plot iso-f1 curves in plot_precision_recall

SACHIN-13 · SACHIN-13 · commit a1b4837da3c8 · 2017-02-17T00:56:33.000+05:30
diff --git a/examples/model_selection/plot_precision_recall.py b/examples/model_selection/plot_precision_recall.py
@@ -139,20 +139,32 @@
 plt.legend(loc="lower left")
 plt.show()
 
-# Plot Precision-Recall curve for each class
+# Plot Precision-Recall curve for each class and iso-f1 curves
 plt.clf()
+f_scores = np.linspace(0.1, 0.9, num=9)
+lines = []
+labels = []
+for F in f_scores:
+    x = R = np.linspace(0.01, 1)
+    y = F * R / (2 * R - F)
+    l, = plt.plot(x[y >= 0], y[y >= 0], color='gray')
+    plt.annotate('f1={0:0.1f}'.format(F), xy=(0.9, y[45] + 0.02))
+
+lines.append(l)
+labels.append('iso-f1 curves')
 plt.plot(recall["micro"], precision["micro"], color='gold', lw=lw,
          label='micro-average Precision-recall curve (area = {0:0.2f})'
                ''.format(average_precision["micro"]))
 for i, color in zip(range(n_classes), colors):
-    plt.plot(recall[i], precision[i], color=color, lw=lw,
-             label='Precision-recall curve of class {0} (area = {1:0.2f})'
-                   ''.format(i, average_precision[i]))
+    l, = plt.plot(recall[i], precision[i], color=color, lw=lw)
+    lines.append(l)
+    labels.append('Precision-recall curve of class {0} (area = {1:0.2f})'
+                  ''.format(i, average_precision[i]))
 
 plt.xlim([0.0, 1.0])
 plt.ylim([0.0, 1.05])
 plt.xlabel('Recall')
 plt.ylabel('Precision')
 plt.title('Extension of Precision-Recall curve to multi-class')
-plt.legend(loc="lower right")
+plt.legend(lines, labels, loc='lower left')
 plt.show()
