def test_regression_split(reg_dataset):
splitter = StratifiedSplitter()
train_ind, valid_ind, test_ind = splitter._split(reg_dataset)
assert type(train_ind) == numpy.ndarray
assert train_ind.shape[0] == 80
assert valid_ind.shape[0] == 10
assert test_ind.shape[0] == 10
train = NumpyTupleDataset(*reg_dataset.features[train_ind])
valid = NumpyTupleDataset(*reg_dataset.features[valid_ind])
test = NumpyTupleDataset(*reg_dataset.features[test_ind])
assert 45.0 < train.features[:, -1].mean() < 55.0
assert 45.0 < valid.features[:, -1].mean() < 55.0
assert 45.0 < test.features[:, -1].mean() < 55.0
train_ind, valid_ind, test_ind = splitter._split(reg_dataset,
frac_train=0.5,
frac_valid=0.3,
frac_test=0.2)
assert type(train_ind) == numpy.ndarray
assert train_ind.shape[0] == 50
assert valid_ind.shape[0] == 30
assert test_ind.shape[0] == 20
train = NumpyTupleDataset(*reg_dataset.features[train_ind])
valid = NumpyTupleDataset(*reg_dataset.features[valid_ind])
test = NumpyTupleDataset(*reg_dataset.features[test_ind])
assert 45.0 < train.features[:, -1].mean() < 55.0
assert 45.0 < valid.features[:, -1].mean() < 55.0
assert 45.0 < test.features[:, -1].mean() < 55.0
def test_classification_split_by_labels_ndarray(cls_dataset, cls_label):
splitter = StratifiedSplitter()
train_ind, valid_ind, test_ind = splitter._split(cls_dataset,
labels=cls_label)
assert type(train_ind) == numpy.ndarray
assert train_ind.shape[0] == 24
assert valid_ind.shape[0] == 3
assert test_ind.shape[0] == 3
train = NumpyTupleDataset(*cls_dataset.features[train_ind])
valid = NumpyTupleDataset(*cls_dataset.features[valid_ind])
test = NumpyTupleDataset(*cls_dataset.features[test_ind])
assert (train.features[:, -1] == 1).sum() == 8
assert (valid.features[:, -1] == 1).sum() == 1
assert (test.features[:, -1] == 1).sum() == 1
train_ind, valid_ind, test_ind = splitter._split(cls_dataset,
labels=cls_label,
frac_train=0.5,
frac_valid=0.3,
frac_test=0.2)
assert type(train_ind) == numpy.ndarray
assert train_ind.shape[0] == 15
assert valid_ind.shape[0] == 9
assert test_ind.shape[0] == 6
train = NumpyTupleDataset(*cls_dataset.features[train_ind])
valid = NumpyTupleDataset(*cls_dataset.features[valid_ind])
test = NumpyTupleDataset(*cls_dataset.features[test_ind])
assert (train.features[:, -1] == 1).sum() == 5
assert (valid.features[:, -1] == 1).sum() == 3
assert (test.features[:, -1] == 1).sum() == 2
def test_split_fail_by_frac_ratio(cls_dataset):
splitter = StratifiedSplitter()
with pytest.raises(AssertionError):
train_ind, valid_ind, test_ind = splitter._split(cls_dataset,
frac_train=0.4,
frac_valid=0.3,
frac_test=0.2)
def test_regression_split(reg_dataset):
splitter = StratifiedSplitter()
train_ind, valid_ind, test_ind = splitter._split(reg_dataset)
assert type(train_ind) == numpy.ndarray
assert train_ind.shape[0] == 80
assert valid_ind.shape[0] == 10
assert test_ind.shape[0] == 10
train = NumpyTupleDataset(*reg_dataset.features[train_ind])
valid = NumpyTupleDataset(*reg_dataset.features[valid_ind])
test = NumpyTupleDataset(*reg_dataset.features[test_ind])
assert 45.0 < train.features[:, -1].mean() < 55.0
assert 45.0 < valid.features[:, -1].mean() < 55.0
assert 45.0 < test.features[:, -1].mean() < 55.0
train_ind, valid_ind, test_ind = splitter._split(reg_dataset,
frac_train=0.5,
frac_valid=0.3,
frac_test=0.2)
assert type(train_ind) == numpy.ndarray
assert train_ind.shape[0] == 50
assert valid_ind.shape[0] == 30
assert test_ind.shape[0] == 20
train = NumpyTupleDataset(*reg_dataset.features[train_ind])
valid = NumpyTupleDataset(*reg_dataset.features[valid_ind])
test = NumpyTupleDataset(*reg_dataset.features[test_ind])
assert 45.0 < train.features[:, -1].mean() < 55.0
assert 45.0 < valid.features[:, -1].mean() < 55.0
assert 45.0 < test.features[:, -1].mean() < 55.0
def test_classification_split_by_labels_ndarray(cls_dataset, cls_label):
splitter = StratifiedSplitter()
train_ind, valid_ind, test_ind = splitter._split(cls_dataset,
labels=cls_label)
assert type(train_ind) == numpy.ndarray
assert train_ind.shape[0] == 24
assert valid_ind.shape[0] == 3
assert test_ind.shape[0] == 3
train = NumpyTupleDataset(*cls_dataset.features[train_ind])
valid = NumpyTupleDataset(*cls_dataset.features[valid_ind])
test = NumpyTupleDataset(*cls_dataset.features[test_ind])
assert (train.features[:, -1] == 1).sum() == 8
assert (valid.features[:, -1] == 1).sum() == 1
assert (test.features[:, -1] == 1).sum() == 1
train_ind, valid_ind, test_ind = splitter._split(cls_dataset,
labels=cls_label,
frac_train=0.5,
frac_valid=0.3,
frac_test=0.2)
assert type(train_ind) == numpy.ndarray
assert train_ind.shape[0] == 15
assert valid_ind.shape[0] == 9
assert test_ind.shape[0] == 6
train = NumpyTupleDataset(*cls_dataset.features[train_ind])
valid = NumpyTupleDataset(*cls_dataset.features[valid_ind])
test = NumpyTupleDataset(*cls_dataset.features[test_ind])
assert (train.features[:, -1] == 1).sum() == 5
assert (valid.features[:, -1] == 1).sum() == 3
assert (test.features[:, -1] == 1).sum() == 2
def test_split_fail_by_frac_ratio(cls_dataset):
splitter = StratifiedSplitter()
with pytest.raises(AssertionError):
train_ind, valid_ind, test_ind = splitter._split(cls_dataset,
frac_train=0.4,
frac_valid=0.3,
frac_test=0.2)
def test_split_fail_by_invalid_task_type(cls_dataset):
splitter = StratifiedSplitter()
with pytest.raises(ValueError):
train_ind, valid_ind, test_ind = splitter._split(cls_dataset,
frac_train=0.5,
frac_valid=0.3,
frac_test=0.2,
task_type='mix')
def test_split_fail_by_invalid_task_type(cls_dataset):
splitter = StratifiedSplitter()
with pytest.raises(ValueError):
train_ind, valid_ind, test_ind = splitter._split(cls_dataset,
frac_train=0.5,
frac_valid=0.3,
frac_test=0.2,
task_type='mix')
def test_regression_split_fix_seed(reg_dataset):
splitter = StratifiedSplitter()
train_ind1, valid_ind1, test_ind1 = splitter._split(reg_dataset, seed=44)
train_ind2, valid_ind2, test_ind2 = splitter._split(reg_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_regression_split_fix_seed(reg_dataset):
splitter = StratifiedSplitter()
train_ind1, valid_ind1, test_ind1 = splitter._split(reg_dataset, seed=44)
train_ind2, valid_ind2, test_ind2 = splitter._split(reg_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_regression_split_return_dataset(reg_dataset):
splitter = StratifiedSplitter()
train, valid = splitter.train_valid_split(reg_dataset, return_index=False)
assert type(train) == NumpyTupleDataset
assert type(valid) == NumpyTupleDataset
assert len(train) == 90
assert len(valid) == 10
assert 45.0 < train.features[:, -1].mean() < 55.0
assert 45.0 < valid.features[:, -1].mean() < 55.0
def test_train_valid_classification_split_return_dataset(cls_dataset):
splitter = StratifiedSplitter()
train, valid = splitter.train_valid_split(cls_dataset, return_index=False)
assert type(train) == NumpyTupleDataset
assert type(valid) == NumpyTupleDataset
assert len(train) == 27
assert len(valid) == 3
assert (train.features[:, -1] == 1).sum() == 9
assert (valid.features[:, -1] == 1).sum() == 1
def test_train_valid_classification_split_return_dataset(cls_dataset):
splitter = StratifiedSplitter()
train, valid = splitter.train_valid_split(cls_dataset, return_index=False)
assert type(train) == NumpyTupleDataset
assert type(valid) == NumpyTupleDataset
assert len(train) == 27
assert len(valid) == 3
assert (train.features[:, -1] == 1).sum() == 9
assert (valid.features[:, -1] == 1).sum() == 1
def test_train_valid_regression_split_return_dataset(reg_dataset):
splitter = StratifiedSplitter()
train, valid = splitter.train_valid_split(reg_dataset, return_index=False)
assert type(train) == NumpyTupleDataset
assert type(valid) == NumpyTupleDataset
assert len(train) == 90
assert len(valid) == 10
assert 45.0 < train.features[:, -1].mean() < 55.0
assert 45.0 < valid.features[:, -1].mean() < 55.0
def test_train_valid_classification_split(cls_dataset):
splitter = StratifiedSplitter()
train_ind, valid_ind = splitter.train_valid_split(cls_dataset)
assert type(train_ind) == numpy.ndarray
assert train_ind.shape[0] == 27
assert valid_ind.shape[0] == 3
train = NumpyTupleDataset(*cls_dataset.features[train_ind])
valid = NumpyTupleDataset(*cls_dataset.features[valid_ind])
assert (train.features[:, -1] == 1).sum() == 9
assert (valid.features[:, -1] == 1).sum() == 1
def test_train_valid_regression_split(reg_dataset):
splitter = StratifiedSplitter()
train_ind, valid_ind = splitter.train_valid_split(reg_dataset)
assert type(train_ind) == numpy.ndarray
assert train_ind.shape[0] == 90
assert valid_ind.shape[0] == 10
train = NumpyTupleDataset(*reg_dataset.features[train_ind])
valid = NumpyTupleDataset(*reg_dataset.features[valid_ind])
assert 45.0 < train.features[:, -1].mean() < 55.0
assert 45.0 < valid.features[:, -1].mean() < 55.0
def test_train_valid_classification_split(cls_dataset):
splitter = StratifiedSplitter()
train_ind, valid_ind = splitter.train_valid_split(cls_dataset)
assert type(train_ind) == numpy.ndarray
assert train_ind.shape[0] == 27
assert valid_ind.shape[0] == 3
train = NumpyTupleDataset(*cls_dataset.features[train_ind])
valid = NumpyTupleDataset(*cls_dataset.features[valid_ind])
assert (train.features[:, -1] == 1).sum() == 9
assert (valid.features[:, -1] == 1).sum() == 1
def test_train_valid_regression_split(reg_dataset):
splitter = StratifiedSplitter()
train_ind, valid_ind = splitter.train_valid_split(reg_dataset)
assert type(train_ind) == numpy.ndarray
assert train_ind.shape[0] == 90
assert valid_ind.shape[0] == 10
train = NumpyTupleDataset(*reg_dataset.features[train_ind])
valid = NumpyTupleDataset(*reg_dataset.features[valid_ind])
assert 45.0 < train.features[:, -1].mean() < 55.0
assert 45.0 < valid.features[:, -1].mean() < 55.0
def test_train_valid_classification_split_ndarray_return_dataset(
cls_ndarray_dataset):
cls_dataset, cls_label = cls_ndarray_dataset
splitter = StratifiedSplitter()
train, valid = splitter.train_valid_split(cls_dataset, labels=cls_label,
return_index=False)
assert type(train) == numpy.ndarray
assert type(valid) == numpy.ndarray
assert len(train) == 27
assert len(valid) == 3
assert (train == 1).sum() == 9
assert (valid == 1).sum() == 1
def test_train_valid_classification_split_ndarray_return_dataset(
cls_ndarray_dataset):
cls_dataset, cls_label = cls_ndarray_dataset
splitter = StratifiedSplitter()
train, valid = splitter.train_valid_split(cls_dataset,
labels=cls_label,
return_index=False)
assert type(train) == numpy.ndarray
assert type(valid) == numpy.ndarray
assert len(train) == 27
assert len(valid) == 3
assert (train == 1).sum() == 9
assert (valid == 1).sum() == 1
def test_train_valid_test_cls_split_by_labels_return_dataset(
cls_dataset, cls_label):
splitter = StratifiedSplitter()
train, valid, test = splitter.train_valid_test_split(cls_dataset,
labels=cls_label,
return_index=False)
assert type(train) == NumpyTupleDataset
assert type(valid) == NumpyTupleDataset
assert type(test) == NumpyTupleDataset
assert len(train) == 24
assert len(valid) == 3
assert len(test) == 3
assert (train.features[:, -1] == 1).sum() == 8
assert (valid.features[:, -1] == 1).sum() == 1
assert (test.features[:, -1] == 1).sum() == 1
def test_train_valid_test_cls_split_by_labels_return_dataset(cls_dataset,
cls_label):
splitter = StratifiedSplitter()
train, valid, test = splitter.train_valid_test_split(cls_dataset,
labels=cls_label,
return_index=False)
assert type(train) == NumpyTupleDataset
assert type(valid) == NumpyTupleDataset
assert type(test) == NumpyTupleDataset
assert len(train) == 24
assert len(valid) == 3
assert len(test) == 3
assert (train.features[:, -1] == 1).sum() == 8
assert (valid.features[:, -1] == 1).sum() == 1
assert (test.features[:, -1] == 1).sum() == 1