Reformat source code with Black; add pre-commit hooks and CI checks

miplearn/instance.py

@@ -12,7 +12,7 @@ import numpy as np
 class Instance(ABC):
     """
     Abstract class holding all the data necessary to generate a concrete model of the problem.
-    
+
     In the knapsack problem, for example, this class could hold the number of items, their weights
     and costs, as well as the size of the knapsack. Objects implementing this class are able to
     convert themselves into a concrete optimization model, which can be optimized by a solver, or
@@ -29,17 +29,17 @@ class Instance(ABC):
     def get_instance_features(self):
         """
         Returns a 1-dimensional Numpy array of (numerical) features describing the entire instance.
-        
+
         The array is used by LearningSolver to determine how similar two instances are. It may also
         be used to predict, in combination with variable-specific features, the values of binary
         decision variables in the problem.
-        
+
         There is not necessarily a one-to-one correspondence between models and instance features:
         the features may encode only part of the data necessary to generate the complete model.
         Features may also be statistics computed from the original data. For example, in the
         knapsack problem, an implementation may decide to provide as instance features only
         the average weights, average prices, number of items and the size of the knapsack.
-        
+
         The returned array MUST have the same length for all relevant instances of the problem. If
         two instances map into arrays of different lengths, they cannot be solved by the same
         LearningSolver object.
@@ -52,16 +52,16 @@ class Instance(ABC):
         """
         Returns a 1-dimensional array of (numerical) features describing a particular decision
         variable.
-        
+
         The argument `var` is a pyomo.core.Var object, which represents a collection of decision
         variables. The argument `index` specifies which variable in the collection is the relevant
         one.
-        
+
         In combination with instance features, variable features are used by LearningSolver to
         predict, among other things, the optimal value of each decision variable before the
         optimization takes place. In the knapsack problem, for example, an implementation could
         provide as variable features the weight and the price of a specific item.
-        
+
         Like instance features, the arrays returned by this method MUST have the same length for
         all variables within the same category, for all relevant instances of the problem.
 
@@ -73,7 +73,7 @@ class Instance(ABC):
         """
         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. If the returned category is None, ML
         models will ignore the variable.
@@ -100,18 +100,18 @@ class Instance(ABC):
     def find_violated_lazy_constraints(self, model):
         """
         Returns lazy constraint violations found for the current solution.
-        
+
         After solving a model, LearningSolver will ask the instance to identify which lazy
         constraints are violated by the current solution. For each identified violation,
         LearningSolver will then call the build_lazy_constraint, add the generated Pyomo
         constraint to the model, then resolve the problem. The process repeats until no further
         lazy constraint violations are found.
-        
+
         Each "violation" is simply a string, a tuple or any other hashable type which allows the
         instance to identify unambiguously which lazy constraint should be generated. In the
         Traveling Salesman Problem, for example, a subtour violation could be a frozen set
         containing the cities in the subtour.
-        
+
         For a concrete example, see TravelingSalesmanInstance.
         """
         return []
@@ -119,17 +119,17 @@ class Instance(ABC):
     def build_lazy_constraint(self, model, violation):
         """
         Returns a Pyomo constraint which fixes a given violation.
-        
+
         This method is typically called immediately after find_violated_lazy_constraints. The violation object
         provided to this method is exactly the same object returned earlier by find_violated_lazy_constraints.
         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_violated_lazy_constraints.
-        
+
         The implementation should not directly add the constraint to the model. The constraint
         will be added by LearningSolver after the method returns.
-        
+
         For a concrete example, see TravelingSalesmanInstance.
         """
         pass
@@ -141,11 +141,11 @@ class Instance(ABC):
         pass
 
     def load(self, filename):
-        with gzip.GzipFile(filename, 'r') as f:
-            data = json.loads(f.read().decode('utf-8'))
+        with gzip.GzipFile(filename, "r") as f:
+            data = json.loads(f.read().decode("utf-8"))
         self.__dict__ = data
 
     def dump(self, filename):
-        data = json.dumps(self.__dict__, indent=2).encode('utf-8')
-        with gzip.GzipFile(filename, 'w') as f:
+        data = json.dumps(self.__dict__, indent=2).encode("utf-8")
+        with gzip.GzipFile(filename, "w") as f:
             f.write(data)