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Finish TSP implementation; improve performance of Extractors

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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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14
miplearn/instance.py
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@@ -65,11 +65,12 @@ class Instance(ABC):
def get_variable_category(self, var, index):
"""
Returns the category (a string, an integer or any hashable type) for each decision variable.
Returns the category (a string, an integer or any hashable type) for each decision
variable.
If two variables have the same category, LearningSolver will use the same internal ML model
to predict the values of both variables. By default, all variables belong to the "default"
category, and therefore only one ML model is used for all variables.
If two variables have the same category, LearningSolver will use the same internal ML
model to predict the values of both variables. By default, all variables belong to the
"default" category, and therefore only one ML model is used for all variables.
If the returned category is None, ML models will ignore the variable.
"""
@@ -99,10 +100,11 @@ class Instance(ABC):
Returns a Pyomo constraint which fixes a given violation.
This method is typically called immediately after find_violations. The violation object
provided by this method is exactly the same object returned earlier by find_violations.
provided to this method is exactly the same object returned earlier by find_violations.
After some training, LearningSolver may decide to proactively build some lazy constraints
at the beginning of the optimization process, before a solution is even available. In this
case, build_lazy_constraints will be called without a corresponding call to find_violations.
case, build_lazy_constraints will be called without a corresponding call to
find_violations.
The implementation should not directly add the constraint to the model. The constraint
will be added by LearningSolver after the method returns.