Merge pull request #183 from JuliaRobotics/feature/147_PointParametricEst

Closes #147 PointParametricEst implementation

Author: GearsAD

src/CloudGraphsDFG/services/CloudGraphsDFG.jl

@@ -182,7 +182,8 @@ function getVariable(dfg::CloudGraphsDFG, variableId::Int64)::DFGVariable
     # props["label"] = Symbol(variable.label)
     timestamp = DateTime(props["timestamp"])
     tags =  JSON2.read(props["tags"], Vector{Symbol})
-    estimateDict = JSON2.read(props["estimateDict"], Dict{Symbol, Dict{Symbol, VariableEstimate}})
+    #TODO this will work for some time, but unpacking in an <: AbstractPointParametricEst would be lekker.
+    estimateDict = JSON2.read(props["estimateDict"], Dict{Symbol, MeanMaxPPE})
     smallData = nothing
     smallData = JSON2.read(props["smallData"], Dict{String, String})
 

src/DistributedFactorGraphs.jl

@@ -33,13 +33,16 @@ export hasFactor, hasVariable, isInitialized, getFactorFunction, isVariable, isF
 export mergeUpdateVariableSolverData!, mergeUpdateGraphSolverData!
 
 # Solver (IIF) Exports
-export VariableNodeData, PackedVariableNodeData, VariableEstimate
+export VariableNodeData, PackedVariableNodeData
 export GenericFunctionNodeData#, FunctionNodeData
 export getSerializationModule, setSerializationModule!
 export pack, unpack
 # Resolve with above
 export packVariable, unpackVariable, packFactor, unpackFactor
 
+#PPE exports
+export MeanMaxPPE
+
 #Interfaces
 export getAdjacencyMatrixSparse
 

src/entities/DFGVariable.jl

@@ -63,26 +63,28 @@ mutable struct PackedVariableNodeData
                          x15::Bool ) = new(x1,x2,x3,x4,x5,x6,x7,x8,x9,x10,x11,x12,x13,x14,x15)
 end
 
-
-abstract type AbstractVariableEstimate end
+# AbstractPointParametricEst interface
+abstract type AbstractPointParametricEst end
 """
     $TYPEDEF
 
-Data container to store Parameteric Point Estimate (PPE) from a variety of types.
-
-Notes
-- `ppeType` is something like `:max/:mean/:modefit` etc.
-- `solveKey` is from super-solve concept, starting with `:default`,
-- `estimate` is the actual numerical estimate value,
-- Additional information such as how the data is represented (ie softtype) is stored alongside this data container in the `DFGVariableSummary` container.
+Data container to store Parameteric Point Estimate (PPE) for mean and max.
 """
-struct VariableEstimate <: AbstractVariableEstimate
-  solverKey::Symbol
-  ppeType::Symbol
-  estimate::Vector{Float64}
-  lastUpdatedTimestamp::DateTime
+struct MeanMaxPPE <: AbstractPointParametricEst
+    solverKey::Symbol #repeated because of Sam's request
+    max::Vector{Float64}
+    mean::Vector{Float64}
+    lastUpdatedTimestamp::DateTime
 end
-VariableEstimate(solverKey::Symbol, type::Symbol, estimate::Vector{Float64}) = VariableEstimate(solverKey, type, estimate, now())
+MeanMaxPPE(solverKey::Symbol,max::Vector{Float64},mean::Vector{Float64}) = MeanMaxPPE(solverKey, max, mean, now())
+
+getMaxPPE(est::AbstractPointParametricEst) = est.max
+getMeanPPE(est::AbstractPointParametricEst) = est.mean
+getLastUpdatedTimestamp(est::AbstractPointParametricEst) = est.lastUpdatedTimestamp
+
+
+VariableEstimate(params...) = errror("VariableEstimate is depreciated, please use MeanMaxPPE")
+
 
 """
     $(TYPEDEF)
@@ -93,7 +95,7 @@ mutable struct DFGVariable <: AbstractDFGVariable
     label::Symbol
     timestamp::DateTime
     tags::Vector{Symbol}
-    estimateDict::Dict{Symbol, Dict{Symbol, <: AbstractVariableEstimate}}
+    estimateDict::Dict{Symbol, <: AbstractPointParametricEst}
     solverDataDict::Dict{Symbol, VariableNodeData}
     smallData::Dict{String, String}
     bigData::Dict{Symbol, AbstractBigDataEntry}
@@ -107,10 +109,10 @@ end
 DFGVariable constructors.
 """
 DFGVariable(label::Symbol, _internalId::Int64) =
-        DFGVariable(label, now(), Symbol[], Dict{Symbol, Dict{Symbol, VariableEstimate}}(), Dict{Symbol, VariableNodeData}(:default => VariableNodeData()), Dict{String, String}(), Dict{Symbol,AbstractBigDataEntry}(), 0, 0, _internalId)
+        DFGVariable(label, now(), Symbol[], Dict{Symbol, MeanMaxPPE}(), Dict{Symbol, VariableNodeData}(:default => VariableNodeData()), Dict{String, String}(), Dict{Symbol,AbstractBigDataEntry}(), 0, 0, _internalId)
 
 DFGVariable(label::Symbol) =
-        DFGVariable(label, now(), Symbol[], Dict{Symbol, VariableEstimate}(), Dict{Symbol, VariableNodeData}(:default => VariableNodeData()), Dict{String, String}(), Dict{Symbol,AbstractBigDataEntry}(), 0, 0, 0)
+        DFGVariable(label, now(), Symbol[], Dict{Symbol, MeanMaxPPE}(), Dict{Symbol, VariableNodeData}(:default => VariableNodeData()), Dict{String, String}(), Dict{Symbol,AbstractBigDataEntry}(), 0, 0, 0)
 
 # Accessors
 label(v::DFGVariable) = v.label
@@ -138,12 +140,7 @@ solverData(v::DFGVariable, key::Symbol=:default) = haskey(v.solverDataDict, key)
 Retrieve data structure stored in a variable.
 """
 function getData(v::DFGVariable; solveKey::Symbol=:default)::VariableNodeData
-  #FIXME but back in later, it just slows everything down
-  if !(@isdefined getDataWarnOnce)
-    @warn "getData is deprecated, please use solverData(), future warnings in getData is suppressed"
-    global getDataWarnOnce = true
-  end
-  # @warn "getData is deprecated, please use solverData()"
+  @warn "getData is deprecated, please use solverData()"
   return v.solverDataDict[solveKey]
 end
 """
@@ -166,7 +163,7 @@ mutable struct DFGVariableSummary <: AbstractDFGVariable
     label::Symbol
     timestamp::DateTime
     tags::Vector{Symbol}
-    estimateDict::Dict{Symbol, Dict{Symbol, <:AbstractVariableEstimate}}
+    estimateDict::Dict{Symbol, <:AbstractPointParametricEst}
     softtypename::Symbol
     _internalId::Int64
 end

src/services/DFGVariable.jl

@@ -17,7 +17,8 @@ function unpackVariable(dfg::G, packedProps::Dict{String, Any})::DFGVariable whe
     label = Symbol(packedProps["label"])
     timestamp = DateTime(packedProps["timestamp"])
     tags =  JSON2.read(packedProps["tags"], Vector{Symbol})
-    estimateDict = JSON2.read(packedProps["estimateDict"], Dict{Symbol, Dict{Symbol, VariableEstimate}})
+    #TODO this will work for some time, but unpacking in an <: AbstractPointParametricEst would be lekker.
+    estimateDict = JSON2.read(packedProps["estimateDict"], Dict{Symbol, MeanMaxPPE})
     smallData = nothing
     smallData = JSON2.read(packedProps["smallData"], Dict{String, String})
 
@@ -112,15 +113,11 @@ function ==(a::VariableNodeData,b::VariableNodeData, nt::Symbol=:var)
 end
 
 """
-    $(SIGNATURES)
-Equality check for VariableEstimate.
+    ==(x::T, y::T) where T <: AbstractPointParametricEst
+Equality check for AbstractPointParametricEst.
 """
-function ==(a::VariableEstimate, b::VariableEstimate)::Bool
-  a.solverKey != b.solverKey && @debug("solverKey are not equal")==nothing && return false
-  a.ppeType != b.ppeType && @debug("ppeType is not equal")==nothing && return false
-  a.estimate != b.estimate && @debug("estimate are not equal")==nothing && return false
-  a.lastUpdatedTimestamp != b.lastUpdatedTimestamp && @debug("lastUpdatedTimestamp is not equal")==nothing && return false
-  return true
+@generated function ==(x::T, y::T) where T <: AbstractPointParametricEst
+    mapreduce(n -> :(x.$n == y.$n), (a,b)->:($a && $b), fieldnames(x))
 end
 
 """

test/LightDFGSummaryTypes.jl

@@ -1,6 +1,6 @@
 dfg = LightDFG{NoSolverParams, DFGVariableSummary, DFGFactorSummary}()
 
-DistributedFactorGraphs.DFGVariableSummary(label::Symbol) = DFGVariableSummary(label, DistributedFactorGraphs.now(), Symbol[], Dict{Symbol, VariableEstimate}(), :NA, 0)
+DistributedFactorGraphs.DFGVariableSummary(label::Symbol) = DFGVariableSummary(label, DistributedFactorGraphs.now(), Symbol[], Dict{Symbol, MeanMaxPPE}(), :NA, 0)
 
 DistributedFactorGraphs.DFGFactorSummary(label::Symbol) = DFGFactorSummary(label, Symbol[], 0, Symbol[])
 
@@ -124,30 +124,16 @@ end
     var = getVariable(dfg, :a)
     #make a copy and simulate external changes
     newvar = deepcopy(var)
-    estimates(newvar)[:default] = Dict{Symbol, VariableEstimate}(
-        :max => VariableEstimate(:default, :max, [100.0]),
-        :mean => VariableEstimate(:default, :mean, [50.0]),
-        :modefit => VariableEstimate(:default, :modefit, [75.0]))
+    estimates(newvar)[:default] = MeanMaxPPE(:default, [100.0], [50.0])
     #update
     mergeUpdateVariableSolverData!(dfg, newvar)
-    #TODO maybe implement ==; @test newvar==var
-    Base.:(==)(varest1::VariableEstimate, varest2::VariableEstimate) = begin
-        varest1.lastUpdatedTimestamp == varest2.lastUpdatedTimestamp || return false
-        varest1.ppeType == varest2.ppeType || return false
-        varest1.solverKey == varest2.solverKey || return false
-        varest1.estimate == varest2.estimate || return false
-        return true
-    end
     #For now spot check
     # @test solverDataDict(newvar) == solverDataDict(var)
     @test estimates(newvar) == estimates(var)
 
     # Delete :default and replace to see if new ones can be added
     delete!(estimates(newvar), :default)
-    estimates(newvar)[:second] = Dict{Symbol, VariableEstimate}(
-        :max => VariableEstimate(:default, :max, [10.0]),
-        :mean => VariableEstimate(:default, :mean, [5.0]),
-        :ppe => VariableEstimate(:default, :ppe, [7.0]))
+    estimates(newvar)[:second] = MeanMaxPPE(:second, [10.0], [5.0])
 
     # Persist to the original variable.
     mergeUpdateVariableSolverData!(dfg, newvar)

test/iifInterfaceTests.jl

@@ -234,10 +234,7 @@ end
     var = getVariable(dfg, :a)
     #make a copy and simulate external changes
     newvar = deepcopy(var)
-    estimates(newvar)[:default] = Dict{Symbol, VariableEstimate}(
-        :max => VariableEstimate(:default, :max, [100.0]),
-        :mean => VariableEstimate(:default, :mean, [50.0]),
-        :modefit => VariableEstimate(:default, :modefit, [75.0]))
+    estimates(newvar)[:default] = MeanMaxPPE(:default, [100.0], [50.0])
     #update
     mergeUpdateVariableSolverData!(dfg, newvar)
 
@@ -246,10 +243,7 @@ end
     @test estimates(newvar) == estimates(var)
 
     # Add a new estimate.
-    estimates(newvar)[:second] = Dict{Symbol, VariableEstimate}(
-        :max => VariableEstimate(:default, :max, [10.0]),
-        :mean => VariableEstimate(:default, :mean, [5.0]),
-        :modefit => VariableEstimate(:default, :modefit, [7.0]))
+    estimates(newvar)[:second] = MeanMaxPPE(:second, [10.0], [5.0])
 
     # Confirm they're different
     @test estimates(newvar) != estimates(var)

test/interfaceTests.jl

@@ -208,30 +208,17 @@ end
     var = getVariable(dfg, :a)
     #make a copy and simulate external changes
     newvar = deepcopy(var)
-    estimates(newvar)[:default] = Dict{Symbol, VariableEstimate}(
-        :max => VariableEstimate(:default, :max, [100.0]),
-        :mean => VariableEstimate(:default, :mean, [50.0]),
-        :modefit => VariableEstimate(:default, :modefit, [75.0]))
+    estimates(newvar)[:default] = MeanMaxPPE(:default, [100.0], [50.0])
     #update
     mergeUpdateVariableSolverData!(dfg, newvar)
-    #TODO maybe implement ==; @test newvar==var
-    Base.:(==)(varest1::VariableEstimate, varest2::VariableEstimate) = begin
-        varest1.lastUpdatedTimestamp == varest2.lastUpdatedTimestamp || return false
-        varest1.ppeType == varest2.ppeType || return false
-        varest1.solverKey == varest2.solverKey || return false
-        varest1.estimate == varest2.estimate || return false
-        return true
-    end
+
     #For now spot check
     @test solverDataDict(newvar) == solverDataDict(var)
     @test estimates(newvar) == estimates(var)
 
     # Delete :default and replace to see if new ones can be added
     delete!(estimates(newvar), :default)
-    estimates(newvar)[:second] = Dict{Symbol, VariableEstimate}(
-        :max => VariableEstimate(:default, :max, [10.0]),
-        :mean => VariableEstimate(:default, :mean, [5.0]),
-        :modefit => VariableEstimate(:default, :modefit, [7.0]))
+    estimates(newvar)[:second] = MeanMaxPPE(:second, [10.0], [5.0])
 
     # Persist to the original variable.
     mergeUpdateVariableSolverData!(dfg, newvar)