def test_invalid_input_partition_data():
malware_rate = 0.87
valid_input = "invalid!"
mlf.load_json_data(malware_rate)
with pytest.raises(ValueError):
mlf.partition_data(valid_input)
def test_cross_validate_with_invalid_k_fold():
k_fold = "invalid!"
malware_rate = 0.33
mlf.load_json_data(malware_rate)
mlf.select_classifier("Decision Tree")
ret_val = mlf.cross_validate(k_fold)
assert isinstance(ret_val, str)
def test_cross_validate_with_classifier():
k_fold = 5
malware_rate = 0.33
mlf.load_json_data(malware_rate) # TODO: value error
mlf.select_classifier("Decision Tree")
ret_val = mlf.cross_validate(k_fold)
assert isinstance(ret_val, numpy.ndarray)
def test_train_classifier_with_classifier():
malware_rate = 0.45
partition_size = 0.92
mlf.load_json_data(malware_rate)
mlf.partition_data(partition_size)
mlf.select_classifier("Decision Tree")
mlf.train_classifier()
def test_display_confusion_matrix_without_classifier():
malware_rate = 0.33
partition_size = 0.33
mlf.load_json_data(malware_rate)
mlf.partition_data(partition_size)
mlf.classify_new_data()
ret_val = mlf.display_confusion_matrix()
assert isinstance(ret_val, str)
def test_valid_input_partition_data():
malware_rate = 0.67
valid_input = 0.23
mlf.load_json_data(malware_rate)
mlf.partition_data(valid_input)
success_count = 0
for key, item in mlf.PARTITIONED_DATA_DICT.items():
if item:
success_count += 1
assert success_count == len(mlf.PARTITIONED_DATA_DICT)
def test_calculate_classifier_accuracy_with_classifier():
malware_rate = 0.33
partition_size = 0.33
mlf.load_json_data(malware_rate)
mlf.partition_data(partition_size)
mlf.select_classifier("Decision Tree")
mlf.train_classifier()
ret_val = mlf.calculate_classifier_accuracy()
assert isinstance(ret_val, float)
def test_classify_new_data_with_classifier():
malware_rate = 0.33
partition_size = 0.33
mlf.load_json_data(malware_rate)
mlf.partition_data(partition_size)
mlf.select_classifier("Decision Tree")
mlf.train_classifier()
ret_val = mlf.classify_new_data()
assert isinstance(ret_val, numpy.ndarray)
def test_display_confusion_matrix_pass():
malware_rate = 0.33
partition_size = 0.33
mlf.REPORT.create_new_report_directory()
mlf.load_json_data(malware_rate)
mlf.partition_data(partition_size)
mlf.select_classifier("Decision Tree")
mlf.train_classifier()
mlf.classify_new_data()
mlf.display_confusion_matrix()
def load_data_menu():
selected_opt = click.prompt("Enter malware rate", default=None)
mlf.load_json_data(float(selected_opt))
def test_load_json_data_invalid_parameter():
malware_rate = "invalid!"
mlf.load_json_data(malware_rate)
def test_load_json_data_valid_parameter():
malware_rate = 0.33
mlf.load_json_data(malware_rate)