FLAML: New feature about flaml HOT 3 CLOSED

def plot_confusion_matrix(estimator, X_train, y_train, X_test, y_test):
    '''Binarize the data for multi-class tasks and plot confusion matrix

    Args:
        estimator: A multi-class classification estimator
        X_train: A numpy array or a pandas dataframe of training data
        y_train: A numpy array or a pandas series of training labels
        X_test: A numpy array or a pandas dataframe of test data
        y_test: A numpy array or a pandas series of test labels
    '''

def norm_confusion_matrix(y_true, y_pred):
    '''normalized confusion matrix

    Args:
        estimator: A multi-class classification estimator
        y_true: A numpy array or a pandas series of true labels
        y_pred: A numpy array or a pandas series of predicted labels

    Returns:
        A normalized confusion matrix
    '''
    from sklearn.metrics import confusion_matrix
    conf_mat = confusion_matrix(y_true, y_pred)
    norm_conf_mat = conf_mat.astype('float') / conf_mat.sum(axis=1)[:, np.newaxis]
    return norm_conf_mat


def multi_class_curves(y_true, y_pred_proba, curve_func):
    '''Binarize the data for multi-class tasks and produce ROC or precision-recall curves

    Args:
        y_true: A numpy array or a pandas series of true labels
        y_pred_proba: A numpy array or a pandas dataframe of predicted probabilites
        curve_func: A function to produce a curve (e.g., roc_curve or precision_recall_curve)

    Returns:
        A tuple of two dictionaries with the same set of keys (class indices)
        The first dictionary curve_x stores the x coordinates of each curve, e.g.,
            curve_x[0] is an 1D array of the x coordinates of class 0
        The second dictionary curve_y stores the y coordinates of each curve, e.g.,
            curve_y[0] is an 1D array of the y coordinates of class 0
    '''
    from sklearn.preprocessing import label_binarize
    classes = np.unique(y_true)
    y_true_binary = label_binarize(y_true, classes=classes)

    curve_x, curve_y = {}, {}
    for i in range(len(classes)):
        curve_x[i], curve_y[i], _ = curve_func(y_true_binary[:, i], y_pred_proba[:, i])
    return curve_x, curve_y