Closed marko7460 closed 3 years ago
in my case edit lambda function like this
def eval(labelpred):
'''
data = (label, pred)
data[0] = label
data[1] = pred
'''
cancel = labelpred.filter(lambda data: data[1] < 0.7)
nocancel = labelpred.filter(lambda data: data[1] >= 0.7)
corr_cancel = cancel.filter(lambda data: data[0] == int(data[1] >= 0.7)).count()
corr_nocancel = nocancel.filter(lambda data: data[0] == int(data[1] >= 0.7)).count()
cancel_denom = cancel.count()
nocancel_denom = nocancel.count()
if cancel_denom == 0:
cancel_denom = 1
if nocancel_denom == 0:
nocancel_denom = 1
return {'total_cancel': cancel.count(), \
'correct_cancel': float(corr_cancel)/cancel_denom, \
'total_noncancel': nocancel.count(), \
'correct_noncancel': float(corr_nocancel)/nocancel_denom \
}
thanks! merged your PR.
This exception is thrown in the last step of
logistic_regression.ipynb
Code:Exception:
Up to this point I was able to train the model and execute all other steps in the notebook. It's only this step that is failing.