[UPD] Change name of function argsup_proba_risk to risk_proba

Author: Henri-Gerard

examples/risk-example.jl

@@ -62,7 +62,7 @@ end
 s_bounds = [(0, 1)] 			# bounds on the state
 u_bounds = [(CONTROL_MIN, CONTROL_MAX)] # bounds on controls
 
-println("Initialzing functions to compare execution time")
+println("Initializing functions to compare execution time")
 spmodel = LinearSPModel(N_STAGES,u_bounds,[S0],cost_t,dynamic,xi_laws, riskMeasure = Expectation())
 set_state_bounds(spmodel, s_bounds) 	# adding the bounds to the model
 # 10 forward pass, stop at MAX_ITER

src/StochDynamicProgramming.jl

@@ -25,7 +25,7 @@ export solve_SDDP,
         StochDynProgModel, SDPparameters, solve_dp,
         sampling, get_control, get_bellman_value,
         benchmark_parameters, SDDPInterface,
-        argsup_proba_risk,
+        risk_proba,
         RiskMeasure, AVaR, Expectation, WorstCase, ConvexCombi, PolyhedralRisk
 
 include("noises.jl")

src/forwardBackwardIterations.jl

@@ -345,7 +345,7 @@ function compute_cuts_hd!(model::SPModel, param::SDDPparameters,
         proba /= sum(proba)
 
         #Modify the probability vector to compute the value of the risk measure
-        proba = argsup_proba_risk(proba,model.riskMeasure,costs)
+        proba = risk_proba(proba,model.riskMeasure,costs)
 
         # Compute expectation of subgradient λ:
         subgradient = vec(sum(proba' .* subgradient_array, 2))

src/risk.jl

@@ -33,16 +33,16 @@ allowing to compute the risk
     a new probability distribution taking risk into account.
 
 """
-function argsup_proba_risk(prob, riskMeasure::RiskMeasure,costs)
-    error("'argsup_proba_risk' not defined for $(typedof(s))")
+function risk_proba(prob, riskMeasure::RiskMeasure,costs)
+    error("'risk_proba' not defined for $(typedof(s))")
 end
 
 """
 $(TYPEDEF)
 
 Return the probability distribution to compute a Average Value at Risk of level beta.
 """
-function argsup_proba_risk(prob,riskMeasure::AVaR,costs)
+function risk_proba(prob,riskMeasure::AVaR,costs)
     perm = sortperm(costs,rev = true)
     beta = riskMeasure.beta
     proba = zeros(length(prob))
@@ -69,7 +69,7 @@ $(TYPEDEF)
 
 Leave the probability distribution unchanged to compute the expectation.
 """
-function argsup_proba_risk(prob,riskMeasure::Expectation,costs)
+function risk_proba(prob,riskMeasure::Expectation,costs)
     return prob
 end
 
@@ -78,7 +78,7 @@ $(TYPEDEF)
 perm
 Return a dirac on the worst cost as a probability distribution.
 """
-function argsup_proba_risk(prob,riskMeasure::WorstCase,costs)
+function risk_proba(prob,riskMeasure::WorstCase,costs)
     proba = zeros(length(prob))
     proba[indmax(costs)] = 1
     return proba
@@ -90,7 +90,7 @@ $(TYPEDEF)
 Return the probability distribution to compute a convex combination
 between expactation and an Average Value at Risk of level beta.
 """
-function argsup_proba_risk(prob,riskMeasure::ConvexCombi,costs)
+function risk_proba(prob,riskMeasure::ConvexCombi,costs)
     perm = sortperm(costs,rev = true)
     beta = riskMeasure.beta
     lambda = riskMeasure.lambda
@@ -119,7 +119,7 @@ $(TYPEDEF)
 Return the worst extreme probability distribution
 defining the convex set P
 """
-function argsup_proba_risk(prob,riskMeasure::PolyhedralRisk,costs)
+function risk_proba(prob,riskMeasure::PolyhedralRisk,costs)
     P = riskMeasure.polyset
     valuesup = P*costs
     return P[indmax(valuesup),:]

test/changeprob.jl

@@ -27,26 +27,26 @@ end
     @testset "Equality WorstCase AVaR(0)" begin
         n = 100
         prob = 1/n*ones(n)
-        @test sum(abs.(argsup_proba_risk(prob,AVaR(0),1:n)-argsup_proba_risk(prob,WorstCase(),1:n)) .<= EPSILON*ones(n)) == n
+        @test sum(abs.(risk_proba(prob,AVaR(0),1:n)-risk_proba(prob,WorstCase(),1:n)) .<= EPSILON*ones(n)) == n
     end
 
     @testset "Equality Expectation AVaR(1)" begin
         n = 100
         prob = 1/n*ones(n)
-        @test sum(abs.(argsup_proba_risk(prob,AVaR(1),1:n)-argsup_proba_risk(prob,Expectation(),1:n)) .<= EPSILON*ones(n)) == n
+        @test sum(abs.(risk_proba(prob,AVaR(1),1:n)-risk_proba(prob,Expectation(),1:n)) .<= EPSILON*ones(n)) == n
     end
 
     @testset "Equality Expectation ConvexCombi(beta,1)" begin
         n = 100
         prob = 1/n*ones(n)
-        @test sum(abs.(argsup_proba_risk(prob,Expectation(),1:n)-argsup_proba_risk(prob,ConvexCombi(rand(),1),1:n)) .<= EPSILON*ones(n)) == n
+        @test sum(abs.(risk_proba(prob,Expectation(),1:n)-risk_proba(prob,ConvexCombi(rand(),1),1:n)) .<= EPSILON*ones(n)) == n
     end
 
     @testset "Equality AVaR(beta) ConvexCombi(beta,0)" begin
         n = 100
         beta = rand()
         prob = 1/n*ones(n)
-        @test sum(abs.(argsup_proba_risk(prob,AVaR(beta),1:n)-argsup_proba_risk(prob,ConvexCombi(beta,0),1:n)) .<= EPSILON*ones(n)) == n
+        @test sum(abs.(risk_proba(prob,AVaR(beta),1:n)-risk_proba(prob,ConvexCombi(beta,0),1:n)) .<= EPSILON*ones(n)) == n
     end
 
     # Check that in the case of minimization, AVaR find the worst costs
@@ -55,7 +55,7 @@ end
         betamin = rand()/2+0.5
         n = 100
         prob = 1/n*ones(n)
-        @test argsup_proba_risk(prob, AVaR(betamin), 1:n)'*(n:-1:1) - argsup_proba_risk(prob, AVaR(betamax), 1:n)'*(n:-1:1) >= 0
+        @test risk_proba(prob, AVaR(betamin), 1:n)'*(n:-1:1) - risk_proba(prob, AVaR(betamax), 1:n)'*(n:-1:1) >= 0
     end
 end
 
@@ -78,7 +78,7 @@ end
 
     status = solve(m)
 
-    probaAVaR = argsup_proba_risk(prob, AVaR(beta), X)
+    probaAVaR = risk_proba(prob, AVaR(beta), X)
 
     @test abs(probaAVaR'*X - getobjectivevalue(m)) <= EPSILON
 end
@@ -97,8 +97,8 @@ end
         polyset[i,i] = (beta*n-n+1)/(n*beta)
     end
 
-    probaAVaR = argsup_proba_risk(prob, AVaR(beta), X)
-    probaPolyhedral = argsup_proba_risk(prob, PolyhedralRisk(polyset), X)
+    probaAVaR = risk_proba(prob, AVaR(beta), X)
+    probaPolyhedral = risk_proba(prob, PolyhedralRisk(polyset), X)
 
     @test sum(abs.(probaAVaR-probaPolyhedral) .<= EPSILON*ones(n)) == n
 end