def test_train_valid_split_return_dataset(dataset):
splitter = RandomSplitter()
train, valid = splitter.train_valid_split(dataset, return_index=False)
assert type(train) == NumpyTupleDataset
assert type(valid) == NumpyTupleDataset
assert len(train) == 9
assert len(valid) == 1
def test_train_valid_test_split(dataset):
splitter = RandomSplitter()
train_ind, valid_ind, test_ind = splitter.train_valid_test_split(dataset)
assert type(train_ind) == numpy.ndarray
assert train_ind.shape[0] == 8
assert valid_ind.shape[0] == 1
assert test_ind.shape[0] == 1
def test_split_fail(dataset):
splitter = RandomSplitter()
with pytest.raises(AssertionError):
train_ind, valid_ind, test_ind = splitter._split(dataset,
frac_train=0.4,
frac_valid=0.3,
frac_test=0.2)
def test_split_fix_seed(dataset):
splitter = RandomSplitter()
train_ind1, valid_ind1, test_ind1 = splitter._split(dataset, seed=44)
train_ind2, valid_ind2, test_ind2 = splitter._split(dataset, seed=44)
assert numpy.array_equal(train_ind1, train_ind2)
assert numpy.array_equal(valid_ind1, valid_ind2)
assert numpy.array_equal(test_ind1, test_ind2)
def test_train_valid_test_split_ndarray_return_dataset(ndarray_dataset):
splitter = RandomSplitter()
train, valid, test = splitter.train_valid_test_split(ndarray_dataset,
return_index=False)
assert type(train) == numpy.ndarray
assert type(valid) == numpy.ndarray
assert type(test) == numpy.ndarray
assert len(train) == 8
assert len(valid) == 1
assert len(test) == 1
def test_split(dataset):
splitter = RandomSplitter()
train_ind, valid_ind, test_ind = splitter._split(dataset)
assert type(train_ind) == numpy.ndarray
assert train_ind.shape[0] == 8
assert valid_ind.shape[0] == 1
assert test_ind.shape[0] == 1
train_ind, valid_ind, test_ind = splitter._split(dataset,
frac_train=0.5,
frac_valid=0.3,
frac_test=0.2)
assert type(train_ind) == numpy.ndarray
assert train_ind.shape[0] == 5
assert valid_ind.shape[0] == 3
assert test_ind.shape[0] == 2