mirror of https://github.com/explosion/spaCy.git
* Bridge to Theano working. Very disorganised. Using thinc adb60aba966ed2
This commit is contained in:
Matthew Honnibal
parent
2fe98b8a9a
commit
f8bb43475e
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#!/usr/bin/env python
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from __future__ import division
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from __future__ import unicode_literals
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import os
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from os import path
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import shutil
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import codecs
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import random
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import plac
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import cProfile
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import pstats
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import re
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import spacy.util
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from spacy.en import English
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from spacy.en.pos import POS_TEMPLATES, POS_TAGS, setup_model_dir
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from spacy.syntax.util import Config
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from spacy.gold import read_json_file
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from spacy.gold import GoldParse
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from spacy.scorer import Scorer
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from thinc.theano_nn import compile_theano_model
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from spacy.syntax.parser import Parser
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from spacy._theano import TheanoModel
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def _corrupt(c, noise_level):
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if random.random() >= noise_level:
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return c
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elif c == ' ':
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return '\n'
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elif c == '\n':
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return ' '
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elif c in ['.', "'", "!", "?"]:
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return ''
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else:
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return c.lower()
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def add_noise(orig, noise_level):
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if random.random() >= noise_level:
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return orig
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elif type(orig) == list:
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corrupted = [_corrupt(word, noise_level) for word in orig]
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corrupted = [w for w in corrupted if w]
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return corrupted
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else:
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return ''.join(_corrupt(c, noise_level) for c in orig)
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def score_model(scorer, nlp, raw_text, annot_tuples, verbose=False):
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if raw_text is None:
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tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
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else:
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tokens = nlp.tokenizer(raw_text)
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nlp.tagger(tokens)
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nlp.entity(tokens)
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nlp.parser(tokens)
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gold = GoldParse(tokens, annot_tuples)
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scorer.score(tokens, gold, verbose=verbose)
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def _merge_sents(sents):
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m_deps = [[], [], [], [], [], []]
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m_brackets = []
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i = 0
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for (ids, words, tags, heads, labels, ner), brackets in sents:
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m_deps[0].extend(id_ + i for id_ in ids)
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m_deps[1].extend(words)
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m_deps[2].extend(tags)
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m_deps[3].extend(head + i for head in heads)
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m_deps[4].extend(labels)
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m_deps[5].extend(ner)
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m_brackets.extend((b['first'] + i, b['last'] + i, b['label']) for b in brackets)
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i += len(ids)
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return [(m_deps, m_brackets)]
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def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic',
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seed=0, gold_preproc=False, n_sents=0, corruption_level=0,
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verbose=False,
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eta=0.01, mu=0.9, n_hidden=100, word_vec_len=10, pos_vec_len=10):
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dep_model_dir = path.join(model_dir, 'deps')
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pos_model_dir = path.join(model_dir, 'pos')
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ner_model_dir = path.join(model_dir, 'ner')
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if path.exists(dep_model_dir):
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shutil.rmtree(dep_model_dir)
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if path.exists(pos_model_dir):
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shutil.rmtree(pos_model_dir)
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if path.exists(ner_model_dir):
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shutil.rmtree(ner_model_dir)
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os.mkdir(dep_model_dir)
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os.mkdir(pos_model_dir)
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os.mkdir(ner_model_dir)
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setup_model_dir(sorted(POS_TAGS.keys()), POS_TAGS, POS_TEMPLATES, pos_model_dir)
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Config.write(dep_model_dir, 'config', features=feat_set, seed=seed,
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labels=Language.ParserTransitionSystem.get_labels(gold_tuples))
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Config.write(ner_model_dir, 'config', features='ner', seed=seed,
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labels=Language.EntityTransitionSystem.get_labels(gold_tuples),
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beam_width=0)
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if n_sents > 0:
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gold_tuples = gold_tuples[:n_sents]
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nlp = Language(data_dir=model_dir)
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def make_model(n_classes, input_spec, model_dir):
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print input_spec
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n_in = sum(n_cols * len(fields) for (n_cols, fields) in input_spec)
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print 'Compiling'
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debug, train_func, predict_func = compile_theano_model(n_classes, n_hidden,
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n_in, 0.0, 0.0)
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print 'Done'
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return TheanoModel(
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n_classes,
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input_spec,
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train_func,
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predict_func,
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model_loc=model_dir,
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debug=debug)
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nlp._parser = Parser(nlp.vocab.strings, dep_model_dir, nlp.ParserTransitionSystem,
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make_model)
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print "Itn.\tP.Loss\tUAS\tNER F.\tTag %\tToken %"
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for itn in range(n_iter):
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scorer = Scorer()
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loss = 0
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for raw_text, sents in gold_tuples:
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if gold_preproc:
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raw_text = None
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else:
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sents = _merge_sents(sents)
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for annot_tuples, ctnt in sents:
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if len(annot_tuples[1]) == 1:
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continue
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score_model(scorer, nlp, raw_text, annot_tuples,
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verbose=verbose if itn >= 2 else False)
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if raw_text is None:
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words = add_noise(annot_tuples[1], corruption_level)
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tokens = nlp.tokenizer.tokens_from_list(words)
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else:
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raw_text = add_noise(raw_text, corruption_level)
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tokens = nlp.tokenizer(raw_text)
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nlp.tagger(tokens)
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gold = GoldParse(tokens, annot_tuples, make_projective=True)
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if not gold.is_projective:
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raise Exception(
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"Non-projective sentence in training, after we should "
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"have enforced projectivity: %s" % annot_tuples
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)
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loss += nlp.parser.train(tokens, gold)
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nlp.entity.train(tokens, gold)
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nlp.tagger.train(tokens, gold.tags)
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random.shuffle(gold_tuples)
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print '%d:\t%d\t%.3f\t%.3f\t%.3f\t%.3f' % (itn, loss, scorer.uas, scorer.ents_f,
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scorer.tags_acc,
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scorer.token_acc)
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nlp.parser.model.end_training()
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nlp.entity.model.end_training()
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nlp.tagger.model.end_training()
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nlp.vocab.strings.dump(path.join(model_dir, 'vocab', 'strings.txt'))
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return nlp
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def evaluate(nlp, gold_tuples, gold_preproc=True):
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scorer = Scorer()
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for raw_text, sents in gold_tuples:
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if gold_preproc:
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raw_text = None
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else:
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sents = _merge_sents(sents)
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for annot_tuples, brackets in sents:
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if raw_text is None:
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tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
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nlp.tagger(tokens)
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nlp.entity(tokens)
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nlp.parser(tokens)
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else:
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tokens = nlp(raw_text, merge_mwes=False)
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gold = GoldParse(tokens, annot_tuples)
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scorer.score(tokens, gold)
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return scorer
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def write_parses(Language, dev_loc, model_dir, out_loc, beam_width=None):
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nlp = Language(data_dir=model_dir)
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if beam_width is not None:
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nlp.parser.cfg.beam_width = beam_width
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gold_tuples = read_json_file(dev_loc)
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scorer = Scorer()
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out_file = codecs.open(out_loc, 'w', 'utf8')
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for raw_text, sents in gold_tuples:
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sents = _merge_sents(sents)
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for annot_tuples, brackets in sents:
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if raw_text is None:
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tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
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nlp.tagger(tokens)
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nlp.entity(tokens)
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nlp.parser(tokens)
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else:
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tokens = nlp(raw_text, merge_mwes=False)
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gold = GoldParse(tokens, annot_tuples)
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scorer.score(tokens, gold, verbose=False)
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for t in tokens:
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out_file.write(
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'%s\t%s\t%s\t%s\n' % (t.orth_, t.tag_, t.head.orth_, t.dep_)
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)
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return scorer
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@plac.annotations(
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train_loc=("Location of training file or directory"),
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dev_loc=("Location of development file or directory"),
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model_dir=("Location of output model directory",),
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eval_only=("Skip training, and only evaluate", "flag", "e", bool),
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corruption_level=("Amount of noise to add to training data", "option", "c", float),
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gold_preproc=("Use gold-standard sentence boundaries in training?", "flag", "g", bool),
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out_loc=("Out location", "option", "o", str),
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n_sents=("Number of training sentences", "option", "n", int),
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n_iter=("Number of training iterations", "option", "i", int),
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verbose=("Verbose error reporting", "flag", "v", bool),
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debug=("Debug mode", "flag", "d", bool),
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)
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def main(train_loc, dev_loc, model_dir, n_sents=0, n_iter=15, out_loc="", verbose=False,
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debug=False, corruption_level=0.0, gold_preproc=False, beam_width=1,
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eval_only=False):
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gold_train = list(read_json_file(train_loc))
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nlp = train(English, gold_train, model_dir,
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feat_set='embed',
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gold_preproc=gold_preproc, n_sents=n_sents,
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corruption_level=corruption_level, n_iter=n_iter,
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verbose=verbose)
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#if out_loc:
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# write_parses(English, dev_loc, model_dir, out_loc, beam_width=beam_width)
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scorer = evaluate(nlp, list(read_json_file(dev_loc)), gold_preproc=gold_preproc)
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print 'TOK', 100-scorer.token_acc
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print 'POS', scorer.tags_acc
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print 'UAS', scorer.uas
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print 'LAS', scorer.las
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print 'NER P', scorer.ents_p
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print 'NER R', scorer.ents_r
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print 'NER F', scorer.ents_f
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if __name__ == '__main__':
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plac.call(main)
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@ -0,0 +1,13 @@
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from ._ml cimport Model
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from thinc.nn cimport InputLayer
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cdef class TheanoModel(Model):
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cdef InputLayer input_layer
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cdef object train_func
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cdef object predict_func
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cdef object debug
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cdef public float eta
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cdef public float mu
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cdef public float t
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@ -9,7 +9,8 @@ from os import path
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cdef class TheanoModel(Model):
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def __init__(self, n_classes, input_spec, train_func, predict_func, model_loc=None):
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def __init__(self, n_classes, input_spec, train_func, predict_func, model_loc=None,
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debug=None):
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if model_loc is not None and path.isdir(model_loc):
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model_loc = path.join(model_loc, 'model')
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@ -20,6 +21,7 @@ cdef class TheanoModel(Model):
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self.input_layer = InputLayer(input_spec, initializer)
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self.train_func = train_func
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self.predict_func = predict_func
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self.debug = debug
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self.n_classes = n_classes
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self.n_feats = len(self.input_layer)
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@ -27,7 +29,7 @@ cdef class TheanoModel(Model):
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def predict(self, Example eg):
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self.input_layer.fill(eg.embeddings, eg.atoms)
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theano_scores = self.predict_func(eg.embeddings)
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theano_scores = self.predict_func(eg.embeddings)[0]
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cdef int i
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for i in range(self.n_classes):
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eg.scores[i] = theano_scores[i]
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@ -35,10 +37,17 @@ cdef class TheanoModel(Model):
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self.n_classes)
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def train(self, Example eg):
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self.predict(eg)
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update, t, eta, mu = self.train_func(eg.embeddings, eg.scores, eg.costs)
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self.input_layer.update(eg.atoms, update, self.t, self.eta, self.mu)
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self.input_layer.fill(eg.embeddings, eg.atoms)
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theano_scores, update, y = self.train_func(eg.embeddings, eg.costs, self.eta)
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self.input_layer.update(update, eg.atoms, self.t, self.eta, self.mu)
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for i in range(self.n_classes):
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eg.scores[i] = theano_scores[i]
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eg.guess = arg_max_if_true(<weight_t*>eg.scores.data, <int*>eg.is_valid.data,
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self.n_classes)
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eg.best = arg_max_if_zero(<weight_t*>eg.scores.data, <int*>eg.costs.data,
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self.n_classes)
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eg.cost = eg.costs[eg.guess]
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self.t += 1
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def end_training(self):
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pass
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@ -355,3 +355,7 @@ trigrams = (
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(N0W, N0p, N0lL, N0l2L),
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(N0p, N0lL, N0l2L),
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)
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words = (S0w, N0w, S1w, N1w)
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tags = (S0p, N0p, S1p, N1p)
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labels = (S0L, N0L, S1L, S2L)
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@ -52,18 +52,21 @@ def get_templates(name):
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return pf.ner
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elif name == 'debug':
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return pf.unigrams
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elif name.startswith('embed'):
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return ((10, pf.words), (10, pf.tags), (10, pf.labels))
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else:
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return (pf.unigrams + pf.s0_n0 + pf.s1_n0 + pf.s1_s0 + pf.s0_n1 + pf.n0_n1 + \
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pf.tree_shape + pf.trigrams)
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cdef class Parser:
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def __init__(self, StringStore strings, model_dir, transition_system):
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def __init__(self, StringStore strings, model_dir, transition_system,
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get_model=Model):
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assert os.path.exists(model_dir) and os.path.isdir(model_dir)
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self.cfg = Config.read(model_dir, 'config')
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self.moves = transition_system(strings, self.cfg.labels)
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templates = get_templates(self.cfg.features)
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self.model = Model(self.moves.n_moves, templates, model_dir)
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self.model = get_model(self.moves.n_moves, templates, model_dir)
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def __call__(self, Tokens tokens):
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cdef StateClass stcls = StateClass.init(tokens.data, tokens.length)
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