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How to use is_space method in yandex-tank

Best Python code snippet using yandex-tank

segmenter.py

Source:segmenter.py

1# -*- coding: utf-8 -*-2"""3@author: Yan Shao, yan.shao@lingfil.uu.se4"""5import reader6import toolbox7from model import Model8from transducer_model import Seq2seq9import sys10import argparse11import os12import codecs13import tensorflow as tf14import cPickle as pickle15from time import time16parser = argparse.ArgumentParser(description='A Universal Tokeniser. Written by Y. Shao, Uppsala University')17parser.add_argument('action', default='tag', choices=['train', 'test', 'tag'], help='train, test or tag')18parser.add_argument('-f', '--format', default='conll', help='Data format of different tasks, conll, mlp1 or mlp2')19parser.add_argument('-p', '--path', default=None, help='Path of the workstation')20parser.add_argument('-t', '--train', default=None, help='File for training')21parser.add_argument('-d', '--dev', default=None, help='File for validation')22parser.add_argument('-e', '--test', default=None, help='File for evaluation')23parser.add_argument('-r', '--raw', default=None, help='Raw file for tagging')24parser.add_argument('-m', '--model', default='trained_model', help='Name of the trained model')25parser.add_argument('-crf', '--crf', default=1, type=int, help='Using CRF interface')26parser.add_argument('-bt', '--bucket_size', default=50, type=int, help='Bucket size')27parser.add_argument('-sl', '--sent_limit', default=300, type=int, help='Long sentences will be chopped')28parser.add_argument('-tg', '--tags', default='BIES', help='Boundary Tagging, default is BIES')29parser.add_argument('-ed', '--emb_dimension', default=50, type=int, help='Dimension of the embeddings')30parser.add_argument('-emb', '--embeddings', default=None, help='Path and name of pre-trained char embeddings')31parser.add_argument('-ng', '--ngram', default=1, type=int, help='Using ngrams')32parser.add_argument('-cell', '--cell', default='gru', help='Use GRU as the recurrent cell', choices=['gru', 'lstm'])33parser.add_argument('-rnn', '--rnn_cell_dimension', default=200, type=int, help='Dimension of the RNN cells')34parser.add_argument('-layer', '--rnn_layer_number', default=1, type=int, help='Numbers of the RNN layers')35parser.add_argument('-dr', '--dropout_rate', default=0.5, type=float, help='Dropout rate')36parser.add_argument('-iter', '--epochs', default=30, type=int, help='Numbers of epochs')37parser.add_argument('-iter_trans', '--epochs_trans', default=50, type=int, help='Epochs for training the transducer')38parser.add_argument('-op', '--optimizer', default='adagrad', help='Optimizer')39parser.add_argument('-lr', '--learning_rate', default=0.2, type=float, help='Initial learning rate')40parser.add_argument('-lr_trans', '--learning_rate_trans', default=0.3, type=float, help='Initial learning rate')41parser.add_argument('-ld', '--decay_rate', default=0.05, type=float, help='Learning rate decay')42parser.add_argument('-mt', '--momentum', default=None, type=float, help='Momentum')43parser.add_argument('-ncp', '--no_clipping', default=False, action='store_true', help='Do not apply gradient clipping')44parser.add_argument("-tb","--train_batch", help="Training batch size", default=10, type=int)45parser.add_argument("-eb","--test_batch", help="Testing batch size", default=500, type=int)46parser.add_argument("-rb","--tag_batch", help="Tagging batch size", default=500, type=int)47parser.add_argument("-g","--gpu", help="the id of gpu, the default is 0", default=0, type=int)48parser.add_argument('-opth', '--output_path', default=None, help='Output path')49parser.add_argument('-sea', '--sea', help='Process languages like Vietamese', default=False, action='store_true')50parser.add_argument('-ss', '--sent_seg', help='Perform sentence seg', default=False, action='store_true')51parser.add_argument('-ens', '--ensemble', default=False, help='Ensemble several weights', action='store_true')52parser.add_argument('-sgl', '--segment_large', default=False, help='Segment (very) large file', action='store_true')53parser.add_argument('-lgs', '--large_size', default=10000, type=int, help='Segment (very) large file')54parser.add_argument('-ot', '--only_tokenised', default=False,55                    help='Only output the tokenised file when segment (very) large file', action='store_true')56parser.add_argument('-ts', '--train_size', default=-1, type=int, help='No. of sentences used for training')57parser.add_argument('-rs', '--reset', default=False, help='Delete and re-initialise the intermediate files',58                    action='store_true')59parser.add_argument('-rst', '--reset_trans', default=False, help='Retrain the transducers', action='store_true')60parser.add_argument('-isp', '--ignore_space', default=False, help='Ignore space delimiters', action='store_true')61parser.add_argument('-imt', '--ignore_mwt', default=False, help='Ignore multi-word tokens to be transcribed',62                    action='store_true')63parser.add_argument('-sb', '--segmentation_bias', default=-1, type=float,64                    help='Add segmentation bias to under(over)-splitting')65parser.add_argument('-tt', '--transduction_type', default='mix', choices=['mix', 'dict', 'trans', 'none'],66                    help='Different ways of transducing the non-segmental MWTs')67args = parser.parse_args()68sys = reload(sys)69sys.setdefaultencoding('utf-8')70print 'Encoding: ', sys.getdefaultencoding()71if args.action == 'train':72    assert args.path is not None73    path = args.path74    train_file = args.train75    dev_file = args.dev76    model_file = args.model77    print 'Reading data......'78    f_names = os.listdir(path)79    if train_file is None or dev_file is None:80        for f_n in f_names:81            if 'ud-train.conllu' in f_n or 'training.segd' in f_n or 'ud-sample.conllu' in f_n:82                train_file = f_n83            elif 'ud-dev.conllu' in f_n or 'development.segd' in f_n:84                dev_file = f_n85    assert train_file is not None86    is_space = True87    if 'Chinese' in path or 'Japanese' in path or args.format == 'mlp2':88        is_space = False89    if args.sea:90        is_space = 'sea'91    if args.reset or not os.path.isfile(path + '/raw_train.txt') or not os.path.isfile(path + '/raw_dev.txt'):92        cat = 'other'93        if 'Chinese' in path or 'Japanese' in path:94            cat = 'zh'95        for line in codecs.open(path + '/' + train_file, 'r', encoding='utf-8'):96            if len(line) < 2:97                break98            if '# sentence' in line or '# text' in line:99                cat = 'gold'100        if dev_file is None:101            reader.get_raw(path, train_file, '/raw_train.txt', cat, is_dev=False, form=args.format, is_space=is_space)102        else:103            reader.get_raw(path, train_file, '/raw_train.txt', cat, form=args.format, is_space=is_space)104            reader.get_raw(path, dev_file, '/raw_dev.txt', cat, form=args.format, is_space=is_space)105    if args.reset or not os.path.isfile(path + '/tag_train.txt') or not os.path.isfile(path + '/tag_dev.txt') or \106            not os.path.isfile(path + '/tag_dev_gold.txt'):107        if dev_file is None:108            raws_train = reader.raw(path + '/raw_train.txt')109            raws_dev = reader.raw(path + '/raw_dev.txt')110            sents_train, sents_dev = reader.gold(path + '/' + train_file, False, form=args.format, is_space=is_space)111        else:112            raws_train = reader.raw(path + '/raw_train.txt')113            sents_train = reader.gold(path + '/' + train_file, form=args.format, is_space=is_space)114            raws_dev = reader.raw(path + '/raw_dev.txt')115            sents_dev = reader.gold(path + '/' + dev_file, form=args.format, is_space=is_space)116        if is_space != 'sea':117            toolbox.raw2tags(raws_train, sents_train, path, 'tag_train.txt', ignore_space=args.ignore_space,118                             reset=args.reset, tag_scheme=args.tags, ignore_mwt=args.ignore_mwt)119            toolbox.raw2tags(raws_dev, sents_dev, path, 'tag_dev.txt', creat_dict=False, gold_path='tag_dev_gold.txt',120                             ignore_space=args.ignore_space, tag_scheme=args.tags, ignore_mwt=args.ignore_mwt)121        else:122            toolbox.raw2tags_sea(raws_train, sents_train, path, 'tag_train.txt', reset=args.reset, tag_scheme=args.tags)123            toolbox.raw2tags_sea(raws_dev, sents_dev, path, 'tag_dev.txt', gold_path='tag_dev_gold.txt',124                                 tag_scheme=args.tags)125    if args.reset or not os.path.isfile(path + '/chars.txt'):126        toolbox.get_chars(path, ['raw_train.txt', 'raw_dev.txt'], sea=is_space)127    char2idx, unk_chars_idx, idx2char, tag2idx, idx2tag, trans_dict = toolbox.get_dicts(path, args.sent_seg, args.tags,128                                                                                        args.crf)129    if args.embeddings is not None:130        print 'Reading embeddings...'131        short_emb = args.embeddings[args.embeddings.index('/') + 1: args.embeddings.index('.')]132        if args.reset or not os.path.isfile(path + '/' + short_emb + '_sub.txt'):133            toolbox.get_sample_embedding(path, args.embeddings, char2idx)134        emb_dim, emb, valid_chars = toolbox.read_sample_embedding(path, short_emb, char2idx)135        for vch in valid_chars:136            if char2idx[vch] in unk_chars_idx:137                unk_chars_idx.remove(char2idx[vch])138    else:139        emb_dim = args.emb_dimension140        emb = None141    train_x, train_y, max_len_train = toolbox.get_input_vec(path, 'tag_train.txt', char2idx, tag2idx,142                                                            limit=args.sent_limit, sent_seg=args.sent_seg,143                                                            is_space=is_space, train_size=args.train_size,144                                                            ignore_space=args.ignore_space)145    dev_x, max_len_dev = toolbox.get_input_vec_raw(path, 'raw_dev.txt', char2idx, limit=args.sent_limit,146                                                   sent_seg=args.sent_seg, is_space=is_space,147                                                   ignore_space=args.ignore_space)148    if args.sent_seg:149        print 'Joint sentence segmentation...'150    else:151        print 'Training set: %d instances; Dev set: %d instances.' % (len(train_x[0]), len(dev_x[0]))152    nums_grams = None153    ng_embs = None154    if args.ngram > 1 and (args.reset or  not os.path.isfile(path + '/' + str(args.ngram) + 'gram.txt')):155        toolbox.get_ngrams(path, args.ngram, is_space)156    ngram = toolbox.read_ngrams(path, args.ngram)157    if args.ngram > 1:158        gram2idx = toolbox.get_ngram_dic(ngram)159        train_gram = toolbox.get_gram_vec(path, 'tag_train.txt', gram2idx, limit=args.sent_limit,sent_seg=args.sent_seg,160                                          is_space=is_space, ignore_space=args.ignore_space)161        dev_gram = toolbox.get_gram_vec(path, 'raw_dev.txt', gram2idx, is_raw=True, limit=args.sent_limit,162                                        sent_seg=args.sent_seg, is_space=is_space, ignore_space=args.ignore_space)163        train_x += train_gram164        dev_x += dev_gram165        nums_grams = []166        for dic in gram2idx:167            nums_grams.append(len(dic.keys()))168    max_len = max(max_len_train, max_len_dev)169    b_train_x, b_train_y = toolbox.buckets(train_x, train_y, size=args.bucket_size)170    b_train_x, b_train_y, b_lens, b_count = toolbox.pad_bucket(b_train_x, b_train_y, max_len)171    b_dev_x = [toolbox.pad_zeros(dev_x_i, max_len) for dev_x_i in dev_x]172    b_dev_y_gold = [line.strip() for line in codecs.open(path + '/tag_dev_gold.txt', 'r', encoding='utf-8')]173    nums_tag = len(tag2idx)174    config = tf.ConfigProto(allow_soft_placement=True)175    gpu_config = "/gpu:" + str(args.gpu)176    transducer = None177    transducer_graph = None178    trans_model = None179    trans_init = None180    if len(trans_dict) > 200 and not args.ignore_mwt:181        transducer = toolbox.get_dict_vec(trans_dict, char2idx)182    t = time()183    initializer = tf.contrib.layers.xavier_initializer()184    if transducer is not None:185        transducer_graph = tf.Graph()186        with transducer_graph.as_default():187            with tf.variable_scope("transducer") as scope:188                trans_model = Seq2seq(path + '/' + model_file + '_transducer')189                print 'Defining transducer...'190                trans_model.define(char_num=len(char2idx), rnn_dim=args.rnn_cell_dimension, emb_dim=args.emb_dimension,191                                   max_x=len(transducer[0][0]), max_y=len(transducer[1][0]))192            trans_init = tf.global_variables_initializer()193        transducer_graph.finalize()194    print 'Initialization....'195    main_graph = tf.Graph()196    with main_graph.as_default():197        with tf.variable_scope("tagger") as scope:198            model = Model(nums_chars=len(char2idx) + 2, nums_tags=nums_tag, buckets_char=b_lens, counts=b_count,199                          crf=args.crf, ngram=nums_grams, batch_size=args.train_batch, sent_seg=args.sent_seg,200                          is_space=is_space, emb_path=args.embeddings, tag_scheme=args.tags)201            model.main_graph(trained_model=path + '/' + model_file + '_model', scope=scope,202                             emb_dim=emb_dim, cell=args.cell, rnn_dim=args.rnn_cell_dimension,203                             rnn_num=args.rnn_layer_number, drop_out=args.dropout_rate, emb=emb)204            t = time()205        model.config(optimizer=args.optimizer, decay=args.decay_rate, lr_v=args.learning_rate,206                     momentum=args.momentum, clipping=not args.no_clipping)207        init = tf.global_variables_initializer()208        print 'Done. Time consumed: %d seconds' % int(time() - t)209    main_graph.finalize()210    main_sess = tf.Session(config=config, graph=main_graph)211    if args.crf > 0:212        decode_graph = tf.Graph()213        with decode_graph.as_default():214            model.decode_graph()215        decode_graph.finalize()216        decode_sess = tf.Session(config=config, graph=decode_graph)217        sess = [main_sess, decode_sess]218    else:219        sess = [main_sess, None]220    with tf.device(gpu_config):221        if transducer is not None:222            print 'Building transducer...'223            t = time()224            trans_sess = tf.Session(config=config, graph=transducer_graph)225            trans_sess.run(trans_init)226            trans_model.train(transducer[0], transducer[1], transducer[2], transducer[3], args.learning_rate_trans,227                              char2idx, trans_sess, args.epochs_trans, batch_size=10, reset=args.reset_trans)228            sess.append(trans_sess)229            print 'Done. Time consumed: %d seconds' % int(time() - t)230            print 'Training the main segmenter..'231        main_sess.run(init)232        print 'Initialisation...'233        print 'Done. Time consumed: %d seconds' % int(time() - t)234        t = time()235        b_dev_raw = [line.strip() for line in codecs.open(path + '/raw_dev.txt', 'r', encoding='utf-8')]236        model.train(b_train_x, b_train_y, b_dev_x, b_dev_raw, b_dev_y_gold, idx2tag, idx2char, unk_chars_idx, trans_dict,237                    sess, args.epochs, path + '/' + model_file + '_weights', transducer=trans_model,238                    lr=args.learning_rate, decay=args.decay_rate, sent_seg=args.sent_seg, outpath=args.output_path)239else:240    assert args.path is not None241    assert args.model is not None242    path = args.path243    assert os.path.isfile(path + '/chars.txt')244    model_file = args.model245    if args.ensemble:246        if not os.path.isfile(path + '/' + model_file + '_1_model') or not os.path.isfile(path + '/' + model_file +247                                                                                          '_1_weights.index'):248            raise Exception('Not any model file or weights file under the name of ' + model_file + '.')249        fin = open(path + '/' + model_file + '_1_model', 'rb')250    else:251        if not os.path.isfile(path + '/' + model_file + '_model') or not os.path.isfile(path + '/' + model_file +252                                                                                        '_weights.index'):253            raise Exception('No model file or weights file under the name of ' + model_file + '.')254        fin = open(path + '/' + model_file + '_model', 'rb')255    weight_path = path + '/' + model_file256    param_dic = pickle.load(fin)257    fin.close()258    nums_chars = param_dic['nums_chars']259    nums_tags = param_dic['nums_tags']260    crf = param_dic['crf']261    emb_dim = param_dic['emb_dim']262    cell = param_dic['cell']263    rnn_dim = param_dic['rnn_dim']264    rnn_num = param_dic['rnn_num']265    drop_out = param_dic['drop_out']266    buckets_char = param_dic['buckets_char']267    nums_ngrams = param_dic['ngram']268    is_space = param_dic['is_space']269    sent_seg = param_dic['sent_seg']270    emb_path = param_dic['emb_path']271    tag_scheme = param_dic['tag_scheme']272    if args.embeddings is not None:273        emb_path = args.embeddings274    ngram = 1275    grams, gram2idx = None, None276    if nums_ngrams is not None:277        ngram = len(nums_ngrams) + 1278    char2idx, unk_chars_idx, idx2char, tag2idx, idx2tag, trans_dict = toolbox.get_dicts(path, sent_seg, tag_scheme, crf)279    trans_char_num = len(char2idx)280    if ngram > 1:281        grams = toolbox.read_ngrams(path, ngram)282    new_chars, new_grams = None, None283    test_x, test_y, raw_x, test_y_gold = None, None, None, None284    sub_dict = None285    max_step = None286    raw_file = None287    if args.action == 'test':288        test_file = args.test289        f_names = os.listdir(path)290        if test_file is None:291            for f_n in f_names:292                if 'ud-test.conllu' in f_n:293                    test_file = f_n294        assert test_file is not None295        cat = 'other'296        if 'Chinese' in path or 'Japanese' in path:297            cat = 'zh'298        for line in codecs.open(path + '/' + test_file, 'r', encoding='utf-8'):299            if len(line) < 2:300                break301            if '# sentence' in line or '# text' in line:302                cat = 'gold'303        reader.get_raw(path, test_file, 'raw_test.txt', cat, form=args.format)304        raws_test = reader.raw(path + '/raw_test.txt')305        test_y_gold = reader.test_gold(path + '/' + test_file, form=args.format, is_space=is_space,306                                       ignore_mwt=args.ignore_mwt)307        new_chars = toolbox.get_new_chars(path + '/raw_test.txt', char2idx, is_space)308        if emb_path is not None:309            valid_chars = toolbox.get_valid_chars(new_chars + char2idx.keys(), emb_path)310        else:311            valid_chars = None312        char2idx, idx2char, unk_chars_idx, sub_dict = toolbox.update_char_dict(char2idx, new_chars, unk_chars_idx, valid_chars)313        test_x, max_len_test = toolbox.get_input_vec_raw(path, 'raw_test.txt', char2idx, limit=args.sent_limit + 100,314                                                         sent_seg=sent_seg, is_space=is_space,315                                                         ignore_space=args.ignore_space)316        max_step = max_len_test317        if sent_seg:318            print 'Joint sentence segmentation...'319        else:320            print 'Test set: %d instances.' % len(test_x[0])321        if ngram > 1:322            gram2idx = toolbox.get_ngram_dic(grams)323            new_grams = toolbox.get_new_grams(path + '/' + test_file, gram2idx, is_space=is_space)324            test_grams = toolbox.get_gram_vec(path, 'raw_test.txt', gram2idx, is_raw=True, limit=args.sent_limit + 100,325                                              sent_seg=sent_seg, is_space=is_space, ignore_space=args.ignore_space)326            test_x += test_grams327        for k in range(len(test_x)):328            test_x[k] = toolbox.pad_zeros(test_x[k], max_step)329    elif args.action == 'tag':330        assert args.raw is not None331        raw_file = args.raw332        new_chars = toolbox.get_new_chars(raw_file, char2idx, is_space)333        if emb_path is not None:334            valid_chars = toolbox.get_valid_chars(new_chars, emb_path)335        else:336            valid_chars = None337        char2idx, idx2char, unk_chars_idx, sub_dict = toolbox.update_char_dict(char2idx, new_chars, unk_chars_idx,338                                                                               valid_chars)339        if not args.segment_large:340            if sent_seg:341                raw_x, raw_len = toolbox.get_input_vec_tag(None, raw_file, char2idx, limit=args.sent_limit + 100,342                                                           is_space=is_space)343            else:344                raw_x, raw_len = toolbox.get_input_vec_raw(None, raw_file, char2idx, limit=args.sent_limit + 100,345                                                           sent_seg=sent_seg, is_space=is_space)346            if sent_seg:347                print 'Joint sentence segmentation...'348            else:349                print 'Raw setences: %d instances.' % len(raw_x[0])350            max_step = raw_len351        else:352            max_step = args.sent_limit353        if ngram > 1:354            gram2idx = toolbox.get_ngram_dic(grams)355            new_grams = toolbox.get_new_grams(raw_file, gram2idx, is_raw=True, is_space=is_space)356            if not args.segment_large:357                if sent_seg:358                    raw_grams = toolbox.get_gram_vec_tag(None, raw_file, gram2idx, limit=args.sent_limit + 100,359                                                         is_space=is_space)360                else:361                    raw_grams = toolbox.get_gram_vec(None, raw_file, gram2idx, is_raw=True, limit=args.sent_limit + 100,362                                                     sent_seg=sent_seg, is_space=is_space)363                raw_x += raw_grams364        if not args.segment_large:365            for k in range(len(raw_x)):366                raw_x[k] = toolbox.pad_zeros(raw_x[k], max_step)367    config = tf.ConfigProto(allow_soft_placement=True)368    gpu_config = "/gpu:" + str(args.gpu)369    transducer = None370    transducer_graph = None371    trans_model = None372    trans_init = None373    if len(trans_dict) > 200:374        transducer = toolbox.get_dict_vec(trans_dict, char2idx)375    t = time()376    initializer = tf.contrib.layers.xavier_initializer()377    if transducer is not None:378        transducer_graph = tf.Graph()379        with transducer_graph.as_default():380            with tf.variable_scope("transducer") as scope:381                trans_model = Seq2seq(path + '/' + model_file + '_transducer')382                trans_fin = open(path + '/' + model_file + '_transducer_model', 'rb')383                trans_param_dic = pickle.load(trans_fin)384                trans_fin.close()385                tr_char_num = trans_param_dic['char_num']386                tr_rnn_dim = trans_param_dic['rnn_dim']387                tr_emb_dim = trans_param_dic['emb_dim']388                tr_max_x = trans_param_dic['max_x']389                tr_max_y = trans_param_dic['max_y']390                print 'Defining transducer...'391                trans_model.define(char_num=tr_char_num, rnn_dim=tr_rnn_dim, emb_dim=tr_emb_dim,392                                   max_x=tr_max_x, max_y=tr_max_y, write_trans_model=False)393            trans_init = tf.global_variables_initializer()394        transducer_graph.finalize()395    print 'Initialization....'396    main_graph = tf.Graph()397    with main_graph.as_default():398        with tf.variable_scope("tagger") as scope:399            model = Model(nums_chars=nums_chars, nums_tags=nums_tags, buckets_char=[max_step], counts=[200],400                          crf=crf, ngram=nums_ngrams, batch_size=args.tag_batch, is_space=is_space)401            model.main_graph(trained_model=None, scope=scope, emb_dim=emb_dim, cell=cell,402                             rnn_dim=rnn_dim, rnn_num=rnn_num, drop_out=drop_out)403        model.define_updates(new_chars=new_chars, emb_path=emb_path, char2idx=char2idx)404        init = tf.global_variables_initializer()405        print 'Done. Time consumed: %d seconds' % int(time() - t)406    main_graph.finalize()407    idx=None408    if args.ensemble:409        idx = 1410        main_sess = []411        while os.path.isfile(path + '/' + model_file + '_' + str(idx) + '_weights.index'):412            main_sess.append(tf.Session(config=config, graph=main_graph))413            idx += 1414    else:415        main_sess = tf.Session(config=config, graph=main_graph)416    if crf:417        decode_graph = tf.Graph()418        with decode_graph.as_default():419            model.decode_graph()420        decode_graph.finalize()421        decode_sess = tf.Session(config=config, graph=decode_graph)422        sess = [main_sess, decode_sess]423    else:424        sess = [main_sess, None]425    with tf.device(gpu_config):426        ens_model = None427        print 'Loading weights....'428        if args.ensemble:429            for i in range(1, idx):430                print 'Ensemble: ' + str(i)431                main_sess[i - 1].run(init)432                model.run_updates(main_sess[i - 1], weight_path + '_' + str(i) + '_weights')433        else:434            main_sess.run(init)435            model.run_updates(main_sess, weight_path + '_weights')436        if transducer is not None:437            print 'Loading transducer...'438            t = time()439            trans_sess = tf.Session(config=config, graph=transducer_graph)440            trans_sess.run(trans_init)441            if os.path.isfile(path + '/' + model_file + '_transducer_weights'):442                trans_weight_path = path + '/' + model_file + '_transducer_weights'443                trans_weight_path = trans_weight_path.replace('//', '/')444                trans_model.saver.restore(trans_sess, trans_weight_path)445            sess.append(trans_sess)446        if args.action == 'test':447            test_y_raw = [line.strip() for line in codecs.open(path + '/raw_test.txt', 'rb', encoding='utf-8')]448            model.test(test_x, test_y_raw, test_y_gold, idx2tag, idx2char, unk_chars_idx, sub_dict, trans_dict, sess,449                       transducer=trans_model, ensemble=args.ensemble, batch_size=args.test_batch, sent_seg=sent_seg,450                       bias=args.segmentation_bias, outpath=args.output_path, trans_type=args.transduction_type)451        if args.action == 'tag':452            if not args.segment_large:453                raw_sents = []454                for line in codecs.open(raw_file, 'rb', encoding='utf-8'):455                    line = line.strip()456                    if len(line) > 0:457                        raw_sents.append(line)458                model.tag(raw_x, raw_sents, idx2tag, idx2char, unk_chars_idx, sub_dict, trans_dict, sess,459                          transducer=trans_model, outpath=args.output_path, ensemble=args.ensemble,460                          batch_size=args.tag_batch, sent_seg=sent_seg, seg_large=args.segment_large, form=args.format)461            else:462                count = 0463                c_line = 0464                l_writer = codecs.open(args.output_path, 'w', encoding='utf-8')465                out = []466                with codecs.open(raw_file, 'r', encoding='utf-8') as l_file:467                    lines = []468                    for line in l_file:469                        line = line.strip()470                        if len(line) > 0:471                            lines.append(line)472                        else:473                            c_line += 1474                        if c_line >= args.large_size:475                            count += len(lines)476                            c_line = 0477                            print count478                            if args.sent_seg:479                                raw_x, _ = toolbox.get_input_vec_tag(None, None, char2idx, lines=lines,480                                                                     limit=args.sent_limit, is_space=is_space)481                            else:482                                raw_x, _ = toolbox.get_input_vec_raw(None, None, char2idx, lines=lines,483                                                                     limit=args.sent_limit, sent_seg=sent_seg,484                                                                     is_space=is_space)485                            if ngram > 1:486                                if sent_seg:487                                    raw_grams = toolbox.get_gram_vec_tag(None, None, gram2idx, lines=lines,488                                                                         limit=args.sent_limit, is_space=is_space)489                                else:490                                    raw_grams = toolbox.get_gram_vec(None, None, gram2idx, lines=lines, is_raw=True,491                                                                     limit=args.sent_limit, sent_seg=sent_seg,492                                                                     is_space=is_space)493                                raw_x += raw_grams494                            for k in range(len(raw_x)):495                                raw_x[k] = toolbox.pad_zeros(raw_x[k], max_step)496                            predition, multi = model.tag(raw_x, lines, idx2tag, idx2char, unk_chars_idx, sub_dict,497                                                         trans_dict, sess, transducer=trans_model,498                                                         outpath=args.output_path, ensemble=args.ensemble,499                                                         batch_size=args.tag_batch, sent_seg=sent_seg,500                                                         seg_large=args.segment_large, form=args.format)501                            if args.only_tokenised:502                                for l_out in predition:503                                    if len(l_out.strip()) > 0:504                                        l_writer.write(l_out + '\n')505                            else:506                                for tagged_t, multi_t in zip(predition, multi):507                                    if len(tagged_t.strip()) > 0:508                                        l_writer.write('#sent_tok: ' + tagged_t + '\n')509                                        idx = 1510                                        tgs = multi_t.split('  ')511                                        pl = ''512                                        for _ in range(8):513                                            pl += '\t' + '_'514                                        for tg in tgs:515                                            if '!#!' in tg:516                                                segs = tg.split('!#!')517                                                l_writer.write(str(idx) + '-' + str(int(segs[1]) + idx - 1) + '\t' +518                                                               segs[0] + pl + '\n')519                                            else:520                                                l_writer.write(str(idx) + '\t' + tg + pl + '\n')521                                                idx += 1522                                        l_writer.write('\n')523                            lines = []524                    if len(lines) > 0:525                        if args.sent_seg:526                            raw_x, _ = toolbox.get_input_vec_tag(None, None, char2idx, lines=lines,527                                                                      limit=args.sent_limit, is_space=is_space)528                        else:529                            raw_x, _ = toolbox.get_input_vec_raw(None, None, char2idx, lines=lines,530                                                                      limit=args.sent_limit, sent_seg=sent_seg,531                                                                      is_space=is_space)532                        if ngram > 1:533                            if sent_seg:534                                raw_grams = toolbox.get_gram_vec_tag(None, None, gram2idx, lines=lines,535                                                                     limit=args.sent_limit, is_space=is_space)536                            else:537                                raw_grams = toolbox.get_gram_vec(None, None, gram2idx, lines=lines, is_raw=True,538                                                                 limit=args.sent_limit, sent_seg=sent_seg,539                                                                 is_space=is_space)540                            raw_x += raw_grams541                        for k in range(len(raw_x)):542                            raw_x[k] = toolbox.pad_zeros(raw_x[k], max_step)543                        prediction, multi = model.tag(raw_x, lines, idx2tag, idx2char, unk_chars_idx, sub_dict,544                                                      trans_dict, sess, transducer=trans_model,545                                                      outpath=args.output_path, ensemble=args.ensemble,546                                                      batch_size=args.tag_batch, sent_seg=sent_seg,547                                                      seg_large=args.segment_large, form=args.format)548                        if args.only_tokenised:549                            for l_out in prediction:550                                if len(l_out.strip()) > 0:551                                    l_writer.write(l_out + '\n')552                        else:553                            for tagged_t, multi_t in zip(prediction, multi):554                                if len(tagged_t.strip()) > 0:555                                    l_writer.write('#sent_tok: ' + tagged_t + '\n')556                                    idx = 1557                                    tgs = multi_t.split('  ')558                                    pl = ''559                                    for _ in range(8):560                                        pl += '\t' + '_'561                                    for tg in tgs:562                                        if '!#!' in tg:563                                            segs = tg.split('!#!')564                                            l_writer.write(str(idx) + '-' + str(int(segs[1]) + idx - 1) + '\t' +565                                                           segs[0] + pl + '\n')566                                        else:567                                            l_writer.write(str(idx) + '\t' + tg + pl + '\n')568                                            idx += 1569                                    l_writer.write('\n')570                l_writer.close()...

test_space_attachment.py

Source:test_space_attachment.py

1# coding: utf-82from __future__ import unicode_literals3import pytest4from spacy.tokens.doc import Doc5from ..util import get_doc, apply_transition_sequence6def test_parser_space_attachment(en_tokenizer):7    text = "This is a test.\nTo ensure  spaces are attached well."8    heads = [1, 0, 1, -2, -3, -1, 1, 4, -1, 2, 1, 0, -1, -2]9    tokens = en_tokenizer(text)10    doc = get_doc(tokens.vocab, words=[t.text for t in tokens], heads=heads)11    for sent in doc.sents:12        if len(sent) == 1:13            assert not sent[-1].is_space14def test_parser_sentence_space(en_tokenizer):15    # fmt: off16    text = "I look forward to using Thingamajig.  I've been told it will make my life easier..."17    heads = [1, 0, -1, -2, -1, -1, -5, -1, 3, 2, 1, 0, 2, 1, -3, 1, 1, -3, -7]18    deps = ["nsubj", "ROOT", "advmod", "prep", "pcomp", "dobj", "punct", "",19            "nsubjpass", "aux", "auxpass", "ROOT", "nsubj", "aux", "ccomp",20            "poss", "nsubj", "ccomp", "punct"]21    # fmt: on22    tokens = en_tokenizer(text)23    doc = get_doc(tokens.vocab, words=[t.text for t in tokens], heads=heads, deps=deps)24    assert len(list(doc.sents)) == 225@pytest.mark.xfail26def test_parser_space_attachment_leading(en_tokenizer, en_parser):27    text = "\t \n This is a sentence ."28    heads = [1, 1, 0, 1, -2, -3]29    tokens = en_tokenizer(text)30    doc = get_doc(tokens.vocab, words=text.split(" "), heads=heads)31    assert doc[0].is_space32    assert doc[1].is_space33    assert doc[2].text == "This"34    with en_parser.step_through(doc) as stepwise:35        pass36    assert doc[0].head.i == 237    assert doc[1].head.i == 238    assert stepwise.stack == set([2])39@pytest.mark.xfail40def test_parser_space_attachment_intermediate_trailing(en_tokenizer, en_parser):41    text = "This is \t a \t\n \n sentence . \n\n \n"42    heads = [1, 0, -1, 2, -1, -4, -5, -1]43    transition = ["L-nsubj", "S", "L-det", "R-attr", "D", "R-punct"]44    tokens = en_tokenizer(text)45    doc = get_doc(tokens.vocab, words=text.split(" "), heads=heads)46    assert doc[2].is_space47    assert doc[4].is_space48    assert doc[5].is_space49    assert doc[8].is_space50    assert doc[9].is_space51    apply_transition_sequence(en_parser, doc, transition)52    for token in doc:53        assert token.dep != 0 or token.is_space54    assert [token.head.i for token in doc] == [1, 1, 1, 6, 3, 3, 1, 1, 7, 7]55@pytest.mark.parametrize("text,length", [(["\n"], 1), (["\n", "\t", "\n\n", "\t"], 4)])56@pytest.mark.xfail57def test_parser_space_attachment_space(en_tokenizer, en_parser, text, length):58    doc = Doc(en_parser.vocab, words=text)59    assert len(doc) == length60    with en_parser.step_through(doc) as _:  # noqa: F84161        pass62    assert doc[0].is_space63    for token in doc:...

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