voiage.methods.preference.PreferenceHeterogeneityResult
Structured preference heterogeneity result.
Attributes
Section titled “Attributes”analysis_id : str
Stable analysis identifier for the preference analysis.
decision_problem_id : str
Stable decision-problem identifier for the preference analysis.
value : float
Weighted switching value relative to the reference preference profile.
individualized_care_value : float
Weighted value of tailoring strategy choice to each preference profile.
preference_profile_ids : list[str]
Preference-profile identifiers in analysis order.
preference_profile_labels : list[str]
Human-readable preference-profile labels.
strategy_names : list[str]
Strategy labels in analysis order.
expected_net_benefits : numpy.ndarray
Expected net benefit with shape (n_profiles, n_strategies).
optimal_strategy_indices : numpy.ndarray
Preference-profile-specific optimal strategy index.
optimal_strategy_names : list[str]
Preference-profile-specific optimal strategy name.
optimal_expected_net_benefits : numpy.ndarray
Expected net benefit of each preference-profile-specific optimum.
regret_matrix : numpy.ndarray
Matrix where row i and column j is regret in profile i
when using the strategy optimal under profile j.
switching_values : numpy.ndarray
Regret avoided by switching away from the reference profile’s strategy
toward each profile’s own optimal strategy.
consensus_strategy_index : int
Strategy maximizing weighted expected net benefit across profiles.
consensus_strategy_name : str
Name of the consensus strategy.
consensus_weighted_expected_net_benefit : float
Weighted expected net benefit of the consensus strategy.
individualized_care_strategy_index : int
Strategy maximizing weighted expected net benefit when tailoring by
preference profile.
individualized_care_strategy_name : str
Name of the individualized-care strategy summary.
individualized_care_weighted_expected_net_benefit : float
Weighted expected net benefit of the individualized-care strategy.
robust_strategy_index : int
Strategy maximizing the minimum expected net benefit across profiles.
robust_strategy_name : str
Name of the robust maximin strategy.
pareto_strategy_indices : list[int]
Strategy indices not dominated across profiles.
pareto_strategy_names : list[str]
Strategy names not dominated across profiles.
preference_profile_weights : numpy.ndarray
Normalized preference-profile weights used for consensus and value
summaries.
reference_preference_profile : str
Preference profile used as the reference decision rule.
method_maturity : str
Maturity label for the fixture-backed preference surface.
diagnostics : dict[str, object]
Deterministic diagnostics for downstream reporting.
Methods
Section titled “Methods”to_dict
Section titled “to_dict”to_dict([positional or keyword] self: None = None) -> dict[str, object]Serialize to a JSON-compatible payload.
Parameters:
self
Returns: dict[str, object]