def evaluation_cross_validation_regression (train_fname=traindat,label_fname=label_traindat,width=0.8,tau=1e-6): from modshogun import CrossValidation, CrossValidationResult from modshogun import MeanSquaredError, CrossValidationSplitting from modshogun import RegressionLabels, RealFeatures from modshogun import GaussianKernel, KernelRidgeRegression, CSVFile # training data features=RealFeatures(CSVFile(train_fname)) labels=RegressionLabels(CSVFile(label_fname)) # kernel and predictor kernel=GaussianKernel() predictor=KernelRidgeRegression(tau, kernel, labels) # splitting strategy for 5 fold cross-validation (for classification its better # to use "StratifiedCrossValidation", but here, the std x-val is used splitting_strategy=CrossValidationSplitting(labels, 5) # evaluation method evaluation_criterium=MeanSquaredError() # cross-validation instance cross_validation=CrossValidation(predictor, features, labels, splitting_strategy, evaluation_criterium) # (optional) repeat x-val 10 times cross_validation.set_num_runs(10) # (optional) tell machine to precompute kernel matrix. speeds up. may not work predictor.data_lock(labels, features) # perform cross-validation and print(results) result=cross_validation.evaluate()
def regression_kernel_ridge_modular (n=100,n_test=100, \ x_range=6,x_range_test=10,noise_var=0.5,width=1, tau=1e-6, seed=1): from modshogun import RegressionLabels, RealFeatures from modshogun import GaussianKernel from modshogun import KernelRidgeRegression # reproducable results random.seed(seed) # easy regression data: one dimensional noisy sine wave n = 15 n_test = 100 x_range_test = 10 noise_var = 0.5 X = random.rand(1, n) * x_range X_test = array([[float(i) / n_test * x_range_test for i in range(n_test)]]) Y_test = sin(X_test) Y = sin(X) + random.randn(n) * noise_var # shogun representation labels = RegressionLabels(Y[0]) feats_train = RealFeatures(X) feats_test = RealFeatures(X_test) kernel = GaussianKernel(feats_train, feats_train, width) krr = KernelRidgeRegression(tau, kernel, labels) krr.train(feats_train) kernel.init(feats_train, feats_test) out = krr.apply().get_labels() # plot results #plot(X[0],Y[0],'x') # training observations #plot(X_test[0],Y_test[0],'-') # ground truth of test #plot(X_test[0],out, '-') # mean predictions of test #legend(["training", "ground truth", "mean predictions"]) #show() return out, kernel, krr
def regression_kernel_ridge_modular (n=100,n_test=100, \ x_range=6,x_range_test=10,noise_var=0.5,width=1, tau=1e-6, seed=1): from modshogun import RegressionLabels, RealFeatures from modshogun import GaussianKernel from modshogun import KernelRidgeRegression # reproducable results random.seed(seed) # easy regression data: one dimensional noisy sine wave n=15 n_test=100 x_range_test=10 noise_var=0.5; X=random.rand(1,n)*x_range X_test=array([[float(i)/n_test*x_range_test for i in range(n_test)]]) Y_test=sin(X_test) Y=sin(X)+random.randn(n)*noise_var # shogun representation labels=RegressionLabels(Y[0]) feats_train=RealFeatures(X) feats_test=RealFeatures(X_test) kernel=GaussianKernel(feats_train, feats_train, width) krr=KernelRidgeRegression(tau, kernel, labels) krr.train(feats_train) kernel.init(feats_train, feats_test) out = krr.apply().get_labels() # plot results #plot(X[0],Y[0],'x') # training observations #plot(X_test[0],Y_test[0],'-') # ground truth of test #plot(X_test[0],out, '-') # mean predictions of test #legend(["training", "ground truth", "mean predictions"]) #show() return out,kernel,krr
def evaluation_cross_validation_regression(train_fname=traindat, label_fname=label_traindat, width=0.8, tau=1e-6): from modshogun import CrossValidation, CrossValidationResult from modshogun import MeanSquaredError, CrossValidationSplitting from modshogun import RegressionLabels, RealFeatures from modshogun import GaussianKernel, KernelRidgeRegression, CSVFile # training data features = RealFeatures(CSVFile(train_fname)) labels = RegressionLabels(CSVFile(label_fname)) # kernel and predictor kernel = GaussianKernel() predictor = KernelRidgeRegression(tau, kernel, labels) # splitting strategy for 5 fold cross-validation (for classification its better # to use "StratifiedCrossValidation", but here, the std x-val is used splitting_strategy = CrossValidationSplitting(labels, 5) # evaluation method evaluation_criterium = MeanSquaredError() # cross-validation instance cross_validation = CrossValidation(predictor, features, labels, splitting_strategy, evaluation_criterium) # (optional) repeat x-val 10 times cross_validation.set_num_runs(10) # (optional) request 95% confidence intervals for results (not actually needed # for this toy example) cross_validation.set_conf_int_alpha(0.05) # (optional) tell machine to precompute kernel matrix. speeds up. may not work predictor.data_lock(labels, features) # perform cross-validation and print(results) result = cross_validation.evaluate()
def modelselection_grid_search_krr_modular (fm_train=traindat,fm_test=testdat,label_train=label_traindat,\ width=2.1,C=1,epsilon=1e-5,tube_epsilon=1e-2): from modshogun import CrossValidation, CrossValidationResult from modshogun import MeanSquaredError from modshogun import CrossValidationSplitting from modshogun import RegressionLabels from modshogun import RealFeatures from modshogun import KernelRidgeRegression from modshogun import GridSearchModelSelection from modshogun import ModelSelectionParameters # training data features_train = RealFeatures(traindat) features_test = RealFeatures(testdat) labels = RegressionLabels(label_traindat) # labels labels = RegressionLabels(label_train) # predictor, set tau=0 here, doesnt matter predictor = KernelRidgeRegression() # splitting strategy for 5 fold cross-validation (for classification its better # to use "StratifiedCrossValidation", but the standard # "StratifiedCrossValidationSplitting" is also available splitting_strategy = CrossValidationSplitting(labels, 5) # evaluation method evaluation_criterium = MeanSquaredError() # cross-validation instance cross_validation = CrossValidation(predictor, features_train, labels, splitting_strategy, evaluation_criterium) # (optional) repeat x-val (set larger to get better estimates) cross_validation.set_num_runs(2) # print all parameter available for modelselection # Dont worry if yours is not included but, write to the mailing list #predictor.print_modsel_params() # build parameter tree to select regularization parameter param_tree_root = create_param_tree() # model selection instance model_selection = GridSearchModelSelection(cross_validation, param_tree_root) # perform model selection with selected methods #print "performing model selection of" #print "parameter tree:" #param_tree_root.print_tree() #print "starting model selection" # print the current parameter combination, if no parameter nothing is printed print_state = False best_parameters = model_selection.select_model(print_state) # print best parameters #print "best parameters:" #best_parameters.print_tree() # apply them and print result best_parameters.apply_to_machine(predictor) result = cross_validation.evaluate()