Scikit-learn获取属于某个类别的样本的可行性

Question

您好，我正在尝试将文本分为 4 个类别，我想打印以及预测，文本属于每个类别的概率。
在阅读了Scikit-learn的文档后，我认为我应该使用predict_proba，到目前为止，我的代码是这样的：

# -*- coding: utf-8 -*-
#!/usr/bin/env python
import sys
import os
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
from sklearn.metrics import confusion_matrix, f1_score
from sklearn.datasets import load_files
from sklearn.svm import SVC
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
string = sys.argv[1] #i will pass text to predict from console
sets = load_files('scikit') #load training set


count_vect = CountVectorizer(analyzer='char_wb', ngram_range=(0, 3), min_df=1) 
X_train_counts = count_vect.fit_transform(sets.data)     

tf_transformer = TfidfTransformer(use_idf=False).fit(X_train_counts)
X_train_tf = tf_transformer.transform(X_train_counts)

tfidf_transformer = TfidfTransformer()
X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)

clf = MultinomialNB().fit(X_train_tfidf, sets.target)
docs_new = [string]
X_new_counts = count_vect.transform(docs_new)
X_new_tfidf = tfidf_transformer.transform(X_new_counts)
predicted = clf.predict(X_new_tfidf)
for doc, category in zip(docs_new, predicted):
     print('%r => %s' % (doc, sets.target_names[category])) #print prediction , and it is correct
     print(clf.predict_proba(sets.target_names)) #trying to get prob for al classes

可悲的是输出是这样的：ValueError: objects are not aligned，我已经尝试了很多不同的方法来实现这一目标，并在网上搜索了很多，但似乎没有一种有效。任何建议将不胜感激。谢谢尼科。

Answer 1

predict_proba（） 函数的输入应该与你提供给 predict（） 方法的输入完全相同。因此，您将获得以下概率

clf.predict_proba(X_new_tfidf)