Add PerVariableTransformer

miplearn/problems/knapsack.py

@@ -0,0 +1,51 @@
+# MIPLearn: A Machine-Learning Framework for Mixed-Integer Optimization
+# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved.
+# Written by Alinson S. Xavier <axavier@anl.gov>
+
+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()
+        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
+    
+    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

miplearn/problems/stab.py

@@ -0,0 +1,60 @@
+# MIPLearn: A Machine-Learning Framework for Mixed-Integer Optimization
+# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved.
+# Written by Alinson S. Xavier <axavier@anl.gov>
+
+import numpy as np
+import pyomo.environ as pe
+import networkx as nx
+from miplearn import Instance
+import random
+
+class MaxStableSetGenerator:
+    def __init__(self, sizes=[50], densities=[0.1]):
+        self.sizes = sizes
+        self.densities = densities
+        
+    def generate(self):
+        size = random.choice(self.sizes)
+        density = random.choice(self.densities)
+        self.graph = nx.generators.random_graphs.binomial_graph(size, density)
+        weights = np.ones(self.graph.number_of_nodes())
+        return MaxStableSetInstance(self.graph, weights)
+    
+
+class MaxStableSetInstance(Instance):
+    def __init__(self, graph, weights):
+        self.graph = graph
+        self.weights = weights
+        
+    def to_model(self):
+        nodes = list(self.graph.nodes)
+        edges = list(self.graph.edges)
+        model = m = pe.ConcreteModel()
+        m.x = pe.Var(nodes, domain=pe.Binary)
+        m.OBJ = pe.Objective(rule=lambda m : sum(m.x[v] * self.weights[v] for v in nodes),
+                              sense=pe.maximize)
+        m.edge_eqs = pe.ConstraintList()
+        for edge in edges:
+            m.edge_eqs.add(m.x[edge[0]] + m.x[edge[1]] <= 1)
+        return m
+    
+    def get_instance_features(self):
+        return np.array([
+            self.graph.number_of_nodes(),
+            self.graph.number_of_edges(),
+        ])
+    
+    def get_variable_features(self, var, index):
+        first_neighbors = list(self.graph.neighbors(index))
+        second_neighbors = [list(self.graph.neighbors(u)) for u in first_neighbors]
+        degree = len(first_neighbors)
+        neighbor_degrees = sorted([len(nn) for nn in second_neighbors])
+        neighbor_degrees = neighbor_degrees + [100.] * 10
+        return np.array([
+            degree,
+            neighbor_degrees[0] - degree,
+            neighbor_degrees[1] - degree,
+            neighbor_degrees[2] - degree,
+        ])
+
+