orange_calculate_and_plot_roc_curve.py

#! /usr/bin/env python
#generate and plot a ROC curve for a classifier using Orange (http://orange.biolab.si/)
#Source Code: https://github.com/biolab/orange/blob/master/Orange/evaluation/scoring.py
import Orange

learners = [Orange.classification.bayes.NaiveLearner(name="bayes"),
            Orange.classification.tree.TreeLearner(name="tree"),
            Orange.classification.svm.SVMLearnerEasy(name="easySVM")]

#split data to train and test set
data = Orange.data.Table("voting")
split_set = Orange.data.sample.SubsetIndices2(p0=0.8)(data) #random zero / 1
train_set = data.select(split_set, 0)
test_set = data.select(split_set, 1)

#the magic
results = Orange.evaluation.testing.learn_and_test_on_test_data(learners,train_set, test_set)
curves = Orange.evaluation.scoring.compute_ROC(results)
Orange.evaluation.scoring.plot_ROC("roc.gp", curves, [l.name for l in learners])

# plot can be displayed by loading it to gnuplot http://people.duke.edu/~hpgavin/gnuplot.html
# > gnuplot
# > load "roc.gp"