MIPLearn/miplearn/problems/tsp.py

#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
#  Released under the modified BSD license. See COPYING.md for more details.

import numpy as np
import pyomo.environ as pe
from miplearn import Instance
from scipy.stats import uniform, randint
from scipy.spatial.distance import pdist, squareform
from scipy.stats.distributions import rv_frozen
import networkx as nx
import random


class ChallengeA:
    def __init__(self,
                 seed=42,
                 n_training_instances=500,
                 n_test_instances=50,
                ):
        
        np.random.seed(seed)
        self.generator = TravelingSalesmanGenerator(x=uniform(loc=0.0, scale=1000.0),
                                                    y=uniform(loc=0.0, scale=1000.0),
                                                    n=randint(low=350, high=351),
                                                    gamma=uniform(loc=0.95, scale=0.1),
                                                    fix_cities=True,
                                                    round=True,
                                                   )

        np.random.seed(seed + 1)
        self.training_instances = self.generator.generate(n_training_instances)
        
        np.random.seed(seed + 2)
        self.test_instances = self.generator.generate(n_test_instances)        
    
        
class TravelingSalesmanGenerator:
    """Random generator for the Traveling Salesman Problem."""

    def __init__(self,
                 x=uniform(loc=0.0, scale=1000.0),
                 y=uniform(loc=0.0, scale=1000.0),
                 n=randint(low=100, high=101),
                 gamma=uniform(loc=1.0, scale=0.0),
                 fix_cities=True,
                 round=True,
                ):
        """Initializes the problem generator.
        
        Initially, the generator creates n cities (x_1,y_1),...,(x_n,y_n) where n, x_i and y_i are
        sampled independently from the provided probability distributions `n`, `x` and `y`. For each
        (unordered) pair of cities (i,j), the distance d[i,j] between them is set to:

            d[i,j] = gamma[i,j] \sqrt{(x_i - x_j)^2 + (y_i - y_j)^2}

        where gamma is sampled from the provided probability distribution `gamma`.

        If fix_cities=True, the list of cities is kept the same for all generated instances. The
        gamma values, and therefore also the distances, are still different.
        
        By default, all distances d[i,j] are rounded to the nearest integer.  If `round=False`
        is provided, this rounding will be disabled.

        Arguments
        ---------
        x: rv_continuous
            Probability distribution for the x-coordinate of each city.
        y: rv_continuous
            Probability distribution for the y-coordinate of each city.
        n: rv_discrete
            Probability distribution for the number of cities.
        fix_cities: bool
            If False, cities will be resampled for every generated instance. Otherwise, list of
            cities will be computed once, during the constructor.
        round: bool
            If True, distances are rounded to the nearest integer.
        """
        assert isinstance(x, rv_frozen), "x should be a SciPy probability distribution"
        assert isinstance(y, rv_frozen), "y should be a SciPy probability distribution"
        assert isinstance(n, rv_frozen), "n should be a SciPy probability distribution"
        assert isinstance(gamma, rv_frozen), "gamma should be a SciPy probability distribution"
        self.x = x
        self.y = y
        self.n = n
        self.gamma = gamma
        self.round = round
        
        if fix_cities:
            self.fixed_n, self.fixed_cities = self._generate_cities()
        else:
            self.fixed_n = None
            self.fixed_cities = None
    
    def generate(self, n_samples):
        def _sample():
            if self.fixed_cities is not None:
                n, cities = self.fixed_n, self.fixed_cities
            else:
                n, cities = self._generate_cities()
            distances = squareform(pdist(cities)) * self.gamma.rvs(size=(n, n))
            distances = np.tril(distances) + np.triu(distances.T, 1)
            if self.round:
                distances = distances.round()
            return TravelingSalesmanInstance(n, distances)
        return [_sample() for _ in range(n_samples)]
            
    def _generate_cities(self):
        n = self.n.rvs()
        cities = np.array([(self.x.rvs(), self.y.rvs()) for _ in range(n)])
        return n, cities
    

class TravelingSalesmanInstance(Instance):
    """An instance ot the Traveling Salesman Problem.
    
    Given a list of cities and the distance between each pair of cities, the problem asks for the
    shortest route starting at the first city, visiting each other city exactly once, then
    returning to the first city. This problem is a generalization of the Hamiltonian path problem,
    one of Karp's 21 NP-complete problems.
    """
    
    def __init__(self, n_cities, distances):
        assert isinstance(distances, np.ndarray)
        assert distances.shape == (n_cities, n_cities)
        self.n_cities = n_cities
        self.distances = distances
    
    def to_model(self):
        model = pe.ConcreteModel()
        model.edges = edges = [(i,j)
                              for i in range(self.n_cities)
                              for j in range(i+1, self.n_cities)]
        model.x = pe.Var(edges, domain=pe.Binary)
        model.obj = pe.Objective(expr=sum(model.x[i,j] * self.distances[i,j]
                                          for (i,j) in edges),
                                 sense=pe.minimize)
        model.eq_degree = pe.ConstraintList()
        model.eq_subtour = pe.ConstraintList()
        for i in range(self.n_cities):
            model.eq_degree.add(sum(model.x[min(i,j), max(i,j)]
                                    for j in range(self.n_cities) if i != j) == 2)
        return model
    
    def get_instance_features(self):
        return np.array([1])
    
    def get_variable_features(self, var_name, index):
        return np.array([1])
    
    def get_variable_category(self, var_name, index):
        return index
    
    def find_violated_lazy_constraints(self, model):
        selected_edges = [e for e in model.edges if model.x[e].value > 0.5]
        graph = nx.Graph()
        graph.add_edges_from(selected_edges)
        components = [frozenset(c) for c in list(nx.connected_components(graph))]
        violations = []
        for c in components:
            if len(c) < self.n_cities:
                violations += [c]
        return violations
    
    def build_lazy_constraint(self, model, component):
        cut_edges = [e for e in model.edges
                     if (e[0] in component and e[1] not in component) or
                        (e[0] not in component and e[1] in component)]
        return model.eq_subtour.add(sum(model.x[e] for e in cut_edges) >= 2)

    def find_violated_user_cuts(self, model):
        return self.find_violated_lazy_constraints(model)

    def build_user_cut(self, model, violation):
        return self.build_lazy_constraint(model, violation)