Upload ZEN/ngram_utils.py

ZEN/ngram_utils.py

@@ -0,0 +1,53 @@
+# coding: utf-8
+# Copyright 2019 Sinovation Ventures AI Institute
+#
+# 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.
+"""utils for ngram for ZEN model."""
+
+import os
+import logging
+
+NGRAM_DICT_NAME = 'ngram.txt'
+
+logger = logging.getLogger(__name__)
+
+class ZenNgramDict(object):
+    """
+    Dict class to store the ngram
+    """
+    def __init__(self, ngram_freq_path, tokenizer, max_ngram_in_seq=128):
+        """Constructs ZenNgramDict
+
+        :param ngram_freq_path: ngrams with frequency
+        """
+        if os.path.isdir(ngram_freq_path):
+            ngram_freq_path = os.path.join(ngram_freq_path, NGRAM_DICT_NAME)
+        self.ngram_freq_path = ngram_freq_path
+        self.max_ngram_in_seq = max_ngram_in_seq
+        self.id_to_ngram_list = ["[pad]"]
+        self.ngram_to_id_dict = {"[pad]": 0}
+        self.ngram_to_freq_dict = {}
+
+        logger.info("loading ngram frequency file {}".format(ngram_freq_path))
+        with open(ngram_freq_path, "r", encoding="utf-8") as fin:
+            for i, line in enumerate(fin):
+                ngram,freq = line.split(",")
+                tokens = tuple(tokenizer.tokenize(ngram))
+                self.ngram_to_freq_dict[ngram] = freq
+                self.id_to_ngram_list.append(tokens)
+                self.ngram_to_id_dict[tokens] = i + 1
+
+    def save(self, ngram_freq_path):
+        with open(ngram_freq_path, "w", encoding="utf-8") as fout:
+            for ngram,freq in self.ngram_to_freq_dict.items():
+                fout.write("{},{}\n".format(ngram, freq))