Closed rikhuijzer closed 9 months ago
This was a notebook that I used to debug (not committing because it's noise):
### A Pluto.jl notebook ###
# v0.19.32
using Markdown
using InteractiveUtils
# ╔═╡ 44f5f78b-d317-4662-ab6d-307f0701a11b
using Revise
# ╔═╡ f67cddf9-7304-47e2-aa2f-48ec93275554
root_dir = @__DIR__
# ╔═╡ 106c3646-8d4a-11ee-0601-27fe3abdc084
# ╠═╡ show_logs = false
x = let
using Pkg: Pkg
Pkg.add("TestEnv")
Pkg.activate(root_dir)
using TestEnv
TestEnv.activate()
Pkg.add("CairoMakie")
using CairoMakie
end;
# ╔═╡ ff2b378b-75a6-4e0a-b13b-6bc69442180b
let
x
using CategoricalArrays:
CategoricalValue,
CategoricalVector,
categorical,
unwrap
using CSV: CSV
using DataDeps: DataDeps, DataDep, @datadep_str
using Documenter: DocMeta, doctest
using MLDatasets:
BostonHousing,
Iris,
Titanic
using DataFrames:
DataFrames,
DataFrame,
Not,
dropmissing!,
rename!,
rename,
select
using DecisionTree: DecisionTree
using MLJBase:
CV,
MLJBase,
PerformanceEvaluation,
evaluate,
mode,
fit!,
machine,
make_blobs,
make_moons,
make_regression,
predict
using MLJDecisionTreeInterface: DecisionTreeClassifier, DecisionTreeRegressor
using MLJLinearModels: LogisticClassifier, LinearRegressor, MultinomialClassifier
using MLJTestInterface: MLJTestInterface
using MLJXGBoostInterface: XGBoostClassifier, XGBoostRegressor
using Random: shuffle, seed!
using StableRNGs: StableRNG
using StatisticalMeasures:
accuracy,
auc,
rsq
using SIRUS
using Statistics: mean, var
using Tables: Tables
using Test
end
# ╔═╡ 5e0aa5fc-c78d-4f2c-94dd-c73fbc726a8d
include(joinpath(root_dir, "test/preliminaries.jl"))
# ╔═╡ 14957f30-40c5-4445-8efc-68ea3c8e3e0f
X, y = boston();
# ╔═╡ 24366d12-3df7-4934-b57e-f489e1b76e9a
X
# ╔═╡ 693b7971-878f-40be-9989-1a37f1d8511f
y
# ╔═╡ ee87d4f9-08c6-4d25-ad68-8e69e675d3c7
fr = let
hyper = (; rng=_rng(), max_depth=2, n_trees=100)
measure = accuracy
_evaluate!(results, "iris", StableForestClassifier, hyper; measure)
end
# ╔═╡ ebc2ba6f-a7da-4290-877a-59a40045d021
fr.fitted_params_per_fold[1].fitresult
# ╔═╡ f306ae6f-fa3d-484b-af78-47f012c9ac07
forest = fr.fitted_params_per_fold[2].fitresult
# ╔═╡ 932a9f80-6ec4-41e1-b550-4fca88ecb68a
rr = let
# Increasing max_rules decreases score, which makes no sense.
hyper = (; rng=_rng(), max_depth=2, max_rules=30, lambda=100, q=20)
measure = rsq
_evaluate!(results, "boston", StableRulesRegressor, hyper; measure)
end
# ╔═╡ c9b5c558-30c2-4b53-a10d-4396878d24b0
only(rr.per_fold)
# ╔═╡ 7dc325e0-07b5-4f65-9d4e-ba98c5e38dc5
rr.fitted_params_per_fold[1].fitresult
# ╔═╡ 4af16b84-683f-4a16-abb5-855a98a14263
rr.fitted_params_per_fold[1].fitresult.weights
# ╔═╡ d2d7d82b-18bb-4b7c-ac77-4b8ceaa1a8e9
forest
# ╔═╡ Cell order:
# ╠═44f5f78b-d317-4662-ab6d-307f0701a11b
# ╠═f67cddf9-7304-47e2-aa2f-48ec93275554
# ╠═106c3646-8d4a-11ee-0601-27fe3abdc084
# ╠═5e0aa5fc-c78d-4f2c-94dd-c73fbc726a8d
# ╠═ff2b378b-75a6-4e0a-b13b-6bc69442180b
# ╠═14957f30-40c5-4445-8efc-68ea3c8e3e0f
# ╠═24366d12-3df7-4934-b57e-f489e1b76e9a
# ╠═693b7971-878f-40be-9989-1a37f1d8511f
# ╠═ee87d4f9-08c6-4d25-ad68-8e69e675d3c7
# ╠═ebc2ba6f-a7da-4290-877a-59a40045d021
# ╠═f306ae6f-fa3d-484b-af78-47f012c9ac07
# ╠═932a9f80-6ec4-41e1-b550-4fca88ecb68a
# ╠═c9b5c558-30c2-4b53-a10d-4396878d24b0
# ╠═7dc325e0-07b5-4f65-9d4e-ba98c5e38dc5
# ╠═4af16b84-683f-4a16-abb5-855a98a14263
# ╠═d2d7d82b-18bb-4b7c-ac77-4b8ceaa1a8e9
(not committing because it's noise)
Why this file is created in root dir?
Maybe edit the PR commit and then git push -f
?
Another shot at #27 and https://github.com/rikhuijzer/SIRUS.jl/issues/42.