8000 [MRG + 1] Plot iso-f1 curves in plot_precision_recall example by SACHIN-13 · Pull Request #8378 · scikit-learn/scikit-learn · GitHub
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[MRG + 1] Plot iso-f1 curves in plot_precision_recall example #8378

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Feb 20, 2017
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[MRG + 1] Plot iso-f1 curves in plot_precision_recall example #8378

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34 changes: 25 additions & 9 deletions examples/model_selection/plot_precision_recall.py
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Original file line number Diff line number Diff line change
Expand Up @@ -139,20 +139,36 @@
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()
plt.plot(recall["micro"], precision["micro"], color='gold', lw=lw,
label='micro-average Precision-recall curve (area = {0:0.2f})'
''.format(average_precision["micro"]))
f_scores = np.linspace(0.2, 0.8, num=4)
lines = []
labels = []
for f_score in f_scores:
x = np.linspace(0.01, 1)
y = f_score * x / (2 * x - f_score)
l, = plt.plot(x[y >= 0], y[y >= 0], color='gray', alpha=0.2)
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unneeded , ?

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Also can you directly do append here?

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without the , , l becomes [<matplotlib.lines.Line2D object at 0x7f43d13912e8>] , which is not supported by legend (a UserWarning is raised). Using a , returns a line instance Line2D(_line3) which is what the legend function accepts! So, the lines have to be appended separately.

plt.annotate('f1={0:0.1f}'.format(f_score), xy=(0.9, y[45] + 0.02))

lines.append(l)
labels.append('iso-f1 curves')
l, = plt.plot(recall["micro"], precision["micro"], color='gold', lw=lw)
lines.append(l)
labels.append('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)
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same comment as above...

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same as above.

labels.append('Precision-recall curve of class {0} (area = {1:0.2f})'
''.format(i, average_precision[i]))

fig = plt.gcf()
fig.set_size_inches(7, 7)
fig.subplots_adjust(bottom=0.25)
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.figlegend(lines, labels, loc='lower center')
plt.show()
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