Add build_raw_data.py

fluid/PaddleNLP/text_classification/async_executor/data_generator/build_raw_data.py

+#   Copyright (c) 2018 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.
+"""
+Build lego raw data
+"""
+from __future__ import print_function
+import sys
+import os
+import random
+import re
+data_type = sys.argv[1]
+
+if not (data_type == "train" or data_type == "test"):
+    print("python %s [test/train]" % sys.argv[0], file=sys.stderr)
+    sys.exit(-1)
+
+pos_folder = "aclImdb/" + data_type + "/pos/"
+neg_folder = "aclImdb/" + data_type + "/neg/"
+
+pos_train_list = [(pos_folder + x, "1") for x in os.listdir(pos_folder)]
+neg_train_list = [(neg_folder + x, "0") for x in os.listdir(neg_folder)]
+
+all_train_list = pos_train_list + neg_train_list
+random.shuffle(all_train_list)
+
+
+def load_dict(dictfile):
+    """
+    Load word id dict
+    """
+    vocab = {}
+    wid = 0
+    with open(dictfile) as f:
+        for line in f:
+            vocab[line.strip()] = str(wid)
+            wid += 1
+    return vocab
+
+
+vocab = load_dict("aclImdb/imdb.vocab")
+unk_id = str(len(vocab))
+print("vocab size: ", len(vocab), file=sys.stderr)
+pattern = re.compile(r'(;|,|\.|\?|!|\s|\(|\))')
+
+for fitem in all_train_list:
+    label = str(fitem[1])
+    fname = fitem[0]
+    with open(fname) as f:
+        sent = f.readline().lower().replace("<br />", " ").strip()
+        out_s = "%s | %s" % (sent, label)
+        print(out_s, file=sys.stdout)