def test_clusterevaluation(self):
"""
Tests the ClusterEvaluation class.
"""
loader = converters.Loader(classname="weka.core.converters.ArffLoader")
data = loader.load_file(self.datafile("anneal.arff"))
self.assertIsNotNone(data)
data.delete_last_attribute()
# simple test set
test = dataset.Instances.copy_instances(data, 0, 10)
cls = clusterers.Clusterer(classname="weka.clusterers.SimpleKMeans")
self.assertIsNotNone(cls, msg="Failed to instantiate clusterer!")
cls.build_clusterer(data)
evl = clusterers.ClusterEvaluation()
self.assertIsNotNone(cls, msg="Failed to instantiate evaluation!")
evl.set_model(cls)
evl.test_model(test)
self.assertGreater(len(evl.cluster_results), 0, msg="No evaluation string!")
self.assertEqual(0.0, evl.log_likelihood, msg="log_likelihood differs")
self.assertEqual(2, evl.num_clusters, msg="num_clusters differs")
self.assertEquals(
[1., 0., 0., 1., 1., 1., 0., 0., 0., 0.], evl.cluster_assignments.tolist(),
msg="cluster_assignments differs")
# cross-validation
cls = clusterers.Clusterer(classname="weka.clusterers.EM", options=["-I", "3", "-X", "2", "-max", "5"])
self.assertIsNotNone(cls, msg="Failed to instantiate clusterer!")
llh = clusterers.ClusterEvaluation.crossvalidate_model(cls, data, 10, classes.Random(1))
self.assertAlmostEqual(-34.397, llh, places=3, msg="Failed to cross-validate clusterer!")
def test_evaluation(self):
"""
Tests the Evaluation class.
"""
# 1. nominal
loader = converters.Loader(classname="weka.core.converters.ArffLoader")
data = loader.load_file(self.datafile("anneal.arff"))
self.assertIsNotNone(data)
data.class_is_last()
cname = "weka.classifiers.trees.J48"
cls = classifiers.Classifier(classname=cname)
self.assertIsNotNone(cls, msg="Failed to instantiate: " + cname)
evl = classifiers.Evaluation(data)
self.assertIsNotNone(evl, msg="Failed to instantiate Evaluation")
self.assertIsNone(evl.header.equal_headers(data))
self.assertEqual([9, 100, 685, 1, 68, 41], evl.class_priors.tolist())
# cross-validate
evl.crossvalidate_model(cls, data, 10, classes.Random(1))
self.assertEqual([[5.0, 0.0, 3.0, 0.0, 0.0, 0.0], [0.0, 99.0, 0.0, 0.0, 0.0, 0.0], [0.0, 2.0, 680.0, 0.0, 0.0, 2.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 67.0, 0.0], [0.0, 0.0, 7.0, 0.0, 0.0, 33.0]], evl.confusion_matrix.tolist(), msg="confusion matrix differs")
self.assertAlmostEqual(0.749, evl.area_under_prc(0), places=3, msg="area_under_prc(0) differs")
self.assertAlmostEqual(0.931, evl.area_under_roc(0), places=3, msg="area_under_roc(0) differs")
self.assertAlmostEqual(0, evl.avg_cost, places=3, msg="avg_cost differs")
self.assertEqual(884, evl.correct, msg="correct differs")
self.assertAlmostEqual(98.775, evl.coverage_of_test_cases_by_predicted_regions, places=3, msg="coverage_of_test_cases_by_predicted_regions differs")
self.assertAlmostEqual(0.016, evl.error_rate, places=3, msg="error_rate differs")
self.assertAlmostEqual(0.769, evl.f_measure(0), places=3, msg="f_measure(0) differs")
self.assertAlmostEqual(0.375, evl.false_negative_rate(0), places=3, msg="false_negative_rate(0) differs")
self.assertAlmostEqual(0.0, evl.false_positive_rate(0), places=3, msg="false_positive_rate(0) differs")
self.assertEqual(14, evl.incorrect, msg="incorrect differs")
self.assertAlmostEqual(0.961, evl.kappa, places=3, msg="kappa differs")
self.assertAlmostEqual(1017.196, evl.kb_information, places=3, msg="kb_information differs")
self.assertAlmostEqual(1.133, evl.kb_mean_information, places=3, msg="kb_mean_information differs")
self.assertAlmostEqual(95.017, evl.kb_relative_information, places=3, msg="kb_relative_information differs")
self.assertAlmostEqual(0.789, evl.matthews_correlation_coefficient(0), places=3, msg="matthews_correlation_coefficient(0) differs")
self.assertAlmostEqual(0.006, evl.mean_absolute_error, places=3, msg="mean_absolute_error differs")
self.assertAlmostEqual(0.134, evl.mean_prior_absolute_error, places=3, msg="mean_prior_absolute_error differs")
self.assertAlmostEqual(3.0, evl.num_false_negatives(0), places=3, msg="num_false_negatives(0) differs")
self.assertAlmostEqual(0.0, evl.num_false_positives(0), places=3, msg="num_false_positives(0) differs")
self.assertAlmostEqual(898.0, evl.num_instances, places=3, msg="num_instances differs")
self.assertAlmostEqual(890.0, evl.num_true_negatives(0), places=3, msg="num_true_negatives(0) differs")
self.assertAlmostEqual(5.0, evl.num_true_positives(0), places=3, msg="num_true_positives(0) differs")
self.assertAlmostEqual(98.44, evl.percent_correct, places=2, msg="percent_correct differs")
self.assertAlmostEqual(1.56, evl.percent_incorrect, places=2, msg="percent_incorrect differs")
self.assertAlmostEqual(0, evl.percent_unclassified, places=2, msg="percent_unclassified differs")
self.assertAlmostEqual(1.0, evl.precision(0), places=3, msg="precision(0) differs")
self.assertAlmostEqual(0.625, evl.recall(0), places=3, msg="recall(0) differs")
self.assertAlmostEqual(4.187, evl.relative_absolute_error, places=3, msg="relative_absolute_error differs")
self.assertAlmostEqual(0.258, evl.root_mean_prior_squared_error, places=3, msg="root_mean_prior_squared_error differs")
self.assertAlmostEqual(0.067, evl.root_mean_squared_error, places=3, msg="root_mean_squared_error differs")
self.assertAlmostEqual(25.912, evl.root_relative_squared_error, places=3, msg="root_relative_squared_error differs")
self.assertAlmostEqual(-75.044, evl.sf_entropy_gain, places=3, msg="sf_entropy_gain differs")
self.assertAlmostEqual(-0.084, evl.sf_mean_entropy_gain, places=3, msg="sf_mean_entropy_gain differs")
self.assertAlmostEqual(1.192, evl.sf_mean_prior_entropy, places=3, msg="sf_mean_prior_entropy differs")
self.assertAlmostEqual(1.276, evl.sf_mean_scheme_entropy, places=3, msg="sf_mean_scheme_entropy differs")
self.assertAlmostEqual(16.722, evl.size_of_predicted_regions, places=3, msg="size_of_predicted_regions differs")
self.assertEqual(0, evl.unclassified, msg="unclassified differs")
self.assertAlmostEqual(0.0, evl.total_cost, places=3, msg="total_cost differs")
self.assertAlmostEqual(0.926, evl.unweighted_macro_f_measure, places=3, msg="unweighted_macro_f_measure differs")
self.assertAlmostEqual(0.984, evl.unweighted_micro_f_measure, places=3, msg="unweighted_micro_f_measure differs")
self.assertAlmostEqual(0.992, evl.weighted_area_under_prc, places=3, msg="weighted_area_under_prc differs")
self.assertAlmostEqual(0.995, evl.weighted_area_under_roc, places=3, msg="weighted_area_under_roc differs")
self.assertAlmostEqual(0.984, evl.weighted_f_measure, places=3, msg="weighted_f_measure differs")
self.assertAlmostEqual(0.016, evl.weighted_false_negative_rate, places=3, msg="weighted_false_negative_rate differs")
self.assertAlmostEqual(0.036, evl.weighted_false_positive_rate, places=3, msg="weighted_false_positive_rate differs")
self.assertAlmostEqual(0.959, evl.weighted_matthews_correlation, places=3, msg="weighted_matthews_correlation differs")
self.assertAlmostEqual(0.984, evl.weighted_precision, places=3, msg="weighted_precision differs")
self.assertAlmostEqual(0.984, evl.weighted_recall, places=3, msg="weighted_recall differs")
self.assertAlmostEqual(0.964, evl.weighted_true_negative_rate, places=3, msg="weighted_true_negative_rate differs")
self.assertAlmostEqual(0.984, evl.weighted_true_positive_rate, places=3, msg="weighted_true_positive_rate differs")
self.assertIsNotNone(evl.predictions, msg="Should have had predictions")
self.assertEqual(data.num_instances, len(evl.predictions), msg="Number of predictions differ from training set size")
# train/test
evl = classifiers.Evaluation(data)
self.assertIsNotNone(evl, msg="Failed to instantiate Evaluation")
cname = "weka.classifiers.trees.J48"
cls = classifiers.Classifier(classname=cname)
self.assertIsNotNone(cls, msg="Failed to instantiate: " + cname)
cls.build_classifier(data)
evl.test_model(cls, data)
self.assertAlmostEqual(99.777, evl.percent_correct, places=3, msg="percent_correct differs")
# predictions: no
cname = "weka.classifiers.trees.J48"
cls = classifiers.Classifier(classname=cname)
self.assertIsNotNone(cls, msg="Failed to instantiate: " + cname)
evl = classifiers.Evaluation(data)
evl.discard_predictions = True
self.assertIsNotNone(evl, msg="Failed to instantiate Evaluation")
evl.crossvalidate_model(cls, data, 10, classes.Random(1))
self.assertIsNone(evl.predictions, msg="Should have had no predictions")
# output
cname = "weka.classifiers.trees.J48"
cls = classifiers.Classifier(classname=cname)
self.assertIsNotNone(cls, msg="Failed to instantiate: " + cname)
evl = classifiers.Evaluation(data)
cname = "weka.classifiers.evaluation.output.prediction.PlainText"
pout = classifiers.PredictionOutput(classname=cname)
self.assertEqual(cname, pout.classname, msg="Output classnames differ!")
self.assertIsNotNone(evl, msg="Failed to instantiate Evaluation")
evl.crossvalidate_model(cls, data, 10, classes.Random(1), output=pout)
self.assertGreater(len(str(pout)), 0, msg="Should have generated output")
# 2. numeric
loader = converters.Loader(classname="weka.core.converters.ArffLoader")
data = loader.load_file(self.datafile("bolts.arff"))
self.assertIsNotNone(data)
data.class_is_last()
cname = "weka.classifiers.functions.LinearRegression"
cls = classifiers.Classifier(classname=cname)
self.assertIsNotNone(cls, msg="Failed to instantiate: " + cname )
evl = classifiers.Evaluation(data)
self.assertIsNotNone(evl, msg="Failed to instantiate Evaluation")
self.assertIsNone(evl.header.equal_headers(data))
# cross-validate
evl.crossvalidate_model(cls, data, 10, classes.Random(1))
self.assertAlmostEqual(0.919, evl.correlation_coefficient, places=3, msg="correlation_coefficient differs")
self.assertAlmostEqual(10.697, evl.error_rate, places=3, msg="error_rate differs")