### A Pluto.jl notebook ###
# v0.15.1

using Markdown
using InteractiveUtils

# ╔═╡ 85e74b35-5e6e-4e69-b7ad-50cedca5f4cf
using PlutoUI

# ╔═╡ e85323e0-e991-4c15-b41f-59b81571e4c2
begin
	using MLJ
	import RDatasets: dataset
	import DataFrames: DataFrame, select, Not, describe
	using Random
end

# ╔═╡ 274c3df4-713b-4643-b2ab-e002151c3164
using MLJMultivariateStatsInterface

# ╔═╡ 7bd93fe6-f392-45eb-95ff-6bacb7efc974
begin 
	using MLJClusteringInterface
	@load KMeans pkg="Clustering"
end

# ╔═╡ f4aafce6-326e-4c7a-bd90-45669f0494ba
using Plots

# ╔═╡ 50defd99-5eb4-444a-9abb-5003764563e6
using UrlDownload

# ╔═╡ effa8648-8ca7-42ef-bab6-17f52a417ffd
begin
	using MLJScikitLearnInterface #También hay que instalar scikit-learn donde sea que tengamos a Julia, en mi caso, uso pip3 install scikit-learn
	@load KNeighborsClassifier pkg="ScikitLearn"
end

# ╔═╡ 146c09f4-f4c6-41ab-a565-03b2d6825129
using CSV, HTTP

# ╔═╡ a3052245-5f44-4166-922b-18df99c0a49d
using MLJDecisionTreeInterface

# ╔═╡ ca2a2e0c-f6f1-11eb-12d4-5d81b9cb97c1
md"""# Ciencia de Datos pt. 2

	- Oscar Alejando Esquivel Flores
	- Óscar Anuar Alvarado Morán
	- Mario Horacio Garrido Czacki
"""

# ╔═╡ c418450d-0ace-4a13-9fbc-9c4dc1c153c3
PlutoUI.TableOfContents(title="🔬 Ciencia de datos pt. 2 🔭", depth = 5)

# ╔═╡ 379fad8b-59b6-4e41-aa5f-d3ff7dbc4f26
md"# Aprendizaje Automatizado"

# ╔═╡ ae8acf9e-5b1b-4d69-af47-96a66b42189a
md"## MLJ.jl
- [GitHub](https://github.com/alan-turing-institute/MLJ.jl)
- [Alan Turing Institute](https://alan-turing-institute.github.io/MLJ.jl/dev/)
- [DataScience Tutorials](https://alan-turing-institute.github.io/DataScienceTutorials.jl/)

`MLJ.jl` es un ambiente que provee de una _interfaz uniforme_ para **ajustar**, **evaluar** y **tunear** modelos de aprendizaje automatizado. Contiene más de 150 modelos de aprendizaje automatizado escritos en Julia y en otros lenguajes, en particular, MLJ envuelve un gran número de modelos del H.H.H `scikit.learn`."

# ╔═╡ 2bb7c88d-6894-4c48-b1eb-ef297ec33fa3
md"### Aprendizaje no supervisado
	- PCA 
	- Agrupamiento"

# ╔═╡ 3460d637-a598-4bd4-a169-a10ad9e220d1
md"Usaremos el conjunto de datos [USArrests](https://www.rdocumentation.org/packages/datasets/versions/3.6.2/topics/USArrests), que consta de arrestos por cada 100,000 residentes para asalto, asesinato y violación en cada uno de los 50 estados de Estados Unidos en el año de 1973. También se presenta el porcentaje de la población viviendo en áreas urbanas."

# ╔═╡ a7cb38fc-7522-4c25-a062-9c5daae14145
begin
	data = dataset("datasets", "USArrests")
	first(data, 5)
end

# ╔═╡ 7c9406b5-21a8-41d7-a777-e7ba90fb7a00
md"Vemos un poco de descripción de los datos:"

# ╔═╡ 2be4cb86-17b7-4f0f-b66b-380a02038d46
describe(data, :mean, :std, :min, :max)

# ╔═╡ 981fc582-642e-43f1-bacc-340acd40f854
md"Obtengamos los datos numéricos"

# ╔═╡ abac24d2-765c-49aa-befe-291012e61b00
begin
	X = select(data, Not(:State))
	X = coerce(X, :UrbanPop => Continuous, :Assault => Continuous)
end

# ╔═╡ 3dedd727-c900-40c6-95ae-47a697891613
md"#### PCA
Primero, cargamos el modelo"

# ╔═╡ 752fecef-1da9-4943-94f9-d7af84bdf472
@load PCA pkg = MultivariateStats

# ╔═╡ cb9ed21f-996f-4104-b58e-8fe439fbeb06
md"¿Qué pasó aquí? La cosa es que MLJ es sólo una interface para usar paquetes de aprendizaje automatizado, por lo que habrá que instalar diferentes paquetes dependiendo del modelo que querramos usar. Podemos ver algunos modelos a continuación."

# ╔═╡ 8b5e4c86-6083-4977-bb35-0e993d63d7d5
models(matching(X))

# ╔═╡ be737177-8be8-4f0a-abfa-97aa286b1654
md"Si queremos ver todos los modelos disponibles podemos hacerlo como sigue:"

# ╔═╡ 32feb81d-8552-4415-b22f-bb5756581292
models()

# ╔═╡ 431ac7b3-96f3-41c0-a102-edbaf000ce00
md"Creamos el modelo"

# ╔═╡ e46948bc-7bd8-4904-a06e-3d5936c300be
pca_mdl = PCA()

# ╔═╡ 5763d717-d695-40a1-8d14-f3ed8a04fbac
md"Creamos una _máquina_, que es un objeto que vincula un modelo (algoritmo + hiperparámetros) con los datos. Esta almacena los parámetros aprendidos."

# ╔═╡ b65357a5-5af6-4cec-a31c-7ded59155a44
pca = machine(pca_mdl, X)

# ╔═╡ 046dd48c-c757-4cde-8483-8f9f02e1217e
md"¡Ajustamos!"

# ╔═╡ f23080a2-2303-414c-acf9-b642a5e7121f
fit!(pca)

# ╔═╡ 7237e74b-f35d-486e-a769-2f93bbad566d
md"¡Transformamos!"

# ╔═╡ 63ae7fa7-d2aa-48b1-872e-68e035b77422
W = MLJ.transform(pca, X)

# ╔═╡ 130769ec-7515-4149-b925-7ef07a6168b8
begin
	r = report(pca)
	cumsum(r.principalvars ./ r.tvar)
end

# ╔═╡ 9485c2f0-4378-4a1f-90cf-2b992e3714f6
md"Trabajemos con datos más interesantes:"

# ╔═╡ 79cfb0ed-7431-4dc8-ad02-62ca35e4f885
md"""
[Conjunto de datos Orange Juice](https://rdrr.io/cran/ISLR/man/OJ.html):

"The data contains 1070 purchases where the customer either purchased Citrus Hill or Minute Maid Orange Juice. A number of characteristics of the customer and product are recorded."
"""

# ╔═╡ a19df644-cfa2-46cb-875e-fd4a94c00134
begin
	data1 = dataset("ISLR", "OJ")
	X1 = select(data1, [:PriceCH, :PriceMM, :DiscCH, :DiscMM, :SalePriceMM,
                  :SalePriceCH, :PriceDiff, :PctDiscMM, :PctDiscCH]);
end

# ╔═╡ ada8f32b-aab1-4687-9ee1-b1a27ec8ca5d
names(data1)

# ╔═╡ c70c8298-c908-4ef8-a5da-79967d0d8ccb
begin
	SPCA = @pipeline(Standardizer(),
	                 PCA())
	
	spca = machine(SPCA, X1)
	fit!(spca)
	W1 = MLJ.transform(spca, X1)
	W1
end

# ╔═╡ 54b04b9f-731c-4642-a0f8-c1b6df24b6ba
begin
	rpca = collect(values(report(spca).report_given_machine))[2]
	cs = cumsum(rpca.principalvars ./ rpca.tvar)
end

# ╔═╡ bea29f95-25ea-4fb9-8f86-a2860c3f1120
md"#### Agrupamiento
Hagamos algo de agrupamiento con estos mismos datos que acabamos de obtener"

# ╔═╡ 7d961743-32f8-4ad0-896d-74e1484d8a9b
begin
	SPCA2 = @pipeline(Standardizer(),
	                  PCA(),
	                  MLJClusteringInterface.KMeans(k=3))
	
	spca2 = machine(SPCA2, X1)
	fit!(spca2)
end

# ╔═╡ a2bc7993-8570-4863-8dfd-baa107424198
models()

# ╔═╡ 7e54fca2-fadb-403c-b4b1-29aead7a2cb8
assignments = collect(values(report(spca2).report_given_machine))[3].assignments

# ╔═╡ 1bae87f9-2ec3-413c-a445-ddebdc52ea44
begin
	mask1 = assignments .== 1
	mask2 = assignments .== 2
	mask3 = assignments .== 3;
end

# ╔═╡ 0224fea6-3c9d-46cb-b936-9d986e53cd1d
plotly()

# ╔═╡ c091be98-c321-48e0-9b13-05de97c12cd9
begin
	plot(1,1,1, alpha = 0)
	for (m, c) in zip((mask1, mask2, mask3), ("red", "green", "blue"))
	    scatter!(W1[m, 1], W1[m, 2], W1[m, 3], markersize=1, color=c, alpha = 0.5)
	end
	plot!(1,1,1, alpha = 0)
end

# ╔═╡ 0798b789-50c1-4080-8386-1a6429b32f7d
md"### Aprendizaje supervisado"

# ╔═╡ b53de955-1b1d-4be5-9bd7-9ce9f84b83e5
md"#### Clasificación"

# ╔═╡ 098588b6-6c67-4656-b520-00a53f39237f
begin
	url2 = "http://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data"
	header = ["Class", "Alcool", "Malic acid", "Ash", "Alcalinity of ash",
	          "Magnesium", "Total phenols", "Flavanoids",
	          "Nonflavanoid phenols", "Proanthcyanins", "Color intensity",
	          "Hue", "OD280/OD315 of diluted wines", "Proline"]
	data2 = urldownload(url2, true, format=:CSV, header=header)
end

# ╔═╡ eb79421f-624c-418a-8a5e-59b4e0c23bd0
begin
	df2 = DataFrame(data2)
	describe(df2)
end

# ╔═╡ af754478-fa99-414b-a48b-389af4fff2d3
y_wine, X_wine = unpack(df2, ==(:Class), colname->true)

# ╔═╡ 104558aa-b7a3-4008-8f68-c7e7f9241570
scitype(y_wine)

# ╔═╡ 84ed9b09-40a6-45e8-927c-c98217636f6a
yc = coerce(y_wine, OrderedFactor)

# ╔═╡ 84ce479d-acd0-4ac0-9dd1-4670444165d8
scitype(X_wine)

# ╔═╡ 5625705f-d6ca-4cb2-85b1-4d7cc5efe167
schema(X_wine)

# ╔═╡ 9a3d69b4-7342-4426-861a-56f9266a0706
Xc = coerce(X_wine, :Proline=>Continuous, :Magnesium=>Continuous)

# ╔═╡ aacbb24b-1056-48d2-ae51-b02718c6bdf7
describe(Xc, :mean, :std)

# ╔═╡ d7e4f63f-f950-4bbe-9248-ba21145436e7
models()

# ╔═╡ 225d1f19-7354-4d8c-ad8d-80498bdea59b
KnnPipe = @pipeline(Standardizer(), KNeighborsClassifier())

# ╔═╡ 345decc0-554c-431e-9e51-86160bbb296a
train, test = partition(eachindex(yc), 0.8, shuffle=true, rng=111)

# ╔═╡ ba19bae9-4cd0-4cf8-8953-466488e22b47
knn = machine(KnnPipe, Xc, yc)

# ╔═╡ d6bd99e2-57ab-491e-98ee-3f25bad44a66
fit!(knn, rows=train)

# ╔═╡ 9ca9182b-84a9-4a4e-8c66-fa54ac45ce17
ŷ = predict_mode(knn, rows=test)

# ╔═╡ 59db05e0-a2f6-4666-82e7-7508f2563b93
begin
	r3 = x -> round(x, sigdigits=3)
	accuracy(ŷ, yc[test]) |> r3
end

# ╔═╡ c4adf149-93b9-4a4f-8d16-790ea44088a4
begin
	req = HTTP.get("https://raw.githubusercontent.com/rupakc/UCI-Data-Analysis/master/Airfoil%20Dataset/airfoil_self_noise.dat");
	
	df_a = CSV.File(req.body; header=["Frequency","Attack_Angle","Chord+Length",
	                  "Free_Velocity","Suction_Side","Scaled_Sound"])
	df_air = DataFrame(df_a)
end

# ╔═╡ e6d330da-98ba-4689-97b9-119b5a5230f9
schema(df_air)

# ╔═╡ f4b660c3-367b-4207-9083-0d0fdcd2988a
y_q, X_q = unpack(df_air, ==(:Scaled_Sound), colname->true)

# ╔═╡ 97d665ec-0e30-4f4c-8480-2ef070e8350f
begin
	y_air, X_ai = unpack(df_air, ==(:Scaled_Sound), colname -> true)
	X_air = MLJ.transform(fit!(machine(Standardizer(), X_ai)), X_ai);
end

# ╔═╡ 15a264a7-78ee-4597-bfc5-79bc6890fb44
train_air, test_air = partition(eachindex(y_air), 0.7, shuffle=true, rng=111)

# ╔═╡ e664cc85-29f9-4c27-b128-6fbbf85dbb35
coerce!(X_air, :Frequency=>Continuous)

# ╔═╡ 19d8300c-97d0-4a9c-915d-83661c393dea
begin
	@load DecisionTreeRegressor pkg=DecisionTree
	dcr = DecisionTreeRegressor()
end

# ╔═╡ e6112bb6-184e-428c-b586-752403998533
begin
	dcrm = machine(dcr, X_air, y_air)
	
	fit!(dcrm, rows=train_air)
	pred_dcrm = MLJ.predict(dcrm, rows=test_air)
end

# ╔═╡ e1c234e9-7dd2-4079-9bd6-83edc3acf9f8
rms(pred_dcrm, y_air[test_air])

# ╔═╡ 00000000-0000-0000-0000-000000000001
PLUTO_PROJECT_TOML_CONTENTS = """
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UrlDownload = "856ac37a-3032-4c1c-9122-f86d88358c8b"

[compat]
CSV = "~0.8.5"
DataFrames = "~1.2.2"
HTTP = "~0.9.13"
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MLJClusteringInterface = "~0.1.4"
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version = "0.9.5"

[[URIs]]
git-tree-sha1 = "97bbe755a53fe859669cd907f2d96aee8d2c1355"
uuid = "5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4"
version = "1.3.0"

[[UUIDs]]
deps = ["Random", "SHA"]
uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"

[[UnPack]]
git-tree-sha1 = "387c1f73762231e86e0c9c5443ce3b4a0a9a0c2b"
uuid = "3a884ed6-31ef-47d7-9d2a-63182c4928ed"
version = "1.0.2"

[[Unicode]]
uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"

[[UrlDownload]]
deps = ["HTTP", "ProgressMeter"]
git-tree-sha1 = "05f86730c7a53c9da603bd506a4fc9ad0851171c"
uuid = "856ac37a-3032-4c1c-9122-f86d88358c8b"
version = "1.0.0"

[[VersionParsing]]
git-tree-sha1 = "80229be1f670524750d905f8fc8148e5a8c4537f"
uuid = "81def892-9a0e-5fdd-b105-ffc91e053289"
version = "1.2.0"

[[Wayland_jll]]
deps = ["Artifacts", "Expat_jll", "JLLWrappers", "Libdl", "Libffi_jll", "Pkg", "XML2_jll"]
git-tree-sha1 = "3e61f0b86f90dacb0bc0e73a0c5a83f6a8636e23"
uuid = "a2964d1f-97da-50d4-b82a-358c7fce9d89"
version = "1.19.0+0"

[[Wayland_protocols_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll"]
git-tree-sha1 = "2839f1c1296940218e35df0bbb220f2a79686670"
uuid = "2381bf8a-dfd0-557d-9999-79630e7b1b91"
version = "1.18.0+4"

[[XML2_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Libiconv_jll", "Pkg", "Zlib_jll"]
git-tree-sha1 = "1acf5bdf07aa0907e0a37d3718bb88d4b687b74a"
uuid = "02c8fc9c-b97f-50b9-bbe4-9be30ff0a78a"
version = "2.9.12+0"

[[XSLT_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Libgcrypt_jll", "Libgpg_error_jll", "Libiconv_jll", "Pkg", "XML2_jll", "Zlib_jll"]
git-tree-sha1 = "91844873c4085240b95e795f692c4cec4d805f8a"
uuid = "aed1982a-8fda-507f-9586-7b0439959a61"
version = "1.1.34+0"

[[Xorg_libX11_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxcb_jll", "Xorg_xtrans_jll"]
git-tree-sha1 = "5be649d550f3f4b95308bf0183b82e2582876527"
uuid = "4f6342f7-b3d2-589e-9d20-edeb45f2b2bc"
version = "1.6.9+4"

[[Xorg_libXau_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "4e490d5c960c314f33885790ed410ff3a94ce67e"
uuid = "0c0b7dd1-d40b-584c-a123-a41640f87eec"
version = "1.0.9+4"

[[Xorg_libXcursor_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXfixes_jll", "Xorg_libXrender_jll"]
git-tree-sha1 = "12e0eb3bc634fa2080c1c37fccf56f7c22989afd"
uuid = "935fb764-8cf2-53bf-bb30-45bb1f8bf724"
version = "1.2.0+4"

[[Xorg_libXdmcp_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "4fe47bd2247248125c428978740e18a681372dd4"
uuid = "a3789734-cfe1-5b06-b2d0-1dd0d9d62d05"
version = "1.1.3+4"

[[Xorg_libXext_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "b7c0aa8c376b31e4852b360222848637f481f8c3"
uuid = "1082639a-0dae-5f34-9b06-72781eeb8cb3"
version = "1.3.4+4"

[[Xorg_libXfixes_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "0e0dc7431e7a0587559f9294aeec269471c991a4"
uuid = "d091e8ba-531a-589c-9de9-94069b037ed8"
version = "5.0.3+4"

[[Xorg_libXi_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXext_jll", "Xorg_libXfixes_jll"]
git-tree-sha1 = "89b52bc2160aadc84d707093930ef0bffa641246"
uuid = "a51aa0fd-4e3c-5386-b890-e753decda492"
version = "1.7.10+4"

[[Xorg_libXinerama_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXext_jll"]
git-tree-sha1 = "26be8b1c342929259317d8b9f7b53bf2bb73b123"
uuid = "d1454406-59df-5ea1-beac-c340f2130bc3"
version = "1.1.4+4"

[[Xorg_libXrandr_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXext_jll", "Xorg_libXrender_jll"]
git-tree-sha1 = "34cea83cb726fb58f325887bf0612c6b3fb17631"
uuid = "ec84b674-ba8e-5d96-8ba1-2a689ba10484"
version = "1.5.2+4"

[[Xorg_libXrender_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "19560f30fd49f4d4efbe7002a1037f8c43d43b96"
uuid = "ea2f1a96-1ddc-540d-b46f-429655e07cfa"
version = "0.9.10+4"

[[Xorg_libpthread_stubs_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "6783737e45d3c59a4a4c4091f5f88cdcf0908cbb"
uuid = "14d82f49-176c-5ed1-bb49-ad3f5cbd8c74"
version = "0.1.0+3"

[[Xorg_libxcb_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "XSLT_jll", "Xorg_libXau_jll", "Xorg_libXdmcp_jll", "Xorg_libpthread_stubs_jll"]
git-tree-sha1 = "daf17f441228e7a3833846cd048892861cff16d6"
uuid = "c7cfdc94-dc32-55de-ac96-5a1b8d977c5b"
version = "1.13.0+3"

[[Xorg_libxkbfile_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "926af861744212db0eb001d9e40b5d16292080b2"
uuid = "cc61e674-0454-545c-8b26-ed2c68acab7a"
version = "1.1.0+4"

[[Xorg_xcb_util_image_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "0fab0a40349ba1cba2c1da699243396ff8e94b97"
uuid = "12413925-8142-5f55-bb0e-6d7ca50bb09b"
version = "0.4.0+1"

[[Xorg_xcb_util_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxcb_jll"]
git-tree-sha1 = "e7fd7b2881fa2eaa72717420894d3938177862d1"
uuid = "2def613f-5ad1-5310-b15b-b15d46f528f5"
version = "0.4.0+1"

[[Xorg_xcb_util_keysyms_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "d1151e2c45a544f32441a567d1690e701ec89b00"
uuid = "975044d2-76e6-5fbe-bf08-97ce7c6574c7"
version = "0.4.0+1"

[[Xorg_xcb_util_renderutil_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "dfd7a8f38d4613b6a575253b3174dd991ca6183e"
uuid = "0d47668e-0667-5a69-a72c-f761630bfb7e"
version = "0.3.9+1"

[[Xorg_xcb_util_wm_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "e78d10aab01a4a154142c5006ed44fd9e8e31b67"
uuid = "c22f9ab0-d5fe-5066-847c-f4bb1cd4e361"
version = "0.4.1+1"

[[Xorg_xkbcomp_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxkbfile_jll"]
git-tree-sha1 = "4bcbf660f6c2e714f87e960a171b119d06ee163b"
uuid = "35661453-b289-5fab-8a00-3d9160c6a3a4"
version = "1.4.2+4"

[[Xorg_xkeyboard_config_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xkbcomp_jll"]
git-tree-sha1 = "5c8424f8a67c3f2209646d4425f3d415fee5931d"
uuid = "33bec58e-1273-512f-9401-5d533626f822"
version = "2.27.0+4"

[[Xorg_xtrans_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "79c31e7844f6ecf779705fbc12146eb190b7d845"
uuid = "c5fb5394-a638-5e4d-96e5-b29de1b5cf10"
version = "1.4.0+3"

[[Zlib_jll]]
deps = ["Libdl"]
uuid = "83775a58-1f1d-513f-b197-d71354ab007a"

[[Zstd_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "cc4bf3fdde8b7e3e9fa0351bdeedba1cf3b7f6e6"
uuid = "3161d3a3-bdf6-5164-811a-617609db77b4"
version = "1.5.0+0"

[[libass_jll]]
deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"]
git-tree-sha1 = "acc685bcf777b2202a904cdcb49ad34c2fa1880c"
uuid = "0ac62f75-1d6f-5e53-bd7c-93b484bb37c0"
version = "0.14.0+4"

[[libfdk_aac_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "7a5780a0d9c6864184b3a2eeeb833a0c871f00ab"
uuid = "f638f0a6-7fb0-5443-88ba-1cc74229b280"
version = "0.1.6+4"

[[libpng_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"]
git-tree-sha1 = "94d180a6d2b5e55e447e2d27a29ed04fe79eb30c"
uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f"
version = "1.6.38+0"

[[libvorbis_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Ogg_jll", "Pkg"]
git-tree-sha1 = "c45f4e40e7aafe9d086379e5578947ec8b95a8fb"
uuid = "f27f6e37-5d2b-51aa-960f-b287f2bc3b7a"
version = "1.3.7+0"

[[nghttp2_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"

[[p7zip_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0"

[[x264_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "d713c1ce4deac133e3334ee12f4adff07f81778f"
uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a"
version = "2020.7.14+2"

[[x265_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "487da2f8f2f0c8ee0e83f39d13037d6bbf0a45ab"
uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76"
version = "3.0.0+3"

[[xkbcommon_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"]
git-tree-sha1 = "ece2350174195bb31de1a63bea3a41ae1aa593b6"
uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd"
version = "0.9.1+5"
"""

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