Add basic precompiles

Author: Frankie Robertson

Project.toml

@@ -24,6 +24,7 @@ Measurements = "eff96d63-e80a-5855-80a2-b1b0885c5ab7"
 Mmap = "a63ad114-7e13-5084-954f-fe012c677804"
 OrderedCollections = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
 Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
+PrecompileTools = "aea7be01-6a6a-4083-8856-8a6e6704d82a"
 PsychometricsBazaarBase = "b0d9cada-d963-45e9-a4c6-4746243987f1"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
@@ -55,6 +56,7 @@ MacroTools = "^0.5.6"
 Measurements = "^2.10.0"
 Optim = "1.7.3"
 OrderedCollections = "^1.6"
+PrecompileTools = "1.2.1"
 PsychometricsBazaarBase = "^0.8.1"
 Reexport = "1"
 ResumableFunctions = "^0.6"

src/ComputerAdaptiveTesting.jl

@@ -43,4 +43,6 @@ include("./Comparison.jl")
 @reexport using .Sim: run_cat
 @reexport using .NextItemRules: preallocate
 
+include("./precompiles.jl")
+
 end

src/precompiles.jl

@@ -1,20 +1,31 @@
-@precompile_setup begin
-    const response_types = (BooleanResponse(), MultinomialResponse())
-    const domains = (OneDimContinuousDomain(), VectorContinuousDomain())
-    const item_banks = Dict(
-        (OneDimContinuousDomain(), BooleanResponse()) => [],
-        (OneDimContinuousDomain(), MultinomialResponse()) => [],
-        (VectorContinuousDomain(), BooleanResponse()) => [],
-        (VectorContinuousDomain(), MultinomialResponse()) => []
-    )
+using PrecompileTools
 
-    #struct BareResponses{ResponseTypeT <: ResponseType, ConcreteResponseTypeT, IndicesVecT <: AbstractVector}
-    @precompile_all_calls begin
-        for response_type in response_types
-            concrete_type = concrete_response_type(response)
-            single_response = Response(response_type, 1, zero(concrete_type))
-            bare_responses = BareResponses(response_type, [1], [zero(concrete_type)])
-        end
-        Response{ResponseTypeT <: ResponseType, ConcreteResponseTypeT}
+@setup_workload begin
+    using PsychometricsBazaarBase: Integrators
+    using FittedItemBanks: SimpleItemBankSpec, StdModel2PL, OneDimContinuousDomain, BooleanResponse
+    using FittedItemBanks.DummyData: dummy_item_bank
+    using Random: default_rng
+    using .Aggregators: LikelihoodAbilityEstimator, MeanAbilityEstimator, GriddedAbilityTracker,
+                        AbilityIntegrator
+    using .NextItemRules: catr_next_item_aliases, preallocate
+    using .Stateful: Stateful
+
+    rng = default_rng(42)
+    spec = SimpleItemBankSpec(StdModel2PL(), OneDimContinuousDomain(), BooleanResponse())
+    item_bank = dummy_item_bank(rng, spec, 2)
+    @compile_workload begin
+        integrator = Integrators.even_grid(-6.0, 6.0, 61)
+        lh_ability_est = LikelihoodAbilityEstimator()
+        lh_grid_tracker = GriddedAbilityTracker(lh_ability_est, integrator)
+        ability_integrator = AbilityIntegrator(integrator, lh_grid_tracker)
+        ability_estimator = MeanAbilityEstimator(lh_ability_est, ability_integrator)
+        next_item_rule = catr_next_item_aliases["MEPV"](ability_estimator)
+        cat = Stateful.StatefulCatConfig(CatConfig.CatRules(;
+            next_item=next_item_rule,
+            termination_condition=TerminationConditions.RunForeverTerminationCondition(),
+            ability_estimator=ability_estimator
+        ), item_bank)
+        Stateful.add_response!(cat, 1, 0)
+        Stateful.next_item(cat)
     end
-end
+end