TypeError: 不能使用灵活的类型执行 reduce

我一直在使用 scikit-learn 图书馆。我试图使用 scikit-learn 库下面的 Gaussian Naive Bayes 模块，但是我遇到了以下错误。TypeError: 不能使用灵活的类型执行 reduce

下面是代码片段。

training = GaussianNB()
training = training.fit(trainData, target)
prediction = training.predict(testData)

这是目标

['ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'AML', 'AML', 'AML', 'AML', 'AML', 'AML', 'AML', 'AML', 'AML', 'AML', 'AML']

这里是 train Data

[['-214' '-153' '-58' ..., '36' '191' '-37']
['-139' '-73' '-1' ..., '11' '76' '-14']
['-76' '-49' '-307' ..., '41' '228' '-41']
...,
['-32' '-49' '49' ..., '-26' '133' '-32']
['-124' '-79' '-37' ..., '39' '298' '-3']
['-135' '-186' '-70' ..., '-12' '790' '-10']]

下面是堆栈跟踪

Traceback (most recent call last):
File "prediction.py", line 90, in <module>
gaussianNaiveBayes()
File "prediction.py", line 76, in gaussianNaiveBayes
training = training.fit(trainData, target)
File "/Library/Python/2.7/site-packages/sklearn/naive_bayes.py", line 163, in fit
self.theta_[i, :] = np.mean(Xi, axis=0)
File "/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/numpy/ core/fromnumeric.py", line 2716, in mean
out=out, keepdims=keepdims)
File "/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/numpy/core/_methods.py", line 62, in _mean
ret = um.add.reduce(arr, axis=axis, dtype=dtype, out=out, keepdims=keepdims)
TypeError: cannot perform reduce with flexible type
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小开

It looks like your 'trainData' is a list of strings:

['-214' '-153' '-58' ..., '36' '191' '-37']

Change your 'trainData' to a numeric type.

 import numpy as np
np.array(['1','2','3']).astype(np.float)
小开

When your are trying to apply prod on string type of value like:

['-214' '-153' '-58' ..., '36' '191' '-37']

you will get the error.

Solution: Append only integer value like [1,2,3], and you will get your expected output.

If the value is in string format before appending then, in the array you can convert the type into int type and store it in a list.

小开

My best advice facing that error. Typically you have to check the type compatibility of your data. Take few minutes to check it, print it and you should find an incompatibility.


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