spaCy/spacy/cli/train.py

# 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 random
import numpy.random

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=30, 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)
    meta.setdefault('lang', lang)
    meta.setdefault('name', 'unnamed')

    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))
    max_doc_len = util.env_opt('max_doc_len', 5000)
    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()
    meta['pipeline'] = pipeline
    nlp.meta.update(meta)
    if vectors:
        util.load_model(vectors, vocab=nlp.vocab)
    for name in pipeline:
        nlp.add_pipe(nlp.create_pipe(name), name=name)
    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):
                    batch = [(d, g) for (d, g) in batch if len(d) < max_doc_len]
                    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))

            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)
                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)


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 = '\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}',
        '{cpu_wps:.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")