Пример #1
0
def train_rls():
    X_train, Y_train, X_test, Y_test = load_wine()
    #Map labels from set {1,2,3} to one-vs-all encoding
    Y_train = to_one_vs_all(Y_train, False)
    Y_test = to_one_vs_all(Y_test, False)
    regparams = [2.**i for i in range(-15, 16)]
    learner = LeaveOneOutRLS(X_train, Y_train, regparams=regparams, measure=ova_accuracy)
    P_test = learner.predict(X_test)
    #ova_accuracy computes one-vs-all classification accuracy directly between transformed
    #class label matrix, and a matrix of predictions, where each column corresponds to a class
    print("test set accuracy %f" %ova_accuracy(Y_test, P_test))
Пример #2
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import numpy as np

from rlscore.utilities import multiclass
from rlscore.measure import ova_accuracy

Y = [0,0,1,1,2,2]
Y_ova = multiclass.to_one_vs_all(Y)

P_ova = [[1, 0, 0], [1.2,0.5, 0], [0, 1, -1], [1, 1.2, 0.5], [0.2, -1, -1], [0.3, -1, -2]]
acc = ova_accuracy(Y_ova, P_ova)
print("ova-mapped Y")
print(Y_ova)
print("P, class prediction is chosen with argmax")
print(P_ova)
print("Accuracy computed with one-vs-all mapped labels and predictions: %f" %acc)
print("original Y")
print(Y)
print("P mapped to class predictions")
P = multiclass.from_one_vs_all(P_ova)
print(P)
acc = np.mean(Y==P)
print("Accuracy is the same:%f " %acc)
Пример #3
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 def callback(self, learner):
     self.iteration += 1
     P = learner.predict(self.X_test)
     acc = ova_accuracy(self.Y_test, P)
     print("Features selected %d, accuracy %f" %(self.iteration, acc))