Consider making predict_prob give class probabilities like sklearn about monoensemble HOT 6 CLOSED

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Hi Chris, first of all, many thanks for the details. And apologies: I had deleted my original post here before you replied (I think you replied by email, that's why you did not notice it). I deleted it because I thought my original question was actually increasing confusion unnecessarily and I preferred to wait a bit until I understood what was going before re-posting.

It turns out I figured how to do it properly for the multi-class case. In case it helps, here's the temporary snippet I was working with:

import numpy as np
def cumToClassProb(cumPreds):
    preds = 1-cumPreds.copy()
    for col in range(0, preds.shape[1]-1):
        preds[:, col+1] = cumPreds[:, col] - cumPreds[:, col+1]
    preds = np.column_stack((preds, cumPreds[:, cumPreds.shape[1]-1]))
    return preds

Also, now that you have settled the issue, I will quote below the relevant part of my original post just so future readers can understand what you reacted to:

Perhaps I can help with a new Pull Request for this issue. However, I got confused with your notation above. Am I understanding correctly that those are cumulative ordinal class probabilities? That is, suppose the classification problem has n data points and m ordinal classes (ordered ascendingly from 1 to m), where y is the output variable with n observations indexed by i, such that y_i \in {1...m}. Then, for a given test point i, wouldn't it be the case that the predict_prob command() in 'monoensemble' actually returns a vector [Pr(y_i >= 1), Pr(y_i >= 2), ..., Pr(y_i = m)] instead of the one you described?

If yes, then the solution to this issue would be trivial: one could retrieve the actual ordinal class probabilities by simply subtracting backwards from the cumulation, e.g. class Pr(y_i = m-1) = Pr(y_i >= m-1) - Pr(y_i = m). Am I getting right what predict_prob() is returning?

Looking forward to the new commits with your final solution!
Cheers.

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OK, I've just pushed a version that should make predict_proba() give predicted class probabilities. It looks to work well but admittedly I was a bit rushed, let me know if you find problems!

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Oh, and thanks for the code snippet, that looks right! 🙂

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Finished doing a few days of testing. All looks good on this front - I did find one error, which I will open another issue for. But I think you can close this one here.

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Great to hear - thanks for testing Fabricio!

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