def test_NaiveBayes(self):
table = SqlTable(
connection_params(),
"iris",
type_hints=Domain(
[],
DiscreteVariable("iris",
values=[
"Iris-setosa", "Iris-virginica",
"Iris-versicolor"
]),
),
)
table = preprocess.Discretize(table)
bayes = nb.NaiveBayesLearner()
clf = bayes(table)
# Single instance prediction
self.assertEqual(clf(table[0]), table[0].get_class())
# Table prediction
pred = clf(table)
actual = array([ins.get_class() for ins in table])
ca = pred == actual
ca = ca.sum() / len(ca)
self.assertGreater(ca, 0.95)
self.assertLess(ca, 1.0)
def test_PCA(self):
table = SqlTable(connection_params(), 'iris',
type_hints=Domain([], DiscreteVariable("iris",
values=['Iris-setosa', 'Iris-virginica',
'Iris-versicolor'])))
for batch_size in (50, 500):
rpca = RemotePCA(table, batch_size, 20)
self.assertEqual(rpca.components_.shape, (4, 4))
def test_PCA(self):
table = SqlTable(connection_params(),
'iris',
type_hints=Domain([],
DiscreteVariable(
"iris",
values=[
'Iris-setosa',
'Iris-virginica',
'Iris-versicolor'
])))
for batch_size in (50, 500):
rpca = RemotePCA(table, batch_size, 10)
self.assertEqual(rpca.components_.shape, (4, 4))
def test_NaiveBayes(self):
table = SqlTable(connection_params(), 'iris',
type_hints=Domain([], DiscreteVariable("iris",
values=['Iris-setosa', 'Iris-virginica',
'Iris-versicolor'])))
table = preprocess.Discretize(table)
bayes = nb.NaiveBayesLearner()
clf = bayes(table)
# Single instance prediction
self.assertEqual(clf(table[0]), table[0].get_class())
# Table prediction
pred = clf(table)
actual = array([ins.get_class() for ins in table])
ca = pred == actual
ca = ca.sum() / len(ca)
self.assertGreater(ca, 0.95)
self.assertLess(ca, 1.)