def main():
X1_train, X2_train, Y_train, X1_test, X2_test, Y_test = davis_data.setting2_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.setting2_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.setting2_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.leave_x1_out()
perf = cindex(Y_train, P)
print("regparam 2**%d 2**%d, leave-row-out cindex %f" %(log_regparam1, log_regparam2, perf))
