switchign
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@ -326,7 +326,7 @@ class KDEyMLauto2(KDEyML):
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if self.target == 'likelihood':
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loss_fn = neg_loglikelihood_prev
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else:
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loss_fn = lambda prev_hat: qp.error.from_name(self.target)(prev, prev_hat)
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loss_fn = lambda prev_hat: qp.error.from_name(self.target)(prevtrue, prev_hat)
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pred_prev, neglikelihood = optim_minimize(loss_fn, init_prev, return_loss=True)
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loss_accum += neglikelihood
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@ -43,7 +43,7 @@ METHODS = [
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('KDEy-AE', KDEyMLauto2(newLR(), bandwidth='auto', target='mae', search='grid'), wrap_hyper(logreg_grid)),
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('KDEy-AE+', KDEyMLauto2(newLR(), bandwidth='auto', target='mae', search='optim'), wrap_hyper(logreg_grid)),
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('KDEy-RAE', KDEyMLauto2(newLR(), bandwidth='auto', target='mrae', search='grid'), wrap_hyper(logreg_grid)),
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('KDEy-RAE', KDEyMLauto2(newLR(), bandwidth='auto', target='mrae', search='optim'), wrap_hyper(logreg_grid)),
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('KDEy-RAE+', KDEyMLauto2(newLR(), bandwidth='auto', target='mrae', search='optim'), wrap_hyper(logreg_grid)),
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]
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@ -167,4 +167,4 @@ if __name__ == '__main__':
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for method_name, quantifier, param_grid in METHODS + TRANSDUCTIVE_METHODS:
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run_experiment(method_name, quantifier, param_grid)
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show_results(global_result_path)
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show_results(global_result_path)
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