def testNearZeroObjectivesAndDirectionCoordinates(self):
multi_objectives = tf.constant([[3, 0, 0]], dtype=tf.float32)
# direction with small non-zero coordinates.
nonzero = 2 * multi_objective_scalarizer.HyperVolumeScalarizer.ALMOST_ZERO
direction = [1, nonzero, nonzero]
scalarizer = multi_objective_scalarizer.HyperVolumeScalarizer(
direction, self._hv_params)
self.assertAllClose(self.evaluate(scalarizer(multi_objectives)),
self.evaluate(tf.constant([0], dtype=tf.float32)))
# direction with coordinates that are so small in magnitude that would be
# considered zeros.
direction = [1, 1e-30, 1e-30]
scalarizer = multi_objective_scalarizer.HyperVolumeScalarizer(
direction, self._hv_params)
self.assertAllClose(self.evaluate(scalarizer(multi_objectives)),
self.evaluate(tf.constant([3], dtype=tf.float32)))
# direction with absolute zero coordinates.
direction = [1, 0, 0]
scalarizer = multi_objective_scalarizer.HyperVolumeScalarizer(
direction, self._hv_params)
self.assertAllClose(self.evaluate(scalarizer(multi_objectives)),
self.evaluate(tf.constant([3], dtype=tf.float32)))
def testInvalidObjectives(self):
with self.assertRaisesRegex(
ValueError, 'The number of input objectives should be'):
direction = [1, 0, 0]
scalarizer = multi_objective_scalarizer.HyperVolumeScalarizer(
direction, self._hv_params)
multi_objectives = tf.constant([[1, 2, 3, 4]], dtype=tf.float32)
scalarizer(multi_objectives)
def testNegativeObjectives(self):
direction = [1, 0, 0]
scalarizer = multi_objective_scalarizer.HyperVolumeScalarizer(
direction, self._hv_params)
multi_objectives = tf.constant([[-3, 2, 1], [-6, 5, 4]],
dtype=tf.float32)
self.assertAllEqual(
self.evaluate(scalarizer(multi_objectives)),
self.evaluate(tf.constant([0, 0], dtype=tf.float32)))
def testDirectionNormalization(self):
multi_objectives = tf.constant([[3, 2, 1], [6, 5, 4]],
dtype=tf.float32)
for direction in [[1, 2, 2], [0.1, 0.2, 0.2]]:
scalarizer = multi_objective_scalarizer.HyperVolumeScalarizer(
direction, self._hv_params)
self.assertAllClose(
self.evaluate(scalarizer(multi_objectives)),
self.evaluate(tf.constant([1.5, 6], dtype=tf.float32)))
def testAxisAlignedDirection(self):
direction = [0, 1, 0]
scalarizer = multi_objective_scalarizer.HyperVolumeScalarizer(
direction, self._hv_params)
multi_objectives = tf.constant([[3, 2, 1], [6, 5, 4]],
dtype=tf.float32)
self.assertAllEqual(
self.evaluate(scalarizer(multi_objectives)),
self.evaluate(tf.constant([2, 5], dtype=tf.float32)))
def testObjectiveTranformation(self):
hv_params = copy.deepcopy(self._hv_params)
hv_params[0] = multi_objective_scalarizer.HyperVolumeScalarizer.PARAMS(
slope=-1, offset=2)
direction = [1, 0, 0]
scalarizer = multi_objective_scalarizer.HyperVolumeScalarizer(
direction, hv_params)
multi_objectives = tf.constant([[-3, 2, 1], [-6, 5, 4]],
dtype=tf.float32)
self.assertAllEqual(
self.evaluate(scalarizer(multi_objectives)),
self.evaluate(tf.constant([5, 8], dtype=tf.float32)))
def testDirectionWithWrongDimension(self):
with self.assertRaisesRegex(ValueError,
'direction has 2 elements but'):
direction = [1, 0]
multi_objective_scalarizer.HyperVolumeScalarizer(
direction, self._hv_params)
def testZeroLengthDirection(self):
with self.assertRaisesRegex(ValueError, 'nearly-zero vector'):
direction = [0, 0, 0]
multi_objective_scalarizer.HyperVolumeScalarizer(
direction, self._hv_params)
def testDirectionWithNegativeCoordinates(self):
with self.assertRaisesRegex(ValueError, 'has negative coordinates'):
direction = [-1, 0, 0]
multi_objective_scalarizer.HyperVolumeScalarizer(
direction, self._hv_params)
def testInvalidParams(self):
with self.assertRaisesRegex(ValueError, 'nearly-zero vector'):
multi_objective_scalarizer.HyperVolumeScalarizer([], [])
with self.assertRaisesRegex(ValueError, 'at least two objectives'):
multi_objective_scalarizer.HyperVolumeScalarizer(
[1], [self._hv_params[0]])
