TinghuiWang / pyActLearn

logger = logging.getLogger(__file__)

def training_and_test(token, train_data, test_data, num_classes, result):

    """Train and test

    Args:

        token (:obj:`str`): token representing this run

        train_data (:obj:`tuple` of :obj:`numpy.array`): Tuple of training feature and label

        test_data (:obj:`tuple` of :obj:`numpy.array`): Tuple of testing feature and label

        num_classes (:obj:`int`): Number of classes

        result (:obj:`pyActLearn.performance.record.LearningResult`): LearningResult object to hold learning result

    """

    decision_tree = DecisionTree(train_data[0].shape[1], num_classes, log_level=logging.WARNING)

    decision_tree.build(train_data[0], train_data[1].flatten())

    # Test

    predicted_y = decision_tree.classify(test_data[0])

    # Evaluate the Test and Store Result

    confusion_matrix = get_confusion_matrix(num_classes=num_classes,

                                            label=test_data[1].flatten(), predicted=predicted_y)

    result.add_record(decision_tree.export_to_dict(), key=token, confusion_matrix=confusion_matrix)

    return predicted_y

def load_and_test(token, test_data, num_classes, result):

logger = logging.getLogger(__file__)

def training_and_test(token, train_data, test_data, num_classes, result):

    """Train and test

    Args:

        token (:obj:`str`): token representing this run

        train_data (:obj:`tuple` of :obj:`numpy.array`): Tuple of training feature and label

        test_data (:obj:`tuple` of :obj:`numpy.array`): Tuple of testing feature and label

        num_classes (:obj:`int`): Number of classes

        result (:obj:`pyActLearn.performance.record.LearningResult`): LearningResult object to hold learning result

    """

    svm_model = sklearn.svm.SVC(kernel='rbf')

    svm_model.fit(train_data[0], train_data[1].flatten())

    # Test

    predicted_y = svm_model.predict(test_data[0])

    # Evaluate the Test and Store Result

    confusion_matrix = get_confusion_matrix(num_classes=num_classes,

                                            label=test_data[1].flatten(), predicted=predicted_y)

    result.add_record(svm_model, key=token, confusion_matrix=confusion_matrix)

    return predicted_y

def load_and_test(token, test_data, num_classes, result):