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https://github.com/explosion/spaCy.git
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Merge pull request #1801 from sorenlind/avoid_dummy_args
Don't pass CLI command name as dummy argument
This commit is contained in:
commit
f246fab0c1
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@ -25,4 +25,4 @@ def blank(name, **kwargs):
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def info(model=None, markdown=False):
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return cli_info(None, model, markdown)
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return cli_info(model, markdown)
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@ -28,7 +28,7 @@ if __name__ == '__main__':
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command = sys.argv.pop(1)
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sys.argv[0] = 'spacy %s' % command
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if command in commands:
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plac.call(commands[command])
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plac.call(commands[command], sys.argv[1:])
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else:
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prints(
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"Available: %s" % ', '.join(commands),
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@ -24,8 +24,7 @@ CONVERTERS = {
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n_sents=("Number of sentences per doc", "option", "n", int),
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converter=("Name of converter (auto, iob, conllu or ner)", "option", "c", str),
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morphology=("Enable appending morphology to tags", "flag", "m", bool))
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def convert(_cmd, input_file, output_dir, n_sents=1, morphology=False,
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converter='auto'):
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def convert(input_file, output_dir, n_sents=1, morphology=False, converter='auto'):
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"""
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Convert files into JSON format for use with train command and other
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experiment management functions.
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@ -16,7 +16,7 @@ from .. import about
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model=("model to download, shortcut or name)", "positional", None, str),
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direct=("force direct download. Needs model name with version and won't "
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"perform compatibility check", "flag", "d", bool))
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def download(_cmd, model, direct=False):
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def download(model, direct=False):
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"""
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Download compatible model from default download path using pip. Model
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can be shortcut, model name or, if --direct flag is set, full model name
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@ -38,8 +38,7 @@ def download(_cmd, model, direct=False):
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# package, which fails if model was just installed via
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# subprocess
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package_path = get_package_path(model_name)
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link(None, model_name, model, force=True,
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model_path=package_path)
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link(model_name, model, force=True, model_path=package_path)
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except:
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# Dirty, but since spacy.download and the auto-linking is
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# mostly a convenience wrapper, it's best to show a success
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@ -25,8 +25,8 @@ numpy.random.seed(0)
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displacy_path=("directory to output rendered parses as HTML", "option",
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"dp", str),
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displacy_limit=("limit of parses to render as HTML", "option", "dl", int))
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def evaluate(_cmd, model, data_path, gpu_id=-1, gold_preproc=False,
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displacy_path=None, displacy_limit=25):
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def evaluate(model, data_path, gpu_id=-1, gold_preproc=False, displacy_path=None,
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displacy_limit=25):
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"""
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Evaluate a model. To render a sample of parses in a HTML file, set an
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output directory as the displacy_path argument.
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@ -13,7 +13,7 @@ from .. import util
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@plac.annotations(
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model=("optional: shortcut link of model", "positional", None, str),
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markdown=("generate Markdown for GitHub issues", "flag", "md", str))
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def info(_cmd, model=None, markdown=False):
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def info(model=None, markdown=False):
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"""Print info about spaCy installation. If a model shortcut link is
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speficied as an argument, print model information. Flag --markdown
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prints details in Markdown for easy copy-pasting to GitHub issues.
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@ -25,7 +25,7 @@ from ..util import prints, ensure_path, get_lang_class
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prune_vectors=("optional: number of vectors to prune to",
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"option", "V", int)
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)
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def init_model(_cmd, lang, output_dir, freqs_loc, clusters_loc=None, vectors_loc=None, prune_vectors=-1):
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def init_model(lang, output_dir, freqs_loc, clusters_loc=None, vectors_loc=None, prune_vectors=-1):
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"""
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Create a new model from raw data, like word frequencies, Brown clusters
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and word vectors.
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@ -13,7 +13,7 @@ from .. import util
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origin=("package name or local path to model", "positional", None, str),
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link_name=("name of shortuct link to create", "positional", None, str),
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force=("force overwriting of existing link", "flag", "f", bool))
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def link(_cmd, origin, link_name, force=False, model_path=None):
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def link(origin, link_name, force=False, model_path=None):
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"""
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Create a symlink for models within the spacy/data directory. Accepts
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either the name of a pip package, or the local path to the model data
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@ -20,7 +20,7 @@ from .. import about
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"the command line prompt", "flag", "c", bool),
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force=("force overwriting of existing model directory in output directory",
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"flag", "f", bool))
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def package(_cmd, input_dir, output_dir, meta_path=None, create_meta=False,
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def package(input_dir, output_dir, meta_path=None, create_meta=False,
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force=False):
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"""
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Generate Python package for model data, including meta and required
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@ -29,7 +29,7 @@ def read_inputs(loc):
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@plac.annotations(
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lang=("model/language", "positional", None, str),
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inputs=("Location of input file", "positional", None, read_inputs))
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def profile(_cmd, lang, inputs=None):
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def profile(lang, inputs=None):
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"""
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Profile a spaCy pipeline, to find out which functions take the most time.
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"""
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@ -38,7 +38,7 @@ numpy.random.seed(0)
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version=("Model version", "option", "V", str),
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meta_path=("Optional path to meta.json. All relevant properties will be "
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"overwritten.", "option", "m", Path))
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def train(_cmd, lang, output_dir, train_data, dev_data, n_iter=30, n_sents=0,
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def train(lang, output_dir, train_data, dev_data, n_iter=30, n_sents=0,
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use_gpu=-1, vectors=None, no_tagger=False,
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no_parser=False, no_entities=False, gold_preproc=False,
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version="0.0.0", meta_path=None):
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@ -10,7 +10,7 @@ from ..util import prints, get_data_path, read_json
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from .. import about
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def validate(_cmd):
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def validate():
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"""Validate that the currently installed version of spaCy is compatible
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with the installed models. Should be run after `pip install -U spacy`.
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"""
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@ -21,8 +21,7 @@ from ..util import prints, ensure_path
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prune_vectors=("optional: number of vectors to prune to.",
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"option", "V", int)
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)
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def make_vocab(_cmd, lang, output_dir, lexemes_loc,
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vectors_loc=None, prune_vectors=-1):
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def make_vocab(lang, output_dir, lexemes_loc, vectors_loc=None, prune_vectors=-1):
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"""Compile a vocabulary from a lexicon jsonl file and word vectors."""
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if not lexemes_loc.exists():
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prints(lexemes_loc, title="Can't find lexical data", exits=1)
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@ -9,7 +9,6 @@ from ...cli.train import train
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@pytest.mark.xfail
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def test_cli_trained_model_can_be_saved(tmpdir):
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cmd = None
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lang = 'nl'
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output_dir = str(tmpdir)
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train_file = NamedTemporaryFile('wb', dir=output_dir, delete=False)
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@ -86,6 +85,6 @@ def test_cli_trained_model_can_be_saved(tmpdir):
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# spacy train -n 1 -g -1 nl output_nl training_corpus.json training \
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# corpus.json
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train(cmd, lang, output_dir, train_data, dev_data, n_iter=1)
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train(lang, output_dir, train_data, dev_data, n_iter=1)
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assert True
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