def testBinaryBootstrapError(self):
testY = numpy.array([-1, -1, 1, 1, 1])
predY = 1 - testY
trainY = numpy.array([-1, -1, 1, 1, 1])
predTrainY = 1 - trainY
self.assertEquals(Evaluator.binaryBootstrapError(testY, testY, trainY, trainY, 0.5), 0.0)
self.assertEquals(Evaluator.binaryBootstrapError(testY, testY, trainY, predTrainY, 0.5), 0.5)
self.assertEquals(Evaluator.binaryBootstrapError(testY, testY, trainY, predTrainY, 0.1), 0.9)
self.assertEquals(Evaluator.binaryBootstrapError(testY, predY, trainY, trainY, 0.1), 0.1)
def computeBootstrapError(args):
"""
Used in conjunction with the parallel model selection. Trains and then tests
on a seperate test set and evaluated the bootstrap error.
"""
(trainX, trainY, testX, testY, learner) = args
learner.learnModel(trainX, trainY)
predTestY = learner.predict(testX)
predTrainY = learner.predict(trainX)
weight = 0.632
return Evaluator.binaryBootstrapError(predTestY, testY, predTrainY, trainY, weight)
def testBinaryBootstrapError(self):
testY = numpy.array([-1, -1, 1, 1, 1])
predY = 1 - testY
trainY = numpy.array([-1, -1, 1, 1, 1])
predTrainY = 1 - trainY
self.assertEquals(
Evaluator.binaryBootstrapError(testY, testY, trainY, trainY, 0.5),
0.0)
self.assertEquals(
Evaluator.binaryBootstrapError(testY, testY, trainY, predTrainY,
0.5), 0.5)
self.assertEquals(
Evaluator.binaryBootstrapError(testY, testY, trainY, predTrainY,
0.1), 0.9)
self.assertEquals(
Evaluator.binaryBootstrapError(testY, predY, trainY, trainY, 0.1),
0.1)
