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Multithreaded support for apply_forest_proba (Issue #209) #210

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Multithreaded support for apply_forest_proba (Issue #209) #210

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876dd49
Multithreaded support for apply_forest_proba
salbert83 Jan 15, 2023
686a44b
Unit tests for multithreaded support for apply_forest_proba
salbert83 Jan 15, 2023
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33 changes: 22 additions & 11 deletions src/classification/main.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -23,12 +23,19 @@ end

# Applies `row_fun(X_row)::AbstractVector` to each row in X
# and returns a matrix containing the resulting vectors, stacked vertically
function stack_function_results(row_fun::Function, X::AbstractMatrix)
function stack_function_results(row_fun::Function, X::AbstractMatrix;
use_multithreading = false)
N = size(X, 1)
N_cols = length(row_fun(X[1, :])) # gets the number of columns
out = Array{Float64}(undef, N, N_cols)
for i in 1:N
out[i, :] = row_fun(X[i, :])
if use_multithreading
for i in 1:N
out[i, :] = row_fun(X[i, :])
end
else
for i in 1:N
out[i, :] = row_fun(X[i, :])
end
Comment on lines +32 to +38
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Cases are the same?

end
return out
end
Expand Down Expand Up @@ -329,10 +336,10 @@ function apply_tree_proba(
return apply_tree_proba(tree.right, features, labels)
end
end
apply_tree_proba(tree::Root{S, T}, features::AbstractMatrix{S}, labels) where {S, T} =
apply_tree_proba(tree.node, features, labels)
apply_tree_proba(tree::LeafOrNode{S, T}, features::AbstractMatrix{S}, labels) where {S, T} =
stack_function_results(row->apply_tree_proba(tree, row, labels), features)
apply_tree_proba(tree::Root{S, T}, features::AbstractMatrix{S}, labels; use_multithreading = false) where {S, T} =
apply_tree_proba(tree.node, features, labels, use_multithreading = use_multithreading)
apply_tree_proba(tree::LeafOrNode{S, T}, features::AbstractMatrix{S}, labels; use_multithreading = false) where {S, T} =
stack_function_results(row->apply_tree_proba(tree, row, labels), features, use_multithreading = use_multithreading)
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Suggested change
stack_function_results(row->apply_tree_proba(tree, row, labels), features, use_multithreading = use_multithreading)
stack_function_results(row->apply_tree_proba(tree, row, labels), features; use_multithreading)

Lower bound is set to 1.6 so no need to repeat the keyword name


function build_forest(
labels :: AbstractVector{T},
Expand Down Expand Up @@ -488,10 +495,12 @@ end
apply_forest_proba(
forest::Ensemble{S, T},
features::AbstractMatrix{S},
labels
labels;
use_multithreading = false
) where {S, T} =
stack_function_results(row->apply_forest_proba(forest, row, labels),
features)
features,
use_multithreading = use_multithreading)

function build_adaboost_stumps(
labels :: AbstractVector{T},
Expand Down Expand Up @@ -597,10 +606,12 @@ function apply_adaboost_stumps_proba(
stumps::Ensemble{S, T},
coeffs::AbstractVector{Float64},
features::AbstractMatrix{S},
labels::AbstractVector{T}
labels::AbstractVector{T};
use_multithreading = false
) where {S, T}
stack_function_results(
row->apply_adaboost_stumps_proba(stumps, coeffs, row, labels),
features
features,
use_multithreading = use_multithreading
)
end
6 changes: 6 additions & 0 deletions test/classification/iris.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -16,6 +16,8 @@ cm = confusion_matrix(labels, preds)
@test depth(model) == 1
probs = apply_tree_proba(model, features, classes)
@test reshape(sum(probs, dims=2), n) ≈ ones(n)
probs_m = apply_tree_proba(model, features, classes, use_multithreading=true)
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@rikhuijzer rikhuijzer Jan 15, 2023 •
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Although there isn't a format style guide for this repository, consistent use of spaces around keyword argument equal signs seems like a good start. Here at line 19 are no spaces and at 33 and 59 there are. MLJ style is no spaces around keyword arguments equals I think.

Same holds for using the semicolon to separate the arguments from the keyword arguments. At some places in this PR it is done and and some not. Here, it's generally advised to use semicolons because they improve clarity.

@test reshape(sum(probs_m, dims=2), n) ≈ ones(n)

# train full-tree classifier (over-fit)
model = build_tree(labels, features)
Expand All @@ -28,6 +30,8 @@ cm = confusion_matrix(labels, preds)
print_tree(model)
probs = apply_tree_proba(model, features, classes)
@test reshape(sum(probs, dims=2), n) ≈ ones(n)
probs_m = apply_tree_proba(model, features, classes, use_multithreading = true)
@test reshape(sum(probs_m, dims=2), n) ≈ ones(n)
i1 = impurity_importance(model)
s1 = split_importance(model)

Expand All @@ -52,6 +56,8 @@ cm = confusion_matrix(labels, preds)
@test 0.95 < cm.accuracy < 1.0
probs = apply_tree_proba(model, features, classes)
@test reshape(sum(probs, dims=2), n) ≈ ones(n)
probs_m = apply_tree_proba(model, features, classes, use_multithreading = true)
@test reshape(sum(probs_m, dims=2), n) ≈ ones(n)

# prune tree to a stump, 2 leaves
pruning_purity = 0.5
Expand Down