duo_ai.algorithms.pyod

Classes

PyODAlgorithmConfig	Configuration dataclass for PyODAlgorithm.
PyODAlgorithm	Algorithm for out-of-distribution (OOD) detection using PyOD models.

Module Contents

class duo_ai.algorithms.pyod.PyODAlgorithmConfig

Configuration dataclass for PyODAlgorithm.

Parameters:

• cls (str, optional) – Name of the algorithm class. Default is “PyODAlgorithm”.

• num_rollouts (int, optional) – Number of rollouts to use for data generation. Default is 128.

• percentiles (list of float, optional) – List of percentiles to use for threshold selection. Default is range(0, 101, 10).

• explore_temps (list of float, optional) – List of temperatures to use during exploration rollouts. Default is [1.0].

• accept_rate (float, optional) – Acceptance rate for sampling data during rollouts. Default is 0.05.

name: str = 'pyod'

num_rollouts: int = 128

percentiles: List[float]

explore_temps: List[float] = [1.0]

accept_rate: float = 0.05

class duo_ai.algorithms.pyod.PyODAlgorithm(config: PyODAlgorithmConfig)

Bases: duo_ai.core.Algorithm

Algorithm for out-of-distribution (OOD) detection using PyOD models.

Examples

>>> algo = PyODAlgorithm(PyODAlgorithmConfig())

config_cls

config

random

train(policy: duo.policies.PPOPolicy, env: gym.Env, validators: Dict[str, duo.core.Evaluator]) → None

Train the PyODAlgorithm by searching for the best threshold parameter that maximizes evaluation reward.

Parameters:

• policy (duo.policies.PPOPolicy) – The policy to be evaluated and tuned.

• env (gym.Env) – The environment instance for training and data generation.

• validators (dict of str to duo.core.Evaluator) – Dictionary mapping split names to evaluator instances for evaluation.

Return type:

None

Examples

>>> algorithm = PyODAlgorithm(PyODAlgorithmConfig())
>>> algorithm.train(policy, env, validators)

save_checkpoint(policy: duo.policies.PPOPolicy, name: str) → None

Save the current policy configuration and parameters to a checkpoint file.

Parameters:

• policy (duo.policies.PPOPolicy) – The policy whose parameters are to be saved.

• name (str) – Name for the checkpoint file.

Return type:

None

Examples

>>> self.save_checkpoint(policy, "best_test")

_generate_data(env: gym.Env, policy: duo.policies.PPOPolicy, temperature: float, num_rollouts: int, accept_rate: float) → dict

Generate data for OOD detection by rolling out the policy in the environment.

Parameters:

• env (gym.Env) – The environment used for rollouts.

• policy (duo.policies.PPOPolicy) – The policy to be evaluated.

• temperature (float) – Temperature parameter for action selection.

• num_rollouts (int) – Total number of rollout episodes to generate.

• accept_rate (float) – Acceptance rate for sampling data during rollouts.

Returns:

data – Dictionary containing collected data arrays for each feature.

Return type:

dict

Examples

>>> data = self._generate_data(env, policy, 1.0, 128, 0.05)