10 lines
455 B
Python
10 lines
455 B
Python
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from quacc.evaluation.report import DatasetReport
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dr = DatasetReport.unpickle("output/main/imdb/imdb.pickle")
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_estimators = ["sld_lr_gs", "bin_sld_lr_gs", "mul_sld_lr_gs", "m3w_sld_lr_gs"]
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_data = dr.data(metric="acc", estimators=_estimators)
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for idx, cr in zip(_data.index.unique(0), dr.crs[::-1]):
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print(cr.train_prev)
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print({k: v for k, v in cr.fit_scores.items() if k in _estimators})
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print(_data.loc[(idx, slice(None), slice(None)), :])
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