ANL-CEEESA/MIPLearn
1
0
Fork 0
mirror of https://github.com/ANL-CEEESA/MIPLearn.git synced 2025-12-08 18:38:51 -06:00
Code Issues Packages Projects Releases Wiki Activity

Add training_data argument to after_solve

Browse Source
This commit is contained in:
Alinson S. Xavier
2021-01-14 10:37:48 -06:00
parent 30d6ea0a9b
commit e12a896504
15 changed files with 148 additions and 58 deletions
Download Patch File Download Diff File Expand all files Collapse all files

26
miplearn/components/steps/convert_tight.py
View File

@@ -74,13 +74,21 @@ class ConvertTightIneqsIntoEqsStep(Component):
logger.info(f"Converted {self.n_converted} inequalities")
def after_solve(self, solver, instance, model, results):
instance.slacks = solver.internal_solver.get_inequality_slacks()
results["ConvertTight: Kept"] = self.n_kept
results["ConvertTight: Converted"] = self.n_converted
results["ConvertTight: Restored"] = self.n_restored
results["ConvertTight: Inf iterations"] = self.n_infeasible_iterations
results["ConvertTight: Subopt iterations"] = self.n_suboptimal_iterations
def after_solve(
self,
solver,
instance,
model,
stats,
training_data,
):
if "slacks" not in training_data.keys():
training_data["slacks"] = solver.internal_solver.get_inequality_slacks()
stats["ConvertTight: Kept"] = self.n_kept
stats["ConvertTight: Converted"] = self.n_converted
stats["ConvertTight: Restored"] = self.n_restored
stats["ConvertTight: Inf iterations"] = self.n_infeasible_iterations
stats["ConvertTight: Subopt iterations"] = self.n_suboptimal_iterations
def fit(self, training_instances):
logger.debug("Extracting x and y...")
@@ -108,7 +116,7 @@ class ConvertTightIneqsIntoEqsStep(Component):
if constraint_ids is not None:
cids = constraint_ids
else:
cids = instance.slacks.keys()
cids = instance.training_data[0]["slacks"].keys()
for cid in cids:
category = instance.get_constraint_category(cid)
if category is None:
@@ -130,7 +138,7 @@ class ConvertTightIneqsIntoEqsStep(Component):
desc="Extract (rlx:conv_ineqs:y)",
disable=len(instances) < 5,
):
for (cid, slack) in instance.slacks.items():
for (cid, slack) in instance.training_data[0]["slacks"].items():
category = instance.get_constraint_category(cid)
if category is None:
continue