Python predict_proba












0















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






python-3.x machine-learning scikit-learn







share|improve this question























  • Show the full stack trace of error and how log_loss is imported?

    – Vivek Kumar
    Nov 14 '18 at 12:39
















0















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






python-3.x machine-learning scikit-learn







share|improve this question























  • Show the full stack trace of error and how log_loss is imported?

    – Vivek Kumar
    Nov 14 '18 at 12:39














0












0








0








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






python-3.x machine-learning scikit-learn







share|improve this question














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






python-3.x machine-learning scikit-learn




python-3.x machine-learning scikit-learn






share|improve this question













share|improve this question











share|improve this question




share|improve this question










asked Nov 14 '18 at 11:46









user10651723user10651723

51




51













  • Show the full stack trace of error and how log_loss is imported?

    – Vivek Kumar
    Nov 14 '18 at 12:39



















  • Show the full stack trace of error and how log_loss is imported?

    – Vivek Kumar
    Nov 14 '18 at 12:39

















Show the full stack trace of error and how log_loss is imported?

– Vivek Kumar
Nov 14 '18 at 12:39





Show the full stack trace of error and how log_loss is imported?

– Vivek Kumar
Nov 14 '18 at 12:39












1 Answer
1






active

oldest

votes


















-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)





share|improve this answer
























  • Thanks a lot!!!

    – user10651723
    Nov 14 '18 at 15:05











  • thanks but downvote? is your problem solved or do you need further help to get there?

    – Bonlenfum
    Nov 15 '18 at 11:02













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1 Answer
1






active

oldest

votes








1 Answer
1






active

oldest

votes









active

oldest

votes






active

oldest

votes









-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)





share|improve this answer
























  • Thanks a lot!!!

    – user10651723
    Nov 14 '18 at 15:05











  • thanks but downvote? is your problem solved or do you need further help to get there?

    – Bonlenfum
    Nov 15 '18 at 11:02


















-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)





share|improve this answer
























  • Thanks a lot!!!

    – user10651723
    Nov 14 '18 at 15:05











  • thanks but downvote? is your problem solved or do you need further help to get there?

    – Bonlenfum
    Nov 15 '18 at 11:02
















-1












-1








-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)





share|improve this answer













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)






share|improve this answer












share|improve this answer



share|improve this answer










answered Nov 14 '18 at 12:47









BonlenfumBonlenfum

11.3k13142




11.3k13142













  • Thanks a lot!!!

    – user10651723
    Nov 14 '18 at 15:05











  • thanks but downvote? is your problem solved or do you need further help to get there?

    – Bonlenfum
    Nov 15 '18 at 11:02





















  • Thanks a lot!!!

    – user10651723
    Nov 14 '18 at 15:05











  • thanks but downvote? is your problem solved or do you need further help to get there?

    – Bonlenfum
    Nov 15 '18 at 11:02



















Thanks a lot!!!

– user10651723
Nov 14 '18 at 15:05





Thanks a lot!!!

– user10651723
Nov 14 '18 at 15:05













thanks but downvote? is your problem solved or do you need further help to get there?

– Bonlenfum
Nov 15 '18 at 11:02







thanks but downvote? is your problem solved or do you need further help to get there?

– Bonlenfum
Nov 15 '18 at 11:02




















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