parent
db3e27b039
commit
741af8506b
@ -1,44 +1,23 @@
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PYTHON := python3
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PYTEST := pytest
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PIP := pip3
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JULIA := julia
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PYTEST_ARGS := -W ignore::DeprecationWarning -vv -x --log-level=DEBUG
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JULIA_ARGS := --color=yes --project=src/julia
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JULIA_SYSIMAGE_ARGS := $(JULIA_ARGS) --sysimage build/sysimage.so
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PYTHON := python3
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PYTEST := pytest
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PIP := pip3
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PYTEST_ARGS := -W ignore::DeprecationWarning -vv -x --log-level=DEBUG
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all: docs test
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build/sysimage.so: src/julia/Manifest.toml src/julia/Project.toml
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mkdir -p build
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$(JULIA) $(JULIA_ARGS) src/julia/sysimage.jl
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develop:
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cd src/python && $(PYTHON) setup.py develop
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docs:
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mkdocs build
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install: install-python
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install-python:
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install:
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cd src/python && $(PYTHON) setup.py install
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install-julia:
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$(JULIA) $(JULIA_ARGS) src/julia/setup.jl `which $(PYTHON)`
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uninstall:
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$(PIP) uninstall miplearn
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test: test-python test-julia
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test-python:
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test:
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cd src/python && $(PYTEST) $(PYTEST_ARGS)
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test-python-watch:
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cd src/python && pytest-watch -- $(PYTEST_ARGS)
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test-julia: build/sysimage.so
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$(JULIA) $(JULIA_SYSIMAGE_ARGS) src/julia/test/runtests.jl
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.PHONY: test test-python test-julia test-watch docs install
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.PHONY: test test-watch docs install
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@ -1,360 +0,0 @@
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# This file is machine-generated - editing it directly is not advised
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[[Base64]]
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uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
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[[BenchmarkTools]]
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deps = ["JSON", "Logging", "Printf", "Statistics", "UUIDs"]
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git-tree-sha1 = "9e62e66db34540a0c919d72172cc2f642ac71260"
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||||
uuid = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
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version = "0.5.0"
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||||
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[[Bzip2_jll]]
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deps = ["Libdl", "Pkg"]
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git-tree-sha1 = "03a44490020826950c68005cafb336e5ba08b7e8"
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uuid = "6e34b625-4abd-537c-b88f-471c36dfa7a0"
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version = "1.0.6+4"
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||||
|
||||
[[CEnum]]
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||||
git-tree-sha1 = "215a9aa4a1f23fbd05b92769fdd62559488d70e9"
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uuid = "fa961155-64e5-5f13-b03f-caf6b980ea82"
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version = "0.4.1"
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[[CPLEX]]
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deps = ["Libdl", "LinearAlgebra", "MathOptInterface", "MathProgBase", "SparseArrays"]
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git-tree-sha1 = "c3d7c4c3e4d4bd01c5ac89dee420be93ef7ef20b"
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uuid = "a076750e-1247-5638-91d2-ce28b192dca0"
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version = "0.6.6"
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[[CPLEXW]]
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deps = ["CEnum", "Libdl"]
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git-tree-sha1 = "2259e91418573278d919d6a163634a34ff1adb61"
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repo-rev = "master"
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repo-url = "https://github.com/iSoron/CPLEXW.git"
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uuid = "cfecb002-79c2-11e9-35be-cb59aa640f85"
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version = "1.0.0"
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[[Calculus]]
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deps = ["LinearAlgebra"]
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git-tree-sha1 = "f641eb0a4f00c343bbc32346e1217b86f3ce9dad"
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uuid = "49dc2e85-a5d0-5ad3-a950-438e2897f1b9"
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version = "0.5.1"
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[[CodeTracking]]
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deps = ["InteractiveUtils", "UUIDs"]
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git-tree-sha1 = "ccc043a0df446cac279dca29d13e2827b40aceb5"
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uuid = "da1fd8a2-8d9e-5ec2-8556-3022fb5608a2"
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version = "0.5.12"
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[[CodecBzip2]]
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deps = ["Bzip2_jll", "Libdl", "TranscodingStreams"]
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git-tree-sha1 = "2e62a725210ce3c3c2e1a3080190e7ca491f18d7"
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uuid = "523fee87-0ab8-5b00-afb7-3ecf72e48cfd"
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version = "0.7.2"
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[[CodecZlib]]
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deps = ["TranscodingStreams", "Zlib_jll"]
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||||
git-tree-sha1 = "ded953804d019afa9a3f98981d99b33e3db7b6da"
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||||
uuid = "944b1d66-785c-5afd-91f1-9de20f533193"
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version = "0.7.0"
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||||
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[[CommonSubexpressions]]
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deps = ["MacroTools", "Test"]
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||||
git-tree-sha1 = "7b8a93dba8af7e3b42fecabf646260105ac373f7"
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||||
uuid = "bbf7d656-a473-5ed7-a52c-81e309532950"
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version = "0.3.0"
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[[CompilerSupportLibraries_jll]]
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deps = ["Libdl", "Pkg"]
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||||
git-tree-sha1 = "7c4f882c41faa72118841185afc58a2eb00ef612"
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uuid = "e66e0078-7015-5450-92f7-15fbd957f2ae"
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version = "0.3.3+0"
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||||
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||||
[[Conda]]
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deps = ["JSON", "VersionParsing"]
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||||
git-tree-sha1 = "7a58bb32ce5d85f8bf7559aa7c2842f9aecf52fc"
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||||
uuid = "8f4d0f93-b110-5947-807f-2305c1781a2d"
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version = "1.4.1"
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||||
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[[DataStructures]]
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deps = ["InteractiveUtils", "OrderedCollections"]
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git-tree-sha1 = "88d48e133e6d3dd68183309877eac74393daa7eb"
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uuid = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
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||||
version = "0.17.20"
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||||
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||||
[[Dates]]
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||||
deps = ["Printf"]
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uuid = "ade2ca70-3891-5945-98fb-dc099432e06a"
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[[DiffResults]]
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||||
deps = ["StaticArrays"]
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git-tree-sha1 = "da24935df8e0c6cf28de340b958f6aac88eaa0cc"
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uuid = "163ba53b-c6d8-5494-b064-1a9d43ac40c5"
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version = "1.0.2"
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[[DiffRules]]
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deps = ["NaNMath", "Random", "SpecialFunctions"]
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git-tree-sha1 = "eb0c34204c8410888844ada5359ac8b96292cfd1"
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uuid = "b552c78f-8df3-52c6-915a-8e097449b14b"
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version = "1.0.1"
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||||
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[[Distributed]]
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deps = ["Random", "Serialization", "Sockets"]
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uuid = "8ba89e20-285c-5b6f-9357-94700520ee1b"
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||||
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[[FileWatching]]
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||||
uuid = "7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"
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[[ForwardDiff]]
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||||
deps = ["CommonSubexpressions", "DiffResults", "DiffRules", "NaNMath", "Random", "SpecialFunctions", "StaticArrays"]
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git-tree-sha1 = "1d090099fb82223abc48f7ce176d3f7696ede36d"
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||||
uuid = "f6369f11-7733-5829-9624-2563aa707210"
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version = "0.10.12"
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[[Gurobi]]
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deps = ["Libdl", "LinearAlgebra", "MathOptInterface", "MathProgBase", "SparseArrays"]
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||||
git-tree-sha1 = "f36a2fa62909675681aec582ccfc4a4a629406e4"
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||||
uuid = "2e9cd046-0924-5485-92f1-d5272153d98b"
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||||
version = "0.8.1"
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||||
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||||
[[HTTP]]
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||||
deps = ["Base64", "Dates", "IniFile", "MbedTLS", "Sockets"]
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||||
git-tree-sha1 = "2ac03263ce44be4222342bca1c51c36ce7566161"
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||||
uuid = "cd3eb016-35fb-5094-929b-558a96fad6f3"
|
||||
version = "0.8.17"
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||||
|
||||
[[IniFile]]
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||||
deps = ["Test"]
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||||
git-tree-sha1 = "098e4d2c533924c921f9f9847274f2ad89e018b8"
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||||
uuid = "83e8ac13-25f8-5344-8a64-a9f2b223428f"
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||||
version = "0.5.0"
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||||
|
||||
[[InteractiveUtils]]
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||||
deps = ["Markdown"]
|
||||
uuid = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
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||||
|
||||
[[JSON]]
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||||
deps = ["Dates", "Mmap", "Parsers", "Unicode"]
|
||||
git-tree-sha1 = "b34d7cef7b337321e97d22242c3c2b91f476748e"
|
||||
uuid = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
|
||||
version = "0.21.0"
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||||
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||||
[[JSON2]]
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||||
deps = ["Dates", "Parsers", "Test"]
|
||||
git-tree-sha1 = "66397cc6c08922f98a28ab05a8d3002f9853b129"
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||||
uuid = "2535ab7d-5cd8-5a07-80ac-9b1792aadce3"
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||||
version = "0.3.2"
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||||
|
||||
[[JSONSchema]]
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||||
deps = ["HTTP", "JSON", "ZipFile"]
|
||||
git-tree-sha1 = "a9ecdbc90be216912a2e3e8a8e38dc4c93f0d065"
|
||||
uuid = "7d188eb4-7ad8-530c-ae41-71a32a6d4692"
|
||||
version = "0.3.2"
|
||||
|
||||
[[JuMP]]
|
||||
deps = ["Calculus", "DataStructures", "ForwardDiff", "LinearAlgebra", "MathOptInterface", "MutableArithmetics", "NaNMath", "Random", "SparseArrays", "Statistics"]
|
||||
git-tree-sha1 = "cbab42e2e912109d27046aa88f02a283a9abac7c"
|
||||
uuid = "4076af6c-e467-56ae-b986-b466b2749572"
|
||||
version = "0.21.3"
|
||||
|
||||
[[JuliaInterpreter]]
|
||||
deps = ["CodeTracking", "InteractiveUtils", "Random", "UUIDs"]
|
||||
git-tree-sha1 = "7b2a1b650cec61a7d8cd8ee9ee7a818b5764d502"
|
||||
uuid = "aa1ae85d-cabe-5617-a682-6adf51b2e16a"
|
||||
version = "0.7.26"
|
||||
|
||||
[[LibGit2]]
|
||||
deps = ["Printf"]
|
||||
uuid = "76f85450-5226-5b5a-8eaa-529ad045b433"
|
||||
|
||||
[[Libdl]]
|
||||
uuid = "8f399da3-3557-5675-b5ff-fb832c97cbdb"
|
||||
|
||||
[[LinearAlgebra]]
|
||||
deps = ["Libdl"]
|
||||
uuid = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
|
||||
|
||||
[[Logging]]
|
||||
uuid = "56ddb016-857b-54e1-b83d-db4d58db5568"
|
||||
|
||||
[[LoweredCodeUtils]]
|
||||
deps = ["JuliaInterpreter"]
|
||||
git-tree-sha1 = "dbd9336b43c2d6fa492efa09ba3bb10fbdbeeb64"
|
||||
uuid = "6f1432cf-f94c-5a45-995e-cdbf5db27b0b"
|
||||
version = "0.4.9"
|
||||
|
||||
[[MacroTools]]
|
||||
deps = ["Markdown", "Random"]
|
||||
git-tree-sha1 = "f7d2e3f654af75f01ec49be82c231c382214223a"
|
||||
uuid = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
|
||||
version = "0.5.5"
|
||||
|
||||
[[Markdown]]
|
||||
deps = ["Base64"]
|
||||
uuid = "d6f4376e-aef5-505a-96c1-9c027394607a"
|
||||
|
||||
[[MathOptInterface]]
|
||||
deps = ["BenchmarkTools", "CodecBzip2", "CodecZlib", "JSON", "JSONSchema", "LinearAlgebra", "MutableArithmetics", "OrderedCollections", "SparseArrays", "Test", "Unicode"]
|
||||
git-tree-sha1 = "cd2049c055c7d192a235670d50faa375361624ba"
|
||||
uuid = "b8f27783-ece8-5eb3-8dc8-9495eed66fee"
|
||||
version = "0.9.14"
|
||||
|
||||
[[MathProgBase]]
|
||||
deps = ["LinearAlgebra", "SparseArrays"]
|
||||
git-tree-sha1 = "9abbe463a1e9fc507f12a69e7f29346c2cdc472c"
|
||||
uuid = "fdba3010-5040-5b88-9595-932c9decdf73"
|
||||
version = "0.7.8"
|
||||
|
||||
[[MbedTLS]]
|
||||
deps = ["Dates", "MbedTLS_jll", "Random", "Sockets"]
|
||||
git-tree-sha1 = "426a6978b03a97ceb7ead77775a1da066343ec6e"
|
||||
uuid = "739be429-bea8-5141-9913-cc70e7f3736d"
|
||||
version = "1.0.2"
|
||||
|
||||
[[MbedTLS_jll]]
|
||||
deps = ["Libdl", "Pkg"]
|
||||
git-tree-sha1 = "a0cb0d489819fa7ea5f9fa84c7e7eba19d8073af"
|
||||
uuid = "c8ffd9c3-330d-5841-b78e-0817d7145fa1"
|
||||
version = "2.16.6+1"
|
||||
|
||||
[[Mmap]]
|
||||
uuid = "a63ad114-7e13-5084-954f-fe012c677804"
|
||||
|
||||
[[MutableArithmetics]]
|
||||
deps = ["LinearAlgebra", "SparseArrays", "Test"]
|
||||
git-tree-sha1 = "6cf09794783b9de2e662c4e8b60d743021e338d0"
|
||||
uuid = "d8a4904e-b15c-11e9-3269-09a3773c0cb0"
|
||||
version = "0.2.10"
|
||||
|
||||
[[NaNMath]]
|
||||
git-tree-sha1 = "c84c576296d0e2fbb3fc134d3e09086b3ea617cd"
|
||||
uuid = "77ba4419-2d1f-58cd-9bb1-8ffee604a2e3"
|
||||
version = "0.3.4"
|
||||
|
||||
[[OpenSpecFun_jll]]
|
||||
deps = ["CompilerSupportLibraries_jll", "Libdl", "Pkg"]
|
||||
git-tree-sha1 = "d51c416559217d974a1113522d5919235ae67a87"
|
||||
uuid = "efe28fd5-8261-553b-a9e1-b2916fc3738e"
|
||||
version = "0.5.3+3"
|
||||
|
||||
[[OrderedCollections]]
|
||||
git-tree-sha1 = "293b70ac1780f9584c89268a6e2a560d938a7065"
|
||||
uuid = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
|
||||
version = "1.3.0"
|
||||
|
||||
[[PackageCompiler]]
|
||||
deps = ["Libdl", "Pkg", "UUIDs"]
|
||||
git-tree-sha1 = "98aa9c653e1dc3473bb5050caf8501293db9eee1"
|
||||
uuid = "9b87118b-4619-50d2-8e1e-99f35a4d4d9d"
|
||||
version = "1.2.1"
|
||||
|
||||
[[Parsers]]
|
||||
deps = ["Dates", "Test"]
|
||||
git-tree-sha1 = "8077624b3c450b15c087944363606a6ba12f925e"
|
||||
uuid = "69de0a69-1ddd-5017-9359-2bf0b02dc9f0"
|
||||
version = "1.0.10"
|
||||
|
||||
[[Pkg]]
|
||||
deps = ["Dates", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "REPL", "Random", "SHA", "UUIDs"]
|
||||
uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
|
||||
|
||||
[[Printf]]
|
||||
deps = ["Unicode"]
|
||||
uuid = "de0858da-6303-5e67-8744-51eddeeeb8d7"
|
||||
|
||||
[[PyCall]]
|
||||
deps = ["Conda", "Dates", "Libdl", "LinearAlgebra", "MacroTools", "Serialization", "VersionParsing"]
|
||||
git-tree-sha1 = "3a3fdb9000d35958c9ba2323ca7c4958901f115d"
|
||||
uuid = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0"
|
||||
version = "1.91.4"
|
||||
|
||||
[[REPL]]
|
||||
deps = ["InteractiveUtils", "Markdown", "Sockets"]
|
||||
uuid = "3fa0cd96-eef1-5676-8a61-b3b8758bbffb"
|
||||
|
||||
[[Random]]
|
||||
deps = ["Serialization"]
|
||||
uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
|
||||
|
||||
[[Revise]]
|
||||
deps = ["CodeTracking", "Distributed", "FileWatching", "JuliaInterpreter", "LibGit2", "LoweredCodeUtils", "OrderedCollections", "Pkg", "REPL", "UUIDs", "Unicode"]
|
||||
git-tree-sha1 = "0992d4643e27b2deb9f2e4ec7a56b7033813a027"
|
||||
uuid = "295af30f-e4ad-537b-8983-00126c2a3abe"
|
||||
version = "2.7.3"
|
||||
|
||||
[[SHA]]
|
||||
uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce"
|
||||
|
||||
[[Serialization]]
|
||||
uuid = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
|
||||
|
||||
[[Sockets]]
|
||||
uuid = "6462fe0b-24de-5631-8697-dd941f90decc"
|
||||
|
||||
[[SparseArrays]]
|
||||
deps = ["LinearAlgebra", "Random"]
|
||||
uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
|
||||
|
||||
[[SpecialFunctions]]
|
||||
deps = ["OpenSpecFun_jll"]
|
||||
git-tree-sha1 = "d8d8b8a9f4119829410ecd706da4cc8594a1e020"
|
||||
uuid = "276daf66-3868-5448-9aa4-cd146d93841b"
|
||||
version = "0.10.3"
|
||||
|
||||
[[StaticArrays]]
|
||||
deps = ["LinearAlgebra", "Random", "Statistics"]
|
||||
git-tree-sha1 = "016d1e1a00fabc556473b07161da3d39726ded35"
|
||||
uuid = "90137ffa-7385-5640-81b9-e52037218182"
|
||||
version = "0.12.4"
|
||||
|
||||
[[Statistics]]
|
||||
deps = ["LinearAlgebra", "SparseArrays"]
|
||||
uuid = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
|
||||
|
||||
[[Test]]
|
||||
deps = ["Distributed", "InteractiveUtils", "Logging", "Random"]
|
||||
uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
|
||||
|
||||
[[TimerOutputs]]
|
||||
deps = ["Printf"]
|
||||
git-tree-sha1 = "f458ca23ff80e46a630922c555d838303e4b9603"
|
||||
uuid = "a759f4b9-e2f1-59dc-863e-4aeb61b1ea8f"
|
||||
version = "0.5.6"
|
||||
|
||||
[[TinyBnB]]
|
||||
deps = ["CPLEXW", "Printf", "Random", "Revise", "Test"]
|
||||
git-tree-sha1 = "b66df27e41e9b83a34e9f48e67876064d5b92a62"
|
||||
repo-rev = "master"
|
||||
repo-url = "https://github.com/iSoron/TinyBnB.jl.git"
|
||||
uuid = "1b2a1171-e557-4eeb-a4d6-6c23d7e94fcd"
|
||||
version = "1.1.0"
|
||||
|
||||
[[TranscodingStreams]]
|
||||
deps = ["Random", "Test"]
|
||||
git-tree-sha1 = "7c53c35547de1c5b9d46a4797cf6d8253807108c"
|
||||
uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa"
|
||||
version = "0.9.5"
|
||||
|
||||
[[UUIDs]]
|
||||
deps = ["Random", "SHA"]
|
||||
uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
|
||||
|
||||
[[Unicode]]
|
||||
uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"
|
||||
|
||||
[[VersionParsing]]
|
||||
git-tree-sha1 = "80229be1f670524750d905f8fc8148e5a8c4537f"
|
||||
uuid = "81def892-9a0e-5fdd-b105-ffc91e053289"
|
||||
version = "1.2.0"
|
||||
|
||||
[[ZipFile]]
|
||||
deps = ["Libdl", "Printf", "Zlib_jll"]
|
||||
git-tree-sha1 = "254975fef2fc526583bb9b7c9420fe66ffe09f2f"
|
||||
uuid = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea"
|
||||
version = "0.9.2"
|
||||
|
||||
[[Zlib_jll]]
|
||||
deps = ["Libdl", "Pkg"]
|
||||
git-tree-sha1 = "fdd89e5ab270ea0f2a0174bd9093e557d06d4bfa"
|
||||
uuid = "83775a58-1f1d-513f-b197-d71354ab007a"
|
||||
version = "1.2.11+16"
|
||||
@ -1,24 +0,0 @@
|
||||
name = "MIPLearn"
|
||||
uuid = "2b1277c3-b477-4c49-a15e-7ba350325c68"
|
||||
authors = ["Alinson S Xavier <git@axavier.org>"]
|
||||
version = "0.1.0"
|
||||
|
||||
[deps]
|
||||
CPLEX = "a076750e-1247-5638-91d2-ce28b192dca0"
|
||||
CPLEXW = "cfecb002-79c2-11e9-35be-cb59aa640f85"
|
||||
Gurobi = "2e9cd046-0924-5485-92f1-d5272153d98b"
|
||||
JSON2 = "2535ab7d-5cd8-5a07-80ac-9b1792aadce3"
|
||||
JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
|
||||
Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
|
||||
MathOptInterface = "b8f27783-ece8-5eb3-8dc8-9495eed66fee"
|
||||
PackageCompiler = "9b87118b-4619-50d2-8e1e-99f35a4d4d9d"
|
||||
Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
|
||||
PyCall = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0"
|
||||
Revise = "295af30f-e4ad-537b-8983-00126c2a3abe"
|
||||
Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
|
||||
TimerOutputs = "a759f4b9-e2f1-59dc-863e-4aeb61b1ea8f"
|
||||
TinyBnB = "1b2a1171-e557-4eeb-a4d6-6c23d7e94fcd"
|
||||
|
||||
[compat]
|
||||
CPLEX = "0.6"
|
||||
JuMP = "0.21"
|
||||
@ -1,8 +0,0 @@
|
||||
ENV["PYTHON"] = ARGS[1]
|
||||
|
||||
using Pkg
|
||||
Pkg.instantiate()
|
||||
Pkg.build("CPLEX")
|
||||
Pkg.build("Gurobi")
|
||||
Pkg.build("PyCall")
|
||||
|
||||
@ -1,41 +0,0 @@
|
||||
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
__precompile__(false)
|
||||
module MIPLearn
|
||||
|
||||
using PyCall
|
||||
miplearn = pyimport("miplearn")
|
||||
Instance = miplearn.Instance
|
||||
BenchmarkRunner = miplearn.BenchmarkRunner
|
||||
|
||||
macro pycall(expr)
|
||||
quote
|
||||
err_msg = nothing
|
||||
result = nothing
|
||||
try
|
||||
result = $(esc(expr))
|
||||
catch err
|
||||
args = err.val.args[1]
|
||||
if (err isa PyCall.PyError) && (args isa String) && startswith(args, "Julia")
|
||||
err_msg = replace(args, r"Stacktrace.*" => "")
|
||||
else
|
||||
rethrow(err)
|
||||
end
|
||||
end
|
||||
if err_msg != nothing
|
||||
error(err_msg)
|
||||
end
|
||||
result
|
||||
end
|
||||
end
|
||||
|
||||
include("log.jl")
|
||||
include("jump_solver.jl")
|
||||
include("learning_solver.jl")
|
||||
include("instance.jl")
|
||||
|
||||
export Instance, BenchmarkRunner
|
||||
|
||||
end # module
|
||||
@ -1,70 +0,0 @@
|
||||
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
import Base.Threads.@threads
|
||||
using TinyBnB, CPLEXW, Printf
|
||||
|
||||
instance_name = ARGS[1]
|
||||
output_filename = ARGS[2]
|
||||
node_limit = parse(Int, ARGS[3])
|
||||
|
||||
mip = open_mip(instance_name)
|
||||
n_vars = CPXgetnumcols(mip.cplex_env[1], mip.cplex_lp[1])
|
||||
|
||||
pseudocost_count_up = [0 for i in 1:n_vars]
|
||||
pseudocost_count_down = [0 for i in 1:n_vars]
|
||||
pseudocost_sum_up = [0. for i in 1:n_vars]
|
||||
pseudocost_sum_down = [0. for i in 1:n_vars]
|
||||
|
||||
function full_strong_branching_track(node::Node, progress::Progress)::TinyBnB.Variable
|
||||
N = length(node.fractional_variables)
|
||||
scores = Array{Float64}(undef, N)
|
||||
rates_up = Array{Float64}(undef, N)
|
||||
rates_down = Array{Float64}(undef, N)
|
||||
|
||||
@threads for v in 1:N
|
||||
fix_vars!(node.mip, node.branch_variables, node.branch_values)
|
||||
obj_up, obj_down = TinyBnB.probe(node.mip, node.fractional_variables[v])
|
||||
unfix_vars!(node.mip, node.branch_variables)
|
||||
delta_up = obj_up - node.obj
|
||||
delta_down = obj_down - node.obj
|
||||
frac_up = ceil(node.fractional_values[v]) - node.fractional_values[v]
|
||||
frac_down = node.fractional_values[v] - floor(node.fractional_values[v])
|
||||
rates_up[v] = delta_up / frac_up
|
||||
rates_down[v] = delta_down / frac_down
|
||||
scores[v] = delta_up * delta_down
|
||||
end
|
||||
|
||||
max_score, max_offset = findmax(scores)
|
||||
selected_var = node.fractional_variables[max_offset]
|
||||
|
||||
if abs(rates_up[max_offset]) < 1e6
|
||||
pseudocost_count_up[selected_var.index] += 1
|
||||
pseudocost_sum_up[selected_var.index] += rates_up[max_offset]
|
||||
end
|
||||
|
||||
if abs(rates_down[max_offset]) < 1e6
|
||||
pseudocost_count_down[selected_var.index] += 1
|
||||
pseudocost_sum_down[selected_var.index] += rates_down[max_offset]
|
||||
end
|
||||
|
||||
return selected_var
|
||||
end
|
||||
|
||||
branch_and_bound(mip,
|
||||
node_limit = node_limit,
|
||||
branch_rule = full_strong_branching_track,
|
||||
node_rule = best_bound,
|
||||
print_interval = 100)
|
||||
|
||||
priority = [(pseudocost_count_up[v] == 0 || pseudocost_count_down[v] == 0) ? 0 :
|
||||
(pseudocost_sum_up[v] / pseudocost_count_up[v]) *
|
||||
(pseudocost_sum_down[v] / pseudocost_count_down[v])
|
||||
for v in 1:n_vars];
|
||||
|
||||
open(output_filename, "w") do file
|
||||
for var in mip.binary_variables
|
||||
write(file, @sprintf("%s,%.0f\n", name(mip, var), priority[var.index]))
|
||||
end
|
||||
end
|
||||
@ -1,61 +0,0 @@
|
||||
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
using JSON2
|
||||
import Base: dump
|
||||
|
||||
get_instance_features(instance) = [0.]
|
||||
get_variable_features(instance, var, index) = [0.]
|
||||
find_violated_lazy_constraints(instance, model) = []
|
||||
build_lazy_constraint(instance, model, v) = nothing
|
||||
|
||||
dump(instance::PyCall.PyObject, filename) = @pycall instance.dump(filename)
|
||||
load!(instance::PyCall.PyObject, filename) = @pycall instance.load(filename)
|
||||
|
||||
macro Instance(klass)
|
||||
quote
|
||||
@pydef mutable struct Wrapper <: Instance
|
||||
function __init__(self, args...; kwargs...)
|
||||
self.data = $(esc(klass))(args...; kwargs...)
|
||||
end
|
||||
|
||||
function dump(self, filename)
|
||||
prev_data = self.data
|
||||
self.data = JSON2.write(prev_data)
|
||||
Instance.dump(self, filename)
|
||||
self.data = prev_data
|
||||
end
|
||||
|
||||
function load(self, filename)
|
||||
Instance.load(self, filename)
|
||||
self.data = JSON2.read(self.data, $(esc(klass)))
|
||||
end
|
||||
|
||||
to_model(self) =
|
||||
$(esc(:to_model))(self.data)
|
||||
|
||||
get_instance_features(self) =
|
||||
get_instance_features(self.data)
|
||||
|
||||
get_variable_features(self, var, index) =
|
||||
get_variable_features(self.data, var, index)
|
||||
|
||||
function find_violated_lazy_constraints(self, model)
|
||||
find_violated_lazy_constraints(self.data, model)
|
||||
end
|
||||
|
||||
function build_lazy_constraint(self, model, v)
|
||||
build_lazy_constraint(self.data, model, v)
|
||||
end
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
export get_instance_features,
|
||||
get_variable_features,
|
||||
find_violated_lazy_constraints,
|
||||
build_lazy_constraint,
|
||||
dump,
|
||||
load!,
|
||||
@Instance
|
||||
@ -1,254 +0,0 @@
|
||||
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
using JuMP
|
||||
using CPLEX
|
||||
using MathOptInterface
|
||||
const MOI = MathOptInterface
|
||||
using TimerOutputs
|
||||
|
||||
|
||||
mutable struct JuMPSolverData
|
||||
basename_idx_to_var
|
||||
var_to_basename_idx
|
||||
optimizer
|
||||
instance
|
||||
model
|
||||
bin_vars
|
||||
solution::Union{Nothing,Dict{String,Dict{String,Float64}}}
|
||||
time_limit::Union{Nothing, Float64}
|
||||
end
|
||||
|
||||
|
||||
function varname_split(varname::String)
|
||||
m = match(r"([^[]*)\[(.*)\]", varname)
|
||||
if m == nothing
|
||||
return varname, ""
|
||||
end
|
||||
return m.captures[1], m.captures[2]
|
||||
end
|
||||
|
||||
|
||||
"""
|
||||
optimize_and_capture_output!(model; tee=tee)
|
||||
|
||||
Optimizes a given JuMP model while capturing the solver log, then returns that log.
|
||||
If tee=true, prints the solver log to the standard output as the optimization takes place.
|
||||
"""
|
||||
function optimize_and_capture_output!(model; tee::Bool=false)
|
||||
original_stdout = stdout
|
||||
rd, wr = redirect_stdout()
|
||||
task = @async begin
|
||||
log = ""
|
||||
while true
|
||||
line = String(readavailable(rd))
|
||||
isopen(rd) || break
|
||||
log *= String(line)
|
||||
if tee
|
||||
print(original_stdout, line)
|
||||
flush(original_stdout)
|
||||
end
|
||||
end
|
||||
return log
|
||||
end
|
||||
JuMP.optimize!(model)
|
||||
sleep(1)
|
||||
redirect_stdout(original_stdout)
|
||||
close(rd)
|
||||
return fetch(task)
|
||||
end
|
||||
|
||||
|
||||
function solve(data::JuMPSolverData; tee::Bool=false)
|
||||
instance, model = data.instance, data.model
|
||||
if data.time_limit != nothing
|
||||
JuMP.set_time_limit_sec(model, data.time_limit)
|
||||
end
|
||||
wallclock_time = 0
|
||||
found_lazy = []
|
||||
log = ""
|
||||
while true
|
||||
log *= optimize_and_capture_output!(model, tee=tee)
|
||||
wallclock_time += JuMP.solve_time(model)
|
||||
violations = instance.find_violated_lazy_constraints(model)
|
||||
if length(violations) == 0
|
||||
break
|
||||
end
|
||||
append!(found_lazy, violations)
|
||||
for v in violations
|
||||
instance.build_lazy_constraint(data.model, v)
|
||||
end
|
||||
end
|
||||
update_solution!(data)
|
||||
instance.found_violated_lazy_constraints = found_lazy
|
||||
instance.found_violated_user_cuts = []
|
||||
primal_bound = JuMP.objective_value(model)
|
||||
dual_bound = JuMP.objective_bound(model)
|
||||
if JuMP.objective_sense(model) == MOI.MIN_SENSE
|
||||
sense = "min"
|
||||
lower_bound = dual_bound
|
||||
upper_bound = primal_bound
|
||||
else
|
||||
sense = "max"
|
||||
lower_bound = primal_bound
|
||||
upper_bound = dual_bound
|
||||
end
|
||||
return Dict("Lower bound" => lower_bound,
|
||||
"Upper bound" => upper_bound,
|
||||
"Sense" => sense,
|
||||
"Wallclock time" => wallclock_time,
|
||||
"Nodes" => 1,
|
||||
"Log" => log,
|
||||
"Warm start value" => nothing)
|
||||
end
|
||||
|
||||
|
||||
function solve_lp(data::JuMPSolverData; tee::Bool=false)
|
||||
model, bin_vars = data.model, data.bin_vars
|
||||
for var in bin_vars
|
||||
JuMP.unset_binary(var)
|
||||
JuMP.set_upper_bound(var, 1.0)
|
||||
JuMP.set_lower_bound(var, 0.0)
|
||||
end
|
||||
log = optimize_and_capture_output!(model, tee=tee)
|
||||
update_solution!(data)
|
||||
obj_value = JuMP.objective_value(model)
|
||||
for var in bin_vars
|
||||
JuMP.set_binary(var)
|
||||
end
|
||||
return Dict("Optimal value" => obj_value,
|
||||
"Log" => log)
|
||||
end
|
||||
|
||||
|
||||
function update_solution!(data::JuMPSolverData)
|
||||
var_to_basename_idx, model = data.var_to_basename_idx, data.model
|
||||
solution = Dict{String,Dict{String,Float64}}()
|
||||
for var in JuMP.all_variables(model)
|
||||
var in keys(var_to_basename_idx) || continue
|
||||
basename, idx = var_to_basename_idx[var]
|
||||
if !haskey(solution, basename)
|
||||
solution[basename] = Dict{String,Float64}()
|
||||
end
|
||||
solution[basename][idx] = JuMP.value(var)
|
||||
end
|
||||
data.solution = solution
|
||||
end
|
||||
|
||||
|
||||
function get_variables(data::JuMPSolverData)
|
||||
var_to_basename_idx, model = data.var_to_basename_idx, data.model
|
||||
variables = Dict()
|
||||
for var in JuMP.all_variables(model)
|
||||
var in keys(var_to_basename_idx) || continue
|
||||
basename, idx = var_to_basename_idx[var]
|
||||
if !haskey(variables, basename)
|
||||
variables[basename] = []
|
||||
end
|
||||
push!(variables[basename], idx)
|
||||
end
|
||||
return variables
|
||||
end
|
||||
|
||||
|
||||
function set_instance!(data::JuMPSolverData, instance, model)
|
||||
data.instance = instance
|
||||
data.model = model
|
||||
data.var_to_basename_idx = Dict(var => varname_split(JuMP.name(var))
|
||||
for var in JuMP.all_variables(model))
|
||||
data.basename_idx_to_var = Dict(varname_split(JuMP.name(var)) => var
|
||||
for var in JuMP.all_variables(model))
|
||||
data.bin_vars = [var
|
||||
for var in JuMP.all_variables(model)
|
||||
if JuMP.is_binary(var)]
|
||||
if data.optimizer != nothing
|
||||
JuMP.set_optimizer(model, data.optimizer)
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
function fix!(data::JuMPSolverData, solution)
|
||||
count = 0
|
||||
for (basename, subsolution) in solution
|
||||
for (idx, value) in subsolution
|
||||
value != nothing || continue
|
||||
var = data.basename_idx_to_var[basename, idx]
|
||||
JuMP.fix(var, value, force=true)
|
||||
count += 1
|
||||
end
|
||||
end
|
||||
@info "Fixing $count variables"
|
||||
end
|
||||
|
||||
|
||||
function set_warm_start!(data::JuMPSolverData, solution)
|
||||
count = 0
|
||||
for (basename, subsolution) in solution
|
||||
for (idx, value) in subsolution
|
||||
value != nothing || continue
|
||||
var = data.basename_idx_to_var[basename, idx]
|
||||
JuMP.set_start_value(var, value)
|
||||
count += 1
|
||||
end
|
||||
end
|
||||
@info "Setting warm start values for $count variables"
|
||||
end
|
||||
|
||||
@pydef mutable struct JuMPSolver <: miplearn.solvers.internal.InternalSolver
|
||||
function __init__(self; optimizer)
|
||||
self.data = JuMPSolverData(nothing, # basename_idx_to_var
|
||||
nothing, # var_to_basename_idx
|
||||
optimizer,
|
||||
nothing, # instance
|
||||
nothing, # model
|
||||
nothing, # bin_vars
|
||||
nothing, # solution
|
||||
nothing, # time limit
|
||||
)
|
||||
end
|
||||
|
||||
set_warm_start(self, solution) =
|
||||
set_warm_start!(self.data, solution)
|
||||
|
||||
fix(self, solution) =
|
||||
fix!(self.data, solution)
|
||||
|
||||
set_instance(self, instance, model) =
|
||||
set_instance!(self.data, instance, model)
|
||||
|
||||
solve(self; tee=false) =
|
||||
solve(self.data, tee=tee)
|
||||
|
||||
solve_lp(self; tee=false) =
|
||||
solve_lp(self.data, tee=tee)
|
||||
|
||||
get_solution(self) =
|
||||
self.data.solution
|
||||
|
||||
get_variables(self) =
|
||||
get_variables(self.data)
|
||||
|
||||
set_time_limit(self, time_limit) =
|
||||
self.data.time_limit = time_limit
|
||||
|
||||
set_gap_tolerance(self, gap_tolerance) =
|
||||
@warn "JuMPSolver: set_gap_tolerance not implemented"
|
||||
|
||||
set_node_limit(self) =
|
||||
@warn "JuMPSolver: set_node_limit not implemented"
|
||||
|
||||
set_threads(self, threads) =
|
||||
@warn "JuMPSolver: set_threads not implemented"
|
||||
|
||||
set_branching_priorities(self, priorities) =
|
||||
@warn "JuMPSolver: set_branching_priorities not implemented"
|
||||
|
||||
add_constraint(self, constraint) = nothing
|
||||
|
||||
clear_warm_start(self) =
|
||||
error("JuMPSolver.clear_warm_start should never be called")
|
||||
|
||||
end
|
||||
|
||||
export JuMPSolver, solve!, fit!, add!
|
||||
@ -1,28 +0,0 @@
|
||||
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
struct LearningSolver
|
||||
py::PyCall.PyObject
|
||||
end
|
||||
|
||||
function LearningSolver(;
|
||||
optimizer,
|
||||
kwargs...,
|
||||
)::LearningSolver
|
||||
py = @pycall miplearn.LearningSolver(;
|
||||
kwargs...,
|
||||
solver=JuMPSolver(optimizer=optimizer))
|
||||
return LearningSolver(py)
|
||||
end
|
||||
|
||||
solve!(solver::LearningSolver, instance; kwargs...) =
|
||||
@pycall solver.py.solve(instance; kwargs...)
|
||||
|
||||
fit!(solver::LearningSolver, instances; kwargs...) =
|
||||
@pycall solver.py.fit(instances; kwargs...)
|
||||
|
||||
add!(solver::LearningSolver, component; kwargs...) =
|
||||
@pycall solver.py.add(component; kwargs...)
|
||||
|
||||
export LearningSolver
|
||||
@ -1,62 +0,0 @@
|
||||
import Logging: min_enabled_level, shouldlog, handle_message
|
||||
using Base.CoreLogging, Logging, Printf
|
||||
|
||||
struct TimeLogger <: AbstractLogger
|
||||
initial_time::Float64
|
||||
file::Union{Nothing, IOStream}
|
||||
screen_log_level
|
||||
io_log_level
|
||||
end
|
||||
|
||||
function TimeLogger(;
|
||||
initial_time::Float64,
|
||||
file::Union{Nothing, IOStream} = nothing,
|
||||
screen_log_level = CoreLogging.Info,
|
||||
io_log_level = CoreLogging.Info,
|
||||
) :: TimeLogger
|
||||
return TimeLogger(initial_time, file, screen_log_level, io_log_level)
|
||||
end
|
||||
|
||||
min_enabled_level(logger::TimeLogger) = logger.io_log_level
|
||||
shouldlog(logger::TimeLogger, level, _module, group, id) = true
|
||||
|
||||
function handle_message(logger::TimeLogger,
|
||||
level,
|
||||
message,
|
||||
_module,
|
||||
group,
|
||||
id,
|
||||
filepath,
|
||||
line;
|
||||
kwargs...)
|
||||
elapsed_time = time() - logger.initial_time
|
||||
time_string = @sprintf("[%12.3f] ", elapsed_time)
|
||||
|
||||
if level >= Logging.Error
|
||||
color = :light_red
|
||||
elseif level >= Logging.Warn
|
||||
color = :light_yellow
|
||||
else
|
||||
color = :light_green
|
||||
end
|
||||
|
||||
if level >= logger.screen_log_level
|
||||
printstyled(time_string, color=color)
|
||||
println(message)
|
||||
end
|
||||
if logger.file != nothing && level >= logger.io_log_level
|
||||
write(logger.file, time_string)
|
||||
write(logger.file, message)
|
||||
write(logger.file, "\n")
|
||||
flush(logger.file)
|
||||
end
|
||||
end
|
||||
|
||||
function setup_logger()
|
||||
initial_time = time()
|
||||
global_logger(TimeLogger(initial_time=initial_time))
|
||||
miplearn = pyimport("miplearn")
|
||||
miplearn.setup_logger(initial_time)
|
||||
end
|
||||
|
||||
export TimeLogger
|
||||
@ -1,22 +0,0 @@
|
||||
using PackageCompiler
|
||||
|
||||
using CPLEX
|
||||
using CPLEXW
|
||||
using Gurobi
|
||||
using JuMP
|
||||
using MathOptInterface
|
||||
using PyCall
|
||||
using TimerOutputs
|
||||
using TinyBnB
|
||||
|
||||
pkg = [:CPLEX
|
||||
:CPLEXW
|
||||
:Gurobi
|
||||
:JuMP
|
||||
:MathOptInterface
|
||||
:PyCall
|
||||
:TimerOutputs
|
||||
:TinyBnB]
|
||||
|
||||
@info "Building system image..."
|
||||
create_sysimage(pkg, sysimage_path="build/sysimage.so")
|
||||
@ -1,65 +0,0 @@
|
||||
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
using Test
|
||||
using MIPLearn
|
||||
using CPLEX
|
||||
using Gurobi
|
||||
|
||||
@testset "varname_split" begin
|
||||
@test MIPLearn.varname_split("x[1]") == ("x", "1")
|
||||
end
|
||||
|
||||
|
||||
@testset "JuMPSolver" begin
|
||||
for optimizer in [CPLEX.Optimizer, Gurobi.Optimizer]
|
||||
instance = KnapsackInstance([23., 26., 20., 18.],
|
||||
[505., 352., 458., 220.],
|
||||
67.0)
|
||||
model = instance.to_model()
|
||||
|
||||
solver = JuMPSolver(optimizer=optimizer)
|
||||
solver.set_instance(instance, model)
|
||||
solver.set_time_limit(30)
|
||||
solver.set_warm_start(Dict("x" => Dict(
|
||||
"1" => 1.0,
|
||||
"2" => 0.0,
|
||||
"3" => 0.0,
|
||||
"4" => 1.0,
|
||||
)))
|
||||
stats = solver.solve()
|
||||
|
||||
@test stats["Lower bound"] == 1183.0
|
||||
@test stats["Upper bound"] == 1183.0
|
||||
@test stats["Sense"] == "max"
|
||||
@test stats["Wallclock time"] > 0
|
||||
@test length(stats["Log"]) > 100
|
||||
|
||||
solution = solver.get_solution()
|
||||
@test solution["x"]["1"] == 1.0
|
||||
@test solution["x"]["2"] == 0.0
|
||||
@test solution["x"]["3"] == 1.0
|
||||
@test solution["x"]["4"] == 1.0
|
||||
|
||||
stats = solver.solve_lp()
|
||||
@test round(stats["Optimal value"], digits=3) == 1287.923
|
||||
@test length(stats["Log"]) > 100
|
||||
|
||||
solution = solver.get_solution()
|
||||
@test round(solution["x"]["1"], digits=3) == 1.000
|
||||
@test round(solution["x"]["2"], digits=3) == 0.923
|
||||
@test round(solution["x"]["3"], digits=3) == 1.000
|
||||
@test round(solution["x"]["4"], digits=3) == 0.000
|
||||
|
||||
solver.fix(Dict("x" => Dict(
|
||||
"1" => 1.0,
|
||||
"2" => 0.0,
|
||||
"3" => 0.0,
|
||||
"4" => 1.0,
|
||||
)))
|
||||
stats = solver.solve()
|
||||
@test stats["Lower bound"] == 725.0
|
||||
@test stats["Upper bound"] == 725.0
|
||||
end
|
||||
end
|
||||
@ -1,34 +0,0 @@
|
||||
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
import MIPLearn: get_instance_features,
|
||||
get_variable_features
|
||||
find_violated_lazy_constraints
|
||||
using JuMP
|
||||
|
||||
struct KnapsackData
|
||||
weights
|
||||
prices
|
||||
capacity
|
||||
end
|
||||
|
||||
function to_model(data::KnapsackData)
|
||||
model = Model()
|
||||
n = length(data.weights)
|
||||
@variable(model, x[1:n], Bin)
|
||||
@objective(model, Max, sum(x[i] * data.prices[i] for i in 1:n))
|
||||
@constraint(model, sum(x[i] * data.weights[i] for i in 1:n) <= data.capacity)
|
||||
return model
|
||||
end
|
||||
|
||||
function get_instance_features(data::KnapsackData)
|
||||
return [0.]
|
||||
end
|
||||
|
||||
|
||||
function get_variable_features(data::KnapsackData, var, index)
|
||||
return [0.]
|
||||
end
|
||||
|
||||
KnapsackInstance = @Instance(KnapsackData)
|
||||
@ -1,50 +0,0 @@
|
||||
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
using Test
|
||||
using MIPLearn
|
||||
using CPLEX
|
||||
using Gurobi
|
||||
|
||||
|
||||
@testset "Instance" begin
|
||||
weights = [23., 26., 20., 18.]
|
||||
prices = [505., 352., 458., 220.]
|
||||
capacity = 67.0
|
||||
|
||||
instance = KnapsackInstance(weights, prices, capacity)
|
||||
dump(instance, "tmp/instance.json.gz")
|
||||
|
||||
instance = KnapsackInstance([0.0], [0.0], 0.0)
|
||||
load!(instance, "tmp/instance.json.gz")
|
||||
@test instance.data.weights == weights
|
||||
@test instance.data.prices == prices
|
||||
@test instance.data.capacity == capacity
|
||||
end
|
||||
|
||||
|
||||
@testset "LearningSolver" begin
|
||||
for optimizer in [CPLEX.Optimizer, Gurobi.Optimizer]
|
||||
instance = KnapsackInstance([23., 26., 20., 18.],
|
||||
[505., 352., 458., 220.],
|
||||
67.0)
|
||||
solver = LearningSolver(optimizer=optimizer,
|
||||
mode="heuristic",
|
||||
time_limit=90)
|
||||
stats = solve!(solver, instance)
|
||||
@test instance.solution["x"]["1"] == 1.0
|
||||
@test instance.solution["x"]["2"] == 0.0
|
||||
@test instance.solution["x"]["3"] == 1.0
|
||||
@test instance.solution["x"]["4"] == 1.0
|
||||
@test instance.lower_bound == 1183.0
|
||||
@test instance.upper_bound == 1183.0
|
||||
@test round(instance.lp_solution["x"]["1"], digits=3) == 1.000
|
||||
@test round(instance.lp_solution["x"]["2"], digits=3) == 0.923
|
||||
@test round(instance.lp_solution["x"]["3"], digits=3) == 1.000
|
||||
@test round(instance.lp_solution["x"]["4"], digits=3) == 0.000
|
||||
@test round(instance.lp_value, digits=3) == 1287.923
|
||||
fit!(solver, [instance])
|
||||
solve!(solver, instance)
|
||||
end
|
||||
end
|
||||
@ -1,14 +0,0 @@
|
||||
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
using Test
|
||||
using MIPLearn
|
||||
|
||||
MIPLearn.setup_logger()
|
||||
|
||||
@testset "MIPLearn" begin
|
||||
include("knapsack.jl")
|
||||
include("jump_solver_test.jl")
|
||||
include("learning_solver_test.jl")
|
||||
end
|
||||
@ -1,125 +0,0 @@
|
||||
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
import logging
|
||||
import os
|
||||
import sys
|
||||
import subprocess
|
||||
import tempfile
|
||||
from copy import deepcopy
|
||||
|
||||
import numpy as np
|
||||
from pyomo.core import Var
|
||||
from pyomo.core.base.label import TextLabeler
|
||||
from sklearn.neighbors import KNeighborsRegressor
|
||||
from tqdm import tqdm
|
||||
|
||||
from .component import Component
|
||||
from ..extractors import Extractor, VariableFeaturesExtractor
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class BranchPriorityExtractor(Extractor):
|
||||
def extract(self, instances):
|
||||
result = {}
|
||||
for instance in tqdm(instances,
|
||||
desc="Extract (branch)",
|
||||
disable=len(instances) < 5 or (not sys.stdout.isatty()),
|
||||
):
|
||||
var_split = self.split_variables(instance)
|
||||
for (category, var_index_pairs) in var_split.items():
|
||||
if category not in result:
|
||||
result[category] = []
|
||||
for (var_name, index) in var_index_pairs:
|
||||
result[category] += [instance.branch_priorities[var_name][index]]
|
||||
for category in result:
|
||||
result[category] = np.array(result[category])
|
||||
return result
|
||||
|
||||
|
||||
class BranchPriorityComponent(Component):
|
||||
def __init__(self,
|
||||
node_limit=10_000,
|
||||
regressor=KNeighborsRegressor(n_neighbors=1),
|
||||
):
|
||||
self.node_limit = node_limit
|
||||
self.regressors = {}
|
||||
self.regressor_prototype = regressor
|
||||
|
||||
def before_solve(self, solver, instance, model):
|
||||
logger.info("Predicting branching priorities...")
|
||||
priorities = self.predict(instance)
|
||||
solver.internal_solver.set_branching_priorities(priorities)
|
||||
|
||||
def after_solve(self, solver, instance, model, results):
|
||||
pass
|
||||
|
||||
def fit(self, training_instances, n_jobs=1):
|
||||
for instance in tqdm(training_instances,
|
||||
desc="Fit (branch)",
|
||||
disable=not sys.stdout.isatty(),
|
||||
):
|
||||
if not hasattr(instance, "branch_priorities"):
|
||||
instance.branch_priorities = self.compute_priorities(instance)
|
||||
x, y = self.x(training_instances), self.y(training_instances)
|
||||
for category in x.keys():
|
||||
self.regressors[category] = deepcopy(self.regressor_prototype)
|
||||
self.regressors[category].fit(x[category], y[category])
|
||||
|
||||
def x(self, instances):
|
||||
return VariableFeaturesExtractor().extract(instances)
|
||||
|
||||
def y(self, instances):
|
||||
return BranchPriorityExtractor().extract(instances)
|
||||
|
||||
def compute_priorities(self, instance, model=None):
|
||||
# Create LP file
|
||||
lp_file = tempfile.NamedTemporaryFile(suffix=".lp")
|
||||
if model is None:
|
||||
model = instance.to_model()
|
||||
model.write(lp_file.name)
|
||||
|
||||
# Run Julia script
|
||||
src_dirname = os.path.dirname(os.path.realpath(__file__))
|
||||
julia_dirname = "%s/../../../julia" % src_dirname
|
||||
priority_file = tempfile.NamedTemporaryFile(mode="r")
|
||||
subprocess.run(["julia",
|
||||
"--project=%s" % julia_dirname,
|
||||
"%s/src/branching.jl" % julia_dirname,
|
||||
lp_file.name,
|
||||
priority_file.name,
|
||||
str(self.node_limit)],
|
||||
check=True)
|
||||
|
||||
# Parse output
|
||||
tokens = [line.strip().split(",") for line in priority_file.readlines()]
|
||||
lp_varname_to_priority = {t[0]: int(t[1]) for t in tokens}
|
||||
|
||||
# Map priorities back to Pyomo variables
|
||||
labeler = TextLabeler()
|
||||
symbol_map = list(model.solutions.symbol_map.values())[0]
|
||||
priorities = {}
|
||||
for var in model.component_objects(Var):
|
||||
priorities[var.name] = {}
|
||||
for index in var:
|
||||
category = instance.get_variable_category(var, index)
|
||||
if category is None:
|
||||
continue
|
||||
lp_varname = symbol_map.getSymbol(var[index], labeler)
|
||||
var_priority = lp_varname_to_priority[lp_varname]
|
||||
priorities[var.name][index] = var_priority
|
||||
return priorities
|
||||
|
||||
def predict(self, instance):
|
||||
priority = {}
|
||||
x_test = self.x([instance])
|
||||
var_split = Extractor.split_variables(instance)
|
||||
for category in self.regressors.keys():
|
||||
y_test = self.regressors[category].predict(x_test[category])
|
||||
for (i, (var, index)) in enumerate(var_split[category]):
|
||||
if var not in priority.keys():
|
||||
priority[var] = {}
|
||||
priority[var][index] = y_test[i]
|
||||
return priority
|
||||
@ -1,45 +0,0 @@
|
||||
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
from unittest.mock import Mock
|
||||
|
||||
import numpy as np
|
||||
from miplearn import BranchPriorityComponent, BranchPriorityExtractor
|
||||
from miplearn.classifiers import Regressor
|
||||
from miplearn.tests import get_test_pyomo_instances
|
||||
|
||||
|
||||
def test_branch_extract():
|
||||
instances, models = get_test_pyomo_instances()
|
||||
instances[0].branch_priorities = {"x": {0: 100, 1: 200, 2: 300, 3: 400}}
|
||||
instances[1].branch_priorities = {"x": {0: 150, 1: 250, 2: 350, 3: 450}}
|
||||
priorities = BranchPriorityExtractor().extract(instances)
|
||||
assert priorities["default"].tolist() == [100, 200, 300, 400, 150, 250, 350, 450]
|
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|
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|
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def test_branch_calculate():
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instances, models = get_test_pyomo_instances()
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comp = BranchPriorityComponent()
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|
||||
# If instances do not have branch_priority property, fit should compute them
|
||||
comp.fit(instances)
|
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assert instances[0].branch_priorities == {"x": {0: 5730, 1: 24878, 2: 0, 3: 0,}}
|
||||
|
||||
# If instances already have branch_priority, fit should not modify them
|
||||
instances[0].branch_priorities = {"x": {0: 100, 1: 200, 2: 300, 3: 400}}
|
||||
comp.fit(instances)
|
||||
assert instances[0].branch_priorities == {"x": {0: 100, 1: 200, 2: 300, 3: 400}}
|
||||
|
||||
|
||||
def test_branch_x_y_predict():
|
||||
instances, models = get_test_pyomo_instances()
|
||||
instances[0].branch_priorities = {"x": {0: 100, 1: 200, 2: 300, 3: 400}}
|
||||
instances[1].branch_priorities = {"x": {0: 150, 1: 250, 2: 350, 3: 450}}
|
||||
comp = BranchPriorityComponent()
|
||||
comp.regressors["default"] = Mock(spec=Regressor)
|
||||
comp.regressors["default"].predict = Mock(return_value=np.array([150., 100., 0., 0.]))
|
||||
x, y = comp.x(instances), comp.y(instances)
|
||||
assert x["default"].shape == (8, 5)
|
||||
assert y["default"].shape == (8,)
|
||||
pred = comp.predict(instances[0])
|
||||
assert pred == {"x": {0: 150., 1: 100., 2: 0., 3: 0.}}
|
||||
Loading…
Reference in new issue