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https://github.com/ANL-CEEESA/MIPLearn.git
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55 lines
1.4 KiB
Python
55 lines
1.4 KiB
Python
from os.path import dirname
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import numpy as np
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from scipy.stats import uniform, randint
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from miplearn.collectors.basic import BasicCollector
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from miplearn.io import write_pkl_gz
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from miplearn.problems.tsp import (
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TravelingSalesmanGenerator,
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TravelingSalesmanPerturber,
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build_tsp_model_gurobipy,
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build_tsp_model_pyomo,
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)
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np.random.seed(42)
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gen = TravelingSalesmanGenerator(
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x=uniform(loc=0.0, scale=1000.0),
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y=uniform(loc=0.0, scale=1000.0),
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n=randint(low=20, high=21),
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gamma=uniform(loc=1.0, scale=0.0),
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round=True,
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)
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# Generate a reference instance with fixed cities
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reference_instance = gen.generate(1)[0]
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# Generate perturbed instances with same cities but different distance scaling
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perturber = TravelingSalesmanPerturber(
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gamma=uniform(loc=1.0, scale=0.25),
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round=True,
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)
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data = perturber.perturb(reference_instance, 3)
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params = {"seed": 42, "threads": 1}
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# Gurobipy
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data_filenames = write_pkl_gz(data, dirname(__file__), prefix="tsp-gp-n20-")
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collector = BasicCollector()
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collector.collect(
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data_filenames,
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lambda d: build_tsp_model_gurobipy(d, params=params),
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progress=True,
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verbose=True,
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)
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# Pyomo
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data_filenames = write_pkl_gz(data, dirname(__file__), prefix="tsp-pyo-n20-")
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collector = BasicCollector()
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collector.collect(
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data_filenames,
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lambda d: build_tsp_model_pyomo(d, params=params),
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progress=True,
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verbose=True,
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)
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