Refactor accessors on containers/arrays

src/JuMP.jl

@@ -13,6 +13,7 @@ isdefined(Base, :__precompile__) && __precompile__()
 module JuMP
 
 importall Base.Operators
+import Base.map
 
 import MathProgBase
 
@@ -394,10 +395,12 @@ end
 # internal method that doesn't print a warning if the value is NaN
 _getValue(v::Variable) = v.m.colVal[v.col]
 
+getvaluewarn(v) = Base.warn("Variable value not defined for $(getname(v)). Check that the model was properly solved.")
+
 function getvalue(v::Variable)
     ret = _getValue(v)
     if isnan(ret)
-        Base.warn("Variable value not defined for $(getname(v)). Check that the model was properly solved.")
+        getvaluewarn(v)
     end
     ret
 end
@@ -434,11 +437,17 @@ function getvalue(arr::Array{Variable})
 end
 
 # Dual value (reduced cost) getter
+
+# internal method that doesn't print a warning if the value is NaN
+_getDual(v::Variable) = v.m.redCosts[v.col]
+
+getdualwarn(::Variable) = error("Variable bound duals (reduced costs) not available. Check that the model was properly solved and no integer variables are present.")
+
 function getdual(v::Variable)
     if length(v.m.redCosts) < MathProgBase.numvar(v.m)
-        error("Variable bound duals (reduced costs) not available. Check that the model was properly solved and no integer variables are present.")
+        getdualwarn(v)
     end
-    return v.m.redCosts[v.col]
+    return _getDual(v)
 end
 
 const var_cats = [:Cont, :Int, :Bin, :SemiCont, :SemiInt]
@@ -538,11 +547,16 @@ LinearConstraint(ref::LinConstrRef) = ref.m.linconstr[ref.idx]::LinearConstraint
 
 linearindex(x::ConstraintRef) = x.idx
 
+# internal method that doesn't print a warning if the value is NaN
+_getDual(c::LinConstrRef) = c.m.linconstrDuals[c.idx]
+
+getdualwarn(::LinConstrRef) = error("Dual solution not available. Check that the model was properly solved and no integer variables are present.")
+
 function getdual(c::LinConstrRef)
     if length(c.m.linconstrDuals) != MathProgBase.numlinconstr(c.m)
-        error("Dual solution not available. Check that the model was properly solved and no integer variables are present.")
+        warnnan(_getDual)
     end
-    return c.m.linconstrDuals[c.idx]
+    return _getDual(c)
 end
 
 # Returns the number of non-infinity and nonzero bounds on variables
@@ -796,13 +810,14 @@ function getconstraint(m::Model, conname::Symbol)
 end
 
 # usage warnings
-function getvalue_warn(x::JuMPContainer{Variable})
+function mapcontainer_warn(f, x::JuMPContainer{Variable})
     isempty(x) && return
     v = first(values(x))
     m = v.m
-    m.getvalue_counter += 1
-    if m.getvalue_counter > 400
-        Base.warn_once("getvalue has been called on a collection of variables a large number of times. For performance reasons, this should be avoided. Instead of getvalue(x)[a,b,c], use getvalue(x[a,b,c]) to avoid temporary allocations.")
+    m.map_counter += 1
+    if m.map_counter > 400
+        # It might not be f that was called the 400 first times but most probably it is f
+        Base.warn_once("$f has been called on a collection of variables a large number of times. For performance reasons, this should be avoided. Instead of $f(x)[a,b,c], use $f(x[a,b,c]) to avoid temporary allocations.")
     end
     return
 end

src/JuMPContainer.jl

@@ -42,6 +42,9 @@ type JuMPContainerData
     condition
 end
 
+# Needed by getvaluewarn when called by _mapInner
+getname(data::JuMPContainerData) = data.name
+
 #JuMPDict{T,N}(name::AbstractString) =
 #    JuMPDict{T,N}(Dict{NTuple{N},T}(), name)
 
@@ -98,25 +101,40 @@ pushmeta!(x::JuMPContainer, sym::Symbol, val) = (x.meta[sym] = val)
 getmeta(x::JuMPContainer, sym::Symbol) = x.meta[sym]
 
 # duck typing approach -- if eltype(innerArray) doesn't support accessor, will fail
-for accessor in (:getdual, :getlowerbound, :getupperbound)
-    @eval $accessor(x::Union{JuMPContainer,Array}) = map($accessor,x)
+for accessor in (:getdual, :getlowerbound, :getupperbound, :getvalue)
+    @eval $accessor(x::AbstractArray) = map($accessor,x)
+end
+# With JuMPContainer, we take care in _mapInner of the warning if NaN values are returned
+# by the accessor so we use the inner accessor that does not generate warnings
+for (accessor, inner) in ((:getdual, :_getDual), (:getlowerbound, :getlowerbound), (:getupperbound, :getupperbound), (:getvalue, :_getValue))
+    @eval $accessor(x::JuMPContainer) = map($inner,x)
 end
 
+
 _similar(x::Array) = Array(Float64,size(x))
 _similar{T}(x::Dict{T}) = Dict{T,Float64}()
 
 _innercontainer(x::JuMPArray) = x.innerArray
 _innercontainer(x::JuMPDict)  = x.tupledict
 
-function _getValueInner(x)
+# Warning for getter returning NaN
+function _warnnan(f, data)
+    if f === _getValue
+        getvaluewarn(data)
+    elseif f === _getDual
+        getdualwarn(data)
+    end
+end
+
+function _mapInner(f, x::JuMPContainer)
     vars = _innercontainer(x)
     vals = _similar(vars)
     data = printdata(x)
     warnedyet = false
     for I in eachindex(vars)
-        tmp = _getValue(vars[I])
+        tmp = f(vars[I])
         if isnan(tmp) && !warnedyet
-            warn("Variable value not defined for entry of $(data.name). Check that the model was properly solved.")
+            _warnnan(f, data.name)
             warnedyet = true
         end
         vals[I] = tmp
@@ -127,9 +145,9 @@ end
 JuMPContainer_from(x::JuMPDict,inner) = JuMPDict(inner)
 JuMPContainer_from(x::JuMPArray,inner) = JuMPArray(inner, x.indexsets)
 
-function getvalue(x::JuMPContainer)
-    getvalue_warn(x)
-    ret = JuMPContainer_from(x,_getValueInner(x))
+function map(f, x::JuMPContainer)
+    mapcontainer_warn(f, x)
+    ret = JuMPContainer_from(x,_getValueInner(f, x))
     # I guess copy!(::Dict, ::Dict) isn't defined, so...
     for (key,val) in x.meta
         ret.meta[key] = val

test/variable.jl

@@ -224,3 +224,19 @@ end
         @test MathProgBase.numvar(m) == 4
     end
 end
+
+@testset "[variable] getvalue on sparse array #889" begin
+    m = Model()
+    @variable(m, x)
+    @variable(m, y)
+    X = sparse([1, 3], [2, 3], [x, y])
+
+    @test typeof(X) == SparseMatrixCSC{Variable, Int}
+
+    setvalue(x, 1)
+    setvalue(y, 2)
+
+    Y = getvalue(X)
+    @test typeof(Y) == SparseMatrixCSC{Float64, Int}
+    @test Y == sparse([1, 3], [2, 3], [1, 2])
+end