def test_kfolds_should_not_have_repeated_indexes(self):
n_rows = 30
n_folds = 10
folds = kfolds(n_rows, n_folds)
for i in range(0, n_folds):
indexes = folds[i].train_index + folds[i].test_index
self.assertTrue(len(indexes) == len(set(indexes)))
def test_kfolds_should_split_data_evenly_if_n_rows_equals_n_folds(self):
n_rows_and_folds = 10
folds = kfolds(n_rows_and_folds, n_rows_and_folds)
self.assertEqual(len(folds), n_rows_and_folds)
for i in range(0, n_rows_and_folds):
self.assertEqual((len(folds[i].train_index)), 1)
self.assertEqual((len(folds[i].test_index)), 9)
def test_kfolds_should_split_data_evenly(self):
n_rows = 30
n_folds = 10
folds = kfolds(n_rows, n_folds)
self.assertEqual(len(folds), n_folds)
for i in range(0, n_folds):
self.assertEqual((len(folds[i].train_index)), 3)
self.assertEqual((len(folds[i].test_index)), 27)
def test_kfolds_should_account_for_uneven_data(self):
n_rows = 29
n_folds = 10
folds = kfolds(n_rows, n_folds)
self.assertEqual(len(folds), n_folds)
for i in range(0, n_folds - 1):
self.assertEqual((len(folds[i].train_index)), 3)
self.assertEqual((len(folds[i].test_index)), 26)
self.assertEqual((len(folds[n_folds - 1].train_index)), 2)
self.assertEqual((len(folds[n_folds - 1].test_index)), 27)
def test_kfolds_should_not_generate_test_folds_with_repeated_indexes(self):
n_rows = 30
n_folds = 10
folds = kfolds(n_rows, n_folds)
train_indexes = set()
test_indexes = set()
for i in range(0, n_folds):
train_indexes |= set(folds[i].train_index)
test_indexes |= set(folds[i].test_index)
self.assertEqual(len(train_indexes), n_rows)
self.assertEqual(len(test_indexes), n_rows)
def cross_validate(data, n_folds, n_iterations):
# TODO Should I make it so you can pass in a ML algorithm?
# TODO Should this be a part of the perceptron?
sum_accuracy = 0
perceptron = Perceptron(get_n_columns(data.features), data.feature_names)
folds = kfolds(get_n_rows(data.features), n_folds)
for fold in folds:
perceptron.train(fold.train_index, data, n_iterations)
accuracy = perceptron.test(fold.test_index, data)
sum_accuracy += accuracy
print('Accuracy: %.9f' % accuracy)
print('Average accuracy: %.9f' % (sum_accuracy / n_folds))
def test_kfolds_should_throw_exception_if_not_enough_data(self):
n_rows = 9
n_folds = 10
with self.assertRaises(ValueError):
kfolds(n_rows, n_folds)