Creatring a custom scoring metric

Hello @Alex_Combessie
,

I want to code a custom scoring function for scikit-learn's average-precision-score. It's a for a Keras image binary classification model. I'm not familiar with how the Dataiku API works, but I want to pass the array dimensions to y_valid and y_pred.

Here's what I have:

from sklearn.metrics import average_precision_score

def score(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"
"""
average_precision = average_precision_score(y_valid, y_pred)

return average_precision