dataset reader for wmt14

example usage:

import paddle.v2 as paddle

if __name__ == '__main__':
    dict_en, dict_fr =	paddle.dataset.wmt14.build_dict()
    train = paddle.dataset.wmt14.train(dict_en, dict_fr)
    test = paddle.dataset.wmt14.test(dict_en, dict_fr)

    total_train = 0
    for i in train():
        total_train += 1

    total_test = 0
    for i in test():
        total_test += 1

    print total_train, total_test

Author: Helin Wang

python/paddle/v2/dataset/__init__.py

@@ -20,8 +20,9 @@
 import conll05
 import uci_housing
 import sentiment
+import wmt14
 
 __all__ = [
     'mnist', 'imikolov', 'imdb', 'cifar', 'movielens', 'conll05', 'sentiment'
-    'uci_housing'
+    'uci_housing', 'wmt14'
 ]

python/paddle/v2/dataset/wmt14.py

@@ -0,0 +1,142 @@
+# 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.
+"""
+wmt14 dataset
+"""
+import paddle.v2.dataset.common
+import tarfile
+import os.path
+import itertools
+
+__all__ = ['train', 'test', 'build_dict']
+
+URL_DEV_TEST = 'http://www-lium.univ-lemans.fr/~schwenk/cslm_joint_paper/data/dev+test.tgz'
+MD5_DEV_TEST = '7d7897317ddd8ba0ae5c5fa7248d3ff5'
+URL_TRAIN = 'http://localhost:8000/train.tgz'
+MD5_TRAIN = '72de99da2830ea5a3a2c4eb36092bbc7'
+
+
+def word_count(f, word_freq=None):
+    add = paddle.v2.dataset.common.dict_add
+    if word_freq == None:
+        word_freq = {}
+
+    for l in f:
+        for w in l.strip().split():
+            add(word_freq, w)
+        add(word_freq, '<s>')
+        add(word_freq, '<e>')
+
+    return word_freq
+
+
+def get_word_dix(word_freq):
+    TYPO_FREQ = 50
+    word_freq = filter(lambda x: x[1] > TYPO_FREQ, word_freq.items())
+    word_freq_sorted = sorted(word_freq, key=lambda x: (-x[1], x[0]))
+    words, _ = list(zip(*word_freq_sorted))
+    word_idx = dict(zip(words, xrange(len(words))))
+    word_idx['<unk>'] = len(words)
+    return word_idx
+
+
+def get_word_freq(train, dev):
+    word_freq = word_count(train, word_count(dev))
+    if '<unk>' in word_freq:
+        # remove <unk> for now, since we will set it as last index
+        del word_freq['<unk>']
+    return word_freq
+
+
+def build_dict():
+    base_dir = './wmt14-data'
+    train_en_filename = base_dir + '/train/train.en'
+    train_fr_filename = base_dir + '/train/train.fr'
+    dev_en_filename = base_dir + '/dev/ntst1213.en'
+    dev_fr_filename = base_dir + '/dev/ntst1213.fr'
+
+    if not os.path.exists(train_en_filename) or not os.path.exists(
+            train_fr_filename):
+        with tarfile.open(
+                paddle.v2.dataset.common.download(URL_TRAIN, 'wmt14',
+                                                  MD5_TRAIN)) as tf:
+            tf.extractall(base_dir)
+
+    if not os.path.exists(dev_en_filename) or not os.path.exists(
+            dev_fr_filename):
+        with tarfile.open(
+                paddle.v2.dataset.common.download(URL_DEV_TEST, 'wmt14',
+                                                  MD5_DEV_TEST)) as tf:
+            tf.extractall(base_dir)
+
+    f_en = open(train_en_filename)
+    f_fr = open(train_fr_filename)
+    f_en_dev = open(dev_en_filename)
+    f_fr_dev = open(dev_fr_filename)
+
+    word_freq_en = get_word_freq(f_en, f_en_dev)
+    word_freq_fr = get_word_freq(f_fr, f_fr_dev)
+
+    f_en.close()
+    f_fr.close()
+    f_en_dev.close()
+    f_fr_dev.close()
+
+    return get_word_dix(word_freq_en), get_word_dix(word_freq_fr)
+
+
+def reader_creator(directory, path_en, path_fr, URL, MD5, dict_en, dict_fr):
+    def reader():
+        if not os.path.exists(path_en) or not os.path.exists(path_fr):
+            with tarfile.open(
+                    paddle.v2.dataset.common.download(URL, 'wmt14', MD5)) as tf:
+                tf.extractall(directory)
+
+        f_en = open(path_en)
+        f_fr = open(path_fr)
+        UNK_en = dict_en['<unk>']
+        UNK_fr = dict_fr['<unk>']
+
+        for en, fr in itertools.izip(f_en, f_fr):
+            src_ids = [dict_en.get(w, UNK_en) for w in en.strip().split()]
+            tar_ids = [
+                dict_fr.get(w, UNK_fr)
+                for w in ['<s>'] + fr.strip().split() + ['<e>']
+            ]
+
+            # remove sequence whose length > 80 in training mode
+            if len(src_ids) == 0 or len(tar_ids) <= 1 or len(
+                    src_ids) > 80 or len(tar_ids) > 80:
+                continue
+
+            yield src_ids, tar_ids[:-1], tar_ids[1:]
+
+        f_en.close()
+        f_fr.close()
+
+    return reader
+
+
+def train(dict_en, dict_fr):
+    directory = './wmt14-data'
+    return reader_creator(directory, directory + '/train/train.en',
+                          directory + '/train/train.fr', URL_TRAIN, MD5_TRAIN,
+                          dict_en, dict_fr)
+
+
+def test(dict_en, dict_fr):
+    directory = './wmt14-data'
+    return reader_creator(directory, directory + '/dev/ntst1213.en',
+                          directory + '/dev/ntst1213.fr', URL_DEV_TEST,
+                          MD5_DEV_TEST, dict_en, dict_fr)