# Copyright 2017 The TensorFlow 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. # ============================================================================== """We calculate n-Grams from the training text. We will use this as an evaluation metric.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from six.moves import xrange def hash_function(input_tuple): """Hash function for a tuple.""" return hash(input_tuple) def find_all_ngrams(dataset, n): """Generate a list of all ngrams.""" return zip(*[dataset[i:] for i in xrange(n)]) def construct_ngrams_dict(ngrams_list): """Construct a ngram dictionary which maps an ngram tuple to the number of times it appears in the text.""" counts = {} for t in ngrams_list: key = hash_function(t) if key in counts: counts[key] += 1 else: counts[key] = 1 return counts def percent_unique_ngrams_in_train(train_ngrams_dict, gen_ngrams_dict): """Compute the percent of ngrams generated by the model that are present in the training text and are unique.""" # *Total* number of n-grams produced by the generator. total_ngrams_produced = 0 for _, value in gen_ngrams_dict.iteritems(): total_ngrams_produced += value # The unique ngrams in the training set. unique_ngrams_in_train = 0. for key, _ in gen_ngrams_dict.iteritems(): if key in train_ngrams_dict: unique_ngrams_in_train += 1 return float(unique_ngrams_in_train) / float(total_ngrams_produced)