Hi,
I am trying to build a custom metric like below:
from sklearn.metrics import cohen_kappa_score
def quadratic_weighted_kappa(y_valid, y_pred):
"""
Custom scoring function.
Must return a float quantifying the estimator prediction quality.
- y_valid is a pandas Series
- y_pred is a numpy ndarray with shape:
- (nb_records,) for regression problems and classification problems
where 'needs probas' (see below) is false
(for classification, the values are the numeric class indexes)
- (nb_records, nb_classes) for classification problems where
'needs probas' is true
- [optional] X_valid is a dataframe with shape (nb_records, nb_input_features)
- [optional] sample_weight is a numpy ndarray with shape (nb_records,)
NB: this option requires a variable set as "Sample weights"
"""
metric = cohen_kappa_score(y_valid, y_pred, weights='quadratic')
return metric
However I am getting a value error:
Trying to enrich exception: com.dataiku.dip.io.SocketBlockLinkKernelException: Failed to train : <class 'ValueError'> : Custom evaluation function not defined from kernel
Any Help with this would be greatly appreciated.
