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License: Other
A library for calibrating classifiers and computing calibration metrics
License: Other
... instead of directly calling plt.figure()
, so as to be able to include them in subplots or modify them in any way
It's basically impossible to find the pages without access to the project settings.
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.
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'
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))
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.
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