forked from moreo/QuaPy
setting experiments for ensembles mrae
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@ -58,6 +58,7 @@ def quantification_ensembles():
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param_mod_sel = {
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'sample_size': settings.SAMPLE_SIZE,
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'n_repetitions': 1000,
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'protocol': 'npp',
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'verbose': False
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}
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common = {
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@ -72,13 +73,13 @@ def quantification_ensembles():
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# hyperparameters will be evaluated within each quantifier of the ensemble, and so the typical model selection
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# will be skipped (by setting hyperparameters to None)
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hyper_none = None
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#yield 'epaccmaeptr', EPACC(newLR(), optim='mae', policy='ptr', **common), hyper_none
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yield 'epaccmaeptr', EPACC(newLR(), optim='mae', policy='ptr', **common), hyper_none
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yield 'epaccmaemae1k', EPACC(newLR(), optim='mae', policy='mae', **common), hyper_none
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# yield 'esldmaeptr', EEMQ(newLR(), optim='mae', policy='ptr', **common), hyper_none
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# yield 'esldmaemae', EEMQ(newLR(), optim='mae', policy='mae', **common), hyper_none
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#yield 'epaccmraeptr', EPACC(newLR(), optim='mrae', policy='ptr', **common), hyper_none
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#yield 'epaccmraemrae', EPACC(newLR(), optim='mrae', policy='mrae', **common), hyper_none
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yield 'epaccmraeptr', EPACC(newLR(), optim='mrae', policy='ptr', **common), hyper_none
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yield 'epaccmraemrae', EPACC(newLR(), optim='mrae', policy='mrae', **common), hyper_none
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#yield 'esldmraeptr', EEMQ(newLR(), optim='mrae', policy='ptr', **common), hyper_none
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#yield 'esldmraemrae', EEMQ(newLR(), optim='mrae', policy='mrae', **common), hyper_none
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