Use expected_item_information for previous items in multidim case (mirtcat compatible)

Author: Frankie Robertson

src/next_item_rules/criteria/item/information.jl

@@ -26,14 +26,15 @@ function compute_criterion(
     return -item_criterion.expected_item_information(ir, ability)
 end
 
-struct InformationMatrixCriteria{AbilityEstimatorT <: AbilityEstimator, F} <:
+struct InformationMatrixCriteria{AbilityEstimatorT <: AbilityEstimator, F, G} <:
        ItemMultiCriterion
     ability_estimator::AbilityEstimatorT
-    expected_item_information::F
+    known_item_information::F
+    expected_item_information::G
 end
 
 function InformationMatrixCriteria(ability_estimator)
-    InformationMatrixCriteria(ability_estimator, expected_item_information)
+    InformationMatrixCriteria(ability_estimator, expected_item_information, expected_item_information)
 end
 
 function init_thread(item_criterion::InformationMatrixCriteria,
@@ -42,7 +43,8 @@ function init_thread(item_criterion::InformationMatrixCriteria,
     # θ update.
     # TODO: Update this to use track!(...) mechanism
     ability = maybe_tracked_ability_estimate(responses, item_criterion.ability_estimator)
-    responses_information(responses.item_bank, responses.responses, ability)
+    responses_information(responses.item_bank, responses.responses, ability;
+        information_func=item_criterion.known_item_information)
 end
 
 function compute_multi_criterion(
@@ -52,9 +54,9 @@ function compute_multi_criterion(
     # TODO: Add in information from the prior
     ability = maybe_tracked_ability_estimate(
         tracked_responses, item_criterion.ability_estimator)
-    return acc_info .+
-           item_criterion.expected_item_information(
+    exp_info = item_criterion.expected_item_information(
         ItemResponse(tracked_responses.item_bank, item_idx), ability)
+    return acc_info .+ exp_info
 end
 
 should_minimize(::InformationMatrixCriteria) = false

src/next_item_rules/criteria/pointwise/information_support.jl

@@ -94,14 +94,16 @@ function expected_item_information(ir::ItemResponse, θ::Vector)
     return -sum(eachslice(hess, dims=1) .* exp_resp)
 end
 
+expected_item_information(ir::ItemResponse, _, θ::Vector) = expected_item_information(ir, θ)
+
 function known_item_information(ir::ItemResponse, resp_value, θ)
     -ForwardDiff.hessian(θ -> log_resp(ir, resp_value, θ), θ)
 end
 
-function responses_information(item_bank::AbstractItemBank, responses::BareResponses, θ)
+function responses_information(item_bank::AbstractItemBank, responses::BareResponses, θ; information_func=known_item_information)
     d = domdims(item_bank)
     reduce(.+,
-        (known_item_information(ItemResponse(item_bank, resp_idx), resp_value > 0, θ)
+        (information_func(ItemResponse(item_bank, resp_idx), resp_value > 0, θ)
         for (resp_idx, resp_value)
         in zip(responses.indices, responses.values)); init = zeros(d, d))
 end