def main():
X1_train, X2_train, Y_train, X1_test, X2_test, Y_test = davis_data.setting4_split()
learner = KronRLS(X1 = X1_train, X2 = X2_train, Y = Y_train)
log_regparams = range(15, 35)
for log_regparam in log_regparams:
learner.solve(2.**log_regparam)
P = learner.predict(X1_test, X2_test)
perf = cindex(Y_test, P)
print("regparam 2**%d, cindex %f" %(log_regparam, perf))
def main():
X1_train, X2_train, Y_train, X1_test, X2_test, Y_test = davis_data.setting4_split(
)
learner = KronRLS(X1=X1_train, X2=X2_train, Y=Y_train)
log_regparams = range(15, 35)
for log_regparam in log_regparams:
learner.solve(2.**log_regparam)
P = learner.predict(X1_test, X2_test)
perf = cindex(Y_test, P)
print("regparam 2**%d, cindex %f" % (log_regparam, perf))
def main():
X1_train, X2_train, Y_train, X1_test, X2_test, Y_test = davis_data.setting4_split()
learner = TwoStepRLS(X1 = X1_train, X2 = X2_train, Y = Y_train, regparam1=1.0, regparam2=1.0)
log_regparams1 = range(-8, -4)
log_regparams2 = range(20,25)
for log_regparam1 in log_regparams1:
for log_regparam2 in log_regparams2:
learner.solve(2.**log_regparam1, 2.**log_regparam2)
P = learner.predict(X1_test, X2_test)
perf = cindex(Y_test, P)
print("regparam 2**%d 2**%d, test set cindex %f" %(log_regparam1, log_regparam2, perf))
P = learner.out_of_sample_loo()
perf = cindex(Y_train, P)
print("regparam 2**%d 2**%d, out-of-sample loo cindex %f" %(log_regparam1, log_regparam2, perf))
def main():
X1_train, X2_train, Y_train, X1_test, X2_test, Y_test = davis_data.setting4_split()
learner = KronRLS(X1 = X1_train, X2 = X2_train, Y = Y_train, regparam=2.**30)
predictor = learner.predictor
print predictor.W
#Predict labels for all X1_test - X2_test combinations)
#Order: column-major: [(X1[0], X2[0]), (X1[1], X2[0])...]
P = predictor.predict(X1_test, X2_test)
print("Number of predictions: %d" %P.shape)
print("three first predictions: " +str(P[:3]))
x1_ind = [0,1,2]
x2_ind = [0,0,0]
P2 = predictor.predict(X1_test, X2_test, x1_ind, x2_ind)
print("three first predictions again: " +str(P2))
print("Number of coefficients %d x %d" %predictor.W.shape)
def main():
X1_train, X2_train, Y_train, X1_test, X2_test, Y_test = davis_data.setting4_split(
)
learner = KronRLS(X1=X1_train, X2=X2_train, Y=Y_train, regparam=2.**30)
predictor = learner.predictor
print predictor.W
#Predict labels for all X1_test - X2_test combinations)
#Order: column-major: [(X1[0], X2[0]), (X1[1], X2[0])...]
P = predictor.predict(X1_test, X2_test)
print("Number of predictions: %d" % P.shape)
print("three first predictions: " + str(P[:3]))
x1_ind = [0, 1, 2]
x2_ind = [0, 0, 0]
P2 = predictor.predict(X1_test, X2_test, x1_ind, x2_ind)
print("three first predictions again: " + str(P2))
print("Number of coefficients %d x %d" % predictor.W.shape)
def main():
X1_train, X2_train, Y_train, X1_test, X2_test, Y_test = davis_data.setting4_split(
)
kernel1 = GaussianKernel(X1_train, gamma=0.01)
kernel2 = GaussianKernel(X2_train, gamma=10**-9)
K1_train = kernel1.getKM(X1_train)
K1_test = kernel1.getKM(X1_test)
K2_train = kernel2.getKM(X2_train)
K2_test = kernel2.getKM(X2_test)
learner = KronRLS(K1=K1_train, K2=K2_train, Y=Y_train)
log_regparams = range(-15, 15)
for log_regparam in log_regparams:
learner.solve(2.**log_regparam)
P = learner.predict(K1_test, K2_test)
perf = cindex(Y_test, P)
print("regparam 2**%d, cindex %f" % (log_regparam, perf))
def main():
X1_train, X2_train, Y_train, X1_test, X2_test, Y_test = davis_data.setting4_split()
kernel1 = GaussianKernel(X1_train, gamma=0.01)
kernel2 = GaussianKernel(X2_train, gamma=10**-9)
K1_train = kernel1.getKM(X1_train)
K1_test = kernel1.getKM(X1_test)
K2_train = kernel2.getKM(X2_train)
K2_test = kernel2.getKM(X2_test)
learner = KronRLS(K1 = K1_train, K2 = K2_train, Y = Y_train, regparam=2**-5)
predictor = learner.predictor
P = predictor.predict(K1_test, K2_test)
print("Number of predictions: %d" %P.shape)
print("three first predictions: " +str(P[:3]))
x1_ind = [0,1,2]
x2_ind = [0,0,0]
P2 = predictor.predict(K1_test, K2_test, x1_ind, x2_ind)
print("three first predictions again: " +str(P2))
print("Number of coefficients %d" %predictor.A.shape)
def main():
X1_train, X2_train, Y_train, X1_test, X2_test, Y_test = davis_data.setting4_split(
)
learner = TwoStepRLS(X1=X1_train,
X2=X2_train,
Y=Y_train,
regparam1=1.0,
regparam2=1.0)
log_regparams1 = range(-8, -4)
log_regparams2 = range(20, 25)
for log_regparam1 in log_regparams1:
for log_regparam2 in log_regparams2:
learner.solve(2.**log_regparam1, 2.**log_regparam2)
P = learner.predict(X1_test, X2_test)
perf = cindex(Y_test, P)
print("regparam 2**%d 2**%d, test set cindex %f" %
(log_regparam1, log_regparam2, perf))
P = learner.out_of_sample_loo()
perf = cindex(Y_train, P)
print("regparam 2**%d 2**%d, out-of-sample loo cindex %f" %
(log_regparam1, log_regparam2, perf))
