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Adding the UCI Wine dataset (#207)
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* Adding the UCI Wine dataset.

This commit contains the code to build the dataset, a .csv file from https://gist.github.com/tijptjik/9408623, the documentation and test.
Everything is based on the current work present in the master branch.

* Adding data source for the Wine dataset
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Tambu authored Feb 11, 2023
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179 changes: 179 additions & 0 deletions data/wine.csv
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Wine,Alcohol,Malic.acid,Ash,Acl,Mg,Phenols,Flavanoids,Nonflavanoid.phenols,Proanth,Color.int,Hue,OD,Proline
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3,13.4,4.6,2.86,25,112,1.98,.96,.27,1.11,8.5,.67,1.92,630
3,12.2,3.03,2.32,19,96,1.25,.49,.4,.73,5.5,.66,1.83,510
3,12.77,2.39,2.28,19.5,86,1.39,.51,.48,.64,9.899999,.57,1.63,470
3,14.16,2.51,2.48,20,91,1.68,.7,.44,1.24,9.7,.62,1.71,660
3,13.71,5.65,2.45,20.5,95,1.68,.61,.52,1.06,7.7,.64,1.74,740
3,13.4,3.91,2.48,23,102,1.8,.75,.43,1.41,7.3,.7,1.56,750
3,13.27,4.28,2.26,20,120,1.59,.69,.43,1.35,10.2,.59,1.56,835
3,13.17,2.59,2.37,20,120,1.65,.68,.53,1.46,9.3,.6,1.62,840
3,14.13,4.1,2.74,24.5,96,2.05,.76,.56,1.35,9.2,.61,1.6,560
1 change: 1 addition & 0 deletions docs/src/datasets/misc.md
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Expand Up @@ -13,4 +13,5 @@ BostonHousing
Iris
Mutagenesis
Titanic
Wine
```
3 changes: 2 additions & 1 deletion src/MLDatasets.jl
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Expand Up @@ -70,7 +70,8 @@ include("datasets/misc/mutagenesis.jl")
export Mutagenesis
include("datasets/misc/titanic.jl")
export Titanic

include("datasets/misc/wine.jl")
export Wine

## Vision

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89 changes: 89 additions & 0 deletions src/datasets/misc/wine.jl
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export Wine

"""
Wine(; as_df = true, dir = nothing)
The UCI Wine dataset.
These data are the results of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars.
The analysis determined the quantities of 13 constituents found in each of the three types of wines.
Data source is the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/wine) where further details can be retrieved.
# Arguments
$ARGUMENTS_SUPERVISED_TABLE
# Fields
$FIELDS_SUPERVISED_TABLE
# Methods
$METHODS_SUPERVISED_TABLE
# Examples
```julia-repl
julia> using MLDatasets: Wine
julia> using DataFrames
julia> dataset = Wine()
dataset Wine:
metadata => Dict{String, Any} with 5 entries
features => 178×13 DataFrame
targets => 178×1 DataFrame
dataframe => 178×14 DataFrame
julia> describe(dataset.dataframe)
14×7 DataFrame
Row │ variable mean min median max nmissing eltype
│ Symbol Float64 Real Float64 Real Int64 DataType
─────┼────────────────────────────────────────────────────────────────────────────────
1 │ Wine 1.9382 1 2.0 3 0 Int64
2 │ Alcohol 13.0006 11.03 13.05 14.83 0 Float64
3 │ Malic.acid 2.33635 0.74 1.865 5.8 0 Float64
4 │ Ash 2.36652 1.36 2.36 3.23 0 Float64
5 │ Acl 19.4949 10.6 19.5 30.0 0 Float64
6 │ Mg 99.7416 70 98.0 162 0 Int64
7 │ Phenols 2.29511 0.98 2.355 3.88 0 Float64
8 │ Flavanoids 2.02927 0.34 2.135 5.08 0 Float64
9 │ Nonflavanoid.phenols 0.361854 0.13 0.34 0.66 0 Float64
10 │ Proanth 1.5909 0.41 1.555 3.58 0 Float64
11 │ Color.int 5.05809 1.28 4.69 13.0 0 Float64
12 │ Hue 0.957449 0.48 0.965 1.71 0 Float64
13 │ OD 2.61169 1.27 2.78 4.0 0 Float64
14 │ Proline 746.893 278 673.5 1680 0 Int64
```
"""
struct Wine <: SupervisedDataset
metadata::Dict{String, Any}
features
targets
dataframe
end

function Wine(; as_df = true, dir = nothing)
@assert dir === nothing "custom `dir` is not supported at the moment."
path = joinpath(@__DIR__, "..", "..", "..", "data", "wine.csv")
df = read_csv(path)

features = df[!, DataFrames.Not(:Wine)]
targets = df[!, [:Wine]]

metadata = Dict{String, Any}()
metadata["path"] = path
metadata["feature_names"] = names(features)
metadata["target_names"] = names(targets)
metadata["n_observations"] = size(df, 1)

if !as_df
features = df_to_matrix(features)
targets = df_to_matrix(targets)
df = nothing
end

return Wine(metadata, features, targets, df)
end
22 changes: 22 additions & 0 deletions test/datasets/misc.jl
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Expand Up @@ -90,3 +90,25 @@ end

@test isequal(d[1].features, [1, 3, "Braund, Mr. Owen Harris", "male", 22, 1, 0, "A/5 21171", 7.25, missing, "S"])
end

@testset "Wine" begin
n_obs = 178
n_features = 13
n_targets = 1
feature_names = ["Alcohol", "Malic.acid", "Ash", "Acl", "Mg", "Phenols", "Flavanoids", "Nonflavanoid.phenols", "Proanth", "Color.int", "Hue", "OD", "Proline"]
target_names = ["Wine"]

d = Wine()
test_inmemory_supervised_table_dataset(d;
n_obs, n_features, n_targets,
feature_names, target_names)


d = Wine(as_df=false)
test_inmemory_supervised_table_dataset(d;
n_obs, n_features, n_targets,
feature_names, target_names,
Tx=Any, Ty=Int)

@test isequal(d[1].features, [14.23, 1.71, 2.43, 15.6, 127, 2.8, 3.06, 0.28, 2.29, 5.64, 1.04, 3.92, 1065])
end

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