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Add generic MLJ interface tests for categorical features #48

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merged 4 commits into from
Feb 28, 2024
Merged

Add generic MLJ interface tests for categorical features #48

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2fb0d61
add tests for categorical features
ablaom Feb 26, 2024
9d32af8
try to fix problem with new test in julia 1.6
ablaom Feb 28, 2024
048c55b
try again
ablaom Feb 28, 2024
886deee
and again...
ablaom Feb 28, 2024
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28 changes: 26 additions & 2 deletions test/runtests.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -8,19 +8,41 @@ using MLJGLMInterface
using GLM: coeftable
import GLM
import MLJTestInterface
using Tables

using Distributions: Normal, Poisson, Uniform
import StableRNGs
using Tables

expit(X) = 1 ./ (1 .+ exp.(-X))

# TODO: Add more datasets to the following generic interface tests after #45 is merged
# synthesize small data sets with mixed features:

n = 100
X_regression, y_regression = MLJBase.make_regression(n, 3)
outlook = categorical(rand(["sunny", "overcast", "rainy"], n))
temperature = categorical(
rand(["cold", "mild", "hot"], n),
ordered=true,
levels=["cold", "mild", "hot"],
)
X = merge(
Tables.columntable(X_regression),
(; outlook, temperature),
)
y_binary = (temperature .== "mild" .|| outlook .== "sunny") |> categorical
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y_count = map(X.x1) do x
floor(Int, 10*abs(x))
end
mixed_binary = (X, y_binary)
mixed_count = (X, y_count)
mixed_regression = (X, y_regression)

@testset "generic interface tests" begin
@testset "LinearRegressor" begin
for data in [
MLJTestInterface.make_regression(),
MLJTestInterface.make_regression(),
mixed_regression,
]
failures, summary = MLJTestInterface.test(
[LinearRegressor,],
Expand All @@ -36,6 +58,7 @@ expit(X) = 1 ./ (1 .+ exp.(-X))
@testset "LinearCountRegressor" begin
for data in [
MLJTestInterface.make_count(),
mixed_count,
]
failures, summary = MLJTestInterface.test(
[LinearCountRegressor,],
Expand All @@ -51,6 +74,7 @@ expit(X) = 1 ./ (1 .+ exp.(-X))
@testset "LinearBinaryClassifier" begin
for data in [
MLJTestInterface.make_binary(),
mixed_binary,
]
failures, summary = MLJTestInterface.test(
[LinearBinaryClassifier,],
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