def test_split_in_three_unequal(self):
"""Tests a simple linear split into three unequal pieces."""
i = 0
expected = [0, 0, 0, 1, 1, 2, 2, 2, 2, 2]
for partition in partitioner(10, [1, 1, 2],
LinearFakeRandomFunction(10)):
self.assertEqual(expected[i], partition)
i += 1
def __init__(self,
dataset,
training_percent=0.8,
partitioning_function=pseudorandom_function):
self.dataset = dataset
self.training_percent = training_percent
self.n_of_elements = len(dataset)
self.partitioner = partitioner(
self.n_of_elements,
[training_percent, 1 - training_percent],
partitioning_function,
)
self.test_indexes = []
self.training_indexes = []
self.index = 0
def test_split_in_two(self):
"""Tests a simple linear split into two pieces."""
i = 0
for partition in partitioner(10, [1, 1], LinearFakeRandomFunction(10)):
self.assertEqual(i // 5, partition)
i += 1
def test_non_positive_n_of_elements(self):
"""Assert that using a weight thats' not positive is a failure."""
with self.assertRaises(AssertionError):
partitioner(0, [1, 1]).__next__()
def test_weight_sum_0(self):
"""Assert that using a weight thats' not positive is a failure."""
with self.assertRaises(AssertionError):
partitioner(10, [0, 0]).__next__()