mirror of
https://github.com/explosion/spaCy.git
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117 lines
4.2 KiB
Python
117 lines
4.2 KiB
Python
# coding: utf8
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from __future__ import unicode_literals, division, print_function
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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 ..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
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from ..util import prints
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from .. import util
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from .. import displacy
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def train(lang_id, output_dir, train_data, dev_data, n_iter, n_sents,
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use_gpu, no_tagger, no_parser, no_entities):
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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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if not output_path.exists():
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prints(output_path, title="Output directory not found", exits=True)
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if not train_path.exists():
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prints(train_path, title="Training data not found", exits=True)
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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=True)
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lang_class = util.get_lang_class(lang_id)
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pipeline = ['token_vectors', '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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nlp = lang_class(pipeline=pipeline)
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corpus = GoldCorpus(train_path, dev_path)
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dropout = util.env_opt('dropout', 0.0)
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optimizer = nlp.begin_training(lambda: corpus.train_tuples, use_gpu=use_gpu)
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n_train_docs = corpus.count_train()
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print("Itn.\tDep. Loss\tUAS\tNER F.\tTag %\tToken %")
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for i in range(n_iter):
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with tqdm.tqdm(total=n_train_docs) as pbar:
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train_docs = corpus.train_docs(nlp, shuffle=i)
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for batch in cytoolz.partition_all(20, train_docs):
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docs, golds = zip(*batch)
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docs = list(docs)
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golds = list(golds)
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nlp.update(docs, golds, drop=dropout, sgd=optimizer)
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pbar.update(len(docs))
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scorer = nlp.evaluate(corpus.dev_docs(nlp))
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print_progress(i, {}, scorer.scores)
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with (output_path / 'model.bin').open('wb') as file_:
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dill.dump(nlp, file_, -1)
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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 evaluate(Language, gold_tuples, path):
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with (path / 'model.bin').open('rb') as file_:
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nlp = dill.load(file_)
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# TODO:
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# 1. This code is duplicate with spacy.train.Trainer.evaluate
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# 2. There's currently a semantic difference between pipe and
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# not pipe! It matters whether we batch the inputs. Must fix!
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all_docs = []
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all_golds = []
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for raw_text, paragraph_tuples in dev_sents:
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if gold_preproc:
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raw_text = None
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else:
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paragraph_tuples = merge_sents(paragraph_tuples)
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docs = self.make_docs(raw_text, paragraph_tuples)
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golds = self.make_golds(docs, paragraph_tuples)
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all_docs.extend(docs)
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all_golds.extend(golds)
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scorer = Scorer()
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for doc, gold in zip(self.nlp.pipe(all_docs), all_golds):
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scorer.score(doc, gold)
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return scorer
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def print_progress(itn, losses, dev_scores):
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# TODO: Fix!
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scores = {}
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for col in ['dep_loss', 'tag_loss', 'uas', 'tags_acc', 'token_acc', 'ents_f']:
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scores[col] = 0.0
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scores.update(losses)
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scores.update(dev_scores)
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tpl = '{:d}\t{dep_loss:.3f}\t{tag_loss:.3f}\t{uas:.3f}\t{ents_f:.3f}\t{tags_acc:.3f}\t{token_acc:.3f}'
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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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