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Refactor MAB and Strategy Classes with Cold Start Methods and Enhance…
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…d Validation

 Change log:
 1. Moved Strategy, Model, and MAB to strategy.py, model.py, and to the new mab.py. base.py is now only for definitions and abstract PyBanditsBaseModel.
 2. Introduced TailoredBaseMab as a child of BaseMab. The new abstract MAB should hold any common functionality for all MABs that are tailored for a specific strategy. Consequently, all MABs except for BaseMab are now inheriting from the new class.
 3. The from_state functionality is now directly inherited by all MABs from BaseMab.
 4. Replaced all cold_start methods in cmab.py and smab.py with cold_start_instantiate stemming from TailoredBaseMab. Correspondingly, updated test cases to use the new cold_start_instantiate methods.
 5. Introduced numerize_field and get_expected_value_from_state methods in the Strategy class to handle default values and state extraction. Added field_validator for exploit_p in BestActionIdentification and subsidy_factor in CostControlBandit to ensure proper default handling and validation.
 6. Merged common functionality into a new CostControlStrategy abstract class, which is now inherited by CostControlBandit and MultiObjectiveCostControlBandit. Simplified the select_action methods by using helper methods like _evaluate_and_select and _reduce.
 7. Plugged get_pareto_front into a new MultiObjectiveStrategy abstract class, which is now inherited by MultiObjectiveBandit and MultiObjectiveCostControlBandit.
 8. In model.py. Removed the redundant BaseBetaMO and BaseBayesianLogisticRegression. Added cold_start_instantiate method to BetaMO and BayesianLogisticRegression models.
 9. Added extract_argument_names_from_function under utils.py to allow extract function parameter names by handle.
 10. Changed tes_base.py into test_mab.py.
 11. Updated deprecated linter settings in pyproject.toml.
 12. Added test_smab_mo_cc_update test on test_smab.py.
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@Shahar-Bar
Shahar-Bar committed Aug 12, 2024
1 parent 95122f4 commit 0748967
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6 changes: 3 additions & 3 deletions docs/tutorials/mab.ipynb
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Expand Up @@ -20,7 +20,7 @@
"from rich import print\n",
"\n",
"from pybandits.model import Beta\n",
"from pybandits.smab import SmabBernoulli, create_smab_bernoulli_cold_start"
"from pybandits.smab import SmabBernoulli"
]
},
{
Expand Down Expand Up @@ -137,7 +137,7 @@
"id": "564914fd-73cc-4854-8ec7-548970f794a6",
"metadata": {},
"source": [
"You can initialize the bandit via the utility function `create_smab_bernoulli_mo_cc_cold_start()`. This is particulary useful in a cold start setting when there is no prior knowledge on the Beta distruibutions. In this case for all Betas `n_successes` and `n_failures` are set to `1`."
"You can initialize the bandit via the utility function `SmabBernoulliMOCC.cold_start_instantiate()`. This is particulary useful in a cold start setting when there is no prior knowledge on the Beta distruibutions. In this case for all Betas `n_successes` and `n_failures` are set to `1`."
]
},
{
Expand All @@ -148,7 +148,7 @@
"outputs": [],
"source": [
"# generate a smab bernoulli in cold start settings\n",
"mab = create_smab_bernoulli_cold_start(action_ids=[\"a1\", \"a2\", \"a3\"])"
"mab = SmabBernoulli.cold_start_instantiate(action_ids=[\"a1\", \"a2\", \"a3\"])"
]
},
{
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6 changes: 3 additions & 3 deletions docs/tutorials/smab_mo_cc.ipynb
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Expand Up @@ -20,7 +20,7 @@
"from rich import print\n",
"\n",
"from pybandits.model import Beta, BetaMOCC\n",
"from pybandits.smab import SmabBernoulliMOCC, create_smab_bernoulli_mo_cc_cold_start"
"from pybandits.smab import SmabBernoulliMOCC"
]
},
{
Expand Down Expand Up @@ -153,7 +153,7 @@
"id": "564914fd-73cc-4854-8ec7-548970f794a6",
"metadata": {},
"source": [
"You can initialize the bandit via the utility function `create_smab_bernoulli_mo_cc_cold_start()`. This is particulary useful in a cold start setting when there is no prior knowledge on the Beta distruibutions. In this case for all Betas `n_successes` and `n_failures` are set to `1`."
"You can initialize the bandit via the utility function `SmabBernoulliMOCC.cold_start_instantiate()`. This is particulary useful in a cold start setting when there is no prior knowledge on the Beta distruibutions. In this case for all Betas `n_successes` and `n_failures` are set to `1`."
]
},
{
Expand All @@ -168,7 +168,7 @@
"n_objectives = 2\n",
"\n",
"# generate a smab bernoulli in cold start settings\n",
"mab = create_smab_bernoulli_mo_cc_cold_start(action_ids_cost=action_ids_cost, n_objectives=n_objectives)"
"mab = SmabBernoulliMOCC.cold_start_instantiate(action_ids_cost=action_ids_cost, n_objectives=n_objectives)"
]
},
{
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219 changes: 2 additions & 217 deletions pybandits/base.py
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Expand Up @@ -21,20 +21,9 @@
# SOFTWARE.


from abc import ABC, abstractmethod
from typing import Any, Dict, List, NewType, Optional, Set, Tuple, Union
from typing import Dict, List, NewType, Tuple

import numpy as np
from pydantic import (
BaseModel,
NonNegativeInt,
confloat,
conint,
constr,
field_validator,
model_validator,
validate_call,
)
from pydantic import BaseModel, confloat, conint, constr

ActionId = NewType("ActionId", constr(min_length=1))
Float01 = NewType("Float_0_1", confloat(ge=0, le=1))
Expand All @@ -47,207 +36,3 @@ class PyBanditsBaseModel(BaseModel, extra="forbid"):
"""
BaseModel of the PyBandits library.
"""


class Model(PyBanditsBaseModel, ABC):
"""
Class to model the prior distributions.
"""

@abstractmethod
def sample_proba(self) -> Probability:
"""
Sample the probability of getting a positive reward.
"""

@abstractmethod
def update(self, rewards: List[Any]):
"""
Update the model parameters.
"""


class Strategy(PyBanditsBaseModel, ABC):
"""
Strategy to select actions in multi-armed bandits.
"""

@abstractmethod
def select_action(self, p: Dict[ActionId, Probability], actions: Optional[Dict[ActionId, Model]]) -> ActionId:
"""
Select the action.
"""


class BaseMab(PyBanditsBaseModel, ABC):
"""
Multi-armed bandit superclass.
Parameters
----------
actions: Dict[ActionId, Model]
The list of possible actions, and their associated Model.
strategy: Strategy
The strategy used to select actions.
epsilon: Optional[Float01]
The probability of selecting a random action.
default_action: Optional[ActionId]
The default action to select with a probability of epsilon when using the epsilon-greedy approach.
If `default_action` is None, a random action from the action set will be selected with a probability of epsilon.
"""

actions: Dict[ActionId, Model]
strategy: Strategy
epsilon: Optional[Float01]
default_action: Optional[ActionId]

@field_validator("actions", mode="before")
@classmethod
def at_least_2_actions_are_defined(cls, v):
if len(v) < 2:
raise AttributeError("At least 2 actions should be defined.")
return v

@model_validator(mode="after")
def check_default_action(self):
if not self.epsilon and self.default_action:
raise AttributeError("A default action should only be defined when epsilon is defined.")
if self.default_action and self.default_action not in self.actions:
raise AttributeError("The default action should be defined in the actions.")
return self

def _get_valid_actions(self, forbidden_actions: Optional[Set[ActionId]]) -> Set[ActionId]:
"""
Given a set of forbidden action IDs, return a set of valid action IDs.
Parameters
----------
forbidden_actions: Optional[Set[ActionId]]
The set of forbidden action IDs.
Returns
-------
valid_actions: Set[ActionId]
The list of valid (i.e. not forbidden) action IDs.
"""
if forbidden_actions is None:
forbidden_actions = set()

if not all(a in self.actions.keys() for a in forbidden_actions):
raise ValueError("forbidden_actions contains invalid action IDs.")
valid_actions = set(self.actions.keys()) - forbidden_actions
if len(valid_actions) == 0:
raise ValueError("All actions are forbidden. You must allow at least 1 action.")
if self.default_action and self.default_action not in valid_actions:
raise ValueError("The default action is forbidden.")

return valid_actions

def _check_update_params(self, actions: List[ActionId], rewards: List[Union[NonNegativeInt, List[NonNegativeInt]]]):
"""
Verify that the given list of action IDs is a subset of the currently defined actions.
Parameters
----------
actions : List[ActionId]
The selected action for each sample.
rewards: List[Union[BinaryReward, List[BinaryReward]]]
The reward for each sample.
"""
invalid = set(actions) - set(self.actions.keys())
if invalid:
raise AttributeError(f"The following invalid action(s) were specified: {invalid}.")
if len(actions) != len(rewards):
raise AttributeError(f"Shape mismatch: actions and rewards should have the same length {len(actions)}.")

@abstractmethod
@validate_call
def update(self, actions: List[ActionId], rewards: List[Union[BinaryReward, List[BinaryReward]]], *args, **kwargs):
"""
Update the stochastic multi-armed bandit model.
actions: List[ActionId]
The selected action for each sample.
rewards: List[Union[BinaryReward, List[BinaryReward]]]
The reward for each sample.
"""

@abstractmethod
@validate_call
def predict(self, forbidden_actions: Optional[Set[ActionId]] = None):
"""
Predict actions.
Parameters
----------
forbidden_actions : Optional[Set[ActionId]], default=None
Set of forbidden actions. If specified, the model will discard the forbidden_actions and it will only
consider the remaining allowed_actions. By default, the model considers all actions as allowed_actions.
Note that: actions = allowed_actions U forbidden_actions.
Returns
-------
actions: List[ActionId] of shape (n_samples,)
The actions selected by the multi-armed bandit model.
probs: List[Dict[ActionId, float]] of shape (n_samples,)
The probabilities of getting a positive reward for each action.
"""

def get_state(self) -> (str, dict):
"""
Access the complete model internal state, enough to create an exact copy of the same model from it.
Returns
-------
model_class_name: str
The name of the class of the model.
model_state: dict
The internal state of the model (actions, scores, etc.).
"""
model_name = self.__class__.__name__
state: dict = self.dict()
return model_name, state

@validate_call
def _select_epsilon_greedy_action(
self,
p: Union[Dict[ActionId, float], Dict[ActionId, Probability], Dict[ActionId, List[Probability]]],
actions: Optional[Dict[ActionId, Model]] = None,
) -> ActionId:
"""
Wraps self.strategy.select_action function with epsilon-greedy strategy,
such that with probability epsilon a default_action is selected,
and with probability 1-epsilon the select_action function is triggered to choose action.
If no default_action is provided, a random action is selected.
Reference: Reinforcement Learning: An Introduction, Ch. 2 (Sutton and Burto, 2018)
https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf&ved=2ahUKEwjMy8WV9N2HAxVe0gIHHVjjG5sQFnoECEMQAQ&usg=AOvVaw3bKK-Y_1kf6XQVwR-UYrBY
Parameters
----------
p: Union[Dict[ActionId, float], Dict[ActionId, Probability], Dict[ActionId, List[Probability]]]
The dictionary or actions and their sampled probability of getting a positive reward.
For MO strategy, the sampled probability is a list with elements corresponding to the objectives.
actions: Optional[Dict[ActionId, Model]]
The dictionary of actions and their associated Model.
Returns
-------
selected_action: ActionId
The selected action.
Raises
------
KeyError
If self.default_action is not present as a key in the probabilities dictionary.
"""

if self.epsilon:
if self.default_action and self.default_action not in p.keys():
raise KeyError(f"Default action {self.default_action} not in actions.")
if np.random.binomial(1, self.epsilon):
selected_action = self.default_action if self.default_action else np.random.choice(list(p.keys()))
else:
selected_action = self.strategy.select_action(p=p, actions=actions)
else:
selected_action = self.strategy.select_action(p=p, actions=actions)
return selected_action
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