Add spacy train work in progress

This commit is contained in:
ines 2017-03-23 11:08:41 +01:00
parent d5ebf583a4
commit 0035fd9efe
3 changed files with 129 additions and 2 deletions

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@ -8,12 +8,14 @@ from spacy.cli import download as cli_download
from spacy.cli import link as cli_link
from spacy.cli import info as cli_info
from spacy.cli import package as cli_package
from spacy.cli import train as cli_train
from spacy.cli import train_config as cli_train_config
class CLI(object):
"""Command-line interface for spaCy"""
commands = ('download', 'link', 'info', 'package')
commands = ('download', 'link', 'info', 'package', 'train', 'train_config')
@plac.annotations(
model=("model to download (shortcut or model name)", "positional", None, str),
@ -61,7 +63,7 @@ class CLI(object):
@plac.annotations(
input_dir=("directory with model data", "positional", None, str),
output_dir=("output directory", "positional", None, str),
output_dir=("output parent directory", "positional", None, str),
force=("force overwriting of existing folder in output directory", "flag", "f", bool)
)
def package(self, input_dir, output_dir, force=False):
@ -74,6 +76,32 @@ class CLI(object):
cli_package(input_dir, output_dir, force)
@plac.annotations(
lang=("language", "positional", None, str),
output_dir=("output directory", "positional", None, str),
train_data=("training data", "positional", None, str),
dev_data=("development data", "positional", None, str),
n_iter=("number of iterations", "flag", "n", int),
tagger=("train tagger", "flag", "t", bool),
parser=("train parser", "flag", "p", bool),
ner=("train NER", "flag", "n", bool)
)
def train(self, lang, output_dir, train_data, dev_data, n_iter=15, tagger=True,
parser=True, ner=True):
"""Train a model."""
cli_train(output_dir, train_data, dev_data, tagger, parser, ner)
@plac.annotations(
config=("config", "positional", None, str),
)
def train_config(self, config):
"""Train a model from config file."""
cli_train_config(config)
def __missing__(self, name):
print("\n Command %r does not exist\n" % name)

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@ -2,3 +2,4 @@ from .download import download
from .info import info
from .link import link
from .package import package
from .train import train, train_config

98
spacy/cli/train.py Normal file
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@ -0,0 +1,98 @@
# coding: utf8
from __future__ import unicode_literals, division, print_function
import json
from pathlib import Path
from ..scorer import Scorer
from ..tagger import Tagger
from ..syntax.parser import Parser
from ..gold import GoldParse, merge_sents
from ..gold import read_json_file as read_gold_json
from .. import util
def train(language, output_dir, train_data, dev_data, n_iter, tagger, parser, ner):
output_path = Path(output_dir)
train_path = Path(train_data)
dev_path = Path(dev_data)
check_dirs(output_path, data_path, dev_path)
lang = util.get_lang_class(language)
parser_cfg = dict(locals())
tagger_cfg = dict(locals())
entity_cfg = dict(locals())
parser_cfg['features'] = lang.Defaults.parser_features
entity_cfg['features'] = lang.Defaults.entity_features
gold_train = list(read_gold_json(train_path))
gold_dev = list(read_gold_json(dev_path))
train_model(lang, gold_train, gold_dev, output_path, tagger_cfg, parser_cfg,
entity_cfg, n_iter)
scorer = evaluate(lang, list(read_gold_json(dev_loc)), output_path)
print_results(scorer)
def train_config(config):
config_path = Path(config)
if not config_path.is_file():
util.sys_exit(config_path.as_posix(), title="Config file not found")
config = json.load(config_path)
for setting in []:
if setting not in config.keys():
util.sys_exit("{s} not found in config file.".format(s=setting),
title="Missing setting")
def train_model(Language, train_data, dev_data, output_path, tagger_cfg, parser_cfg,
entity_cfg, n_iter):
print("Itn.\tN weight\tN feats\tUAS\tNER F.\tTag %\tToken %")
with Language.train(output_path, train_data, tagger_cfg, parser_cfg, entity_cfg) as trainer:
loss = 0
for itn, epoch in enumerate(trainer.epochs(n_iter, augment_data=None)):
for doc, gold in epoch:
trainer.update(doc, gold)
dev_scores = trainer.evaluate(dev_data)
print_progress(itn, trainer.nlp.parser.model.nr_weight,
trainer.nlp.parser.model.nr_active_feat,
**dev_scores.scores)
def evaluate(Language, gold_tuples, output_path):
print("Load parser", output_path)
nlp = Language(path=output_path)
scorer = Scorer()
for raw_text, sents in gold_tuples:
sents = merge_sents(sents)
for annot_tuples, brackets in sents:
if raw_text is None:
tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
nlp.tagger(tokens)
nlp.parser(tokens)
nlp.entity(tokens)
else:
tokens = nlp(raw_text)
gold = GoldParse.from_annot_tuples(tokens, annot_tuples)
scorer.score(tokens, gold)
return scorer
def check_dirs(input_path, train_path, dev_path):
if not output_path.exists():
util.sys_exit(output_path.as_posix(), title="Output directory not found")
if not train_path.exists() and train_path.is_file():
util.sys_exit(train_path.as_posix(), title="Training data not found")
def print_progress(itn, nr_weight, nr_active_feat, **scores):
tpl = '{:d}\t{:d}\t{:d}\t{uas:.3f}\t{ents_f:.3f}\t{tags_acc:.3f}\t{token_acc:.3f}'
print(tpl.format(itn, nr_weight, nr_active_feat, **scores))
def print_results(scorer):
results = {'TOK': scorer.token_acc, 'POS': scorer.tags_acc, 'UAS': scorer.uas,
'LAS': scorer.las, 'NER P': scorer.ents_p, 'NER R': scorer.ents_r,
'NER F': scorer.ents_f}
util.print_table(results, title="Results")