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Add meta.json option to cli.train and add relevant properties

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Add accuracy scores to meta.json instead of accuracy.json and replace
all relevant properties like lang, pipeline, spacy_version in existing
meta.json. If not present, also add name and version placeholders to
make it packagable.
This commit is contained in:
ines 2017-09-25 19:00:47 +02:00
parent d2d35b63b7
commit edf7e4881d
1 changed files with 20 additions and 5 deletions
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25
spacy/cli/train.py
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@ -18,6 +18,7 @@ from ..gold import GoldParse, merge_sents
from ..gold import GoldCorpus, minibatch from ..gold import GoldCorpus, minibatch
from ..util import prints from ..util import prints
from .. import util from .. import util
from .. import about
from .. import displacy from .. import displacy
from ..compat import json_dumps from ..compat import json_dumps
@ -35,10 +36,11 @@ from ..compat import json_dumps
no_parser=("Don't train parser", "flag", "P", bool), no_parser=("Don't train parser", "flag", "P", bool),
no_entities=("Don't train NER", "flag", "N", bool), no_entities=("Don't train NER", "flag", "N", bool),
gold_preproc=("Use gold preprocessing", "flag", "G", bool), gold_preproc=("Use gold preprocessing", "flag", "G", bool),
meta_path=("Optional path to meta.json. All relevant properties will be overwritten.", "option", "m", Path)
) )
def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0, def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
use_gpu=-1, vectors=None, no_tagger=False, no_parser=False, no_entities=False, use_gpu=-1, vectors=None, no_tagger=False, no_parser=False, no_entities=False,
gold_preproc=False): gold_preproc=False, meta_path=None):
""" """
Train a model. Expects data in spaCy's JSON format. Train a model. Expects data in spaCy's JSON format.
""" """
@ -47,13 +49,19 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
output_path = util.ensure_path(output_dir) output_path = util.ensure_path(output_dir)
train_path = util.ensure_path(train_data) train_path = util.ensure_path(train_data)
dev_path = util.ensure_path(dev_data) dev_path = util.ensure_path(dev_data)
meta_path = util.ensure_path(meta_path)
if not output_path.exists(): if not output_path.exists():
output_path.mkdir() output_path.mkdir()
if not train_path.exists(): if not train_path.exists():
prints(train_path, title="Training data not found", exits=1) prints(train_path, title="Training data not found", exits=1)
if dev_path and not dev_path.exists(): if dev_path and not dev_path.exists():
prints(dev_path, title="Development data not found", exits=1) prints(dev_path, title="Development data not found", exits=1)
if meta_path is not None and not meta_path.exists():
prints(meta_path, title="meta.json not found", exits=1)
meta = util.read_json(meta_path) if meta_path else {}
if not isinstance(meta, dict):
prints("Expected dict but got: {}".format(type(meta)),
title="Not a valid meta.json format", exits=1)
pipeline = ['token_vectors', 'tags', 'dependencies', 'entities'] pipeline = ['token_vectors', 'tags', 'dependencies', 'entities']
if no_tagger and 'tags' in pipeline: pipeline.remove('tags') if no_tagger and 'tags' in pipeline: pipeline.remove('tags')
@ -105,9 +113,16 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
corpus.dev_docs( corpus.dev_docs(
nlp, nlp,
gold_preproc=gold_preproc)) gold_preproc=gold_preproc))
acc_loc =(output_path / ('model%d' % i) / 'accuracy.json') meta_loc = output_path / ('model%d' % i) / 'meta.json'
with acc_loc.open('w') as file_: meta['accuracy'] = scorer.scores
file_.write(json_dumps(scorer.scores)) meta['lang'] = nlp.lang
meta['pipeline'] = pipeline
meta['spacy_version'] = '>=%s' % about.__version__
meta.setdefault('name', 'model%d' % i)
meta.setdefault('version', '0.0.0')
with meta_loc.open('w') as file_:
file_.write(json_dumps(meta))
util.set_env_log(True) util.set_env_log(True)
print_progress(i, losses, scorer.scores) print_progress(i, losses, scorer.scores)
finally: finally: