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def input_evalution(input_processed_text,tenant_id, df_train_mtrx,tfidf_vector,df_act): print("Into Input Evaluation function") text=input_processed_text print("Text : ",text) tfidf_vector=tfidf_vector print("TFIDF Vector : ",tfidf_vector) tenant_id = tenant_id print("Tenant ID Inside INput Evaluation : ",tenant_id) df_train_mtrx=df_train_mtrx print("DF Train Matrix : ",df_train_mtrx) input_tfidf=tfidf_vector.transform([text]) print("Input TF IDF : ",input_tfidf) x=input_tfidf.todense() print("X : ",x) df_tst = pd.DataFrame(x) #print("Df Test Input Evaluation : ",df_tst) ## Replacing Nan values in matrix with 0 df_train_mtrx_nan=np.isnan(df_train_mtrx) #print("DF Train MAtrix Nan : ",df_train_mtrx_nan) df_train_mtrx[df_train_mtrx_nan] = 0 scr=cosine_similarity(df_train_mtrx, df_tst) print("Cosine Similarity : ",scr) Here if you see we are getting tenant_id , and now we want to filter the df_train_mtrx by tenant_id and get only relevant matching records with tenant_id , for eg if tenant_id is 32, so only relevant records should be present. Can we create a new matrix by filtering with tenant id on df_train_mtrx and then calculate the cosine similarity for for df_tst.
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