Adapt examples; remaining: example 4-onwards
not working: 15 (qunfold)

Solve the warnings issue; right now there is a warning ignore in method/__init__.py:

Add 'platt' to calib options in EMQ?

Allow n_prevpoints in APP to be specified by a user-defined grid?

Update READMEs, wiki, & examples for new fit-predict interface

Add the fix suggested by Alexander:

For a more general application, I would maybe first establish a per-class threshold value of plausible prevalence
based on the number of actual positives and the required sample size; e.g., for sample_size=100 and actual
positives [10, 100, 500] -> [0.1, 1.0, 1.0], meaning that class 0 can be sampled at most at 0.1 prevalence, while
the others can be sampled up to 1. prevalence. Then, when a prevalence value is requested, e.g., [0.33, 0.33, 0.33],
we may either clip each value and normalize (as you suggest for the extreme case, e.g., [0.1, 0.33, 0.33]/sum) or
scale each value by per-class thresholds, i.e., [0.33*0.1, 0.33*1, 0.33*1]/sum.
- This affects LabelledCollection
- This functionality should be accessible via sampling protocols and evaluation functions

Solve the pre-trained classifier issues. An example is the coptic-codes script I did, which needed a mock_lr to
work for having access to classes_; think also the case in which the precomputed outputs are already generated
as in the unifying problems code.

Para quitar el labelledcollection de los métodos:

- El follón viene por la semántica confusa de fit en agregativos, que recibe 3 parámetros:
    - data: LabelledCollection, que puede ser:
        - el training set si hay que entrenar el clasificador
        - None si no hay que entregar el clasificador
        - el validation, que entra en conflicto con val_split, si no hay que entrenar clasificador
    - fit_classifier: dice si hay que entrenar el clasificador o no, y estos cambia la semántica de los otros
    - val_split: que puede ser:
        - un número: el número de kfcv, lo cual implica fit_classifier=True y data=todo el training set
        - una fración en [0,1]: que indica la parte que usamos para validation; implica fit_classifier=True y data=train+val
        - un labelled collection: el conjunto de validación específico; no implica fit_classifier=True ni False
- La forma de quitar la dependencia de los métodos con LabelledCollection debería ser así:
    - En el constructor se dice si el clasificador que se recibe por parámetro hay que entrenarlo o ya está entrenado;
        es decir, hay un fit_classifier=True o False.
        - fit_classifier=True:
            - data en fit es todo el training incluyendo el validation y todo
            - val_split:
                - int: número de folds en kfcv
                - proporción en [0,1]
        - fit_classifier=False:



- [TODO] document confidence in manuals
- [TODO] Test the return_type="index" in protocols and finish the "distributing_samples.py" example
- [TODO] Add EDy (an implementation is available at quantificationlib)
- [TODO] add ensemble methods SC-MQ, MC-SQ, MC-MQ
- [TODO] add HistNetQ
- [TODO] add CDE-iteration and Bayes-CDE methods
- [TODO] add Friedman's method and DeBias
- [TODO] check ignore warning stuff
    check https://docs.python.org/3/library/warnings.html#temporarily-suppressing-warnings
- [TODO] nmd and md are not selectable from qp.evaluation.evaluate as a string