def show_res(raw_set, n, k, new_min, new_max):
for i in range(n):
training_set, test_set = mylib.n_fold(n, i, raw_set)
training_data, training_label = mylib.get_data_label(training_set)
factory = KnnFactory(training_data, training_label)
knn_machine_5 = factory.get_KnnMachine(k, new_min, new_max)
test_data, test_label = mylib.get_data_label(test_set)
res_label = knn_machine_5.predict(test_data)
confusion = mylib.confusion_matrix(test_label, res_label)
accuracy = mylib.get_accuracy(confusion)
precision = mylib.get_precision(confusion)
recall = mylib.get_recall(confusion)
f1_score = mylib.get_f1_score(confusion)
print("***itr: ", i, " ***")
print("confusion matrix:")
print(confusion)
print("accuracy: ", accuracy)
print("precision: ", precision)
print("recall: ", recall)
print("f1_score: ", f1_score)
def show_res(raw_set, n, sec_num):
for i in range(n):
training_set, test_set = mylib.n_fold(n, i, raw_set)
training_data, training_label = mylib.get_data_label(training_set)
factory = NaiveBayesFactory(training_data, training_label)
bayes_5 = factory.get_naiveBayes_machine(sec_num)
test_data, test_label = mylib.get_data_label(test_set)
res_label = bayes_5.predict(test_data)
confusion = mylib.confusion_matrix(test_label, res_label)
accuracy = mylib.get_accuracy(confusion)
precision = mylib.get_precision(confusion)
recall = mylib.get_recall(confusion)
f1_score = mylib.get_f1_score(confusion)
print("**************itr: ", i, " **************")
print("confusion matrix:")
print(confusion)
print("accuracy: ", accuracy)
print("precision: ", precision)
print("recall: ", recall)
print("f1_score: ", f1_score)
def show_res(raw_set, n, k, branch_num, impurity_fun, sub_space_fun, seed):
for i in range(n):
training_set, test_set = mylib.n_fold(n, i, raw_set)
training_data, training_label = mylib.get_data_label(training_set)
factory = ForestFactory(training_data, training_label)
randForest = factory.get_RF(k, branch_num, impurity_fun, sub_space_fun,
seed)
test_data, test_label = mylib.get_data_label(test_set)
true_label = mylib.convert_label(test_label)
res_label = randForest.predict(test_data)
confusion = mylib.confusion_matrix(true_label, res_label)
accuracy = mylib.get_accuracy(confusion)
precision = mylib.get_precision(confusion)
recall = mylib.get_recall(confusion)
f1_score = mylib.get_f1_score(confusion)
print("**************itr: ", i, " **************")
print("confusion matrix:")
print(confusion)
print("accuracy: ", accuracy)
print("precision: ", precision)
print("recall: ", recall)
print("f1_score: ", f1_score)
def show_res(raw_set, n, gate_num, sub_space_fun, seed):
for i in range(n):
training_set, test_set = mylib.n_fold(n, i, raw_set)
training_data, training_label = mylib.get_data_label(training_set)
factory = TreeFactory(training_data, training_label)
dtree = factory.get_DT_machine(gate_num, mylib.entropy, sub_space_fun,
seed)
test_data, test_label = mylib.get_data_label(test_set)
true_label = mylib.convert_label(test_label)
res_label = dtree.predict(test_data)
confusion = mylib.confusion_matrix(true_label, res_label)
accuracy = mylib.get_accuracy(confusion)
precision = mylib.get_precision(confusion)
recall = mylib.get_recall(confusion)
f1_score = mylib.get_f1_score(confusion)
print("**************itr: ", i, " **************")
print("confusion matrix:")
print(confusion)
print("accuracy: ", accuracy)
print("precision: ", precision)
print("recall: ", recall)
print("f1_score: ", f1_score)