desdeo_mcdm.interactive.NautilusNavigator

Module Contents

Classes

NautilusNavigatorStopRequest

Request to stop navigation and return the solution found (or the

NautilusNavigatorRequest

Request to handle interactions with NAUTILUS Navigator. See the

NautilusNavigator

Implementations of the NAUTILUS Navigator algorithm.

Attributes

f1

class desdeo_mcdm.interactive.NautilusNavigator.NautilusNavigatorStopRequest(reachable_idx: List[int], pareto_front: numpy.ndarray, decision_variables: numpy.ndarray | None = None)[source]

Bases: desdeo_tools.interaction.request.BaseRequest

Request to stop navigation and return the solution found (or the currently reachable solutions, if stopped before the navigation ends.

class desdeo_mcdm.interactive.NautilusNavigator.NautilusNavigatorRequest(ideal: numpy.ndarray, nadir: numpy.ndarray, reachable_lb: numpy.ndarray, reachable_ub: numpy.ndarray, user_bounds: List[float], reachable_idx: List[int], step_number: int, steps_remaining: int, distance: float, allowed_speeds: List[int], current_speed: int, navigation_point: numpy.ndarray)[source]

Bases: desdeo_tools.interaction.request.BaseRequest

Request to handle interactions with NAUTILUS Navigator. See the NautilusNavigator class for further details.

classmethod init_with_method(method)[source]
validator(response: Dict) → None[source]
exception desdeo_mcdm.interactive.NautilusNavigator.NautilusNavigatorException[source]

Bases: Exception

Raised when an exception related to NAUTILUS Navigator is encountered.

class desdeo_mcdm.interactive.NautilusNavigator.NautilusNavigator(pareto_front: numpy.ndarray, ideal: numpy.ndarray, nadir: numpy.ndarray, decision_variables: numpy.ndarray | None = None)[source]

Bases: desdeo_mcdm.interactive.InteractiveMethod.InteractiveMethod

Implementations of the NAUTILUS Navigator algorithm.

Parameters:

  • pareto_front (np.ndarray) – A two dimensional numpy array representing a Pareto front with objective vectors on each of its rows.

  • ideal (np.ndarray) – The ideal objective vector of the problem being represented by the Pareto front.

  • nadir (np.ndarray) – The nadir objective vector of the problem being represented by the Pareto front.

  • decision_variables (Optional[np.ndarray]) – Two dimensinoal numpy array of decision variables that can be optionally supplied. The i’th vector in decision_variables should result in the i’th objective vector in pareto_front. Defaults to None.

Raises:
start() → NautilusNavigatorRequest[source]

Returns the first Request object to begin iterating.

Returns:

The Request.

Return type:

NautilusNavigatorRequest

iterate(request: NautilusNavigatorRequest) → NautilusNavigatorRequest[source]

Perform the next logical step based on the response in the Request.

handle_request(request: NautilusNavigatorRequest) → NautilusNavigatorRequest | NautilusNavigatorStopRequest[source]

Handle the Request and its contents.

Parameters:

request (NautilusNavigatorRequest) – A Request with a defined response.

Returns:

Some of the contents of the response are invalid.

Return type:

NautilusNavigatorRequest

update(ref_point: numpy.ndarray, speed: int, go_to_previous: bool, stop: bool, step_number: int | None = None, nav_point: numpy.ndarray | None = None, lower_bounds: numpy.ndarray | None = None, upper_bounds: numpy.ndarray | None = None, user_bounds: numpy.ndarray | None = None, reachable_idx: List[int] | None = None, distance: float | None = None, steps_remaining: int | None = None) → NautilusNavigatorRequest | None[source]

Update the internal state of self.

Parameters:

  • ref_point (np.ndarray) – A reference point given by a decision maker.

  • speed (int) – An integer value between 1-5 indicating the navigation speed.

  • go_to_previous (bool) – If True, the parameters indicate the state of a previous state, and the request is handled accordingly.

  • stop (bool) – If the navigation should stop. If True, returns a request with self’s current state.

  • step_number (Optional[int], optional) – Current step number, or previous step number if go_to_previous is True. Defaults to None.

  • nav_point (Optional[np.ndarray], optional) – The current navigation point. Relevant if go_to_previous is True. Defaults to None.

  • lower_bounds (Optional[np.ndarray], optional) – Lower bounds of the reachable objective vector values. Relevant if go_to_previous is True. Defaults to None.

  • upper_bounds (Optional[np.ndarray], optional) – Upper bounds of the reachable objective vector values. Relevant if go_to_previous is True. Defaults to None.

  • user_bounds (Optional[np.ndarray], optional) – The user given bounds for each objective. The reachable lower limit with attempt to not exceed the given bounds for each objective value.

  • reachable_idx (Optional[List[int]], optional) – Indices of the reachable Pareto optimal solutions. Relevant if go_to_previous is True. Defaults to None.

  • distance (Optional[float], optional) – Distance to the Pareto optimal front. Relevant if go_to_previous is True. Defaults to None.

  • steps_remaining (Optional[int], optional) – Remaining steps in the navigation. Relevant if go_to_previous is True. Defaults to None.

Returns:

Some of the given parameters are erroneous.

Return type:

NautilusNavigatorRequest

calculate_reachable_point_indices(pareto_front: numpy.ndarray, lower_bounds: numpy.ndarray, upper_bounds: numpy.ndarray) → List[int][source]

Calculate the indices of the reachable Pareto optimal solutions based on lower and upper bounds.

Returns:

List of the indices of the reachable solutions.

Return type:

List[int]

static solve_nautilus_asf_problem(pareto_f: numpy.ndarray, subset_indices: List[int], ref_point: numpy.ndarray, ideal: numpy.ndarray, nadir: numpy.ndarray, user_bounds: numpy.ndarray) → int[source]

Forms and solves the achievement scalarizing function to find the closest point on the Pareto optimal front to the given reference point.

Parameters:

  • pareto_f (np.ndarray) – The whole Pareto optimal front.

  • subset_indices ([type]) – Indices of the currently reachable solutions.

  • ref_point (np.ndarray) – The reference point indicating a decision maker’s preference.

  • ideal (np.ndarray) – Ideal point.

  • nadir (np.ndarray) – Nadir point.

  • user_bounds (np.ndarray) – Bounds given by the user (the DM) for each objective,which should not be exceeded. A 1D array where NaN’s indicate ‘no bound is given’ for the respective objective value.

Returns:

Index of the closest point according the minimized value of the ASF.

Return type:

int

calculate_navigation_point(projection: numpy.ndarray, nav_point: numpy.ndarray, steps_remaining: int) → numpy.ndarray[source]

Calculate a new navigation point based on the projection of the preference point to the Pareto optimal front.

Parameters:

  • projection (np.ndarray) – The point on the Pareto optimal front closest to the preference point given by a decision maker.

  • nav_point (np.ndarray) – The previous navigation point.

  • steps_remaining (int) – How many steps are remaining in the navigation.

Returns:

The new navigation point.

Return type:

np.ndarray

static calculate_bounds(pareto_front: numpy.ndarray, nav_point: numpy.ndarray, user_bounds: numpy.ndarray, previous_lb: numpy.ndarray, previous_ub: numpy.ndarray) → Tuple[numpy.ndarray, numpy.ndarray][source]

Calculate the new bounds of the reachable points on the Pareto optimal front from a navigation point.

Parameters:

  • pareto_front (np.ndarray) – The Pareto optimal front.

  • nav_point (np.ndarray) – The current navigation point.

  • user_bounds (np.ndarray) – Bounds given by the user (the DM) for each objective,which should not be exceeded. A 1D array where NaN’s indicate ‘no bound is given’ for the respective objective value.

  • previous_lb (np.ndarray) – If no new lower bound can be found for an objective, this value is used.

  • previous_ub (np.ndarray) – If no new upper bound can be found for an objective, this value is used.

Returns:

The lower and upper bounds.

Return type:

Tuple[np.ndarray, np.ndarray]

calculate_distance(nav_point: numpy.ndarray, projection: numpy.ndarray, nadir: numpy.ndarray) → float[source]

Calculate the distance to the Pareto optimal front from a navigation point. The distance is calculated to the supplied projection which is assumed to lay on the front.

Parameters:

  • nav_point (np.ndarray) – The navigation point.

  • projection (np.ndarray) – The point of the Pareto optimal front the distance is calculated to.

  • nadir (np.ndarray) – The nadir point of the Pareto optimal set.

Returns:

The distance.

Return type:

float

desdeo_mcdm.interactive.NautilusNavigator.f1[source]