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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||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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@@ -24,14 +24,14 @@ end | |
| accuracy(confusion::AbstractMatrix) | ||
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| Returns the percentage of matching classifications out of total classifications, | ||
| or `NaN` if `all(iszero, confusion)`. | ||
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| Note that `accuracy(confusion)` is equivalent to overall percent agreement | ||
| between `confusion`'s row classifier and column classifier. | ||
| """ | ||
| function accuracy(confusion::AbstractMatrix) | ||
| total = sum(confusion) | ||
| total == 0 && return NaN | ||
| return tr(confusion) / total | ||
| end | ||
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@@ -78,15 +78,12 @@ function binary_statistics(confusion::AbstractMatrix, class_index::Integer) | |
| false_negative_rate = (false_negatives == 0 && actual_positives == 0) ? | ||
| (zero(false_negatives) / one(actual_positives)) : | ||
| (false_negatives / actual_positives) | ||
| precision = (true_positives == 0 && predicted_positives == 0) ? NaN : | ||
| (true_positives / predicted_positives) | ||
| return (; predicted_positives, predicted_negatives, actual_positives, actual_negatives, | ||
| true_positives, true_negatives, false_positives, false_negatives, | ||
| true_positive_rate, true_negative_rate, false_positive_rate, | ||
| false_negative_rate, precision) | ||
| end | ||
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| function binary_statistics(confusion::AbstractMatrix) | ||
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@@ -105,7 +102,8 @@ Return `(κ, p₀)` where `κ` is Cohen's kappa and `p₀` percent agreement giv | |
| their equivalents in [`confusion_matrix`](@ref)). | ||
| """ | ||
| function cohens_kappa(class_count, hard_label_pairs) | ||
| all(issubset(pair, 1:class_count) for pair in hard_label_pairs) || | ||
| throw(ArgumentError("Unexpected class in `hard_label_pairs`.")) | ||
| p₀ = accuracy(confusion_matrix(class_count, hard_label_pairs)) | ||
| pₑ = _probability_of_chance_agreement(class_count, hard_label_pairs) | ||
| return _cohens_kappa(p₀, pₑ), p₀ | ||
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@@ -137,7 +135,7 @@ where: | |
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| - `bins` a vector with `bin_count` `Pairs` specifying the calibration curve's probability bins | ||
| - `fractions`: a vector where `fractions[i]` is the number of values in `probabilities` | ||
| that falls within `bin[i]` over the total number of values within `bin[i]`, or `NaN` | ||
| if the total number of values in `bin[i]` is zero. | ||
| - `totals`: a vector where `totals[i]` the total number of values within `bin[i]`. | ||
| - `mean_squared_error`: The mean squared error of `fractions` vs. an ideal calibration curve. | ||
|
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@@ -150,12 +148,12 @@ function calibration_curve(probabilities, bitmask; bin_count=10) | |
| bins = probability_bins(bin_count) | ||
| per_bin = [fraction_within(probabilities, bitmask, bin...) for bin in bins] | ||
| fractions, totals = first.(per_bin), last.(per_bin) | ||
| nonempty_indices = findall(!isnan, fractions) | ||
| if !isempty(nonempty_indices) | ||
| ideal = range(mean(first(bins)), mean(last(bins)); length=length(bins)) | ||
| mean_squared_error = mse(fractions[nonempty_indices], ideal[nonempty_indices]) | ||
| else | ||
| mean_squared_error = NaN | ||
| end | ||
| return (bins=bins, fractions=fractions, totals=totals, | ||
| mean_squared_error=mean_squared_error) | ||
|
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@@ -179,6 +177,6 @@ function fraction_within(values, bitmask, start, stop) | |
| total += 1 | ||
| end | ||
| end | ||
| fraction = iszero(total) ? NaN : (count / total) | ||
| return (fraction=fraction, total=total) | ||
| end | ||
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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).