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

using Markdown
using InteractiveUtils

# ╔═╡ b107d7f9-90c3-4a9f-9b55-0e1dc323ee56
using PlutoUI

# ╔═╡ ad6469ef-68a5-4819-bf98-85d06d37b960
using CSV

# ╔═╡ f31b4062-c763-4647-84ef-b3db25406fc8
using HTTP

# ╔═╡ 6cab610d-e713-4b08-8a5b-5eab8820732f
using DataFrames

# ╔═╡ 6fc301b3-18f2-44e8-91d2-268e10c3a0e6
using BenchmarkTools

# ╔═╡ 7ef54d4e-a43d-4b37-8b2e-021d9a5ea99f
begin
	using MLJ
	import RDatasets: dataset
	import DataFrames: DataFrame, select, Not, describe
	using Random
end

# ╔═╡ b33417a7-6263-4954-a923-c6716c61f2bf
using MLJMultivariateStatsInterface

# ╔═╡ 76e7ce0e-7c62-45e2-a02e-2f2283c56c3b
using MLJClusteringInterface

# ╔═╡ 9ecc20b5-538e-415b-80c3-c4fad54b0ccf
using Plots

# ╔═╡ 65e9979c-ec08-4093-9a35-3b872da2dee1
using UrlDownload

# ╔═╡ cfc8f87e-3502-4115-9da4-c137acd880cb
using MLJScikitLearnInterface #También hay que instalar scikit-learn donde sea que tengamos a Julia, en mi caso, uso pip3 install scikit-learn

# ╔═╡ 6b4a8fd1-60f5-4760-a453-86a75a201a5f
md"""# Ciencia de Datos pt. 1

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

# ╔═╡ ef6340c4-f4f7-11eb-30f9-599dcebb98be
PlutoUI.TableOfContents(title="🔬 Ciencia de datos pt. 1 🔭", depth = 5)

# ╔═╡ d24051dc-5efa-48c8-b65b-c756ba43b864
md"# Manejo de datos"

# ╔═╡ b1a67905-09f3-41aa-9a87-362d051fc69f
md"## CSV.jl
- [GitHub](https://github.com/JuliaData/CSV.jl)
- [JuliaData](https://csv.juliadata.org/stable/)"

# ╔═╡ 6069ba43-4a4d-4c59-a465-098385eb6e80
md"### Leyendo csv
Usemos la función `CSV.File` para leer archivos `.csv`. Podemos hacerlo desde archivos locales:"

# ╔═╡ a3c21e5d-a030-4cf3-aed3-e7100a885d37
hotel = CSV.File("../..//datos/hotel_bookings.csv")

# ╔═╡ 6baca64d-6dcb-44d7-b03d-69c0322631fe
md"O también podemos leer desde una página con la paquetería `HTTP.jl`"

# ╔═╡ e32d05c9-5896-4435-8f4c-15f887d6b256
begin
	url = "https://raw.githubusercontent.com/OscarAlvaradoM/Pokemon/master/datos/Disp8_completo.csv"
	pokemon = CSV.File(HTTP.get(url).body)
end

# ╔═╡ bf9cd655-eafe-41d2-ab06-f0a53cde9455
pokemon.names

# ╔═╡ f653a134-d37a-4959-bf81-40c2d20d4a87
pokemon.cols

# ╔═╡ 7ea3c9a3-a369-4ee9-908d-ed8b7d1ae3cb
md"### Delimitador"

# ╔═╡ 36b56e8b-dde9-4fcb-a3f9-a93fb3101cdf
md"""
	col1::col2
	1::2
	3::4
```Julia
CSV.File(file; delim="::")
```
"""

# ╔═╡ fcd2724d-aa29-4e7b-8d39-818463e7ceb5
md"### Sin encabezado"

# ╔═╡ 6165b59f-2c9c-44d8-b914-a307f99a5a48
md"""
	1,2,3
	4,5,6
	7,8,9
```Julia
CSV.File(file; header=false)
CSV.File(file; header=["col1", "col2", "col3"])
CSV.File(file; header=[:col1, :col2, :col3])
```
"""

# ╔═╡ c36c1741-21fe-4758-a82d-dbf3439830cd
md"### Datarow"

# ╔═╡ 51e1ee64-9541-4ee2-9499-748811dacac9
md"""
	col1,col2,col3
	metadata1,metadata2,metadata3
	extra1,extra2,extra3
	1,2,3
	4,5,6
	7,8,9

```Julia
CSV.File(file; datarow=4)
CSV.File(file; skipto=4)
```
"""

# ╔═╡ e2da47f5-d1bf-4060-8113-5fa2a7de56ff
md"### Leyendo lotes"

# ╔═╡ 4ece0521-051e-475c-bec9-10f38beb009d
md"""
	col1,col2,col3
	1,2,3
	4,5,6
	7,8,9
	10,11,12
	13,14,15
	16,17,18
	19,20,21

```Julia
CSV.File(file; limit=3)
CSV.File(file; skipto=4, limit=1)
CSV.File(file; skipto=7, footerskip=1)
```
"""

# ╔═╡ 236f6a7d-86a5-4bbc-a0d8-49f06e1714bf
md"### Reenglones comentados"

# ╔═╡ 66e15e9e-f283-487d-8e73-ae5ec23edb54
md"""
	col1,col2,col3
	# this row is commented and we'd like to ignore it while parsing
	1,2,3
	4,5,6

```Julia
CSV.File(file; comment="#")
CSV.File(file; datarow=3)
```
"""

# ╔═╡ 72e80a6f-8d30-4a85-a154-d2fbfc653421
md"### Valores nulos"

# ╔═╡ 0b5f90c0-7b51-484a-a023-0f533167bf7b
md"""
	code,age,score
	0,21,3.42
	1,42,6.55
	-999,81,NA
	-999,83,NA

```Julia
CSV.File(file; missingstring="-999")
CSV.File(file; missingstrings=["-999", "NA"])
```
"""

# ╔═╡ 720f5fe8-0616-4480-be24-b81764c4361f
md"### Fechas"

# ╔═╡ 7cbe09ae-5dcc-4c42-b824-bd401ed7f18b
md"""
	code,date
	0,2019/01/01
	1,2019/01/02

```Julia
CSV.File(file; dateformat="yyyy/mm/dd")
```
"""

# ╔═╡ 6b8336a6-fac6-4042-84e5-2cf4a0bf83b7
md"### Asignando tipos"

# ╔═╡ 9d3653a3-a617-4694-bc87-72d2606245c9
md"""
	col1,col2,col3
	1,2,3
	4,5,invalid
	6,7,8

```Julia
CSV.File(file; types=Dict(3 => Int))
CSV.File(file; types=Dict(:col3 => Int))
CSV.File(file; types=Dict("col3" => Int))
CSV.File(file; types=[Int, Int, Int])
CSV.File(file; types=[Int, Int, Int], silencewarnings=true)
CSV.File(file; types=[Int, Int, Int], strict=true)
```
"""

# ╔═╡ 7f43e8c7-7ac8-4aa2-acae-87e0c3814da1
md"### Seleccionar o eliminar columnas de un archivo"

# ╔═╡ ffb89c6b-cdd1-4a64-8ad7-b512373dfa1a
md"""
	a,b,c
	1,2,3
	4,5,6
	7,8,9

```Julia
# select
CSV.File(file; select=[1, 3])
CSV.File(file; select=[:a, :c])
CSV.File(file; select=["a", "c"])
CSV.File(file; select=[true, false, true])
CSV.File(file; select=(i, nm) -> i in (1, 3))
# drop
CSV.File(file; drop=[2])
CSV.File(file; drop=[:b])
CSV.File(file; drop=["b"])
CSV.File(file; drop=[false, true, false])
CSV.File(file; drop=(i, nm) -> i == 2)
```
"""

# ╔═╡ acf3fc88-d7cd-4bf9-ba4b-dd166984d7aa
md"## DataFrames.jl
- [GitHub](https://github.com/JuliaData/DataFrames.jl)
- [JuliaData](https://dataframes.juliadata.org/stable/#)"

# ╔═╡ 92056bbd-8dde-4b38-8780-02e248c6cfd3
md"### Transformando de un CSV.File a DataFrame"

# ╔═╡ fdbd56bb-4a02-436a-9ee3-552b373547c7
df_poke = CSV.read(HTTP.get("https://raw.githubusercontent.com/OscarAlvaradoM/Pokemon/master/datos/Disp8_completo.csv").body, DataFrame)

# ╔═╡ ccd0662b-455d-453f-be8c-d0a998910a7f
df_pokemon = DataFrame(pokemon)

# ╔═╡ 97d67ce7-cfb5-47fd-9ea3-272ded84e9fd
methodswith(typeof(df_pokemon))

# ╔═╡ 21f1d7dd-a865-4e8b-ab03-cd81b91f5f29
names(df_pokemon)

# ╔═╡ 6dc1461e-ad0e-4020-825a-a696de245ca8
nrow(df_pokemon)

# ╔═╡ 7dd8b809-0870-4945-97d2-2a655d0fcc63
md"### Creando DataFrames desde 0"

# ╔═╡ fd76cca7-48c0-474a-b826-3be1d7c54c14
DataFrame(A=1:3, B=5:7, fixed="uno")

# ╔═╡ a5f4bad2-2919-4241-9178-e944e42d69a5
DataFrame("Edad" => ["?", 23, 24], "Nombre" => ["Oscar", "Mario", "Anuar"])

# ╔═╡ b2fbf649-32b6-4dd8-894c-9ca2e2fa1e9c
begin
	dict_edades1 = Dict("Edad" => ["?", 23, 24], "Nombre" => ["Oscar", "Mario", "Anuar"])
	DataFrame(dict_edades1)
end

# ╔═╡ 8b1d8c99-7fa1-4054-ae7d-52b01a97b0b3
begin
	dict_edades2 = Dict(:Edad => ["?", 23, 24], :Nombre => ["Oscar", "Mario", "Anuar"])
	DataFrame(dict_edades2)
end

# ╔═╡ 76dbe6fa-7426-4ef1-874a-7463602e6c7c
DataFrame((a=[1, 2], b=[3, 4]))

# ╔═╡ bbae294a-01f4-47a3-9cfb-99e8e0978796
DataFrame([(a=1, b=0), (a=2, b=0)])

# ╔═╡ 064bea34-0890-4d27-8c5c-63d7f66e10ac
DataFrame([1 0; 2 0], ["A", "B"])

# ╔═╡ 35fef6ab-eaf7-4e39-8424-1e77ef29a4f1
DataFrame([1 0; 2 0], :auto)

# ╔═╡ e13caf23-fe50-456a-a7f0-1df74580e6c6
md"### Operaciones básicas sobre DataFrames"

# ╔═╡ 2d0dd71a-d577-4d56-bf42-31ce47d0bebe
names(df_pokemon)

# ╔═╡ 41424723-6627-4462-9e31-36ef20933230
md"Esto no es una copia"

# ╔═╡ 816b06b0-82b9-45ce-8cbb-22061363c936
df_pokemon.Nombre

# ╔═╡ 8784abe2-688d-401f-bce0-0ad02b4b7e76
md"Esto es una copia:"

# ╔═╡ efca9fcb-2378-49ad-b82b-a528674944da
df_pokemon[:, :Nombre]

# ╔═╡ 0cd53836-8853-46da-86f4-2a11bee5bed8
md"Y esto ¿qué es?"

# ╔═╡ 0eb90afd-4e1c-4bf2-b8a4-b7241ed7d042
df_pokemon[!, :Nombre]

# ╔═╡ 7c787df1-3f7e-4057-91e2-db9b0a42b11f
df_pokemon.Nombre === df_pokemon[!, :Nombre]

# ╔═╡ e426364d-dc2d-45a7-8e3f-a7d63424433e
df_pokemon.Nombre === df_pokemon[:, :Nombre]

# ╔═╡ d96f14f8-dc5c-4adf-899e-5e9497e9bba1
eltype.(eachcol(df_pokemon))

# ╔═╡ 79bf46e7-96c3-488d-9ced-e35798d6ed09
size(df_pokemon)

# ╔═╡ d6b8b7f2-2eff-4201-b53f-c10848942f9b
nrow(df_pokemon)

# ╔═╡ dfe4c5c0-d51d-4aae-abe7-604619a16e17
ncol(df_pokemon)

# ╔═╡ 22b933e1-9763-4922-b72d-a9f59477aaaf
describe(df_pokemon, cols=1:10, :min)

# ╔═╡ 4e6a6592-72dd-4e59-9cb8-e4aa75b82c3f
mapcols(Altura_m -> Altura_m.^2, df_pokemon)

# ╔═╡ dc16d155-379f-4d2a-8e01-825c384c117c
df_pokemon.Altura_m

# ╔═╡ f413974c-b210-4d9e-bc3f-c5b0d3f28e69
first(df_pokemon, 10)

# ╔═╡ 5f96e230-7e61-4a1e-962f-8ca5f0d0be60
last(df_pokemon)

# ╔═╡ 466e107d-1c7a-4a15-a08b-c9120b84a917
df_pokemon[1:5, [:Nombre, :Clasificación]]

# ╔═╡ a50fb16a-f22e-4983-9df5-d118429dbca5
df_pokemon[1:5, 3:5]

# ╔═╡ b7935362-ac6b-47a8-9106-cc2beaac70a8
df_pokemon[[1, 6, end], :]

# ╔═╡ 8d0b9978-b04a-436d-bf13-6946b043e48d
@view df_pokemon[end:-1:1, [:Nombre, :Generación]]

# ╔═╡ 38452d24-76bd-4d75-9f9b-39d5d0d2dae8
@btime @view $df_pokemon[1:end-1, 1:end-1]

# ╔═╡ 68733a99-4d7e-4256-a168-2032de50ab80
@btime $df_pokemon[1:end-1, 1:end-1];

# ╔═╡ e0968f67-1995-45d1-81e5-f6fb875312d5
df_pokemon.Contra_acero .= 1.

# ╔═╡ e51c2334-fd90-4847-9877-05ed2390d45a
insertcols!(df_pokemon, 1, :Juego => "Pokemon")

# ╔═╡ 432e6d0a-7671-4b3c-a8c4-0508a418cee7
df_pokemon[:, Not(:Evolución)]

# ╔═╡ 7e693874-9155-4c2f-8b4c-3543454fbb56
df_pokemon[:, Between(:Juego, :Generación)]

# ╔═╡ 7cc17b78-8f2a-49de-a7a6-99ab6d89b0e0
df_pokemon[:, Cols(:Altura_m, Between(:Juego, :Generación))]

# ╔═╡ ab72fb5f-90ac-446c-88d6-938852f3816e
md"Haciendo algunos joins"

# ╔═╡ 9c851f5a-4c8f-4388-86a1-ff6a3d83c8fe
people = DataFrame(ID = [20, 40], Name = ["John Doe", "Jane Doe"])

# ╔═╡ 6f6459c8-8242-4340-a588-0512eff5d5c3
jobs = DataFrame(ID = [20, 60], Job = ["Lawyer", "Doctor"])

# ╔═╡ db565011-1563-41cc-84d0-e14c8237aa8a
innerjoin(people, jobs, on = :ID)

# ╔═╡ f518b097-a6f4-4bf5-ba36-0088f5fc6b94
leftjoin(people, jobs, on = :ID)

# ╔═╡ 98693a39-71de-4e60-bf66-42e30618e254
rightjoin(people, jobs, on = :ID)

# ╔═╡ f1ea0441-6910-4704-906a-3e6cf05b4105
df = DataFrame(i = 1:5,x = [missing, 4, missing, 2, 1],y = [missing, missing, "c", "d", "e"])

# ╔═╡ a0d7c872-8c54-4eed-9a01-1ca92c2d6a28
dropmissing(df)

# ╔═╡ 65729744-b221-488d-b1ad-85981489916c
dropmissing(df, :x)

# ╔═╡ c0b3f6a7-341c-435a-b528-3a190f5c500c
md"Una buena comparación con Pandas y R:

[DataFrames (JuliaData)](https://dataframes.juliadata.org/stable/man/comparisons/)"

# ╔═╡ 185015b8-d252-4beb-96f0-4d2483f79344
md"# Tarea
Buscar un conjunto de datos de tu interés con el cuál trabajar para aprendizaje automatizado, leerlo y analizarlo con julia"

# ╔═╡ e9fcbd2a-6c9b-4e07-bcae-b990ebc1a3da
md"# Aprendizaje Automatizado"

# ╔═╡ 042c51dc-80d8-4c19-9c87-4e3e83de5e19
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`."

# ╔═╡ eda00801-1169-4f24-ad20-56585835002a
md"### Aprendizaje no supervisado
	- PCA 
	- Agrupamiento"

# ╔═╡ 6f396986-414e-4a2a-a1b4-fb097e1759df
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."

# ╔═╡ 37aef421-934c-4d0e-a78e-7da96f8b3edc
begin
	data = dataset("datasets", "USArrests")
	first(data, 5)
end

# ╔═╡ 6d09d7e5-57d8-4619-b409-3fea32f427a4
md"Vemos un poco de descripción de los datos:"

# ╔═╡ 9d3e1566-7dbf-4cdc-9a79-796b1bb9b961
describe(data, :mean, :std, :min, :max)

# ╔═╡ eef96558-9a43-447f-a52d-a44636e5015d
md"Obtengamos los datos numéricos"

# ╔═╡ 9ead4d07-7586-480a-a24a-7eb866d4b130
begin
	X = select(data, Not(:State))
	X = coerce(X, :UrbanPop => Continuous, :Assault => Continuous)
end

# ╔═╡ a75720f2-9d56-4e1e-8fdd-e4d248871367
md"#### PCA
Primero, cargamos el modelo"

# ╔═╡ 0eea30b5-7729-40c8-aadf-8017fa3e740d
@load PCA pkg = MultivariateStats

# ╔═╡ 9c3f3c32-ec53-46db-8b06-a446d3871175
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."

# ╔═╡ 35cfad5a-cc8b-4c14-81e6-808ec0f67008
models(matching(X))

# ╔═╡ 6c8ee67c-66ee-480f-9154-01cf26583215
md"Si queremos ver todos los modelos disponibles podemos hacerlo como sigue:"

# ╔═╡ 2d5b8029-d913-43ff-bab4-3319c5681e71
models()

# ╔═╡ 7959d6c7-5b4a-4865-8fd1-3934cef54755
md"Creamos el modelo"

# ╔═╡ f7fd7736-f30d-40ea-af14-c422a31fbd77
pca_mdl = PCA()

# ╔═╡ 1cb799aa-dfe3-4b24-baf1-a2ad9c68c6d3
md"Creamos una _máquina_, que es un objeto que vincula un modelo (algoritmo + hiperparámetros) con los datos. Esta almacena los parámetros aprendidos."

# ╔═╡ 1274b8b5-8aa2-4b83-8170-fac74bebb946
pca = machine(pca_mdl, X)

# ╔═╡ 73691cc0-f43d-46ab-952b-d877a16de744
md"¡Ajustamos!"

# ╔═╡ 0bff6458-b298-4ea2-b1f8-01b00ce185b5
fit!(pca)

# ╔═╡ f48cce8b-6b4d-47e7-9827-26cd2a847a38
md"¡Transformamos!"

# ╔═╡ 565cb36b-96a6-4a05-b1b0-5ab8b95478d5
W = MLJ.transform(pca, X)

# ╔═╡ 8f44f8fa-53f3-490d-8835-0bb32cbc23d2
begin
	r = report(pca)
	cumsum(r.principalvars ./ r.tvar)
end

# ╔═╡ 610fa1c7-8c0c-4608-bc36-f7c8aa3f0f3b
md"Trabajemos con datos más interesantes:"

# ╔═╡ b211535c-fd8b-4f84-b88b-2ea2286a58f4
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."
"""

# ╔═╡ 45bb9873-5eac-4e62-a041-0c4f47975462
begin
	data1 = dataset("ISLR", "OJ")
	X1 = select(data1, [:PriceCH, :PriceMM, :DiscCH, :DiscMM, :SalePriceMM,
                  :SalePriceCH, :PriceDiff, :PctDiscMM, :PctDiscCH]);
end

# ╔═╡ 728d6e97-9e60-4e42-b9ad-39c1a2e32808
names(data1)

# ╔═╡ 43594135-f686-4649-8f03-c87c7294315c
begin
	SPCA = @pipeline(Standardizer(),
	                 PCA())
	
	spca = machine(SPCA, X1)
	fit!(spca)
	W1 = MLJ.transform(spca, X1)
	W1
end

# ╔═╡ 60751597-b4b6-46e4-b2fe-a18de27301ef
begin
	rpca = collect(values(report(spca).report_given_machine))[2]
	cs = cumsum(rpca.principalvars ./ rpca.tvar)
end

# ╔═╡ 131dc691-87b0-46f6-ba6e-1a421b491f7d
md"#### Agrupamiento
Hagamos algo de agrupamiento con estos mismos datos que acabamos de obtener"

# ╔═╡ 5f5f9329-d62f-48cb-9ddb-c68e4f7886b5
@load KMeans pkg=ScikitLearn

# ╔═╡ 7a981cc3-1a22-407c-aafe-69f170c64062
begin
	SPCA2 = @pipeline(Standardizer(),
	                  PCA(),
	                  KMeans(k=3))
	
	spca2 = machine(SPCA2, X1)
	fit!(spca2)
end

# ╔═╡ 44491e74-109f-4ae3-a91a-651ed86209a7
models()

# ╔═╡ b15087d4-51d5-4903-8064-2f7cfac3f3c2
assignments = collect(values(report(spca2).report_given_machine))[3].assignments

# ╔═╡ 51a697bf-c7ce-4fa0-864c-57f93eb41d92
begin
	mask1 = assignments .== 1
	mask2 = assignments .== 2
	mask3 = assignments .== 3;
end

# ╔═╡ 95be3be8-af10-4286-97b0-5366792ad349
plotly()

# ╔═╡ a0ef1009-87bb-4f04-86f8-cf61f69577a1
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

# ╔═╡ a506982f-6ab8-4f90-9974-8ccbc5e15d35
md"### Aprendizaje supervisado"

# ╔═╡ 353d48a4-20ea-4167-bf93-c517e3488220
md"#### Clasificación"

# ╔═╡ c0385ff2-3c5a-4f01-acb8-a036cd5c85ff
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

# ╔═╡ 8934fed8-f0ba-4635-9d96-74b9751c8b95
begin
	df2 = DataFrame(data2)
	describe(df2)
end

# ╔═╡ 26fac042-79b9-46d7-ade0-dd893fd4280e
y_wine, X_wine = unpack(df2, ==(:Class), colname->true)

# ╔═╡ 59160fa4-83dc-4d23-8759-ce8ab979b7b9
scitype(y_wine)

# ╔═╡ 1645c96b-84d0-43d2-b57f-09adf5d04803
yc = coerce(y_wine, OrderedFactor)

# ╔═╡ 04c66bc0-c607-46c2-8625-042f50e0a32b
scitype(X_wine)

# ╔═╡ 3c813b9e-1859-47b5-911f-06b36dc9b5c2
schema(X_wine)

# ╔═╡ b4aff21d-885a-44c4-abf0-a686fa44ae7a
Xc = coerce(X_wine, :Proline=>Continuous, :Magnesium=>Continuous)

# ╔═╡ 7787818f-b621-4039-94cf-91059e45d200
describe(Xc, :mean, :std)

# ╔═╡ 6c050cbf-ce13-4fa1-a992-2261702369f7
@load KNeighborsClassifier pkg="ScikitLearn"

# ╔═╡ 483537db-0253-4083-86b4-9678f66e137e
models()

# ╔═╡ 525d3cd2-f231-4138-b6be-c35d8a317bbc
KnnPipe = @pipeline(Standardizer(), KNeighborsClassifier())

# ╔═╡ 02d2647b-951a-41ad-9a50-767fdbdf54ec
train, test = partition(eachindex(yc), 0.8, shuffle=true, rng=111)

# ╔═╡ a22e4fc2-5ae3-4e64-8589-9c2ce68b01a2
knn = machine(KnnPipe, Xc, yc)

# ╔═╡ 8b9e878d-6f32-48d7-abeb-005bbcd14549
fit!(knn, rows=train)

# ╔═╡ 8b6374e9-47c2-4417-ae11-1ff909f9e5e1
ŷ = predict_mode(knn, rows=test)

# ╔═╡ 7ddff453-bb4b-4086-9d29-1f37ab99643a
begin
	r3 = x -> round(x, sigdigits=3)
	accuracy(ŷ, yc[test]) |> r3
end

# ╔═╡ eee433ec-6493-48d6-bcd0-e0e3b9c34bdc
md"#### Regresión"

# ╔═╡ 20ecc9ce-db93-4733-8fc1-24e058c84fb5
begin
	req = HTTP.get("https://raw.githubusercontent.com/rupakc/UCI-Data-Analysis/master/Airfoil%20Dataset/airfoil_self_noise.dat");
	
	df_air = CSV.File(req.body; header=["Frequency","Attack_Angle","Chord+Length",
	                  "Free_Velocity","Suction_Side","Scaled_Sound"])
end

# ╔═╡ 3e6d49ab-53ea-4d74-9478-4e3b48798977
schema(df_air)

# ╔═╡ 0d9281c5-3560-4a2c-9cec-21d16d4af695
y_q, X_q = unpack(df_air, ==(:Scaled_Sound), colname->true)

# ╔═╡ 11c5a4f7-ff46-4298-a05d-c75f48b5c483
df_air

# ╔═╡ 98483c7e-7cb0-42a6-b52d-da7d4ee43cce
begin
	y_air, X_ai = unpack(df_air, ==(:Scaled_Sound), colname -> true)
	X_air = MLJ.transform(fit!(machine(Standardizer(), X_ai)), X_ai);
end

# ╔═╡ 37483f0a-b23e-4f26-a592-8c539b47eb0f
train_air, test_air = partition(eachindex(y_air), 0.7, shuffle=true, rng=111)

# ╔═╡ 7e1d9ecc-be3a-418b-bcfc-e8268d1e4512
coerce!(X_air, :Frequency=>Continuous)

# ╔═╡ 46cd4e9c-e9f5-4cd6-a216-7ddb185ac35c
dcr = @load DecisionTreeRegressor pkg=DecisionTree

# ╔═╡ cd79d6f8-eeae-484d-af38-3b52574c030f
dcrm = machine(dcr, X_air, y_air)

fit!(dcrm, rows=train)
pred_dcrm = MLJ.predict(dcrm, rows=test)

# ╔═╡ 33117a1d-ac85-44db-b75f-bf56d1d39f4f
rms(pred_dcrm, y_air[test])

# ╔═╡ 00000000-0000-0000-0000-000000000001
PLUTO_PROJECT_TOML_CONTENTS = """
[deps]
BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
HTTP = "cd3eb016-35fb-5094-929b-558a96fad6f3"
MLJ = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7"
MLJClusteringInterface = "d354fa79-ed1c-40d4-88ef-b8c7bd1568af"
MLJMultivariateStatsInterface = "1b6a4a23-ba22-4f51-9698-8599985d3728"
MLJScikitLearnInterface = "5ae90465-5518-4432-b9d2-8a1def2f0cab"
Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
PlutoUI = "7f904dfe-b85e-4ff6-b463-dae2292396a8"
RDatasets = "ce6b1742-4840-55fa-b093-852dadbb1d8b"
Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
UrlDownload = "856ac37a-3032-4c1c-9122-f86d88358c8b"

[compat]
BenchmarkTools = "~1.1.1"
CSV = "~0.8.5"
DataFrames = "~1.2.2"
HTTP = "~0.9.13"
MLJ = "~0.16.7"
MLJClusteringInterface = "~0.1.4"
MLJMultivariateStatsInterface = "~0.2.2"
MLJScikitLearnInterface = "~0.1.10"
Plots = "~1.20.0"
PlutoUI = "~0.7.9"
RDatasets = "~0.7.5"
UrlDownload = "~1.0.0"
"""

# ╔═╡ 00000000-0000-0000-0000-000000000002
PLUTO_MANIFEST_TOML_CONTENTS = """
# This file is machine-generated - editing it directly is not advised

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[[GR]]
deps = ["Base64", "DelimitedFiles", "GR_jll", "HTTP", "JSON", "Libdl", "LinearAlgebra", "Pkg", "Printf", "Random", "Serialization", "Sockets", "Test", "UUIDs"]
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[[GR_jll]]
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[[GeometryBasics]]
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[[Gettext_jll]]
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[[Glib_jll]]
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[[Grisu]]
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[[HTTP]]
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[[IniFile]]
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[[InteractiveUtils]]
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[[InvertedIndices]]
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[[JLLWrappers]]
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[[JLSO]]
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[[JSON]]
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[[JpegTurbo_jll]]
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[[LAME_jll]]
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git-tree-sha1 = "f6250b16881adf048549549fba48b1161acdac8c"
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[[LZO_jll]]
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[[LaTeXStrings]]
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[[Latexify]]
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[[LatinHypercubeSampling]]
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[[LazyArtifacts]]
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[[LearnBase]]
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[[LibCURL]]
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[[LibCURL_jll]]
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uuid = "deac9b47-8bc7-5906-a0fe-35ac56dc84c0"

[[LibGit2]]
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[[LibSSH2_jll]]
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uuid = "29816b5a-b9ab-546f-933c-edad1886dfa8"

[[LibVPX_jll]]
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[[Libdl]]
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[[Libffi_jll]]
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[[Libgcrypt_jll]]
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[[Libglvnd_jll]]
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[[Libgpg_error_jll]]
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[[Libiconv_jll]]
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[[Libmount_jll]]
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[[Libtiff_jll]]
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[[Libuuid_jll]]
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[[LinearAlgebra]]
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[[LogExpFunctions]]
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[[Logging]]
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[[LossFunctions]]
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[[MLJ]]
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[[MLJBase]]
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[[MLJClusteringInterface]]
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[[MLJEnsembles]]
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[[MLJIteration]]
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[[MLJModelInterface]]
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[[MLJModels]]
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[[MLJMultivariateStatsInterface]]
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[[MLJOpenML]]
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[[MLJScikitLearnInterface]]
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[[MLJSerialization]]
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[[MLJTuning]]
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[[MacroTools]]
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[[Markdown]]
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[[MbedTLS]]
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[[MbedTLS_jll]]
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[[Measures]]
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[[Memento]]
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[[Missings]]
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[[Mmap]]
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[[Mocking]]
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[[MozillaCACerts_jll]]
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[[MultivariateStats]]
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[[NaNMath]]
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[[NearestNeighbors]]
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[[NetworkOptions]]
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[[Ogg_jll]]
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[[OpenBLAS_jll]]
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[[OpenSSL_jll]]
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[[OpenSpecFun_jll]]
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[[Opus_jll]]
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[[OrderedCollections]]
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[[PCRE_jll]]
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uuid = "2f80f16e-611a-54ab-bc61-aa92de5b98fc"
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[[PDMats]]
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[[Parameters]]
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[[Parsers]]
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[[PersistenceDiagramsBase]]
deps = ["Compat", "Tables"]
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[[Pixman_jll]]
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git-tree-sha1 = "b4f5d02549a10e20780a24fce72bea96b6329e29"
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[[Pkg]]
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[[PlotThemes]]
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[[PlotUtils]]
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[[Plots]]
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[[PlutoUI]]
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[[PooledArrays]]
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[[Preferences]]
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[[PrettyTables]]
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[[Printf]]
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[[ProgressMeter]]
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[[PyCall]]
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[[Qt5Base_jll]]
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[[QuadGK]]
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[[RData]]
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[[RDatasets]]
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[[REPL]]
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[[Random]]
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[[RecipesBase]]
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[[RecipesPipeline]]
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[[Reexport]]
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[[Requires]]
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[[Rmath]]
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[[Rmath_jll]]
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[[SHA]]
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[[ScientificTypes]]
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[[ScientificTypesBase]]
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[[ScikitLearn]]
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[[ScikitLearnBase]]
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[[Scratch]]
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[[SentinelArrays]]
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[[SharedArrays]]
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[[Showoff]]
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[[Sockets]]
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[[SortingAlgorithms]]
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[[SparseArrays]]
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[[SpecialFunctions]]
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[[StableRNGs]]
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[[StaticArrays]]
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[[StatisticalTraits]]
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[[Statistics]]
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[[StatsAPI]]
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[[StatsBase]]
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[[StatsFuns]]
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[[StructArrays]]
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[[StructTypes]]
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[[SuiteSparse]]
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[[Suppressor]]
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[[Syslogs]]
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[[TOML]]
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[[TableTraits]]
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[[Tables]]
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[[Tar]]
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[[Test]]
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[[TimeZones]]
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[[TranscodingStreams]]
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[[URIs]]
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[[UUIDs]]
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[[UnPack]]
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[[Unicode]]
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[[UrlDownload]]
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[[Wayland_protocols_jll]]
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[[XML2_jll]]
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[[XSLT_jll]]
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[[Xorg_libX11_jll]]
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[[Xorg_libXau_jll]]
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[[Xorg_libXcursor_jll]]
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[[Xorg_libXdmcp_jll]]
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[[Xorg_libXext_jll]]
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[[Xorg_libXi_jll]]
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[[Xorg_libXinerama_jll]]
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[[Xorg_libXrandr_jll]]
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[[Xorg_libXrender_jll]]
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[[Xorg_libpthread_stubs_jll]]
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[[Xorg_libxcb_jll]]
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[[Xorg_libxkbfile_jll]]
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[[Xorg_xcb_util_image_jll]]
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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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