def test_select_random_size_none(monkeypatch):
# Monkeypatch so we know that random returns
monkeypatch.setattr(random, 'randint',
lambda x, y: 0) # randint always returns 0
input_matrix, target_matrix = datasets.get_xor()
new_inp_matrix, new_tar_matrix = base.select_random(
input_matrix, target_matrix)
assert new_inp_matrix.shape == input_matrix.shape
assert new_tar_matrix.shape == target_matrix.shape
for inp_vec in new_inp_matrix:
assert (inp_vec == input_matrix[0]).all() # due to monkeypatch
for tar_vec in new_tar_matrix:
assert (tar_vec == target_matrix[0]).all() # due to monkeypatch
def test_select_random_size_none(seed_random):
# Default to size smaller than number of samples
input_matrix, target_matrix = datasets.get_random_classification(
1000, 1, 2)
new_inp_matrix, new_tar_matrix = base.select_random(
input_matrix, target_matrix)
assert len(new_inp_matrix) < len(input_matrix)
assert len(new_inp_matrix) == base._selection_size_heuristic(
len(input_matrix))
assert len(new_tar_matrix) < len(input_matrix)
assert len(new_tar_matrix) == base._selection_size_heuristic(
len(input_matrix))
def test_select_random_size_none_few_samples(monkeypatch):
# When number of samples is low, default to all samples
# Monkeypatch so we know that random returns
# randint always returns 0
monkeypatch.setattr(random, 'randint', lambda x, y: 0)
input_matrix, target_matrix = datasets.get_xor()
new_inp_matrix, new_tar_matrix = base.select_random(
input_matrix, target_matrix)
assert new_inp_matrix.shape == input_matrix.shape
assert new_tar_matrix.shape == target_matrix.shape
for inp_vec in new_inp_matrix:
assert (inp_vec == input_matrix[0]).all() # Due to monkeypatch
for tar_vec in new_tar_matrix:
assert (tar_vec == target_matrix[0]).all() # Due to monkeypatch
def test_select_random(monkeypatch):
# Monkeypatch so we know that random returns
# randint always returns 0
monkeypatch.setattr(random, 'randint', lambda x, y: 0)
input_matrix, target_matrix = datasets.get_xor()
# Test size param
new_inp_matrix, new_tar_matrix = base.select_random(input_matrix,
target_matrix,
size=2)
assert new_inp_matrix.shape[0] == 2
assert new_tar_matrix.shape[0] == 2
for inp_vec in new_inp_matrix:
assert (inp_vec == input_matrix[0]).all() # Due to monkeypatch
for tar_vec in new_tar_matrix:
assert (tar_vec == target_matrix[0]).all() # Due to monkeypatch