def main():
X1, X2, Y = davis_data.load_davis()
Y = Y.ravel(order='F')
learner = TwoStepRLS(X1 = X1, X2 = X2, Y = Y, 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.in_sample_loo()
perf = cindex(Y, P)
print("regparam 2**%d 2**%d, cindex %f" %(log_regparam1, log_regparam2, perf))
def main():
X = np.loadtxt("drug-drug_similarities_2D.txt")
Y = np.loadtxt("drug-drug_similarities_ECFP4.txt")
Y = Y.ravel(order='F')
K = np.dot(X, X)
learner = TwoStepRLS(K1 = K, K2 = K, Y = Y, regparam1=1.0, regparam2=1.0)
log_regparams = range(-10, 0)
for log_regparam in log_regparams:
learner.solve(2.**log_regparam, 2.**log_regparam)
P = learner.out_of_sample_loo_symmetric()
perf = cindex(Y, P)
print("regparam 2**%d, cindex %f" %(log_regparam, perf))
def main():
X = np.loadtxt("drug-drug_similarities_2D.txt")
Y = np.loadtxt("drug-drug_similarities_ECFP4.txt")
Y = Y.ravel(order='F')
K = np.dot(X, X)
learner = TwoStepRLS(K1=K, K2=K, Y=Y, regparam1=1.0, regparam2=1.0)
log_regparams = range(-10, 0)
for log_regparam in log_regparams:
learner.solve(2.**log_regparam, 2.**log_regparam)
P = learner.out_of_sample_loo_symmetric()
perf = cindex(Y, P)
print("regparam 2**%d, cindex %f" % (log_regparam, perf))
def main():
X1, X2, Y = davis_data.load_davis()
Y = Y.ravel(order='F')
learner = TwoStepRLS(X1=X1, X2=X2, Y=Y, 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.in_sample_loo()
perf = cindex(Y, P)
print("regparam 2**%d 2**%d, cindex %f" %
(log_regparam1, log_regparam2, perf))
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, X2, Y = davis_data.load_davis()
n = X1.shape[0]
m = X2.shape[0]
Y = Y.ravel(order='F')
learner = TwoStepRLS(X1 = X1, X2 = X2, Y = Y, regparam1=1.0, regparam2=1.0)
log_regparams1 = range(-8, -4)
log_regparams2 = range(20,25)
#Create random split to 5 folds for the drug-target pairs
folds = random_folds(n*m, 5, seed=12345)
#Map the indices back to (drug_indices, target_indices)
folds = [np.unravel_index(fold, (n,m)) for fold in folds]
for log_regparam1 in log_regparams1:
for log_regparam2 in log_regparams2:
learner.solve(2.**log_regparam1, 2.**log_regparam2)
P = learner.in_sample_kfoldcv(folds)
perf = cindex(Y, P)
print("regparam 2**%d 2**%d, cindex %f" %(log_regparam1, log_regparam2, perf))
def main():
X1, X2, Y = davis_data.load_davis()
n = X1.shape[0]
m = X2.shape[0]
Y = Y.ravel(order='F')
learner = TwoStepRLS(X1=X1, X2=X2, Y=Y, regparam1=1.0, regparam2=1.0)
log_regparams1 = range(-8, -4)
log_regparams2 = range(20, 25)
#Create random split to 5 folds for the drug-target pairs
folds = random_folds(n * m, 5, seed=12345)
#Map the indices back to (drug_indices, target_indices)
folds = [np.unravel_index(fold, (n, m)) for fold in folds]
for log_regparam1 in log_regparams1:
for log_regparam2 in log_regparams2:
learner.solve(2.**log_regparam1, 2.**log_regparam2)
P = learner.in_sample_kfoldcv(folds)
perf = cindex(Y, P)
print("regparam 2**%d 2**%d, 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))
