import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve, auc


class ROCEvaluation(object):

    linewidth = 2

    def __init__(self, prepare_figure=False):
        self.prepare_figure = prepare_figure
        self.epoch = 0

    def __call__(self, prediction, label, plotting=False):

        # Compute ROC curve and ROC area
        fpr, tpr, _ = roc_curve(prediction, label)
        roc_auc = auc(fpr, tpr)
        if plotting:
            fig = plt.gcf()
            fig.plot(fpr, tpr, color='darkorange', lw=self.linewidth, label=f'ROC curve (area = {roc_auc})')
        return roc_auc, fpr, tpr

    def _prepare_fig(self):
        fig = plt.gcf()
        fig.plot([0, 1], [0, 1], color='navy', lw=self.linewidth, linestyle='--')
        fig.xlim([0.0, 1.0])
        fig.ylim([0.0, 1.05])
        fig.xlabel('False Positive Rate')
        fig.ylabel('True Positive Rate')
        fig.legend(loc="lower right")

        return fig