436 dont pass entire data container #439
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src/counterfactuals/core_struct.jl
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| :iteration_count => 0, | ||
| :mutability => DataPreprocessing.mutability_constraints(data), |
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| :iteration_count => 0, | |
| :mutability => DataPreprocessing.mutability_constraints(data), | |
| :iteration_count => 0, :mutability => DataPreprocessing.mutability_constraints(data) |
src/objectives/distance_utils.jl
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| @@ -39,6 +39,7 @@ end | |||
| Computes the distance of the counterfactual from a point in the target main. | |||
| """ | |||
| function distance_from_target(ce::AbstractCounterfactualExplanation; K::Int=50, kwrgs...) | |||
| get!(ce.search, :potential_neighbours, CounterfactualExplanations.find_potential_neighbours(ce)) | |||
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| get!(ce.search, :potential_neighbours, CounterfactualExplanations.find_potential_neighbours(ce)) | |
| get!( | |
| ce.search, | |
| :potential_neighbours, | |
| CounterfactualExplanations.find_potential_neighbours(ce), | |
| ) |
src/counterfactuals/core_struct.jl
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| 4. Initializes the loss. | ||
| """ | ||
| function initialize!(ce::CounterfactualExplanation) | ||
|
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src/objectives/penalties.jl
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| return loss | ||
| end | ||
| end |
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| end | |
| end |
src/objectives/traits.jl
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| PenaltyRequirements(::Type{<:typeof(distance_from_target)}) = | ||
| NeedsNeighbours() |
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| PenaltyRequirements(::Type{<:typeof(distance_from_target)}) = | |
| NeedsNeighbours() | |
| PenaltyRequirements(::Type{<:typeof(distance_from_target)}) = NeedsNeighbours() |
src/generators/Generators.jl
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| Computes the total loss of a counterfactual explanation with respect to the search objective. | ||
| """ | ||
| total_loss(ce::AbstractCounterfactualExplanation) = ℓ(ce.generator, ce) + h(ce.generator, ce) |
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| total_loss(ce::AbstractCounterfactualExplanation) = ℓ(ce.generator, ce) + h(ce.generator, ce) | |
| total_loss(ce::AbstractCounterfactualExplanation) = | |
| ℓ(ce.generator, ce) + h(ce.generator, ce) |
src/generators/Generators.jl
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| Computes the total loss of a counterfactual explanation with respect to the search objective. | ||
| """ | ||
| total_loss(ce::AbstractCounterfactualExplanation) = hasfield(typeof(ce.generator), :loss) ? ℓ(ce.generator, ce) + h(ce.generator, ce) : nothing |
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| total_loss(ce::AbstractCounterfactualExplanation) = hasfield(typeof(ce.generator), :loss) ? ℓ(ce.generator, ce) + h(ce.generator, ce) : nothing | |
| total_loss(ce::AbstractCounterfactualExplanation) = if hasfield(typeof(ce.generator), :loss) | |
| ℓ(ce.generator, ce) + h(ce.generator, ce) | |
| else | |
| nothing | |
| end |
src/objectives/traits.jl
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| Check if a generator needs access to neighbors in the target class. | ||
| """ | ||
| needs_neighbours(gen::AbstractGenerator) = hasfield(typeof(gen), :penalty) ? any(needs_neighbours.(gen.penalty)) : false |
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| needs_neighbours(gen::AbstractGenerator) = hasfield(typeof(gen), :penalty) ? any(needs_neighbours.(gen.penalty)) : false | |
| needs_neighbours(gen::AbstractGenerator) = | |
| hasfield(typeof(gen), :penalty) ? any(needs_neighbours.(gen.penalty)) : false |
src/generators/Generators.jl
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| total_loss(ce::AbstractCounterfactualExplanation) = if hasfield(typeof(ce.generator), :loss) | ||
| ℓ(ce.generator, ce) + h(ce.generator, ce) | ||
| else | ||
| nothing | ||
| end |
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| total_loss(ce::AbstractCounterfactualExplanation) = if hasfield(typeof(ce.generator), :loss) | |
| ℓ(ce.generator, ce) + h(ce.generator, ce) | |
| else | |
| nothing | |
| end | |
| total_loss(ce::AbstractCounterfactualExplanation) = | |
| if hasfield(typeof(ce.generator), :loss) | |
| ℓ(ce.generator, ce) + h(ce.generator, ce) | |
| else | |
| nothing | |
| end |
|
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| Fit a transformer to the data for an `InputTransformer` object. This is a no-op. | ||
| """ | ||
| function fit_transformer(data::CounterfactualData, input_encoder::InputTransformer; kwargs...) |
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| function fit_transformer(data::CounterfactualData, input_encoder::InputTransformer; kwargs...) | |
| function fit_transformer( | |
| data::CounterfactualData, input_encoder::InputTransformer; kwargs... | |
| ) |
|
It turns out that passing the data container was not the issue: I tried using Instead, there were a few unnecessary forward passes through the entire dataset that have now been removed. There is now little to no dependency of performance on the dataset size (as in number of samples). |
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