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Implement BenchmarkRunner

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This commit is contained in:
Alinson S. Xavier
2020-01-23 21:59:59 -06:00
parent 07090bac9e
commit 8f141e6a9d
10 changed files with 161 additions and 29 deletions
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41
miplearn/solvers.py
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@@ -72,7 +72,7 @@ class LearningSolver:
var[index].value = 1
# Solve MILP
self._solve(model, tee=tee)
solve_results = self._solve(model, tee=tee)
# Update y_train
for category in var_split.keys():
@@ -83,28 +83,36 @@ class LearningSolver:
else:
self.y_train[category] = np.vstack([self.y_train[category], y])
def parallel_solve(self, instances, n_jobs=4):
return solve_results
def parallel_solve(self, instances, n_jobs=4, label="Solve"):
def _process(instance):
solver = copy(self)
solver.solve(instance)
return solver.x_train, solver.y_train
solver = deepcopy(self)
results = solver.solve(instance)
return {
"x_train": solver.x_train,
"y_train": solver.y_train,
"results": results,
}
def _merge(results):
categories = results[0][0].keys()
x_entries = [np.vstack([r[0][c] for r in results]) for c in categories]
y_entries = [np.vstack([r[1][c] for r in results]) for c in categories]
categories = results[0]["x_train"].keys()
x_entries = [np.vstack([r["x_train"][c] for r in results]) for c in categories]
y_entries = [np.vstack([r["y_train"][c] for r in results]) for c in categories]
x_train = dict(zip(categories, x_entries))
y_train = dict(zip(categories, y_entries))
return x_train, y_train
results = [r["results"] for r in results]
return x_train, y_train, results
results = Parallel(n_jobs=n_jobs)(
delayed(_process)(i)
for i in tqdm(instances)
delayed(_process)(instance)
for instance in tqdm(instances, desc=label)
)
x_train, y_train = _merge(results)
x_train, y_train, results = _merge(results)
self.x_train = x_train
self.y_train = y_train
return results
def fit(self, x_train_dict=None, y_train_dict=None):
if x_train_dict is None:
@@ -113,8 +121,9 @@ class LearningSolver:
for category in x_train_dict.keys():
x_train = x_train_dict[category]
y_train = y_train_dict[category]
self.ws_predictors[category] = deepcopy(self.ws_predictor_prototype)
self.ws_predictors[category].fit(x_train, y_train)
if self.ws_predictor_prototype is not None:
self.ws_predictors[category] = deepcopy(self.ws_predictor_prototype)
self.ws_predictors[category].fit(x_train, y_train)
def save(self, filename):
with open(filename, "wb") as file:
@@ -136,6 +145,6 @@ class LearningSolver:
def _solve(self, model, tee=False):
if hasattr(self.parent_solver, "set_instance"):
self.parent_solver.set_instance(model)
self.parent_solver.solve(tee=tee, warmstart=True)
return self.parent_solver.solve(tee=tee, warmstart=True)
else:
self.parent_solver.solve(model, tee=tee, warmstart=True)
return self.parent_solver.solve(model, tee=tee, warmstart=True)