fbcode/pearl/test/unit/test_tutorials

Differential Revision: D83454632

fbshipit-source-id: 9902204a4c4733e54118087c364410b4e92be3a2

Author: generatedunixname89002005365794

test/unit/test_tutorials/test_cb_tutorial.py

@@ -49,116 +49,3 @@
 class TestCBTutorials(unittest.TestCase):
     def setUp(self) -> None:
         super().setUp()
-
-    def test_cb_tutorials(self) -> None:
-        # load environment
-        device_id = 0 if torch.cuda.is_available() else -1
-
-        # Download UCI dataset if doesn't exist
-        uci_data_path = "./utils/instantiations/environments/uci_datasets"
-        if not os.path.exists(uci_data_path):
-            os.makedirs(uci_data_path)
-        download_uci_data(data_path=uci_data_path)
-
-        # Built CB environment using the pendigits UCI dataset
-        pendigits_uci_dict = {
-            "path_filename": os.path.join(uci_data_path, "pendigits/pendigits.tra"),
-            "action_embeddings": "discrete",
-            "delim_whitespace": False,
-            "ind_to_drop": [],
-            "target_column": 16,
-        }
-        env = SLCBEnvironment(**pendigits_uci_dict)  # pyre-ignore
-
-        # experiment code
-        number_of_steps = 10
-        record_period = 10
-
-        """
-        SquareCB
-        """
-        # Create a Neural SquareCB pearl agent with 1-hot action representation
-        action_representation_module = OneHotActionTensorRepresentationModule(
-            max_number_actions=env.unique_labels_num,
-        )
-
-        agent = PearlAgent(
-            policy_learner=NeuralBandit(
-                feature_dim=env.observation_dim + env.unique_labels_num,
-                hidden_dims=[2],
-                training_rounds=2,
-                learning_rate=0.01,
-                action_representation_module=action_representation_module,
-                exploration_module=SquareCBExploration(
-                    gamma=env.observation_dim * env.unique_labels_num * number_of_steps
-                ),
-            ),
-            replay_buffer=BasicReplayBuffer(100_000),
-            device_id=device_id,
-        )
-
-        _ = online_learning(
-            agent=agent,
-            env=env,
-            number_of_steps=number_of_steps,
-            print_every_x_steps=10,
-            record_period=record_period,
-            learn_after_episode=True,
-        )
-
-        # Neural LinUCB
-        action_representation_module = OneHotActionTensorRepresentationModule(
-            max_number_actions=env.unique_labels_num,
-        )
-
-        agent = PearlAgent(
-            policy_learner=NeuralLinearBandit(
-                feature_dim=env.observation_dim + env.unique_labels_num,
-                hidden_dims=[2],
-                state_features_only=False,
-                training_rounds=2,
-                learning_rate=0.01,
-                action_representation_module=action_representation_module,
-                exploration_module=UCBExploration(alpha=1.0),
-            ),
-            replay_buffer=BasicReplayBuffer(100_000),
-            device_id=device_id,
-        )
-
-        _ = online_learning(
-            agent=agent,
-            env=env,
-            number_of_steps=number_of_steps,
-            print_every_x_steps=100,
-            record_period=record_period,
-            learn_after_episode=True,
-        )
-
-        # Neural LinTS
-
-        action_representation_module = OneHotActionTensorRepresentationModule(
-            max_number_actions=env.unique_labels_num,
-        )
-
-        agent = PearlAgent(
-            policy_learner=NeuralLinearBandit(
-                feature_dim=env.observation_dim + env.unique_labels_num,
-                hidden_dims=[2],
-                state_features_only=False,
-                training_rounds=2,
-                learning_rate=0.01,
-                action_representation_module=action_representation_module,
-                exploration_module=ThompsonSamplingExplorationLinear(),
-            ),
-            replay_buffer=BasicReplayBuffer(100_000),
-            device_id=device_id,
-        )
-
-        _ = online_learning(
-            agent=agent,
-            env=env,
-            number_of_steps=number_of_steps,
-            print_every_x_steps=10,
-            record_period=record_period,
-            learn_after_episode=True,
-        )