mirror of
https://github.com/ANL-CEEESA/MIPLearn.git
synced 2025-12-06 01:18:52 -06:00
Reformat additional files
This commit is contained in:
2
Makefile
2
Makefile
@@ -35,7 +35,7 @@ uninstall:
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$(PIP) uninstall miplearn
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reformat:
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$(PYTHON) -m black miplearn
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$(PYTHON) -m black .
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test:
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$(PYTEST) $(PYTEST_ARGS)
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@@ -14,22 +14,26 @@ Usage:
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Options:
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-h --help Show this screen
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"""
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from docopt import docopt
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import importlib, pathlib
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from miplearn import (LearningSolver, BenchmarkRunner)
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from numpy import median
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import pyomo.environ as pe
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import pickle
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import importlib
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import logging
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import pathlib
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import pickle
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import sys
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logging.basicConfig(format='%(asctime)s %(levelname).1s %(name)s: %(message)12s',
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datefmt='%H:%M:%S',
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level=logging.INFO,
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stream=sys.stdout)
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logging.getLogger('gurobipy').setLevel(logging.ERROR)
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logging.getLogger('pyomo.core').setLevel(logging.ERROR)
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logging.getLogger('miplearn').setLevel(logging.INFO)
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from docopt import docopt
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from numpy import median
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from miplearn import LearningSolver, BenchmarkRunner
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logging.basicConfig(
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format="%(asctime)s %(levelname).1s %(name)s: %(message)12s",
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datefmt="%H:%M:%S",
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level=logging.INFO,
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stream=sys.stdout,
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)
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logging.getLogger("gurobipy").setLevel(logging.ERROR)
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logging.getLogger("pyomo.core").setLevel(logging.ERROR)
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logging.getLogger("miplearn").setLevel(logging.INFO)
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logger = logging.getLogger("benchmark")
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n_jobs = 10
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@@ -46,28 +50,31 @@ def save(obj, filename):
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logger.info("Writing %s..." % filename)
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with open(filename, "wb") as file:
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pickle.dump(obj, file)
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def load(filename):
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import pickle
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with open(filename, "rb") as file:
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return pickle.load(file)
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return pickle.load(file)
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def train():
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problem_name, challenge_name = args["<challenge>"].split("/")
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pkg = importlib.import_module("miplearn.problems.%s" % problem_name)
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challenge = getattr(pkg, challenge_name)()
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train_instances = challenge.training_instances
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test_instances = challenge.test_instances
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solver = LearningSolver(time_limit=train_time_limit,
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solver=internal_solver,
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components={})
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test_instances = challenge.test_instances
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solver = LearningSolver(
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time_limit=train_time_limit,
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solver=internal_solver,
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components={},
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)
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solver.parallel_solve(train_instances, n_jobs=n_jobs)
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save(train_instances, "%s/train_instances.bin" % basepath)
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save(test_instances, "%s/test_instances.bin" % basepath)
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def test_baseline():
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test_instances = load("%s/test_instances.bin" % basepath)
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solvers = {
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@@ -79,8 +86,8 @@ def test_baseline():
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benchmark = BenchmarkRunner(solvers)
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benchmark.parallel_solve(test_instances, n_jobs=n_jobs)
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benchmark.save_results("%s/benchmark_baseline.csv" % basepath)
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def test_ml():
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logger.info("Loading instances...")
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train_instances = load("%s/train_instances.bin" % basepath)
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@@ -105,17 +112,18 @@ def test_ml():
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benchmark.parallel_solve(test_instances, n_jobs=n_jobs)
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benchmark.save_results("%s/benchmark_ml.csv" % basepath)
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def charts():
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import matplotlib.pyplot as plt
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import seaborn as sns
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sns.set_style("whitegrid")
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sns.set_palette("Blues_r")
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benchmark = BenchmarkRunner({})
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benchmark.load_results("%s/benchmark_ml.csv" % basepath)
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results = benchmark.raw_results()
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results["Gap (%)"] = results["Gap"] * 100.0
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sense = results.loc[0, "Sense"]
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if sense == "min":
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primal_column = "Relative Upper Bound"
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@@ -125,70 +133,71 @@ def charts():
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primal_column = "Relative Lower Bound"
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obj_column = "Lower Bound"
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predicted_obj_column = "Predicted LB"
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palette={
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"baseline": "#9b59b6",
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"ml-exact": "#3498db",
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"ml-heuristic": "#95a5a6"
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}
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fig, (ax1, ax2, ax3, ax4) = plt.subplots(nrows=1,
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ncols=4,
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figsize=(12,4),
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gridspec_kw={'width_ratios': [2, 1, 1, 2]},
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)
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sns.stripplot(x="Solver",
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y="Wallclock Time",
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data=results,
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ax=ax1,
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jitter=0.25,
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palette=palette,
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size=4.0,
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);
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sns.barplot(x="Solver",
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y="Wallclock Time",
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data=results,
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ax=ax1,
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errwidth=0.,
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alpha=0.4,
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palette=palette,
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estimator=median,
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);
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ax1.set(ylabel='Wallclock Time (s)')
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palette = {"baseline": "#9b59b6", "ml-exact": "#3498db", "ml-heuristic": "#95a5a6"}
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fig, (ax1, ax2, ax3, ax4) = plt.subplots(
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nrows=1,
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ncols=4,
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figsize=(12, 4),
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gridspec_kw={"width_ratios": [2, 1, 1, 2]},
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)
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sns.stripplot(
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x="Solver",
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y="Wallclock Time",
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data=results,
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ax=ax1,
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jitter=0.25,
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palette=palette,
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size=4.0,
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)
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sns.barplot(
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x="Solver",
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y="Wallclock Time",
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data=results,
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ax=ax1,
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errwidth=0.0,
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alpha=0.4,
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palette=palette,
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estimator=median,
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)
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ax1.set(ylabel="Wallclock Time (s)")
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ax2.set_ylim(-0.5, 5.5)
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sns.stripplot(x="Solver",
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y="Gap (%)",
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jitter=0.25,
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data=results[results["Solver"] != "ml-heuristic"],
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ax=ax2,
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palette=palette,
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size=4.0,
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);
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ax3.set_ylim(0.95,1.05)
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sns.stripplot(x="Solver",
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y=primal_column,
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jitter=0.25,
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data=results[results["Solver"] == "ml-heuristic"],
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ax=ax3,
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palette=palette,
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);
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sns.scatterplot(x=obj_column,
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y=predicted_obj_column,
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hue="Solver",
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data=results[results["Solver"] == "ml-exact"],
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ax=ax4,
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palette=palette,
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);
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sns.stripplot(
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x="Solver",
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y="Gap (%)",
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jitter=0.25,
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data=results[results["Solver"] != "ml-heuristic"],
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ax=ax2,
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palette=palette,
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size=4.0,
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)
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ax3.set_ylim(0.95, 1.05)
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sns.stripplot(
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x="Solver",
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y=primal_column,
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jitter=0.25,
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data=results[results["Solver"] == "ml-heuristic"],
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ax=ax3,
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palette=palette,
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)
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sns.scatterplot(
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x=obj_column,
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y=predicted_obj_column,
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hue="Solver",
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data=results[results["Solver"] == "ml-exact"],
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ax=ax4,
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palette=palette,
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)
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xlim, ylim = ax4.get_xlim(), ax4.get_ylim()
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ax4.plot([-1e10, 1e10], [-1e10, 1e10], ls='-', color="#cccccc");
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ax4.plot([-1e10, 1e10], [-1e10, 1e10], ls="-", color="#cccccc")
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ax4.set_xlim(xlim)
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ax4.set_ylim(ylim)
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ax4.get_legend().remove()
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fig.tight_layout()
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plt.savefig("%s/performance.png" % basepath,
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bbox_inches='tight',
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dpi=150)
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plt.savefig("%s/performance.png" % basepath, bbox_inches="tight", dpi=150)
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if __name__ == "__main__":
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if args["train"]:
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34
setup.py
34
setup.py
@@ -4,27 +4,27 @@ with open("README.md", "r") as fh:
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long_description = fh.read()
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setup(
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name='miplearn',
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version='0.2.0',
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author='Alinson S. Xavier',
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author_email='axavier@anl.gov',
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name="miplearn",
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version="0.2.0",
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author="Alinson S. Xavier",
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author_email="axavier@anl.gov",
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description="Extensible framework for Learning-Enhanced Mixed-Integer Optimization",
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long_description=long_description,
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long_description_content_type="text/markdown",
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url="https://github.com/ANL-CEEESA/MIPLearn/",
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packages=find_namespace_packages(),
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python_requires='>=3.6',
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python_requires=">=3.6",
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install_requires=[
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'docopt',
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'matplotlib',
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'networkx',
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'numpy',
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'pandas',
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'p_tqdm',
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'pyomo',
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'python-markdown-math',
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'seaborn',
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'sklearn',
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'tqdm',
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],
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"docopt",
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"matplotlib",
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"networkx",
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"numpy",
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"pandas",
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"p_tqdm",
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"pyomo",
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"python-markdown-math",
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"seaborn",
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"sklearn",
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"tqdm",
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],
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)
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