def test_tfdata(self):
ground_truth_data = dummy_data.DummyData()
dataset = util.tf_data_set_from_ground_truth_data(ground_truth_data, 0)
one_shot_iterator = dataset.make_one_shot_iterator()
next_element = one_shot_iterator.get_next()
with self.test_session() as sess:
for _ in range(10):
sess.run(next_element)
def test_metric(self):
gin.bind_parameter("predictor.predictor_fn",
utils.gradient_boosting_classifier)
ground_truth_data = dummy_data.DummyData()
def representation_function(x):
return np.array(x, dtype=np.float64)[:, :, 0, 0]
random_state = np.random.RandomState(0)
_ = fairness.compute_fairness(ground_truth_data,
representation_function, random_state,
None, 1000, 1000)
def get_named_ground_truth_data(name):
"""Returns ground truth data set based on name.
Args:
name: String with the name of the dataset.
Raises:
ValueError: if an invalid data set name is provided.
"""
if name == "dsprites_full":
return dsprites.DSprites([1, 2, 3, 4, 5])
elif name == "dsprites_noshape":
return dsprites.DSprites([2, 3, 4, 5])
elif name == "color_dsprites":
return dsprites.ColorDSprites([1, 2, 3, 4, 5])
elif name == "noisy_dsprites":
return dsprites.NoisyDSprites([1, 2, 3, 4, 5])
elif name == "scream_dsprites":
return dsprites.ScreamDSprites([1, 2, 3, 4, 5])
elif name == "smallnorb":
return norb.SmallNORB()
elif name == "cars3d":
return cars3d.Cars3D()
elif name == "mpi3d_toy":
return mpi3d.MPI3D(mode="mpi3d_toy")
elif name == "mpi3d_realistic":
return mpi3d.MPI3D(mode="mpi3d_realistic")
elif name == "mpi3d_real":
return mpi3d.MPI3D(mode="mpi3d_real")
elif name == "shapes3d":
return shapes3d.Shapes3D()
elif name == "dummy_data":
return dummy_data.DummyData()
else:
raise ValueError("Invalid data set name.")