Question

在Kaggle的训练/测试集上尝试多项NB分类器时，我得到一个奇怪的ValueError。我的（练习）目标是根据他们的名字来预测乘客是男性还是女性，这些是进入CountVectorizer的。

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-97-ad69ee9ed02b> in <module>()
     12 
     13 # classifier prediction - for test set now!
---> 14 predict_Test = nb.predict(name_test)
     15 score = accuracy_score(sex_test, predictions, normalize=True)
     16 print score

C:\Users\Evan Chow\Anaconda\lib\site-packages\sklearn\naive_bayes.pyc in predict(self, X)
     61             Predicted target values for X
     62         """
---> 63         jll = self._joint_log_likelihood(X)
     64         return self.classes_[np.argmax(jll, axis=1)]
     65 

C:\Users\Evan Chow\Anaconda\lib\site-packages\sklearn\naive_bayes.pyc in _joint_log_likelihood(self, X)
    455         """Calculate the posterior log probability of the samples X"""
    456         X = atleast2d_or_csr(X)
--> 457         return (safe_sparse_dot(X, self.feature_log_prob_.T)
    458                 + self.class_log_prior_)
    459 

C:\Users\Evan Chow\Anaconda\lib\site-packages\sklearn\utils\extmath.pyc in safe_sparse_dot(a, b, dense_output)
     81         return ret
     82     else:
---> 83         return np.dot(a, b)
     84 
     85 

ValueError: matrices are not aligned

我的代码是：

rawDataTrain = pd.read_csv('train.csv')
trainData = pd.concat([rawDataTrain.Name, rawDataTrain.Sex], axis=1)

# get name, sex training data
cv_train = CountVectorizer(min_df = 0)
cv_train.fit(trainData.Name)
name_train = cv_train.transform(trainData.Name).toarray() # name_train
sex_train = np.asarray(trainData.Sex, dtype='S') # name_test

# get name, sex testing data
rawDataTest = pd.read_csv('test.csv')
testData = pd.concat([rawDataTest.Name, rawDataTest.Sex], axis=1)
cv_test = CountVectorizer(min_df = 0)
cv_test.fit(testData.Name)
name_test = cv_test.transform(testData.Name).toarray() # name test
sex_test = np.asarray(testData.Sex, dtype='S') # sex test

# classifier prediction - test quickly on training set. you should get 1.0
predictionsTrain = nb.predict(name_train)
scoreTrain = accuracy_score(sex_train, predictionsTrain, normalize=True)
print scoreTrain # returns probability of 1.0

# classifier prediction - this is what goes weird!
predict_Test = nb.predict(name_test)
score = accuracy_score(sex_test, predictions, normalize=True)
print score

此外，name_train，name_test，sex_train和sex_test的尺寸为：

（891,1509）（418,825）（891）（418）

似乎name_train和name_test的第一个坐标必须相同，但如果这是真的，那么预测只适用于具有与训练集相同的样本数的矩阵！关于如何摆脱这个ValueError的任何想法？

Answer 1

他们需要具有相同的第二维度（即相同数量的列）。你给出的任何形状都没有与其他形状相同的第二维（除了两个根本没有第二维的情况，在这种情况下使用它们进行训练或测试没有多大意义）。

MultinomialNB：ValueError

1 个答案: