#!/usr/bin/env python from __future__ import division from __future__ import unicode_literals import os from os import path import shutil import codecs import random import plac import cProfile import pstats import re import spacy.util from spacy.en import English from spacy.en.pos import POS_TEMPLATES, POS_TAGS, setup_model_dir from spacy.syntax.orig_arc_eager import OrigArcEager from spacy.syntax.tree_arc_eager import TreeArcEager from spacy.syntax.util import Config from spacy.gold import read_json_file from spacy.gold import GoldParse from spacy.scorer import Scorer def _corrupt(c, noise_level): if random.random() >= noise_level: return c elif c == ' ': return '\n' elif c == '\n': return ' ' elif c in ['.', "'", "!", "?"]: return '' else: return c.lower() def add_noise(orig, noise_level): if random.random() >= noise_level: return orig elif type(orig) == list: corrupted = [_corrupt(word, noise_level) for word in orig] corrupted = [w for w in corrupted if w] return corrupted else: return ''.join(_corrupt(c, noise_level) for c in orig) def score_model(scorer, nlp, raw_text, annot_tuples, verbose=False): if raw_text is None: tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1]) else: tokens = nlp.tokenizer(raw_text) nlp.tagger(tokens) nlp.entity(tokens) nlp.parser(tokens) gold = GoldParse(tokens, annot_tuples) scorer.score(tokens, gold, verbose=verbose) def _merge_sents(sents): m_deps = [[], [], [], [], [], []] m_brackets = [] i = 0 for (ids, words, tags, heads, labels, ner), brackets in sents: m_deps[0].extend(id_ + i for id_ in ids) m_deps[1].extend(words) m_deps[2].extend(tags) m_deps[3].extend(head + i for head in heads) m_deps[4].extend(labels) m_deps[5].extend(ner) m_brackets.extend((b['first'] + i, b['last'] + i, b['label']) for b in brackets) i += len(ids) return [(m_deps, m_brackets)] def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic', seed=0, gold_preproc=False, n_sents=0, corruption_level=0, beam_width=1, verbose=False, use_orig_arc_eager=False): dep_model_dir = path.join(model_dir, 'deps') pos_model_dir = path.join(model_dir, 'pos') ner_model_dir = path.join(model_dir, 'ner') if path.exists(dep_model_dir): shutil.rmtree(dep_model_dir) if path.exists(pos_model_dir): shutil.rmtree(pos_model_dir) if path.exists(ner_model_dir): shutil.rmtree(ner_model_dir) os.mkdir(dep_model_dir) os.mkdir(pos_model_dir) os.mkdir(ner_model_dir) setup_model_dir(sorted(POS_TAGS.keys()), POS_TAGS, POS_TEMPLATES, pos_model_dir) Config.write(dep_model_dir, 'config', features=feat_set, seed=seed, labels=Language.ParserTransitionSystem.get_labels(gold_tuples), beam_width=beam_width) Config.write(ner_model_dir, 'config', features='ner', seed=seed, labels=Language.EntityTransitionSystem.get_labels(gold_tuples), beam_width=0) if n_sents > 0: gold_tuples = gold_tuples[:n_sents] nlp = Language(data_dir=model_dir) print "Itn.\tP.Loss\tUAS\tNER F.\tTag %\tToken %" for itn in range(n_iter): scorer = Scorer() loss = 0 for raw_text, sents in gold_tuples: if gold_preproc: raw_text = None else: sents = _merge_sents(sents) for annot_tuples, ctnt in sents: if len(annot_tuples[1]) == 1: continue score_model(scorer, nlp, raw_text, annot_tuples, verbose=verbose if itn >= 2 else False) if raw_text is None: words = add_noise(annot_tuples[1], corruption_level) tokens = nlp.tokenizer.tokens_from_list(words) else: raw_text = add_noise(raw_text, corruption_level) tokens = nlp.tokenizer(raw_text) nlp.tagger(tokens) gold = GoldParse(tokens, annot_tuples, make_projective=True) loss += nlp.parser.train(tokens, gold) nlp.entity.train(tokens, gold) nlp.tagger.train(tokens, gold.tags) random.shuffle(gold_tuples) print '%d:\t%d\t%.3f\t%.3f\t%.3f\t%.3f' % (itn, loss, scorer.uas, scorer.ents_f, scorer.tags_acc, scorer.token_acc) nlp.parser.model.end_training() nlp.entity.model.end_training() nlp.tagger.model.end_training() nlp.vocab.strings.dump(path.join(model_dir, 'vocab', 'strings.txt')) def evaluate(Language, gold_tuples, model_dir, gold_preproc=False, verbose=False, beam_width=None): nlp = Language(data_dir=model_dir) if beam_width is not None: nlp.parser.cfg.beam_width = beam_width scorer = Scorer() for raw_text, sents in gold_tuples: if gold_preproc: raw_text = None else: sents = _merge_sents(sents) for annot_tuples, brackets in sents: if raw_text is None: tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1]) nlp.tagger(tokens) nlp.entity(tokens) nlp.parser(tokens) else: tokens = nlp(raw_text, merge_mwes=False) gold = GoldParse(tokens, annot_tuples) scorer.score(tokens, gold, verbose=verbose) return scorer def write_parses(Language, dev_loc, model_dir, out_loc, beam_width=None): nlp = Language(data_dir=model_dir) if beam_width is not None: nlp.parser.cfg.beam_width = beam_width gold_tuples = read_json_file(dev_loc) scorer = Scorer() out_file = codecs.open(out_loc, 'w', 'utf8') for raw_text, sents in gold_tuples: sents = _merge_sents(sents) for annot_tuples, brackets in sents: if raw_text is None: tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1]) nlp.tagger(tokens) nlp.entity(tokens) nlp.parser(tokens) else: tokens = nlp(raw_text, merge_mwes=False) gold = GoldParse(tokens, annot_tuples) scorer.score(tokens, gold, verbose=False) for t in tokens: out_file.write( '%s\t%s\t%s\t%s\n' % (t.orth_, t.tag_, t.head.orth_, t.dep_) ) return scorer @plac.annotations( train_loc=("Location of training file or directory"), dev_loc=("Location of development file or directory"), model_dir=("Location of output model directory",), eval_only=("Skip training, and only evaluate", "flag", "e", bool), corruption_level=("Amount of noise to add to training data", "option", "c", float), gold_preproc=("Use gold-standard sentence boundaries in training?", "flag", "g", bool), out_loc=("Out location", "option", "o", str), n_sents=("Number of training sentences", "option", "n", int), n_iter=("Number of training iterations", "option", "i", int), beam_width=("Number of candidates to maintain in the beam", "option", "k", int), verbose=("Verbose error reporting", "flag", "v", bool), debug=("Debug mode", "flag", "d", bool), use_orig_arc_eager=("Use the original, monotonic arc-eager system", "flag", "m", bool) ) def main(train_loc, dev_loc, model_dir, n_sents=0, n_iter=15, out_loc="", verbose=False, debug=False, corruption_level=0.0, gold_preproc=False, beam_width=1, eval_only=False, use_orig_arc_eager=False): if use_orig_arc_eager: English.ParserTransitionSystem = TreeArcEager if not eval_only: gold_train = list(read_json_file(train_loc)) train(English, gold_train, model_dir, feat_set='basic' if not debug else 'debug', gold_preproc=gold_preproc, n_sents=n_sents, corruption_level=corruption_level, n_iter=n_iter, beam_width=beam_width, verbose=verbose, use_orig_arc_eager=use_orig_arc_eager) #if out_loc: # write_parses(English, dev_loc, model_dir, out_loc, beam_width=beam_width) scorer = evaluate(English, list(read_json_file(dev_loc)), model_dir, gold_preproc=gold_preproc, verbose=verbose, beam_width=beam_width) print 'TOK', 100-scorer.token_acc print 'POS', scorer.tags_acc print 'UAS', scorer.uas print 'LAS', scorer.las print 'NER P', scorer.ents_p print 'NER R', scorer.ents_r print 'NER F', scorer.ents_f if __name__ == '__main__': plac.call(main)