#13 Add mock example

Author: pockerman

src/apps/qlearning_on_mock.py

@@ -0,0 +1,73 @@
+from src.algorithms.q_learning import QLearning, QLearnConfig
+from src.algorithms.trainer import Trainer
+from src.utils.string_distance_calculator import DistanceType
+from src.spaces.actions import ActionSuppress, ActionIdentity, ActionGeneralize, ActionTransform
+from src.spaces.environment import Environment
+from src.spaces.action_space import ActionSpace
+from src.datasets.datasets_loaders import MockSubjectsLoader
+from src.utils.reward_manager import RewardManager
+from src.policies.epsilon_greedy_policy import EpsilonGreedyPolicy, EpsilonDecreaseOption
+from src.utils.serial_hierarchy import SerialHierarchy
+
+
+if __name__ == '__main__':
+
+    EPS = 1.0
+    GAMMA = 0.99
+    ALPHA = 0.1
+
+    # load the dataset
+    ds = MockSubjectsLoader()
+
+    # specify the action space. We need to establish how these actions
+    # are performed
+    action_space = ActionSpace(n=4)
+
+    generalization_table = {"Mixed White/Asian": SerialHierarchy(values=["Mixed", ]),
+                            "Chinese": SerialHierarchy(values=["Asian", ]),
+                            "Indian": SerialHierarchy(values=["Asian", ]),
+                            "Mixed White/Black African": SerialHierarchy(values=["Mixed", ]),
+                            "Black African": SerialHierarchy(values=["Black", ]),
+                            "Asian other": SerialHierarchy(values=["Asian", ]),
+                            "Black other": SerialHierarchy(values=["Black", ]),
+                            "Mixed White/Black Caribbean": SerialHierarchy(values=["Mixed", ]),
+                            "Mixed other": SerialHierarchy(values=["Mixed", ]),
+                            "Arab": SerialHierarchy(values=["Asian", ]),
+                            "White Irish": SerialHierarchy(values=["White", ]),
+                            "Not stated": SerialHierarchy(values=["Not stated"]),
+                            "White Gypsy/Traveller": SerialHierarchy(values=["White", ]),
+                            "White British": SerialHierarchy(values=["White", ]),
+                            "Bangladeshi": SerialHierarchy(values=["Asian", ]),
+                            "White other": SerialHierarchy(values=["White", ]),
+                            "Black Caribbean": SerialHierarchy(values=["Black", ]),
+                            "Pakistani": SerialHierarchy(values=["Asian", ])}
+
+    action_space.add_many(ActionSuppress(column_name="gender", suppress_table={"F": SerialHierarchy(values=['*', ]),
+                                                                               'M': SerialHierarchy(values=['*', ])}),
+                          ActionIdentity(column_name="salary"), ActionIdentity(column_name="education"),
+                          ActionGeneralize(column_name="ethnicity", generalization_table=generalization_table))
+
+    # specify the reward manager to use
+    reward_manager = RewardManager()
+
+    # create the environment
+    env = Environment(data_set=ds, action_space=action_space,
+                      gamma=0.99, start_column="gender", reward_manager=reward_manager)
+    # initialize text distances
+    env.initialize_text_distances(distance_type=DistanceType.COSINE)
+
+    algo_config = QLearnConfig()
+    algo_config.n_itrs_per_episode = 1000
+    algo_config.gamma = 0.99
+    algo_config.alpha = 0.1
+    algo_config.policy = EpsilonGreedyPolicy(eps=EPS, env=env,
+                                             decay_op=EpsilonDecreaseOption.INVERSE_STEP)
+
+    agent = QLearning(algo_config=algo_config)
+
+    configuration = {"n_episodes": 10, "update_frequency": 100}
+
+    # create a trainer to train the A2C agent
+    trainer = Trainer(env=env, agent=agent, configuration=configuration)
+
+    trainer.train()