def main():
X1_train, X2_train, Y_train, X1_test, X2_test, Y_test = davis_data.setting3_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_x2_out()
perf = cindex(Y_train, P)
print("regparam 2**%d 2**%d, leave-column-out 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 = 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.settingC_split()
m = X2_train.shape[0]
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)
#Create random split to 5 folds for the targets
folds = random_folds(m, 5, seed=12345)
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.x2_kfold_cv(folds)
perf = cindex(Y_train, P)
print("regparam 2**%d 2**%d, K-fold cindex %f" %(log_regparam1, log_regparam2, perf))
def main():
X1_train, X2_train, Y_train, X1_test, X2_test, Y_test = davis_data.settingD_split()
n = X1_train.shape[0]
m = X2_train.shape[0]
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)
#Create random split to 5 folds for both drugs and targets
drug_folds = random_folds(n, 5, seed=123)
target_folds = random_folds(m, 5, seed=456)
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_kfold_cv(drug_folds, target_folds)
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.settingB_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))
def main():
X1_train, X2_train, Y_train, X1_test, X2_test, Y_test = davis_data.settingC_split(
)
m = X2_train.shape[0]
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)
#Create random split to 5 folds for the targets
folds = random_folds(m, 5, seed=12345)
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.x2_kfold_cv(folds)
perf = cindex(Y_train, P)
print("regparam 2**%d 2**%d, K-fold cindex %f" %
(log_regparam1, log_regparam2, perf))
