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migrate CI to GitHub Actions (JuliaML#64)
* migrate CI to GitHub Actions * fix jl version * CI * refine runtests.jl * remove appveyor
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‎.github/workflows/CI.yml

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name: CI
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# Run on master, tags, or any pull request
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on:
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push:
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branches: [master]
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tags: ["*"]
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pull_request:
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jobs:
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test:
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name: Julia ${{ matrix.version }} - ${{ matrix.os }} - ${{ matrix.arch }}
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runs-on: ${{ matrix.os }}
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strategy:
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fail-fast: false
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matrix:
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version:
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- '1.3'
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- '1.4'
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- '1' # Latest Release
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os:
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- ubuntu-latest
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- windows-latest
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- macos-latest
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arch:
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- x64
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steps:
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- uses: actions/checkout@v2
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- uses: julia-actions/setup-julia@v1
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with:
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version: ${{ matrix.version }}
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arch: ${{ matrix.arch }}
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- uses: actions/cache@v1
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env:
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cache-name: cache-artifacts
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with:
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path: ~/.julia/artifacts
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key: ${{ runner.os }}-test-${{ env.cache-name }}-${{ hashFiles('**/Project.toml') }}
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restore-keys: |
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${{ runner.os }}-test-${{ env.cache-name }}-
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${{ runner.os }}-test-
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${{ runner.os }}-
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- uses: julia-actions/julia-buildpkg@latest
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- run: |
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git config --global user.name Tester
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git config --global user.email [email protected]
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- uses: julia-actions/julia-runtest@latest
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- uses: julia-actions/julia-processcoverage@v1
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- uses: codecov/codecov-action@v1
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with:
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file: lcov.info
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# ATM, we only have README
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#
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# docs:
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# name: Documentation
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# runs-on: ubuntu-latest
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# steps:
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# - uses: actions/checkout@v2
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# - uses: julia-actions/setup-julia@v1
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# with:
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# version: '1'
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# - run: |
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# git config --global user.name name
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# git config --global user.email email
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# git config --global github.user username
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# - run: |
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# julia --project=docs -e '
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# using Pkg;
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# Pkg.develop(PackageSpec(path=pwd()));
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# Pkg.instantiate();'
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# - run: |
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# julia --project=docs -e '
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# using Documenter: doctest
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# using TimeSeries
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# doctest(TimeSeries)'
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# - run: julia --project=docs docs/make.jl
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# env:
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# GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

‎.github/workflows/CompatHelper.yml

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name: CompatHelper
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on:
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schedule:
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- cron: '00 00 * * *'
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jobs:
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CompatHelper:
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runs-on: ubuntu-latest
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steps:
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- uses: julia-actions/setup-julia@latest
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- name: Pkg.add("CompatHelper")
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run: julia -e 'using Pkg; Pkg.add("CompatHelper")'
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- name: CompatHelper.main()
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env:
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GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
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run: julia -e 'using CompatHelper; CompatHelper.main()'

‎.github/workflows/TagBot.yml

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name: TagBot
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on:
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issue_comment:
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types:
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- created
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workflow_dispatch:
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jobs:
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TagBot:
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if: github.event_name == 'workflow_dispatch' || github.actor == 'JuliaTagBot'
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runs-on: ubuntu-latest
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steps:
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- uses: JuliaRegistries/TagBot@v1
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with:
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token: ${{ secrets.GITHUB_TOKEN }}

‎.travis.yml

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This file was deleted.

‎README.md

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# LIBSVM.jl
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[![Build Status](https://travis-ci.org/mpastell/LIBSVM.jl.svg?branch=master)](https://travis-ci.org/mpastell/LIBSVM.jl)
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[![Build status](https://ci.appveyor.com/api/projects/status/1v8jpbiy1o7mpi6q/branch/master?svg=true)](https://ci.appveyor.com/project/mpastell/libsvm-jl/branch/master)
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[![Coverage Status](https://coveralls.io/repos/github/mpastell/LIBSVM.jl/badge.svg?branch=master)](https://coveralls.io/github/mpastell/LIBSVM.jl?branch=master)
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[![Build Status](https://github.com/JuliaML/LIBSVM.jl/workflows/CI/badge.svg)](https://github.com/JuliaML/LIBSVM.jl/actions?query=workflow%3ACI)
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[![Coverage Status](https://coveralls.io/repos/github/JuliaML/LIBSVM.jl/badge.svg?branch=master)](https://coveralls.io/github/JuliaML/LIBSVM.jl?branch=master)
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This is a Julia interface for [LIBSVM](http://www.csie.ntu.edu.tw/~cjlin/libsvm/).
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‎appveyor.yml

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This file was deleted.

‎test/runtests.jl

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using LIBSVM, Test
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using DelimitedFiles
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using LIBSVM
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using SparseArrays
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using Test
45

5-
iris = readdlm(joinpath(dirname(@__FILE__), "iris.csv"), ',')
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labels = iris[:, 5]
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instances = convert(Matrix{Float64}, iris[:, 1:4]')
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model = svmtrain(instances[:, 1:2:end], labels[1:2:end]; verbose=true)
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GC.gc()
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(class, decvalues) = svmpredict(model, instances[:, 2:2:end])
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correct = Bool[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1]
12-
@test (class .== labels[2:2:end]) == correct
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@testset "LibSVM" begin
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14-
skmodel = fit!(SVC(), instances[:,1:2:end]', labels[1:2:end])
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skclass = predict(skmodel, instances[:, 2:2:end]')
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@test skclass == class
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18-
model = svmtrain(sparse(instances[:, 1:2:end]), labels[1:2:end]; verbose=true)
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GC.gc()
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(class, decvalues) = svmpredict(model, sparse(instances[:, 2:2:end]))
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correct = Bool[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1]
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@test (class .== labels[2:2:end]) == correct
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@testset "IRIS" begin
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iris = readdlm(joinpath(@__DIR__, "iris.csv"), ',')
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labels = iris[:, 5]
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#Regression tests, results confirmed using e1071 R-package
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#whiteside = RDatasets.dataset("MASS", "whiteside")
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whiteside, hdr = readdlm(joinpath(dirname(@__FILE__), "whiteside.csv"), ',', header=true)
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ws = convert(Matrix{Float64}, whiteside[:,2:3])
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X = Array{Float64, 2}(ws[:, 2]')
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y = ws[:, 1]
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instances = convert(Matrix{Float64}, iris[:, 1:4]')
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model = svmtrain(instances[:, 1:2:end], labels[1:2:end]; verbose = true)
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GC.gc()
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(class, decvalues) = svmpredict(model, instances[:, 2:2:end])
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correct = Bool[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1]
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@test (class .== labels[2:2:end]) == correct
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m = svmtrain(X, y, svmtype = EpsilonSVR, cost = 10., gamma = 1.)
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yeps, d = svmpredict(m, X)
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@test sum(yeps - y) ≈ 7.455509045783046
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skm = fit!(EpsilonSVR(cost = 10., gamma = 1.), X', y)
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ysk = predict(skm, X')
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@test isapprox(yeps,ysk)
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skmodel = fit!(SVC(), instances[:,1:2:end]', labels[1:2:end])
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skclass = predict(skmodel, instances[:, 2:2:end]')
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@test skclass == class
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model = svmtrain(sparse(instances[:, 1:2:end]), labels[1:2:end]; verbose=true)
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GC.gc()
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(class, decvalues) = svmpredict(model, sparse(instances[:, 2:2:end]))
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correct = Bool[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1]
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@test (class .== labels[2:2:end]) == correct
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end
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38-
nu1 = svmtrain(X, y, svmtype = NuSVR, cost = 10.,
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nu = .7, gamma = 2., tolerance = .001)
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ynu1, d = svmpredict(nu1, X)
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@test sum(ynu1 - y) ≈ 14.184665717092
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sknu1 = fit!(NuSVR(cost = 10., nu=.7, gamma = 2.), X', y)
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ysknu1 = predict(sknu1, X')
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@test isapprox(ysknu1,ynu1)
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46-
nu2 = svmtrain(X, y, svmtype = NuSVR, cost = 10., nu = .9)
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ynu2, d =svmpredict(nu2, X)
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@test sum(ynu2 - y) ≈ 6.686819661799177
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sknu2 = fit!(NuSVR(cost = 10., nu=.9), X', y)
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ysknu2 = predict(sknu2, X')
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@test isapprox(ysknu2,ynu2)
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@testset "Whiteside" begin
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#Regression tests, results confirmed using e1071 R-package
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#whiteside = RDatasets.dataset("MASS", "whiteside")
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whiteside, hdr = readdlm(joinpath(@__DIR__, "whiteside.csv"), ',', header=true)
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ws = convert(Matrix{Float64}, whiteside[:,2:3])
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X = Array{Float64, 2}(ws[:, 2]')
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y = ws[:, 1]
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# Multithreading testing
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m = svmtrain(X, y, svmtype = EpsilonSVR, cost = 10., gamma = 1.)
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yeps, d = svmpredict(m, X)
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@test sum(yeps - y) ≈ 7.455509045783046
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skm = fit!(EpsilonSVR(cost = 10., gamma = 1.), X', y)
43+
ysk = predict(skm, X')
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@test isapprox(yeps,ysk)
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55-
# Assign by maximum number of threads
56-
ntnu1 = svmtrain(X, y, svmtype = NuSVR, cost = 10.,
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nu = .7, gamma = 2., tolerance = .001,
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nt = -1)
59-
ntynu1, ntd = svmpredict(ntnu1, X)
60-
@test sum(ntynu1 - y) ≈ 14.184665717092
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nu1 = svmtrain(X, y, svmtype = NuSVR, cost = 10.,
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nu = .7, gamma = 2., tolerance = .001)
48+
ynu1, d = svmpredict(nu1, X)
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@test sum(ynu1 - y) ≈ 14.184665717092
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sknu1 = fit!(NuSVR(cost = 10., nu=.7, gamma = 2.), X', y)
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ysknu1 = predict(sknu1, X')
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@test isapprox(ysknu1,ynu1)
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62-
# Assign by environment
63-
ENV["OMP_NUM_THREADS"] = 2
54+
nu2 = svmtrain(X, y, svmtype = NuSVR, cost = 10., nu = .9)
55+
ynu2, d =svmpredict(nu2, X)
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@test sum(ynu2 - y) ≈ 6.686819661799177
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sknu2 = fit!(NuSVR(cost = 10., nu=.9), X', y)
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ysknu2 = predict(sknu2, X')
59+
@test isapprox(ysknu2, ynu2)
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65-
ntm = svmtrain(X, y, svmtype = EpsilonSVR, cost = 10., gamma = 1.)
66-
ntyeps, ntd = svmpredict(m, X)
67-
@test sum(ntyeps - y) ≈ 7.455509045783046
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@testset "Multithreading" begin
62+
# Assign by maximum number of threads
63+
ntnu1 = svmtrain(X, y, svmtype = NuSVR, cost = 10.,
64+
nu = .7, gamma = 2., tolerance = .001,
65+
nt = -1)
66+
ntynu1, ntd = svmpredict(ntnu1, X)
67+
@test sum(ntynu1 - y) ≈ 14.184665717092
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69+
# Assign by environment
70+
ENV["OMP_NUM_THREADS"] = 2
71+
72+
ntm = svmtrain(X, y, svmtype = EpsilonSVR, cost = 10., gamma = 1.)
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ntyeps, ntd = svmpredict(m, X)
74+
@test sum(ntyeps - y) ≈ 7.455509045783046
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end
76+
end
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78+
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end # @testset "LIBSVM"

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