def test_store_data(self):
t = self.random_table
learners = [NaiveBayesLearner()]
res = LeaveOneOut(store_data=False)(t, learners)
self.assertIsNone(res.data)
res = LeaveOneOut(store_data=True)(t, learners)
self.assertIs(res.data, t)
def test_store_models(self):
t = self.random_table
learners = [NaiveBayesLearner(), MajorityLearner()]
res = LeaveOneOut()(t, learners)
self.assertIsNone(res.models)
res = LeaveOneOut(store_models=True)(t, learners)
self.check_models(res, learners, self.nrows)
def test_store_data(self):
nrows, ncols = 50, 10
t = random_data(nrows, ncols)
learners = [NaiveBayesLearner()]
res = LeaveOneOut(t, learners)
self.assertIsNone(res.data)
res = LeaveOneOut(t, learners, store_data=True)
self.assertIs(res.data, t)
def test_store_models(self):
nrows, ncols = 50, 10
t = random_data(nrows, ncols)
learners = [NaiveBayesLearner(), MajorityLearner()]
res = LeaveOneOut(t, learners)
self.assertIsNone(res.models)
res = LeaveOneOut(t, learners, store_models=True)
self.assertEqual(len(res.models), 50)
for models in res.models:
self.assertEqual(len(models), 2)
self.assertIsInstance(models[0], NaiveBayesModel)
self.assertIsInstance(models[1], ConstantModel)
def test_miss_majority():
x = np.zeros((50, 3))
y = x[:, -1]
x[49] = 1
data = Table(x, y)
res = LeaveOneOut()(data, [MajorityLearner()])
np.testing.assert_equal(res.predicted[0][:49], 0)
x[49] = 0
res = LeaveOneOut()(data, [MajorityLearner()])
np.testing.assert_equal(res.predicted[0][:49], 0)
x[25:] = 1
data = Table(x, y)
res = LeaveOneOut()(data, [MajorityLearner()])
np.testing.assert_equal(res.predicted[0],
1 - data.Y[res.row_indices].flatten())
def test_call(self):
nrows = self.nrows
t = self.random_table
res = LeaveOneOut()(t, [NaiveBayesLearner()])
y = t.Y
np.testing.assert_equal(res.actual, y[res.row_indices].reshape(nrows))
np.testing.assert_equal(res.predicted[0],
y[res.row_indices].reshape(nrows))
np.testing.assert_equal(np.argmax(res.probabilities[0], axis=1),
y[res.row_indices].reshape(nrows))
def test_call(self):
nrows, ncols = 100, 10
t = random_data(nrows, ncols)
res = LeaveOneOut(t, [NaiveBayesLearner()])
y = t.Y
np.testing.assert_equal(res.actual, y[res.row_indices].reshape(nrows))
np.testing.assert_equal(res.predicted[0],
y[res.row_indices].reshape(nrows))
np.testing.assert_equal(np.argmax(res.probabilities[0], axis=1),
y[res.row_indices].reshape(nrows))
def test_probs(self):
data = Table('iris')[30:130]
learners = [MajorityLearner(), MajorityLearner()]
results = LeaveOneOut()(data, learners)
self.assertEqual(results.predicted.shape, (2, len(data)))
np.testing.assert_equal(results.predicted, np.ones((2, 100)))
probs = results.probabilities
self.assertTrue((probs[:, :, 0] < probs[:, :, 2]).all())
self.assertTrue((probs[:, :, 2] < probs[:, :, 1]).all())