Merge pull request #652 from joshua-whittemore/add-module-to-download-iris-dataset
Add module to make iris dataset available.
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NEWS.md
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NEWS.md
@ -9,6 +9,7 @@
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* New "performance tips" [section of the docs](https://github.com/FluxML/Flux.jl/pull/615).
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* The training loop is [now more readable](https://github.com/FluxML/Flux.jl/pull/651) and better shows how to use the lower-level APIs.
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* New [AlphaDropout](https://github.com/FluxML/Flux.jl/pull/656).
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* [Data.Iris](https://github.com/FluxML/Flux.jl/pull/652) makes Fisher's Iris dataset available with `Iris.labels` and `Iris.features`.
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* New [InstanceNorm](https://github.com/FluxML/Flux.jl/pull/634), as popularized by [Instance Normalization: The Missing Ingredient for Fast Stylization](https://arxiv.org/abs/1607.08022).
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AD Changes:
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@ -6,6 +6,7 @@ AbstractTrees = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"
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Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
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CodecZlib = "944b1d66-785c-5afd-91f1-9de20f533193"
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Colors = "5ae59095-9a9b-59fe-a467-6f913c188581"
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DelimitedFiles = "8bb1440f-4735-579b-a4ab-409b98df4dab"
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Juno = "e5e0dc1b-0480-54bc-9374-aad01c23163d"
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LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
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MacroTools = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
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@ -39,4 +39,7 @@ include("tree.jl")
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include("sentiment.jl")
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using .Sentiment
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include("iris.jl")
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export Iris
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end
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src/data/iris.jl
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src/data/iris.jl
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"""
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Iris
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Fisher's classic iris dataset.
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Measurements from 3 different species of iris: setosa, versicolor and
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virginica. There are 50 examples of each species.
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There are 4 measurements for each example: sepal length, sepal width, petal
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length and petal width. The measurements are in centimeters.
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The module retrieves the data from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/iris).
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"""
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module Iris
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using DelimitedFiles
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using ..Data: deps, download_and_verify
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const cache_prefix = ""
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# Uncomment if the iris.data file is cached to cache.julialang.org.
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# const cache_prefix = "https://cache.julialang.org/"
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function load()
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isfile(deps("iris.data")) && return
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@info "Downloading iris dataset."
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download_and_verify("$(cache_prefix)https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data",
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deps("iris.data"),
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"6f608b71a7317216319b4d27b4d9bc84e6abd734eda7872b71a458569e2656c0")
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end
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"""
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labels()
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Get the labels of the iris dataset, a 150 element array of strings listing the
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species of each example.
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```jldoctest
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julia> labels = Flux.Data.Iris.labels();
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julia> summary(labels)
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"150-element Array{String,1}"
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julia> labels[1]
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"Iris-setosa"
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```
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"""
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function labels()
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load()
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iris = readdlm(deps("iris.data"), ',')
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Vector{String}(iris[1:end, end])
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end
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"""
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features()
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Get the features of the iris dataset. This is a 4x150 matrix of Float64
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elements. It has a row for each feature (sepal length, sepal width,
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petal length, petal width) and a column for each example.
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```jldoctest
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julia> features = Flux.Data.Iris.features();
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julia> summary(features)
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"4×150 Array{Float64,2}"
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julia> features[:, 1]
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4-element Array{Float64,1}:
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5.1
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3.5
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1.4
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0.2
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```
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"""
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function features()
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load()
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iris = readdlm(deps("iris.data"), ',')
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Matrix{Float64}(iris[1:end, 1:4]')
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end
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end
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@ -14,3 +14,9 @@ using Test
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@test FashionMNIST.labels() isa Vector{Int64}
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@test Data.Sentiment.train() isa Vector{Data.Tree{Any}}
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@test Iris.features() isa Matrix
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@test size(Iris.features()) == (4,150)
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@test Iris.labels() isa Vector{String}
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@test size(Iris.labels()) == (150,)
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