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))