# 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 from ._messages import Messages from ..attrs import PROB, IS_OOV, CLUSTER, LANG from ..gold import GoldCorpus, minibatch from ..util import prints 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), 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)) 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, 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.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 output_path.exists(): output_path.mkdir() 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') 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: 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) 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.\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")