
text = utf8open(loc).read()
loc = util.ensure_path(__file__).parent / file_name
text = loc.open('r', encoding='utf8').read()
assert len(text) != 0
tokens = tokenizer(text)
assert len(tokens) > 100

65
spacy/tokenizer.pyx
View File

@ -1,17 +1,11 @@
# cython: embedsignature=True
# coding: utf8
from __future__ import unicode_literals
import pathlib
import ujson
from cython.operator cimport dereference as deref
from cython.operator cimport preincrement as preinc
try:
import ujson as json
except ImportError:
import json
from cymem.cymem cimport Pool
from preshed.maps cimport PreshMap
@ -23,12 +17,15 @@ from .tokens.doc cimport Doc
cdef class Tokenizer:
"""Segment text, and create Doc objects with the discovered segment boundaries."""
"""
Segment text, and create Doc objects with the discovered segment boundaries.
"""
@classmethod
def load(cls, path, Vocab vocab, rules=None, prefix_search=None, suffix_search=None,
infix_finditer=None, token_match=None):
'''Load a Tokenizer, reading unsupplied components from the path.
"""
Load a Tokenizer, reading unsupplied components from the path.
Arguments:
path (Path):
The path to load from.
@ -45,13 +42,11 @@ cdef class Tokenizer:
infix_finditer:
Signature of re.compile(string).finditer
Returns Tokenizer
'''
if isinstance(path, basestring):
path = pathlib.Path(path)
"""
path = util.ensure_path(path)
if rules is None:
with (path / 'tokenizer' / 'specials.json').open('r', encoding='utf8') as file_:
rules = json.load(file_)
rules = ujson.load(file_)
if prefix_search in (None, True):
with (path / 'tokenizer' / 'prefix.txt').open() as file_:
entries = file_.read().split('\n')
@ -67,8 +62,9 @@ cdef class Tokenizer:
return cls(vocab, rules, prefix_search, suffix_search, infix_finditer, token_match)
def __init__(self, Vocab vocab, rules, prefix_search, suffix_search, infix_finditer, token_match=None):
'''Create a Tokenizer, to create Doc objects given unicode text.
"""
Create a Tokenizer, to create Doc objects given unicode text.
Arguments:
vocab (Vocab):
A storage container for lexical types.
@ -85,7 +81,7 @@ cdef class Tokenizer:
to find infixes.
token_match:
A boolean function matching strings that becomes tokens.
'''
"""
self.mem = Pool()
self._cache = PreshMap()
self._specials = PreshMap()
@ -107,7 +103,7 @@ cdef class Tokenizer:
self.token_match)
return (self.__class__, args, None, None)
cpdef Doc tokens_from_list(self, list strings):
return Doc(self.vocab, words=strings)
#raise NotImplementedError(
@ -117,7 +113,8 @@ cdef class Tokenizer:
@cython.boundscheck(False)
def __call__(self, unicode string):
"""Tokenize a string.
"""
Tokenize a string.
Arguments:
string (unicode): The string to tokenize.
@ -170,7 +167,8 @@ cdef class Tokenizer:
return tokens
def pipe(self, texts, batch_size=1000, n_threads=2):
"""Tokenize a stream of texts.
"""
Tokenize a stream of texts.
Arguments:
texts: A sequence of unicode texts.
@ -270,7 +268,7 @@ cdef class Tokenizer:
cache_hit = self._try_cache(hash_string(string), tokens)
if cache_hit:
pass
elif self.token_match and self.token_match(string):
elif self.token_match and self.token_match(string):
# We're always saying 'no' to spaces here -- the caller will
# fix up the outermost one, with reference to the original.
# See Issue #859
@ -324,7 +322,8 @@ cdef class Tokenizer:
self._cache.set(key, cached)
def find_infix(self, unicode string):
"""Find internal split points of the string, such as hyphens.
"""
Find internal split points of the string, such as hyphens.
string (unicode): The string to segment.
@ -337,7 +336,8 @@ cdef class Tokenizer:
return list(self.infix_finditer(string))
def find_prefix(self, unicode string):
"""Find the length of a prefix that should be segmented from the string,
"""
Find the length of a prefix that should be segmented from the string,
or None if no prefix rules match.
Arguments:
@ -350,7 +350,8 @@ cdef class Tokenizer:
return (match.end() - match.start()) if match is not None else 0
def find_suffix(self, unicode string):
"""Find the length of a suffix that should be segmented from the string,
"""
Find the length of a suffix that should be segmented from the string,
or None if no suffix rules match.
Arguments:
@ -363,13 +364,15 @@ cdef class Tokenizer:
return (match.end() - match.start()) if match is not None else 0
def _load_special_tokenization(self, special_cases):
'''Add special-case tokenization rules.
'''
"""
Add special-case tokenization rules.
"""
for chunk, substrings in sorted(special_cases.items()):
self.add_special_case(chunk, substrings)
def add_special_case(self, unicode string, substrings):
'''Add a special-case tokenization rule.
"""
Add a special-case tokenization rule.
Arguments:
string (unicode): The string to specially tokenize.
@ -378,7 +381,7 @@ cdef class Tokenizer:
attributes. The ORTH fields of the attributes must exactly match
the string when they are concatenated.
Returns None
'''
"""
substrings = list(substrings)
cached = <_Cached*>self.mem.alloc(1, sizeof(_Cached))
cached.length = len(substrings)

93
spacy/tokens/doc.pyx
View File

@ -1,15 +1,18 @@
# coding: utf8
from __future__ import unicode_literals
cimport cython
cimport numpy as np
import numpy
import numpy.linalg
import struct
from libc.string cimport memcpy, memset
from libc.stdint cimport uint32_t
from libc.math cimport sqrt
import numpy
import numpy.linalg
import struct
cimport numpy as np
import six
import warnings
from .span cimport Span
from .token cimport Token
from ..lexeme cimport Lexeme
from ..lexeme cimport EMPTY_LEXEME
from ..typedefs cimport attr_t, flags_t
@ -19,11 +22,10 @@ from ..attrs cimport POS, LEMMA, TAG, DEP, HEAD, SPACY, ENT_IOB, ENT_TYPE
from ..parts_of_speech cimport CCONJ, PUNCT, NOUN
from ..parts_of_speech cimport univ_pos_t
from ..lexeme cimport Lexeme
from .span cimport Span
from .token cimport Token
from ..serialize.bits cimport BitArray
from ..util import normalize_slice
from ..syntax.iterators import CHUNKERS
from ..compat import is_config
DEF PADDING = 5
@ -76,7 +78,7 @@ cdef class Doc:
"""
def __init__(self, Vocab vocab, words=None, spaces=None, orths_and_spaces=None):
'''
"""
Create a Doc object.
Aside: Implementation
@ -97,7 +99,7 @@ cdef class Doc:
A list of boolean values, of the same length as words. True
means that the word is followed by a space, False means it is not.
If None, defaults to [True]*len(words)
'''
"""
self.vocab = vocab
size = 20
self.mem = Pool()
@ -158,7 +160,7 @@ cdef class Doc:
self.is_parsed = True
def __getitem__(self, object i):
'''
"""
doc[i]
Get the Token object at position i, where i is an integer.
Negative indexing is supported, and follows the usual Python
@ -172,7 +174,7 @@ cdef class Doc:
are not supported, as `Span` objects must be contiguous (cannot have gaps).
You can use negative indices and open-ended ranges, which have their
normal Python semantics.
'''
"""
if isinstance(i, slice):
start, stop = normalize_slice(len(self), i.start, i.stop, i.step)
return Span(self, start, stop, label=0)
@ -186,7 +188,7 @@ cdef class Doc:
return Token.cinit(self.vocab, &self.c[i], i, self)
def __iter__(self):
'''
"""
for token in doc
Iterate over `Token` objects, from which the annotations can
be easily accessed. This is the main way of accessing Token
@ -194,7 +196,7 @@ cdef class Doc:
Python. If faster-than-Python speeds are required, you can
instead access the annotations as a numpy array, or access the
underlying C data directly from Cython.
'''
"""
cdef int i
for i in range(self.length):
if self._py_tokens[i] is not None:
@ -203,10 +205,10 @@ cdef class Doc:
yield Token.cinit(self.vocab, &self.c[i], i, self)
def __len__(self):
'''
"""
len(doc)
The number of tokens in the document.
'''
"""
return self.length
def __unicode__(self):
@ -216,7 +218,7 @@ cdef class Doc:
return u''.join([t.text_with_ws for t in self]).encode('utf-8')
def __str__(self):
if six.PY3:
if is_config(python3=True):
return self.__unicode__()
return self.__bytes__()
@ -228,7 +230,8 @@ cdef class Doc:
return self
def similarity(self, other):
'''Make a semantic similarity estimate. The default estimate is cosine
"""
Make a semantic similarity estimate. The default estimate is cosine
similarity using an average of word vectors.
Arguments:
@ -237,7 +240,7 @@ cdef class Doc:
Return:
score (float): A scalar similarity score. Higher is more similar.
'''
"""
if 'similarity' in self.user_hooks:
return self.user_hooks['similarity'](self, other)
if self.vector_norm == 0 or other.vector_norm == 0:
@ -245,9 +248,9 @@ cdef class Doc:
return numpy.dot(self.vector, other.vector) / (self.vector_norm * other.vector_norm)
property has_vector:
'''
"""
A boolean value indicating whether a word vector is associated with the object.
'''
"""
def __get__(self):
if 'has_vector' in self.user_hooks:
return self.user_hooks['has_vector'](self)
@ -255,11 +258,11 @@ cdef class Doc:
return any(token.has_vector for token in self)
property vector:
'''
"""
A real-valued meaning representation. Defaults to an average of the token vectors.
Type: numpy.ndarray[ndim=1, dtype='float32']
'''
"""
def __get__(self):
if 'vector' in self.user_hooks:
return self.user_hooks['vector'](self)
@ -294,17 +297,21 @@ cdef class Doc:
return self.text
property text:
'''A unicode representation of the document text.'''
"""
A unicode representation of the document text.
"""
def __get__(self):
return u''.join(t.text_with_ws for t in self)
property text_with_ws:
'''An alias of Doc.text, provided for duck-type compatibility with Span and Token.'''
"""
An alias of Doc.text, provided for duck-type compatibility with Span and Token.
"""
def __get__(self):
return self.text
property ents:
'''
"""
Yields named-entity `Span` objects, if the entity recognizer
has been applied to the document. Iterate over the span to get
individual Token objects, or access the label:
@ -318,7 +325,7 @@ cdef class Doc:
assert ents[0].label_ == 'PERSON'
assert ents[0].orth_ == 'Best'
assert ents[0].text == 'Mr. Best'
'''
"""
def __get__(self):
cdef int i
cdef const TokenC* token
@ -382,13 +389,13 @@ cdef class Doc:
self.c[start].ent_iob = 3
property noun_chunks:
'''
"""
Yields base noun-phrase #[code Span] objects, if the document
has been syntactically parsed. A base noun phrase, or
'NP chunk', is a noun phrase that does not permit other NPs to
be nested within it – so no NP-level coordination, no prepositional
phrases, and no relative clauses. For example:
'''
phrases, and no relative clauses.
"""
def __get__(self):
if not self.is_parsed:
raise ValueError(
@ -496,7 +503,8 @@ cdef class Doc:
return output
def count_by(self, attr_id_t attr_id, exclude=None, PreshCounter counts=None):
"""Produce a dict of {attribute (int): count (ints)} frequencies, keyed
"""
Produce a dict of {attribute (int): count (ints)} frequencies, keyed
by the values of the given attribute ID.
Example:
@ -563,8 +571,9 @@ cdef class Doc:
self.c[i] = parsed[i]
def from_array(self, attrs, array):
'''Write to a `Doc` object, from an `(M, N)` array of attributes.
'''
"""
Write to a `Doc` object, from an `(M, N)` array of attributes.
"""
cdef int i, col
cdef attr_id_t attr_id
cdef TokenC* tokens = self.c
@ -603,19 +612,23 @@ cdef class Doc:
return self
def to_bytes(self):
'''Serialize, producing a byte string.'''
"""
Serialize, producing a byte string.
"""
byte_string = self.vocab.serializer.pack(self)
cdef uint32_t length = len(byte_string)
return struct.pack('I', length) + byte_string
def from_bytes(self, data):
'''Deserialize, loading from bytes.'''
"""
Deserialize, loading from bytes.
"""
self.vocab.serializer.unpack_into(data[4:], self)
return self
@staticmethod
def read_bytes(file_):
'''
"""
A static method, used to read serialized #[code Doc] objects from
a file. For example:
@ -630,7 +643,7 @@ cdef class Doc:
for byte_string in Doc.read_bytes(file_):
docs.append(Doc(nlp.vocab).from_bytes(byte_string))
assert len(docs) == 2
'''
"""
keep_reading = True
while keep_reading:
try:
@ -644,7 +657,8 @@ cdef class Doc:
yield n_bytes_str + data
def merge(self, int start_idx, int end_idx, *args, **attributes):
"""Retokenize the document, such that the span at doc.text[start_idx : end_idx]
"""
Retokenize the document, such that the span at doc.text[start_idx : end_idx]
is merged into a single token. If start_idx and end_idx do not mark start
and end token boundaries, the document remains unchanged.
@ -658,7 +672,6 @@ cdef class Doc:
token (Token):
The newly merged token, or None if the start and end indices did
not fall at token boundaries.
"""
cdef unicode tag, lemma, ent_type
if len(args) == 3:

72
spacy/tokens/span.pyx
View File

@ -1,26 +1,31 @@
# coding: utf8
from __future__ import unicode_literals
from collections import defaultdict
cimport numpy as np
import numpy
import numpy.linalg
cimport numpy as np
from libc.math cimport sqrt
import six
from .doc cimport token_by_start, token_by_end
from ..structs cimport TokenC, LexemeC
from ..typedefs cimport flags_t, attr_t, hash_t
from ..attrs cimport attr_id_t
from ..parts_of_speech cimport univ_pos_t
from ..util import normalize_slice
from .doc cimport token_by_start, token_by_end
from ..attrs cimport IS_PUNCT, IS_SPACE
from ..lexeme cimport Lexeme
from ..compat import is_config
cdef class Span:
"""A slice from a Doc object."""
"""
A slice from a Doc object.
"""
def __cinit__(self, Doc doc, int start, int end, int label=0, vector=None,
vector_norm=None):
'''Create a Span object from the slice doc[start : end]
"""
Create a Span object from the slice doc[start : end]
Arguments:
doc (Doc): The parent document.
@ -30,7 +35,7 @@ cdef class Span:
vector (ndarray[ndim=1, dtype='float32']): A meaning representation of the span.
Returns:
Span The newly constructed object.
'''
"""
if not (0 <= start <= end <= len(doc)):
raise IndexError
@ -68,7 +73,7 @@ cdef class Span:
return self.end - self.start
def __repr__(self):
if six.PY3:
if is_config(python3=True):
return self.text
return self.text.encode('utf-8')
@ -89,7 +94,8 @@ cdef class Span:
yield self.doc[i]
def merge(self, *args, **attributes):
"""Retokenize the document, such that the span is merged into a single token.
"""
Retokenize the document, such that the span is merged into a single token.
Arguments:
**attributes:
@ -102,7 +108,8 @@ cdef class Span:
return self.doc.merge(self.start_char, self.end_char, *args, **attributes)
def similarity(self, other):
'''Make a semantic similarity estimate. The default estimate is cosine
"""
Make a semantic similarity estimate. The default estimate is cosine
similarity using an average of word vectors.
Arguments:
@ -111,7 +118,7 @@ cdef class Span:
Return:
score (float): A scalar similarity score. Higher is more similar.
'''
"""
if 'similarity' in self.doc.user_span_hooks:
self.doc.user_span_hooks['similarity'](self, other)
if self.vector_norm == 0.0 or other.vector_norm == 0.0:
@ -133,11 +140,12 @@ cdef class Span:
self.end = end + 1
property sent:
'''The sentence span that this span is a part of.
"""
The sentence span that this span is a part of.
Returns:
Span The sentence this is part of.
'''
"""
def __get__(self):
if 'sent' in self.doc.user_span_hooks:
return self.doc.user_span_hooks['sent'](self)
@ -198,13 +206,13 @@ cdef class Span:
return u''.join([t.text_with_ws for t in self])
property noun_chunks:
'''
"""
Yields base noun-phrase #[code Span] objects, if the document
has been syntactically parsed. A base noun phrase, or
'NP chunk', is a noun phrase that does not permit other NPs to
be nested within it – so no NP-level coordination, no prepositional
phrases, and no relative clauses. For example:
'''
"""
def __get__(self):
if not self.doc.is_parsed:
raise ValueError(
@ -223,17 +231,16 @@ cdef class Span:
yield span
property root:
"""The token within the span that's highest in the parse tree. If there's a tie, the earlist is prefered.
"""
The token within the span that's highest in the parse tree. If there's a
tie, the earlist is prefered.
Returns:
Token: The root token.
i.e. has the
shortest path to the root of the sentence (or is the root itself).
If multiple words are equally high in the tree, the first word is taken.
For example:
i.e. has the shortest path to the root of the sentence (or is the root
itself). If multiple words are equally high in the tree, the first word
is taken. For example:
>>> toks = nlp(u'I like New York in Autumn.')
@ -303,7 +310,8 @@ cdef class Span:
return self.doc[root]
property lefts:
"""Tokens that are to the left of the span, whose head is within the Span.
"""
Tokens that are to the left of the span, whose head is within the Span.
Yields: Token A left-child of a token of the span.
"""
@ -314,7 +322,8 @@ cdef class Span:
yield left
property rights:
"""Tokens that are to the right of the Span, whose head is within the Span.
"""
Tokens that are to the right of the Span, whose head is within the Span.
Yields: Token A right-child of a token of the span.
"""
@ -325,7 +334,8 @@ cdef class Span:
yield right
property subtree:
"""Tokens that descend from tokens in the span, but fall outside it.
"""
Tokens that descend from tokens in the span, but fall outside it.
Yields: Token A descendant of a token within the span.
"""
@ -337,7 +347,9 @@ cdef class Span:
yield from word.subtree
property ent_id:
'''An (integer) entity ID. Usually assigned by patterns in the Matcher.'''
"""
An (integer) entity ID. Usually assigned by patterns in the Matcher.
"""
def __get__(self):
return self.root.ent_id
@ -345,9 +357,11 @@ cdef class Span:
# TODO
raise NotImplementedError(
"Can't yet set ent_id from Span. Vote for this feature on the issue "
"tracker: http://github.com/spacy-io/spaCy")
"tracker: http://github.com/explosion/spaCy/issues")
property ent_id_:
'''A (string) entity ID. Usually assigned by patterns in the Matcher.'''
"""
A (string) entity ID. Usually assigned by patterns in the Matcher.
"""
def __get__(self):
return self.root.ent_id_
@ -355,7 +369,7 @@ cdef class Span:
# TODO
raise NotImplementedError(
"Can't yet set ent_id_ from Span. Vote for this feature on the issue "
"tracker: http://github.com/spacy-io/spaCy")
"tracker: http://github.com/explosion/spaCy/issues")
property orth_:
def __get__(self):
@ -397,5 +411,5 @@ cdef int _count_words_to_root(const TokenC* token, int sent_length) except -1:
raise RuntimeError(
"Array bounds exceeded while searching for root word. This likely "
"means the parse tree is in an invalid state. Please report this "
"issue here: http://github.com/honnibal/spaCy/")
"issue here: http://github.com/explosion/spaCy/issues")
return n

107
spacy/tokens/token.pyx
View File

@ -1,5 +1,5 @@
# coding: utf8
# cython: infer_types=True
# coding: utf8
from __future__ import unicode_literals
from libc.string cimport memcpy
@ -8,20 +8,15 @@ from cpython.mem cimport PyMem_Malloc, PyMem_Free
from cython.view cimport array as cvarray
cimport numpy as np
np.import_array()
import numpy
import six
from ..typedefs cimport hash_t
from ..lexeme cimport Lexeme
from .. import parts_of_speech
from ..attrs cimport LEMMA
from ..attrs cimport ID, ORTH, NORM, LOWER, SHAPE, PREFIX, SUFFIX, LENGTH, CLUSTER
from ..attrs cimport POS, LEMMA, TAG, DEP
from ..parts_of_speech cimport CCONJ, PUNCT
from ..attrs cimport IS_ALPHA, IS_ASCII, IS_DIGIT, IS_LOWER, IS_PUNCT, IS_SPACE
from ..attrs cimport IS_BRACKET
from ..attrs cimport IS_QUOTE
@ -29,12 +24,13 @@ from ..attrs cimport IS_LEFT_PUNCT
from ..attrs cimport IS_RIGHT_PUNCT
from ..attrs cimport IS_TITLE, IS_UPPER, LIKE_URL, LIKE_NUM, LIKE_EMAIL, IS_STOP
from ..attrs cimport IS_OOV
from ..lexeme cimport Lexeme
from ..compat import is_config
cdef class Token:
"""An individual token --- i.e. a word, punctuation symbol, whitespace, etc.
"""
An individual token --- i.e. a word, punctuation symbol, whitespace, etc.
"""
def __cinit__(self, Vocab vocab, Doc doc, int offset):
self.vocab = vocab
@ -46,7 +42,9 @@ cdef class Token:
return hash((self.doc, self.i))
def __len__(self):
'''Number of unicode characters in token.text'''
"""
Number of unicode characters in token.text.
"""
return self.c.lex.length
def __unicode__(self):
@ -56,7 +54,7 @@ cdef class Token:
return self.text.encode('utf8')
def __str__(self):
if six.PY3:
if is_config(python3=True):
return self.__unicode__()
return self.__bytes__()
@ -83,27 +81,30 @@ cdef class Token:
raise ValueError(op)
cpdef bint check_flag(self, attr_id_t flag_id) except -1:
'''Check the value of a boolean flag.
"""
Check the value of a boolean flag.
Arguments:
flag_id (int): The ID of the flag attribute.
Returns:
is_set (bool): Whether the flag is set.
'''
"""
return Lexeme.c_check_flag(self.c.lex, flag_id)
def nbor(self, int i=1):
'''Get a neighboring token.
"""
Get a neighboring token.
Arguments:
i (int): The relative position of the token to get. Defaults to 1.
Returns:
neighbor (Token): The token at position self.doc[self.i+i]
'''
"""
return self.doc[self.i+i]
def similarity(self, other):
'''Compute a semantic similarity estimate. Defaults to cosine over vectors.
"""
Compute a semantic similarity estimate. Defaults to cosine over vectors.
Arguments:
other:
@ -111,7 +112,7 @@ cdef class Token:
Token and Lexeme objects.
Returns:
score (float): A scalar similarity score. Higher is more similar.
'''
"""
if 'similarity' in self.doc.user_token_hooks:
return self.doc.user_token_hooks['similarity'](self)
if self.vector_norm == 0 or other.vector_norm == 0:
@ -191,6 +192,8 @@ cdef class Token:
property lemma:
def __get__(self):
return self.c.lemma
def __set__(self, int lemma):
self.c.lemma = lemma
property pos:
def __get__(self):
@ -209,9 +212,9 @@ cdef class Token:
self.c.dep = label
property has_vector:
'''
"""
A boolean value indicating whether a word vector is associated with the object.
'''
"""
def __get__(self):
if 'has_vector' in self.doc.user_token_hooks:
return self.doc.user_token_hooks['has_vector'](self)
@ -223,11 +226,11 @@ cdef class Token:
return False
property vector:
'''
"""
A real-valued meaning representation.
Type: numpy.ndarray[ndim=1, dtype='float32']
'''
"""
def __get__(self):
if 'vector' in self.doc.user_token_hooks:
return self.doc.user_token_hooks['vector'](self)
@ -245,6 +248,7 @@ cdef class Token:
property repvec:
def __get__(self):
raise AttributeError("repvec was renamed to vector in v0.100")
property has_repvec:
def __get__(self):
raise AttributeError("has_repvec was renamed to has_vector in v0.100")
@ -265,7 +269,8 @@ cdef class Token:
property lefts:
def __get__(self):
"""The leftward immediate children of the word, in the syntactic
"""
The leftward immediate children of the word, in the syntactic
dependency parse.
"""
cdef int nr_iter = 0
@ -282,8 +287,10 @@ cdef class Token:
property rights:
def __get__(self):
"""The rightward immediate children of the word, in the syntactic
dependency parse."""
"""
The rightward immediate children of the word, in the syntactic
dependency parse.
"""
cdef const TokenC* ptr = self.c + (self.c.r_edge - self.i)
tokens = []
cdef int nr_iter = 0
@ -300,19 +307,21 @@ cdef class Token:
yield t
property children:
'''A sequence of the token's immediate syntactic children.
"""
A sequence of the token's immediate syntactic children.
Yields: Token A child token such that child.head==self
'''
"""
def __get__(self):
yield from self.lefts
yield from self.rights
property subtree:
'''A sequence of all the token's syntactic descendents.
"""
A sequence of all the token's syntactic descendents.
Yields: Token A descendent token such that self.is_ancestor(descendent)
'''
"""
def __get__(self):
for word in self.lefts:
yield from word.subtree
@ -321,26 +330,29 @@ cdef class Token:
yield from word.subtree
property left_edge:
'''The leftmost token of this token's syntactic descendents.
"""
The leftmost token of this token's syntactic descendents.
Returns: Token The first token such that self.is_ancestor(token)
'''
"""
def __get__(self):
return self.doc[self.c.l_edge]
property right_edge:
'''The rightmost token of this token's syntactic descendents.
"""
The rightmost token of this token's syntactic descendents.
Returns: Token The last token such that self.is_ancestor(token)
'''
"""
def __get__(self):
return self.doc[self.c.r_edge]
property ancestors:
'''A sequence of this token's syntactic ancestors.
"""
A sequence of this token's syntactic ancestors.
Yields: Token A sequence of ancestor tokens such that ancestor.is_ancestor(self)
'''
"""
def __get__(self):
cdef const TokenC* head_ptr = self.c
# guard against infinite loop, no token can have
@ -356,25 +368,29 @@ cdef class Token:
return self.is_ancestor(descendant)
def is_ancestor(self, descendant):
'''Check whether this token is a parent, grandparent, etc. of another
"""
Check whether this token is a parent, grandparent, etc. of another
in the dependency tree.
Arguments:
descendant (Token): Another token.
Returns:
is_ancestor (bool): Whether this token is the ancestor of the descendant.
'''
"""
if self.doc is not descendant.doc:
return False
return any( ancestor.i == self.i for ancestor in descendant.ancestors )
property head:
'''The syntactic parent, or "governor", of this token.
"""
The syntactic parent, or "governor", of this token.
Returns: Token
'''
"""
def __get__(self):
"""The token predicted by the parser to be the head of the current token."""
"""
The token predicted by the parser to be the head of the current token.
"""
return self.doc[self.i + self.c.head]
def __set__(self, Token new_head):
# this function sets the head of self to new_head
@ -467,10 +483,11 @@ cdef class Token:
self.c.head = rel_newhead_i
property conjuncts:
'''A sequence of coordinated tokens, including the token itself.
"""
A sequence of coordinated tokens, including the token itself.
Yields: Token A coordinated token
'''
"""
def __get__(self):
"""Get a list of conjoined words."""
cdef Token word
@ -501,7 +518,9 @@ cdef class Token:
return iob_strings[self.c.ent_iob]
property ent_id:
'''An (integer) entity ID. Usually assigned by patterns in the Matcher.'''
"""
An (integer) entity ID. Usually assigned by patterns in the Matcher.
"""
def __get__(self):
return self.c.ent_id
@ -509,7 +528,9 @@ cdef class Token:
self.c.ent_id = key
property ent_id_:
'''A (string) entity ID. Usually assigned by patterns in the Matcher.'''
"""
A (string) entity ID. Usually assigned by patterns in the Matcher.
"""
def __get__(self):
return self.vocab.strings[self.c.ent_id]
@ -551,6 +572,8 @@ cdef class Token:
property lemma_:
def __get__(self):
return self.vocab.strings[self.c.lemma]
def __set__(self, unicode lemma_):
self.c.lemma = self.vocab.strings[lemma_]
property pos_:
def __get__(self):

11
spacy/train.py
View File

@ -1,15 +1,16 @@
from __future__ import absolute_import
from __future__ import unicode_literals
# coding: utf8
from __future__ import absolute_import, unicode_literals
import random
import tqdm
from .gold import GoldParse
from .gold import GoldParse, merge_sents
from .scorer import Scorer
from .gold import merge_sents
class Trainer(object):
'''Manage training of an NLP pipeline.'''
"""
Manage training of an NLP pipeline.
"""
def __init__(self, nlp, gold_tuples):
self.nlp = nlp
self.gold_tuples = gold_tuples

157
spacy/util.py
View File

@ -1,29 +1,17 @@
# coding: utf8
from __future__ import unicode_literals, print_function
import os
import io
import json
import ujson
import re
import os.path
import pathlib
from pathlib import Path
import sys
import textwrap
try:
basestring
except NameError:
basestring = str
try:
raw_input
except NameError: # Python 3
raw_input = input
from .compat import basestring_, unicode_, input_
LANGUAGES = {}
_data_path = pathlib.Path(__file__).parent / 'data'
_data_path = Path(__file__).parent / 'data'
def set_lang_class(name, cls):
@ -32,9 +20,11 @@ def set_lang_class(name, cls):
def get_lang_class(name):
if name in LANGUAGES:
return LANGUAGES[name]
lang = re.split('[^a-zA-Z0-9]', name, 1)[0]
if lang not in LANGUAGES:
raise RuntimeError('Language not supported: %s' % lang)
raise RuntimeError('Language not supported: %s' % name)
return LANGUAGES[lang]
@ -47,55 +37,18 @@ def get_data_path(require_exists=True):
def set_data_path(path):
global _data_path
if isinstance(path, basestring):
path = pathlib.Path(path)
_data_path = path
_data_path = ensure_path(path)
def or_(val1, val2):
if val1 is not None:
return val1
elif callable(val2):
return val2()
def ensure_path(path):
if isinstance(path, basestring_):
return Path(path)
else:
return val2
def match_best_version(target_name, target_version, path):
path = path if not isinstance(path, basestring) else pathlib.Path(path)
if path is None or not path.exists():
return None
matches = []
for data_name in path.iterdir():
name, version = split_data_name(data_name.parts[-1])
if name == target_name and constraint_match(target_version, version):
matches.append((tuple(float(v) for v in version.split('.')), data_name))
if matches:
return pathlib.Path(max(matches)[1])
else:
return None
def split_data_name(name):
return name.split('-', 1) if '-' in name else (name, '')
def constraint_match(constraint_string, version):
# From http://github.com/spacy-io/sputnik
if not constraint_string:
return True
constraints = [c.strip() for c in constraint_string.split(',') if c.strip()]
for c in constraints:
if not re.match(r'[><=][=]?\d+(\.\d+)*', c):
raise ValueError('invalid constraint: %s' % c)
return all(semver.match(version, c) for c in constraints)
return path
def read_regex(path):
path = path if not isinstance(path, basestring) else pathlib.Path(path)
path = ensure_path(path)
with path.open() as file_:
entries = file_.read().split('\n')
expression = '|'.join(['^' + re.escape(piece) for piece in entries if piece.strip()])
@ -142,32 +95,28 @@ def normalize_slice(length, start, stop, step=None):
return start, stop
def utf8open(loc, mode='r'):
return io.open(loc, mode, encoding='utf8')
def check_renamed_kwargs(renamed, kwargs):
for old, new in renamed.items():
if old in kwargs:
raise TypeError("Keyword argument %s now renamed to %s" % (old, new))
def is_windows():
"""Check if user is on Windows."""
return sys.platform.startswith('win')
def is_python2():
"""Check if Python 2 is used."""
return sys.version.startswith('2.')
def read_json(location):
with location.open('r', encoding='utf8') as f:
return ujson.load(f)
def parse_package_meta(package_path, package, require=True):
location = os.path.join(str(package_path), package, 'meta.json')
if os.path.isfile(location):
with io.open(location, encoding='utf8') as f:
meta = json.load(f)
return meta
"""
Check if a meta.json exists in a package and return its contents as a
dictionary. If require is set to True, raise an error if no meta.json found.
"""
# TODO: Allow passing in full model path and only require one argument
# instead of path and package name. This lets us avoid passing in an awkward
# empty string in spacy.load() if user supplies full model path.
location = package_path / package / 'meta.json'
if location.is_file():
return read_json(location)
elif require:
raise IOError("Could not read meta.json from %s" % location)
else:
@ -175,20 +124,22 @@ def parse_package_meta(package_path, package, require=True):
def get_raw_input(description, default=False):
"""Get user input via raw_input / input and return input value. Takes a
"""
Get user input via raw_input / input and return input value. Takes a
description for the prompt, and an optional default value that's displayed
with the prompt."""
with the prompt.
"""
additional = ' (default: {d})'.format(d=default) if default else ''
prompt = ' {d}{a}: '.format(d=description, a=additional)
user_input = raw_input(prompt)
user_input = input_(prompt)
return user_input
def print_table(data, **kwargs):
"""Print data in table format. Can either take a list of tuples or a
dictionary, which will be converted to a list of tuples."""
"""
Print data in table format. Can either take a list of tuples or a
dictionary, which will be converted to a list of tuples.
"""
if type(data) == dict:
data = list(data.items())
@ -204,15 +155,15 @@ def print_table(data, **kwargs):
def print_markdown(data, **kwargs):
"""Print listed data in GitHub-flavoured Markdown format so it can be
"""
Print listed data in GitHub-flavoured Markdown format so it can be
copy-pasted into issues. Can either take a list of tuples or a dictionary,
which will be converted to a list of tuples."""
which will be converted to a list of tuples.
"""
def excl_value(value):
# don't print value if it contains absolute path of directory
# (i.e. personal info that shouldn't need to be shared)
# other conditions can be included here if necessary
if str(pathlib.Path(__file__).parent) in value:
# don't print value if it contains absolute path of directory (i.e.
# personal info). Other conditions can be included here if necessary.
if unicode_(Path(__file__).parent) in value:
return True
if type(data) == dict:
@ -225,16 +176,16 @@ def print_markdown(data, **kwargs):
if 'title' in kwargs and kwargs['title']:
print(tpl_title.format(msg=kwargs['title']))
print(tpl_msg.format(msg=markdown))
def print_msg(*text, **kwargs):
"""Print formatted message. Each positional argument is rendered as newline-
"""
Print formatted message. Each positional argument is rendered as newline-
separated paragraph. If kwarg 'title' exist, title is printed above the text
and highlighted (using ANSI escape sequences manually to avoid unnecessary
dependency)."""
dependency).
"""
message = '\n\n'.join([_wrap_text(t) for t in text])
tpl_msg = '\n{msg}\n'
tpl_title = '\n\033[93m{msg}\033[0m'
@ -246,9 +197,10 @@ def print_msg(*text, **kwargs):
def _wrap_text(text):
"""Wrap text at given width using textwrap module. Indent should consist of
spaces. Its length is deducted from wrap width to ensure exact wrapping."""
"""
Wrap text at given width using textwrap module. Indent should consist of
spaces. Its length is deducted from wrap width to ensure exact wrapping.
"""
wrap_max = 80
indent = ' '
wrap_width = wrap_max - len(indent)
@ -258,10 +210,11 @@ def _wrap_text(text):
def sys_exit(*messages, **kwargs):
"""Performs SystemExit. For modules used from the command line, like
"""
Performs SystemExit. For modules used from the command line, like
download and link. To print message, use the same arguments as for
print_msg()."""
print_msg().
"""
if messages:
print_msg(*messages, **kwargs)
sys.exit(0)

119
spacy/vocab.pyx
View File

@ -1,41 +1,29 @@
# coding: utf8
from __future__ import unicode_literals
import bz2
import ujson
import re
from libc.string cimport memset
from libc.stdint cimport int32_t
from libc.math cimport sqrt
from pathlib import Path
import bz2
import ujson as json
import re
try:
import cPickle as pickle
except ImportError:
import pickle
from cymem.cymem cimport Address
from .lexeme cimport EMPTY_LEXEME
from .lexeme cimport Lexeme
from .strings cimport hash_string
from .typedefs cimport attr_t
from .cfile cimport CFile, StringCFile
from .lemmatizer import Lemmatizer
from .attrs import intify_attrs
from .tokens.token cimport Token
from . import attrs
from . import symbols
from cymem.cymem cimport Address
from .serialize.packer cimport Packer
from .attrs cimport PROB, LANG
from .compat import copy_reg, pickle
from .lemmatizer import Lemmatizer
from .attrs import intify_attrs
from . import util
try:
import copy_reg
except ImportError:
import copyreg as copy_reg
from . import attrs
from . import symbols
DEF MAX_VEC_SIZE = 100000
@ -48,8 +36,9 @@ EMPTY_LEXEME.vector = EMPTY_VEC
cdef class Vocab:
'''A map container for a language's LexemeC structs.
'''
"""
A map container for a language's LexemeC structs.
"""
@classmethod
def load(cls, path, lex_attr_getters=None, lemmatizer=True,
tag_map=True, serializer_freqs=True, oov_prob=True, **deprecated_kwargs):
@ -72,8 +61,7 @@ cdef class Vocab:
Returns:
Vocab: The newly constructed vocab object.
"""
if isinstance(path, basestring):
path = Path(path)
path = util.ensure_path(path)
util.check_renamed_kwargs({'get_lex_attr': 'lex_attr_getters'}, deprecated_kwargs)
if 'vectors' in deprecated_kwargs:
raise AttributeError(
@ -81,7 +69,7 @@ cdef class Vocab:
"Install vectors after loading.")
if tag_map is True and (path / 'vocab' / 'tag_map.json').exists():
with (path / 'vocab' / 'tag_map.json').open('r', encoding='utf8') as file_:
tag_map = json.load(file_)
tag_map = ujson.load(file_)
elif tag_map is True:
tag_map = None
if lex_attr_getters is not None \
@ -94,12 +82,12 @@ cdef class Vocab:
lemmatizer = Lemmatizer.load(path)
if serializer_freqs is True and (path / 'vocab' / 'serializer.json').exists():
with (path / 'vocab' / 'serializer.json').open('r', encoding='utf8') as file_:
serializer_freqs = json.load(file_)
serializer_freqs = ujson.load(file_)
else:
serializer_freqs = None
with (path / 'vocab' / 'strings.json').open('r', encoding='utf8') as file_:
strings_list = json.load(file_)
strings_list = ujson.load(file_)
cdef Vocab self = cls(lex_attr_getters=lex_attr_getters, tag_map=tag_map,
lemmatizer=lemmatizer, serializer_freqs=serializer_freqs,
strings=strings_list)
@ -108,7 +96,8 @@ cdef class Vocab:
def __init__(self, lex_attr_getters=None, tag_map=None, lemmatizer=None,
serializer_freqs=None, strings=tuple(), **deprecated_kwargs):
'''Create the vocabulary.
"""
Create the vocabulary.
lex_attr_getters (dict):
A dictionary mapping attribute IDs to functions to compute them.
@ -123,7 +112,7 @@ cdef class Vocab:
Returns:
Vocab: The newly constructed vocab object.
'''
"""
util.check_renamed_kwargs({'get_lex_attr': 'lex_attr_getters'}, deprecated_kwargs)
lex_attr_getters = lex_attr_getters if lex_attr_getters is not None else {}
@ -172,17 +161,19 @@ cdef class Vocab:
return langfunc('_') if langfunc else ''
def __len__(self):
"""The current number of lexemes stored."""
"""
The current number of lexemes stored.
"""
return self.length
def resize_vectors(self, int new_size):
'''
"""
Set vectors_length to a new size, and allocate more memory for the Lexeme
vectors if necessary. The memory will be zeroed.
Arguments:
new_size (int): The new size of the vectors.
'''
"""
cdef hash_t key
cdef size_t addr
if new_size > self.vectors_length:
@ -193,7 +184,8 @@ cdef class Vocab:
self.vectors_length = new_size
def add_flag(self, flag_getter, int flag_id=-1):
'''Set a new boolean flag to words in the vocabulary.
"""
Set a new boolean flag to words in the vocabulary.
The flag_setter function will be called over the words currently in the
vocab, and then applied to new words as they occur. You'll then be able
@ -213,7 +205,7 @@ cdef class Vocab:
Returns:
flag_id (int): The integer ID by which the flag value can be checked.
'''
"""
if flag_id == -1:
for bit in range(1, 64):
if bit not in self.lex_attr_getters:
@ -234,9 +226,11 @@ cdef class Vocab:
return flag_id
cdef const LexemeC* get(self, Pool mem, unicode string) except NULL:
'''Get a pointer to a LexemeC from the lexicon, creating a new Lexeme
"""
Get a pointer to a LexemeC from the lexicon, creating a new Lexeme
if necessary, using memory acquired from the given pool. If the pool
is the lexicon's own memory, the lexeme is saved in the lexicon.'''
is the lexicon's own memory, the lexeme is saved in the lexicon.
"""
if string == u'':
return &EMPTY_LEXEME
cdef LexemeC* lex
@ -252,9 +246,11 @@ cdef class Vocab:
return self._new_lexeme(mem, string)
cdef const LexemeC* get_by_orth(self, Pool mem, attr_t orth) except NULL:
'''Get a pointer to a LexemeC from the lexicon, creating a new Lexeme
"""
Get a pointer to a LexemeC from the lexicon, creating a new Lexeme
if necessary, using memory acquired from the given pool. If the pool
is the lexicon's own memory, the lexeme is saved in the lexicon.'''
is the lexicon's own memory, the lexeme is saved in the lexicon.
"""
if orth == 0:
return &EMPTY_LEXEME
cdef LexemeC* lex
@ -297,30 +293,33 @@ cdef class Vocab:
self.length += 1
def __contains__(self, unicode string):
'''Check whether the string has an entry in the vocabulary.
"""
Check whether the string has an entry in the vocabulary.
Arguments:
string (unicode): The ID string.
Returns:
bool Whether the string has an entry in the vocabulary.
'''
"""
key = hash_string(string)
lex = self._by_hash.get(key)
return lex is not NULL
def __iter__(self):
'''Iterate over the lexemes in the vocabulary.
"""
Iterate over the lexemes in the vocabulary.
Yields: Lexeme An entry in the vocabulary.
'''
"""
cdef attr_t orth
cdef size_t addr
for orth, addr in self._by_orth.items():
yield Lexeme(self, orth)
def __getitem__(self, id_or_string):
'''Retrieve a lexeme, given an int ID or a unicode string. If a previously
"""
Retrieve a lexeme, given an int ID or a unicode string. If a previously
unseen unicode string is given, a new lexeme is created and stored.
Arguments:
@ -332,7 +331,7 @@ cdef class Vocab:
Returns:
lexeme (Lexeme): The lexeme indicated by the given ID.
'''
"""
cdef attr_t orth
if type(id_or_string) == unicode:
orth = self.strings[id_or_string]
@ -355,7 +354,8 @@ cdef class Vocab:
return tokens
def dump(self, loc=None):
"""Save the lexemes binary data to the given location, or
"""
Save the lexemes binary data to the given location, or
return a byte-string with the data if loc is None.
Arguments:
@ -392,14 +392,15 @@ cdef class Vocab:
return fp.string_data()
def load_lexemes(self, loc):
'''Load the binary vocabulary data from the given location.
"""
Load the binary vocabulary data from the given location.
Arguments:
loc (Path): The path to load from.
Returns:
None
'''
"""
fp = CFile(loc, 'rb',
on_open_error=lambda: IOError('LexemeCs file not found at %s' % loc))
cdef LexemeC* lexeme = NULL
@ -440,8 +441,9 @@ cdef class Vocab:
fp.close()
def _deserialize_lexemes(self, CFile fp):
'''Load the binary vocabulary data from the given CFile.
'''
"""
Load the binary vocabulary data from the given CFile.
"""
cdef LexemeC* lexeme = NULL
cdef hash_t key
cdef unicode py_str
@ -494,13 +496,14 @@ cdef class Vocab:
fp.close()
def dump_vectors(self, out_loc):
'''Save the word vectors to a binary file.
"""
Save the word vectors to a binary file.
Arguments:
loc (Path): The path to save to.
Returns:
None
'''
"""
cdef int32_t vec_len = self.vectors_length
cdef int32_t word_len
cdef bytes word_str
@ -522,7 +525,8 @@ cdef class Vocab:
out_file.close()
def load_vectors(self, file_):
"""Load vectors from a text-based file.
"""
Load vectors from a text-based file.
Arguments:
file_ (buffer): The file to read from. Entries should be separated by newlines,
@ -561,7 +565,8 @@ cdef class Vocab:
return vec_len
def load_vectors_from_bin_loc(self, loc):
"""Load vectors from the location of a binary file.
"""
Load vectors from the location of a binary file.
Arguments:
loc (unicode): The path of the binary file to load from.

2
website/_harp.json
View File

@ -12,7 +12,7 @@
"COMPANY_URL": "https://explosion.ai",
"DEMOS_URL": "https://demos.explosion.ai",
"SPACY_VERSION": "1.7",
"SPACY_VERSION": "1.8",
"LATEST_NEWS": {
"url": "https://survey.spacy.io/",
"title": "Take the spaCy user survey and help us improve the library!"

8
website/assets/img/graphics.svg
View File

@ -2,9 +2,11 @@
<defs>
<symbol id="usersurvey" viewBox="0 0 200 111">
<title>spaCy user survey 2017</title>
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20
website/docs/api/language.jade
View File

@ -55,14 +55,14 @@ p Create or load the pipeline.
+table(["Name", "Type", "Description"])
+row
+cell #[code **kwrags]
+cell #[code **overrides]
+cell -
+cell Keyword arguments indicating which defaults to override.
+footrow
+cell return
+cell #[code Language]
+cell #[code self]
+cell The newly constructed object.
+h(2, "call") Language.__call__
+tag method
@ -136,3 +136,19 @@ p
+cell yield
+cell #[code Doc]
+cell Containers for accessing the linguistic annotations.
+h(2, "save_to_directory") Language.save_to_directory
+tag method
p Save the #[code Vocab], #[code StringStore] and pipeline to a directory.
+table(["Name", "Type", "Description"])
+row
+cell #[code path]
+cell string or pathlib path
+cell Path to save the model.
+footrow
+cell return
+cell #[code None]
+cell -

30
website/docs/usage/_data.json
View File

@ -20,8 +20,10 @@
"Word vectors": "word-vectors-similarities",
"Deep learning": "deep-learning",
"Custom tokenization": "customizing-tokenizer",
"Adding languages": "adding-languages",
"Training": "training",
"Adding languages": "adding-languages"
"Training NER": "training-ner",
"Saving & loading": "saving-loading"
},
"Examples": {
"Tutorials": "tutorials",
@ -101,11 +103,21 @@
"customizing-tokenizer": {
"title": "Customizing the tokenizer",
"next": "training"
"next": "adding-languages"
},
"training": {
"title": "Training the tagger, parser and entity recognizer"
"title": "Training spaCy's statistical models",
"next": "saving-loading"
},
"training-ner": {
"title": "Training the Named Entity Recognizer",
"next": "saving-loading"
},
"saving-loading": {
"title": "Saving and loading models"
},
"pos-tagging": {
@ -356,6 +368,18 @@
},
"code": {
"Training a new entity type": {
"url": "https://github.com/explosion/spaCy/blob/master/examples/training/train_new_entity_type.py",
"author": "Matthew Honnibal",
"tags": ["ner", "training"]
},
"Training an NER system from scratch": {
"url": "https://github.com/explosion/spaCy/blob/master/examples/training/train_ner_standalone.py",
"author": "Matthew Honnibal",
"tags": ["ner", "training"]
},
"Information extraction": {
"url": "https://github.com/explosion/spaCy/blob/master/examples/information_extraction.py",
"author": "Matthew Honnibal",

26
website/docs/usage/adding-languages.jade
View File

@ -63,14 +63,16 @@ p
tag_map = TAG_MAP
stop_words = STOP_WORDS
p Additionally, the new #[code Language] class needs to be registered in #[+src(gh("spaCy", "spacy/__init__.py")) spacy/__init__.py] using the #[code set_lang_class()] function, so that you can use #[code spacy.load()].
p
| Additionally, the new #[code Language] class needs to be added to the
| list of available languages in #[+src(gh("spaCy", "spacy/__init__.py")) __init__.py].
| The languages are then registered using the #[code set_lang_class()] function.
+code("spacy/__init__.py").
from . import en
from . import xx
set_lang_class(en.English.lang, en.English)
set_lang_class(xx.Xxxxx.lang, xx.Xxxxx)
_languages = (en.English, ..., xx.Xxxxx)
p You'll also need to list the new package in #[+src(gh("spaCy", "spacy/setup.py")) setup.py]:
@ -398,11 +400,12 @@ p
| vectors files, you can use the
| #[+src(gh("spacy-dev-resources", "training/init.py")) init.py]
| script from our
| #[+a(gh("spacy-dev-resources")) developer resources] to create a
| spaCy data directory:
| #[+a(gh("spacy-dev-resources")) developer resources], or use the new
| #[+a("/docs/usage/cli#model") #[code model] command] to create a data
| directory:
+code(false, "bash").
python training/init.py xx your_data_directory/ my_data/word_freqs.txt my_data/clusters.txt my_data/word_vectors.bz2
python -m spacy model [lang] [model_dir] [freqs_data] [clusters_data] [vectors_data]
+aside-code("your_data_directory", "yaml").
├── vocab/
@ -421,17 +424,14 @@ p
p
| This creates a spaCy data directory with a vocabulary model, ready to be
| loaded. By default, the
| #[+src(gh("spacy-dev-resources", "training/init.py")) init.py]
| script expects to be able to find your language class using
| #[code spacy.util.get_lang_class(lang_id)]. You can edit the script to
| help it find your language class if necessary.
| loaded. By default, the command expects to be able to find your language
| class using #[code spacy.util.get_lang_class(lang_id)].
+h(3, "word-frequencies") Word frequencies
p
| The #[+src(gh("spacy-dev-resources", "training/init.py")) init.py]
| script expects a tab-separated word frequencies file with three columns:
| The #[+a("/docs/usage/cli#model") #[code model] command] expects a
| tab-separated word frequencies file with three columns:
+list("numbers")
+item The number of times the word occurred in your language sample.

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@ -145,7 +145,9 @@ p
+h(2, "model") Model
+tag experimental
p Initialise a new model and its data directory.
p
| Initialise a new model and its data directory. For more info on this, see
| the documentation on #[+a("/docs/usage/adding-languages") adding languages].
+code(false, "bash").
python -m spacy model [lang] [model_dir] [freqs_data] [clusters_data] [vectors_data]
@ -246,15 +248,17 @@ p
+tag experimental
p
| Generate a #[+a("/docs/usage/models#own-models") model Python package]
| from an existing model data directory. All data files are copied over,
| and the meta data can be entered directly from the command line. While
| this feature is still experimental, the required file templates are
| downloaded from #[+src(gh("spacy-dev-resources", "templates/model")) GitHub].
| This means you need to be connected to the internet to use this command.
| Generate a #[+a("/docs/usage/saving-loading#generating") model Python package]
| from an existing model data directory. All data files are copied over.
| If the path to a meta.json is supplied, or a meta.json is found in the
| input directory, this file is used. Otherwise, the data can be entered
| directly from the command line. While this feature is still experimental,
| the required file templates are downloaded from
| #[+src(gh("spacy-dev-resources", "templates/model")) GitHub]. This means
| you need to be connected to the internet to use this command.
+code(false, "bash").
python -m spacy package [input_dir] [output_dir] [--force]
python -m spacy package [input_dir] [output_dir] [--meta] [--force]
+table(["Argument", "Type", "Description"])
+row
@ -267,6 +271,11 @@ p
+cell positional
+cell Directory to create package folder in.
+row
+cell #[code meta]
+cell option
+cell Path to meta.json file (optional).
+row
+cell #[code --force], #[code -f]
+cell flag

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@ -57,7 +57,7 @@ p
doc.ents = [Span(doc, 0, 1, label=doc.vocab.strings['GPE'])]
assert doc[0].ent_type_ == 'GPE'
doc.ents = []
doc.ents = [(u'LondonCity', doc.vocab.strings['GPE']), 0, 1)]
doc.ents = [(u'LondonCity', doc.vocab.strings['GPE'], 0, 1)]
p
| The value you assign should be a sequence, the values of which

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@ -137,7 +137,7 @@ p
return word.ent_type != 0
def count_parent_verb_by_person(docs):
counts = defaultdict(defaultdict(int))
counts = defaultdict(lambda: defaultdict(int))
for doc in docs:
for ent in doc.ents:
if ent.label_ == 'PERSON' and ent.root.head.pos == VERB:

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@ -235,62 +235,13 @@ p
p
| If you've trained your own model, for example for
| #[+a("/docs/usage/adding-languages") additional languages], you can
| create a shortuct link for it by pointing #[code spacy.link] to the
| model's data directory. To allow your model to be downloaded and
| installed via pip, you'll also need to generate a package for it. You can
| do this manually, or via the new
| #[+a("/docs/usage/cli#package") #[code spacy package] command] that will
| create all required files, and walk you through generating the meta data.
| #[+a("/docs/usage/adding-languages") additional languages] or
| #[+a("/docs/usage/train-ner") custom named entities], you can save its
| state using the #[code Language.save_to_directory()] method. To make the
| model more convenient to deploy, we recommend wrapping it as a Python
| package.
+infobox("Important note")
| The model packages are #[strong not suitable] for the public
| #[+a("https://pypi.python.org") pypi.python.org] directory, which is not
| designed for binary data and files over 50 MB. However, if your company
| is running an internal installation of pypi, publishing your models on
| there can be a convenient solution to share them with your team.
p The model directory should look like this:
+code("Directory structure", "yaml").
└── /
├── MANIFEST.in # to include meta.json
├── meta.json # model meta data
├── setup.py # setup file for pip installation
└── en_core_web_md # model directory
├── __init__.py # init for pip installation
└── en_core_web_md-1.2.0 # model data
p
| You can find templates for all files in our
| #[+a(gh("spacy-dev-resouces", "templates/model")) spaCy dev resources].
| Unless you want to customise installation and loading, the only file
| you'll need to modify is #[code meta.json], which includes the model's
| meta data. It will later be copied into the package and data directory.
+code("meta.json", "json").
{
"name": "core_web_md",
"lang": "en",
"version": "1.2.0",
"spacy_version": "1.7.0",
"description": "English model for spaCy",
"author": "Explosion AI",
"email": "contact@explosion.ai",
"license": "MIT"
}
p
| Keep in mind that the directories need to be named according to the
| naming conventions. The #[code lang] setting is also used to create the
| respective #[code Language] class in spaCy, which will later be returned
| by the model's #[code load()] method.
p
| To generate the package, run the following command from within the
| directory. This will create a #[code .tar.gz] archive in a directory
| #[code /dist].
+code(false, "bash").
python setup.py sdist
+infobox("Saving and loading models")
| For more information and a detailed guide on how to package your model,
| see the documentation on
| #[+a("/docs/usage/saving-loading") saving and loading models].

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@ -0,0 +1,109 @@
include ../../_includes/_mixins
p
| After training your model, you'll usually want to save its state, and load
| it back later. You can do this with the
| #[+api("language#save_to_directory") #[code Language.save_to_directory()]]
| method:
+code.
nlp.save_to_directory('/home/me/data/en_example_model')
p
| The directory will be created if it doesn't exist, and the whole pipeline
| will be written out. To make the model more convenient to deploy, we
| recommend wrapping it as a Python package.
+h(2, "generating") Generating a model package
+infobox("Important note")
| The model packages are #[strong not suitable] for the public
| #[+a("https://pypi.python.org") pypi.python.org] directory, which is not
| designed for binary data and files over 50 MB. However, if your company
| is running an internal installation of pypi, publishing your models on
| there can be a convenient solution to share them with your team.
p
| spaCy comes with a handy CLI command that will create all required files,
| and walk you through generating the meta data. You can also create the
| meta.json manually and place it in the model data directory, or supply a
| path to it using the #[code --meta] flag. For more info on this, see the
| #[+a("/docs/usage/cli/#package") #[code package] command] documentation.
+aside-code("meta.json", "json").
{
"name": "example_model",
"lang": "en",
"version": "1.0.0",
"spacy_version": "&gt;=1.7.0,&lt;2.0.0",
"description": "Example model for spaCy",
"author": "You",
"email": "you@example.com",
"license": "CC BY-SA 3.0"
}
+code(false, "bash").
python -m spacy package /home/me/data/en_example_model /home/me/my_models
p This command will create a model package directory that should look like this:
+code("Directory structure", "yaml").
└── /
├── MANIFEST.in # to include meta.json
├── meta.json # model meta data
├── setup.py # setup file for pip installation
└── en_example_model # model directory
├── __init__.py # init for pip installation
└── en_example_model-1.0.0 # model data
p
| You can also find templates for all files in our
| #[+a(gh("spacy-dev-resouces", "templates/model")) spaCy dev resources].
| If you're creating the package manually, keep in mind that the directories
| need to be named according to the naming conventions of
| #[code [language]_[type]] and #[code [language]_[type]-[version]]. The
| #[code lang] setting in the meta.json is also used to create the
| respective #[code Language] class in spaCy, which will later be returned
| by the model's #[code load()] method.
+h(2, "building") Building a model package
p
| To build the package, run the following command from within the
| directory. This will create a #[code .tar.gz] archive in a directory
| #[code /dist].
+code(false, "bash").
python setup.py sdist
p
| For more information on building Python packages, see the
| #[+a("https://setuptools.readthedocs.io/en/latest/") Python Setuptools documentation].
+h(2, "loading") Loading a model package
p
| Model packages can be installed by pointing pip to the model's
| #[code .tar.gz] archive:
+code(false, "bash").
pip install /path/to/en_example_model-1.0.0.tar.gz
p You'll then be able to load the model as follows:
+code.
import en_example_model
nlp = en_example_model.load()
p
| To load the model via #[code spacy.load()], you can also
| create a #[+a("/docs/usage/models#usage") shortcut link] that maps the
| package name to a custom model name of your choice:
+code(false, "bash").
python -m spacy link en_example_model example
+code.
import spacy
nlp = spacy.load('example')

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@ -0,0 +1,174 @@
include ../../_includes/_mixins
p
| All #[+a("/docs/usage/models") spaCy models] support online learning, so
| you can update a pre-trained model with new examples. You can even add
| new classes to an existing model, to recognise a new entity type,
| part-of-speech, or syntactic relation. Updating an existing model is
| particularly useful as a "quick and dirty solution", if you have only a
| few corrections or annotations.
+h(2, "improving-accuracy") Improving accuracy on existing entity types
p
| To update the model, you first need to create an instance of
| #[+api("goldparse") #[code spacy.gold.GoldParse]], with the entity labels
| you want to learn. You will then pass this instance to the
| #[+api("entityrecognizer#update") #[code EntityRecognizer.update()]]
| method. For example:
+code.
import spacy
from spacy.gold import GoldParse
nlp = spacy.load('en')
doc = nlp.make_doc(u'Facebook released React in 2014')
gold = GoldParse(doc, entities=['U-ORG', 'O', 'U-TECHNOLOGY', 'O', 'U-DATE'])
nlp.entity.update(doc, gold)
p
| You'll usually need to provide many examples to meaningfully improve the
| system — a few hundred is a good start, although more is better. You
| should avoid iterating over the same few examples multiple times, or the
| model is likely to "forget" how to annotate other examples. If you
| iterate over the same few examples, you're effectively changing the loss
| function. The optimizer will find a way to minimize the loss on your
| examples, without regard for the consequences on the examples it's no
| longer paying attention to.
p
| One way to avoid this "catastrophic forgetting" problem is to "remind"
| the model of other examples by augmenting your annotations with sentences
| annotated with entities automatically recognised by the original model.
| Ultimately, this is an empirical process: you'll need to
| #[strong experiment on your own data] to find a solution that works best
| for you.
+h(2, "adding") Adding a new entity type
p
| You can add new entity types to an existing model. Let's say we want to
| recognise the category #[code TECHNOLOGY]. The new category will include
| programming languages, frameworks and platforms. First, we need to
| register the new entity type:
+code.
nlp.entity.add_label('TECHNOLOGY')
p
| Next, iterate over your examples, calling #[code entity.update()]. As
| above, we want to avoid iterating over only a small number of sentences.
| A useful compromise is to run the model over a number of plain-text
| sentences, and pass the entities to #[code GoldParse], as "true"
| annotations. This encourages the optimizer to find a solution that
| predicts the new category with minimal difference from the previous
| output.
+h(2, "saving-loading") Saving and loading
p
| After training our model, you'll usually want to save its state, and load
| it back later. You can do this with the #[code Language.save_to_directory()]
| method:
+code.
nlp.save_to_directory('/home/me/data/en_technology')
p
| To make the model more convenient to deploy, we recommend wrapping it as
| a Python package, so that you can install it via pip and load it as a
| module. spaCy comes with a handy #[+a("/docs/usage/cli#package") CLI command]
| to create all required files and directories.
+code(false, "bash").
python -m spacy package /home/me/data/en_technology /home/me/my_models
p
| To build the package and create a #[code .tar.gz] archive, run
| #[code python setup.py sdist] from within its directory.
+infobox("Saving and loading models")
| For more information and a detailed guide on how to package your model,
| see the documentation on
| #[+a("/docs/usage/saving-loading") saving and loading models].
p
| After you've generated and installed the package, you'll be able to
| load the model as follows:
+code.
import en_technology
nlp = en_technology.load()
+h(2, "example") Example: Adding and training an #[code ANIMAL] entity
p
| This script shows how to add a new entity type to an existing pre-trained
| NER model. To keep the example short and simple, only four sentences are
| provided as examples. In practice, you'll need many more —
| #[strong a few hundred] would be a good start. You will also likely need
| to mix in #[strong examples of other entity types], which might be
| obtained by running the entity recognizer over unlabelled sentences, and
| adding their annotations to the training set.
p
| For the full, runnable script of this example, see
| #[+src(gh("spacy", "examples/training/train_new_entity_type.py")) train_new_entity_type.py].
+code("Training the entity recognizer").
import spacy
from spacy.pipeline import EntityRecognizer
from spacy.gold import GoldParse
from spacy.tagger import Tagger
import random
model_name = 'en'
entity_label = 'ANIMAL'
output_directory = '/path/to/model'
train_data = [
("Horses are too tall and they pretend to care about your feelings",
[(0, 6, 'ANIMAL')]),
("horses are too tall and they pretend to care about your feelings",
[(0, 6, 'ANIMAL')]),
("horses pretend to care about your feelings",
[(0, 6, 'ANIMAL')]),
("they pretend to care about your feelings, those horses",
[(48, 54, 'ANIMAL')])
]
nlp = spacy.load(model_name)
nlp.entity.add_label(entity_label)
ner = train_ner(nlp, train_data, output_directory)
def train_ner(nlp, train_data, output_dir):
# Add new words to vocab
for raw_text, _ in train_data:
doc = nlp.make_doc(raw_text)
for word in doc:
_ = nlp.vocab[word.orth]
for itn in range(20):
random.shuffle(train_data)
for raw_text, entity_offsets in train_data:
gold = GoldParse(doc, entities=entity_offsets)
doc = nlp.make_doc(raw_text)
nlp.tagger(doc)
loss = nlp.entity.update(doc, gold)
nlp.end_training()
nlp.save_to_directory(output_dir)
p
+button(gh("spaCy", "examples/training/train_new_entity_type.py"), false, "secondary") Full example
p
| The actual training is performed by looping over the examples, and
| calling #[code nlp.entity.update()]. The #[code update()] method steps
| through the words of the input. At each word, it makes a prediction. It
| then consults the annotations provided on the #[code GoldParse] instance,
| to see whether it was right. If it was wrong, it adjusts its weights so
| that the correct action will score higher next time.
p
| After training your model, you can
| #[+a("/docs/usage/saving-loading") save it to a directory]. We recommend wrapping
| models as Python packages, for ease of deployment.

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@ -1,13 +1,10 @@
include ../../_includes/_mixins
p
| This tutorial describes how to train new statistical models for spaCy's
| This workflow describes how to train new statistical models for spaCy's
| part-of-speech tagger, named entity recognizer and dependency parser.
p
| I'll start with some quick code examples, that describe how to train
| each model. I'll then provide a bit of background about the algorithms,
| and explain how the data and feature templates work.
| Once the model is trained, you can then
| #[+a("/docs/usage/saving-loading") save and load] it.
+h(2, "train-pos-tagger") Training the part-of-speech tagger
@ -48,7 +45,21 @@ p
p
+button(gh("spaCy", "examples/training/train_ner.py"), false, "secondary") Full example
+h(2, "train-entity") Training the dependency parser
+h(2, "extend-entity") Extending the named entity recognizer
p
| All #[+a("/docs/usage/models") spaCy models] support online learning, so
| you can update a pre-trained model with new examples. You can even add
| new classes to an existing model, to recognise a new entity type,
| part-of-speech, or syntactic relation. Updating an existing model is
| particularly useful as a "quick and dirty solution", if you have only a
| few corrections or annotations.
p.o-inline-list
+button(gh("spaCy", "examples/training/train_new_entity_type.py"), true, "secondary") Full example
+button("/docs/usage/training-ner", false, "secondary") Usage Workflow
+h(2, "train-dependency") Training the dependency parser
+code.
from spacy.vocab import Vocab
@ -67,7 +78,7 @@ p
p
+button(gh("spaCy", "examples/training/train_parser.py"), false, "secondary") Full example
+h(2, 'feature-templates') Customizing the feature extraction
+h(2, "feature-templates") Customizing the feature extraction
p
| spaCy currently uses linear models for the tagger, parser and entity