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36 lines
1.0 KiB
36 lines
1.0 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 json
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import logging
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from typing import Dict, Any, List
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from miplearn.components.cuts.mem import convert_lists_to_tuples
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from miplearn.h5 import H5File
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from miplearn.solvers.abstract import AbstractModel
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logger = logging.getLogger(__name__)
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class ExpertCutsComponent:
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def fit(
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self,
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_: List[str],
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) -> None:
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pass
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def before_mip(
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self,
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test_h5: str,
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model: AbstractModel,
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stats: Dict[str, Any],
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) -> None:
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with H5File(test_h5, "r") as h5:
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cuts_str = h5.get_scalar("mip_cuts")
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assert cuts_str is not None
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assert isinstance(cuts_str, str)
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cuts = list(set(convert_lists_to_tuples(json.loads(cuts_str))))
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model.set_cuts(cuts)
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stats["Cuts: AOT"] = len(cuts)
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