spaCy/bin/parser/train.py

#!/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.util import Config
from spacy.gold import read_json_file
from spacy.gold import GoldParse

from spacy.scorer import Scorer


def add_noise(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 score_model(scorer, nlp, raw_text, annot_tuples, train_tags=None):
    if raw_text is None:
        tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
    else:
        tokens = nlp.tokenizer(raw_text)
    if train_tags is not None:
        key = hash(tokens.string)
        nlp.tagger.tag_from_strings(tokens, train_tags[key])
    else:
        nlp.tagger(tokens)

    nlp.entity(tokens)
    nlp.parser(tokens)
    gold = GoldParse(tokens, annot_tuples)
    scorer.score(tokens, gold, verbose=False)


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 get_train_tags(Language, model_dir, docs, gold_preproc):
    taggings = {}
    for train_part, test_part in get_partitions(docs, 5):
        nlp = _train_tagger(Language, model_dir, train_part, gold_preproc)
        for tokens in _tag_partition(nlp, test_part):
            taggings[hash(tokens.string)] = [w.tag_ for w in tokens]
    return taggings

def get_partitions(docs, n_parts):
    random.shuffle(docs)
    n_test = len(docs) / n_parts
    n_train = len(docs) - n_test
    for part in range(n_parts):
        start = int(part * n_test)
        end = int(start + n_test)
        yield docs[:start] + docs[end:], docs[start:end]


def _train_tagger(Language, model_dir, docs, gold_preproc=False, n_iter=5):
    pos_model_dir = path.join(model_dir, 'pos')
    if path.exists(pos_model_dir):
        shutil.rmtree(pos_model_dir)
    os.mkdir(pos_model_dir)
    setup_model_dir(sorted(POS_TAGS.keys()), POS_TAGS, POS_TEMPLATES, pos_model_dir)

    nlp = Language(data_dir=model_dir)

    print "Itn.\tTag %"
    for itn in range(n_iter):
        scorer = Scorer()
        correct = 0
        total = 0
        for raw_text, sents in docs:
            if gold_preproc:
                raw_text = None
            else:
                sents = _merge_sents(sents)
            for annot_tuples, ctnt in sents:
                if raw_text is None:
                    tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
                else:
                    tokens = nlp.tokenizer(raw_text)
                gold = GoldParse(tokens, annot_tuples)
                correct += nlp.tagger.train(tokens, gold.tags)
                total += len(tokens)
        random.shuffle(docs)
        print itn, '%.3f' % (correct / total)
    nlp.tagger.model.end_training()
    nlp.vocab.strings.dump(path.join(model_dir, 'vocab', 'strings.txt'))
    return nlp


def _tag_partition(nlp, docs, gold_preproc=False):
    for raw_text, sents in docs:
        if gold_preproc:
            raw_text = None
        else:
            sents = _merge_sents(sents)
        for annot_tuples, _ in sents:
            if raw_text is None:
                tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
            else:
                tokens = nlp.tokenizer(raw_text)

            nlp.tagger(tokens)
            yield tokens


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,
          train_tags=None, beam_width=1):
    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=1)

    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:
                score_model(scorer, nlp, raw_text, annot_tuples, train_tags)
                if raw_text is None:
                    tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
                else:
                    tokens = nlp.tokenizer(raw_text)
                if train_tags is not None:
                    sent_id = hash(tokens.string)
                    nlp.tagger.tag_from_strings(tokens, train_tags[sent_id])
                else:
                    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):
    nlp = Language(data_dir=model_dir)
    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):
    nlp = Language()
    gold_tuples = read_docparse_file(dev_loc)
    scorer = Scorer()
    out_file = codecs.open(out_loc, 'w', 'utf8')
    for raw_text, segmented_text, annot_tuples in gold_tuples:
        tokens = nlp(raw_text)
        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"),
    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),
    model_dir=("Location of output model directory",),
    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)
)
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):
    gold_train = list(read_json_file(train_loc))
    #taggings = get_train_tags(English, model_dir, gold_train, gold_preproc)
    taggings = None
    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,
          train_tags=taggings, beam_width=beam_width)
    if out_loc:
        write_parses(English, dev_loc, model_dir, out_loc)
    scorer = evaluate(English, list(read_json_file(dev_loc)),
                      model_dir, gold_preproc=gold_preproc, verbose=verbose)
    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)