Temporarily remove unused files; make package work with Cbc

miplearn/problems/knapsack.py

@@ -6,46 +6,48 @@ import miplearn
 import numpy as np
 import pyomo.environ as pe
 
+
 class KnapsackInstance(miplearn.Instance):
     def __init__(self, weights, prices, capacity):
         self.weights = weights
         self.prices = prices
         self.capacity = capacity
-        
+
     def to_model(self):
-        model = m = pe.ConcreteModel()
+        model = pe.ConcreteModel()
         items = range(len(self.weights))
-        m.x = pe.Var(items, domain=pe.Binary)
-        m.OBJ = pe.Objective(rule=lambda m : sum(m.x[v] * self.prices[v] for v in items),
-                              sense=pe.maximize)
-        m.eq_capacity = pe.Constraint(rule = lambda m :
-                                      sum(m.x[v] * self.weights[v]
-                                          for v in items) <= self.capacity)
-        return m
-    
+        model.x = pe.Var(items, domain=pe.Binary)
+        model.OBJ = pe.Objective(rule=lambda m: sum(m.x[v] * self.prices[v] for v in items),
+                                 sense=pe.maximize)
+        model.eq_capacity = pe.Constraint(rule=lambda m: sum(m.x[v] * self.weights[v]
+                                                             for v in items) <= self.capacity)
+        return model
+
     def get_instance_features(self):
         return np.array([
             self.capacity,
             np.average(self.weights),
         ])
-    
+
     def get_variable_features(self, var, index):
         return np.array([
             self.weights[index],
             self.prices[index],
         ])
-    
+
+
 class KnapsackInstance2(KnapsackInstance):
     """
     Alternative implementation of the Knapsack Problem, which assigns a different category for each
     decision variable, and therefore trains one machine learning model per variable.
     """
+
     def get_instance_features(self):
         return np.hstack([self.weights, self.prices])
-    
+
     def get_variable_features(self, var, index):
         return np.array([
         ])
-    
+
     def get_variable_category(self, var, index):
-        return index
+        return index