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I have a question on a classification problem in machine learning using the log_loss function in scikit-learn.

from sklearn.ensemble import RandomForestClassifier
classifier = RandomForestClassifier()
classifier.fit(Xtrain, ytrain)
soft = classifier.predict_proba(Xtest)[:,1]
log_loss = log_loss(ytest, soft)

I would to compute the log loss but an error appears :

'numpy.float64' object is not callable

I think that this problem may come from the fact that there is some 0 in the vector soft. But I do know to solve this problem ? 

s = 0
for x in soft : 
    if x == 0 : 
        s+=1
print(s)
>> 17729

Thanks in advance

Sociopath
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user10651723
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1 Answers1

-1

It appears as if your issue here is not really with the log_loss inputs, but just to do with your variable naming. Everything in python is an object and so in the line:

log_loss = log_loss(ytest, soft)

you assigned the answer, a number (of type numpy.float64), to the token log_loss. So your variable shadows the function. Then, subsequent calls, as if it were a function, fail. 

from sklearn.metrics import log_loss
print(log_loss)
>>> <function log_loss at 0x7f9f692db1b8>

log_loss = log_loss(ytest, soft)
print(log_loss)
>>> 0.11895972559889094
log_loss = log_loss(ytest, soft)
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-40-b423b2324b92> in <module>()
----> 1 log_loss = log_loss(ytest, soft)

TypeError: 'numpy.float64' object is not callable

Simplest resolution is not to call your variable log_loss, but more generally you might find some level of namespacing helps, e.g. instead of 

from sklearn.metrics import log_loss
...
loss = log_loss(ytest, soft)

you could use

from sklearn import metrics
...
loss = metrics.log_loss(ytest, soft)
Bonlenfum
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