pr curve

prec_recall.py

+########### PRECISION - RECALL CURVE ##########
+from sklearn.metrics import average_precision_score
+from sklearn.metrics import precision_recall_curve
+
+def pr(num_classes, y_test, y_pred):
+    # For each class
+    precision = dict()
+    recall = dict()
+    average_precision = dict()
+    for i in range(num_classes):
+        precision[i], recall[i], _ = precision_recall_curve(y_test_one_hot[:, i], y_pred[:, i])
+        average_precision[i] = average_precision_score(y_test[:, i], y_pred[:, i])
+
+    # A "micro-average": quantifying score on all classes jointly
+    precision["micro"], recall["micro"], _ = precision_recall_curve(y_test.ravel(), y_pred.ravel())
+    average_precision["micro"] = average_precision_score(y_test, y_pred, average="micro")
+    print('Average precision score, micro-averaged over all classes: {0:0.2f}'.format(average_precision["micro"]))
+
+    return recall, precision, avg_precision
+
+def plot_pr(recall, precision, average_precision):
+    #plotting
+    plt.figure()
+    plt.step(recall['micro'], precision['micro'], color='b', alpha=0.2, where='post')
+    plt.fill_between(recall["micro"], precision["micro"], alpha=0.2, color='b')
+
+    plt.xlabel('Recall')
+    plt.ylabel('Precision')
+    plt.ylim([0.0, 1.05])
+    plt.xlim([0.0, 1.0])
+    plt.title('Average precision score, micro-averaged over all classes: AP={0:0.2f}'.format(average_precision["micro"]))
+    plt.savefig("precision_recall_curve")
+    #plt.show()
+    plt.close()