refactor: faster calc_ground_truth_policy_value in SyntheticSlateBanditDataset

[aiueola]

new feature

• allowed len_list >= n_unique_actions setting when using is_factorizable
  https://github.com/aiueola/zr-obp/blob/97ff9987716a7e1351ae30e135313a54e6e9fbbe/obp/dataset/synthetic_slate.py#L201

bug fix

• fixed _calc_epsilon_greedy_pscore when using is_factorizable
  https://github.com/aiueola/zr-obp/blob/97ff9987716a7e1351ae30e135313a54e6e9fbbe/obp/dataset/synthetic_slate.py#L1122

refactor

• implemented faster calc_ground_truth_policy_value by avoiding for loops with n_rounds.
  • refactored pscore calculation when is_factorizable=True.
    https://github.com/aiueola/zr-obp/blob/f126964583c8d265eeb323301874a8060fbddd28/obp/dataset/synthetic_slate.py#L828
  • refactored action_interaction_reward_function (to calculate expected_reward_factual) with batch processing.
    https://github.com/aiueola/zr-obp/blob/f126964583c8d265eeb323301874a8060fbddd28/obp/dataset/synthetic_slate.py#L852
• unified both action_interaction_additive_reward_function and action_interaction_decay_reward_function into action_interaction_reward_function for the faster implementation and preventing memory error.
  https://github.com/aiueola/zr-obp/blob/f126964583c8d265eeb323301874a8060fbddd28/obp/dataset/synthetic_slate.py#L1162
• add tqdm for pscore calclation when is_factorizable=False
  https://github.com/aiueola/zr-obp/blob/f126964583c8d265eeb323301874a8060fbddd28/obp/dataset/synthetic_slate.py#L838

result

• setting: len_list=5, n_unique_actions=10, n_rounds=1000,is_factorizable=True
• pscore when is_factorizable=True: 1.5min
• expected_reward_factural
  • before: standard_additive = 7.5min, cascade_additive = 7min, standard_decay = 17min, cascade_decay = 17min, independent = 17min
  • after: standard_additive = 7min, cascade_additive = 6min, standard_decay = 8.5min, cascade_decay = 6.5min, independent = 4min

test

• add and edit corresponding tests.

others

• minor fix on typos and docstrings

[usaito]

@aiueola Thanks!

[imo]

I think action_interaction_reward_function should be given action and enumerated_action separately (then, is_enumerated will be deleted). That's because enumerated_action is not sampled from a prob distribution and they are conceptually different.
https://github.com/aiueola/zr-obp/blob/b20d8158b67683c0a479ff8fe8fe6451a2a132c8/obp/dataset/synthetic_slate.py#L1171

Some minor points are below.

• Desc of the function ("Reward function incorporating additive interactions among combinatorial action") should be updated
  https://github.com/aiueola/zr-obp/blob/b20d8158b67683c0a479ff8fe8fe6451a2a132c8/obp/dataset/synthetic_slate.py#L1175

• Do you mean If is_enumerated=True by saying If not is_enumerated=False? If so, please avoid indirect expressions as possible as you can.
  https://github.com/aiueola/zr-obp/blob/b20d8158b67683c0a479ff8fe8fe6451a2a132c8/obp/dataset/synthetic_slate.py#L1187

• How about

is_additive = reward_structure in ["standard_additive", "cascade_additive"]
is_cascade = reward_structure in ["cascade_additive", "cascade_decay"]

instead of
https://github.com/aiueola/zr-obp/blob/b20d8158b67683c0a479ff8fe8fe6451a2a132c8/obp/dataset/synthetic_slate.py#L1263