It's basically impossible to find the pages without access to the project settings.

I would suggest to remove the static html page from the build, make gh pages host only docs (if not completely dropped in favour of RTD), and use coveralls for (fancy) coverage reports.

Docs are built from develop => out of sync with releases. I think pages should only be built from master, after a release to pypi. Alternatively, one could maintain versions for all tags, allowing to switch between them, like in read the docs, but doing that manually would be a pita.

Explanation

Instances of HistogramBinning cannot be cloned using sklearn's clone function.
This is relevant because it is used for example in the sklearn cross_val_score function.

This error is due to the HistogramBinning estimator class not following sklearn's requirements for an estimator, specifically this following one:

In addition, every keyword argument accepted by init should correspond to an attribute on the instance. Scikit-learn relies on this to find the relevant attributes to set on an estimator when doing model selection.

How to reproduce

from kyle.calibration.calibration_methods import HistogramBinning
from sklearn import clone

clone(HistogramBinning())

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/anesbenmerzoug/workdir/repositories/transferlab/experiments-for-the-calibration-paper/venv/lib/python3.9/site-packages/sklearn/base.py", line 84, in clone
    new_object_params = estimator.get_params(deep=False)
  File "/home/anesbenmerzoug/workdir/repositories/transferlab/experiments-for-the-calibration-paper/venv/lib/python3.9/site-packages/sklearn/base.py", line 210, in get_params
    value = getattr(self, key)
AttributeError: 'HistogramBinning' object has no attribute 'bins'

Suggested fix

To fix this we could simply change the HistogramBinning class from:

class HistogramBinning(NetcalBasedCalibration[bn.HistogramBinning]):
    def __init__(self, bins=20):
        super().__init__(bn.HistogramBinning(bins=bins))

to:

class HistogramBinning(NetcalBasedCalibration[bn.HistogramBinning]):
    def __init__(self, bins=20):
        self.bins = bins
        super().__init__(bn.HistogramBinning(bins=bins))

... instead of directly calling plt.figure(), so as to be able to include them in subplots or modify them in any way