>>>metrics.classification_report(test.target,prediction,target_names=test.target_names)precisionrecallf1-scoresupportalt.atheism0.210.590.31319comp.graphics0.530.630.58389comp.os.ms-windows.misc0.000.000.00394comp.sys.ibm.pc.hardware0.540.530.54392comp.sys.mac.hardware0.610.600.60385comp.windows.x0.770.600.673...
Visualize Term Score Decline.visualize_term_rank() Visualize Topic Probability Distribution.visualize_distribution(probs[0]) Visualize Topics over Time.visualize_topics_over_time(topics_over_time) Visualize Topics per Class.visualize_topics_per_class(topics_per_class) ...
dict(zip(feature_names, tfidf_matrix[i].toarray()[0])) sorted_scores = sorted(tfidf_scores.items(), key=lambda x: x[1], reverse=True) # 输出前3个关键词及其TF-IDF值 print(f"Document {i+1} top keywords:") for word, score in sorted_scores[:3]: print(f"{word}: {score}")...
对结果进行输出打印,只打印每个文本中IF-IDF值top3: # outputprint("\nTraining by gensim Tfidf Model...\n")fori,docinenumerate(corpus_tfidf):print("Top words in document %d"%(i+1))sorted_words=sorted(doc,key=lambdax:x[1],reverse=True)#type=listfornum,scoreinsorted_words[:3]:print("...
Visualize Term Score Decline.visualize_term_rank() Visualize Topic Probability Distribution.visualize_distribution(probs[0]) Visualize Topics over Time.visualize_topics_over_time(topics_over_time) Visualize Topics per Class.visualize_topics_per_class(topics_per_class) ...