コード例 #1
0
 def _make_rvs(self):
     r_min, r_max = self._get_return_ranges()
     # Variables for expected return given T under the candidate policy
     R0 = rvs.BoundedRealSampleSet(name='R0', lower=r_min, upper=r_max)
     R1 = rvs.BoundedRealSampleSet(name='R1', lower=r_min, upper=r_max)
     ER0 = R0.expected_value('E[R|T=0]', mode=mode)
     ER1 = R1.expected_value('E[R|T=1]', mode=mode)
     # Constants
     eM = rvs.constant(self.epsilons[0], name='eM')
     eF = rvs.constant(self.epsilons[1], name='eF')
     r_ref0 = rvs.constant(self._ref_return_T0, name='Avg[R_ref|T=0]')
     r_ref1 = rvs.constant(self._ref_return_T1, name='Avg[R_ref|T=1]')
     # BQF and Constraint Objectives
     #   g0(theta) := (E[R_ref|T=0] - Average(R|T=0,D) - eM
     #   g1(theta) := (E[R_ref|T=1] - Average(R|T=1,D) - eF
     BQF0 = rvs.sum(r_ref0, -ER0, name='BQF0')
     BQF1 = rvs.sum(r_ref1, -ER1, name='BQF1')
     CO0 = rvs.sum(BQF0, -eM, name='CO0')
     SCO0 = rvs.sum(BQF0, -eM, name='SCO0', scaling=scaling)
     CO1 = rvs.sum(BQF1, -eF, name='CO1')
     SCO1 = rvs.sum(BQF1, -eF, name='SCO1', scaling=scaling)
     # Store the sample sets and variables
     self._scheck_rvs = [CO0, CO1]
     self._ccheck_rvs = [SCO0, SCO1]
     self._eval_rvs = {
         'bqf_0_mean': BQF0,
         'co_0_mean': CO0,
         'bqf_1_mean': BQF1,
         'co_1_mean': CO1
     }
     # Add the sample sets and variables to the manager
     self._vm = rvs.VariableManager(self._preprocessor)
     self._vm.add_sample_set(R0, R1)
     self._vm.add(ER0, ER1, BQF0, CO0, SCO0, BQF1, CO1, SCO1)
コード例 #2
0
        def _make_rvs(self):
            r_min, r_max = self._get_return_ranges()
            # Variables for expected return given T under the candidate policy
            R1 = rvs.BoundedRealSampleSet(name='R1', lower=r_min, upper=r_max)
            ER1 = R1.expected_value('E[R|T=1]', mode=mode)
            # Variables for expected return given T under the reference policy
            R_ref1 = rvs.BoundedRealSampleSet(name='R_ref1',
                                              lower=r_min,
                                              upper=r_max)
            ER_ref1 = R_ref1.expected_value('E[R_ref|T=1]', mode=mode)

            # Constants
            e = rvs.constant(self.epsilons[0], name='e')
            # BQF and Constraint Objectives
            #   g(theta) := (E[R_ref|T=1] - E[R|T=1]) - e
            BQF = rvs.sum(ER_ref1, -ER1, name='BQF')
            CO = rvs.sum(BQF, -e, name='CO')
            SCO = rvs.sum(BQF, -e, name='SCO', scaling=scaling)
            # Store the sample sets and variables
            self._scheck_rvs = [CO]
            self._ccheck_rvs = [SCO]
            self._eval_rvs = {'bqf_0_mean': BQF, 'co_0_mean': CO}
            # Add the sample sets and variables to the manager
            self._vm = rvs.VariableManager(self._preprocessor)
            self._vm.add_sample_set(R1, R_ref1)
            self._vm.add(ER1, ER_ref1, BQF, CO, SCO)
コード例 #3
0
 def _make_rvs(self):
     # Sample sets for false-positive classifications conditioned on T
     Pos0 = rvs.BoundedRealSampleSet(name='Pos0', lower=0, upper=1)
     Pos1 = rvs.BoundedRealSampleSet(name='Pos1', lower=0, upper=1)
     # Variables representing the conditional false-positive rates and the BQF
     PrPos0 = Pos0.expected_value('Pr(Pos|T=0)', mode=mode)
     PrPos1 = Pos1.expected_value('Pr(Pos|T=1)', mode=mode)
     # Constants
     pct = rvs.constant(self.epsilons[0], name='pct')
     # BQF
     BQF = rvs.maxrec(-(PrPos0 / PrPos1), name='BQF')
     CO = rvs.sum(BQF, -pct, name='CO')
     SCO = rvs.sum(BQF, -pct, name='SCO', scaling=scaling)
     # Store the sample sets and variables
     self._scheck_rvs = [CO]
     self._ccheck_rvs = [SCO]
     self._eval_rvs = {'bqf_0_mean': BQF, 'co_0_mean': CO}
     # Add the sample sets and variables to the manager
     self._vm = rvs.VariableManager(self._preprocessor)
     self._vm.add_sample_set(Pos0, Pos1)
     self._vm.add(PrPos0, PrPos1, BQF, CO, SCO)