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switching to kde

This commit is contained in:
Alejandro Moreo Fernandez 2024-09-27 10:18:20 +02:00
parent 641228bf62
commit 14ff3c9884
2 changed files with 4 additions and 4 deletions
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4
LocalStack/experiments.py
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@ -3,7 +3,7 @@ from time import time
import numpy as np import numpy as np
from sklearn.linear_model import LogisticRegression from sklearn.linear_model import LogisticRegression
import quapy as qp import quapy as qp
from KDEy.kdey_devel import KDEyMLauto, KDEyMLauto2, KDEyMLred #from KDEy.kdey_devel import KDEyMLauto, KDEyMLauto2, KDEyMLred
from LocalStack.method import LocalStackingQuantification, LocalStackingQuantification2 from LocalStack.method import LocalStackingQuantification, LocalStackingQuantification2
from quapy.method.aggregative import PACC, EMQ, KDEyML from quapy.method.aggregative import PACC, EMQ, KDEyML
from quapy.model_selection import GridSearchQ from quapy.model_selection import GridSearchQ
@ -123,4 +123,4 @@ if __name__ == '__main__':
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') 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')
csv.flush() csv.flush()
show_results(global_result_path) show_results(global_result_path)

4
LocalStack/method.py
View File

@ -3,7 +3,7 @@ import quapy as qp
from sklearn.multioutput import MultiOutputRegressor from sklearn.multioutput import MultiOutputRegressor
from sklearn.svm import SVR from sklearn.svm import SVR
from data import LabelledCollection from quapy.data import LabelledCollection
from quapy.method.base import BaseQuantifier from quapy.method.base import BaseQuantifier
from quapy.method.aggregative import AggregativeSoftQuantifier from quapy.method.aggregative import AggregativeSoftQuantifier
@ -109,4 +109,4 @@ class LocalStackingQuantification2(BaseQuantifier):
corrected_prev = reg.predict([pred_prevs])[0] corrected_prev = reg.predict([pred_prevs])[0]
corrected_prev = self.normalize(corrected_prev) corrected_prev = self.normalize(corrected_prev)
return corrected_prev return corrected_prev