def test_plot_experiment(self):
"""
Tests the plot_experiment method.
"""
datasets = [
self.datafile("bolts.arff"),
self.datafile("bodyfat.arff"),
self.datafile("autoPrice.arff")
]
cls = [
classifiers.Classifier("weka.classifiers.trees.REPTree"),
classifiers.Classifier(
"weka.classifiers.functions.LinearRegression"),
classifiers.Classifier("weka.classifiers.functions.SMOreg"),
]
outfile = self.tempfile("results-rs.arff")
exp = experiments.SimpleRandomSplitExperiment(classification=False,
runs=10,
percentage=66.6,
preserve_order=False,
datasets=datasets,
classifiers=cls,
result=outfile)
exp.setup()
exp.run()
# evaluate
loader = converters.loader_for_file(outfile)
data = loader.load_file(outfile)
matrix = experiments.ResultMatrix(
"weka.experiment.ResultMatrixPlainText")
tester = experiments.Tester("weka.experiment.PairedCorrectedTTester")
tester.resultmatrix = matrix
comparison_col = data.attribute_by_name(
"Correlation_coefficient").index
tester.instances = data
tester.header(comparison_col)
tester.multi_resultset_full(0, comparison_col)
# plot
plot.plot_experiment(matrix,
title="Random split (w/ StdDev)",
measure="Correlation coefficient",
show_stdev=True,
wait=False)
plot.plot_experiment(matrix,
title="Random split",
measure="Correlation coefficient",
wait=False)
def test_randomsplit_regression(self):
"""
Tests random split on regression.
"""
datasets = [self.datafile("bolts.arff"), self.datafile("bodyfat.arff")]
cls = [
classifiers.Classifier(classname="weka.classifiers.rules.ZeroR"),
classifiers.Classifier(
classname="weka.classifiers.functions.LinearRegression")
]
outfile = self.tempfile("results-rs.arff")
exp = experiments.SimpleRandomSplitExperiment(classification=False,
runs=10,
percentage=66.6,
preserve_order=False,
datasets=datasets,
classifiers=cls,
result=outfile)
self.assertIsNotNone(exp, msg="Failed to instantiate!")
exp.setup()
exp.run()
# evaluate
loader = converters.loader_for_file(outfile)
data = loader.load_file(outfile)
self.assertIsNotNone(data, msg="Failed to load data: " + outfile)
matrix = experiments.ResultMatrix(
classname="weka.experiment.ResultMatrixPlainText")
self.assertIsNotNone(matrix, msg="Failed to instantiate!")
tester = experiments.Tester(
classname="weka.experiment.PairedCorrectedTTester")
self.assertIsNotNone(tester, msg="Failed to instantiate!")
tester.resultmatrix = matrix
comparison_col = data.attribute_by_name(
"Correlation_coefficient").index
tester.instances = data
self.assertGreater(len(tester.header(comparison_col)),
0,
msg="Generated no header")
self.assertGreater(len(tester.multi_resultset_full(0, comparison_col)),
0,
msg="Generated no result")
def test_crossvalidation_classification(self):
"""
Tests cross-validated classification.
"""
datasets = [self.datafile("iris.arff"), self.datafile("anneal.arff")]
cls = [
classifiers.Classifier(classname="weka.classifiers.rules.ZeroR"),
classifiers.Classifier(classname="weka.classifiers.trees.J48")
]
outfile = self.tempfile("results-cv.arff")
exp = experiments.SimpleCrossValidationExperiment(classification=True,
runs=10,
folds=10,
datasets=datasets,
classifiers=cls,
result=outfile)
self.assertIsNotNone(exp, msg="Failed to instantiate!")
exp.setup()
exp.run()
# evaluate
loader = converters.loader_for_file(outfile)
data = loader.load_file(outfile)
self.assertIsNotNone(data, msg="Failed to load data: " + outfile)
matrix = experiments.ResultMatrix(
classname="weka.experiment.ResultMatrixPlainText")
self.assertIsNotNone(matrix, msg="Failed to instantiate!")
tester = experiments.Tester(
classname="weka.experiment.PairedCorrectedTTester")
self.assertIsNotNone(tester, msg="Failed to instantiate!")
tester.resultmatrix = matrix
comparison_col = data.attribute_by_name("Percent_correct").index
tester.instances = data
self.assertGreater(len(tester.header(comparison_col)),
0,
msg="Generated no header")
self.assertGreater(len(tester.multi_resultset_full(0, comparison_col)),
0,
msg="Generated no result")
def test_result_matrix(self):
"""
Tests the ResultMatrix class.
"""
datasets = [self.datafile("iris.arff"), self.datafile("anneal.arff")]
cls = [
classifiers.Classifier(classname="weka.classifiers.rules.ZeroR"),
classifiers.Classifier(classname="weka.classifiers.trees.J48")
]
outfile = self.tempfile("results-cv.arff")
exp = experiments.SimpleCrossValidationExperiment(classification=True,
runs=10,
folds=10,
datasets=datasets,
classifiers=cls,
result=outfile)
self.assertIsNotNone(exp, msg="Failed to instantiate!")
exp.setup()
exp.run()
# evaluate
loader = converters.loader_for_file(outfile)
data = loader.load_file(outfile)
self.assertIsNotNone(data, msg="Failed to load data: " + outfile)
matrix = experiments.ResultMatrix(
classname="weka.experiment.ResultMatrixPlainText")
self.assertIsNotNone(matrix, msg="Failed to instantiate!")
tester = experiments.Tester(
classname="weka.experiment.PairedCorrectedTTester")
self.assertIsNotNone(tester, msg="Failed to instantiate!")
tester.resultmatrix = matrix
comparison_col = data.attribute_by_name("Percent_correct").index
tester.instances = data
self.assertGreater(len(tester.header(comparison_col)),
0,
msg="Generated no header")
self.assertGreater(len(tester.multi_resultset_full(0, comparison_col)),
0,
msg="Generated no result")
# dimensions
self.assertEqual(2, matrix.rows, msg="# of rows differ")
self.assertEqual(2, matrix.columns, msg="# of rows differ")
# cols
self.assertTrue(matrix.get_col_name(0).find("ZeroR") > -1,
msg="ZeroR should be part of col name")
self.assertTrue(matrix.get_col_name(1).find("J48") > -1,
msg="J48 should be part of col name")
self.assertIsNone(matrix.get_col_name(2),
msg="Column name should not exist")
matrix.set_col_name(0, "zeror")
self.assertTrue(matrix.get_col_name(0).find("zeror") > -1,
msg="zeror should be part of col name")
self.assertFalse(matrix.is_col_hidden(1),
msg="Column should be visible")
matrix.hide_col(1)
self.assertTrue(matrix.is_col_hidden(1), msg="Column should be hidden")
matrix.show_col(1)
self.assertFalse(matrix.is_col_hidden(1),
msg="Column should be visible again")
# rows
self.assertEqual("iris",
matrix.get_row_name(0),
msg="Row names differ")
self.assertEqual("anneal",
matrix.get_row_name(1),
msg="Row names differ")
self.assertIsNone(matrix.get_col_name(2),
msg="Row name should not exist")
matrix.set_row_name(0, "IRIS")
self.assertEqual("IRIS",
matrix.get_row_name(0),
msg="Row names differ")
self.assertFalse(matrix.is_row_hidden(1), msg="Row should be visible")
matrix.hide_row(1)
self.assertTrue(matrix.is_row_hidden(1), msg="Row should be hidden")
matrix.show_row(1)
self.assertFalse(matrix.is_row_hidden(1),
msg="Row should be visible again")
# mean
self.assertAlmostEqual(33.3,
matrix.get_mean(0, 0),
places=1,
msg="Means differ")
self.assertAlmostEqual(54.75,
matrix.average(0),
places=2,
msg="Averages differ")
matrix.set_mean(0, 0, 10)
self.assertAlmostEqual(10.0,
matrix.get_mean(0, 0),
places=1,
msg="Means differ")
# stdev
self.assertAlmostEqual(0.0,
matrix.get_stdev(0, 0),
places=1,
msg="Means differ")
matrix.set_stdev(0, 0, 0.3)
self.assertAlmostEqual(0.3,
matrix.get_stdev(0, 0),
places=1,
msg="Means differ")
