Create a Split Conformal API that operates directly on labels, predictions arrays. about mapie HOT 3 CLOSED

Hi @RudrakshTuwani , thanks for raising this issue ! I guess a simple solution to this problem would be to add an optional argument y in the predict method. If y is not None then y should be used as y_pred throughout predict. What do you think ?

By the way, sorry for not replying yet to your PR but we are currently really busy and we hope to find some to address your contributions within the next few weeks.

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Hey @vtaquet , thanks for getting back and no worries! :)

I guess that could work for predict but I can't think of a clean solution for fit. Also, the primary motivations behind this is to have a model API agnostic conformal method that can be easily extended to incorporate cutting edge conformal methods.

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Hi @RudrakshTuwani,
This implementation needs quite a huge refactoring to our code. As a simple solution, you could use this wrapper :

class FakeModel:
    
    def __init__(self):
        self.pred_proba = None
        self.trained_ = True
        self.classes_ = np.array([0, 1])


    def fit(self, X, y):
        pass

    def predict_proba(self, X):
        
        return X
    
    def get_params(self, deep=True):
        return {}

    def predict(self, X):
       
        pred_proba = self.predict_proba(X)
        return np.argmax(pred_proba, axis=1)

    def __sklearn_is_fitted__(self):
        return True

where the input X is in fact your predictions that you computed earlier. Doing so, there is no need to call the "real" predictmethod of your model.

We will look for a cleaner solution in the long term. I close the issue for now (Closed as not planned), we will re-open it when integrating this change to our roadmap

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