Dev lane #249
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Dev lane #249
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…rced training data)
…consistency throughout
…om set of provided features (pass selector=None)
…want equal number of points per bin
…ze_df" as dataframe to conduct featurization on. Also added generator .pkl saving in BaseGenerator
…ules using SMILES strings as input (e.g., features from RDKit)
…g required input args, adding query/return of y_true data, adding support for passed features + additional featurizers
…eras models not working
…ost-processing easier
…dml package. Better integration with other plots
Some small bug fixes, cleanup, output of data
CrabNet bug fix
Fixes to conform to newest version of madml package
Adding ability to do per-bin recalibration
Many small bug fixes, adding per-bin recalibration, adding support for domains
Updates to Tutorial 1 for latest code versions
Update Tutorial 2 to fix bugs, update version calls
Updates to tutorial 3
Updating tutorial 4
fix to baggingregressor base estimator call
Updates to tutorial 5
Updating tutorial 6
incrementing version number for merging with main branch
Updates to setup.py for python version and package deps
adding loading of X_train for jacknife-after-bootstrap analysis to work again
Updating tutorial 7
add requirements.txt back in to help with merging
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Integrating MADML into MAST-ML as a dependency. We can pass arguments into MADML through the evaluate method in MAST-ML as follows:
domain = [
('madml', {
'n_repeats': 4,
'uq_coeffs': [0.0, 1.0, 0.0],
}),
]
splitter.evaluate(
X=X,
y=y,
models=[model],
preprocessor=preprocessor,
metrics=metrics,
plots=['Scatter', 'Histogram'],
X_extra=X_extra,
verbosity=3,
domain=domain,
)