Refactor evaluation metrics to output Legolas table rows #45
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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@@ -14,7 +14,7 @@ jobs: | |
| fail-fast: false | ||
| matrix: | ||
| version: | ||
| - '1.7' | ||
| - '1.6' | ||
| os: | ||
| - ubuntu-latest | ||
|
|
||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,133 @@ | ||
| # Arrow can't handle matrices---so when we write/read matrices, we have to pack and unpack them o_O | ||
| # https://github.com/apache/arrow-julia/issues/125 | ||
| vec_to_mat(mat::AbstractMatrix) = mat | ||
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| function vec_to_mat(vec::AbstractVector) | ||
| n = isqrt(length(vec)) | ||
| return reshape(vec, n, n) | ||
| end | ||
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| vec_to_mat(x::Missing) = return missing | ||
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| # Redefinition is workaround for https://github.com/beacon-biosignals/Legolas.jl/issues/9 | ||
| const EVALUATION_ROW_SCHEMA = Legolas.Schema("lighthouse.evaluation@1") | ||
|
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| """ | ||
| const EvaluationRow = Legolas.@row("lighthouse.evaluation@1", | ||
| class_labels::Union{Missing,Vector{String}}, | ||
| confusion_matrix::Union{Missing,Array{Int64}} = vec_to_mat(confusion_matrix), | ||
| discrimination_calibration_curve::Union{Missing, | ||
| Tuple{Vector{Float64}, | ||
| Vector{Float64}}}, | ||
| discrimination_calibration_score::Union{Missing,Float64}, | ||
| multiclass_IRA_kappas::Union{Missing,Float64}, | ||
| multiclass_kappa::Union{Missing,Float64}, | ||
| optimal_threshold::Union{Missing,Float64}, | ||
| optimal_threshold_class::Union{Missing,Int64}, | ||
| per_class_IRA_kappas::Union{Missing,Vector{Float64}}, | ||
| per_class_kappas::Union{Missing,Vector{Float64}}, | ||
| stratified_kappas::Union{Missing, | ||
| Vector{NamedTuple{(:per_class, | ||
| :multiclass, | ||
| :n), | ||
| Tuple{Vector{Float64}, | ||
| Float64, | ||
| Int64}}}}, | ||
| per_class_pr_curves::Union{Missing, | ||
| Vector{Tuple{Vector{Float64}, | ||
| Vector{Float64}}}}, | ||
| per_class_reliability_calibration_curves::Union{Missing, | ||
| Vector{Tuple{Vector{Float64}, | ||
| Vector{Float64}}}}, | ||
| per_class_reliability_calibration_scores::Union{Missing, | ||
| Vector{Float64}}, | ||
| per_class_roc_aucs::Union{Missing,Vector{Float64}}, | ||
| per_class_roc_curves::Union{Missing, | ||
| Vector{Tuple{Vector{Float64}, | ||
| Vector{Float64}}}}, | ||
| per_expert_discrimination_calibration_curves::Union{Missing, | ||
| Vector{Tuple{Vector{Float64}, | ||
| Vector{Float64}}}}, | ||
| per_expert_discrimination_calibration_scores::Union{Missing, | ||
| Vector{Float64}}, | ||
| spearman_correlation::Union{Missing, | ||
| NamedTuple{(:ρ, :n, | ||
| :ci_lower, | ||
| :ci_upper), | ||
| Tuple{Float64, | ||
| Int64, | ||
| Float64, | ||
| Float64}}}, | ||
| thresholds::Union{Missing,Vector{Float64}}) | ||
| EvaluationRow(evaluation_row_dict::Dict{String, Any}) -> EvaluationRow | ||
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| A type alias for [`Legolas.Row{typeof(Legolas.Schema("lighthouse.evaluation@1@1"))}`](https://beacon-biosignals.github.io/Legolas.jl/stable/#Legolas.@row) | ||
| representing the output metrics computed by [`evaluation_metrics_row`](@ref) and | ||
| [`evaluation_metrics`](@ref). | ||
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| Constructor that takes `evaluation_row_dict` converts [`evaluation_metrics`](@ref) | ||
| `Dict` of metrics results (e.g. from Lighthouse <v0.14.0) into an [`EvaluationRow`](@ref). | ||
| """ | ||
| const EvaluationRow = Legolas.@row("lighthouse.evaluation@1", | ||
| class_labels::Union{Missing,Vector{String}}, | ||
| confusion_matrix::Union{Missing,Array{Int64}} = vec_to_mat(confusion_matrix), | ||
| discrimination_calibration_curve::Union{Missing, | ||
| Tuple{Vector{Float64}, | ||
| Vector{Float64}}}, | ||
| discrimination_calibration_score::Union{Missing,Float64}, | ||
| multiclass_IRA_kappas::Union{Missing,Float64}, | ||
| multiclass_kappa::Union{Missing,Float64}, | ||
| optimal_threshold::Union{Missing,Float64}, | ||
| optimal_threshold_class::Union{Missing,Int64}, | ||
| per_class_IRA_kappas::Union{Missing,Vector{Float64}}, | ||
| per_class_kappas::Union{Missing,Vector{Float64}}, | ||
| stratified_kappas::Union{Missing, | ||
| Vector{NamedTuple{(:per_class, | ||
| :multiclass, | ||
| :n), | ||
| Tuple{Vector{Float64}, | ||
| Float64, | ||
| Int64}}}}, | ||
| per_class_pr_curves::Union{Missing, | ||
| Vector{Tuple{Vector{Float64}, | ||
| Vector{Float64}}}}, | ||
| per_class_reliability_calibration_curves::Union{Missing, | ||
| Vector{Tuple{Vector{Float64}, | ||
| Vector{Float64}}}}, | ||
| per_class_reliability_calibration_scores::Union{Missing, | ||
| Vector{Float64}}, | ||
| per_class_roc_aucs::Union{Missing,Vector{Float64}}, | ||
| per_class_roc_curves::Union{Missing, | ||
| Vector{Tuple{Vector{Float64}, | ||
| Vector{Float64}}}}, | ||
| per_expert_discrimination_calibration_curves::Union{Missing, | ||
| Vector{Tuple{Vector{Float64}, | ||
| Vector{Float64}}}}, | ||
| per_expert_discrimination_calibration_scores::Union{Missing, | ||
| Vector{Float64}}, | ||
| spearman_correlation::Union{Missing, | ||
| NamedTuple{(:ρ, :n, | ||
| :ci_lower, | ||
| :ci_upper), | ||
| Tuple{Float64, | ||
| Int64, | ||
| Float64, | ||
| Float64}}}, | ||
| thresholds::Union{Missing,Vector{Float64}}) | ||
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| function Legolas.Row{S}(evaluation_row_dict::Dict) where {S<:Legolas.Schema{Symbol("lighthouse.evaluation"), | ||
| 1}} | ||
| row = (; (Symbol(k) => v for (k, v) in pairs(evaluation_row_dict))...) | ||
| return EvaluationRow(row) | ||
| end | ||
|
|
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| """ | ||
| _evaluation_row_dict(row::EvaluationRow) -> Dict{String,Any} | ||
|
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| Convert [`EvaluationRow`](@ref) into `::Dict{String, Any}` results, as are | ||
| output by `[`evaluation_metrics`](@ref)` (and predated use of `EvaluationRow` in | ||
| Lighthouse <v0.14.0). | ||
| """ | ||
| function _evaluation_row_dict(row::EvaluationRow) | ||
| return Dict(string(k) => v for (k, v) in pairs(NamedTuple(row)) if !ismissing(v)) | ||
| end |
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Bumping the version b/c we've changed the expected output for "failure" cases from
missingtoNaNfor any metrics output types that show up in vectors (to aid in Arrow serialization).