mirror of https://github.com/explosion/spaCy.git
186 lines
7.5 KiB
Python
186 lines
7.5 KiB
Python
# coding: utf8
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from __future__ import unicode_literals, division, print_function
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import plac
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import json
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from collections import defaultdict
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import cytoolz
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from pathlib import Path
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import dill
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import tqdm
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from thinc.neural._classes.model import Model
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from thinc.neural.optimizers import linear_decay
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from timeit import default_timer as timer
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import random
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import numpy.random
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from ..tokens.doc import Doc
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from ..scorer import Scorer
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from ..gold import GoldParse, merge_sents
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from ..gold import GoldCorpus, minibatch
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from ..util import prints
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from .. import util
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from .. import about
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from .. import displacy
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from ..compat import json_dumps
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random.seed(0)
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numpy.random.seed(0)
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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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n_sents=("number of sentences", "option", "ns", int),
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use_gpu=("Use GPU", "option", "g", int),
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vectors=("Model to load vectors from", "option", "v"),
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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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gold_preproc=("Use gold preprocessing", "flag", "G", bool),
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meta_path=("Optional path to meta.json. All relevant properties will be overwritten.", "option", "m", Path)
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)
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def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
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use_gpu=-1, vectors=None, no_tagger=False, no_parser=False, no_entities=False,
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gold_preproc=False, meta_path=None):
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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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util.set_env_log(True)
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n_sents = n_sents or None
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output_path = util.ensure_path(output_dir)
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train_path = util.ensure_path(train_data)
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dev_path = util.ensure_path(dev_data)
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meta_path = util.ensure_path(meta_path)
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if not output_path.exists():
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output_path.mkdir()
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if not train_path.exists():
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prints(train_path, title="Training data not found", exits=1)
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if dev_path and not dev_path.exists():
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prints(dev_path, title="Development data not found", exits=1)
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if meta_path is not None and not meta_path.exists():
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prints(meta_path, title="meta.json not found", exits=1)
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meta = util.read_json(meta_path) if meta_path else {}
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if not isinstance(meta, dict):
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prints("Expected dict but got: {}".format(type(meta)),
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title="Not a valid meta.json format", exits=1)
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pipeline = ['tags', 'dependencies', 'entities']
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if no_tagger and 'tags' in pipeline: pipeline.remove('tags')
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if no_parser and 'dependencies' in pipeline: pipeline.remove('dependencies')
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if no_entities and 'entities' in pipeline: pipeline.remove('entities')
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# Take dropout and batch size as generators of values -- dropout
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# starts high and decays sharply, to force the optimizer to explore.
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# Batch size starts at 1 and grows, so that we make updates quickly
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# at the beginning of training.
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dropout_rates = util.decaying(util.env_opt('dropout_from', 0.2),
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util.env_opt('dropout_to', 0.2),
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util.env_opt('dropout_decay', 0.0))
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batch_sizes = util.compounding(util.env_opt('batch_from', 1),
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util.env_opt('batch_to', 64),
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util.env_opt('batch_compound', 1.001))
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corpus = GoldCorpus(train_path, dev_path, limit=n_sents)
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n_train_words = corpus.count_train()
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lang_class = util.get_lang_class(lang)
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nlp = lang_class(pipeline=pipeline)
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if vectors:
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util.load_model(vectors, vocab=nlp.vocab)
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optimizer = nlp.begin_training(lambda: corpus.train_tuples, device=use_gpu)
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nlp._optimizer = None
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print("Itn.\tLoss\tUAS\tNER P.\tNER R.\tNER F.\tTag %\tToken %")
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try:
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train_docs = corpus.train_docs(nlp, projectivize=True, noise_level=0.0,
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gold_preproc=gold_preproc, max_length=0)
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train_docs = list(train_docs)
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for i in range(n_iter):
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with tqdm.tqdm(total=n_train_words, leave=False) as pbar:
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losses = {}
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for batch in minibatch(train_docs, size=batch_sizes):
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docs, golds = zip(*batch)
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nlp.update(docs, golds, sgd=optimizer,
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drop=next(dropout_rates), losses=losses)
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pbar.update(sum(len(doc) for doc in docs))
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with nlp.use_params(optimizer.averages):
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util.set_env_log(False)
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epoch_model_path = output_path / ('model%d' % i)
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nlp.to_disk(epoch_model_path)
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nlp_loaded = lang_class(pipeline=pipeline)
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nlp_loaded = nlp_loaded.from_disk(epoch_model_path)
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scorer = nlp_loaded.evaluate(
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list(corpus.dev_docs(
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nlp_loaded,
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gold_preproc=gold_preproc)))
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acc_loc =(output_path / ('model%d' % i) / 'accuracy.json')
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with acc_loc.open('w') as file_:
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file_.write(json_dumps(scorer.scores))
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meta_loc = output_path / ('model%d' % i) / 'meta.json'
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meta['accuracy'] = scorer.scores
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meta['lang'] = nlp.lang
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meta['pipeline'] = pipeline
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meta['spacy_version'] = '>=%s' % about.__version__
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meta.setdefault('name', 'model%d' % i)
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meta.setdefault('version', '0.0.0')
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with meta_loc.open('w') as file_:
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file_.write(json_dumps(meta))
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util.set_env_log(True)
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print_progress(i, losses, scorer.scores)
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finally:
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print("Saving model...")
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try:
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with (output_path / 'model-final.pickle').open('wb') as file_:
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with nlp.use_params(optimizer.averages):
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dill.dump(nlp, file_, -1)
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except:
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pass
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def _render_parses(i, to_render):
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to_render[0].user_data['title'] = "Batch %d" % i
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with Path('/tmp/entities.html').open('w') as file_:
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html = displacy.render(to_render[:5], style='ent', page=True)
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file_.write(html)
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with Path('/tmp/parses.html').open('w') as file_:
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html = displacy.render(to_render[:5], style='dep', page=True)
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file_.write(html)
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def print_progress(itn, losses, dev_scores, wps=0.0):
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scores = {}
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for col in ['dep_loss', 'tag_loss', 'uas', 'tags_acc', 'token_acc',
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'ents_p', 'ents_r', 'ents_f', 'wps']:
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scores[col] = 0.0
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scores['dep_loss'] = losses.get('parser', 0.0)
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scores['tag_loss'] = losses.get('tagger', 0.0)
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scores.update(dev_scores)
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scores['wps'] = wps
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tpl = '\t'.join((
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'{:d}',
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'{dep_loss:.3f}',
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'{uas:.3f}',
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'{ents_p:.3f}',
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'{ents_r:.3f}',
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'{ents_f:.3f}',
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'{tags_acc:.3f}',
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'{token_acc:.3f}',
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'{wps:.1f}'))
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print(tpl.format(itn, **scores))
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def print_results(scorer):
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results = {
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'TOK': '%.2f' % scorer.token_acc,
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'POS': '%.2f' % scorer.tags_acc,
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'UAS': '%.2f' % scorer.uas,
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'LAS': '%.2f' % scorer.las,
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'NER P': '%.2f' % scorer.ents_p,
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'NER R': '%.2f' % scorer.ents_r,
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'NER F': '%.2f' % scorer.ents_f}
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util.print_table(results, title="Results")
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