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):
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
overall_performance_te_rf = performance_te_rf/repeats
overall_performance_tr_svm = performance_tr_svm/repeats
overall_performance_te_svm = performance_te_svm/repeats
# And we run our deterministic classifiers:
class_train_y, class_test_y, class_train_prob_y, class_test_prob_y = learner.k_nearest_neighbor(selected_train_X, train_y, selected_test_X, gridsearch=True)
performance_te_nn = 0
performance_te_rf = 0
performance_te_svm = 0
for repeat in range(0, repeats):
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
overall_performance_te_rf = performance_te_rf / repeats
overall_performance_tr_svm = performance_tr_svm / repeats
overall_performance_te_svm = performance_te_svm / repeats
# And we run our deterministic classifiers:
class_train_y, class_test_y, class_train_prob_y, class_test_prob_y = learner.k_nearest_neighbor(
selected_train_X, train_y, selected_test_X, gridsearch=True)
performance_tr_knn = eval.accuracy(train_y, class_train_y)
