forked from moreo/QuaPy
51 lines
1.2 KiB
Python
51 lines
1.2 KiB
Python
import numpy as np
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from sklearn.linear_model import LogisticRegression
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from quapy.method.aggregative import CC, PCC, ACC, PACC, EMQ
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from quapy.benchmarking._base import MethodDescriptor
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lr_hyper = {'C': np.logspace(-3, 3, 7), 'class_weight': ['balanced', None]}
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wrap_cls_params = lambda params: {'classifier__' + key: val for key, val in params.items()}
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cc = MethodDescriptor(
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id='CC',
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name='CC(LR)',
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instance=CC(LogisticRegression()),
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hyperparams=wrap_cls_params(lr_hyper)
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)
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pcc = MethodDescriptor(
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id='PCC',
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name='PCC(LR)',
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instance=PCC(LogisticRegression()),
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hyperparams=wrap_cls_params(lr_hyper)
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)
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acc = MethodDescriptor(
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id='ACC',
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name='ACC(LR)',
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instance=ACC(LogisticRegression()),
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hyperparams=wrap_cls_params(lr_hyper)
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)
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pacc = MethodDescriptor(
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id='PACC',
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name='PACC(LR)',
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instance=PACC(LogisticRegression()),
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hyperparams=wrap_cls_params(lr_hyper)
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)
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sld = MethodDescriptor(
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id='SLD',
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name='SLD',
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instance=EMQ(LogisticRegression()),
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hyperparams=wrap_cls_params(lr_hyper)
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)
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sld_bcts = MethodDescriptor(
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id='SLD-BCTS',
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name='SLD-BCTS',
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instance=EMQ(LogisticRegression(), recalib='bcts', exact_train_prev=False),
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hyperparams=wrap_cls_params(lr_hyper)
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) |