spaCy/spacy/cli/train.py

# coding: utf8
from __future__ import unicode_literals, division, print_function

import plac
import json
from collections import defaultdict
import cytoolz
from pathlib import Path
import dill
import tqdm
from thinc.neural._classes.model import Model
from thinc.neural.optimizers import linear_decay
from timeit import default_timer as timer
import random
import numpy.random

from ..tokens.doc import Doc
from ..scorer import Scorer
from ..gold import GoldParse, merge_sents
from ..gold import GoldCorpus, minibatch
from ..util import prints
from .. import util
from .. import about
from .. import displacy
from ..compat import json_dumps

random.seed(0)
numpy.random.seed(0)


@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),
    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)
)
def train(cmd, lang, output_dir, train_data, dev_data, n_iter=10, n_sents=0,
          use_gpu=-1, vectors=None, no_tagger=False, no_parser=False, no_entities=False,
          gold_preproc=False, version="0.0.0", meta_path=None):
    """
    Train a model. Expects data in spaCy's JSON format.
    """
    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 output_path.exists():
        output_path.mkdir()
    if not train_path.exists():
        prints(train_path, title="Training data not found", exits=1)
    if dev_path and not dev_path.exists():
        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 = ['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', 1),
                                   util.env_opt('batch_to', 16),
                                   util.env_opt('batch_compound', 1.001))
    corpus = GoldCorpus(train_path, dev_path, limit=n_sents)
    n_train_words = corpus.count_train()

    lang_class = util.get_lang_class(lang)
    nlp = lang_class(pipeline=pipeline)
    if vectors:
        util.load_model(vectors, vocab=nlp.vocab)
    optimizer = nlp.begin_training(lambda: corpus.train_tuples, device=use_gpu)
    nlp._optimizer = None

    print("Itn.\tP.Loss\tN.Loss\tUAS\tNER P.\tNER R.\tNER F.\tTag %\tToken %")
    try:
        train_docs = corpus.train_docs(nlp, projectivize=True, noise_level=0.0,
                                       gold_preproc=gold_preproc, max_length=0)
        train_docs = list(train_docs)
        for i in range(n_iter):
            with tqdm.tqdm(total=n_train_words, leave=False) as pbar:
                losses = {}
                for batch in minibatch(train_docs, size=batch_sizes):
                    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))

            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 = lang_class(pipeline=pipeline)
                nlp_loaded = nlp_loaded.from_disk(epoch_model_path)
                scorer = nlp_loaded.evaluate(
                            list(corpus.dev_docs(
                                nlp_loaded,
                                gold_preproc=gold_preproc)))
                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['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)
    finally:
        print("Saving model...")
        try:
            with (output_path / 'model-final.pickle').open('wb') as file_:
                with nlp.use_params(optimizer.averages):
                    dill.dump(nlp, file_, -1)
        except:
            print("Error saving model")


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, wps=0.0):
    scores = {}
    for col in ['dep_loss', 'tag_loss', 'uas', 'tags_acc', 'token_acc',
                'ents_p', 'ents_r', 'ents_f', '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['wps'] = wps
    tpl = '\t'.join((
        '{:d}',
        '{dep_loss:.3f}',
        '{ner_loss:.3f}',
        '{uas:.3f}',
        '{ents_p:.3f}',
        '{ents_r:.3f}',
        '{ents_f:.3f}',
        '{tags_acc:.3f}',
        '{token_acc:.3f}',
        '{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")