文本可视化python包
titulus的Python项目详细描述
提图斯
文本可视化python包
fromtitulusimportcolor,print_test="Nous sommes le 12/24/2018 aujourd'hui. Mon numéro de tel est le (301)227-1340"tokens=test.split()weights=np.random.randint(low=0,high=10,size=len(tokens))print_(' '.join(color(tokens,weights,n=10)))
fromsklearn.datasetsimportfetch_20newsgroupsfromsklearn.feature_extraction.textimportTfidfVectorizerfromsklearn.linear_modelimportSGDClassifierfromsklearn.pipelineimportPipelinecategories=['alt.atheism','talk.religion.misc']newsgroups_train=fetch_20newsgroups(subset='train',categories=categories)newsgroups_test=fetch_20newsgroups(subset='test',categories=categories)X_train,X_test=newsgroups_train.data,newsgroups_test.datay_train,y_test=newsgroups_train.target,newsgroups_test.target
idx=np.random.randint(len(X_vec_list))tokens=tokenizer(X_train[idx])token_idx=[voc.index(t)iftinvocelse-1fortintokens]weights=[X_vec_arr[idx,:][i]ifi>0else0foriintoken_idx]print_(' '.join(color(tokens,weights,start_hex="#FEFEFE",finish_hex="#00a4e4",n=20)))
text_clf=Pipeline([('vect',vectorizer),('clf',SGDClassifier(loss='hinge',penalty='l2',tol=0.2,alpha=1e-3,max_iter=15,random_state=42)),])_=text_clf.fit(X_train,y_train)X_vec=vectorizer.transform(X_train)X_vec_arr=X_vec.toarray()X_vec_list=[list(x)forxinX_vec_arr]voc=vectorizer.get_feature_names()idx=np.random.randint(len(X_vec_list))tokens=tokenizer(X_train[idx])token_idx=[voc.index(t)iftinvocelse-1fortintokens]weights_=np.multiply(X_vec_arr[idx,:],text_clf.named_steps['clf'].coef_[0,:])weights=[weights_[i]ifi>0else0foriintoken_idx]print_(' '.join(color(tokens,weights,start_hex="#34BF49",finish_hex="#BE0027",middle_hex="#FEFEFE",n=20)))
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