TypeError: cannot perform reduce with flexible type

Question

I have been using the scikit-learn library. I'm trying to use the Gaussian Naive Bayes Module under the scikit-learn library but I'm running into the following error. TypeError: cannot perform reduce with flexible type

Below is the code snippet.

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

This is target

['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']

This is trainData

[['-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']]

Below is the stack trace

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

Answer 1

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)

Answer 2

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.