def test_recsim_interest_evolution(self):
num_candidate = 10
env_config = {
"num_candidates": num_candidate,
"slate_size": 3,
"resample_documents": False,
"seed": 1,
}
env = interest_evolution.create_environment(env_config)
env = ValueWrapper(env, ValueMode.INNER_PROD)
obs_preprocessor = make_default_obs_preprocessor(env)
obs = env.reset()
state = obs_preprocessor(obs)
self.assertFalse(state.has_float_features_only)
self.assertEqual(state.float_features.shape, (1, obs["user"].shape[0]))
self.assertEqual(state.float_features.dtype, torch.float32)
self.assertEqual(state.float_features.device, torch.device("cpu"))
npt.assert_array_almost_equal(obs["user"], state.float_features.squeeze(0))
doc_float_features = state.candidate_docs.float_features
self.assertIsNotNone(doc_float_features)
self.assertEqual(
doc_float_features.shape, (1, num_candidate, obs["doc"]["0"].shape[0])
)
self.assertEqual(doc_float_features.dtype, torch.float32)
self.assertEqual(doc_float_features.device, torch.device("cpu"))
for i, v in enumerate(obs["doc"].values()):
npt.assert_array_almost_equal(v, doc_float_features[0, i])
def test_box(self):
env = gym.make("CartPole-v0")
obs_preprocessor = make_default_obs_preprocessor(env)
obs = env.reset()
state = obs_preprocessor(obs)
self.assertTrue(state.has_float_features_only)
self.assertEqual(state.float_features.shape, (1, obs.shape[0]))
self.assertEqual(state.float_features.dtype, torch.float32)
self.assertEqual(state.float_features.device, torch.device("cpu"))
npt.assert_array_almost_equal(obs, state.float_features.squeeze(0))
def test_box_cuda(self):
env = gym.make("CartPole-v0")
device = torch.device("cuda")
obs_preprocessor = make_default_obs_preprocessor(env, device=device)
obs = env.reset()
state = obs_preprocessor(obs)
self.assertTrue(state.has_float_features_only)
self.assertEqual(state.float_features.shape, (1, obs.shape[0]))
self.assertEqual(state.float_features.dtype, torch.float32)
# `device` doesn't have index. So we need this.
x = torch.zeros(1, device=device)
self.assertEqual(state.float_features.device, x.device)
npt.assert_array_almost_equal(obs, state.float_features.cpu().squeeze(0))
def test_recsim_interest_exploration(self):
num_candidate = 10
env_config = {
"num_candidates": num_candidate,
"slate_size": 3,
"resample_documents": False,
"seed": 1,
}
env = interest_exploration.create_environment(env_config)
env = ValueWrapper(env, ValueMode.CONST)
obs_preprocessor = make_default_obs_preprocessor(env)
obs = env.reset()
state = obs_preprocessor(obs)
self.assertFalse(state.has_float_features_only)
self.assertEqual(state.float_features.shape, (1, obs["user"].shape[0]))
self.assertEqual(state.float_features.dtype, torch.float32)
self.assertEqual(state.float_features.device, torch.device("cpu"))
npt.assert_array_almost_equal(obs["user"], state.float_features.squeeze(0))
doc_float_features = state.candidate_docs.float_features
self.assertIsNotNone(doc_float_features)
quality_len = 1
expected_doc_feature_length = (
env.observation_space["doc"]["0"]["cluster_id"].n + quality_len
)
self.assertEqual(
doc_float_features.shape, (1, num_candidate, expected_doc_feature_length)
)
self.assertEqual(doc_float_features.dtype, torch.float32)
self.assertEqual(doc_float_features.device, torch.device("cpu"))
for i, v in enumerate(obs["doc"].values()):
expected_doc_feature = torch.cat(
[
F.one_hot(torch.tensor(v["cluster_id"]), 2).float(),
# This needs unsqueeze because it's a scalar
torch.tensor(v["quality"]).unsqueeze(0).float(),
],
dim=0,
)
npt.assert_array_almost_equal(
expected_doc_feature, doc_float_features[0, i]
)