def test_select_sample_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_sample(
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 _check_obj_and_obj_jac_match(make_model_func, classification=False):
"""obj and obj_jac functions should return the same obj value."""
attrs = random.randint(1, 10)
outs = random.randint(1, 10)
model = make_model_func(attrs, random.randint(1, 10), outs)
if classification:
dataset = datasets.get_random_classification(10, attrs, outs)
else:
dataset = datasets.get_random_regression(10, attrs, outs)
# Don't use exactly the same parameters, to ensure obj functions are actually
# using the given parameters
parameters = random.uniform(-1.0,
1.0) * mlp._flatten(model._weight_matrices)
assert helpers.approx_equal(
mlp._mlp_obj(model, dataset[0], dataset[1], parameters),
mlp._mlp_obj_jac(model, dataset[0], dataset[1], parameters)[0])