def load(self, records): self.records = records self.ratings_matrix = basic_knn.create_ratings_matrix(records) self.reviews_matrix = create_reviews_matrix(records) self.user_dictionary = extractor.initialize_users(self.records, False) self.user_ids = extractor.get_groupby_list(self.records, 'user_id') lda_based_context = LdaBasedContext(self.records, self.reviews) # self.lda_model =\ # lda_context_utils.discover_topics(text_reviews, self.num_topics) # if self.reviews: # lda_based_context = LdaBasedContext() # lda_based_context.reviews = self.reviews # lda_based_context.init_reviews() # else: # text_reviews = [] # for record in self.records: # text_reviews.append(record['text']) # lda_based_context = LdaBasedContext(text_reviews) # lda_based_context.init_reviews() self.context_rich_topics = lda_based_context.get_context_rich_topics() self.lda_model = lda_based_context.topic_model print('building similarity matrix', time.strftime("%H:%M:%S")) self.context_matrix = self.create_context_matrix(records) self.similarity_matrix = self.create_similarity_matrix() print('finished building similarity matrix', time.strftime("%H:%M:%S"))
def train_context_extractor(records, stable=True): print('%s: train context topics model' % time.strftime("%Y/%m/%d-%H:%M:%S")) if Constants.TOPIC_MODEL_TYPE == 'lda': context_extractor = LdaBasedContext(records) context_extractor.generate_review_corpus() context_extractor.build_topic_model() context_extractor.update_reviews_with_topics() context_extractor.get_context_rich_topics() context_extractor.clear_reviews() elif Constants.TOPIC_MODEL_TYPE == 'nmf': context_extractor = NmfContextExtractor(records) context_extractor.generate_review_bows() context_extractor.build_document_term_matrix() if stable: context_extractor.build_stable_topic_model() else: context_extractor.build_topic_model() context_extractor.update_reviews_with_topics() context_extractor.get_context_rich_topics() context_extractor.clear_reviews() else: raise ValueError('Unrecognized topic model type: \'%s\'' % Constants.TOPIC_MODEL_TYPE) print('%s: Trained Topic Model' % time.strftime("%Y/%m/%d-%H:%M:%S")) return context_extractor
def load_context(self, records): if self.reviews: lda_based_context = LdaBasedContext() lda_based_context.reviews = self.reviews lda_based_context.init_reviews() else: text_reviews = [] for record in records: text_reviews.append(record['text']) lda_based_context = LdaBasedContext(text_reviews) lda_based_context.init_reviews() self.context_rich_topics = lda_based_context.get_context_rich_topics() self.lda_model = lda_based_context.topic_model for user in self.user_dictionary.values(): user.item_contexts = lda_context_utils.get_user_item_contexts( records, self.lda_model, user.user_id, True )
def train_context_topics_model(records): print('%s: train context topics model' % time.strftime("%Y/%m/%d-%H:%M:%S")) lda_based_context = LdaBasedContext(records) lda_based_context.generate_review_corpus() lda_based_context.build_topic_model() lda_based_context.update_reviews_with_topics() print('%s: Trained LDA Model' % time.strftime("%Y/%m/%d-%H:%M:%S")) return lda_based_context
def train_topic_model(self, cycle_index, fold_index): if Constants.CACHE_TOPIC_MODEL: print('loading topic model') lda_based_context = topic_model_creator.load_topic_model( cycle_index, fold_index) else: print('train topic model: %s' % time.strftime("%Y/%m/%d-%H:%M:%S")) lda_based_context = LdaBasedContext(self.train_records) lda_based_context.generate_review_corpus() lda_based_context.build_topic_model() lda_based_context.update_reviews_with_topics() lda_based_context.get_context_rich_topics() self.context_rich_topics = lda_based_context.context_rich_topics print('Trained LDA Model: %s' % time.strftime("%Y/%m/%d-%H:%M:%S")) return lda_based_context