Support aggregated metrics, coverage, and novelty in CV

Same set of supported metrics as `evaluate()`
takuti · Nov 25, 2022 · 47fcaed · 47fcaed
1 parent 2eaefd8
commit 47fcaed
Showing 1 changed file with 7 additions and 6 deletions.
diff --git a/src/evaluation/cross_validation.jl b/src/evaluation/cross_validation.jl
@@ -3,27 +3,28 @@ export cross_validation, leave_one_out
 """
     cross_validation(
         n_folds::Integer,
-        metric::RankingMetric,
+        metric::Union{RankingMetric, AggregatedMetric, Coverage, Novelty},
         topk::Integer,
         recommender_type::Type{<:Recommender},
         data::DataAccessor,
-        recommender_args...
+        recommender_args...;
+        allow_repeat::Bool=false
     )
 
 Conduct `n_folds` cross validation for a combination of recommender `recommender_type` and ranking metric `metric`. A recommender is initialized with `recommender_args` and runs top-`k` recommendation.
 """
-function cross_validation(n_folds::Integer, metric::RankingMetric, topk::Integer, recommender_type::Type{<:Recommender}, data::DataAccessor, recommender_args...)
+function cross_validation(n_folds::Integer, metric::Metric, topk::Integer, recommender_type::Type{<:Recommender}, data::DataAccessor, recommender_args...; allow_repeat=false)
     cross_validation(n_folds, [metric], topk, recommender_type, data, recommender_args...)[1]
 end
 
 function cross_validation(n_folds::Integer, metrics::AbstractVector{T}, topk::Integer,
-                          recommender_type::Type{<:Recommender}, data::DataAccessor, recommender_args...
-                         ) where T<:RankingMetric
+                          recommender_type::Type{<:Recommender}, data::DataAccessor, recommender_args...; allow_repeat::Bool=false
+                         ) where T<:Metric
     accum_accuracy = zeros(length(metrics))
     for (train_data, truth_data) in split_data(data, n_folds)
         recommender = recommender_type(train_data, recommender_args...)
         fit!(recommender)
-        accum_accuracy += evaluate(recommender, truth_data, metrics, topk)
+        accum_accuracy += evaluate(recommender, truth_data, metrics, topk; allow_repeat=allow_repeat)
     end
     accum_accuracy / n_folds
 end