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Get the tokens from the original Doc that are also in the comparison Doc.
def overlap_tokens(doc, other_doc):
"""Get the tokens from the original Doc that are also in the comparison Doc.
"""
overlap = []
other_tokens = [token.text for token in other_doc]
for token in doc:
if token.text in other_tokens:
overlap.append(token)
return overlap
|
Convert IOB files into JSON format for use with train cli.
def iob2json(input_data, n_sents=10, *args, **kwargs):
"""
Convert IOB files into JSON format for use with train cli.
"""
docs = []
for group in minibatch(docs, n_sents):
group = list(group)
first = group.pop(0)
to_extend = first["paragraphs"][0]["sentences"]
for sent in group[1:]:
to_extend.extend(sent["paragraphs"][0]["sentences"])
docs.append(first)
return docs
|
Render displaCy visualisation.
docs (list or Doc): Document(s) to visualise.
style (unicode): Visualisation style, 'dep' or 'ent'.
page (bool): Render markup as full HTML page.
minify (bool): Minify HTML markup.
jupyter (bool): Override Jupyter auto-detection.
options (dict): Visualiser-specific options, e.g. colors.
manual (bool): Don't parse `Doc` and instead expect a dict/list of dicts.
RETURNS (unicode): Rendered HTML markup.
DOCS: https://spacy.io/api/top-level#displacy.render
USAGE: https://spacy.io/usage/visualizers
def render(
docs, style="dep", page=False, minify=False, jupyter=None, options={}, manual=False
):
"""Render displaCy visualisation.
docs (list or Doc): Document(s) to visualise.
style (unicode): Visualisation style, 'dep' or 'ent'.
page (bool): Render markup as full HTML page.
minify (bool): Minify HTML markup.
jupyter (bool): Override Jupyter auto-detection.
options (dict): Visualiser-specific options, e.g. colors.
manual (bool): Don't parse `Doc` and instead expect a dict/list of dicts.
RETURNS (unicode): Rendered HTML markup.
DOCS: https://spacy.io/api/top-level#displacy.render
USAGE: https://spacy.io/usage/visualizers
"""
factories = {
"dep": (DependencyRenderer, parse_deps),
"ent": (EntityRenderer, parse_ents),
}
if style not in factories:
raise ValueError(Errors.E087.format(style=style))
if isinstance(docs, (Doc, Span, dict)):
docs = [docs]
docs = [obj if not isinstance(obj, Span) else obj.as_doc() for obj in docs]
if not all(isinstance(obj, (Doc, Span, dict)) for obj in docs):
raise ValueError(Errors.E096)
renderer, converter = factories[style]
renderer = renderer(options=options)
parsed = [converter(doc, options) for doc in docs] if not manual else docs
_html["parsed"] = renderer.render(parsed, page=page, minify=minify).strip()
html = _html["parsed"]
if RENDER_WRAPPER is not None:
html = RENDER_WRAPPER(html)
if jupyter or (jupyter is None and is_in_jupyter()):
# return HTML rendered by IPython display()
from IPython.core.display import display, HTML
return display(HTML(html))
return html
|
Serve displaCy visualisation.
docs (list or Doc): Document(s) to visualise.
style (unicode): Visualisation style, 'dep' or 'ent'.
page (bool): Render markup as full HTML page.
minify (bool): Minify HTML markup.
options (dict): Visualiser-specific options, e.g. colors.
manual (bool): Don't parse `Doc` and instead expect a dict/list of dicts.
port (int): Port to serve visualisation.
host (unicode): Host to serve visualisation.
DOCS: https://spacy.io/api/top-level#displacy.serve
USAGE: https://spacy.io/usage/visualizers
def serve(
docs,
style="dep",
page=True,
minify=False,
options={},
manual=False,
port=5000,
host="0.0.0.0",
):
"""Serve displaCy visualisation.
docs (list or Doc): Document(s) to visualise.
style (unicode): Visualisation style, 'dep' or 'ent'.
page (bool): Render markup as full HTML page.
minify (bool): Minify HTML markup.
options (dict): Visualiser-specific options, e.g. colors.
manual (bool): Don't parse `Doc` and instead expect a dict/list of dicts.
port (int): Port to serve visualisation.
host (unicode): Host to serve visualisation.
DOCS: https://spacy.io/api/top-level#displacy.serve
USAGE: https://spacy.io/usage/visualizers
"""
from wsgiref import simple_server
if is_in_jupyter():
user_warning(Warnings.W011)
render(docs, style=style, page=page, minify=minify, options=options, manual=manual)
httpd = simple_server.make_server(host, port, app)
print("\nUsing the '{}' visualizer".format(style))
print("Serving on http://{}:{} ...\n".format(host, port))
try:
httpd.serve_forever()
except KeyboardInterrupt:
print("Shutting down server on port {}.".format(port))
finally:
httpd.server_close()
|
Generate dependency parse in {'words': [], 'arcs': []} format.
doc (Doc): Document do parse.
RETURNS (dict): Generated dependency parse keyed by words and arcs.
def parse_deps(orig_doc, options={}):
"""Generate dependency parse in {'words': [], 'arcs': []} format.
doc (Doc): Document do parse.
RETURNS (dict): Generated dependency parse keyed by words and arcs.
"""
doc = Doc(orig_doc.vocab).from_bytes(orig_doc.to_bytes())
if not doc.is_parsed:
user_warning(Warnings.W005)
if options.get("collapse_phrases", False):
with doc.retokenize() as retokenizer:
for np in list(doc.noun_chunks):
attrs = {
"tag": np.root.tag_,
"lemma": np.root.lemma_,
"ent_type": np.root.ent_type_,
}
retokenizer.merge(np, attrs=attrs)
if options.get("collapse_punct", True):
spans = []
for word in doc[:-1]:
if word.is_punct or not word.nbor(1).is_punct:
continue
start = word.i
end = word.i + 1
while end < len(doc) and doc[end].is_punct:
end += 1
span = doc[start:end]
spans.append((span, word.tag_, word.lemma_, word.ent_type_))
with doc.retokenize() as retokenizer:
for span, tag, lemma, ent_type in spans:
attrs = {"tag": tag, "lemma": lemma, "ent_type": ent_type}
retokenizer.merge(span, attrs=attrs)
if options.get("fine_grained"):
words = [{"text": w.text, "tag": w.tag_} for w in doc]
else:
words = [{"text": w.text, "tag": w.pos_} for w in doc]
arcs = []
for word in doc:
if word.i < word.head.i:
arcs.append(
{"start": word.i, "end": word.head.i, "label": word.dep_, "dir": "left"}
)
elif word.i > word.head.i:
arcs.append(
{
"start": word.head.i,
"end": word.i,
"label": word.dep_,
"dir": "right",
}
)
return {"words": words, "arcs": arcs, "settings": get_doc_settings(orig_doc)}
|
Generate named entities in [{start: i, end: i, label: 'label'}] format.
doc (Doc): Document do parse.
RETURNS (dict): Generated entities keyed by text (original text) and ents.
def parse_ents(doc, options={}):
"""Generate named entities in [{start: i, end: i, label: 'label'}] format.
doc (Doc): Document do parse.
RETURNS (dict): Generated entities keyed by text (original text) and ents.
"""
ents = [
{"start": ent.start_char, "end": ent.end_char, "label": ent.label_}
for ent in doc.ents
]
if not ents:
user_warning(Warnings.W006)
title = doc.user_data.get("title", None) if hasattr(doc, "user_data") else None
settings = get_doc_settings(doc)
return {"text": doc.text, "ents": ents, "title": title, "settings": settings}
|
Set an optional wrapper function that is called around the generated
HTML markup on displacy.render. This can be used to allow integration into
other platforms, similar to Jupyter Notebooks that require functions to be
called around the HTML. It can also be used to implement custom callbacks
on render, or to embed the visualization in a custom page.
func (callable): Function to call around markup before rendering it. Needs
to take one argument, the HTML markup, and should return the desired
output of displacy.render.
def set_render_wrapper(func):
"""Set an optional wrapper function that is called around the generated
HTML markup on displacy.render. This can be used to allow integration into
other platforms, similar to Jupyter Notebooks that require functions to be
called around the HTML. It can also be used to implement custom callbacks
on render, or to embed the visualization in a custom page.
func (callable): Function to call around markup before rendering it. Needs
to take one argument, the HTML markup, and should return the desired
output of displacy.render.
"""
global RENDER_WRAPPER
if not hasattr(func, "__call__"):
raise ValueError(Errors.E110.format(obj=type(func)))
RENDER_WRAPPER = func
|
Evaluate a model. To render a sample of parses in a HTML file, set an
output directory as the displacy_path argument.
def evaluate(
model,
data_path,
gpu_id=-1,
gold_preproc=False,
displacy_path=None,
displacy_limit=25,
return_scores=False,
):
"""
Evaluate a model. To render a sample of parses in a HTML file, set an
output directory as the displacy_path argument.
"""
msg = Printer()
util.fix_random_seed()
if gpu_id >= 0:
util.use_gpu(gpu_id)
util.set_env_log(False)
data_path = util.ensure_path(data_path)
displacy_path = util.ensure_path(displacy_path)
if not data_path.exists():
msg.fail("Evaluation data not found", data_path, exits=1)
if displacy_path and not displacy_path.exists():
msg.fail("Visualization output directory not found", displacy_path, exits=1)
corpus = GoldCorpus(data_path, data_path)
nlp = util.load_model(model)
dev_docs = list(corpus.dev_docs(nlp, gold_preproc=gold_preproc))
begin = timer()
scorer = nlp.evaluate(dev_docs, verbose=False)
end = timer()
nwords = sum(len(doc_gold[0]) for doc_gold in dev_docs)
results = {
"Time": "%.2f s" % (end - begin),
"Words": nwords,
"Words/s": "%.0f" % (nwords / (end - begin)),
"TOK": "%.2f" % scorer.token_acc,
"POS": "%.2f" % scorer.tags_acc,
"UAS": "%.2f" % scorer.uas,
"LAS": "%.2f" % scorer.las,
"NER P": "%.2f" % scorer.ents_p,
"NER R": "%.2f" % scorer.ents_r,
"NER F": "%.2f" % scorer.ents_f,
}
msg.table(results, title="Results")
if displacy_path:
docs, golds = zip(*dev_docs)
render_deps = "parser" in nlp.meta.get("pipeline", [])
render_ents = "ner" in nlp.meta.get("pipeline", [])
render_parses(
docs,
displacy_path,
model_name=model,
limit=displacy_limit,
deps=render_deps,
ents=render_ents,
)
msg.good("Generated {} parses as HTML".format(displacy_limit), displacy_path)
if return_scores:
return scorer.scores
|
Create a symlink for models within the spacy/data directory. Accepts
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).
def link(origin, link_name, force=False, model_path=None):
"""
Create a symlink for models within the spacy/data directory. Accepts
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).
"""
msg = Printer()
if util.is_package(origin):
model_path = util.get_package_path(origin)
else:
model_path = Path(origin) if model_path is None else Path(model_path)
if not model_path.exists():
msg.fail(
"Can't locate model data",
"The data should be located in {}".format(path2str(model_path)),
exits=1,
)
data_path = util.get_data_path()
if not data_path or not data_path.exists():
spacy_loc = Path(__file__).parent.parent
msg.fail(
"Can't find the spaCy data path to create model symlink",
"Make sure a directory `/data` exists within your spaCy "
"installation and try again. The data directory should be located "
"here:".format(path=spacy_loc),
exits=1,
)
link_path = util.get_data_path() / link_name
if link_path.is_symlink() and not force:
msg.fail(
"Link '{}' already exists".format(link_name),
"To overwrite an existing link, use the --force flag",
exits=1,
)
elif link_path.is_symlink(): # does a symlink exist?
# NB: It's important to check for is_symlink here and not for exists,
# because invalid/outdated symlinks would return False otherwise.
link_path.unlink()
elif link_path.exists(): # does it exist otherwise?
# NB: Check this last because valid symlinks also "exist".
msg.fail(
"Can't overwrite symlink '{}'".format(link_name),
"This can happen if your data directory contains a directory or "
"file of the same name.",
exits=1,
)
details = "%s --> %s" % (path2str(model_path), path2str(link_path))
try:
symlink_to(link_path, model_path)
except: # noqa: E722
# This is quite dirty, but just making sure other errors are caught.
msg.fail(
"Couldn't link model to '{}'".format(link_name),
"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. You can still import the model as a module and "
"call its load() method, or create the symlink manually.",
)
msg.text(details)
raise
msg.good("Linking successful", details)
msg.text("You can now load the model via spacy.load('{}')".format(link_name))
|
Validate that the currently installed version of spaCy is compatible
with the installed models. Should be run after `pip install -U spacy`.
def validate():
"""
Validate that the currently installed version of spaCy is compatible
with the installed models. Should be run after `pip install -U spacy`.
"""
msg = Printer()
with msg.loading("Loading compatibility table..."):
r = requests.get(about.__compatibility__)
if r.status_code != 200:
msg.fail(
"Server error ({})".format(r.status_code),
"Couldn't fetch compatibility table.",
exits=1,
)
msg.good("Loaded compatibility table")
compat = r.json()["spacy"]
version = about.__version__
version = version.rsplit(".dev", 1)[0]
current_compat = compat.get(version)
if not current_compat:
msg.fail(
"Can't find spaCy v{} in compatibility table".format(version),
about.__compatibility__,
exits=1,
)
all_models = set()
for spacy_v, models in dict(compat).items():
all_models.update(models.keys())
for model, model_vs in models.items():
compat[spacy_v][model] = [reformat_version(v) for v in model_vs]
model_links = get_model_links(current_compat)
model_pkgs = get_model_pkgs(current_compat, all_models)
incompat_links = {l for l, d in model_links.items() if not d["compat"]}
incompat_models = {d["name"] for _, d in model_pkgs.items() if not d["compat"]}
incompat_models.update(
[d["name"] for _, d in model_links.items() if not d["compat"]]
)
na_models = [m for m in incompat_models if m not in current_compat]
update_models = [m for m in incompat_models if m in current_compat]
spacy_dir = Path(__file__).parent.parent
msg.divider("Installed models (spaCy v{})".format(about.__version__))
msg.info("spaCy installation: {}".format(path2str(spacy_dir)))
if model_links or model_pkgs:
header = ("TYPE", "NAME", "MODEL", "VERSION", "")
rows = []
for name, data in model_pkgs.items():
rows.append(get_model_row(current_compat, name, data, msg))
for name, data in model_links.items():
rows.append(get_model_row(current_compat, name, data, msg, "link"))
msg.table(rows, header=header)
else:
msg.text("No models found in your current environment.", exits=0)
if update_models:
msg.divider("Install updates")
msg.text("Use the following commands to update the model packages:")
cmd = "python -m spacy download {}"
print("\n".join([cmd.format(pkg) for pkg in update_models]) + "\n")
if na_models:
msg.text(
"The following models are not available for spaCy "
"v{}: {}".format(about.__version__, ", ".join(na_models))
)
if incompat_links:
msg.text(
"You may also want to overwrite the incompatible links using the "
"`python -m spacy link` command with `--force`, or remove them "
"from the data directory. "
"Data path: {path}".format(path=path2str(get_data_path()))
)
if incompat_models or incompat_links:
sys.exit(1)
|
Profile a spaCy pipeline, to find out which functions take the most time.
Input should be formatted as one JSON object per line with a key "text".
It can either be provided as a JSONL file, or be read from sys.sytdin.
If no input file is specified, the IMDB dataset is loaded via Thinc.
def profile(model, inputs=None, n_texts=10000):
"""
Profile a spaCy pipeline, to find out which functions take the most time.
Input should be formatted as one JSON object per line with a key "text".
It can either be provided as a JSONL file, or be read from sys.sytdin.
If no input file is specified, the IMDB dataset is loaded via Thinc.
"""
msg = Printer()
if inputs is not None:
inputs = _read_inputs(inputs, msg)
if inputs is None:
n_inputs = 25000
with msg.loading("Loading IMDB dataset via Thinc..."):
imdb_train, _ = thinc.extra.datasets.imdb()
inputs, _ = zip(*imdb_train)
msg.info("Loaded IMDB dataset and using {} examples".format(n_inputs))
inputs = inputs[:n_inputs]
with msg.loading("Loading model '{}'...".format(model)):
nlp = load_model(model)
msg.good("Loaded model '{}'".format(model))
texts = list(itertools.islice(inputs, n_texts))
cProfile.runctx("parse_texts(nlp, texts)", globals(), locals(), "Profile.prof")
s = pstats.Stats("Profile.prof")
msg.divider("Profile stats")
s.strip_dirs().sort_stats("time").print_stats()
|
If necessary, add a field to the POS tag for UD mapping.
Under Universal Dependencies, sometimes the same Unidic POS tag can
be mapped differently depending on the literal token or its context
in the sentence. This function adds information to the POS tag to
resolve ambiguous mappings.
def resolve_pos(token):
"""If necessary, add a field to the POS tag for UD mapping.
Under Universal Dependencies, sometimes the same Unidic POS tag can
be mapped differently depending on the literal token or its context
in the sentence. This function adds information to the POS tag to
resolve ambiguous mappings.
"""
# TODO: This is a first take. The rules here are crude approximations.
# For many of these, full dependencies are needed to properly resolve
# PoS mappings.
if token.pos == "連体詞,*,*,*":
if re.match(r"[こそあど此其彼]の", token.surface):
return token.pos + ",DET"
if re.match(r"[こそあど此其彼]", token.surface):
return token.pos + ",PRON"
return token.pos + ",ADJ"
return token.pos
|
Format Mecab output into a nice data structure, based on Janome.
def detailed_tokens(tokenizer, text):
"""Format Mecab output into a nice data structure, based on Janome."""
node = tokenizer.parseToNode(text)
node = node.next # first node is beginning of sentence and empty, skip it
words = []
while node.posid != 0:
surface = node.surface
base = surface # a default value. Updated if available later.
parts = node.feature.split(",")
pos = ",".join(parts[0:4])
if len(parts) > 7:
# this information is only available for words in the tokenizer
# dictionary
base = parts[7]
words.append(ShortUnitWord(surface, base, pos))
node = node.next
return words
|
Create a symlink. Used for model shortcut links.
orig (unicode / Path): The origin path.
dest (unicode / Path): The destination path of the symlink.
def symlink_to(orig, dest):
"""Create a symlink. Used for model shortcut links.
orig (unicode / Path): The origin path.
dest (unicode / Path): The destination path of the symlink.
"""
if is_windows:
import subprocess
subprocess.check_call(
["mklink", "/d", path2str(orig), path2str(dest)], shell=True
)
else:
orig.symlink_to(dest)
|
Remove a symlink. Used for model shortcut links.
link (unicode / Path): The path to the symlink.
def symlink_remove(link):
"""Remove a symlink. Used for model shortcut links.
link (unicode / Path): The path to the symlink.
"""
# https://stackoverflow.com/q/26554135/6400719
if os.path.isdir(path2str(link)) and is_windows:
# this should only be on Py2.7 and windows
os.rmdir(path2str(link))
else:
os.unlink(path2str(link))
|
Check if a specific configuration of Python version and operating system
matches the user's setup. Mostly used to display targeted error messages.
python2 (bool): spaCy is executed with Python 2.x.
python3 (bool): spaCy is executed with Python 3.x.
windows (bool): spaCy is executed on Windows.
linux (bool): spaCy is executed on Linux.
osx (bool): spaCy is executed on OS X or macOS.
RETURNS (bool): Whether the configuration matches the user's platform.
DOCS: https://spacy.io/api/top-level#compat.is_config
def is_config(python2=None, python3=None, windows=None, linux=None, osx=None):
"""Check if a specific configuration of Python version and operating system
matches the user's setup. Mostly used to display targeted error messages.
python2 (bool): spaCy is executed with Python 2.x.
python3 (bool): spaCy is executed with Python 3.x.
windows (bool): spaCy is executed on Windows.
linux (bool): spaCy is executed on Linux.
osx (bool): spaCy is executed on OS X or macOS.
RETURNS (bool): Whether the configuration matches the user's platform.
DOCS: https://spacy.io/api/top-level#compat.is_config
"""
return (
python2 in (None, is_python2)
and python3 in (None, is_python3)
and windows in (None, is_windows)
and linux in (None, is_linux)
and osx in (None, is_osx)
)
|
Import module from a file. Used to load models from a directory.
name (unicode): Name of module to load.
loc (unicode / Path): Path to the file.
RETURNS: The loaded module.
def import_file(name, loc):
"""Import module from a file. Used to load models from a directory.
name (unicode): Name of module to load.
loc (unicode / Path): Path to the file.
RETURNS: The loaded module.
"""
loc = path2str(loc)
if is_python_pre_3_5:
import imp
return imp.load_source(name, loc)
else:
import importlib.util
spec = importlib.util.spec_from_file_location(name, str(loc))
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
return module
|
Python2.7's re module chokes when compiling patterns that have ranges
between escaped unicode codepoints if the two codepoints are unrecognised
in the unicode database. For instance:
re.compile('[\\uAA77-\\uAA79]').findall("hello")
Ends up matching every character (on Python 2). This problem doesn't occur
if we're dealing with unicode literals.
def unescape_unicode(string):
"""Python2.7's re module chokes when compiling patterns that have ranges
between escaped unicode codepoints if the two codepoints are unrecognised
in the unicode database. For instance:
re.compile('[\\uAA77-\\uAA79]').findall("hello")
Ends up matching every character (on Python 2). This problem doesn't occur
if we're dealing with unicode literals.
"""
if string is None:
return string
# We only want to unescape the unicode, so we first must protect the other
# backslashes.
string = string.replace("\\", "\\\\")
# Now we remove that protection for the unicode.
string = string.replace("\\\\u", "\\u")
string = string.replace("\\\\U", "\\U")
# Now we unescape by evaling the string with the AST. This can't execute
# code -- it only does the representational level.
return ast.literal_eval("u'''" + string + "'''")
|
Import and load a Language class.
lang (unicode): Two-letter language code, e.g. 'en'.
RETURNS (Language): Language class.
def get_lang_class(lang):
"""Import and load a Language class.
lang (unicode): Two-letter language code, e.g. 'en'.
RETURNS (Language): Language class.
"""
global LANGUAGES
# Check if an entry point is exposed for the language code
entry_point = get_entry_point("spacy_languages", lang)
if entry_point is not None:
LANGUAGES[lang] = entry_point
return entry_point
if lang not in LANGUAGES:
try:
module = importlib.import_module(".lang.%s" % lang, "spacy")
except ImportError as err:
raise ImportError(Errors.E048.format(lang=lang, err=err))
LANGUAGES[lang] = getattr(module, module.__all__[0])
return LANGUAGES[lang]
|
Load a model from a shortcut link, package or data path.
name (unicode): Package name, shortcut link or model path.
**overrides: Specific overrides, like pipeline components to disable.
RETURNS (Language): `Language` class with the loaded model.
def load_model(name, **overrides):
"""Load a model from a shortcut link, package or data path.
name (unicode): Package name, shortcut link or model path.
**overrides: Specific overrides, like pipeline components to disable.
RETURNS (Language): `Language` class with the loaded model.
"""
data_path = get_data_path()
if not data_path or not data_path.exists():
raise IOError(Errors.E049.format(path=path2str(data_path)))
if isinstance(name, basestring_): # in data dir / shortcut
if name in set([d.name for d in data_path.iterdir()]):
return load_model_from_link(name, **overrides)
if is_package(name): # installed as package
return load_model_from_package(name, **overrides)
if Path(name).exists(): # path to model data directory
return load_model_from_path(Path(name), **overrides)
elif hasattr(name, "exists"): # Path or Path-like to model data
return load_model_from_path(name, **overrides)
raise IOError(Errors.E050.format(name=name))
|
Load a model from a shortcut link, or directory in spaCy data path.
def load_model_from_link(name, **overrides):
"""Load a model from a shortcut link, or directory in spaCy data path."""
path = get_data_path() / name / "__init__.py"
try:
cls = import_file(name, path)
except AttributeError:
raise IOError(Errors.E051.format(name=name))
return cls.load(**overrides)
|
Load a model from an installed package.
def load_model_from_package(name, **overrides):
"""Load a model from an installed package."""
cls = importlib.import_module(name)
return cls.load(**overrides)
|
Load a model from a data directory path. Creates Language class with
pipeline from meta.json and then calls from_disk() with path.
def load_model_from_path(model_path, meta=False, **overrides):
"""Load a model from a data directory path. Creates Language class with
pipeline from meta.json and then calls from_disk() with path."""
if not meta:
meta = get_model_meta(model_path)
cls = get_lang_class(meta["lang"])
nlp = cls(meta=meta, **overrides)
pipeline = meta.get("pipeline", [])
disable = overrides.get("disable", [])
if pipeline is True:
pipeline = nlp.Defaults.pipe_names
elif pipeline in (False, None):
pipeline = []
for name in pipeline:
if name not in disable:
config = meta.get("pipeline_args", {}).get(name, {})
component = nlp.create_pipe(name, config=config)
nlp.add_pipe(component, name=name)
return nlp.from_disk(model_path)
|
Helper function to use in the `load()` method of a model package's
__init__.py.
init_file (unicode): Path to model's __init__.py, i.e. `__file__`.
**overrides: Specific overrides, like pipeline components to disable.
RETURNS (Language): `Language` class with loaded model.
def load_model_from_init_py(init_file, **overrides):
"""Helper function to use in the `load()` method of a model package's
__init__.py.
init_file (unicode): Path to model's __init__.py, i.e. `__file__`.
**overrides: Specific overrides, like pipeline components to disable.
RETURNS (Language): `Language` class with loaded model.
"""
model_path = Path(init_file).parent
meta = get_model_meta(model_path)
data_dir = "%s_%s-%s" % (meta["lang"], meta["name"], meta["version"])
data_path = model_path / data_dir
if not model_path.exists():
raise IOError(Errors.E052.format(path=path2str(data_path)))
return load_model_from_path(data_path, meta, **overrides)
|
Get model meta.json from a directory path and validate its contents.
path (unicode or Path): Path to model directory.
RETURNS (dict): The model's meta data.
def get_model_meta(path):
"""Get model meta.json from a directory path and validate its contents.
path (unicode or Path): Path to model directory.
RETURNS (dict): The model's meta data.
"""
model_path = ensure_path(path)
if not model_path.exists():
raise IOError(Errors.E052.format(path=path2str(model_path)))
meta_path = model_path / "meta.json"
if not meta_path.is_file():
raise IOError(Errors.E053.format(path=meta_path))
meta = srsly.read_json(meta_path)
for setting in ["lang", "name", "version"]:
if setting not in meta or not meta[setting]:
raise ValueError(Errors.E054.format(setting=setting))
return meta
|
Get the path to an installed package.
name (unicode): Package name.
RETURNS (Path): Path to installed package.
def get_package_path(name):
"""Get the path to an installed package.
name (unicode): Package name.
RETURNS (Path): Path to installed package.
"""
name = name.lower() # use lowercase version to be safe
# Here we're importing the module just to find it. This is worryingly
# indirect, but it's otherwise very difficult to find the package.
pkg = importlib.import_module(name)
return Path(pkg.__file__).parent
|
Get registered entry points from other packages for a given key, e.g.
'spacy_factories' and return them as a dictionary, keyed by name.
key (unicode): Entry point name.
RETURNS (dict): Entry points, keyed by name.
def get_entry_points(key):
"""Get registered entry points from other packages for a given key, e.g.
'spacy_factories' and return them as a dictionary, keyed by name.
key (unicode): Entry point name.
RETURNS (dict): Entry points, keyed by name.
"""
result = {}
for entry_point in pkg_resources.iter_entry_points(key):
result[entry_point.name] = entry_point.load()
return result
|
Check if registered entry point is available for a given name and
load it. Otherwise, return None.
key (unicode): Entry point name.
value (unicode): Name of entry point to load.
RETURNS: The loaded entry point or None.
def get_entry_point(key, value):
"""Check if registered entry point is available for a given name and
load it. Otherwise, return None.
key (unicode): Entry point name.
value (unicode): Name of entry point to load.
RETURNS: The loaded entry point or None.
"""
for entry_point in pkg_resources.iter_entry_points(key):
if entry_point.name == value:
return entry_point.load()
|
Check if user is running spaCy from a Jupyter notebook by detecting the
IPython kernel. Mainly used for the displaCy visualizer.
RETURNS (bool): True if in Jupyter, False if not.
def is_in_jupyter():
"""Check if user is running spaCy from a Jupyter notebook by detecting the
IPython kernel. Mainly used for the displaCy visualizer.
RETURNS (bool): True if in Jupyter, False if not.
"""
# https://stackoverflow.com/a/39662359/6400719
try:
shell = get_ipython().__class__.__name__
if shell == "ZMQInteractiveShell":
return True # Jupyter notebook or qtconsole
except NameError:
return False # Probably standard Python interpreter
return False
|
Compile a sequence of suffix rules into a regex object.
entries (tuple): The suffix rules, e.g. spacy.lang.punctuation.TOKENIZER_SUFFIXES.
RETURNS (regex object): The regex object. to be used for Tokenizer.suffix_search.
def compile_suffix_regex(entries):
"""Compile a sequence of suffix rules into a regex object.
entries (tuple): The suffix rules, e.g. spacy.lang.punctuation.TOKENIZER_SUFFIXES.
RETURNS (regex object): The regex object. to be used for Tokenizer.suffix_search.
"""
expression = "|".join([piece + "$" for piece in entries if piece.strip()])
return re.compile(expression)
|
Compile a sequence of infix rules into a regex object.
entries (tuple): The infix rules, e.g. spacy.lang.punctuation.TOKENIZER_INFIXES.
RETURNS (regex object): The regex object. to be used for Tokenizer.infix_finditer.
def compile_infix_regex(entries):
"""Compile a sequence of infix rules into a regex object.
entries (tuple): The infix rules, e.g. spacy.lang.punctuation.TOKENIZER_INFIXES.
RETURNS (regex object): The regex object. to be used for Tokenizer.infix_finditer.
"""
expression = "|".join([piece for piece in entries if piece.strip()])
return re.compile(expression)
|
Update and validate tokenizer exceptions. Will overwrite exceptions.
base_exceptions (dict): Base exceptions.
*addition_dicts (dict): Exceptions to add to the base dict, in order.
RETURNS (dict): Combined tokenizer exceptions.
def update_exc(base_exceptions, *addition_dicts):
"""Update and validate tokenizer exceptions. Will overwrite exceptions.
base_exceptions (dict): Base exceptions.
*addition_dicts (dict): Exceptions to add to the base dict, in order.
RETURNS (dict): Combined tokenizer exceptions.
"""
exc = dict(base_exceptions)
for additions in addition_dicts:
for orth, token_attrs in additions.items():
if not all(isinstance(attr[ORTH], unicode_) for attr in token_attrs):
raise ValueError(Errors.E055.format(key=orth, orths=token_attrs))
described_orth = "".join(attr[ORTH] for attr in token_attrs)
if orth != described_orth:
raise ValueError(Errors.E056.format(key=orth, orths=described_orth))
exc.update(additions)
exc = expand_exc(exc, "'", "’")
return exc
|
Find string in tokenizer exceptions, duplicate entry and replace string.
For example, to add additional versions with typographic apostrophes.
excs (dict): Tokenizer exceptions.
search (unicode): String to find and replace.
replace (unicode): Replacement.
RETURNS (dict): Combined tokenizer exceptions.
def expand_exc(excs, search, replace):
"""Find string in tokenizer exceptions, duplicate entry and replace string.
For example, to add additional versions with typographic apostrophes.
excs (dict): Tokenizer exceptions.
search (unicode): String to find and replace.
replace (unicode): Replacement.
RETURNS (dict): Combined tokenizer exceptions.
"""
def _fix_token(token, search, replace):
fixed = dict(token)
fixed[ORTH] = fixed[ORTH].replace(search, replace)
return fixed
new_excs = dict(excs)
for token_string, tokens in excs.items():
if search in token_string:
new_key = token_string.replace(search, replace)
new_value = [_fix_token(t, search, replace) for t in tokens]
new_excs[new_key] = new_value
return new_excs
|
Iterate over batches of items. `size` may be an iterator,
so that batch-size can vary on each step.
def minibatch(items, size=8):
"""Iterate over batches of items. `size` may be an iterator,
so that batch-size can vary on each step.
"""
if isinstance(size, int):
size_ = itertools.repeat(size)
else:
size_ = size
items = iter(items)
while True:
batch_size = next(size_)
batch = list(itertools.islice(items, int(batch_size)))
if len(batch) == 0:
break
yield list(batch)
|
Yield an infinite series of compounding values. Each time the
generator is called, a value is produced by multiplying the previous
value by the compound rate.
EXAMPLE:
>>> sizes = compounding(1., 10., 1.5)
>>> assert next(sizes) == 1.
>>> assert next(sizes) == 1 * 1.5
>>> assert next(sizes) == 1.5 * 1.5
def compounding(start, stop, compound):
"""Yield an infinite series of compounding values. Each time the
generator is called, a value is produced by multiplying the previous
value by the compound rate.
EXAMPLE:
>>> sizes = compounding(1., 10., 1.5)
>>> assert next(sizes) == 1.
>>> assert next(sizes) == 1 * 1.5
>>> assert next(sizes) == 1.5 * 1.5
"""
def clip(value):
return max(value, stop) if (start > stop) else min(value, stop)
curr = float(start)
while True:
yield clip(curr)
curr *= compound
|
Yield an infinite series of values that step from a start value to a
final value over some number of steps. Each step is (stop-start)/steps.
After the final value is reached, the generator continues yielding that
value.
EXAMPLE:
>>> sizes = stepping(1., 200., 100)
>>> assert next(sizes) == 1.
>>> assert next(sizes) == 1 * (200.-1.) / 100
>>> assert next(sizes) == 1 + (200.-1.) / 100 + (200.-1.) / 100
def stepping(start, stop, steps):
"""Yield an infinite series of values that step from a start value to a
final value over some number of steps. Each step is (stop-start)/steps.
After the final value is reached, the generator continues yielding that
value.
EXAMPLE:
>>> sizes = stepping(1., 200., 100)
>>> assert next(sizes) == 1.
>>> assert next(sizes) == 1 * (200.-1.) / 100
>>> assert next(sizes) == 1 + (200.-1.) / 100 + (200.-1.) / 100
"""
def clip(value):
return max(value, stop) if (start > stop) else min(value, stop)
curr = float(start)
while True:
yield clip(curr)
curr += (stop - start) / steps
|
Yield an infinite series of linearly decaying values.
def decaying(start, stop, decay):
"""Yield an infinite series of linearly decaying values."""
curr = float(start)
while True:
yield max(curr, stop)
curr -= (decay)
|
Create minibatches of a given number of words.
def minibatch_by_words(items, size, tuples=True, count_words=len):
"""Create minibatches of a given number of words."""
if isinstance(size, int):
size_ = itertools.repeat(size)
else:
size_ = size
items = iter(items)
while True:
batch_size = next(size_)
batch = []
while batch_size >= 0:
try:
if tuples:
doc, gold = next(items)
else:
doc = next(items)
except StopIteration:
if batch:
yield batch
return
batch_size -= count_words(doc)
if tuples:
batch.append((doc, gold))
else:
batch.append(doc)
if batch:
yield batch
|
Shuffle an iterator. This works by holding `bufsize` items back
and yielding them sometime later. Obviously, this is not unbiased –
but should be good enough for batching. Larger bufsize means less bias.
From https://gist.github.com/andres-erbsen/1307752
iterable (iterable): Iterator to shuffle.
bufsize (int): Items to hold back.
YIELDS (iterable): The shuffled iterator.
def itershuffle(iterable, bufsize=1000):
"""Shuffle an iterator. This works by holding `bufsize` items back
and yielding them sometime later. Obviously, this is not unbiased –
but should be good enough for batching. Larger bufsize means less bias.
From https://gist.github.com/andres-erbsen/1307752
iterable (iterable): Iterator to shuffle.
bufsize (int): Items to hold back.
YIELDS (iterable): The shuffled iterator.
"""
iterable = iter(iterable)
buf = []
try:
while True:
for i in range(random.randint(1, bufsize - len(buf))):
buf.append(next(iterable))
random.shuffle(buf)
for i in range(random.randint(1, bufsize)):
if buf:
yield buf.pop()
else:
break
except StopIteration:
random.shuffle(buf)
while buf:
yield buf.pop()
raise StopIteration
|
Validate data against a given JSON schema (see https://json-schema.org).
data: JSON-serializable data to validate.
validator (jsonschema.DraftXValidator): The validator.
RETURNS (list): A list of error messages, if available.
def validate_json(data, validator):
"""Validate data against a given JSON schema (see https://json-schema.org).
data: JSON-serializable data to validate.
validator (jsonschema.DraftXValidator): The validator.
RETURNS (list): A list of error messages, if available.
"""
errors = []
for err in sorted(validator.iter_errors(data), key=lambda e: e.path):
if err.path:
err_path = "[{}]".format(" -> ".join([str(p) for p in err.path]))
else:
err_path = ""
msg = err.message + " " + err_path
if err.context: # Error has suberrors, e.g. if schema uses anyOf
suberrs = [" - {}".format(suberr.message) for suberr in err.context]
msg += ":\n{}".format("".join(suberrs))
errors.append(msg)
return errors
|
Helper function to validate serialization args and manage transition from
keyword arguments (pre v2.1) to exclude argument.
def get_serialization_exclude(serializers, exclude, kwargs):
"""Helper function to validate serialization args and manage transition from
keyword arguments (pre v2.1) to exclude argument.
"""
exclude = list(exclude)
# Split to support file names like meta.json
options = [name.split(".")[0] for name in serializers]
for key, value in kwargs.items():
if key in ("vocab",) and value is False:
deprecation_warning(Warnings.W015.format(arg=key))
exclude.append(key)
elif key.split(".")[0] in options:
raise ValueError(Errors.E128.format(arg=key))
# TODO: user warning?
return exclude
|
All labels present in the match patterns.
RETURNS (set): The string labels.
DOCS: https://spacy.io/api/entityruler#labels
def labels(self):
"""All labels present in the match patterns.
RETURNS (set): The string labels.
DOCS: https://spacy.io/api/entityruler#labels
"""
all_labels = set(self.token_patterns.keys())
all_labels.update(self.phrase_patterns.keys())
return tuple(all_labels)
|
Get all patterns that were added to the entity ruler.
RETURNS (list): The original patterns, one dictionary per pattern.
DOCS: https://spacy.io/api/entityruler#patterns
def patterns(self):
"""Get all patterns that were added to the entity ruler.
RETURNS (list): The original patterns, one dictionary per pattern.
DOCS: https://spacy.io/api/entityruler#patterns
"""
all_patterns = []
for label, patterns in self.token_patterns.items():
for pattern in patterns:
all_patterns.append({"label": label, "pattern": pattern})
for label, patterns in self.phrase_patterns.items():
for pattern in patterns:
all_patterns.append({"label": label, "pattern": pattern.text})
return all_patterns
|
Add patterns to the entitiy ruler. A pattern can either be a token
pattern (list of dicts) or a phrase pattern (string). For example:
{'label': 'ORG', 'pattern': 'Apple'}
{'label': 'GPE', 'pattern': [{'lower': 'san'}, {'lower': 'francisco'}]}
patterns (list): The patterns to add.
DOCS: https://spacy.io/api/entityruler#add_patterns
def add_patterns(self, patterns):
"""Add patterns to the entitiy ruler. A pattern can either be a token
pattern (list of dicts) or a phrase pattern (string). For example:
{'label': 'ORG', 'pattern': 'Apple'}
{'label': 'GPE', 'pattern': [{'lower': 'san'}, {'lower': 'francisco'}]}
patterns (list): The patterns to add.
DOCS: https://spacy.io/api/entityruler#add_patterns
"""
for entry in patterns:
label = entry["label"]
pattern = entry["pattern"]
if isinstance(pattern, basestring_):
self.phrase_patterns[label].append(self.nlp(pattern))
elif isinstance(pattern, list):
self.token_patterns[label].append(pattern)
else:
raise ValueError(Errors.E097.format(pattern=pattern))
for label, patterns in self.token_patterns.items():
self.matcher.add(label, None, *patterns)
for label, patterns in self.phrase_patterns.items():
self.phrase_matcher.add(label, None, *patterns)
|
Load the entity ruler from a bytestring.
patterns_bytes (bytes): The bytestring to load.
**kwargs: Other config paramters, mostly for consistency.
RETURNS (EntityRuler): The loaded entity ruler.
DOCS: https://spacy.io/api/entityruler#from_bytes
def from_bytes(self, patterns_bytes, **kwargs):
"""Load the entity ruler from a bytestring.
patterns_bytes (bytes): The bytestring to load.
**kwargs: Other config paramters, mostly for consistency.
RETURNS (EntityRuler): The loaded entity ruler.
DOCS: https://spacy.io/api/entityruler#from_bytes
"""
patterns = srsly.msgpack_loads(patterns_bytes)
self.add_patterns(patterns)
return self
|
Load the entity ruler from a file. Expects a file containing
newline-delimited JSON (JSONL) with one entry per line.
path (unicode / Path): The JSONL file to load.
**kwargs: Other config paramters, mostly for consistency.
RETURNS (EntityRuler): The loaded entity ruler.
DOCS: https://spacy.io/api/entityruler#from_disk
def from_disk(self, path, **kwargs):
"""Load the entity ruler from a file. Expects a file containing
newline-delimited JSON (JSONL) with one entry per line.
path (unicode / Path): The JSONL file to load.
**kwargs: Other config paramters, mostly for consistency.
RETURNS (EntityRuler): The loaded entity ruler.
DOCS: https://spacy.io/api/entityruler#from_disk
"""
path = ensure_path(path)
path = path.with_suffix(".jsonl")
patterns = srsly.read_jsonl(path)
self.add_patterns(patterns)
return self
|
Save the entity ruler patterns to a directory. The patterns will be
saved as newline-delimited JSON (JSONL).
path (unicode / Path): The JSONL file to load.
**kwargs: Other config paramters, mostly for consistency.
RETURNS (EntityRuler): The loaded entity ruler.
DOCS: https://spacy.io/api/entityruler#to_disk
def to_disk(self, path, **kwargs):
"""Save the entity ruler patterns to a directory. The patterns will be
saved as newline-delimited JSON (JSONL).
path (unicode / Path): The JSONL file to load.
**kwargs: Other config paramters, mostly for consistency.
RETURNS (EntityRuler): The loaded entity ruler.
DOCS: https://spacy.io/api/entityruler#to_disk
"""
path = ensure_path(path)
path = path.with_suffix(".jsonl")
srsly.write_jsonl(path, self.patterns)
|
Read the CONLLU format into (Doc, GoldParse) tuples. If raw_text=True,
include Doc objects created using nlp.make_doc and then aligned against
the gold-standard sequences. If oracle_segments=True, include Doc objects
created from the gold-standard segments. At least one must be True.
def read_data(
nlp,
conllu_file,
text_file,
raw_text=True,
oracle_segments=False,
max_doc_length=None,
limit=None,
):
"""Read the CONLLU format into (Doc, GoldParse) tuples. If raw_text=True,
include Doc objects created using nlp.make_doc and then aligned against
the gold-standard sequences. If oracle_segments=True, include Doc objects
created from the gold-standard segments. At least one must be True."""
if not raw_text and not oracle_segments:
raise ValueError("At least one of raw_text or oracle_segments must be True")
paragraphs = split_text(text_file.read())
conllu = read_conllu(conllu_file)
# sd is spacy doc; cd is conllu doc
# cs is conllu sent, ct is conllu token
docs = []
golds = []
for doc_id, (text, cd) in enumerate(zip(paragraphs, conllu)):
sent_annots = []
for cs in cd:
sent = defaultdict(list)
for id_, word, lemma, pos, tag, morph, head, dep, _, space_after in cs:
if "." in id_:
continue
if "-" in id_:
continue
id_ = int(id_) - 1
head = int(head) - 1 if head != "0" else id_
sent["words"].append(word)
sent["tags"].append(tag)
sent["heads"].append(head)
sent["deps"].append("ROOT" if dep == "root" else dep)
sent["spaces"].append(space_after == "_")
sent["entities"] = ["-"] * len(sent["words"])
sent["heads"], sent["deps"] = projectivize(sent["heads"], sent["deps"])
if oracle_segments:
docs.append(Doc(nlp.vocab, words=sent["words"], spaces=sent["spaces"]))
golds.append(GoldParse(docs[-1], **sent))
sent_annots.append(sent)
if raw_text and max_doc_length and len(sent_annots) >= max_doc_length:
doc, gold = _make_gold(nlp, None, sent_annots)
sent_annots = []
docs.append(doc)
golds.append(gold)
if limit and len(docs) >= limit:
return docs, golds
if raw_text and sent_annots:
doc, gold = _make_gold(nlp, None, sent_annots)
docs.append(doc)
golds.append(gold)
if limit and len(docs) >= limit:
return docs, golds
return docs, golds
|
Get out the annoying 'tuples' format used by begin_training, given the
GoldParse objects.
def golds_to_gold_tuples(docs, golds):
"""Get out the annoying 'tuples' format used by begin_training, given the
GoldParse objects."""
tuples = []
for doc, gold in zip(docs, golds):
text = doc.text
ids, words, tags, heads, labels, iob = zip(*gold.orig_annot)
sents = [((ids, words, tags, heads, labels, iob), [])]
tuples.append((text, sents))
return tuples
|
check if text resembles a number
def like_num(text):
"""
check if text resembles a number
"""
text = (
text.replace(",", "")
.replace(".", "")
.replace("،", "")
.replace("٫", "")
.replace("/", "")
)
if text.isdigit():
return True
if text in _num_words:
return True
if text in _ordinal_words:
return True
return False
|
Concatenate multiple serialized binders into one byte string.
def merge_bytes(binder_strings):
"""Concatenate multiple serialized binders into one byte string."""
output = None
for byte_string in binder_strings:
binder = Binder().from_bytes(byte_string)
if output is None:
output = binder
else:
output.merge(binder)
return output.to_bytes()
|
Add a doc's annotations to the binder for serialization.
def add(self, doc):
"""Add a doc's annotations to the binder for serialization."""
array = doc.to_array(self.attrs)
if len(array.shape) == 1:
array = array.reshape((array.shape[0], 1))
self.tokens.append(array)
spaces = doc.to_array(SPACY)
assert array.shape[0] == spaces.shape[0]
spaces = spaces.reshape((spaces.shape[0], 1))
self.spaces.append(numpy.asarray(spaces, dtype=bool))
self.strings.update(w.text for w in doc)
|
Recover Doc objects from the annotations, using the given vocab.
def get_docs(self, vocab):
"""Recover Doc objects from the annotations, using the given vocab."""
for string in self.strings:
vocab[string]
orth_col = self.attrs.index(ORTH)
for tokens, spaces in zip(self.tokens, self.spaces):
words = [vocab.strings[orth] for orth in tokens[:, orth_col]]
doc = Doc(vocab, words=words, spaces=spaces)
doc = doc.from_array(self.attrs, tokens)
yield doc
|
Extend the annotations of this binder with the annotations from another.
def merge(self, other):
"""Extend the annotations of this binder with the annotations from another."""
assert self.attrs == other.attrs
self.tokens.extend(other.tokens)
self.spaces.extend(other.spaces)
self.strings.update(other.strings)
|
Serialize the binder's annotations into a byte string.
def to_bytes(self):
"""Serialize the binder's annotations into a byte string."""
for tokens in self.tokens:
assert len(tokens.shape) == 2, tokens.shape
lengths = [len(tokens) for tokens in self.tokens]
msg = {
"attrs": self.attrs,
"tokens": numpy.vstack(self.tokens).tobytes("C"),
"spaces": numpy.vstack(self.spaces).tobytes("C"),
"lengths": numpy.asarray(lengths, dtype="int32").tobytes("C"),
"strings": list(self.strings),
}
return gzip.compress(srsly.msgpack_dumps(msg))
|
Deserialize the binder's annotations from a byte string.
def from_bytes(self, string):
"""Deserialize the binder's annotations from a byte string."""
msg = srsly.msgpack_loads(gzip.decompress(string))
self.attrs = msg["attrs"]
self.strings = set(msg["strings"])
lengths = numpy.fromstring(msg["lengths"], dtype="int32")
flat_spaces = numpy.fromstring(msg["spaces"], dtype=bool)
flat_tokens = numpy.fromstring(msg["tokens"], dtype="uint64")
shape = (flat_tokens.size // len(self.attrs), len(self.attrs))
flat_tokens = flat_tokens.reshape(shape)
flat_spaces = flat_spaces.reshape((flat_spaces.size, 1))
self.tokens = NumpyOps().unflatten(flat_tokens, lengths)
self.spaces = NumpyOps().unflatten(flat_spaces, lengths)
for tokens in self.tokens:
assert len(tokens.shape) == 2, tokens.shape
return self
|
Load data from the IMDB dataset.
def load_data(limit=0, split=0.8):
"""Load data from the IMDB dataset."""
# Partition off part of the train data for evaluation
train_data, _ = thinc.extra.datasets.imdb()
random.shuffle(train_data)
train_data = train_data[-limit:]
texts, labels = zip(*train_data)
cats = [{"POSITIVE": bool(y), "NEGATIVE": not bool(y)} for y in labels]
split = int(len(train_data) * split)
return (texts[:split], cats[:split]), (texts[split:], cats[split:])
|
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. If --create-meta is
set and a meta.json already exists in the output directory, the existing
values will be used as the defaults in the command-line prompt.
def package(input_dir, output_dir, meta_path=None, create_meta=False, 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. If --create-meta is
set and a meta.json already exists in the output directory, the existing
values will be used as the defaults in the command-line prompt.
"""
msg = Printer()
input_path = util.ensure_path(input_dir)
output_path = util.ensure_path(output_dir)
meta_path = util.ensure_path(meta_path)
if not input_path or not input_path.exists():
msg.fail("Can't locate model data", input_path, exits=1)
if not output_path or not output_path.exists():
msg.fail("Output directory not found", output_path, exits=1)
if meta_path and not meta_path.exists():
msg.fail("Can't find model meta.json", meta_path, exits=1)
meta_path = meta_path or input_path / "meta.json"
if meta_path.is_file():
meta = srsly.read_json(meta_path)
if not create_meta: # only print if user doesn't want to overwrite
msg.good("Loaded meta.json from file", meta_path)
else:
meta = generate_meta(input_dir, meta, msg)
for key in ("lang", "name", "version"):
if key not in meta or meta[key] == "":
msg.fail(
"No '{}' setting found in meta.json".format(key),
"This setting is required to build your package.",
exits=1,
)
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
if package_path.exists():
if force:
shutil.rmtree(path2str(package_path))
else:
msg.fail(
"Package directory already exists",
"Please delete the directory and try again, or use the "
"`--force` flag to overwrite existing "
"directories.".format(path=path2str(package_path)),
exits=1,
)
Path.mkdir(package_path, parents=True)
shutil.copytree(path2str(input_path), path2str(package_path / model_name_v))
create_file(main_path / "meta.json", srsly.json_dumps(meta, indent=2))
create_file(main_path / "setup.py", TEMPLATE_SETUP)
create_file(main_path / "MANIFEST.in", TEMPLATE_MANIFEST)
create_file(package_path / "__init__.py", TEMPLATE_INIT)
msg.good("Successfully created package '{}'".format(model_name_v), main_path)
msg.text("To build the package, run `python setup.py sdist` in this directory.")
|
Check whether we're dealing with an uninflected paradigm, so we can
avoid lemmatization entirely.
def is_base_form(self, univ_pos, morphology=None):
"""
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', 'Tense')]
if univ_pos == 'noun' and morphology.get('Number') == 'sing':
return True
elif univ_pos == 'verb' and morphology.get('VerbForm') == 'inf':
return True
# This maps 'VBP' to base form -- probably just need 'IS_BASE'
# morphology
elif univ_pos == 'verb' and (morphology.get('VerbForm') == 'fin' and
morphology.get('Tense') == 'pres' and
morphology.get('Number') is None and
not others):
return True
elif univ_pos == 'adj' and morphology.get('Degree') == 'pos':
return True
elif VerbForm_inf in morphology:
return True
elif VerbForm_none in morphology:
return True
elif Number_sing in morphology:
return True
elif Degree_pos in morphology:
return True
else:
return False
|
Set up the pipeline and entity recognizer, and train the new entity.
def main(model=None, new_model_name="animal", output_dir=None, n_iter=30):
"""Set up the pipeline and entity recognizer, and train the new entity."""
random.seed(0)
if model is not None:
nlp = spacy.load(model) # load existing spaCy model
print("Loaded model '%s'" % model)
else:
nlp = spacy.blank("en") # create blank Language class
print("Created blank 'en' model")
# Add entity recognizer to model if it's not in the pipeline
# nlp.create_pipe works for built-ins that are registered with spaCy
if "ner" not in nlp.pipe_names:
ner = nlp.create_pipe("ner")
nlp.add_pipe(ner)
# otherwise, get it, so we can add labels to it
else:
ner = nlp.get_pipe("ner")
ner.add_label(LABEL) # add new entity label to entity recognizer
# Adding extraneous labels shouldn't mess anything up
ner.add_label("VEGETABLE")
if model is None:
optimizer = nlp.begin_training()
else:
optimizer = nlp.resume_training()
move_names = list(ner.move_names)
# get names of other pipes to disable them during training
other_pipes = [pipe for pipe in nlp.pipe_names if pipe != "ner"]
with nlp.disable_pipes(*other_pipes): # only train NER
sizes = compounding(1.0, 4.0, 1.001)
# batch up the examples using spaCy's minibatch
for itn in range(n_iter):
random.shuffle(TRAIN_DATA)
batches = minibatch(TRAIN_DATA, size=sizes)
losses = {}
for batch in batches:
texts, annotations = zip(*batch)
nlp.update(texts, annotations, sgd=optimizer, drop=0.35, losses=losses)
print("Losses", losses)
# test the trained model
test_text = "Do you like horses?"
doc = nlp(test_text)
print("Entities in '%s'" % test_text)
for ent in doc.ents:
print(ent.label_, ent.text)
# save model to output directory
if output_dir is not None:
output_dir = Path(output_dir)
if not output_dir.exists():
output_dir.mkdir()
nlp.meta["name"] = new_model_name # rename model
nlp.to_disk(output_dir)
print("Saved model to", output_dir)
# test the saved model
print("Loading from", output_dir)
nlp2 = spacy.load(output_dir)
# Check the classes have loaded back consistently
assert nlp2.get_pipe("ner").move_names == move_names
doc2 = nlp2(test_text)
for ent in doc2.ents:
print(ent.label_, ent.text)
|
Convert files in the CoNLL-2003 NER format into JSON format for use with
train cli.
def conll_ner2json(input_data, **kwargs):
"""
Convert files in the CoNLL-2003 NER format into JSON format for use with
train cli.
"""
delimit_docs = "-DOCSTART- -X- O O"
output_docs = []
for doc in input_data.strip().split(delimit_docs):
doc = doc.strip()
if not doc:
continue
output_doc = []
for sent in doc.split("\n\n"):
sent = sent.strip()
if not sent:
continue
lines = [line.strip() for line in sent.split("\n") if line.strip()]
words, tags, chunks, iob_ents = zip(*[line.split() for line in lines])
biluo_ents = iob_to_biluo(iob_ents)
output_doc.append(
{
"tokens": [
{"orth": w, "tag": tag, "ner": ent}
for (w, tag, ent) in zip(words, tags, biluo_ents)
]
}
)
output_docs.append(
{"id": len(output_docs), "paragraphs": [{"sentences": output_doc}]}
)
output_doc = []
return output_docs
|
Create a new model, set up the pipeline and train the tagger. In order to
train the tagger with a custom tag map, we're creating a new Language
instance with a custom vocab.
def main(lang="en", output_dir=None, n_iter=25):
"""Create a new model, set up the pipeline and train the tagger. In order to
train the tagger with a custom tag map, we're creating a new Language
instance with a custom vocab.
"""
nlp = spacy.blank(lang)
# add the tagger to the pipeline
# nlp.create_pipe works for built-ins that are registered with spaCy
tagger = nlp.create_pipe("tagger")
# Add the tags. This needs to be done before you start training.
for tag, values in TAG_MAP.items():
tagger.add_label(tag, values)
nlp.add_pipe(tagger)
optimizer = nlp.begin_training()
for i in range(n_iter):
random.shuffle(TRAIN_DATA)
losses = {}
# batch up the examples using spaCy's minibatch
batches = minibatch(TRAIN_DATA, size=compounding(4.0, 32.0, 1.001))
for batch in batches:
texts, annotations = zip(*batch)
nlp.update(texts, annotations, sgd=optimizer, losses=losses)
print("Losses", losses)
# test the trained model
test_text = "I like blue eggs"
doc = nlp(test_text)
print("Tags", [(t.text, t.tag_, t.pos_) for t in doc])
# save model to output directory
if output_dir is not None:
output_dir = Path(output_dir)
if not output_dir.exists():
output_dir.mkdir()
nlp.to_disk(output_dir)
print("Saved model to", output_dir)
# test the save model
print("Loading from", output_dir)
nlp2 = spacy.load(output_dir)
doc = nlp2(test_text)
print("Tags", [(t.text, t.tag_, t.pos_) for t in doc])
|
Load data from the IMDB dataset.
def load_textcat_data(limit=0):
"""Load data from the IMDB dataset."""
# Partition off part of the train data for evaluation
train_data, eval_data = thinc.extra.datasets.imdb()
random.shuffle(train_data)
train_data = train_data[-limit:]
texts, labels = zip(*train_data)
eval_texts, eval_labels = zip(*eval_data)
cats = [{"POSITIVE": bool(y), "NEGATIVE": not bool(y)} for y in labels]
eval_cats = [{"POSITIVE": bool(y), "NEGATIVE": not bool(y)} for y in eval_labels]
return (texts, cats), (eval_texts, eval_cats)
|
Create a new model from raw data, like word frequencies, Brown clusters
and word vectors. If vectors are provided in Word2Vec format, they can
be either a .txt or zipped as a .zip or .tar.gz.
def init_model(
lang,
output_dir,
freqs_loc=None,
clusters_loc=None,
jsonl_loc=None,
vectors_loc=None,
prune_vectors=-1,
):
"""
Create a new model from raw data, like word frequencies, Brown clusters
and word vectors. If vectors are provided in Word2Vec format, they can
be either a .txt or zipped as a .zip or .tar.gz.
"""
if jsonl_loc is not None:
if freqs_loc is not None or clusters_loc is not None:
settings = ["-j"]
if freqs_loc:
settings.append("-f")
if clusters_loc:
settings.append("-c")
msg.warn(
"Incompatible arguments",
"The -f and -c arguments are deprecated, and not compatible "
"with the -j argument, which should specify the same "
"information. Either merge the frequencies and clusters data "
"into the JSONL-formatted file (recommended), or use only the "
"-f and -c files, without the other lexical attributes.",
)
jsonl_loc = ensure_path(jsonl_loc)
lex_attrs = srsly.read_jsonl(jsonl_loc)
else:
clusters_loc = ensure_path(clusters_loc)
freqs_loc = ensure_path(freqs_loc)
if freqs_loc is not None and not freqs_loc.exists():
msg.fail("Can't find words frequencies file", freqs_loc, exits=1)
lex_attrs = read_attrs_from_deprecated(freqs_loc, clusters_loc)
with msg.loading("Creating model..."):
nlp = create_model(lang, lex_attrs)
msg.good("Successfully created model")
if vectors_loc is not None:
add_vectors(nlp, vectors_loc, prune_vectors)
vec_added = len(nlp.vocab.vectors)
lex_added = len(nlp.vocab)
msg.good(
"Sucessfully compiled vocab",
"{} entries, {} vectors".format(lex_added, vec_added),
)
if not output_dir.exists():
output_dir.mkdir()
nlp.to_disk(output_dir)
return nlp
|
Handle .gz, .tar.gz or unzipped files
def open_file(loc):
"""Handle .gz, .tar.gz or unzipped files"""
loc = ensure_path(loc)
if tarfile.is_tarfile(str(loc)):
return tarfile.open(str(loc), "r:gz")
elif loc.parts[-1].endswith("gz"):
return (line.decode("utf8") for line in gzip.open(str(loc), "r"))
elif loc.parts[-1].endswith("zip"):
zip_file = zipfile.ZipFile(str(loc))
names = zip_file.namelist()
file_ = zip_file.open(names[0])
return (line.decode("utf8") for line in file_)
else:
return loc.open("r", encoding="utf8")
|
Load the model, set up the pipeline and train the entity recognizer.
def main(model=None, output_dir=None, n_iter=100):
"""Load the model, set up the pipeline and train the entity recognizer."""
if model is not None:
nlp = spacy.load(model) # load existing spaCy model
print("Loaded model '%s'" % model)
else:
nlp = spacy.blank("en") # create blank Language class
print("Created blank 'en' model")
# create the built-in pipeline components and add them to the pipeline
# nlp.create_pipe works for built-ins that are registered with spaCy
if "ner" not in nlp.pipe_names:
ner = nlp.create_pipe("ner")
nlp.add_pipe(ner, last=True)
# otherwise, get it so we can add labels
else:
ner = nlp.get_pipe("ner")
# add labels
for _, annotations in TRAIN_DATA:
for ent in annotations.get("entities"):
ner.add_label(ent[2])
# get names of other pipes to disable them during training
other_pipes = [pipe for pipe in nlp.pipe_names if pipe != "ner"]
with nlp.disable_pipes(*other_pipes): # only train NER
# reset and initialize the weights randomly – but only if we're
# training a new model
if model is None:
nlp.begin_training()
for itn in range(n_iter):
random.shuffle(TRAIN_DATA)
losses = {}
# batch up the examples using spaCy's minibatch
batches = minibatch(TRAIN_DATA, size=compounding(4.0, 32.0, 1.001))
for batch in batches:
texts, annotations = zip(*batch)
nlp.update(
texts, # batch of texts
annotations, # batch of annotations
drop=0.5, # dropout - make it harder to memorise data
losses=losses,
)
print("Losses", losses)
# test the trained model
for text, _ in TRAIN_DATA:
doc = nlp(text)
print("Entities", [(ent.text, ent.label_) for ent in doc.ents])
print("Tokens", [(t.text, t.ent_type_, t.ent_iob) for t in doc])
# save model to output directory
if output_dir is not None:
output_dir = Path(output_dir)
if not output_dir.exists():
output_dir.mkdir()
nlp.to_disk(output_dir)
print("Saved model to", output_dir)
# test the saved model
print("Loading from", output_dir)
nlp2 = spacy.load(output_dir)
for text, _ in TRAIN_DATA:
doc = nlp2(text)
print("Entities", [(ent.text, ent.label_) for ent in doc.ents])
print("Tokens", [(t.text, t.ent_type_, t.ent_iob) for t in doc])
|
Pre-train the 'token-to-vector' (tok2vec) layer of pipeline components,
using an approximate language-modelling objective. Specifically, we load
pre-trained vectors, and train a component like a CNN, BiLSTM, etc to predict
vectors which match the pre-trained ones. The weights are saved to a directory
after each epoch. You can then pass a path to one of these pre-trained weights
files to the 'spacy train' command.
This technique may be especially helpful if you have little labelled data.
However, it's still quite experimental, so your mileage may vary.
To load the weights back in during 'spacy train', you need to ensure
all settings are the same between pretraining and training. The API and
errors around this need some improvement.
def pretrain(
texts_loc,
vectors_model,
output_dir,
width=96,
depth=4,
embed_rows=2000,
loss_func="cosine",
use_vectors=False,
dropout=0.2,
n_iter=1000,
batch_size=3000,
max_length=500,
min_length=5,
seed=0,
n_save_every=None,
):
"""
Pre-train the 'token-to-vector' (tok2vec) layer of pipeline components,
using an approximate language-modelling objective. Specifically, we load
pre-trained vectors, and train a component like a CNN, BiLSTM, etc to predict
vectors which match the pre-trained ones. The weights are saved to a directory
after each epoch. You can then pass a path to one of these pre-trained weights
files to the 'spacy train' command.
This technique may be especially helpful if you have little labelled data.
However, it's still quite experimental, so your mileage may vary.
To load the weights back in during 'spacy train', you need to ensure
all settings are the same between pretraining and training. The API and
errors around this need some improvement.
"""
config = dict(locals())
msg = Printer()
util.fix_random_seed(seed)
has_gpu = prefer_gpu()
msg.info("Using GPU" if has_gpu else "Not using GPU")
output_dir = Path(output_dir)
if not output_dir.exists():
output_dir.mkdir()
msg.good("Created output directory")
srsly.write_json(output_dir / "config.json", config)
msg.good("Saved settings to config.json")
# Load texts from file or stdin
if texts_loc != "-": # reading from a file
texts_loc = Path(texts_loc)
if not texts_loc.exists():
msg.fail("Input text file doesn't exist", texts_loc, exits=1)
with msg.loading("Loading input texts..."):
texts = list(srsly.read_jsonl(texts_loc))
msg.good("Loaded input texts")
random.shuffle(texts)
else: # reading from stdin
msg.text("Reading input text from stdin...")
texts = srsly.read_jsonl("-")
with msg.loading("Loading model '{}'...".format(vectors_model)):
nlp = util.load_model(vectors_model)
msg.good("Loaded model '{}'".format(vectors_model))
pretrained_vectors = None if not use_vectors else nlp.vocab.vectors.name
model = create_pretraining_model(
nlp,
Tok2Vec(
width,
embed_rows,
conv_depth=depth,
pretrained_vectors=pretrained_vectors,
bilstm_depth=0, # Requires PyTorch. Experimental.
cnn_maxout_pieces=3, # You can try setting this higher
subword_features=True, # Set to False for Chinese etc
),
)
optimizer = create_default_optimizer(model.ops)
tracker = ProgressTracker(frequency=10000)
msg.divider("Pre-training tok2vec layer")
row_settings = {"widths": (3, 10, 10, 6, 4), "aligns": ("r", "r", "r", "r", "r")}
msg.row(("#", "# Words", "Total Loss", "Loss", "w/s"), **row_settings)
def _save_model(epoch, is_temp=False):
is_temp_str = ".temp" if is_temp else ""
with model.use_params(optimizer.averages):
with (output_dir / ("model%d%s.bin" % (epoch, is_temp_str))).open(
"wb"
) as file_:
file_.write(model.tok2vec.to_bytes())
log = {
"nr_word": tracker.nr_word,
"loss": tracker.loss,
"epoch_loss": tracker.epoch_loss,
"epoch": epoch,
}
with (output_dir / "log.jsonl").open("a") as file_:
file_.write(srsly.json_dumps(log) + "\n")
for epoch in range(n_iter):
for batch_id, batch in enumerate(
util.minibatch_by_words(((text, None) for text in texts), size=batch_size)
):
docs = make_docs(
nlp,
[text for (text, _) in batch],
max_length=max_length,
min_length=min_length,
)
loss = make_update(
model, docs, optimizer, objective=loss_func, drop=dropout
)
progress = tracker.update(epoch, loss, docs)
if progress:
msg.row(progress, **row_settings)
if texts_loc == "-" and tracker.words_per_epoch[epoch] >= 10 ** 7:
break
if n_save_every and (batch_id % n_save_every == 0):
_save_model(epoch, is_temp=True)
_save_model(epoch)
tracker.epoch_loss = 0.0
if texts_loc != "-":
# Reshuffle the texts if texts were loaded from a file
random.shuffle(texts)
|
Perform an update over a single batch of documents.
docs (iterable): A batch of `Doc` objects.
drop (float): The droput rate.
optimizer (callable): An optimizer.
RETURNS loss: A float for the loss.
def make_update(model, docs, optimizer, drop=0.0, objective="L2"):
"""Perform an update over a single batch of documents.
docs (iterable): A batch of `Doc` objects.
drop (float): The droput rate.
optimizer (callable): An optimizer.
RETURNS loss: A float for the loss.
"""
predictions, backprop = model.begin_update(docs, drop=drop)
loss, gradients = get_vectors_loss(model.ops, docs, predictions, objective)
backprop(gradients, sgd=optimizer)
# Don't want to return a cupy object here
# The gradients are modified in-place by the BERT MLM,
# so we get an accurate loss
return float(loss)
|
Compute a mean-squared error loss between the documents' vectors and
the prediction.
Note that this is ripe for customization! We could compute the vectors
in some other word, e.g. with an LSTM language model, or use some other
type of objective.
def get_vectors_loss(ops, docs, prediction, objective="L2"):
"""Compute a mean-squared error loss between the documents' vectors and
the prediction.
Note that this is ripe for customization! We could compute the vectors
in some other word, e.g. with an LSTM language model, or use some other
type of objective.
"""
# The simplest way to implement this would be to vstack the
# token.vector values, but that's a bit inefficient, especially on GPU.
# Instead we fetch the index into the vectors table for each of our tokens,
# and look them up all at once. This prevents data copying.
ids = ops.flatten([doc.to_array(ID).ravel() for doc in docs])
target = docs[0].vocab.vectors.data[ids]
if objective == "L2":
d_target = prediction - target
loss = (d_target ** 2).sum()
elif objective == "cosine":
loss, d_target = get_cossim_loss(prediction, target)
return loss, d_target
|
Define a network for the pretraining. We simply add an output layer onto
the tok2vec input model. The tok2vec input model needs to be a model that
takes a batch of Doc objects (as a list), and returns a list of arrays.
Each array in the output needs to have one row per token in the doc.
def create_pretraining_model(nlp, tok2vec):
"""Define a network for the pretraining. We simply add an output layer onto
the tok2vec input model. The tok2vec input model needs to be a model that
takes a batch of Doc objects (as a list), and returns a list of arrays.
Each array in the output needs to have one row per token in the doc.
"""
output_size = nlp.vocab.vectors.data.shape[1]
output_layer = chain(
LN(Maxout(300, pieces=3)), Affine(output_size, drop_factor=0.0)
)
# This is annoying, but the parser etc have the flatten step after
# the tok2vec. To load the weights in cleanly, we need to match
# the shape of the models' components exactly. So what we cann
# "tok2vec" has to be the same set of processes as what the components do.
tok2vec = chain(tok2vec, flatten)
model = chain(tok2vec, output_layer)
model = masked_language_model(nlp.vocab, model)
model.tok2vec = tok2vec
model.output_layer = output_layer
model.begin_training([nlp.make_doc("Give it a doc to infer shapes")])
return model
|
Round large numbers as integers, smaller numbers as decimals.
def _smart_round(figure, width=10, max_decimal=4):
"""Round large numbers as integers, smaller numbers as decimals."""
n_digits = len(str(int(figure)))
n_decimal = width - (n_digits + 1)
if n_decimal <= 1:
return str(int(figure))
else:
n_decimal = min(n_decimal, max_decimal)
format_str = "%." + str(n_decimal) + "f"
return format_str % figure
|
Detect base noun phrases. Works on both Doc and Span.
def noun_chunks(obj):
"""
Detect base noun phrases. Works on both Doc and Span.
"""
# It follows the logic of the noun chunks finder of English language,
# adjusted to some Greek language special characteristics.
# obj tag corrects some DEP tagger mistakes.
# Further improvement of the models will eliminate the need for this tag.
labels = ["nsubj", "obj", "iobj", "appos", "ROOT", "obl"]
doc = obj.doc # Ensure works on both Doc and Span.
np_deps = [doc.vocab.strings.add(label) for label in labels]
conj = doc.vocab.strings.add("conj")
nmod = doc.vocab.strings.add("nmod")
np_label = doc.vocab.strings.add("NP")
seen = set()
for i, word in enumerate(obj):
if word.pos not in (NOUN, PROPN, PRON):
continue
# Prevent nested chunks from being produced
if word.i in seen:
continue
if word.dep in np_deps:
if any(w.i in seen for w in word.subtree):
continue
flag = False
if word.pos == NOUN:
# check for patterns such as γραμμή παραγωγής
for potential_nmod in word.rights:
if potential_nmod.dep == nmod:
seen.update(
j for j in range(word.left_edge.i, potential_nmod.i + 1)
)
yield word.left_edge.i, potential_nmod.i + 1, np_label
flag = True
break
if flag is False:
seen.update(j for j in range(word.left_edge.i, word.i + 1))
yield word.left_edge.i, word.i + 1, np_label
elif word.dep == conj:
# covers the case: έχει όμορφα και έξυπνα παιδιά
head = word.head
while head.dep == conj and head.head.i < head.i:
head = head.head
# If the head is an NP, and we're coordinated to it, we're an NP
if head.dep in np_deps:
if any(w.i in seen for w in word.subtree):
continue
seen.update(j for j in range(word.left_edge.i, word.i + 1))
yield word.left_edge.i, word.i + 1, np_label
|
Validate and convert arguments. Reused in Doc, Token and Span.
def get_ext_args(**kwargs):
"""Validate and convert arguments. Reused in Doc, Token and Span."""
default = kwargs.get("default")
getter = kwargs.get("getter")
setter = kwargs.get("setter")
method = kwargs.get("method")
if getter is None and setter is not None:
raise ValueError(Errors.E089)
valid_opts = ("default" in kwargs, method is not None, getter is not None)
nr_defined = sum(t is True for t in valid_opts)
if nr_defined != 1:
raise ValueError(Errors.E083.format(nr_defined=nr_defined))
if setter is not None and not hasattr(setter, "__call__"):
raise ValueError(Errors.E091.format(name="setter", value=repr(setter)))
if getter is not None and not hasattr(getter, "__call__"):
raise ValueError(Errors.E091.format(name="getter", value=repr(getter)))
if method is not None and not hasattr(method, "__call__"):
raise ValueError(Errors.E091.format(name="method", value=repr(method)))
return (default, method, getter, setter)
|
Check if an extension attribute is writable.
ext (tuple): The (default, getter, setter, method) tuple available via
{Doc,Span,Token}.get_extension.
RETURNS (bool): Whether the attribute is writable.
def is_writable_attr(ext):
"""Check if an extension attribute is writable.
ext (tuple): The (default, getter, setter, method) tuple available via
{Doc,Span,Token}.get_extension.
RETURNS (bool): Whether the attribute is writable.
"""
default, method, getter, setter = ext
# Extension is writable if it has a setter (getter + setter), if it has a
# default value (or, if its default value is none, none of the other values
# should be set).
if setter is not None or default is not None or all(e is None for e in ext):
return True
return False
|
Check whether we're on OSX >= 10.10
def is_new_osx():
"""Check whether we're on OSX >= 10.10"""
name = distutils.util.get_platform()
if sys.platform != "darwin":
return False
elif name.startswith("macosx-10"):
minor_version = int(name.split("-")[1].split(".")[1])
if minor_version >= 7:
return True
else:
return False
else:
return False
|
Return labels indicating the position of the word in the document.
def get_position_label(i, words, tags, heads, labels, ents):
"""Return labels indicating the position of the word in the document.
"""
if len(words) < 20:
return "short-doc"
elif i == 0:
return "first-word"
elif i < 10:
return "early-word"
elif i < 20:
return "mid-word"
elif i == len(words) - 1:
return "last-word"
else:
return "late-word"
|
Download compatible model from default download path using pip. Model
can be shortcut, model name or, if --direct flag is set, full model name
with version. For direct downloads, the compatibility check will be skipped.
def download(model, direct=False, *pip_args):
"""
Download compatible model from default download path using pip. Model
can be shortcut, model name or, if --direct flag is set, full model name
with version. For direct downloads, the compatibility check will be skipped.
"""
dl_tpl = "{m}-{v}/{m}-{v}.tar.gz#egg={m}=={v}"
if direct:
components = model.split("-")
model_name = "".join(components[:-1])
version = components[-1]
dl = download_model(dl_tpl.format(m=model_name, v=version), pip_args)
else:
shortcuts = get_json(about.__shortcuts__, "available shortcuts")
model_name = shortcuts.get(model, model)
compatibility = get_compatibility()
version = get_version(model_name, compatibility)
dl = download_model(dl_tpl.format(m=model_name, v=version), pip_args)
if dl != 0: # if download subprocess doesn't return 0, exit
sys.exit(dl)
msg.good(
"Download and installation successful",
"You can now load the model via spacy.load('{}')".format(model_name),
)
# Only create symlink if the model is installed via a shortcut like 'en'.
# There's no real advantage over an additional symlink for en_core_web_sm
# and if anything, it's more error prone and causes more confusion.
if model in shortcuts:
try:
# Get package path here because link uses
# pip.get_installed_distributions() to check if model is a
# package, which fails if model was just installed via
# subprocess
package_path = get_package_path(model_name)
link(model_name, model, force=True, model_path=package_path)
except: # noqa: E722
# Dirty, but since spacy.download and the auto-linking is
# mostly a convenience wrapper, it's best to show a success
# message and loading instructions, even if linking fails.
msg.warn(
"Download successful but linking failed",
"Creating a shortcut link for '{}' didn't work (maybe you "
"don't have admin permissions?), but you can still load "
"the model via its full package name: "
"nlp = spacy.load('{}')".format(model, model_name),
)
|
Convert files into JSON format for use with train command and other
experiment management functions. If no output_dir is specified, the data
is written to stdout, so you can pipe them forward to a JSON file:
$ spacy convert some_file.conllu > some_file.json
def convert(
input_file,
output_dir="-",
file_type="json",
n_sents=1,
morphology=False,
converter="auto",
lang=None,
):
"""
Convert files into JSON format for use with train command and other
experiment management functions. If no output_dir is specified, the data
is written to stdout, so you can pipe them forward to a JSON file:
$ spacy convert some_file.conllu > some_file.json
"""
msg = Printer()
input_path = Path(input_file)
if file_type not in FILE_TYPES:
msg.fail(
"Unknown file type: '{}'".format(file_type),
"Supported file types: '{}'".format(", ".join(FILE_TYPES)),
exits=1,
)
if file_type not in FILE_TYPES_STDOUT and output_dir == "-":
# TODO: support msgpack via stdout in srsly?
msg.fail(
"Can't write .{} data to stdout.".format(file_type),
"Please specify an output directory.",
exits=1,
)
if not input_path.exists():
msg.fail("Input file not found", input_path, exits=1)
if output_dir != "-" and not Path(output_dir).exists():
msg.fail("Output directory not found", output_dir, exits=1)
if converter == "auto":
converter = input_path.suffix[1:]
if converter not in CONVERTERS:
msg.fail("Can't find converter for {}".format(converter), exits=1)
# Use converter function to convert data
func = CONVERTERS[converter]
input_data = input_path.open("r", encoding="utf-8").read()
data = func(input_data, n_sents=n_sents, use_morphology=morphology, lang=lang)
if output_dir != "-":
# Export data to a file
suffix = ".{}".format(file_type)
output_file = Path(output_dir) / Path(input_path.parts[-1]).with_suffix(suffix)
if file_type == "json":
srsly.write_json(output_file, data)
elif file_type == "jsonl":
srsly.write_jsonl(output_file, data)
elif file_type == "msg":
srsly.write_msgpack(output_file, data)
msg.good("Generated output file ({} documents)".format(len(data)), output_file)
else:
# Print to stdout
if file_type == "json":
srsly.write_json("-", data)
elif file_type == "jsonl":
srsly.write_jsonl("-", data)
|
Load a specific spaCy model
def load_model(modelname, add_sentencizer=False):
""" Load a specific spaCy model """
loading_start = time.time()
nlp = spacy.load(modelname)
if add_sentencizer:
nlp.add_pipe(nlp.create_pipe('sentencizer'))
loading_end = time.time()
loading_time = loading_end - loading_start
if add_sentencizer:
return nlp, loading_time, modelname + '_sentencizer'
return nlp, loading_time, modelname
|
Load a generic spaCy model and add the sentencizer for sentence tokenization
def load_default_model_sentencizer(lang):
""" Load a generic spaCy model and add the sentencizer for sentence tokenization"""
loading_start = time.time()
lang_class = get_lang_class(lang)
nlp = lang_class()
nlp.add_pipe(nlp.create_pipe('sentencizer'))
loading_end = time.time()
loading_time = loading_end - loading_start
return nlp, loading_time, lang + "_default_" + 'sentencizer'
|
Turn a list of errors into frequency-sorted tuples thresholded by a certain total number
def get_freq_tuples(my_list, print_total_threshold):
""" Turn a list of errors into frequency-sorted tuples thresholded by a certain total number """
d = {}
for token in my_list:
d.setdefault(token, 0)
d[token] += 1
return sorted(d.items(), key=operator.itemgetter(1), reverse=True)[:print_total_threshold]
|
Heuristic to determine whether the treebank has blinded texts or not
def _contains_blinded_text(stats_xml):
""" Heuristic to determine whether the treebank has blinded texts or not """
tree = ET.parse(stats_xml)
root = tree.getroot()
total_tokens = int(root.find('size/total/tokens').text)
unique_lemmas = int(root.find('lemmas').get('unique'))
# assume the corpus is largely blinded when there are less than 1% unique tokens
return (unique_lemmas / total_tokens) < 0.01
|
Fetch the txt files for all treebanks for a given set of languages
def fetch_all_treebanks(ud_dir, languages, corpus, best_per_language):
"""" Fetch the txt files for all treebanks for a given set of languages """
all_treebanks = dict()
treebank_size = dict()
for l in languages:
all_treebanks[l] = []
treebank_size[l] = 0
for treebank_dir in ud_dir.iterdir():
if treebank_dir.is_dir():
for txt_path in treebank_dir.iterdir():
if txt_path.name.endswith('-ud-' + corpus + '.txt'):
file_lang = txt_path.name.split('_')[0]
if file_lang in languages:
gold_path = treebank_dir / txt_path.name.replace('.txt', '.conllu')
stats_xml = treebank_dir / "stats.xml"
# ignore treebanks where the texts are not publicly available
if not _contains_blinded_text(stats_xml):
if not best_per_language:
all_treebanks[file_lang].append(txt_path)
# check the tokens in the gold annotation to keep only the biggest treebank per language
else:
with gold_path.open(mode='r', encoding='utf-8') as gold_file:
gold_ud = conll17_ud_eval.load_conllu(gold_file)
gold_tokens = len(gold_ud.tokens)
if treebank_size[file_lang] < gold_tokens:
all_treebanks[file_lang] = [txt_path]
treebank_size[file_lang] = gold_tokens
return all_treebanks
|
Run an evaluation of a model nlp on a certain specified treebank
def run_single_eval(nlp, loading_time, print_name, text_path, gold_ud, tmp_output_path, out_file, print_header,
check_parse, print_freq_tasks):
"""" Run an evaluation of a model nlp on a certain specified treebank """
with text_path.open(mode='r', encoding='utf-8') as f:
flat_text = f.read()
# STEP 1: tokenize text
tokenization_start = time.time()
texts = split_text(flat_text)
docs = list(nlp.pipe(texts))
tokenization_end = time.time()
tokenization_time = tokenization_end - tokenization_start
# STEP 2: record stats and timings
tokens_per_s = int(len(gold_ud.tokens) / tokenization_time)
print_header_1 = ['date', 'text_path', 'gold_tokens', 'model', 'loading_time', 'tokenization_time', 'tokens_per_s']
print_string_1 = [str(datetime.date.today()), text_path.name, len(gold_ud.tokens),
print_name, "%.2f" % loading_time, "%.2f" % tokenization_time, tokens_per_s]
# STEP 3: evaluate predicted tokens and features
with tmp_output_path.open(mode="w", encoding="utf8") as tmp_out_file:
write_conllu(docs, tmp_out_file)
with tmp_output_path.open(mode="r", encoding="utf8") as sys_file:
sys_ud = conll17_ud_eval.load_conllu(sys_file, check_parse=check_parse)
tmp_output_path.unlink()
scores = conll17_ud_eval.evaluate(gold_ud, sys_ud, check_parse=check_parse)
# STEP 4: format the scoring results
eval_headers = EVAL_PARSE
if not check_parse:
eval_headers = EVAL_NO_PARSE
for score_name in eval_headers:
score = scores[score_name]
print_string_1.extend(["%.2f" % score.precision,
"%.2f" % score.recall,
"%.2f" % score.f1])
print_string_1.append("-" if score.aligned_accuracy is None else "%.2f" % score.aligned_accuracy)
print_string_1.append("-" if score.undersegmented is None else "%.4f" % score.under_perc)
print_string_1.append("-" if score.oversegmented is None else "%.4f" % score.over_perc)
print_header_1.extend([score_name + '_p', score_name + '_r', score_name + '_F', score_name + '_acc',
score_name + '_under', score_name + '_over'])
if score_name in print_freq_tasks:
print_header_1.extend([score_name + '_word_under_ex', score_name + '_shape_under_ex',
score_name + '_word_over_ex', score_name + '_shape_over_ex'])
d_under_words = get_freq_tuples(score.undersegmented, PRINT_TOTAL)
d_under_shapes = get_freq_tuples([word_shape(x) for x in score.undersegmented], PRINT_TOTAL)
d_over_words = get_freq_tuples(score.oversegmented, PRINT_TOTAL)
d_over_shapes = get_freq_tuples([word_shape(x) for x in score.oversegmented], PRINT_TOTAL)
# saving to CSV with ; seperator so blinding ; in the example output
print_string_1.append(
str({k: v for k, v in d_under_words if v > PRINT_FREQ}).replace(";", "*SEMICOLON*"))
print_string_1.append(
str({k: v for k, v in d_under_shapes if v > PRINT_FREQ}).replace(";", "*SEMICOLON*"))
print_string_1.append(
str({k: v for k, v in d_over_words if v > PRINT_FREQ}).replace(";", "*SEMICOLON*"))
print_string_1.append(
str({k: v for k, v in d_over_shapes if v > PRINT_FREQ}).replace(";", "*SEMICOLON*"))
# STEP 5: print the formatted results to CSV
if print_header:
out_file.write(';'.join(map(str, print_header_1)) + '\n')
out_file.write(';'.join(map(str, print_string_1)) + '\n')
|
Run an evaluation for each language with its specified models and treebanks
def run_all_evals(models, treebanks, out_file, check_parse, print_freq_tasks):
"""" Run an evaluation for each language with its specified models and treebanks """
print_header = True
for tb_lang, treebank_list in treebanks.items():
print()
print("Language", tb_lang)
for text_path in treebank_list:
print(" Evaluating on", text_path)
gold_path = text_path.parent / (text_path.stem + '.conllu')
print(" Gold data from ", gold_path)
# nested try blocks to ensure the code can continue with the next iteration after a failure
try:
with gold_path.open(mode='r', encoding='utf-8') as gold_file:
gold_ud = conll17_ud_eval.load_conllu(gold_file)
for nlp, nlp_loading_time, nlp_name in models[tb_lang]:
try:
print(" Benchmarking", nlp_name)
tmp_output_path = text_path.parent / str('tmp_' + nlp_name + '.conllu')
run_single_eval(nlp, nlp_loading_time, nlp_name, text_path, gold_ud, tmp_output_path, out_file,
print_header, check_parse, print_freq_tasks)
print_header = False
except Exception as e:
print(" Ran into trouble: ", str(e))
except Exception as e:
print(" Ran into trouble: ", str(e))
|
Assemble all treebanks and models to run evaluations with.
When setting check_parse to True, the default models will not be evaluated as they don't have parsing functionality
def main(out_path, ud_dir, check_parse=False, langs=ALL_LANGUAGES, exclude_trained_models=False, exclude_multi=False,
hide_freq=False, corpus='train', best_per_language=False):
""""
Assemble all treebanks and models to run evaluations with.
When setting check_parse to True, the default models will not be evaluated as they don't have parsing functionality
"""
languages = [lang.strip() for lang in langs.split(",")]
print_freq_tasks = []
if not hide_freq:
print_freq_tasks = ['Tokens']
# fetching all relevant treebank from the directory
treebanks = fetch_all_treebanks(ud_dir, languages, corpus, best_per_language)
print()
print("Loading all relevant models for", languages)
models = dict()
# multi-lang model
multi = None
if not exclude_multi and not check_parse:
multi = load_model('xx_ent_wiki_sm', add_sentencizer=True)
# initialize all models with the multi-lang model
for lang in languages:
models[lang] = [multi] if multi else []
# add default models if we don't want to evaluate parsing info
if not check_parse:
# Norwegian is 'nb' in spaCy but 'no' in the UD corpora
if lang == 'no':
models['no'].append(load_default_model_sentencizer('nb'))
else:
models[lang].append(load_default_model_sentencizer(lang))
# language-specific trained models
if not exclude_trained_models:
if 'de' in models:
models['de'].append(load_model('de_core_news_sm'))
if 'es' in models:
models['es'].append(load_model('es_core_news_sm'))
models['es'].append(load_model('es_core_news_md'))
if 'pt' in models:
models['pt'].append(load_model('pt_core_news_sm'))
if 'it' in models:
models['it'].append(load_model('it_core_news_sm'))
if 'nl' in models:
models['nl'].append(load_model('nl_core_news_sm'))
if 'en' in models:
models['en'].append(load_model('en_core_web_sm'))
models['en'].append(load_model('en_core_web_md'))
models['en'].append(load_model('en_core_web_lg'))
if 'fr' in models:
models['fr'].append(load_model('fr_core_news_sm'))
models['fr'].append(load_model('fr_core_news_md'))
with out_path.open(mode='w', encoding='utf-8') as out_file:
run_all_evals(models, treebanks, out_file, check_parse, print_freq_tasks)
|
Detect base noun phrases from a dependency parse. Works on both Doc and Span.
def noun_chunks(obj):
"""
Detect base noun phrases from a dependency parse. Works on both Doc and Span.
"""
# this iterator extracts spans headed by NOUNs starting from the left-most
# syntactic dependent until the NOUN itself for close apposition and
# measurement construction, the span is sometimes extended to the right of
# the NOUN. Example: "eine Tasse Tee" (a cup (of) tea) returns "eine Tasse Tee"
# and not just "eine Tasse", same for "das Thema Familie".
labels = [
"sb",
"oa",
"da",
"nk",
"mo",
"ag",
"ROOT",
"root",
"cj",
"pd",
"og",
"app",
]
doc = obj.doc # Ensure works on both Doc and Span.
np_label = doc.vocab.strings.add("NP")
np_deps = set(doc.vocab.strings.add(label) for label in labels)
close_app = doc.vocab.strings.add("nk")
rbracket = 0
for i, word in enumerate(obj):
if i < rbracket:
continue
if word.pos in (NOUN, PROPN, PRON) and word.dep in np_deps:
rbracket = word.i + 1
# try to extend the span to the right
# to capture close apposition/measurement constructions
for rdep in doc[word.i].rights:
if rdep.pos in (NOUN, PROPN) and rdep.dep == close_app:
rbracket = rdep.i + 1
yield word.left_edge.i, rbracket, np_label
|
Wrap a model that should run on CPU, transferring inputs and outputs
as necessary.
def with_cpu(ops, model):
"""Wrap a model that should run on CPU, transferring inputs and outputs
as necessary."""
model.to_cpu()
def with_cpu_forward(inputs, drop=0.0):
cpu_outputs, backprop = model.begin_update(_to_cpu(inputs), drop=drop)
gpu_outputs = _to_device(ops, cpu_outputs)
def with_cpu_backprop(d_outputs, sgd=None):
cpu_d_outputs = _to_cpu(d_outputs)
return backprop(cpu_d_outputs, sgd=sgd)
return gpu_outputs, with_cpu_backprop
return wrap(with_cpu_forward, model)
|
Build a simple CNN text classifier, given a token-to-vector model as inputs.
If exclusive_classes=True, a softmax non-linearity is applied, so that the
outputs sum to 1. If exclusive_classes=False, a logistic non-linearity
is applied instead, so that outputs are in the range [0, 1].
def build_simple_cnn_text_classifier(tok2vec, nr_class, exclusive_classes=False, **cfg):
"""
Build a simple CNN text classifier, given a token-to-vector model as inputs.
If exclusive_classes=True, a softmax non-linearity is applied, so that the
outputs sum to 1. If exclusive_classes=False, a logistic non-linearity
is applied instead, so that outputs are in the range [0, 1].
"""
with Model.define_operators({">>": chain}):
if exclusive_classes:
output_layer = Softmax(nr_class, tok2vec.nO)
else:
output_layer = (
zero_init(Affine(nr_class, tok2vec.nO, drop_factor=0.0)) >> logistic
)
model = tok2vec >> flatten_add_lengths >> Pooling(mean_pool) >> output_layer
model.tok2vec = chain(tok2vec, flatten)
model.nO = nr_class
return model
|
Compose two or more models `f`, `g`, etc, such that their outputs are
concatenated, i.e. `concatenate(f, g)(x)` computes `hstack(f(x), g(x))`
def concatenate_lists(*layers, **kwargs): # pragma: no cover
"""Compose two or more models `f`, `g`, etc, such that their outputs are
concatenated, i.e. `concatenate(f, g)(x)` computes `hstack(f(x), g(x))`
"""
if not layers:
return noop()
drop_factor = kwargs.get("drop_factor", 1.0)
ops = layers[0].ops
layers = [chain(layer, flatten) for layer in layers]
concat = concatenate(*layers)
def concatenate_lists_fwd(Xs, drop=0.0):
drop *= drop_factor
lengths = ops.asarray([len(X) for X in Xs], dtype="i")
flat_y, bp_flat_y = concat.begin_update(Xs, drop=drop)
ys = ops.unflatten(flat_y, lengths)
def concatenate_lists_bwd(d_ys, sgd=None):
return bp_flat_y(ops.flatten(d_ys), sgd=sgd)
return ys, concatenate_lists_bwd
model = wrap(concatenate_lists_fwd, concat)
return model
|
Convert a model into a BERT-style masked language model
def masked_language_model(vocab, model, mask_prob=0.15):
"""Convert a model into a BERT-style masked language model"""
random_words = _RandomWords(vocab)
def mlm_forward(docs, drop=0.0):
mask, docs = _apply_mask(docs, random_words, mask_prob=mask_prob)
mask = model.ops.asarray(mask).reshape((mask.shape[0], 1))
output, backprop = model.begin_update(docs, drop=drop)
def mlm_backward(d_output, sgd=None):
d_output *= 1 - mask
return backprop(d_output, sgd=sgd)
return output, mlm_backward
return wrap(mlm_forward, model)
|
Allocate model, using width from tensorizer in pipeline.
gold_tuples (iterable): Gold-standard training data.
pipeline (list): The pipeline the model is part of.
def begin_training(self, _=tuple(), pipeline=None, sgd=None, **kwargs):
"""Allocate model, using width from tensorizer in pipeline.
gold_tuples (iterable): Gold-standard training data.
pipeline (list): The pipeline the model is part of.
"""
if self.model is True:
self.model = self.Model(pipeline[0].model.nO)
link_vectors_to_models(self.vocab)
if sgd is None:
sgd = self.create_optimizer()
return sgd
|
Render complete markup.
parsed (list): Dependency parses to render.
page (bool): Render parses wrapped as full HTML page.
minify (bool): Minify HTML markup.
RETURNS (unicode): Rendered SVG or HTML markup.
def render(self, parsed, page=False, minify=False):
"""Render complete markup.
parsed (list): Dependency parses to render.
page (bool): Render parses wrapped as full HTML page.
minify (bool): Minify HTML markup.
RETURNS (unicode): Rendered SVG or HTML markup.
"""
# Create a random ID prefix to make sure parses don't receive the
# same ID, even if they're identical
id_prefix = uuid.uuid4().hex
rendered = []
for i, p in enumerate(parsed):
if i == 0:
settings = p.get("settings", {})
self.direction = settings.get("direction", DEFAULT_DIR)
self.lang = settings.get("lang", DEFAULT_LANG)
render_id = "{}-{}".format(id_prefix, i)
svg = self.render_svg(render_id, p["words"], p["arcs"])
rendered.append(svg)
if page:
content = "".join([TPL_FIGURE.format(content=svg) for svg in rendered])
markup = TPL_PAGE.format(
content=content, lang=self.lang, dir=self.direction
)
else:
markup = "".join(rendered)
if minify:
return minify_html(markup)
return markup
|
Render SVG.
render_id (int): Unique ID, typically index of document.
words (list): Individual words and their tags.
arcs (list): Individual arcs and their start, end, direction and label.
RETURNS (unicode): Rendered SVG markup.
def render_svg(self, render_id, words, arcs):
"""Render SVG.
render_id (int): Unique ID, typically index of document.
words (list): Individual words and their tags.
arcs (list): Individual arcs and their start, end, direction and label.
RETURNS (unicode): Rendered SVG markup.
"""
self.levels = self.get_levels(arcs)
self.highest_level = len(self.levels)
self.offset_y = self.distance / 2 * self.highest_level + self.arrow_stroke
self.width = self.offset_x + len(words) * self.distance
self.height = self.offset_y + 3 * self.word_spacing
self.id = render_id
words = [self.render_word(w["text"], w["tag"], i) for i, w in enumerate(words)]
arcs = [
self.render_arrow(a["label"], a["start"], a["end"], a["dir"], i)
for i, a in enumerate(arcs)
]
content = "".join(words) + "".join(arcs)
return TPL_DEP_SVG.format(
id=self.id,
width=self.width,
height=self.height,
color=self.color,
bg=self.bg,
font=self.font,
content=content,
dir=self.direction,
lang=self.lang,
)
|
Render individual word.
text (unicode): Word text.
tag (unicode): Part-of-speech tag.
i (int): Unique ID, typically word index.
RETURNS (unicode): Rendered SVG markup.
def render_word(self, text, tag, i):
"""Render individual word.
text (unicode): Word text.
tag (unicode): Part-of-speech tag.
i (int): Unique ID, typically word index.
RETURNS (unicode): Rendered SVG markup.
"""
y = self.offset_y + self.word_spacing
x = self.offset_x + i * self.distance
if self.direction == "rtl":
x = self.width - x
html_text = escape_html(text)
return TPL_DEP_WORDS.format(text=html_text, tag=tag, x=x, y=y)
|
Render individual arrow.
label (unicode): Dependency label.
start (int): Index of start word.
end (int): Index of end word.
direction (unicode): Arrow direction, 'left' or 'right'.
i (int): Unique ID, typically arrow index.
RETURNS (unicode): Rendered SVG markup.
def render_arrow(self, label, start, end, direction, i):
"""Render individual arrow.
label (unicode): Dependency label.
start (int): Index of start word.
end (int): Index of end word.
direction (unicode): Arrow direction, 'left' or 'right'.
i (int): Unique ID, typically arrow index.
RETURNS (unicode): Rendered SVG markup.
"""
level = self.levels.index(end - start) + 1
x_start = self.offset_x + start * self.distance + self.arrow_spacing
if self.direction == "rtl":
x_start = self.width - x_start
y = self.offset_y
x_end = (
self.offset_x
+ (end - start) * self.distance
+ start * self.distance
- self.arrow_spacing * (self.highest_level - level) / 4
)
if self.direction == "rtl":
x_end = self.width - x_end
y_curve = self.offset_y - level * self.distance / 2
if self.compact:
y_curve = self.offset_y - level * self.distance / 6
if y_curve == 0 and len(self.levels) > 5:
y_curve = -self.distance
arrowhead = self.get_arrowhead(direction, x_start, y, x_end)
arc = self.get_arc(x_start, y, y_curve, x_end)
label_side = "right" if self.direction == "rtl" else "left"
return TPL_DEP_ARCS.format(
id=self.id,
i=i,
stroke=self.arrow_stroke,
head=arrowhead,
label=label,
label_side=label_side,
arc=arc,
)
|
Render individual arc.
x_start (int): X-coordinate of arrow start point.
y (int): Y-coordinate of arrow start and end point.
y_curve (int): Y-corrdinate of Cubic Bézier y_curve point.
x_end (int): X-coordinate of arrow end point.
RETURNS (unicode): Definition of the arc path ('d' attribute).
def get_arc(self, x_start, y, y_curve, x_end):
"""Render individual arc.
x_start (int): X-coordinate of arrow start point.
y (int): Y-coordinate of arrow start and end point.
y_curve (int): Y-corrdinate of Cubic Bézier y_curve point.
x_end (int): X-coordinate of arrow end point.
RETURNS (unicode): Definition of the arc path ('d' attribute).
"""
template = "M{x},{y} C{x},{c} {e},{c} {e},{y}"
if self.compact:
template = "M{x},{y} {x},{c} {e},{c} {e},{y}"
return template.format(x=x_start, y=y, c=y_curve, e=x_end)
|
Render individual arrow head.
direction (unicode): Arrow direction, 'left' or 'right'.
x (int): X-coordinate of arrow start point.
y (int): Y-coordinate of arrow start and end point.
end (int): X-coordinate of arrow end point.
RETURNS (unicode): Definition of the arrow head path ('d' attribute).
def get_arrowhead(self, direction, x, y, end):
"""Render individual arrow head.
direction (unicode): Arrow direction, 'left' or 'right'.
x (int): X-coordinate of arrow start point.
y (int): Y-coordinate of arrow start and end point.
end (int): X-coordinate of arrow end point.
RETURNS (unicode): Definition of the arrow head path ('d' attribute).
"""
if direction == "left":
pos1, pos2, pos3 = (x, x - self.arrow_width + 2, x + self.arrow_width - 2)
else:
pos1, pos2, pos3 = (
end,
end + self.arrow_width - 2,
end - self.arrow_width + 2,
)
arrowhead = (
pos1,
y + 2,
pos2,
y - self.arrow_width,
pos3,
y - self.arrow_width,
)
return "M{},{} L{},{} {},{}".format(*arrowhead)
|
Calculate available arc height "levels".
Used to calculate arrow heights dynamically and without wasting space.
args (list): Individual arcs and their start, end, direction and label.
RETURNS (list): Arc levels sorted from lowest to highest.
def get_levels(self, arcs):
"""Calculate available arc height "levels".
Used to calculate arrow heights dynamically and without wasting space.
args (list): Individual arcs and their start, end, direction and label.
RETURNS (list): Arc levels sorted from lowest to highest.
"""
levels = set(map(lambda arc: arc["end"] - arc["start"], arcs))
return sorted(list(levels))
|
Render complete markup.
parsed (list): Dependency parses to render.
page (bool): Render parses wrapped as full HTML page.
minify (bool): Minify HTML markup.
RETURNS (unicode): Rendered HTML markup.
def render(self, parsed, page=False, minify=False):
"""Render complete markup.
parsed (list): Dependency parses to render.
page (bool): Render parses wrapped as full HTML page.
minify (bool): Minify HTML markup.
RETURNS (unicode): Rendered HTML markup.
"""
rendered = []
for i, p in enumerate(parsed):
if i == 0:
settings = p.get("settings", {})
self.direction = settings.get("direction", DEFAULT_DIR)
self.lang = settings.get("lang", DEFAULT_LANG)
rendered.append(self.render_ents(p["text"], p["ents"], p.get("title")))
if page:
docs = "".join([TPL_FIGURE.format(content=doc) for doc in rendered])
markup = TPL_PAGE.format(content=docs, lang=self.lang, dir=self.direction)
else:
markup = "".join(rendered)
if minify:
return minify_html(markup)
return markup
|
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