Add types to stab.py

miplearn/problems/stab.py

@@ -1,25 +1,27 @@
 #  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
 #  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
+from typing import List
 
 import networkx as nx
 import numpy as np
 import pyomo.environ as pe
+from networkx import Graph
 from overrides import overrides
 from scipy.stats import uniform, randint
 from scipy.stats.distributions import rv_frozen
 
 from miplearn.instance.base import Instance
+from miplearn.types import VariableName, Category
 
 
 class ChallengeA:
     def __init__(
         self,
-        seed=42,
+        seed: int = 42,
-        n_training_instances=500,
+        n_training_instances: int = 500,
-        n_test_instances=50,
+        n_test_instances: int = 50,
-    ):
+    ) -> None:
-
         np.random.seed(seed)
         self.generator = MaxWeightStableSetGenerator(
             w=uniform(loc=100.0, scale=50.0),
@@ -35,24 +37,76 @@ class ChallengeA:
         self.test_instances = self.generator.generate(n_test_instances)
 
 
+class MaxWeightStableSetInstance(Instance):
+    """An instance of the Maximum-Weight Stable Set Problem.
+
+    Given a graph G=(V,E) and a weight w_v for each vertex v, the problem asks for a stable
+    set S of G maximizing sum(w_v for v in S). A stable set (also called independent set) is
+    a subset of vertices, no two of which are adjacent.
+
+    This is one of Karp's 21 NP-complete problems.
+    """
+
+    def __init__(self, graph: Graph, weights: np.ndarray) -> None:
+        super().__init__()
+        self.graph = graph
+        self.weights = weights
+        self.nodes = list(self.graph.nodes)
+        self.varname_to_node = {f"x[{v}]": v for v in self.nodes}
+
+    @overrides
+    def to_model(self) -> pe.ConcreteModel:
+        model = pe.ConcreteModel()
+        model.x = pe.Var(self.nodes, domain=pe.Binary)
+        model.OBJ = pe.Objective(
+            expr=sum(model.x[v] * self.weights[v] for v in self.nodes),
+            sense=pe.maximize,
+        )
+        model.clique_eqs = pe.ConstraintList()
+        for clique in nx.find_cliques(self.graph):
+            model.clique_eqs.add(sum(model.x[v] for v in clique) <= 1)
+        return model
+
+    @overrides
+    def get_variable_features(self, var_name: VariableName) -> List[float]:
+        v1 = self.varname_to_node[var_name]
+        neighbor_weights = [0.0] * 15
+        neighbor_degrees = [100.0] * 15
+        for v2 in self.graph.neighbors(v1):
+            neighbor_weights += [self.weights[v2] / self.weights[v1]]
+            neighbor_degrees += [self.graph.degree(v2) / self.graph.degree(v1)]
+        neighbor_weights.sort(reverse=True)
+        neighbor_degrees.sort()
+        features = []
+        features += neighbor_weights[:5]
+        features += neighbor_degrees[:5]
+        features += [self.graph.degree(v1)]
+        return features
+
+    @overrides
+    def get_variable_category(self, var: VariableName) -> Category:
+        return "default"
+
+
 class MaxWeightStableSetGenerator:
     """Random instance generator for the Maximum-Weight Stable Set Problem.
 
-    The generator has two modes of operation. When `fix_graph=True` is provided, one random
+    The generator has two modes of operation. When `fix_graph=True` is provided,
-    Erdős-Rényi graph $G_{n,p}$ is generated in the constructor, where $n$ and $p$ are sampled
+    one random Erdős-Rényi graph $G_{n,p}$ is generated in the constructor, where $n$
-    from user-provided probability distributions `n` and `p`. To generate each instance, the
+    and $p$ are sampled from user-provided probability distributions `n` and `p`. To
-    generator independently samples each $w_v$ from the user-provided probability distribution `w`.
+    generate each instance, the generator independently samples each $w_v$ from the
+    user-provided probability distribution `w`.
 
-    When `fix_graph=False`, a new random graph is generated for each instance; the remaining
+    When `fix_graph=False`, a new random graph is generated for each instance; the
-    parameters are sampled in the same way.
+    remaining parameters are sampled in the same way.
     """
 
     def __init__(
         self,
-        w=uniform(loc=10.0, scale=1.0),
+        w: rv_frozen = uniform(loc=10.0, scale=1.0),
-        n=randint(low=250, high=251),
+        n: rv_frozen = randint(low=250, high=251),
-        p=uniform(loc=0.05, scale=0.0),
+        p: rv_frozen = uniform(loc=0.05, scale=0.0),
-        fix_graph=True,
+        fix_graph: bool = True,
     ):
         """Initialize the problem generator.
 
@@ -76,8 +130,8 @@ class MaxWeightStableSetGenerator:
         if fix_graph:
             self.graph = self._generate_graph()
 
-    def generate(self, n_samples):
+    def generate(self, n_samples: int) -> List[MaxWeightStableSetInstance]:
-        def _sample():
+        def _sample() -> MaxWeightStableSetInstance:
             if self.graph is not None:
                 graph = self.graph
             else:
@@ -87,56 +141,5 @@ class MaxWeightStableSetGenerator:
 
         return [_sample() for _ in range(n_samples)]
 
-    def _generate_graph(self):
+    def _generate_graph(self) -> Graph:
         return nx.generators.random_graphs.binomial_graph(self.n.rvs(), self.p.rvs())
-
-
-class MaxWeightStableSetInstance(Instance):
-    """An instance of the Maximum-Weight Stable Set Problem.
-
-    Given a graph G=(V,E) and a weight w_v for each vertex v, the problem asks for a stable
-    set S of G maximizing sum(w_v for v in S). A stable set (also called independent set) is
-    a subset of vertices, no two of which are adjacent.
-
-    This is one of Karp's 21 NP-complete problems.
-    """
-
-    def __init__(self, graph, weights):
-        super().__init__()
-        self.graph = graph
-        self.weights = weights
-        self.nodes = list(self.graph.nodes)
-        self.varname_to_node = {f"x[{v}]": v for v in self.nodes}
-
-    @overrides
-    def to_model(self):
-        model = pe.ConcreteModel()
-        model.x = pe.Var(self.nodes, domain=pe.Binary)
-        model.OBJ = pe.Objective(
-            expr=sum(model.x[v] * self.weights[v] for v in self.nodes),
-            sense=pe.maximize,
-        )
-        model.clique_eqs = pe.ConstraintList()
-        for clique in nx.find_cliques(self.graph):
-            model.clique_eqs.add(sum(model.x[v] for v in clique) <= 1)
-        return model
-
-    @overrides
-    def get_variable_features(self, var_name):
-        v1 = self.varname_to_node[var_name]
-        neighbor_weights = [0] * 15
-        neighbor_degrees = [100] * 15
-        for v2 in self.graph.neighbors(v1):
-            neighbor_weights += [self.weights[v2] / self.weights[v1]]
-            neighbor_degrees += [self.graph.degree(v2) / self.graph.degree(v1)]
-        neighbor_weights.sort(reverse=True)
-        neighbor_degrees.sort()
-        features = []
-        features += neighbor_weights[:5]
-        features += neighbor_degrees[:5]
-        features += [self.graph.degree(v1)]
-        return features
-
-    @overrides
-    def get_variable_category(self, var):
-        return "default"