def test_safe_indexing(self):
npt.assert_array_equal(
safe_indexing(np.asarray([3, 2, 1]), np.asarray([1, 2])),
np.asarray([2, 1]))
npt.assert_array_equal(safe_indexing(np.asarray([3, 2, 1]), [1, 2]),
np.asarray([2, 1]))
self.assertListEqual(safe_indexing([3, 2, 1], [1, 2]), [2, 1])
def test_safe_indexing():
assert all([
a == b for a, b in zip(
safe_indexing(np.asarray([3, 2, 1]), np.asarray([1, 2])),
np.asarray([2, 1]))
])
assert all([
a == b for a, b in zip(safe_indexing(np.asarray([3, 2, 1]), [1, 2]),
np.asarray([2, 1]))
])
assert all(
[a == b for a, b in zip(safe_indexing([3, 2, 1], [1, 2]), [2, 1])])
def _split(self):
data_idx = self.rng.permutation(len(self._data))
train_idx = data_idx[:self.train_size]
test_idx = data_idx[-self.test_size:]
val_idx = data_idx[self.train_size:-self.test_size]
train_data = safe_indexing(self._data, train_idx)
test_data = safe_indexing(self._data, test_idx)
val_data = safe_indexing(self._data, val_idx) if len(
val_idx) > 0 else None
self._build_stratified_datasets(train_data=train_data,
test_data=test_data,
val_data=val_data)