nip.parameters.protocol.CommonProtocolParameters

class nip.parameters.protocol.CommonProtocolParameters(verifier_first: bool = True, randomize_prover_stance: bool = False, prover_reward: float = 1.0, prover_invalid_response_penalty: float | None = None, verifier_reward: float = 1.0, verifier_incorrect_penalty: float = -1.0, verifier_neither_accept_nor_reject_reward: float | None = None, verifier_terminated_penalty: float = -1.0, verifier_no_guess_reward: float = 0.0, shared_reward: bool = False, force_guess: Literal['zero', 'one', 'y'] | None = None, zero_knowledge: bool = False, verifier_decision_spectrum: Literal['accept_reject', 'likert_scale_4', 'likert_scale_5', 'likert_scale_6', 'likert_scale_7', 'likert_scale', 'likert_scale_no_undecided', 'out_of_10', 'out_of_100'] = 'accept_reject')

Common additional parameters for the interaction protocol.

Parameters:

• verifier_first (bool) – Whether the verifier goes first in the protocol.

• randomize_prover_stance (bool) – Whether, for each datapoint, the verdict the prover arguing for is randomized. This is only relevant when there is a single prover, and when using a text-based protocol.

• prover_reward (float) – The reward given to the prover when the verifier guesses “accept”.

• prover_invalid_response_penalty (float | None) – The reward given to a prover when it gives an invalid response. If None, provers are not penalized for invalid responses. This is only relevant in pure-text scenarios, where the prover is expected to give a text response.

• verifier_reward (float) – The reward given to the verifier when it guesses correctly.

• verifier_incorrect_penalty (float) – The penalty given to the verifier when it guesses incorrectly.

• verifier_neither_accept_nor_reject_reward (float | None) – The reward given to the verifier when it neither accepts nor rejects. If None, the mid-point between verifier_reward and verifier_incorrect_penalty is used. This value is only relevant for text-based scenarios. Note that when using a verifier decision spectrum (see verifier_decision_spectrum), reward for intermediate decisions is computed by interpolating piece-wise linearly between verifier_incorrect_penalty, verifier_neither_accept_nor_reject_reward and verifier_reward. So in this case you probably want to set this to None.

• verifier_terminated_penalty (float) – The reward given to the verifier if the episode terminates before it guesses.

• verifier_no_guess_reward (float) – The reward given to the verifier if it does not make a guess in a round.

• shared_reward (bool) – Whether to use a shared reward function, where the prover gets the same reward as the verifier. This overrides prover_reward.

• force_guess (GuessType, optional) – The guess to force the verifier to make. If not provided, the verifier makes a guess using its policy.

• zero_knowledge (bool) – Whether to use a zero-knowledge version of the protocol.

• verifier_decision_spectrum (VerifierDecisionSpectrumType) – The scale used by the verifier to make its decision. This allows for more nuanced decisions than just “accept” or “reject”. This is only relevant for text-based scenarios.

Methods Summary

__eq__(other)	Return self==value.
__init__([verifier_first, ...])
__post_init__()
__repr__()	Return repr(self).
_get_param_class_from_dict(param_dict)	Try to get the parameter class from a dictionary of serialised parameters.
construct_test_params()	Construct a set of basic parameters for testing.
from_dict(params_dict[, ignore_extra_keys])	Create a parameters object from a dictionary.
get(address)	Get a value from the parameters object using a dot-separated address.
to_dict()	Convert the parameters object to a dictionary.

Attributes

force_guess
prover_invalid_response_penalty
prover_reward
randomize_prover_stance
shared_reward
verifier_decision_spectrum
verifier_first
verifier_incorrect_penalty
verifier_neither_accept_nor_reject_reward
verifier_no_guess_reward
verifier_reward
verifier_terminated_penalty
zero_knowledge

Methods

__eq__(other)

Return self==value.

__init__(verifier_first: bool = True, randomize_prover_stance: bool = False, prover_reward: float = 1.0, prover_invalid_response_penalty: float | None = None, verifier_reward: float = 1.0, verifier_incorrect_penalty: float = -1.0, verifier_neither_accept_nor_reject_reward: float | None = None, verifier_terminated_penalty: float = -1.0, verifier_no_guess_reward: float = 0.0, shared_reward: bool = False, force_guess: Literal['zero', 'one', 'y'] | None = None, zero_knowledge: bool = False, verifier_decision_spectrum: Literal['accept_reject', 'likert_scale_4', 'likert_scale_5', 'likert_scale_6', 'likert_scale_7', 'likert_scale', 'likert_scale_no_undecided', 'out_of_10', 'out_of_100'] = 'accept_reject') → None

__post_init__()

__repr__()

Return repr(self).

classmethod _get_param_class_from_dict(param_dict: dict) → type[ParameterValue] | None

Try to get the parameter class from a dictionary of serialised parameters.

Parameters:

param_dict (dict) – A dictionary of parameters, which may have come from a to_dict method. This dictionary may contain a _type key, which is used to determine the class of the parameter.

Returns:

param_class (type[ParameterValue] | None) – The class of the parameter, if it can be determined.

Raises:

ValueError – If the class specified in the dictionary is not a valid parameter class.

classmethod construct_test_params() → BaseHyperParameters

Construct a set of basic parameters for testing.

classmethod from_dict(params_dict: dict, ignore_extra_keys: bool = False) → BaseHyperParameters

Create a parameters object from a dictionary.

Parameters:

• params_dict (dict) – A dictionary of the parameters.

• ignore_extra_keys (bool, default=False) – If True, ignore keys in the dictionary that do not correspond to fields in the parameters object.

Returns:

hyper_params (BaseParameters) – The parameters object.

get(address: str) → Any

Get a value from the parameters object using a dot-separated address.

Parameters:

address (str) – The path to the value in the parameters object, separated by dots.

Returns:

value (Any) – The value at the address.

Raises:

KeyError – If the address does not exist.

to_dict() → dict

Convert the parameters object to a dictionary.

Turns enums into strings, and sub-parameters into dictionaries. Includes the is_random parameter if it exists.

Returns:

params_dict (dict) – A dictionary of the parameters.