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Finish TSP implementation; improve performance of Extractors

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This commit is contained in:
Alinson S. Xavier
2020-02-25 22:31:03 -06:00
parent b1f674fcc6
commit 0b04fa93da
33 changed files with 1347 additions and 679 deletions
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11
miplearn/components/primal.py
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@@ -129,7 +129,7 @@ class PrimalSolutionComponent(Component):
self.dynamic_thresholds = dynamic_thresholds
def before_solve(self, solver, instance, model):
solution = self.predict(instance, model)
solution = self.predict(instance)
if self.mode == "heuristic":
solver.internal_solver.fix(solution)
else:
@@ -139,6 +139,7 @@ class PrimalSolutionComponent(Component):
pass
def fit(self, training_instances):
logger.debug("Extracting features...")
features = VariableFeaturesExtractor().extract(training_instances)
solutions = SolutionExtractor().extract(training_instances)
@@ -180,12 +181,10 @@ class PrimalSolutionComponent(Component):
self.thresholds[category, label] = thresholds[k]
def predict(self, instance, model=None):
if model is None:
model = instance.to_model()
x_test = VariableFeaturesExtractor().extract([instance], [model])
def predict(self, instance):
x_test = VariableFeaturesExtractor().extract([instance])
solution = {}
var_split = Extractor.split_variables(instance, model)
var_split = Extractor.split_variables(instance)
for category in var_split.keys():
for (i, (var, index)) in enumerate(var_split[category]):
if var not in solution.keys():