def test1NaiveBayes(self):
fName = os.path.join(RDConfig.RDCodeDir, 'ML', 'NaiveBayes',
'test_data', 'stddata.csv')
data = DataUtils.TextFileToData(fName)
examples = data.GetNamedData()
nvars = data.GetNVars()
attrs = list(range(1, nvars + 1))
npvals = [0] + [3] * nvars + [2]
qBounds = [0] + [2] * nvars + [0]
mod, err = CrossValidate.CrossValidationDriver(examples,
attrs,
npvals,
qBounds,
silent=True)
self.assertAlmostEqual(mod._classProbs[0], 0.5000, 4)
self.assertAlmostEqual(mod._classProbs[1], 0.5000, 4)
self.assertAlmostEqual(mod._QBoundVals[1][0], -0.0360, 4)
self.assertAlmostEqual(mod._QBoundVals[1][1], 0.114)
self.assertAlmostEqual(mod._QBoundVals[2][0], -0.7022, 4)
self.assertAlmostEqual(mod._QBoundVals[2][1], -0.16635, 4)
self.assertAlmostEqual(mod._QBoundVals[3][0], -0.3659, 4)
self.assertAlmostEqual(mod._QBoundVals[3][1], 0.4305, 4)
self.assertAlmostEqual(err, 0.2121, 4)
mod, err = CrossValidate.CrossValidationDriver(examples,
attrs,
npvals,
qBounds,
silent=True,
calcTotalError=True)
self.assertAlmostEqual(mod._classProbs[0], 0.515151, 4)
self.assertAlmostEqual(mod._classProbs[1], 0.484848, 4)
self.assertAlmostEqual(mod._QBoundVals[1][0], -0.40315, 4)
self.assertAlmostEqual(mod._QBoundVals[1][1], 0.114)
self.assertAlmostEqual(mod._QBoundVals[2][0], -0.62185, 4)
self.assertAlmostEqual(mod._QBoundVals[2][1], -0.19965, 4)
self.assertAlmostEqual(mod._QBoundVals[3][0], 0.4305, 4)
self.assertAlmostEqual(mod._QBoundVals[3][1], 0.80305, 4)
self.assertAlmostEqual(err, 0.14563, 4)
mod, err = CrossValidate.CrossValidationDriver(
examples,
attrs,
npvals,
qBounds,
silent=True,
replacementSelection=True)
self.assertAlmostEqual(mod._classProbs[0], 0.5131578, 4)
self.assertAlmostEqual(mod._classProbs[1], 0.4868421, 4)
self.assertAlmostEqual(mod._QBoundVals[1][0], -0.036, 4)
self.assertAlmostEqual(mod._QBoundVals[1][1], 0.93465, 4)
self.assertAlmostEqual(mod._QBoundVals[2][0], -0.6696, 4)
self.assertAlmostEqual(mod._QBoundVals[2][1], -0.19965, 4)
self.assertAlmostEqual(mod._QBoundVals[3][0], -1.06785, 4)
self.assertAlmostEqual(mod._QBoundVals[3][1], 0.4305, 4)
self.assertAlmostEqual(err, 0.3, 4)
def test2NaiveBayes(self):
fName = os.path.join(RDConfig.RDCodeDir, 'ML', 'NaiveBayes',
'test_data', 'stddata.csv')
data = DataUtils.TextFileToData(fName)
examples = data.GetNamedData()
nvars = data.GetNVars()
attrs = list(range(1, nvars + 1))
npvals = [0] + [3] * nvars + [2]
qBounds = [0] + [2] * nvars + [0]
mod, err = CrossValidate.CrossValidationDriver(examples,
attrs,
npvals,
qBounds,
mEstimateVal=20.0,
silent=True)
self.assertTrue(isinstance(mod, NaiveBayesClassifier))
self.assertAlmostEqual(err, 0.1818, 4)
self.assertEqual(mod.GetName(), '')
mod.SetName('modelName')
self.assertEqual(mod.GetName(), 'modelName')
mod.NameModel(None)
self.assertEqual(mod.GetName(), 'NaiveBayesClassifier')
self.assertGreater(len(mod.GetExamples()), 0)
self.assertGreater(len(mod.GetTrainingExamples()), 0)
self.assertEqual(sorted(mod.GetTrainingExamples() + mod.GetExamples()),
sorted(examples))
def test1NaiveBayes(self):
fName = os.path.join(RDConfig.RDCodeDir, 'ML', 'NaiveBayes',
'test_data', 'stddata.csv')
data = DataUtils.TextFileToData(fName)
examples = data.GetNamedData()
nvars = data.GetNVars()
attrs = range(1, nvars + 1)
npvals = [0] + [3] * nvars + [2]
qBounds = [0] + [2] * nvars + [0]
mod, err = CrossValidate.CrossValidationDriver(examples,
attrs,
npvals,
qBounds,
silent=True)
self.assertAlmostEqual(mod._classProbs[0], 0.5000, 4)
self.assertAlmostEqual(mod._classProbs[1], 0.5000, 4)
self.assertAlmostEqual(mod._QBoundVals[1][0], -0.0360, 4)
self.assertAlmostEqual(mod._QBoundVals[1][1], 0.114)
self.assertAlmostEqual(mod._QBoundVals[2][0], -0.7022, 4)
self.assertAlmostEqual(mod._QBoundVals[2][1], -0.16635, 4)
self.assertAlmostEqual(mod._QBoundVals[3][0], -0.3659, 4)
self.assertAlmostEqual(mod._QBoundVals[3][1], 0.4305, 4)
self.assertAlmostEqual(err, 0.2121, 4)
def test2NaiveBayes(self):
fName = os.path.join(RDConfig.RDCodeDir, 'ML', 'NaiveBayes',
'test_data', 'stddata.csv')
data = DataUtils.TextFileToData(fName)
examples = data.GetNamedData()
nvars = data.GetNVars()
attrs = range(1, nvars + 1)
npvals = [0] + [3] * nvars + [2]
qBounds = [0] + [2] * nvars + [0]
mod, err = CrossValidate.CrossValidationDriver(examples,
attrs,
npvals,
qBounds,
mEstimateVal=20.0)
assert feq(err, 0.19354)
def test1NaiveBayes(self):
fName = os.path.join(RDConfig.RDCodeDir, 'ML', 'NaiveBayes',
'test_data', 'stddata.csv')
data = DataUtils.TextFileToData(fName)
examples = data.GetNamedData()
nvars = data.GetNVars()
attrs = range(1, nvars + 1)
npvals = [0] + [3] * nvars + [2]
qBounds = [0] + [2] * nvars + [0]
mod, err = CrossValidate.CrossValidationDriver(examples, attrs, npvals,
qBounds)
assert feq(mod._classProbs[0], 0.54167)
assert feq(mod._classProbs[1], 0.45833)
assert feq(mod._QBoundVals[1][0], -0.56995)
assert feq(mod._QBoundVals[1][1], 0.114)
assert feq(mod._QBoundVals[2][0], -0.7022)
assert feq(mod._QBoundVals[2][1], -0.2347)
assert feq(mod._QBoundVals[3][0], -0.3659)
assert feq(mod._QBoundVals[3][1], 1.17275)
assert feq(err, 0.16129)
