keyword.py

def sort_coo(coo_matrix):
    tuples = zip(coo_matrix.col, coo_matrix.data)
    return sorted(tuples, key=lambda x: (x[1], x[0]), reverse=True)

def extract_topn_from_vector(feature_names, sorted_items, topn=10):
    """get the feature names and tf-idf score of top n items"""
    
    #use only topn items from vector
    sorted_items = sorted_items[:topn]

    score_vals = []
    feature_vals = []

    for idx, score in sorted_items:
        fname = feature_names[idx]
        
        #keep track of feature name and its corresponding score
        score_vals.append(round(score, 3))
        feature_vals.append(feature_names[idx])

    #create a tuples of feature,score
    #results = zip(feature_vals,score_vals)
    results= {}
    for idx in range(len(feature_vals)):
        results[feature_vals[idx]]=score_vals[idx]
    
    return results

# get feature names
feature_names=cv.get_feature_names()

def get_keywords(idx, docs):

    #generate tf-idf for the given document
    tf_idf_vector=tfidf_transformer.transform(cv.transform([docs[idx]]))

    #sort the tf-idf vectors by descending order of scores
    sorted_items=sort_coo(tf_idf_vector.tocoo())

    #extract only the top n; n here is 10
    keywords=extract_topn_from_vector(feature_names,sorted_items,10)
    
    return keywords

def print_results(idx,keywords, df):
    # now print the results
    print("\n=====Title=====")
    print(df['title'][idx])
    print("\n=====Abstract=====")
    print(df['abstract'][idx])
    print("\n===Keywords===")
    for k in keywords:
        print(k,keywords[k])
idx=941
keywords=get_keywords(idx, docs)
print_results(idx,keywords, df)