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 time
import gzip

import plac
import cProfile
import pstats

import spacy.util
from spacy.en import English
from spacy.en.pos import POS_TEMPLATES, POS_TAGS, setup_model_dir

from spacy.syntax.parser import GreedyParser
from spacy.syntax.parser import OracleError
from spacy.syntax.util import Config
from spacy.syntax.conll import read_docparse_file
from spacy.syntax.conll import GoldParse

from spacy.scorer import Scorer


def is_punct_label(label):
    return label == 'P' or label.lower() == 'punct'


def read_tokenized_gold(file_):
    """Read a standard CoNLL/MALT-style format"""
    sents = []
    for sent_str in file_.read().strip().split('\n\n'):
        ids = []
        words = []
        heads = []
        labels = []
        tags = []
        for i, line in enumerate(sent_str.split('\n')):
            id_, word, pos_string, head_idx, label = _parse_line(line)
            words.append(word)
            if head_idx == -1:
                head_idx = i
            ids.append(id_)
            heads.append(head_idx)
            labels.append(label)
            tags.append(pos_string)
        text = ' '.join(words)
        sents.append((text, [words], ids, words, tags, heads, labels))
    return sents


def read_docparse_gold(file_):
    paragraphs = []
    for sent_str in file_.read().strip().split('\n\n'):
        if not sent_str.strip():
            continue
        words = []
        heads = []
        labels = []
        tags = []
        ids = []
        lines = sent_str.strip().split('\n')
        raw_text = lines.pop(0).strip()
        tok_text = lines.pop(0).strip()
        for i, line in enumerate(lines):
            id_, word, pos_string, head_idx, label = _parse_line(line)
            if label == 'root':
                label = 'ROOT'
            words.append(word)
            if head_idx < 0:
                head_idx = id_
            ids.append(id_)
            heads.append(head_idx)
            labels.append(label)
            tags.append(pos_string)
        tokenized = [sent_str.replace('<SEP>', ' ').split(' ')
                     for sent_str in tok_text.split('<SENT>')]
        paragraphs.append((raw_text, tokenized, ids, words, tags, heads, labels))
    return paragraphs


def _map_indices_to_tokens(ids, heads):
    mapped = []
    for head in heads:
        if head not in ids:
            mapped.append(None)
        else:
            mapped.append(ids.index(head))
    return mapped


def _parse_line(line):
    pieces = line.split()
    if len(pieces) == 4:
        return 0, pieces[0], pieces[1], int(pieces[2]) - 1, pieces[3]
    else:
        id_ = int(pieces[0])
        word = pieces[1]
        pos = pieces[3]
        head_idx = int(pieces[6])
        label = pieces[7]
        return id_, word, pos, head_idx, label


loss = 0
def _align_annotations_to_non_gold_tokens(tokens, words, annot):
    global loss
    tags = []
    heads = []
    labels = []
    orig_words = list(words)
    missed = []
    for token in tokens:
        while annot and token.idx > annot[0][0]:
            miss_id, miss_tag, miss_head, miss_label = annot.pop(0)
            miss_w = words.pop(0)
            if not is_punct_label(miss_label):
                missed.append(miss_w)
                loss += 1
        if not annot:
            tags.append(None)
            heads.append(None)
            labels.append(None)
            continue
        id_, tag, head, label = annot[0]
        if token.idx == id_:
            tags.append(tag)
            heads.append(head)
            labels.append(label)
            annot.pop(0)
            words.pop(0)
        elif token.idx < id_:
            tags.append(None)
            heads.append(None)
            labels.append(None)
        else:
            raise StandardError
    #if missed:
    #    print orig_words
    #    print missed
    #    for t in tokens:
    #        print t.idx, t.orth_
    return loss, tags, heads, labels

        
def iter_data(paragraphs, tokenizer, gold_preproc=False):
    for raw, tokenized, ids, words, tags, heads, labels in paragraphs:
        if not gold_preproc:
            tokens = tokenizer(raw)
            loss, tags, heads, labels = _align_annotations_to_non_gold_tokens(
                                            tokens, list(words),
                                            zip(ids, tags, heads, labels))
            ids = [t.idx for t in tokens]
            heads = _map_indices_to_tokens(ids, heads)
            yield tokens, tags, heads, labels
        else:
            assert len(words) == len(heads)
            for words in tokenized:
                sent_ids = ids[:len(words)]
                sent_tags = tags[:len(words)]
                sent_heads = heads[:len(words)]
                sent_labels = labels[:len(words)]
                sent_heads = _map_indices_to_tokens(sent_ids, sent_heads)
                tokens = tokenizer.tokens_from_list(words)
                yield tokens, sent_tags, sent_heads, sent_labels
                ids = ids[len(words):]
                tags = tags[len(words):]
                heads = heads[len(words):]
                labels = labels[len(words):]


def get_labels(sents):
    left_labels = set()
    right_labels = set()
    for raw, tokenized, ids, words, tags, heads, labels in sents:
        for child, (head, label) in enumerate(zip(heads, labels)):
            if head > child:
                left_labels.add(label)
            elif head < child:
                right_labels.add(label)
    return list(sorted(left_labels)), list(sorted(right_labels))


def train(Language, train_loc, model_dir, n_iter=15, feat_set=u'basic', seed=0,
          gold_preproc=False, force_gold=False, n_sents=0):
    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)

    gold_tuples = read_docparse_file(train_loc)

    Config.write(dep_model_dir, 'config', features=feat_set, seed=seed,
                 labels=Language.ParserTransitionSystem.get_labels(gold_tuples))
    Config.write(ner_model_dir, 'config', features='ner', seed=seed,
                 labels=Language.EntityTransitionSystem.get_labels(gold_tuples))

    if n_sents > 0:
        gold_tuples = gold_tuples[:n_sents]
    nlp = Language()

    print "Itn.\tUAS\tNER F.\tTag %"
    for itn in range(n_iter):
        scorer = Scorer()
        for raw_text, segmented_text, annot_tuples in gold_tuples:
            if gold_preproc:
                sents = [nlp.tokenizer.tokens_from_list(s) for s in segmented_text]
            else:
                sents = [nlp.tokenizer(raw_text)]
            for tokens in sents:
                gold = GoldParse(tokens, annot_tuples)
                nlp.tagger(tokens)
                nlp.parser.train(tokens, gold, force_gold=force_gold)
                nlp.entity.train(tokens, gold, force_gold=force_gold)
                nlp.tagger.train(tokens, gold.tags)
                
                nlp.entity(tokens)
                nlp.parser(tokens)
                scorer.score(tokens, gold, verbose=False)
        print '%d:\t%.3f\t%.3f\t%.3f' % (itn, scorer.uas, scorer.ents_f, scorer.tags_acc)
        random.shuffle(gold_tuples)
    nlp.parser.model.end_training()
    nlp.entity.model.end_training()
    nlp.tagger.model.end_training()
    print nlp.vocab.strings['NMOD']


def evaluate(Language, dev_loc, model_dir, gold_preproc=False, verbose=True):
    assert not gold_preproc
    nlp = Language()
    gold_tuples = read_docparse_file(dev_loc)
    scorer = Scorer()
    for raw_text, segmented_text, annot_tuples in gold_tuples:
        tokens = nlp(raw_text)
        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_)
            )
    print nlp.vocab.strings['NMOD']
    return scorer


@plac.annotations(
    train_loc=("Training file location",),
    dev_loc=("Dev. file location",),
    model_dir=("Location of output model directory",),
    out_loc=("Out location", "option", "o", str),
    n_sents=("Number of training sentences", "option", "n", int),
    verbose=("Verbose error reporting", "flag", "v", bool),
)
def main(train_loc, dev_loc, model_dir, n_sents=0, out_loc="", verbose=False):
    #train(English, train_loc, model_dir,
    #      gold_preproc=False, force_gold=False, n_sents=n_sents)
    if out_loc:
        write_parses(English, dev_loc, model_dir, out_loc)
    scorer = evaluate(English, dev_loc, model_dir, gold_preproc=False, verbose=verbose)
    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)