Module miplearn.benchmark
+
+
++Expand source code +
+# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+import logging
+import os
+from copy import deepcopy
+
+import pandas as pd
+from tqdm.auto import tqdm
+
+from miplearn.solvers.learning import LearningSolver
+
+
+class BenchmarkRunner:
+ def __init__(self, solvers):
+ assert isinstance(solvers, dict)
+ for solver in solvers.values():
+ assert isinstance(solver, LearningSolver)
+ self.solvers = solvers
+ self.results = None
+
+ def solve(self, instances, tee=False):
+ for (solver_name, solver) in self.solvers.items():
+ for i in tqdm(range(len((instances)))):
+ results = solver.solve(deepcopy(instances[i]), tee=tee)
+ self._push_result(
+ results,
+ solver=solver,
+ solver_name=solver_name,
+ instance=i,
+ )
+
+ def parallel_solve(
+ self,
+ instances,
+ n_jobs=1,
+ n_trials=1,
+ index_offset=0,
+ ):
+ self._silence_miplearn_logger()
+ trials = instances * n_trials
+ for (solver_name, solver) in self.solvers.items():
+ results = solver.parallel_solve(
+ trials,
+ n_jobs=n_jobs,
+ label="Solve (%s)" % solver_name,
+ discard_outputs=True,
+ )
+ for i in range(len(trials)):
+ idx = (i % len(instances)) + index_offset
+ self._push_result(
+ results[i],
+ solver=solver,
+ solver_name=solver_name,
+ instance=idx,
+ )
+ self._restore_miplearn_logger()
+
+ def raw_results(self):
+ return self.results
+
+ def save_results(self, filename):
+ os.makedirs(os.path.dirname(filename), exist_ok=True)
+ self.results.to_csv(filename)
+
+ def load_results(self, filename):
+ self.results = pd.concat([self.results, pd.read_csv(filename, index_col=0)])
+
+ def load_state(self, filename):
+ for (solver_name, solver) in self.solvers.items():
+ solver.load_state(filename)
+
+ def fit(self, training_instances):
+ for (solver_name, solver) in self.solvers.items():
+ solver.fit(training_instances)
+
+ @staticmethod
+ def _compute_gap(ub, lb):
+ if lb is None or ub is None or lb * ub < 0:
+ # solver did not find a solution and/or bound, use maximum gap possible
+ return 1.0
+ elif abs(ub - lb) < 1e-6:
+ # avoid division by zero when ub = lb = 0
+ return 0.0
+ else:
+ # divide by max(abs(ub),abs(lb)) to ensure gap <= 1
+ return (ub - lb) / max(abs(ub), abs(lb))
+
+ def _push_result(self, result, solver, solver_name, instance):
+ if self.results is None:
+ self.results = pd.DataFrame(
+ # Show the following columns first in the CSV file
+ columns=[
+ "Solver",
+ "Instance",
+ ]
+ )
+ result["Solver"] = solver_name
+ result["Instance"] = instance
+ result["Gap"] = self._compute_gap(
+ ub=result["Upper bound"],
+ lb=result["Lower bound"],
+ )
+ result["Mode"] = solver.mode
+ self.results = self.results.append(pd.DataFrame([result]))
+
+ def _silence_miplearn_logger(self):
+ miplearn_logger = logging.getLogger("miplearn")
+ self.prev_log_level = miplearn_logger.getEffectiveLevel()
+ miplearn_logger.setLevel(logging.WARNING)
+
+ def _restore_miplearn_logger(self):
+ miplearn_logger = logging.getLogger("miplearn")
+ miplearn_logger.setLevel(self.prev_log_level)Classes
+-
+
+class BenchmarkRunner +(solvers) +
+-
+
+++
+Expand source code +
+
+class BenchmarkRunner: + def __init__(self, solvers): + assert isinstance(solvers, dict) + for solver in solvers.values(): + assert isinstance(solver, LearningSolver) + self.solvers = solvers + self.results = None + + def solve(self, instances, tee=False): + for (solver_name, solver) in self.solvers.items(): + for i in tqdm(range(len((instances)))): + results = solver.solve(deepcopy(instances[i]), tee=tee) + self._push_result( + results, + solver=solver, + solver_name=solver_name, + instance=i, + ) + + def parallel_solve( + self, + instances, + n_jobs=1, + n_trials=1, + index_offset=0, + ): + self._silence_miplearn_logger() + trials = instances * n_trials + for (solver_name, solver) in self.solvers.items(): + results = solver.parallel_solve( + trials, + n_jobs=n_jobs, + label="Solve (%s)" % solver_name, + discard_outputs=True, + ) + for i in range(len(trials)): + idx = (i % len(instances)) + index_offset + self._push_result( + results[i], + solver=solver, + solver_name=solver_name, + instance=idx, + ) + self._restore_miplearn_logger() + + def raw_results(self): + return self.results + + def save_results(self, filename): + os.makedirs(os.path.dirname(filename), exist_ok=True) + self.results.to_csv(filename) + + def load_results(self, filename): + self.results = pd.concat([self.results, pd.read_csv(filename, index_col=0)]) + + def load_state(self, filename): + for (solver_name, solver) in self.solvers.items(): + solver.load_state(filename) + + def fit(self, training_instances): + for (solver_name, solver) in self.solvers.items(): + solver.fit(training_instances) + + @staticmethod + def _compute_gap(ub, lb): + if lb is None or ub is None or lb * ub < 0: + # solver did not find a solution and/or bound, use maximum gap possible + return 1.0 + elif abs(ub - lb) < 1e-6: + # avoid division by zero when ub = lb = 0 + return 0.0 + else: + # divide by max(abs(ub),abs(lb)) to ensure gap <= 1 + return (ub - lb) / max(abs(ub), abs(lb)) + + def _push_result(self, result, solver, solver_name, instance): + if self.results is None: + self.results = pd.DataFrame( + # Show the following columns first in the CSV file + columns=[ + "Solver", + "Instance", + ] + ) + result["Solver"] = solver_name + result["Instance"] = instance + result["Gap"] = self._compute_gap( + ub=result["Upper bound"], + lb=result["Lower bound"], + ) + result["Mode"] = solver.mode + self.results = self.results.append(pd.DataFrame([result])) + + def _silence_miplearn_logger(self): + miplearn_logger = logging.getLogger("miplearn") + self.prev_log_level = miplearn_logger.getEffectiveLevel() + miplearn_logger.setLevel(logging.WARNING) + + def _restore_miplearn_logger(self): + miplearn_logger = logging.getLogger("miplearn") + miplearn_logger.setLevel(self.prev_log_level)Methods
+-
+
+def fit(self, training_instances) +
+-
+
+++
+Expand source code +
+
+def fit(self, training_instances): + for (solver_name, solver) in self.solvers.items(): + solver.fit(training_instances)
+ +def load_results(self, filename) +
+-
+
+++
+Expand source code +
+
+def load_results(self, filename): + self.results = pd.concat([self.results, pd.read_csv(filename, index_col=0)])
+ +def load_state(self, filename) +
+-
+
+++
+Expand source code +
+
+def load_state(self, filename): + for (solver_name, solver) in self.solvers.items(): + solver.load_state(filename)
+ +def parallel_solve(self, instances, n_jobs=1, n_trials=1, index_offset=0) +
+-
+
+++
+Expand source code +
+
+def parallel_solve( + self, + instances, + n_jobs=1, + n_trials=1, + index_offset=0, +): + self._silence_miplearn_logger() + trials = instances * n_trials + for (solver_name, solver) in self.solvers.items(): + results = solver.parallel_solve( + trials, + n_jobs=n_jobs, + label="Solve (%s)" % solver_name, + discard_outputs=True, + ) + for i in range(len(trials)): + idx = (i % len(instances)) + index_offset + self._push_result( + results[i], + solver=solver, + solver_name=solver_name, + instance=idx, + ) + self._restore_miplearn_logger()
+ +def raw_results(self) +
+-
+
+++
+Expand source code +
+
+def raw_results(self): + return self.results
+ +def save_results(self, filename) +
+-
+
+++
+Expand source code +
+
+def save_results(self, filename): + os.makedirs(os.path.dirname(filename), exist_ok=True) + self.results.to_csv(filename)
+ +def solve(self, instances, tee=False) +
+-
+
+++
+Expand source code +
+
+def solve(self, instances, tee=False): + for (solver_name, solver) in self.solvers.items(): + for i in tqdm(range(len((instances)))): + results = solver.solve(deepcopy(instances[i]), tee=tee) + self._push_result( + results, + solver=solver, + solver_name=solver_name, + instance=i, + )
+
+