def testStandardiseArray(self):
numExamples = 10
numFeatures = 3
preprocessor = Standardiser()
# Test an everyday matrix
X = numpy.random.rand(numExamples, numFeatures)
Xs = preprocessor.standardiseArray(X)
self.assertAlmostEquals(numpy.sum(Xs), 0, places=3)
self.assertAlmostEquals(numpy.sum(Xs * Xs), numFeatures, places=3)
# Now, test on a portion of a matrix
Xss = preprocessor.standardiseArray(X[1:5, :])
self.assertTrue((Xss == Xs[1:5, :]).all())
def testUnstandardiseArray(self):
numExamples = 10
numFeatures = 3
tol = 10 ** -6
preprocessor = Standardiser()
# Test an everyday matrix
X = numpy.random.rand(numExamples, numFeatures)
Xs = preprocessor.standardiseArray(X)
X2 = preprocessor.unstandardiseArray(Xs)
self.assertTrue(numpy.linalg.norm(X2 - X) < tol)
def testLearnModel(self):
numExamples = 50
numFeatures = 200
preprocessor = Standardiser()
X = numpy.random.randn(numExamples, numFeatures)
X = preprocessor.standardiseArray(X)
c = numpy.random.rand(numFeatures)
y = numpy.dot(X, c)
tol = 0.05
kernel = LinearKernel()
lmbda = 0.0001
predictor = KernelShiftRegression(kernel, lmbda)
alpha, b = predictor.learnModel(X, y)
predY = predictor.predict(X)
self.assertTrue(Evaluator.rootMeanSqError(y, predY) < tol)
#Try increasing y
y = y + 5
lmbda = 0.2
predictor = KernelShiftRegression(kernel, lmbda)
alpha, b = predictor.learnModel(X, y)
predY = predictor.predict(X)
self.assertTrue(numpy.abs(b - 5) < 0.1)
self.assertTrue(Evaluator.rootMeanSqError(y, predY) < 0.1)
#Try making prediction for multilabel Y
C = numpy.random.rand(numFeatures, numFeatures)
Y = numpy.dot(X, C)
predictor = KernelShiftRegression(kernel, lmbda)
alpha, b = predictor.learnModel(X, Y)
predY = predictor.predict(X)
self.assertTrue(Evaluator.rootMeanSqError(Y, predY) < 0.1)
#Now, shift the data
s = numpy.random.rand(numFeatures)
Y = Y + s
predictor = KernelShiftRegression(kernel, lmbda)
alpha, b = predictor.learnModel(X, Y)
predY = predictor.predict(X)
self.assertTrue(numpy.linalg.norm(b - s) < 0.1)
self.assertTrue(Evaluator.rootMeanSqError(Y, predY) < 0.1)
def testLearnModel(self):
numExamples = 50
numFeatures = 200
preprocessor = Standardiser()
X = numpy.random.randn(numExamples, numFeatures)
X = preprocessor.standardiseArray(X)
c = numpy.random.rand(numFeatures)
y = numpy.dot(X, c)
tol = 0.05
kernel = LinearKernel()
lmbda = 0.0001
predictor = KernelShiftRegression(kernel, lmbda)
alpha, b = predictor.learnModel(X, y)
predY = predictor.predict(X)
self.assertTrue(Evaluator.rootMeanSqError(y, predY) < tol)
#Try increasing y
y = y + 5
lmbda = 0.2
predictor = KernelShiftRegression(kernel, lmbda)
alpha, b = predictor.learnModel(X, y)
predY = predictor.predict(X)
self.assertTrue(numpy.abs(b - 5) < 0.1)
self.assertTrue(Evaluator.rootMeanSqError(y, predY) < 0.1)
#Try making prediction for multilabel Y
C = numpy.random.rand(numFeatures, numFeatures)
Y = numpy.dot(X, C)
predictor = KernelShiftRegression(kernel, lmbda)
alpha, b = predictor.learnModel(X, Y)
predY = predictor.predict(X)
self.assertTrue(Evaluator.rootMeanSqError(Y, predY) < 0.1)
#Now, shift the data
s = numpy.random.rand(numFeatures)
Y = Y + s
predictor = KernelShiftRegression(kernel, lmbda)
alpha, b = predictor.learnModel(X, Y)
predY = predictor.predict(X)
self.assertTrue(numpy.linalg.norm(b - s) < 0.1)
self.assertTrue(Evaluator.rootMeanSqError(Y, predY) < 0.1)
def testClassify(self):
numExamples = 10
numFeatures = 20
X = numpy.random.randn(numExamples, numFeatures)
y = numpy.sign(numpy.random.randn(numExamples))
logging.debug(y)
preprocessor = Standardiser()
X = preprocessor.standardiseArray(X)
tol = 10**-5
lmbda = 1.0
kernel = LinearKernel()
predictor = KernelRidgeRegression(kernel, lmbda)
predictor.learnModel(X, y)
classY, predY = predictor.classify(X)
self.assertTrue(numpy.logical_or(classY == 1, classY == -1).all())
def testLearnModel2(self):
numExamples = 200
numFeatures = 100
X = numpy.random.randn(numExamples, numFeatures)
y = numpy.random.randn(numExamples)
preprocessor = Standardiser()
X = preprocessor.standardiseArray(X)
tol = 10**-3
kernel = LinearKernel()
#Try using a low-rank matrix
lmbda = 0.001
predictor = KernelShiftRegression(kernel, lmbda)
alpha, b = predictor.learnModel(X, y)
predY = predictor.predict(X)
logging.debug((numpy.linalg.norm(y)))
logging.debug((numpy.linalg.norm(predY - y)))
def testLearnModel2(self):
numExamples = 200
numFeatures = 100
X = numpy.random.randn(numExamples, numFeatures)
y = numpy.random.randn(numExamples)
preprocessor = Standardiser()
X = preprocessor.standardiseArray(X)
tol = 10**-3
kernel = LinearKernel()
#Try using a low-rank matrix
lmbda = 0.001
predictor = KernelShiftRegression(kernel, lmbda)
alpha, b = predictor.learnModel(X, y)
predY = predictor.predict(X)
logging.debug((numpy.linalg.norm(y)))
logging.debug((numpy.linalg.norm(predY - y)))
def testLearnModel(self):
numExamples = 50
numFeatures = 200
X = numpy.random.randn(numExamples, numFeatures)
y = numpy.random.randn(numExamples)
preprocessor = Standardiser()
X = preprocessor.standardiseArray(X)
tol = 10**-3
kernel = LinearKernel()
#Compare Linear kernel with linear ridge regression
lmbda = 0.1
predictor = KernelRidgeRegression(kernel, lmbda)
alpha = predictor.learnModel(X, y)
predY = predictor.predict(X)
K = numpy.dot(X, X.T)
alpha2 = numpy.dot(
numpy.linalg.inv(K + lmbda * numpy.eye(numExamples)), y)
predY2 = X.dot(
numpy.linalg.inv(
numpy.dot(X.T, X) + lmbda * numpy.eye(numFeatures))).dot(
X.T).dot(y)
#logging.debug(numpy.linalg.norm(alpha - alpha2))
self.assertTrue(numpy.linalg.norm(alpha - alpha2) < tol)
self.assertTrue(numpy.linalg.norm(predY - predY2) < tol)
lmbda = 0.5
predictor = KernelRidgeRegression(kernel, lmbda)
alpha = predictor.learnModel(X, y)
predY = predictor.predict(X)
K = numpy.dot(X, X.T)
alpha2 = numpy.dot(
numpy.linalg.inv(K + lmbda * numpy.eye(numExamples)), y)
predY2 = X.dot(
numpy.linalg.inv(
numpy.dot(X.T, X) + lmbda * numpy.eye(numFeatures))).dot(
X.T).dot(y)
self.assertTrue(numpy.linalg.norm(alpha - alpha2) < tol)
self.assertTrue(numpy.linalg.norm(predY - predY2) < tol)
#Now test on an alternative test set
numTestExamples = 50
testX = numpy.random.randn(numTestExamples, numFeatures)
predictor = KernelRidgeRegression(kernel, lmbda)
alpha = predictor.learnModel(X, y)
predY = predictor.predict(testX)
K = numpy.dot(X, X.T)
alpha2 = numpy.dot(
numpy.linalg.inv(K + lmbda * numpy.eye(numExamples)), y)
predY2 = testX.dot(
numpy.linalg.inv(
numpy.dot(X.T, X) + lmbda * numpy.eye(numFeatures))).dot(
X.T).dot(y)
self.assertTrue(numpy.linalg.norm(alpha - alpha2) < tol)
self.assertTrue(numpy.linalg.norm(predY - predY2) < tol)
#Use the method against a multi-label example
Y = numpy.random.randn(numExamples, numFeatures)
alpha = predictor.learnModel(X, Y)
self.assertTrue(alpha.shape == (numExamples, numFeatures))
