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Refactor CLI
This commit is contained in:
Matthew Honnibal
parent
cc569a348d
commit
7811d97339
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@ -13,122 +13,112 @@ from spacy.cli import model as cli_model
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from spacy.cli import convert as cli_convert
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class CLI(object):
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@plac.annotations(
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model=("model to download (shortcut or model 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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)
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def download(model, direct=False):
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"""
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Command-line interface for spaCy
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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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with version.
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"""
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commands = ('download', 'link', 'info', 'package', 'train', 'model', 'convert')
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@plac.annotations(
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model=("model to download (shortcut or model 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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)
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def download(self, 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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with version.
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"""
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cli_download(model, direct)
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cli_download(model, direct)
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@plac.annotations(
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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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)
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def link(self, origin, link_name, force=False):
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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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directory. Linking models allows loading them via spacy.load(link_name).
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"""
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cli_link(origin, link_name, force)
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@plac.annotations(
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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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)
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def link(origin, link_name, force=False):
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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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directory. Linking models allows loading them via spacy.load(link_name).
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"""
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cli_link(origin, link_name, force)
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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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)
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def info(self, model=None, markdown=False):
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"""
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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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"""
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cli_info(model, markdown)
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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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)
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def info(model=None, markdown=False):
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"""
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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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"""
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cli_info(model, markdown)
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@plac.annotations(
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input_dir=("directory with model data", "positional", None, str),
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output_dir=("output parent directory", "positional", None, str),
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meta=("path to meta.json", "option", "m", str),
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force=("force overwriting of existing folder in output directory", "flag", "f", bool)
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)
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def package(self, input_dir, output_dir, meta=None, force=False):
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"""
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Generate Python package for model data, including meta and required
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installation files. A new directory will be created in the specified
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output directory, and model data will be copied over.
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"""
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cli_package(input_dir, output_dir, meta, force)
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@plac.annotations(
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input_dir=("directory with model data", "positional", None, str),
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output_dir=("output parent directory", "positional", None, str),
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meta=("path to meta.json", "option", "m", str),
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force=("force overwriting of existing folder in output directory", "flag", "f", bool)
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)
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def package(input_dir, output_dir, meta=None, force=False):
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"""
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Generate Python package for model data, including meta and required
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installation files. A new directory will be created in the specified
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output directory, and model data will be copied over.
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"""
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cli_package(input_dir, output_dir, meta, force)
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@plac.annotations(
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lang=("model language", "positional", None, str),
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output_dir=("output directory to store model in", "positional", None, str),
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train_data=("location of JSON-formatted training data", "positional", None, str),
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dev_data=("location of JSON-formatted development data (optional)", "positional", None, str),
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n_iter=("number of iterations", "option", "n", int),
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nsents=("number of sentences", "option", None, int),
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parser_L1=("L1 regularization penalty for parser", "option", "L", float),
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use_gpu=("Use GPU", "flag", "g", bool),
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no_tagger=("Don't train tagger", "flag", "T", bool),
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no_parser=("Don't train parser", "flag", "P", bool),
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no_entities=("Don't train NER", "flag", "N", bool)
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)
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def train(self, lang, output_dir, train_data, dev_data=None, n_iter=15,
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nsents=0, parser_L1=0.0, use_gpu=False,
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no_tagger=False, no_parser=False, no_entities=False):
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"""
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Train a model. Expects data in spaCy's JSON format.
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"""
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nsents = nsents or None
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cli_train(lang, output_dir, train_data, dev_data, n_iter, nsents,
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use_gpu, no_tagger, no_parser, no_entities, parser_L1)
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@plac.annotations(
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lang=("model language", "positional", None, str),
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output_dir=("output directory to store model in", "positional", None, str),
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train_data=("location of JSON-formatted training data", "positional", None, str),
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dev_data=("location of JSON-formatted development data (optional)", "positional", None, str),
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n_iter=("number of iterations", "option", "n", int),
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nsents=("number of sentences", "option", None, int),
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parser_L1=("L1 regularization penalty for parser", "option", "L", float),
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use_gpu=("Use GPU", "flag", "g", bool),
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no_tagger=("Don't train tagger", "flag", "T", bool),
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no_parser=("Don't train parser", "flag", "P", bool),
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no_entities=("Don't train NER", "flag", "N", bool)
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)
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def train(lang, output_dir, train_data, dev_data=None, n_iter=15,
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nsents=0, parser_L1=0.0, use_gpu=False,
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no_tagger=False, no_parser=False, no_entities=False):
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"""
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Train a model. Expects data in spaCy's JSON format.
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"""
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nsents = nsents or None
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cli_train(lang, output_dir, train_data, dev_data, n_iter, nsents,
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use_gpu, no_tagger, no_parser, no_entities, parser_L1)
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@plac.annotations(
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lang=("model language", "positional", None, str),
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model_dir=("output directory to store model in", "positional", None, str),
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freqs_data=("tab-separated frequencies file", "positional", None, str),
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clusters_data=("Brown clusters file", "positional", None, str),
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vectors_data=("word vectors file", "positional", None, str)
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)
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def model(self, lang, model_dir, freqs_data, clusters_data=None, vectors_data=None):
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"""
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Initialize a new model and its data directory.
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"""
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cli_model(lang, model_dir, freqs_data, clusters_data, vectors_data)
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@plac.annotations(
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input_file=("input file", "positional", None, str),
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output_dir=("output directory for converted file", "positional", None, str),
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n_sents=("Number of sentences per doc", "option", "n", float),
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morphology=("Enable appending morphology to tags", "flag", "m", bool)
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)
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def convert(input_file, output_dir, n_sents=10, morphology=False):
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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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"""
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cli_convert(input_file, output_dir, n_sents, morphology)
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@plac.annotations(
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input_file=("input file", "positional", None, str),
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output_dir=("output directory for converted file", "positional", None, str),
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n_sents=("Number of sentences per doc", "option", "n", float),
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morphology=("Enable appending morphology to tags", "flag", "m", bool)
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)
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def convert(self, input_file, output_dir, n_sents=10, morphology=False):
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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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"""
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cli_convert(input_file, output_dir, n_sents, morphology)
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@plac.annotations(
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lang=("model language", "positional", None, str),
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model_dir=("output directory to store model in", "positional", None, str),
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freqs_data=("tab-separated frequencies file", "positional", None, str),
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clusters_data=("Brown clusters file", "positional", None, str),
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vectors_data=("word vectors file", "positional", None, str)
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)
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def model(lang, model_dir, freqs_data, clusters_data=None, vectors_data=None):
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"""
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Initialize a new model and its data directory.
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"""
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cli_model(lang, model_dir, freqs_data, clusters_data, vectors_data)
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def __missing__(self, name):
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print("\n Command %r does not exist."
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"\n Use the --help flag for a list of available commands.\n" % name)
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@plac.annotations(
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lang=("model language", "positional", None, str),
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output_dir=("output directory to store model in", "positional", None, str),
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@ -147,6 +137,7 @@ def train(self, lang, output_dir, train_data, dev_data=None, n_iter=15,
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"""
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Train a model. Expects data in spaCy's JSON format.
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"""
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print(train_data, dev_data)
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nsents = nsents or None
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cli_train(lang, output_dir, train_data, dev_data, n_iter, nsents,
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use_gpu, no_tagger, no_parser, no_entities)
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@ -157,3 +148,5 @@ if __name__ == '__main__':
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import sys
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if sys.argv[1] == 'train':
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plac.call(train)
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if sys.argv[1] == 'convert':
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plac.call(convert)
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