conll05.py

# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import paddle.v2.dataset.common
import tarfile
import gzip
import itertools

__all__ = ['test, get_dict', 'get_embedding']
"""
Conll 2005 dataset.  Paddle semantic role labeling Book and demo use this
dataset as an example. Because Conll 2005 is not free in public, the default
downloaded URL is test set of Conll 2005 (which is public). Users can change
URL and MD5 to their Conll dataset.
"""

DATA_URL = 'http://www.cs.upc.edu/~srlconll/conll05st-tests.tar.gz'
DATA_MD5 = '387719152ae52d60422c016e92a742fc'
WORDDICT_URL = 'http://paddlepaddle.bj.bcebos.com/demo/srl_dict_and_embedding/wordDict.txt'
WORDDICT_MD5 = 'ea7fb7d4c75cc6254716f0177a506baa'
VERBDICT_URL = 'http://paddlepaddle.bj.bcebos.com/demo/srl_dict_and_embedding/verbDict.txt'
VERBDICT_MD5 = '0d2977293bbb6cbefab5b0f97db1e77c'
TRGDICT_URL = 'http://paddlepaddle.bj.bcebos.com/demo/srl_dict_and_embedding/targetDict.txt'
TRGDICT_MD5 = 'd8c7f03ceb5fc2e5a0fa7503a4353751'
EMB_URL = 'http://paddlepaddle.bj.bcebos.com/demo/srl_dict_and_embedding/emb'
EMB_MD5 = 'bf436eb0faa1f6f9103017f8be57cdb7'

UNK_IDX = 0


def load_dict(filename):
    d = dict()
    with open(filename, 'r') as f:
        for i, line in enumerate(f):
            d[line.strip()] = i
    return d


def corpus_reader(data_path, words_name, props_name):
    """
    Read one corpus. It returns an iterator. Each element of
    this iterator is a tuple including sentence and labels. The sentence is
    consist of a list of word IDs. The labels include a list of label IDs.
    :return: a iterator of data.
    :rtype: iterator
    """

    def reader():
        tf = tarfile.open(data_path)
        wf = tf.extractfile(words_name)
        pf = tf.extractfile(props_name)
        with gzip.GzipFile(fileobj=wf) as words_file, gzip.GzipFile(
                fileobj=pf) as props_file:
            sentences = []
            labels = []
            one_seg = []
            for word, label in itertools.izip(words_file, props_file):
                word = word.strip()
                label = label.strip().split()

                if len(label) == 0:  # end of sentence
                    for i in xrange(len(one_seg[0])):
                        a_kind_lable = [x[i] for x in one_seg]
                        labels.append(a_kind_lable)

                    if len(labels) >= 1:
                        verb_list = []
                        for x in labels[0]:
                            if x != '-':
                                verb_list.append(x)

                        for i, lbl in enumerate(labels[1:]):
                            cur_tag = 'O'
                            is_in_bracket = False
                            lbl_seq = []
                            verb_word = ''
                            for l in lbl:
                                if l == '*' and is_in_bracket == False:
                                    lbl_seq.append('O')
                                elif l == '*' and is_in_bracket == True:
                                    lbl_seq.append('I-' + cur_tag)
                                elif l == '*)':
                                    lbl_seq.append('I-' + cur_tag)
                                    is_in_bracket = False
                                elif l.find('(') != -1 and l.find(')') != -1:
                                    cur_tag = l[1:l.find('*')]
                                    lbl_seq.append('B-' + cur_tag)
                                    is_in_bracket = False
                                elif l.find('(') != -1 and l.find(')') == -1:
                                    cur_tag = l[1:l.find('*')]
                                    lbl_seq.append('B-' + cur_tag)
                                    is_in_bracket = True
                                else:
                                    raise RuntimeError('Unexpected label: %s' %
                                                       l)

                            yield sentences, verb_list[i], lbl_seq

                    sentences = []
                    labels = []
                    one_seg = []
                else:
                    sentences.append(word)
                    one_seg.append(label)

        pf.close()
        wf.close()
        tf.close()

    return reader


def reader_creator(corpus_reader,
                   word_dict=None,
                   predicate_dict=None,
                   label_dict=None):
    def reader():
        for sentence, predicate, labels in corpus_reader():

            sen_len = len(sentence)

            verb_index = labels.index('B-V')
            mark = [0] * len(labels)
            if verb_index > 0:
                mark[verb_index - 1] = 1
                ctx_n1 = sentence[verb_index - 1]
            else:
                ctx_n1 = 'bos'

            if verb_index > 1:
                mark[verb_index - 2] = 1
                ctx_n2 = sentence[verb_index - 2]
            else:
                ctx_n2 = 'bos'

            mark[verb_index] = 1
            ctx_0 = sentence[verb_index]

            if verb_index < len(labels) - 1:
                mark[verb_index + 1] = 1
                ctx_p1 = sentence[verb_index + 1]
            else:
                ctx_p1 = 'eos'

            if verb_index < len(labels) - 2:
                mark[verb_index + 2] = 1
                ctx_p2 = sentence[verb_index + 2]
            else:
                ctx_p2 = 'eos'

            word_idx = [word_dict.get(w, UNK_IDX) for w in sentence]
            pred_idx = [predicate_dict.get(predicate)] * sen_len

            ctx_n2_idx = [word_dict.get(ctx_n2, UNK_IDX)] * sen_len
            ctx_n1_idx = [word_dict.get(ctx_n1, UNK_IDX)] * sen_len
            ctx_0_idx = [word_dict.get(ctx_0, UNK_IDX)] * sen_len
            ctx_p1_idx = [word_dict.get(ctx_p1, UNK_IDX)] * sen_len
            ctx_p2_idx = [word_dict.get(ctx_p2, UNK_IDX)] * sen_len

            label_idx = [label_dict.get(w) for w in labels]

            yield word_idx, pred_idx, ctx_n2_idx, ctx_n1_idx, \
              ctx_0_idx, ctx_p1_idx, ctx_p2_idx, mark, label_idx

    return reader()


def get_dict():
    word_dict = load_dict(
        common.download(WORDDICT_URL, 'conll05st', WORDDICT_MD5))
    verb_dict = load_dict(
        common.download(VERBDICT_URL, 'conll05st', VERBDICT_MD5))
    label_dict = load_dict(
        common.download(TRGDICT_URL, 'conll05st', TRGDICT_MD5))
    return word_dict, verb_dict, label_dict


def get_embedding():
    return common.download(EMB_URL, 'conll05st', EMB_MD5)


def test():
    word_dict, verb_dict, label_dict = get_dict()
    reader = corpus_reader(
        common.download(DATA_URL, 'conll05st', DATA_MD5),
        words_name='conll05st-release/test.wsj/words/test.wsj.words.gz',
        props_name='conll05st-release/test.wsj/props/test.wsj.props.gz')
    return reader_creator(reader, word_dict, verb_dict, label_dict)


if __name__ == '__main__':
    print get_embedding()
    for f in test():
        print f