try: # If model is already available in disk. model,countVec,countMatrix,n_topic = train.recoverModel(H.modelname,H.datafolder) beta_lda = model.components_/model.components_.sum(axis=1)[:, np.newaxis] except: n_topic = H.n_topic #train_idx, valid_idx = train.splitData(H.df_red,0.1) countVec, countMatrix = train.count(H.df_red,[1,2], 0.002,0.6) model,beta_lda = train.fitLDA(countMatrix,n_topic=H.n_topic,random_state=6,alpha=None) train.saveModel(H.modelname,H.datafolder,model,countVec,countMatrix,H.n_topic) topic_dist, dominant_topic = train.transformer(countMatrix, model) if __name__ == 'main': # Recommender object. R = Recommender(H.df_red,n_topic,topic_dist,H.links) R.clearHistory() # R.history = np.array([],dtype='int64') # search_result = R.searchPage('Orta Doğu Teknik Üniversitesi', True) # search_result = R.searchPage('Makine Öğrenimi', True) # search_result = R.searchPage('Suriye', True) # search_result = R.searchPage('Python (programlama dili)', True) # search_result = R.searchPage('General Dynamics F-16 Fighting Falcon', True) # search_result = R.searchPage('Kadeş Antlaşması', True) # search_result = R.searchPage('Cengiz Han', True) search_result = R.searchPage('Wolfgang Amadeus Mozart', True) R.addToHistory(search_result.index.to_numpy()) print('Lets get recommendations..') rcm = R.recommendWrtHistory() R.evaluateRecommendation(rcm.head(20))