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33 lines
1.1 KiB
Python
33 lines
1.1 KiB
Python
# 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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from math import log
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from typing import List, Dict, Any
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import numpy as np
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import gurobipy as gp
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from ..h5 import H5File
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class ExpertBranchPriorityComponent:
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def __init__(self) -> None:
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pass
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def fit(self, train_h5: List[str]) -> None:
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pass
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def before_mip(self, test_h5: str, model: gp.Model, _: Dict[str, Any]) -> None:
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with H5File(test_h5, "r") as h5:
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var_names = h5.get_array("static_var_names")
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var_priority = h5.get_array("bb_var_priority")
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assert var_priority is not None
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assert var_names is not None
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for var_idx, var_name in enumerate(var_names):
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if np.isfinite(var_priority[var_idx]):
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var = model.getVarByName(var_name.decode())
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assert var is not None, f"unknown var: {var_name}"
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var.BranchPriority = int(log(1 + var_priority[var_idx]))
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