def test_bayes(self):
x = np.random.random_integers(1, 3, (100, 5))
col = np.random.randint(5)
y = x[:, col].copy().reshape(100, 1)
t = Table(x, y)
t = discretization.DiscretizeTable(t, method=EqualWidth(n=3))
res = testing.TestOnTrainingData(t, [naive_bayes.NaiveBayesLearner()])
np.testing.assert_almost_equal(scoring.CA(res), [1])
t.Y[-20:] = 4 - t.Y[-20:]
res = testing.TestOnTrainingData(t, [naive_bayes.NaiveBayesLearner()])
self.assertGreaterEqual(scoring.CA(res)[0], 0.75)
self.assertLess(scoring.CA(res)[0], 1)
def test_NaiveBayes(self):
table = Orange.data.Table('titanic')
bayes = nb.NaiveBayesLearner()
results = testing.CrossValidation(table[::20], [bayes], k=10)
ca = scoring.CA(results)
self.assertGreater(ca, 0.7)
self.assertLess(ca, 0.9)
def test_predict_numpy(self):
table = Orange.data.Table('titanic')
bayes = nb.NaiveBayesLearner()
c = bayes(table)
X = table.X[::20]
c(X)
vals, probs = c(X, c.ValueProbs)
def test_predict_table(self):
table = Orange.data.Table('titanic')
bayes = nb.NaiveBayesLearner()
c = bayes(table)
table = table[::20]
c(table)
vals, probs = c(table, c.ValueProbs)
def test_predict_single_instance(self):
table = Orange.data.Table('titanic')
bayes = nb.NaiveBayesLearner()
c = bayes(table)
for ins in table[::20]:
c(ins)
val, prob = c(ins, c.ValueProbs)
def test_predict_single_instance(self):
table = data.Table("titanic")
learn = nb.NaiveBayesLearner()
clf = learn(table)
pred = []
for row in table:
pred.append(clf(row))
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 main():
from Orange.classification import \
logistic_regression as lr, naive_bayes as nb
app = QtGui.QApplication([])
data = Orange.data.Table("iris")
w = OWTestLearners()
w.show()
w.set_train_data(data)
w.set_test_data(data)
w.set_learner(lr.LogisticRegressionLearner(), 1)
w.set_learner(nb.NaiveBayesLearner(), 2)
w.handleNewSignals()
return app.exec_()
def test_NaiveBayes(self):
table = SqlTable(dict(host='localhost', database='test'), '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.)
def test_NaiveBayes(self):
iris_v = ['Iris-setosa', 'Iris-virginica', 'Iris-versicolor']
table = SqlTable(self.conn,
self.iris,
type_hints=Domain([],
DiscreteVariable("iris",
values=iris_v)))
disc = preprocess.Discretize()
table = disc(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.)
