:py:mod:`desdeo_mcdm.interactive.ParetoNavigator` ================================================= .. py:module:: desdeo_mcdm.interactive.ParetoNavigator Module Contents --------------- Classes ~~~~~~~ .. autoapisummary:: desdeo_mcdm.interactive.ParetoNavigator.ParetoNavigatorInitialRequest desdeo_mcdm.interactive.ParetoNavigator.ParetoNavigatorRequest desdeo_mcdm.interactive.ParetoNavigator.ParetoNavigatorSolutionRequest desdeo_mcdm.interactive.ParetoNavigator.ParetoNavigatorStopRequest desdeo_mcdm.interactive.ParetoNavigator.ParetoNavigator Functions ~~~~~~~~~ .. autoapisummary:: desdeo_mcdm.interactive.ParetoNavigator.f1 .. py:exception:: ParetoNavigatorException Bases: :py:obj:`Exception` Raised when an exception related to Pareto Navigator is encountered. .. py:class:: ParetoNavigatorInitialRequest(ideal: numpy.ndarray, nadir: numpy.ndarray, allowed_speeds: numpy.ndarray, po_solutions: numpy.ndarray) Bases: :py:obj:`desdeo_tools.interaction.request.BaseRequest` A request class to handle the Decision Maker's initial preferences for the first iteration round. :param ideal: Ideal vector :type ideal: np.ndarray :param nadir: Nadir vector :type nadir: np.ndarray :param allowed_speeds: Allowed movement speeds :type allowed_speeds: np.ndarray :param po_solutions: (np.ndarray): A set of pareto optimal solutions .. py:attribute:: msg :annotation: = Please specify a starting point as 'preferred_solution'. Or specify a reference point as 'reference_point'. .. py:method:: init_with_method(method: desdeo_mcdm.interactive.InteractiveMethod.InteractiveMethod) :classmethod: Initialize request with given instance of ParetoNavigator. :param method: Instance of ReferencePointMethod-class. :type method: ParetoNavigator :returns: Initial request. :rtype: ParetoNavigatorInitialRequest .. py:class:: ParetoNavigatorRequest(current_solution: numpy.ndarray, ideal: numpy.ndarray, nadir: numpy.ndarray, allowed_speeds: numpy.ndarray, valid_classifications: numpy.ndarray) Bases: :py:obj:`desdeo_tools.interaction.request.BaseRequest` A request class to handle navigation preferences after the first iteration round. :param current_solution: Current solution. :type current_solution: np.ndarray :param ideal: Ideal vector. :type ideal: np.ndarray :param nadir: Nadir vector. :type nadir: np.ndarray :param allowed_speeds: Allowed movement speeds :type allowed_speeds: np.ndarray :param valid_classifications: Valid classifications :type valid_classifications: np.ndarray .. py:method:: init_with_method(method: desdeo_mcdm.interactive.InteractiveMethod.InteractiveMethod) :classmethod: Initialize request with given instance of ParetoNavigator. :param method: Instance of ParetoNavigator-class. :type method: ParetoNavigator :returns: Initial request. :rtype: ParetoNavigatorRequest .. py:class:: ParetoNavigatorSolutionRequest(approx_solution: numpy.ndarray, pareto_optimal_solution: numpy.ndarray, objective_values: numpy.ndarray) Bases: :py:obj:`desdeo_tools.interaction.request.BaseRequest` A request class to handle requests to see pareto optimal solution. :param approx_solution: The approximated solution received by navigation :type approx_solution: np.ndarray :param pareto_optimal_solution: A pareto optimal solution (decision variables). :type pareto_optimal_solution: np.ndarray :param objective_values: Objective vector. :type objective_values: np.ndarray .. py:class:: ParetoNavigatorStopRequest(approx_solution: numpy.ndarray, final_solution: numpy.ndarray, objective_values: numpy.ndarray) Bases: :py:obj:`desdeo_tools.interaction.request.BaseRequest` A request class to handle termination. :param approx_solution: The approximated solution received by navigation. :type approx_solution: np.ndarray :param final_solution: Solution (decision variables). :type final_solution: np.ndarray :param objective_values: Objective values. :type objective_values: np.ndarray .. py:class:: ParetoNavigator(problem: Union[desdeo_problem.problem.MOProblem, desdeo_problem.problem.DiscreteDataProblem], pareto_optimal_solutions: Optional[numpy.ndarray] = None, scalar_method: Optional[desdeo_tools.solver.ScalarSolver.ScalarMethod] = None) Bases: :py:obj:`desdeo_mcdm.interactive.InteractiveMethod.InteractiveMethod` Paretonavigator as described in 'Pareto navigator for interactive nonlinear multiobjective optimization' (2008) [Petri Eskelinen · Kaisa Miettinen · Kathrin Klamroth · Jussi Hakanen]. :param problem: The problem to be solved. :type problem: MOProblem :param pareto_optimal_solutions: Some pareto optimal solutions to construct the polyhedral set. :type pareto_optimal_solutions: np.ndarray :param scalar_method: (Optional[ScalarMethod], optional): The scalar method used to solve asf .. note:: pareto_optimal_solutions must be provided for problems of type MOProblem. For DiscreteDataProblems if no pareto optimal solutions are provided the method will use the objective values from the problem. .. py:method:: start() Start the solving process :returns: Initial request :rtype: ParetoNavigatorInitialRequest .. py:method:: iterate(request: Union[ParetoNavigatorInitialRequest, ParetoNavigatorRequest, ParetoNavigatorSolutionRequest, ParetoNavigatorStopRequest]) -> Union[ParetoNavigatorRequest, ParetoNavigatorSolutionRequest, ParetoNavigatorStopRequest] Perform the next logical iteration step based on the given request type. :param request (Union[ParetoNavigatorInitialRequest: ParetoNavigatorSolutionRequest, ParetoNavigatorStopRequest]): A ParetoNavigatorRequest :param ParetoNavigatorRequest: ParetoNavigatorSolutionRequest, ParetoNavigatorStopRequest]): A ParetoNavigatorRequest :param : ParetoNavigatorSolutionRequest, ParetoNavigatorStopRequest]): A ParetoNavigatorRequest :returns: A new request with content depending on the Decision Maker's preferences. :rtype: Union[ParetoNavigatorRequest, ParetoNavigatorSolutionRequest, ParetoNavigatorStopRequest] .. py:method:: handle_initial_request(request: ParetoNavigatorInitialRequest) -> ParetoNavigatorRequest Handles the initial request. :param request: Initial request :type request: ParetoNavigatorInitialRequest :returns: A navigation request :rtype: ParetoNavigatorRequest .. py:method:: handle_request(request: ParetoNavigatorRequest) -> Union[ParetoNavigatorRequest, ParetoNavigatorSolutionRequest, ParetoNavigatorStopRequest] Handles a navigation request. :param request: A request :type request: ParetoNavigatorRequest :returns: Next request corresponding the DM's preferences :rtype: Union[ParetoNavigatorRequest, ParetoNavigatorSolutionRequest, ParetoNavigatorStopRequest] .. py:method:: handle_solution_request(request: ParetoNavigatorSolutionRequest) -> Union[ParetoNavigatorRequest, ParetoNavigatorStopRequest] Handle a solution request :param request: A solution request :type request: ParetoNavigatorSolutionRequest :returns: A navigation request or a stop request depending on whether the DM wishes to continue or stop :rtype: Union[ParetoNavigatorRequest, ParetoNavigatorStopRequest] .. py:method:: calculate_extremes(points: numpy.ndarray) -> Tuple[numpy.ndarray, numpy.ndarray] Calculate the minimum and maximum points of a given array. :param points: A two dimensional array :type points: np.ndarray :returns: The min and max values of each column :rtype: Tuple[np.ndarray, np.ndarray] .. py:method:: calculate_speed(given_speed: int) -> float Calculate a speed value from given integer value. :param given_speed: a speed value where 1 is slowest and 5 fastest :type given_speed: int :returns: A speed value calculated from given integer value. Is between 0 and 1 :rtype: float .. note:: The denominator 10 is not mentioned in the article, but it is included because the navigation speed seems to be too fast without it. .. py:method:: calculate_weights(ideal: numpy.ndarray, nadir: numpy.ndarray) Calculate the scaling coefficients w from ideal and nadir. :param ideal: Ideal vector :type ideal: np.ndarray :param nadir: Nadir vector :type nadir: np.ndarray :returns: The scaling coefficients :rtype: np.ndarray .. py:method:: polyhedral_set_eq(po_solutions: numpy.ndarray) -> Tuple[numpy.ndarray, numpy.ndarray] Construct a polyhedral set as convex hull from the set of pareto optimal solutions :param po_solutions: Some pareto optimal solutions :type po_solutions: np.ndarray :returns: Matrix A and vector b from the convex hull inequality representation Az <= b :rtype: Tuple[np.ndarray, np.ndarray] .. py:method:: construct_lppp_A(weights, A) The matrix A used in the linear parametric programming problem :param weights: Scaling coefficients :type weights: np.ndarray :param A: Matrix A from the convex hull representation Ax < b :type A: np.ndarray :returns: The matrix A' in the linear parametric programming problem A'x numpy.ndarray Solves the linear parametric programming problem as defined in (3) :param current_sol: Current solution :type current_sol: np.ndarray :param ideal: Ideal vector :type ideal: np.ndarray :param nadir: Nadir vector :type nadir: np.ndarray :param direction: Navigation direction :type direction: np.ndarray :param a: Alpha in problem (3) :type a: float :param A: Matrix A from Az <= b :type A: np.ndarray :param b: Vector b from Az <= b :type b: np.ndarray :returns: Optimal vector from the linear parametric programming problem. This is the new solution to be used in the navigation. :rtype: np.ndarray .. py:method:: solve_asf(problem: Union[desdeo_problem.problem.MOProblem, desdeo_problem.problem.DiscreteDataProblem], ref_point: numpy.ndarray, method: Optional[desdeo_tools.solver.ScalarSolver.ScalarMethod] = None) Solve the achievement scalarizing function :param problem: The problem :type problem: MOProblem :param ref_point: A reference point :param method: A method provided to the scalar minimizer :type method: Optional[ScalarMethod], optional :returns: The decision vector which solves the achievement scalarizing function :rtype: np.ndarray .. py:function:: f1(xs)