# 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=20, 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 = ['tags', 'dependencies', 'entities'] if no_tagger and 'tags' in pipeline: pipeline.remove('tags') if no_parser and 'dependencies' in pipeline: pipeline.remove('dependencies') if no_entities and 'entities' in pipeline: pipeline.remove('entities') # 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', 32), 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.\tLoss\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: pass 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['tag_loss'] = losses.get('tagger', 0.0) scores.update(dev_scores) scores['wps'] = wps tpl = '\t'.join(( '{:d}', '{dep_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")