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* Batch v2 + summary resusts candidates in README + results as json * WIP development of searching campaign * Tweak campaign * Add developments from train journey * Some more refactoring and adding first tests * More refactoring and linting * Install optimade from github * Update tests * Download naccarato dataset in CI * smaller kpoints grid * Some acquisition functions + v4 dielectric calculation v1 candidates * Add gitignore * Removed unecessary metadata line * Add first wave of results from human guided and fake active learning --------- Co-authored-by: Victor Trinquet <victor.trinquet@uclouvain.be>
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ENV/ | ||
.matplotlibrc | ||
matplotlibrc | ||
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# data | ||
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*pkl* | ||
*mdd* | ||
*mod.* | ||
*json* |
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# Exploration - highest uncertainties | ||
def exploration(predictions, uncertainties, **kwargs): | ||
unc = uncertainties.copy() | ||
target = unc.columns.values | ||
unc['score'] = unc[target].rank(pct=True) | ||
return unc |
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# Exploration - highest uncertainties | ||
def exploration(predictions, uncertainties, **kwargs): | ||
unc = uncertainties.copy() | ||
target = unc.columns.values | ||
unc['score'] = unc[target].rank(pct=True) | ||
return unc |
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