selected_test_X = test_X[possible_feature_sets[i]] # First we run our non deterministic classifiers a number of times to average their score. performance_tr_nn = 0 performance_tr_rf = 0 performance_tr_svm = 0 performance_te_nn = 0 performance_te_rf = 0 performance_te_svm = 0 for repeat in range(0, repeats): print repeat class_train_y, class_test_y, class_train_prob_y, class_test_prob_y = learner.feedforward_neural_network( selected_train_X, train_y, selected_test_X, gridsearch=True) performance_tr_nn += eval.accuracy(train_y, class_train_y) performance_te_nn += eval.accuracy(test_y, class_test_y) class_train_y, class_test_y, class_train_prob_y, class_test_prob_y = learner.random_forest( selected_train_X, train_y, selected_test_X, gridsearch=True) performance_tr_rf += eval.accuracy(train_y, class_train_y) performance_te_rf += eval.accuracy(test_y, class_test_y) class_train_y, class_test_y, class_train_prob_y, class_test_prob_y = learner.support_vector_machine_with_kernel( selected_train_X, train_y, selected_test_X, gridsearch=True) performance_tr_svm += eval.accuracy(train_y, class_train_y) performance_te_svm += eval.accuracy(test_y, class_test_y) overall_performance_tr_nn = performance_tr_nn / repeats overall_performance_te_nn = performance_te_nn / repeats overall_performance_tr_rf = performance_tr_rf / repeats
repeats = 20 for reg_param in reg_parameters: performance_tr = 0 performance_te = 0 for i in range(0, repeats): # besluiten of we dit gebruiken... class_train_y, class_test_y, class_train_prob_y, class_test_prob_y = learner.feedforward_neural_network( train_X, train_y, test_X, hidden_layer_sizes=(250, ), alpha=reg_param, max_iter=500, gridsearch=False) performance_tr += eval.accuracy(train_y, class_train_y) performance_te += eval.accuracy(test_y, class_test_y) performance_training.append(performance_tr / repeats) performance_test.append(performance_te / repeats) plot.hold(True) plot.semilogx(reg_parameters, performance_training, 'r-') plot.semilogx(reg_parameters, performance_test, 'b:') print performance_training print performance_test plot.xlabel('regularization parameter value') plot.ylabel('accuracy') plot.ylim([0.95, 1.01]) plot.legend(['training', 'test'], loc=4) plot.hold(False)
# First we run our non deterministic classifiers a number of times to average their score. performance_tr_nn = 0 performance_tr_rf = 0 performance_tr_svm = 0 performance_te_nn = 0 performance_te_rf = 0 performance_te_svm = 0 for repeat in range(0, repeats): print repeat class_train_y, class_test_y, class_train_prob_y, class_test_prob_y = learner.feedforward_neural_network(selected_train_X, train_y, selected_test_X, gridsearch=True, alpha=10, max_iter=50) performance_tr_nn += eval.accuracy(train_y, class_train_y) performance_te_nn += eval.accuracy(test_y, class_test_y) """ class_train_y, class_test_y, class_train_prob_y, class_test_prob_y = learner.random_forest(selected_train_X, train_y, selected_test_X, gridsearch=True) performance_tr_rf += eval.accuracy(train_y, class_train_y) performance_te_rf += eval.accuracy(test_y, class_test_y) class_train_y, class_test_y, class_train_prob_y, class_test_prob_y = learner.support_vector_machine_with_kernel(selected_train_X, train_y, selected_test_X, gridsearch=True) performance_tr_svm += eval.accuracy(train_y, class_train_y) performance_te_svm += eval.accuracy(test_y, class_test_y) """ overall_performance_tr_nn = performance_tr_nn/repeats overall_performance_te_nn = performance_te_nn/repeats """