您好，我正在尝试将文本分为4个类别，一个是我要打印的，另一个是预测，即文本属于每个类别的概率。
阅读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，我尝试了许多不同的方法来实现这一目标，并在网络上进行了大量搜索，但是似乎都没有用。任何建议，将不胜感激。谢谢
尼科
最佳答案
对predict_proba（）函数的输入应与您对predict（）方法的输入完全相同。因此，您将获得
clf.predict_proba(X_new_tfidf)