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mlj.jl
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mlj.jl
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### A Pluto.jl notebook ###
# v0.19.9
using Markdown
using InteractiveUtils
# ╔═╡ 5628c458-bad1-4464-a14d-81ff238f8c18
using CSV, DataFrames, Plots, MLJ, MLJLinearModels, MLJDecisionTreeInterface, LinearAlgebra, Random, MLJNaiveBayesInterface, MLJLIBSVMInterface, NearestNeighborModels, LIBSVM
# ╔═╡ 8ee68ceb-3fa2-4002-a060-c9b51a295e27
begin
pessoas_file = download("https://raw.githubusercontent.com/abelsiqueira/intro-a-ml/main/data/pessoas.csv", "pessoas.csv")
df = CSV.read(pessoas_file, DataFrame)
end
# ╔═╡ 17e95eca-8b4e-4d7c-90d2-2627903ed3c2
begin
y, X = unpack(
df,
==(:salario),
x -> x == :idade || x == :cargo;
:idade => Continuous,
:cargo => Multiclass,
)
if size(X, 2) == 1
X = coerce(df[!,[:idade]], :idade => Continuous)
end
X[!,:idade_pow2] = X.idade .^ 2
hot = OneHotEncoder()
hot_mach = machine(hot, X)
MLJ.fit!(hot_mach)
X = MLJ.transform(hot_mach, X)
schema(X)
end
# ╔═╡ 28665e2c-4abb-41fd-98c3-f0cabb402255
train, test = partition(1:length(y), 0.7, rng=123)
# ╔═╡ 5f26cffd-9001-4976-a689-b2ae164e9d62
# ╠═╡ show_logs = false
let
LinearRegressor = @load LinearRegressor pkg=MLJLinearModels verbosity=0
model = LinearRegressor()
cv = CV(nfolds = 5)
evaluate(model, X, y, resampling = cv, measure = rsq)
# mach = machine(model, X, y)
# MLJ.fit!(mach, rows=train)
# ŷ = MLJ.predict(mach, X[test,:])
# compara2d(X[test,:], y[test], ŷ, "LinearRegressor")
end
# ╔═╡ 5cbf9be2-01a8-4d96-9a4b-c1a033b4695f
MLJ.models(matching(X, y))
# ╔═╡ c3425abf-8c47-4674-b550-4123947573b2
begin
function compara2d!(plt, X, y, ŷ, nome = "no name")
R2 = round(rsq(ŷ, y), digits=4)
scatter!(plt, X.idade, y, lab="real")
scatter!(plt, X.idade, ŷ, lab="pred")
title!(plt, "$nome, R2 = $R2")
end
function compara2d(args...; kwargs...)
plt = plot()
compara2d!(plt, args...; kwargs...)
return plt
end
end
# ╔═╡ 298cc300-082c-490b-b71b-4714e1b7d9ce
let
LinearRegressor = @load LinearRegressor pkg=MLJLinearModels verbosity=0
RidgeRegressor = @load RidgeRegressor pkg=MLJLinearModels verbosity=0
RandomForestRegressor = @load RandomForestRegressor pkg=DecisionTree verbosity=0
plt = plot(layout = (2, 2), size = (1200, 800))
for (i, reg) in enumerate([
LinearRegressor,
RidgeRegressor,
RandomForestRegressor,
])
model = reg()
mach = machine(model, X, y)
MLJ.fit!(mach, rows=train)
ŷ = MLJ.predict(mach, X[test,:])
compara2d!(plt[i], X[test,:], y[test], ŷ, "$reg")
end
plt
end
# ╔═╡ 5dc2c937-2a7d-4319-8a61-610241c8b1de
let
RandomForestRegressor = @load RandomForestRegressor pkg=DecisionTree
model = RandomForestRegressor()
mach = machine(model, X, y)
MLJ.fit!(mach, rows=train)
ŷ = MLJ.predict(mach, X[test,:])
compara2d(X[test,:], y[test], ŷ, "RandomForestRegressor")
end
# ╔═╡ a0096a1c-dc60-422c-9d67-4bd1d431cb75
begin
Random.seed!(0)
X2 = zeros(0, 2)
for j = 1:5
global X2
μ = randn(2) * 2
Σ = rand(2, 2)
Σ = Σ' * Σ + 0.3I
X2 = [X2; randn(100, 2) * Σ .+ μ']
end
X2 = DataFrame(X2, :auto)
n = size(X2, 1)
y2 = coerce([
-(X2[i,2] - 0.5) * X2[i,1] > randn() * 1.5
for i = 1:n
], OrderedFactor)
X2 = X2 .* [1000 3] .+ [10_000 10]
function plot_2d_class!(plt, X, y)
yu = unique(y)
colors = [:red, :blue, :green]
if length(yu) > length(colors)
error("Abel, mais cores!")
end
for (i, yi) in enumerate(yu)
idx = findall(y .== yi)
scatter!(plt, X[idx,1], X[idx,2], c=colors[i], lab="$yi", opacity=0.7, m=(4,stroke(0)))
end
plt
end
function plot_machine_class!(plt, mach, X; use_pdf = false)
N = 51
X1 = range(extrema(X[:,1])..., length=N)
X2 = range(extrema(X[:,2])..., length=N)
Xg = DataFrame(vcat([
[X1[i]; X2[j]]'
for i = 1:N, j = 1:N
][:]...), names(X))
ŷ = MLJ.predict(mach, Xg)
if use_pdf
ŷ = pdf.(ŷ, true)
end
contourf!(plt, X1, X2, ŷ, c=cgrad([:pink, :white, :lightblue]), lw=0)
contour!(plt, X1, X2, ŷ, lw=2, c=:black, levels=[0.5])
end
plot_2d_class!(plot(), X2, y2)
end
# ╔═╡ 3e6470ec-aa64-49b5-8996-dd3fe407a6e7
begin
LogisticClassifier = @load LogisticClassifier pkg=MLJLinearModels
KNNClassifier = @load KNNClassifier pkg=NearestNeighborModels
SVC = @load SVC pkg=LIBSVM
GaussianNBClassifier = @load GaussianNBClassifier pkg=NaiveBayes
DecisionTreeClassifier = @load DecisionTreeClassifier pkg=DecisionTree
RandomForestClassifier = @load RandomForestClassifier pkg=DecisionTree
end
# ╔═╡ cad53c61-fe06-4052-a127-e6534f660431
let
using Logging
clfs = [
LogisticClassifier(),
KNNClassifier(),
SVC(gamma = 10.0),
GaussianNBClassifier(),
DecisionTreeClassifier(),
RandomForestClassifier(n_trees = 100),
]
use_pdfs = [true, true, false, true, true, true]
cv = CV(nfolds = 3)
nclfs = length(clfs)
plts = plot(
layout = grid(ceil(Int, 0.5nclfs), 2),
size = (1200, 400 * ceil(Int, 0.5nclfs)),
leg = false
)
for (i, clf) in enumerate(clfs)
pipe = Pipeline(
Standardizer(),
ContinuousEncoder(),
clf,
)
mach = machine(pipe, X2, y2)
ev = evaluate!(
mach,
resampling = cv,
measure = [accuracy, f1score],
verbosity = 0,
)
ŷ = MLJ.predict(mach, X2)
if use_pdfs[i]
ŷ = pdf.(ŷ, true)
ŷ = round.(Int, ŷ)
end
plot_machine_class!(plts[i], mach, X2, use_pdf=use_pdfs[i])
plot_2d_class!(plts[i], X2, y2)
acc = round(ev.measurement[1], digits = 2)
f1s = round(ev.measurement[2], digits = 2)
name = string(clf)
name = name[1:min(5,length(name))]
title!(plts[i], "$(name)..., acc = $acc, f1s = $f1s")
end
plts
end
# ╔═╡ 24a39a09-d31c-4511-93e0-dee43dcd0bb3
let
# model = SVC(
# kernel = LIBSVM.Kernel.RadialBasis,
# gamma = 0.001
# )
# model = KNNClassifier()
model = Pipeline(
Standardizer(),
ContinuousEncoder(),
SVC(),
# KNNClassifier(),
)
mach = machine(model, X2, y2)
MLJ.fit!(mach)
ŷ = MLJ.predict(mach, X2)
# pdf.(ŷ, true)
# evaluate!(
# mach,
# resampling=CV(nfolds=5),
# measure=[accuracy, f1score],
# verbosity=0
# )
plt = plot()
plot_machine_class!(plt, mach, X2; use_pdf = false)
plot_2d_class!(plt, X2, y2)
end
# ╔═╡ 00000000-0000-0000-0000-000000000001
PLUTO_PROJECT_TOML_CONTENTS = """
[deps]
CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
LIBSVM = "b1bec4e5-fd48-53fe-b0cb-9723c09d164b"
LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
MLJ = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7"
MLJDecisionTreeInterface = "c6f25543-311c-4c74-83dc-3ea6d1015661"
MLJLIBSVMInterface = "61c7150f-6c77-4bb1-949c-13197eac2a52"
MLJLinearModels = "6ee0df7b-362f-4a72-a706-9e79364fb692"
MLJNaiveBayesInterface = "33e4bacb-b9e2-458e-9a13-5d9a90b235fa"
NearestNeighborModels = "636a865e-7cf4-491e-846c-de09b730eb36"
Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
[compat]
CSV = "~0.10.4"
DataFrames = "~1.3.4"
LIBSVM = "~0.8.0"
MLJ = "~0.18.3"
MLJDecisionTreeInterface = "~0.2.2"
MLJLIBSVMInterface = "~0.2.0"
MLJLinearModels = "~0.6.3"
MLJNaiveBayesInterface = "~0.1.5"
NearestNeighborModels = "~0.2.0"
Plots = "~1.31.1"
"""
# ╔═╡ 00000000-0000-0000-0000-000000000002
PLUTO_MANIFEST_TOML_CONTENTS = """
# This file is machine-generated - editing it directly is not advised
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