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Add training_data argument to after_solve

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Alinson S. Xavier
2021-01-14 10:37:48 -06:00
parent 30d6ea0a9b
commit e12a896504
15 changed files with 148 additions and 58 deletions
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19
miplearn/solvers/learning.py
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@@ -287,22 +287,27 @@ class LearningSolver:
lazy_cb = lazy_cb_wrapper
logger.info("Solving MILP...")
results = self.internal_solver.solve(
stats = self.internal_solver.solve(
tee=tee,
iteration_cb=iteration_cb,
lazy_cb=lazy_cb,
)
results["LP value"] = instance.lp_value
stats["LP value"] = instance.lp_value
# Read MIP solution and bounds
instance.lower_bound = results["Lower bound"]
instance.upper_bound = results["Upper bound"]
instance.solver_log = results["Log"]
instance.lower_bound = stats["Lower bound"]
instance.upper_bound = stats["Upper bound"]
instance.solver_log = stats["Log"]
instance.solution = self.internal_solver.get_solution()
logger.debug("Calling after_solve callbacks...")
training_data = {}
for component in self.components.values():
component.after_solve(self, instance, model, results)
component.after_solve(self, instance, model, stats, training_data)
if not hasattr(instance, "training_data"):
instance.training_data = []
instance.training_data += [training_data]
if filename is not None and output is not None:
output_filename = output
@@ -316,7 +321,7 @@ class LearningSolver:
with gzip.GzipFile(output_filename, "wb") as file:
pickle.dump(instance, file)
return results
return stats
def parallel_solve(self, instances, n_jobs=4, label="Solve", output=[]):
"""