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