switching to kde
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@ -3,7 +3,7 @@ from time import time
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import numpy as np
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from sklearn.linear_model import LogisticRegression
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import quapy as qp
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from KDEy.kdey_devel import KDEyMLauto, KDEyMLauto2, KDEyMLred
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#from KDEy.kdey_devel import KDEyMLauto, KDEyMLauto2, KDEyMLred
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from LocalStack.method import LocalStackingQuantification, LocalStackingQuantification2
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from quapy.method.aggregative import PACC, EMQ, KDEyML
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from quapy.model_selection import GridSearchQ
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@ -123,4 +123,4 @@ if __name__ == '__main__':
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csv.write(f'{method_name}\t{dataset}\t{means["mae"]:.5f}\t{means["mrae"]:.5f}\t{means["kld"]:.5f}\t{means["tr_time"]:.3f}\t{means["te_time"]:.3f}\n')
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csv.flush()
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show_results(global_result_path)
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show_results(global_result_path)
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@ -3,7 +3,7 @@ import quapy as qp
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from sklearn.multioutput import MultiOutputRegressor
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from sklearn.svm import SVR
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from data import LabelledCollection
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from quapy.data import LabelledCollection
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from quapy.method.base import BaseQuantifier
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from quapy.method.aggregative import AggregativeSoftQuantifier
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@ -109,4 +109,4 @@ class LocalStackingQuantification2(BaseQuantifier):
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corrected_prev = reg.predict([pred_prevs])[0]
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corrected_prev = self.normalize(corrected_prev)
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return corrected_prev
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return corrected_prev
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