Reformat additional files

2025-12-06 01:18:52 -06:00 · 2020-12-05 11:14:15 -06:00
parent d99600f101
commit 718ac0da06
3 changed files with 113 additions and 104 deletions
--- a/2
+++ b/2
@@ -35,7 +35,7 @@ uninstall:
 	$(PIP) uninstall miplearn

 reformat:
-	$(PYTHON) -m black miplearn
+	$(PYTHON) -m black .

 test:
 	$(PYTEST) $(PYTEST_ARGS)
--- a/benchmark/benchmark.py
+++ b/benchmark/benchmark.py
@@ -14,22 +14,26 @@ Usage:
 Options:
    -h --help    Show this screen
 """
-from docopt import docopt
-import importlib, pathlib
-from miplearn import (LearningSolver, BenchmarkRunner)
-from numpy import median
-import pyomo.environ as pe
-import pickle
+import importlib
 import logging
+import pathlib
+import pickle
 import sys

-logging.basicConfig(format='%(asctime)s %(levelname).1s %(name)s: %(message)12s',
-                    datefmt='%H:%M:%S',
-                    level=logging.INFO,
-                    stream=sys.stdout)
-logging.getLogger('gurobipy').setLevel(logging.ERROR)
-logging.getLogger('pyomo.core').setLevel(logging.ERROR)
-logging.getLogger('miplearn').setLevel(logging.INFO)
+from docopt import docopt
+from numpy import median
+
+from miplearn import LearningSolver, BenchmarkRunner
+
+logging.basicConfig(
+    format="%(asctime)s %(levelname).1s %(name)s: %(message)12s",
+    datefmt="%H:%M:%S",
+    level=logging.INFO,
+    stream=sys.stdout,
+)
+logging.getLogger("gurobipy").setLevel(logging.ERROR)
+logging.getLogger("pyomo.core").setLevel(logging.ERROR)
+logging.getLogger("miplearn").setLevel(logging.INFO)
 logger = logging.getLogger("benchmark")

 n_jobs = 10
@@ -50,6 +54,7 @@ def save(obj, filename):

 def load(filename):
    import pickle
+
    with open(filename, "rb") as file:
        return pickle.load(file)

@@ -59,10 +64,12 @@ def train():
    pkg = importlib.import_module("miplearn.problems.%s" % problem_name)
    challenge = getattr(pkg, challenge_name)()
    train_instances = challenge.training_instances
-    test_instances  = challenge.test_instances
-    solver = LearningSolver(time_limit=train_time_limit,
-                            solver=internal_solver,
-                            components={})
+    test_instances = challenge.test_instances
+    solver = LearningSolver(
+        time_limit=train_time_limit,
+        solver=internal_solver,
+        components={},
+    )
    solver.parallel_solve(train_instances, n_jobs=n_jobs)
    save(train_instances, "%s/train_instances.bin" % basepath)
    save(test_instances, "%s/test_instances.bin" % basepath)
@@ -109,6 +116,7 @@ def test_ml():
 def charts():
    import matplotlib.pyplot as plt
    import seaborn as sns
+
    sns.set_style("whitegrid")
    sns.set_palette("Blues_r")
    benchmark = BenchmarkRunner({})
@@ -126,69 +134,70 @@ def charts():
        obj_column = "Lower Bound"
        predicted_obj_column = "Predicted LB"

-    palette={
-        "baseline": "#9b59b6", 
-        "ml-exact": "#3498db",
-        "ml-heuristic": "#95a5a6"
-    }
-    fig, (ax1, ax2, ax3, ax4) = plt.subplots(nrows=1,
-                                             ncols=4,
-                                             figsize=(12,4),
-                                             gridspec_kw={'width_ratios': [2, 1, 1, 2]},
-                                            )
-    sns.stripplot(x="Solver",
-                  y="Wallclock Time",
-                  data=results,
-                  ax=ax1,
-                  jitter=0.25,
-                  palette=palette,
-                  size=4.0,
-               );
-    sns.barplot(x="Solver",
-                y="Wallclock Time",
-                data=results,
-                ax=ax1,
-                errwidth=0.,
-                alpha=0.4,
-                palette=palette,
-                estimator=median,
-               );
-    ax1.set(ylabel='Wallclock Time (s)')
+    palette = {"baseline": "#9b59b6", "ml-exact": "#3498db", "ml-heuristic": "#95a5a6"}
+    fig, (ax1, ax2, ax3, ax4) = plt.subplots(
+        nrows=1,
+        ncols=4,
+        figsize=(12, 4),
+        gridspec_kw={"width_ratios": [2, 1, 1, 2]},
+    )
+    sns.stripplot(
+        x="Solver",
+        y="Wallclock Time",
+        data=results,
+        ax=ax1,
+        jitter=0.25,
+        palette=palette,
+        size=4.0,
+    )
+    sns.barplot(
+        x="Solver",
+        y="Wallclock Time",
+        data=results,
+        ax=ax1,
+        errwidth=0.0,
+        alpha=0.4,
+        palette=palette,
+        estimator=median,
+    )
+    ax1.set(ylabel="Wallclock Time (s)")
    ax2.set_ylim(-0.5, 5.5)
-    sns.stripplot(x="Solver",
-                  y="Gap (%)",
-                  jitter=0.25,
-                  data=results[results["Solver"] != "ml-heuristic"],
-                  ax=ax2,
-                  palette=palette,
-                  size=4.0,
-                 );
-    ax3.set_ylim(0.95,1.05)
-    sns.stripplot(x="Solver",
-                  y=primal_column,
-                  jitter=0.25,
-                  data=results[results["Solver"] == "ml-heuristic"],
-                  ax=ax3,
-                  palette=palette,
-                 );
+    sns.stripplot(
+        x="Solver",
+        y="Gap (%)",
+        jitter=0.25,
+        data=results[results["Solver"] != "ml-heuristic"],
+        ax=ax2,
+        palette=palette,
+        size=4.0,
+    )
+    ax3.set_ylim(0.95, 1.05)
+    sns.stripplot(
+        x="Solver",
+        y=primal_column,
+        jitter=0.25,
+        data=results[results["Solver"] == "ml-heuristic"],
+        ax=ax3,
+        palette=palette,
+    )

-    sns.scatterplot(x=obj_column,
-                    y=predicted_obj_column,
-                    hue="Solver",
-                    data=results[results["Solver"] == "ml-exact"],
-                    ax=ax4,
-                    palette=palette,
-                   );
+    sns.scatterplot(
+        x=obj_column,
+        y=predicted_obj_column,
+        hue="Solver",
+        data=results[results["Solver"] == "ml-exact"],
+        ax=ax4,
+        palette=palette,
+    )
    xlim, ylim = ax4.get_xlim(), ax4.get_ylim()
-    ax4.plot([-1e10, 1e10], [-1e10, 1e10], ls='-', color="#cccccc");
+    ax4.plot([-1e10, 1e10], [-1e10, 1e10], ls="-", color="#cccccc")
    ax4.set_xlim(xlim)
    ax4.set_ylim(ylim)
    ax4.get_legend().remove()

    fig.tight_layout()
-    plt.savefig("%s/performance.png" % basepath,
-                bbox_inches='tight',
-                dpi=150)
+    plt.savefig("%s/performance.png" % basepath, bbox_inches="tight", dpi=150)
+

 if __name__ == "__main__":
    if args["train"]:
--- a/setup.py
+++ b/setup.py
@@ -4,27 +4,27 @@ with open("README.md", "r") as fh:
    long_description = fh.read()

 setup(
-    name='miplearn',
-    version='0.2.0',
-    author='Alinson S. Xavier',
-    author_email='axavier@anl.gov',
+    name="miplearn",
+    version="0.2.0",
+    author="Alinson S. Xavier",
+    author_email="axavier@anl.gov",
    description="Extensible framework for Learning-Enhanced Mixed-Integer Optimization",
    long_description=long_description,
    long_description_content_type="text/markdown",
    url="https://github.com/ANL-CEEESA/MIPLearn/",
    packages=find_namespace_packages(),
-    python_requires='>=3.6',
+    python_requires=">=3.6",
    install_requires=[
-        'docopt',
-        'matplotlib',
-        'networkx',
-        'numpy',
-        'pandas',
-        'p_tqdm',
-        'pyomo',
-        'python-markdown-math',
-        'seaborn',
-        'sklearn',
-        'tqdm',
-   ],
+        "docopt",
+        "matplotlib",
+        "networkx",
+        "numpy",
+        "pandas",
+        "p_tqdm",
+        "pyomo",
+        "python-markdown-math",
+        "seaborn",
+        "sklearn",
+        "tqdm",
+    ],
 )