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))