spaCy/spacy/cli/train.py
2018-08-16 00:41:44 +02:00

288 lines
12 KiB
Python

# coding: utf8
from __future__ import unicode_literals, division, print_function
import plac
from pathlib import Path
import tqdm
from thinc.neural._classes.model import Model
from timeit import default_timer as timer
import json
import shutil
from ._messages import Messages
from ..attrs import PROB, IS_OOV, CLUSTER, LANG
from ..gold import GoldCorpus
from ..util import prints, minibatch, minibatch_by_words
from .. import util
from .. import about
from .. import displacy
from ..compat import json_dumps
@plac.annotations(
lang=("model language", "positional", None, str),
output_dir=("output directory to store model in", "positional", None, str),
train_data=("location of JSON-formatted training data", "positional",
None, str),
dev_data=("location of JSON-formatted development data (optional)",
"positional", None, str),
n_iter=("number of iterations", "option", "n", int),
n_sents=("number of sentences", "option", "ns", int),
use_gpu=("Use GPU", "option", "g", int),
vectors=("Model to load vectors from", "option", "v"),
no_tagger=("Don't train tagger", "flag", "T", bool),
no_parser=("Don't train parser", "flag", "P", bool),
no_entities=("Don't train NER", "flag", "N", bool),
parser_multitasks=("Side objectives for parser CNN, e.g. dep dep,tag", "option", "pt", str),
noise_level=("Amount of corruption to add for data augmentation", "option", "nl", float),
entity_multitasks=("Side objectives for ner CNN, e.g. dep dep,tag", "option", "et", str),
gold_preproc=("Use gold preprocessing", "flag", "G", bool),
version=("Model version", "option", "V", str),
meta_path=("Optional path to meta.json. All relevant properties will be "
"overwritten.", "option", "m", Path),
verbose=("Display more information for debug", "option", None, bool))
def train(lang, output_dir, train_data, dev_data, n_iter=30, n_sents=0,
parser_multitasks='', entity_multitasks='',
use_gpu=-1, vectors=None, no_tagger=False, noise_level=0.0,
no_parser=False, no_entities=False, gold_preproc=False,
version="0.0.0", meta_path=None, verbose=False):
"""
Train a model. Expects data in spaCy's JSON format.
"""
util.fix_random_seed()
util.set_env_log(True)
n_sents = n_sents or None
output_path = util.ensure_path(output_dir)
train_path = util.ensure_path(train_data)
dev_path = util.ensure_path(dev_data)
meta_path = util.ensure_path(meta_path)
if not train_path.exists():
prints(train_path, title=Messages.M050, exits=1)
if dev_path and not dev_path.exists():
prints(dev_path, title=Messages.M051, exits=1)
if meta_path is not None and not meta_path.exists():
prints(meta_path, title=Messages.M020, exits=1)
meta = util.read_json(meta_path) if meta_path else {}
if not isinstance(meta, dict):
prints(Messages.M053.format(meta_type=type(meta)),
title=Messages.M052, exits=1)
meta.setdefault('lang', lang)
meta.setdefault('name', 'unnamed')
if not output_path.exists():
output_path.mkdir()
print("Counting training words (limit=%s" % n_sents)
corpus = GoldCorpus(train_path, dev_path, limit=n_sents)
n_train_words = corpus.count_train()
print(n_train_words)
pipeline = ['tagger', 'parser', 'ner']
if no_tagger and 'tagger' in pipeline:
pipeline.remove('tagger')
if no_parser and 'parser' in pipeline:
pipeline.remove('parser')
if no_entities and 'ner' in pipeline:
pipeline.remove('ner')
# Take dropout and batch size as generators of values -- dropout
# starts high and decays sharply, to force the optimizer to explore.
# Batch size starts at 1 and grows, so that we make updates quickly
# at the beginning of training.
dropout_rates = util.decaying(util.env_opt('dropout_from', 0.2),
util.env_opt('dropout_to', 0.2),
util.env_opt('dropout_decay', 0.0))
batch_sizes = util.compounding(util.env_opt('batch_from', 1000),
util.env_opt('batch_to', 1000),
util.env_opt('batch_compound', 1.001))
lang_class = util.get_lang_class(lang)
nlp = lang_class()
meta['pipeline'] = pipeline
nlp.meta.update(meta)
if vectors:
print("Load vectors model", vectors)
util.load_model(vectors, vocab=nlp.vocab)
for lex in nlp.vocab:
values = {}
for attr, func in nlp.vocab.lex_attr_getters.items():
# These attrs are expected to be set by data. Others should
# be set by calling the language functions.
if attr not in (CLUSTER, PROB, IS_OOV, LANG):
values[lex.vocab.strings[attr]] = func(lex.orth_)
lex.set_attrs(**values)
lex.is_oov = False
for name in pipeline:
nlp.add_pipe(nlp.create_pipe(name), name=name)
nlp.add_pipe(nlp.create_pipe('merge_subtokens'))
if parser_multitasks:
for objective in parser_multitasks.split(','):
nlp.parser.add_multitask_objective(objective)
if entity_multitasks:
for objective in entity_multitasks.split(','):
nlp.entity.add_multitask_objective(objective)
optimizer = nlp.begin_training(lambda: corpus.train_tuples, device=use_gpu)
nlp._optimizer = None
print("Itn. Dep Loss NER Loss UAS NER P. NER R. NER F. Tag % Token % CPU WPS GPU WPS")
try:
for i in range(n_iter):
train_docs = corpus.train_docs(nlp, noise_level=noise_level,
gold_preproc=gold_preproc, max_length=0)
words_seen = 0
with tqdm.tqdm(total=n_train_words, leave=False) as pbar:
losses = {}
for batch in minibatch_by_words(train_docs, size=batch_sizes):
if not batch:
continue
docs, golds = zip(*batch)
nlp.update(docs, golds, sgd=optimizer,
drop=next(dropout_rates), losses=losses)
pbar.update(sum(len(doc) for doc in docs))
words_seen += sum(len(doc) for doc in docs)
with nlp.use_params(optimizer.averages):
util.set_env_log(False)
epoch_model_path = output_path / ('model%d' % i)
nlp.to_disk(epoch_model_path)
nlp_loaded = util.load_model_from_path(epoch_model_path)
dev_docs = list(corpus.dev_docs(
nlp_loaded,
gold_preproc=gold_preproc))
nwords = sum(len(doc_gold[0]) for doc_gold in dev_docs)
start_time = timer()
scorer = nlp_loaded.evaluate(dev_docs, verbose)
end_time = timer()
if use_gpu < 0:
gpu_wps = None
cpu_wps = nwords/(end_time-start_time)
else:
gpu_wps = nwords/(end_time-start_time)
with Model.use_device('cpu'):
nlp_loaded = util.load_model_from_path(epoch_model_path)
dev_docs = list(corpus.dev_docs(
nlp_loaded, gold_preproc=gold_preproc))
start_time = timer()
scorer = nlp_loaded.evaluate(dev_docs)
end_time = timer()
cpu_wps = nwords/(end_time-start_time)
acc_loc = (output_path / ('model%d' % i) / 'accuracy.json')
with acc_loc.open('w') as file_:
file_.write(json_dumps(scorer.scores))
meta_loc = output_path / ('model%d' % i) / 'meta.json'
meta['accuracy'] = scorer.scores
meta['speed'] = {'nwords': nwords, 'cpu': cpu_wps,
'gpu': gpu_wps}
meta['vectors'] = {'width': nlp.vocab.vectors_length,
'vectors': len(nlp.vocab.vectors),
'keys': nlp.vocab.vectors.n_keys}
meta['lang'] = nlp.lang
meta['pipeline'] = pipeline
meta['spacy_version'] = '>=%s' % about.__version__
meta.setdefault('name', 'model%d' % i)
meta.setdefault('version', version)
with meta_loc.open('w') as file_:
file_.write(json_dumps(meta))
util.set_env_log(True)
print_progress(i, losses, scorer.scores, cpu_wps=cpu_wps,
gpu_wps=gpu_wps)
finally:
print("Saving model...")
with nlp.use_params(optimizer.averages):
final_model_path = output_path / 'model-final'
nlp.to_disk(final_model_path)
components = []
if not no_parser:
components.append('parser')
if not no_tagger:
components.append('tagger')
if not no_entities:
components.append('ner')
_collate_best_model(meta, output_path, components)
def _collate_best_model(meta, output_path, components):
bests = {}
for component in components:
bests[component] = _find_best(output_path, component)
best_dest = output_path / 'model-best'
shutil.copytree(output_path / 'model-final', best_dest)
for component, best_component_src in bests.items():
shutil.rmtree(best_dest / component)
shutil.copytree(best_component_src / component, best_dest / component)
with (best_component_src / 'accuracy.json').open() as file_:
accs = json.load(file_)
for metric in _get_metrics(component):
meta['accuracy'][metric] = accs[metric]
with (best_dest / 'meta.json').open('w') as file_:
file_.write(json_dumps(meta))
def _find_best(experiment_dir, component):
accuracies = []
for epoch_model in experiment_dir.iterdir():
if epoch_model.is_dir() and epoch_model.parts[-1] != "model-final":
accs = json.load((epoch_model / "accuracy.json").open())
scores = [accs.get(metric, 0.0) for metric in _get_metrics(component)]
accuracies.append((scores, epoch_model))
if accuracies:
return max(accuracies)[1]
else:
return None
def _get_metrics(component):
if component == "parser":
return ("las", "uas", "token_acc")
elif component == "tagger":
return ("tags_acc",)
elif component == "ner":
return ("ents_f", "ents_p", "ents_r")
return ("token_acc",)
def _render_parses(i, to_render):
to_render[0].user_data['title'] = "Batch %d" % i
with Path('/tmp/entities.html').open('w') as file_:
html = displacy.render(to_render[:5], style='ent', page=True)
file_.write(html)
with Path('/tmp/parses.html').open('w') as file_:
html = displacy.render(to_render[:5], style='dep', page=True)
file_.write(html)
def print_progress(itn, losses, dev_scores, cpu_wps=0.0, gpu_wps=0.0):
scores = {}
for col in ['dep_loss', 'tag_loss', 'uas', 'tags_acc', 'token_acc',
'ents_p', 'ents_r', 'ents_f', 'cpu_wps', 'gpu_wps']:
scores[col] = 0.0
scores['dep_loss'] = losses.get('parser', 0.0)
scores['ner_loss'] = losses.get('ner', 0.0)
scores['tag_loss'] = losses.get('tagger', 0.0)
scores.update(dev_scores)
scores['cpu_wps'] = cpu_wps
scores['gpu_wps'] = gpu_wps or 0.0
tpl = ''.join((
'{:<6d}',
'{dep_loss:<10.3f}',
'{ner_loss:<10.3f}',
'{uas:<8.3f}',
'{ents_p:<8.3f}',
'{ents_r:<8.3f}',
'{ents_f:<8.3f}',
'{tags_acc:<8.3f}',
'{token_acc:<9.3f}',
'{cpu_wps:<9.1f}',
'{gpu_wps:.1f}',
))
print(tpl.format(itn, **scores))
def print_results(scorer):
results = {
'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}
util.print_table(results, title="Results")