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Add iris classification using xgboost
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@stefanocampanella
stefanocampanella committed Jan 16, 2024
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251 changes: 230 additions & 21 deletions examples/dataframes.jl
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Expand Up @@ -10,9 +10,12 @@ using DataFrames
# ╔═╡ 525b4126-e6fa-4616-9676-b8b6f31e403f
using CSV

# ╔═╡ 9e807a3f-d861-4b98-a119-54b8b69bae27
# ╔═╡ 93b98458-2288-48e5-acf2-0b849bc4aaba
using Statistics

# ╔═╡ 7d05f65d-ff26-41a6-889e-b03df04dd48b
using XGBoost

# ╔═╡ 884905d7-ef97-4b62-acc9-843ce2e79f79
md"""
# Julia DataFrames
Expand Down Expand Up @@ -77,36 +80,75 @@ iris[1:10, 1:4]
# ╔═╡ e8cb2729-37ec-4f60-a529-d7b139ac5133
iris[!, r"sepal_*"]

# ╔═╡ 41446fa3-fd89-4ee8-a3bc-045d9003e92c
md"""
## Mutating DataFrames
"""
# ╔═╡ f865cb0d-c4a5-4dcc-89ed-a44d0acf88d2
iris[!, Not(:class)]

# ╔═╡ d8f04fc9-8ba6-48d5-8c64-2ef3103e5077
# ╔═╡ 9db64168-338c-4bf7-b430-98c2efd39db3
md"""
## Missing values
## Classifying with XGBoost
"""

# ╔═╡ d5350cc4-f91a-4208-9a97-8440b7e637d9
md"""
## Broadcasting
"""
# ╔═╡ cbef1767-4c50-4f40-aa29-e9bddbaeb2a0
import Random: shuffle

# ╔═╡ f7567427-9c14-462c-b507-368e561ed540
md"""
## Selectors and Transformation functions
"""
# ╔═╡ 226ecb13-12d4-407c-b58a-924c421e2423
function split(df, at=0.8)
df = shuffle(df)
endpoint = round(Int, nrow(df) * at)
df[1:endpoint, :], df[endpoint + 1:end, :]
end

# ╔═╡ 1815eda4-0d09-4b47-ba41-5c568f6804bc
train, test = split(iris)

# ╔═╡ d684258f-a215-4b1a-a6ab-8d4d114e7b16
classes = unique(iris.class)

# ╔═╡ 42dc519a-506b-4013-bace-5c7f1f1c18f7
encode(x) :: Float32 = findfirst(==(x), classes)

# ╔═╡ b9ee45ad-37a7-4f6f-a044-28dd60e04de6
decode(x) = classes[round(Int, x)]

# ╔═╡ d96c37be-da2f-4c0a-93c4-9ebf7f060603
train_labels = map(encode, train.class)

# ╔═╡ 1a112052-f7e3-4bc8-bec6-70fcaa28eb1f
model = xgboost((train[:, Not(:class)], train_labels))

# ╔═╡ e36eecca-feb6-4c76-9a14-0ffb675e904d
predictions = predict(model, test[:, Not(:class)])

# ╔═╡ d0000e98-e712-4769-94b5-dc918637d7d5
rmse(xs, ys) = sqrt(mean((xs - ys) .^ 2))

# ╔═╡ 7557839c-a580-44c3-a135-b9384cff89c3
rmse(encode.(test.class), predictions)

# ╔═╡ 73413e5f-5c7f-4955-9acb-f5c5c996a903
count(encode.(test.class) .!= round.(predictions))

# ╔═╡ 697135f2-cf29-4769-bb75-0c615d30a2ab
let
df = copy(iris)
predictions = decode.(predict(model, df[:, Not(:class)]))
df.prediction = predictions
df[df.class .!= df.prediction, :]
end

# ╔═╡ 00000000-0000-0000-0000-000000000001
PLUTO_PROJECT_TOML_CONTENTS = """
[deps]
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DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
XGBoost = "009559a3-9522-5dbb-924b-0b6ed2b22bb9"
[compat]
CSV = "~0.10.12"
DataFrames = "~1.6.1"
XGBoost = "~2.5.1"
"""

# ╔═╡ 00000000-0000-0000-0000-000000000002
Expand All @@ -115,20 +157,46 @@ PLUTO_MANIFEST_TOML_CONTENTS = """
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Expand Down Expand Up @@ -181,12 +249,20 @@ version = "1.0.0"
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Expand All @@ -211,11 +287,63 @@ git-tree-sha1 = "a3f24677c21f5bbe9d2a714f95dcd58337fb2856"
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Expand All @@ -230,6 +358,11 @@ uuid = "56ddb016-857b-54e1-b83d-db4d58db5568"
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Expand All @@ -239,6 +372,14 @@ version = "1.1.0"
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Expand All @@ -255,6 +396,11 @@ git-tree-sha1 = "8489905bcdbcfac64d1daa51ca07c0d8f0283821"
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Expand Down Expand Up @@ -323,6 +469,12 @@ deps = ["Libdl", "LinearAlgebra", "Random", "Serialization", "SuiteSparse_jll"]
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Expand All @@ -334,6 +486,16 @@ git-tree-sha1 = "a04cabe79c5f01f4d723cc6704070ada0b9d46d5"
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Expand All @@ -356,6 +518,11 @@ git-tree-sha1 = "cb76cf677714c095e535e3501ac7954732aeea2d"
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Expand Down Expand Up @@ -387,6 +554,26 @@ git-tree-sha1 = "cd1659ba0d57b71a464a29e64dbc67cfe83d54e7"
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Expand All @@ -396,6 +583,16 @@ version = "1.2.13+1"
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"""

# ╔═╡ Cell order:
Expand All @@ -409,7 +606,7 @@ version = "5.8.0+1"
# ╠═32944ad3-641d-4da4-80aa-e3fa29ecbbe9
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Expand All @@ -418,9 +615,21 @@ version = "5.8.0+1"
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