def autism_sets_process():
print(
"---------------------Autism Adult Data Sets(CART)------------------------"
)
autism_data_sets, autism_labels = utils.get_autism_data_set()
autism_data_test_df, autism_data_train_df = utils.handle_data(
autism_data_sets, autism_labels)
autism_labels = array(autism_data_train_df.columns)
# print(autism_labels)
target_names = unique(autism_data_train_df.iloc[:, -1])
# print(target_names)
dt = DecisionTree()
fit_begin_time = datetime.datetime.now()
print("Begin Time: " + str(fit_begin_time))
tree = dt.fit(autism_data_train_df)
# print(tree)
# print(autism_data_test_df)
fit_end_time = datetime.datetime.now()
print("End Time: " + str(fit_end_time))
print("Training used Time: " + str(fit_end_time - fit_begin_time))
autism_test_result_df = predict_data(dt, autism_data_test_df,
autism_labels)
report = utils.generate_report(autism_data_test_df, autism_test_result_df,
target_names)
print(report)
def iris_sets_process():
print("---------------------Iris Data Sets------------------------")
iris_data_sets, iris_labels = utils.get_iris_data_set()
iris_data_test_df, iris_data_train_df = utils.handle_data(iris_data_sets, iris_labels)
target_names = np.unique(iris_data_train_df.iloc[:, -1])
svm = SVM(max_iter=200)
fit_begin_time = datetime.datetime.now()
print("Begin Time: " + str(fit_begin_time))
svm.fit(iris_data_train_df, iris_labels)
fit_end_time = datetime.datetime.now()
print("End Time: " + str(fit_end_time))
print("Training used Time: " + str(fit_end_time - fit_begin_time))
def healthy_sets_process():
print("---------------------Healthy Older People Sets(ID3)------------------------")
healthy_data_sets, healthy_labels = utils.get_healthy_data_set()
healthy_data_test_df, healthy_data_train_df = utils.handle_data(healthy_data_sets, healthy_labels)
target_names = unique(healthy_data_train_df.iloc[:, -1])
# print(target_names)
dt = DecisionTree()
fit_begin_time = datetime.datetime.now()
print("Begin Time: " + str(fit_begin_time))
tree = dt.fit(healthy_data_train_df)
fit_end_time = datetime.datetime.now()
print("End Time: " + str(fit_end_time))
print("Training used Time: " + str(fit_end_time - fit_begin_time))
healthy_test_result_df = predict_data(dt, healthy_data_test_df, healthy_labels)
report = utils.generate_report(healthy_data_test_df, healthy_test_result_df, target_names)
print(report)
def iris_sets_process():
print("---------------------Iris Data Sets(ID3)------------------------")
iris_data_sets, iris_labels = utils.get_iris_data_set()
iris_data_test_df, iris_data_train_df = utils.handle_data(iris_data_sets, iris_labels)
target_names = unique(iris_data_train_df.iloc[:, -1])
# print(target_names)
dt = DecisionTree()
fit_begin_time = datetime.datetime.now()
print("Begin Time: " + str(fit_begin_time))
tree = dt.fit(iris_data_train_df)
fit_end_time = datetime.datetime.now()
print("End Time: " + str(fit_end_time))
print("Training used Time: " + str(fit_end_time - fit_begin_time))
# print(tree)
# print(iris_data_test_df)
# r1 = dt.predict(["7.0", "3.2", "4.7", "1.4"])
# print(r1)
iris_test_result_df = predict_data(dt, iris_data_test_df, iris_labels)
# print(iris_test_result_df)
report = utils.generate_report(iris_data_test_df, iris_test_result_df, target_names)
print(report)
