Function plot_roc_curve

alphapy/plots.py:529–591

r"""Display ROC Curves with Cross-Validation. Parameters ---------- model : alphapy.Model The model object with plotting specifications. partition : alphapy.Partition Reference to the dataset. Returns ------- None : None References ----------

(model, partition)

#

def plot_roc_curve(model, partition):
    r"""Display ROC Curves with Cross-Validation.

    Parameters
    ----------
    model : alphapy.Model
        The model object with plotting specifications.
    partition : alphapy.Partition
        Reference to the dataset.

    Returns
    -------
    None : None

    References
    ----------

    http://scikit-learn.org/stable/modules/model_evaluation.html#receiver-operating-characteristic-roc

    """

    logger.info("Generating ROC Curves")
    pstring = datasets[partition]

    # For classification only

    if model.specs['model_type'] != ModelType.classification:
        logger.info('ROC Curves are for classification only')
        return None

    # Get X, Y for correct partition.

    X, y = get_partition_data(model, partition)

    # Initialize plot parameters.

    plt.style.use('classic')
    plt.figure()
    lw = 2

    # Plot a ROC Curve for each algorithm.

    for algo in model.algolist:
        logger.info("ROC Curve for Algorithm: %s", algo)
        # compute ROC curve and ROC area for each class
        probas = model.probas[(algo, partition)]
        fpr, tpr, _ = roc_curve(y, probas)
        roc_auc = auc(fpr, tpr)
        plt.plot(fpr, tpr, lw=lw, label='%s (area = %0.2f)' % (algo, roc_auc))

    # draw the luck line
    plt.plot([0, 1], [0, 1], linestyle='--', color='k', label='Luck')
    # define plot characteristics
    plt.xlim([-0.05, 1.05])
    plt.ylim([-0.05, 1.05])
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    title = BSEP.join([algo, "ROC Curve [", pstring, "]"])