Exemplo n.º 1
0
    def fit(self, time_limit=None, node_limit=None, display_flag=False):

        if self._mip is None:
            return self.infeasible_info

        mip = self.mip
        mip = set_cpx_display_options(mip,
                                      display_mip=False,
                                      display_lp=display_flag,
                                      display_parameters=display_flag)

        # update time limit
        if time_limit is not None:
            mip = set_mip_time_limit(mip, time_limit)

        if node_limit is not None:
            mip = set_mip_node_limit(mip, node_limit)

        # solve
        start_time = mip.get_time()
        mip.solve()
        end_time = mip.get_time() - start_time

        info = self.solution_info
        info['runtime'] = end_time
        # assert self._check_solution(info)
        return info
Exemplo n.º 2
0
    def populate(self,
                 total_items=10,
                 time_limit=None,
                 node_limit=None,
                 display_flag=False,
                 enumeration_type='distinct_subsets'):

        mip = self.mip
        mip = set_cpx_display_options(mip,
                                      display_mip=False,
                                      display_lp=display_flag,
                                      display_parameters=display_flag)

        # update time limit
        if time_limit is not None:
            mip = set_mip_time_limit(mip, time_limit)

        if node_limit is not None:
            mip = set_mip_node_limit(mip, node_limit)

        if enumeration_type == 'mutually_exclusive':
            remove_solution = self.remove_all_features
        else:
            remove_solution = self.remove_feature_combination

        # enumerate soluitions
        k = 0
        all_info = []
        populate_start_time = mip.get_time()

        while k < total_items:

            # solve mip
            start_time = mip.get_time()
            mip.solve()
            run_time = mip.get_time() - start_time

            info = self.solution_info
            info['runtime'] = run_time

            if not info['feasible']:
                if self.print_flag:
                    print('recovered all minimum-cost items')
                break

            all_info.append(info)
            remove_solution()
            k += 1

        if self.print_flag:
            print('mined %d items in %1.1f seconds' %
                  (k, mip.get_time() - populate_start_time))

        return all_info
    def __init__(self, action_set, x = None, **kwargs):
        """
        :param x: vector of input variables for person x
        :param intercept: intercept value of score function
        :param coefs: coefficients of score function
        :param action_set: action set
        :param params: parameters for flipset form/flipset generation
                       (e.g. type of cost function to use / max items etc.)
        """
        # initialize Cplex MIP
        self._cpx_parameters = kwargs.get('cplex_parameters', self._default_cplex_parameters)
        self._set_mip_time_limit = set_cpx_time_limit
        self._set_mip_node_limit = set_cpx_node_limit
        self._set_mip_display = lambda mip, flag: set_cpx_display_options(mip, display_lp = flag, display_mip = flag, display_parameters = flag)

        ## initialize base class
        super().__init__(action_set = action_set, x = x, **kwargs)