From 9b55804cbde86c1a8ed1e4214811f3cf4e4a33d3 Mon Sep 17 00:00:00 2001
From: tiemvanderdeure <tiemvanderdeure@gmail.com>
Date: Tue, 16 Sep 2025 18:25:21 +0200
Subject: [PATCH] first draft

---
 src/probabilistic.jl | 110 +++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 110 insertions(+)

diff --git a/src/probabilistic.jl b/src/probabilistic.jl
index a3de1b8..59960d7 100644
--- a/src/probabilistic.jl
+++ b/src/probabilistic.jl
@@ -536,3 +536,113 @@ $DOC_DISTRIBUTIONS
 SphericalScore
 "$SphericalScoreDoc"
 const spherical_score = SphericalScore()
+
+
+# ---------------------------------------------------------------------
+# Continuous Boyce Index
+struct _ContinuousBoyceIndex 
+    n_bins::Integer
+    bin_overlap::AbstractFloat
+    min::Union{AbstractFloat, Nothing}
+    max::Union{AbstractFloat, Nothing}
+    cor::Function
+    function _ContinuousBoyceIndex(; n_bins = 101, bin_overlap = 0.1, min = nothing, max = nothing, cor = StatsBase.corspearman)
+        new(n_bins, bin_overlap, min, max, cor)
+    end
+end
+
+ContinuousBoyceIndex(; kw...) = _ContinuousBoyceIndex(; kw...) |> robust_measure |> fussy_measure
+
+function (m::_ContinuousBoyceIndex)(ŷ::UnivariateFiniteArray, y::NonMissingCatArrOrSub; warn=true)
+    warn && @warn ConfusionMatrices.WARN_UNORDERED(levels(y))
+    positive_class = classes(first(ŷ))|> last
+    scores = pdf.(ŷ, positive_class)
+    ma = isnothing(m.max) ? maximum(scores) : m.max
+    mi = isnothing(m.min) ? minimum(scores) : m.min
+    binwidth = m.bin_overlap * (ma - mi)
+
+    return _cbi(scores, y, positive_class, m.n_bins, binwidth, ma, mi, m.cor)
+end
+
+function _cbi(scores, y, positive_class, nbins, binwidth, ma, mi, cor)
+    binstarts = range(mi, stop=ma-binwidth, length=nbins)
+    binends = range(mi + binwidth, stop=ma, length=nbins)
+
+    sorted_indices = sortperm(scores)
+    sorted_scores = view(scores, sorted_indices)
+    sorted_y = view(y, sorted_indices)
+
+    tot_positive = count(==(positive_class), y)
+    tot_negative = length(y) - tot_positive
+
+    n_positive = zeros(Int, nbins)
+    n_negative = zeros(Int, nbins)
+
+    @inbounds for i in 1:nbins
+        bin_index_first = searchsortedfirst(sorted_scores, binstarts[i])
+        bin_index_last = searchsortedlast(sorted_scores, binends[i])
+        @inbounds for j in bin_index_first:bin_index_last
+            if sorted_y[j] == positive_class 
+                n_positive[i] += 1
+            end
+        end
+        n_negative[i] = bin_index_last - bin_index_first + 1 - n_positive[i]
+    end
+
+    n_total = n_positive .+ n_negative
+
+    # omit bins with no negative - we don't want to divide by zero
+    no_obs = n_negative .== 0
+    deleteat!(n_positive, no_obs)
+    deleteat!(n_negative, no_obs)
+    binstarts = binstarts[.!no_obs]
+
+    binmeans = (n_positive ./ tot_positive) ./ (n_negative ./ tot_negative)
+    r = cor(binmeans, binstarts)
+    isnan(r) && error()
+    return r
+end
+
+const ContinuousBoyceIndexType = API.FussyMeasure{<:API.RobustMeasure{<:_ContinuousBoyceIndex}}
+
+@fix_show ContinuousBoyceIndex::ContinuousBoyceIndexType
+
+StatisticalMeasures.@trait(
+    _ContinuousBoyceIndex,
+    consumes_multiple_observations=true,
+    observation_scitype = Finite{2},
+    kind_of_proxy=StatisticalMeasures.LearnAPI.Distribution(),
+    orientation=Score(),
+    external_aggregation_mode=Mean(),
+    human_name = "continuous boyce index",
+)
+
+register(ContinuousBoyceIndex, "continuous_boyce_index", "cbi")
+
+const ContinuousBoyceIndexDoc = docstring(
+    "ContinuousBoyceIndex(; n_bins=101, bin_overlap=0.1, min=nothing, max=nothing, cor=StatsBase.corspearman)",
+    body=
+"""
+The Continuous Boyce Index is a measure for evaluating the performance of probabilistic predictions for binary classification, 
+especially for presence-background data in ecological modeling. 
+It compares the predicted probability scores for the positive class across bins, giving higher scores if the ratio of positive
+    and negative samples in each bin is strongly correlated to the value at that bin.
+
+- `n_bins`: Number of bins to use for score partitioning.
+- `bin_overlap`: Fractional overlap between bins.
+- `min`, `max`: Optional minimum and maximum score values for binning.
+- `cor`: Correlation function (default: Spearman correlation).
+
+The predictions `ŷ` should be a vector of `UnivariateFinite` distributions from CategoricalDistributions.jl, and `y` a vector of ground truth labels.
+
+Returns the correlation between the ratio of positive to negative samples in each bin and the bin centers.
+
+See also [Boyce Index (Wikipedia)](https://en.wikipedia.org/wiki/Boyce_index).
+""",
+    scitype="",
+)
+
+"$ContinuousBoyceIndexDoc"
+ContinuousBoyceIndex
+"$ContinuousBoyceIndexDoc"
+const cbi = ContinuousBoyceIndex()