原因：数据生成的稀疏矩阵，模型需要的稠密矩阵，两者不兼容

解决：改模型，例如我用高斯朴素贝叶斯出现了这个bug，换用多项式朴素贝叶斯就可以得到较好的结果

model = Pipeline([
    ('vect',TfidfVectorizer()),
    ('clf',GaussianNB()),
])
model.fit(X_train,y_train)
Traceback (most recent call last):

  File "<ipython-input-55-8ed8bbc4bd13>", line 6, in <module>
    model.fit(X_train,y_train)

  File "D:\Anaconda\python\lib\site-packages\sklearn\pipeline.py", line 335, in fit
    self._final_estimator.fit(Xt, y, **fit_params_last_step)

  File "D:\Anaconda\python\lib\site-packages\sklearn\naive_bayes.py", line 210, in fit
    X, y = self._validate_data(X, y)

  File "D:\Anaconda\python\lib\site-packages\sklearn\base.py", line 432, in _validate_data
    X, y = check_X_y(X, y, **check_params)

  File "D:\Anaconda\python\lib\site-packages\sklearn\utils\validation.py", line 72, in inner_f
    return f(**kwargs)

  File "D:\Anaconda\python\lib\site-packages\sklearn\utils\validation.py", line 795, in check_X_y
    X = check_array(X, accept_sparse=accept_sparse,

  File "D:\Anaconda\python\lib\site-packages\sklearn\utils\validation.py", line 72, in inner_f
    return f(**kwargs)

  File "D:\Anaconda\python\lib\site-packages\sklearn\utils\validation.py", line 575, in check_array
    array = _ensure_sparse_format(array, accept_sparse=accept_sparse,

  File "D:\Anaconda\python\lib\site-packages\sklearn\utils\validation.py", line 353, in _ensure_sparse_format
    raise TypeError('A sparse matrix was passed, but dense '

TypeError: A sparse matrix was passed, but dense data is required. Use X.toarray() to convert to a dense numpy array.





model = Pipeline([
    ('vect',TfidfVectorizer()),
    ('clf',MultinomialNB(alpha=1.0)),
])
model.fit(X_train,y_train)
predict = model.predict(X_test)
from sklearn.metrics import accuracy_score
accuracy=accuracy_score(y_test,predict)
print(accuracy)
###0.8888

解决二：也可以进行转化为稠密矩阵，能力不足，目前这个方法还没弄出来，大家有会的可以教教我