Esempi in Python per load_json_data

Esempio n. 1

File: test_machineLearningFramework.py Progetto: marcusCarpenter97/Machine-Learning-for-Malware-Detection

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)

Esempio n. 2

File: test_machineLearningFramework.py Progetto: marcusCarpenter97/Machine-Learning-for-Malware-Detection

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)

Esempio n. 3

File: test_machineLearningFramework.py Progetto: marcusCarpenter97/Machine-Learning-for-Malware-Detection

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)

Esempio n. 4

File: test_machineLearningFramework.py Progetto: marcusCarpenter97/Machine-Learning-for-Malware-Detection

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()

Esempio n. 5

File: test_machineLearningFramework.py Progetto: marcusCarpenter97/Machine-Learning-for-Malware-Detection

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)

Esempio n. 6

File: test_machineLearningFramework.py Progetto: marcusCarpenter97/Machine-Learning-for-Malware-Detection

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)

Esempio n. 7

File: test_machineLearningFramework.py Progetto: marcusCarpenter97/Machine-Learning-for-Malware-Detection

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)

Esempio n. 8

File: test_machineLearningFramework.py Progetto: marcusCarpenter97/Machine-Learning-for-Malware-Detection

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)

Esempio n. 9

File: test_machineLearningFramework.py Progetto: marcusCarpenter97/Machine-Learning-for-Malware-Detection

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()

Esempio n. 10

def load_data_menu():

    selected_opt = click.prompt("Enter malware rate", default=None)

    mlf.load_json_data(float(selected_opt))

Esempio n. 11

File: test_machineLearningFramework.py Progetto: marcusCarpenter97/Machine-Learning-for-Malware-Detection

def test_load_json_data_invalid_parameter():

    malware_rate = "invalid!"

    mlf.load_json_data(malware_rate)

Esempio n. 12

File: test_machineLearningFramework.py Progetto: marcusCarpenter97/Machine-Learning-for-Malware-Detection

def test_load_json_data_valid_parameter():

    malware_rate = 0.33

    mlf.load_json_data(malware_rate)