initial

evaluation/classification.py

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+import matplotlib.pyplot as plt
+
+from sklearn.metrics import roc_curve, auc
+
+
+class ROCEvaluation(object):
+
+    BINARY_PROBLEM = 2
+    linewidth = 2
+
+    def __init__(self, save_fig=True):
+        self.epoch = 0
+        pass
+
+    def __call__(self, prediction, label, prepare_fig=True):
+
+        # Compute ROC curve and ROC area
+        fpr, tpr, _ = roc_curve(prediction, label)
+        roc_auc = auc(fpr, tpr)
+
+        if prepare_fig:
+            fig = self._prepare_fig()
+            fig.plot(fpr, tpr, color='darkorange',
+                     lw=2, label=f'ROC curve (area = {roc_auc})')
+            self._prepare_fig()
+        return roc_auc
+
+    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