def test_generate_thresholdcurve_data(self):
"""
Tests the generate_thresholdcurve_data method.
"""
loader = converters.Loader(classname="weka.core.converters.ArffLoader")
data = loader.load_file(self.datafile("diabetes.arff"))
data.class_is_last()
remove = filters.Filter(
classname="weka.filters.unsupervised.attribute.Remove",
options=["-R", "1-3"])
cls = classifiers.Classifier(
classname="weka.classifiers.bayes.NaiveBayes")
fc = classifiers.FilteredClassifier()
fc.filter = remove
fc.classifier = cls
evl = classifiers.Evaluation(data)
evl.crossvalidate_model(cls, data, 10, Random(1))
data = plot.generate_thresholdcurve_data(evl, 0)
self.assertEqual(13,
data.num_attributes,
msg="number of attributes differs")
self.assertEqual(769, data.num_instances, msg="number of rows differs")
attname = "True Positives"
self.assertIsNotNone(data.attribute_by_name(attname),
msg="Failed to locate attribute: " + attname)
attname = "False Positive Rate"
self.assertIsNotNone(data.attribute_by_name(attname),
msg="Failed to locate attribute: " + attname)
attname = "Lift"
self.assertIsNotNone(data.attribute_by_name(attname),
msg="Failed to locate attribute: " + attname)
def test_plot_prc(self):
"""
Tests the plot_prc method.
"""
loader = converters.Loader(classname="weka.core.converters.ArffLoader")
data = loader.load_file(self.datafile("diabetes.arff"))
data.class_is_last()
remove = filters.Filter(classname="weka.filters.unsupervised.attribute.Remove", options=["-R", "1-3"])
cls = classifiers.Classifier(classname="weka.classifiers.bayes.NaiveBayes")
fc = classifiers.FilteredClassifier()
fc.filter = remove
fc.classifier = cls
evl = classifiers.Evaluation(data)
evl.crossvalidate_model(cls, data, 10, Random(1))
plot.plot_prc(evl, class_index=[0, 1], wait=False)
def test_instantiate_classifier(self):
"""
Tests the instantiation of several classifier classes.
"""
cname = "weka.classifiers.trees.J48"
options = None
cls = classifiers.Classifier(classname=cname, options=options)
self.assertIsNotNone(cls, msg="Failed to instantiate: " + cname + "/" + str(options))
self.assertEqual(cname, cls.classname, msg="Classnames differ!")
cname = "weka.classifiers.trees.J48"
options = ["-C", "0.3"]
cls = classifiers.Classifier(classname=cname, options=options)
self.assertIsNotNone(cls, msg="Failed to instantiate: " + cname + "/" + str(options))
self.assertEqual(cname, cls.classname, msg="Classnames differ!")
cname = "weka.classifiers.meta.FilteredClassifier"
options = ["-W", "weka.classifiers.trees.J48", "--", "-C", "0.3"]
cls = classifiers.SingleClassifierEnhancer(classname=cname, options=options)
self.assertIsNotNone(cls, msg="Failed to instantiate: " + cname + "/" + str(options))
self.assertEqual(cname, cls.classname, msg="Classnames differ!")
fname = "weka.filters.unsupervised.attribute.Remove"
flter = filters.Filter(classname=fname, options=["-R", "last"])
cls.filter = flter
self.assertEqual(fname, cls.filter.classname, msg="Classnames differ!")
cls = classifiers.FilteredClassifier()
self.assertIsNotNone(cls, msg="Failed to instantiate FilteredClassifier!")
self.assertEqual("weka.classifiers.meta.FilteredClassifier", cls.classname, msg="Classnames differ!")
cname = "weka.classifiers.functions.SMO"
cls = classifiers.KernelClassifier(classname=cname)
self.assertIsNotNone(cls, msg="Failed to instantiate KernelClassifier: " + cname)
self.assertEqual(cname, cls.classname, msg="Classnames differ!")
kname = "weka.classifiers.functions.supportVector.RBFKernel"
kernel = classifiers.Kernel(classname=kname)
self.assertIsNotNone(kernel, msg="Failed to instantiate Kernel: " + kname)
cls.kernel = kernel
self.assertEqual(kname, cls.kernel.classname, msg="Kernel classnames differ!")
cname = "weka.classifiers.meta.Vote"
cls = classifiers.MultipleClassifiersCombiner(classname=cname)
self.assertIsNotNone(cls, msg="Failed to instantiate MultipleClassifiersCombiner: " + cname)
self.assertEqual(cname, cls.classname, msg="Classnames differ!")
def test_get_prc(self):
"""
Tests the get_prc method.
"""
loader = converters.Loader(classname="weka.core.converters.ArffLoader")
data = loader.load_file(self.datafile("diabetes.arff"))
data.class_is_last()
remove = filters.Filter(classname="weka.filters.unsupervised.attribute.Remove", options=["-R", "1-3"])
cls = classifiers.Classifier(classname="weka.classifiers.bayes.NaiveBayes")
fc = classifiers.FilteredClassifier()
fc.filter = remove
fc.classifier = cls
evl = classifiers.Evaluation(data)
evl.crossvalidate_model(cls, data, 10, Random(1))
data = plot.generate_thresholdcurve_data(evl, 0)
area = plot.get_prc(data)
self.assertAlmostEqual(0.892, area, places=3, msg="PRC differs")