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50 lines
1.6 KiB
50 lines
1.6 KiB
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020-2022, UChicago Argonne, LLC. All rights reserved.
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# Released under the modified BSD license. See COPYING.md for more details.
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import os
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import subprocess
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from typing import Callable
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from ..h5 import H5File
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class BranchPriorityCollector:
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def __init__(
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self,
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time_limit: float = 900.0,
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print_interval: int = 1,
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node_limit: int = 500,
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) -> None:
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self.time_limit = time_limit
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self.print_interval = print_interval
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self.node_limit = node_limit
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def collect(self, data_filename: str, _: Callable) -> None:
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basename = data_filename.replace(".pkl.gz", "")
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env = os.environ.copy()
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env["JULIA_NUM_THREADS"] = "1"
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ret = subprocess.run(
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[
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"julia",
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"--project=.",
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"-e",
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(
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f"using CPLEX, JuMP, MIPLearn.BB; "
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f"BB.solve!("
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f' optimizer_with_attributes(CPLEX.Optimizer, "CPXPARAM_Threads" => 1),'
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f' "{basename}",'
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f" print_interval={self.print_interval},"
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f" time_limit={self.time_limit:.2f},"
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f" node_limit={self.node_limit},"
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f")"
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),
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],
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check=True,
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capture_output=True,
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env=env,
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)
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h5_filename = f"{basename}.h5"
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with H5File(h5_filename, "r+") as h5:
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h5.put_scalar("bb_log", ret.stdout)
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