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<body>
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<main>
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<article id="content">
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<header>
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<h1 class="title">Module <code>miplearn.problems</code></h1>
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</header>
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<section id="section-intro">
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<details class="source">
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<summary>
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<span>Expand source code</span>
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</summary>
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<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
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# Released under the modified BSD license. See COPYING.md for more details.</code></pre>
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</details>
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</section>
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<section>
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<h2 class="section-title" id="header-submodules">Sub-modules</h2>
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<dl>
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<dt><code class="name"><a title="miplearn.problems.knapsack" href="knapsack.html">miplearn.problems.knapsack</a></code></dt>
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<dd>
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<section class="desc"></section>
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</dd>
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<dt><code class="name"><a title="miplearn.problems.stab" href="stab.html">miplearn.problems.stab</a></code></dt>
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<dd>
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<section class="desc"></section>
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</dd>
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<dt><code class="name"><a title="miplearn.problems.tests" href="tests/index.html">miplearn.problems.tests</a></code></dt>
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<dd>
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<section class="desc"></section>
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</dd>
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<dt><code class="name"><a title="miplearn.problems.tsp" href="tsp.html">miplearn.problems.tsp</a></code></dt>
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<dd>
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<section class="desc"></section>
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</dd>
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</dl>
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</section>
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<section>
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</section>
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<section>
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<section>
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</section>
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</article>
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<nav id="sidebar">
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<h1>Index</h1>
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<div class="toc">
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<ul></ul>
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</div>
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<ul id="index">
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<li><h3>Super-module</h3>
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<ul>
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<li><code><a title="miplearn" href="../index.html">miplearn</a></code></li>
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</ul>
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</li>
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<li><h3><a href="#header-submodules">Sub-modules</a></h3>
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<ul>
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<li><code><a title="miplearn.problems.knapsack" href="knapsack.html">miplearn.problems.knapsack</a></code></li>
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<li><code><a title="miplearn.problems.stab" href="stab.html">miplearn.problems.stab</a></code></li>
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<li><code><a title="miplearn.problems.tests" href="tests/index.html">miplearn.problems.tests</a></code></li>
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<li><code><a title="miplearn.problems.tsp" href="tsp.html">miplearn.problems.tsp</a></code></li>
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881
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<main>
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<article id="content">
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<header>
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<h1 class="title">Module <code>miplearn.problems.knapsack</code></h1>
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</header>
|
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<section id="section-intro">
|
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<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># 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.
|
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|
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import numpy as np
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import pyomo.environ as pe
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from scipy.stats import uniform, randint
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from scipy.stats.distributions import rv_frozen
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from miplearn.instance import Instance
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class ChallengeA:
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"""
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- 250 variables, 10 constraints, fixed weights
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- w ~ U(0, 1000), jitter ~ U(0.95, 1.05)
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- K = 500, u ~ U(0., 1.)
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- alpha = 0.25
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"""
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def __init__(
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self,
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seed=42,
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n_training_instances=500,
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n_test_instances=50,
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):
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np.random.seed(seed)
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self.gen = MultiKnapsackGenerator(
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n=randint(low=250, high=251),
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m=randint(low=10, high=11),
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w=uniform(loc=0.0, scale=1000.0),
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K=uniform(loc=500.0, scale=0.0),
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u=uniform(loc=0.0, scale=1.0),
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alpha=uniform(loc=0.25, scale=0.0),
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fix_w=True,
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w_jitter=uniform(loc=0.95, scale=0.1),
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)
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np.random.seed(seed + 1)
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self.training_instances = self.gen.generate(n_training_instances)
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np.random.seed(seed + 2)
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self.test_instances = self.gen.generate(n_test_instances)
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class MultiKnapsackInstance(Instance):
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"""Representation of the Multidimensional 0-1 Knapsack Problem.
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Given a set of n items and m knapsacks, the problem is to find a subset of items S maximizing
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sum(prices[i] for i in S). If selected, each item i occupies weights[i,j] units of space in
|
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each knapsack j. Furthermore, each knapsack j has limited storage space, given by capacities[j].
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This implementation assigns a different category for each decision variable, and therefore
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trains one ML model per variable. It is only suitable when training and test instances have
|
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same size and items don't shuffle around.
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"""
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def __init__(self, prices, capacities, weights):
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super().__init__()
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assert isinstance(prices, np.ndarray)
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assert isinstance(capacities, np.ndarray)
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assert isinstance(weights, np.ndarray)
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assert len(weights.shape) == 2
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self.m, self.n = weights.shape
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assert prices.shape == (self.n,)
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assert capacities.shape == (self.m,)
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self.prices = prices
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self.capacities = capacities
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self.weights = weights
|
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|
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def to_model(self):
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model = pe.ConcreteModel()
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model.x = pe.Var(range(self.n), domain=pe.Binary)
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model.OBJ = pe.Objective(
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rule=lambda model: sum(model.x[j] * self.prices[j] for j in range(self.n)),
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sense=pe.maximize,
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)
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model.eq_capacity = pe.ConstraintList()
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for i in range(self.m):
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model.eq_capacity.add(
|
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sum(model.x[j] * self.weights[i, j] for j in range(self.n))
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<= self.capacities[i]
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)
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return model
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def get_instance_features(self):
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return np.hstack(
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[
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np.mean(self.prices),
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self.capacities,
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]
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)
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def get_variable_features(self, var, index):
|
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return np.hstack(
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[
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self.prices[index],
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self.weights[:, index],
|
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]
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)
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|
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|
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# def get_variable_category(self, var, index):
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# return index
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class MultiKnapsackGenerator:
|
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def __init__(
|
||||
self,
|
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n=randint(low=100, high=101),
|
||||
m=randint(low=30, high=31),
|
||||
w=randint(low=0, high=1000),
|
||||
K=randint(low=500, high=500),
|
||||
u=uniform(loc=0.0, scale=1.0),
|
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alpha=uniform(loc=0.25, scale=0.0),
|
||||
fix_w=False,
|
||||
w_jitter=uniform(loc=1.0, scale=0.0),
|
||||
round=True,
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||||
):
|
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"""Initialize the problem generator.
|
||||
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Instances have a random number of items (or variables) and a random number of knapsacks
|
||||
(or constraints), as specified by the provided probability distributions `n` and `m`,
|
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respectively. The weight of each item `i` on knapsack `j` is sampled independently from
|
||||
the provided distribution `w`. The capacity of knapsack `j` is set to:
|
||||
|
||||
alpha_j * sum(w[i,j] for i in range(n)),
|
||||
|
||||
where `alpha_j`, the tightness ratio, is sampled from the provided probability
|
||||
distribution `alpha`. To make the instances more challenging, the costs of the items
|
||||
are linearly correlated to their average weights. More specifically, the weight of each
|
||||
item `i` is set to:
|
||||
|
||||
sum(w[i,j]/m for j in range(m)) + K * u_i,
|
||||
|
||||
where `K`, the correlation coefficient, and `u_i`, the correlation multiplier, are sampled
|
||||
from the provided probability distributions. Note that `K` is only sample once for the
|
||||
entire instance.
|
||||
|
||||
If fix_w=True is provided, then w[i,j] are kept the same in all generated instances. This
|
||||
also implies that n and m are kept fixed. Although the prices and capacities are derived
|
||||
from w[i,j], as long as u and K are not constants, the generated instances will still not
|
||||
be completely identical.
|
||||
|
||||
If a probability distribution w_jitter is provided, then item weights will be set to
|
||||
w[i,j] * gamma[i,j] where gamma[i,j] is sampled from w_jitter. When combined with
|
||||
fix_w=True, this argument may be used to generate instances where the weight of each item
|
||||
is roughly the same, but not exactly identical, across all instances. The prices of the
|
||||
items and the capacities of the knapsacks will be calculated as above, but using these
|
||||
perturbed weights instead.
|
||||
|
||||
By default, all generated prices, weights and capacities are rounded to the nearest integer
|
||||
number. If `round=False` is provided, this rounding will be disabled.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
n: rv_discrete
|
||||
Probability distribution for the number of items (or variables)
|
||||
m: rv_discrete
|
||||
Probability distribution for the number of knapsacks (or constraints)
|
||||
w: rv_continuous
|
||||
Probability distribution for the item weights
|
||||
K: rv_continuous
|
||||
Probability distribution for the profit correlation coefficient
|
||||
u: rv_continuous
|
||||
Probability distribution for the profit multiplier
|
||||
alpha: rv_continuous
|
||||
Probability distribution for the tightness ratio
|
||||
fix_w: boolean
|
||||
If true, weights are kept the same (minus the noise from w_jitter) in all instances
|
||||
w_jitter: rv_continuous
|
||||
Probability distribution for random noise added to the weights
|
||||
round: boolean
|
||||
If true, all prices, weights and capacities are rounded to the nearest integer
|
||||
"""
|
||||
assert isinstance(n, rv_frozen), "n should be a SciPy probability distribution"
|
||||
assert isinstance(m, rv_frozen), "m should be a SciPy probability distribution"
|
||||
assert isinstance(w, rv_frozen), "w should be a SciPy probability distribution"
|
||||
assert isinstance(K, rv_frozen), "K should be a SciPy probability distribution"
|
||||
assert isinstance(u, rv_frozen), "u should be a SciPy probability distribution"
|
||||
assert isinstance(
|
||||
alpha, rv_frozen
|
||||
), "alpha should be a SciPy probability distribution"
|
||||
assert isinstance(fix_w, bool), "fix_w should be boolean"
|
||||
assert isinstance(
|
||||
w_jitter, rv_frozen
|
||||
), "w_jitter should be a SciPy probability distribution"
|
||||
|
||||
self.n = n
|
||||
self.m = m
|
||||
self.w = w
|
||||
self.K = K
|
||||
self.u = u
|
||||
self.alpha = alpha
|
||||
self.w_jitter = w_jitter
|
||||
self.round = round
|
||||
|
||||
if fix_w:
|
||||
self.fix_n = self.n.rvs()
|
||||
self.fix_m = self.m.rvs()
|
||||
self.fix_w = np.array([self.w.rvs(self.fix_n) for _ in range(self.fix_m)])
|
||||
self.fix_u = self.u.rvs(self.fix_n)
|
||||
self.fix_K = self.K.rvs()
|
||||
else:
|
||||
self.fix_n = None
|
||||
self.fix_m = None
|
||||
self.fix_w = None
|
||||
self.fix_u = None
|
||||
self.fix_K = None
|
||||
|
||||
def generate(self, n_samples):
|
||||
def _sample():
|
||||
if self.fix_w is not None:
|
||||
n = self.fix_n
|
||||
m = self.fix_m
|
||||
w = self.fix_w
|
||||
u = self.fix_u
|
||||
K = self.fix_K
|
||||
else:
|
||||
n = self.n.rvs()
|
||||
m = self.m.rvs()
|
||||
w = np.array([self.w.rvs(n) for _ in range(m)])
|
||||
u = self.u.rvs(n)
|
||||
K = self.K.rvs()
|
||||
w = w * np.array([self.w_jitter.rvs(n) for _ in range(m)])
|
||||
alpha = self.alpha.rvs(m)
|
||||
p = np.array([w[:, j].sum() / m + K * u[j] for j in range(n)])
|
||||
b = np.array([w[i, :].sum() * alpha[i] for i in range(m)])
|
||||
if self.round:
|
||||
p = p.round()
|
||||
b = b.round()
|
||||
w = w.round()
|
||||
return MultiKnapsackInstance(p, b, w)
|
||||
|
||||
return [_sample() for _ in range(n_samples)]
|
||||
|
||||
|
||||
class KnapsackInstance(Instance):
|
||||
"""
|
||||
Simpler (one-dimensional) Knapsack Problem, used for testing.
|
||||
"""
|
||||
|
||||
def __init__(self, weights, prices, capacity):
|
||||
super().__init__()
|
||||
self.weights = weights
|
||||
self.prices = prices
|
||||
self.capacity = capacity
|
||||
|
||||
def to_model(self):
|
||||
model = pe.ConcreteModel()
|
||||
items = range(len(self.weights))
|
||||
model.x = pe.Var(items, domain=pe.Binary)
|
||||
model.OBJ = pe.Objective(
|
||||
expr=sum(model.x[v] * self.prices[v] for v in items), sense=pe.maximize
|
||||
)
|
||||
model.eq_capacity = pe.Constraint(
|
||||
expr=sum(model.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 GurobiKnapsackInstance(KnapsackInstance):
|
||||
"""
|
||||
Simpler (one-dimensional) knapsack instance, implemented directly in Gurobi
|
||||
instead of Pyomo, used for testing.
|
||||
"""
|
||||
|
||||
def __init__(self, weights, prices, capacity):
|
||||
super().__init__(weights, prices, capacity)
|
||||
|
||||
def to_model(self):
|
||||
import gurobipy as gp
|
||||
from gurobipy import GRB
|
||||
|
||||
model = gp.Model("Knapsack")
|
||||
n = len(self.weights)
|
||||
x = model.addVars(n, vtype=GRB.BINARY, name="x")
|
||||
model.addConstr(
|
||||
gp.quicksum(x[i] * self.weights[i] for i in range(n)) <= self.capacity,
|
||||
"eq_capacity",
|
||||
)
|
||||
model.setObjective(
|
||||
gp.quicksum(x[i] * self.prices[i] for i in range(n)), GRB.MAXIMIZE
|
||||
)
|
||||
return model</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-classes">Classes</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.problems.knapsack.ChallengeA"><code class="flex name class">
|
||||
<span>class <span class="ident">ChallengeA</span></span>
|
||||
<span>(</span><span>seed=42, n_training_instances=500, n_test_instances=50)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><ul>
|
||||
<li>250 variables, 10 constraints, fixed weights</li>
|
||||
<li>w ~ U(0, 1000), jitter ~ U(0.95, 1.05)</li>
|
||||
<li>K = 500, u ~ U(0., 1.)</li>
|
||||
<li>alpha = 0.25</li>
|
||||
</ul></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class ChallengeA:
|
||||
"""
|
||||
- 250 variables, 10 constraints, fixed weights
|
||||
- w ~ U(0, 1000), jitter ~ U(0.95, 1.05)
|
||||
- K = 500, u ~ U(0., 1.)
|
||||
- alpha = 0.25
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
seed=42,
|
||||
n_training_instances=500,
|
||||
n_test_instances=50,
|
||||
):
|
||||
|
||||
np.random.seed(seed)
|
||||
self.gen = MultiKnapsackGenerator(
|
||||
n=randint(low=250, high=251),
|
||||
m=randint(low=10, high=11),
|
||||
w=uniform(loc=0.0, scale=1000.0),
|
||||
K=uniform(loc=500.0, scale=0.0),
|
||||
u=uniform(loc=0.0, scale=1.0),
|
||||
alpha=uniform(loc=0.25, scale=0.0),
|
||||
fix_w=True,
|
||||
w_jitter=uniform(loc=0.95, scale=0.1),
|
||||
)
|
||||
np.random.seed(seed + 1)
|
||||
self.training_instances = self.gen.generate(n_training_instances)
|
||||
|
||||
np.random.seed(seed + 2)
|
||||
self.test_instances = self.gen.generate(n_test_instances)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.problems.knapsack.GurobiKnapsackInstance"><code class="flex name class">
|
||||
<span>class <span class="ident">GurobiKnapsackInstance</span></span>
|
||||
<span>(</span><span>weights, prices, capacity)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Simpler (one-dimensional) knapsack instance, implemented directly in Gurobi
|
||||
instead of Pyomo, used for testing.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class GurobiKnapsackInstance(KnapsackInstance):
|
||||
"""
|
||||
Simpler (one-dimensional) knapsack instance, implemented directly in Gurobi
|
||||
instead of Pyomo, used for testing.
|
||||
"""
|
||||
|
||||
def __init__(self, weights, prices, capacity):
|
||||
super().__init__(weights, prices, capacity)
|
||||
|
||||
def to_model(self):
|
||||
import gurobipy as gp
|
||||
from gurobipy import GRB
|
||||
|
||||
model = gp.Model("Knapsack")
|
||||
n = len(self.weights)
|
||||
x = model.addVars(n, vtype=GRB.BINARY, name="x")
|
||||
model.addConstr(
|
||||
gp.quicksum(x[i] * self.weights[i] for i in range(n)) <= self.capacity,
|
||||
"eq_capacity",
|
||||
)
|
||||
model.setObjective(
|
||||
gp.quicksum(x[i] * self.prices[i] for i in range(n)), GRB.MAXIMIZE
|
||||
)
|
||||
return model</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.problems.knapsack.KnapsackInstance" href="#miplearn.problems.knapsack.KnapsackInstance">KnapsackInstance</a></li>
|
||||
<li><a title="miplearn.instance.Instance" href="../instance.html#miplearn.instance.Instance">Instance</a></li>
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Inherited members</h3>
|
||||
<ul class="hlist">
|
||||
<li><code><b><a title="miplearn.problems.knapsack.KnapsackInstance" href="#miplearn.problems.knapsack.KnapsackInstance">KnapsackInstance</a></b></code>:
|
||||
<ul class="hlist">
|
||||
<li><code><a title="miplearn.problems.knapsack.KnapsackInstance.build_lazy_constraint" href="../instance.html#miplearn.instance.Instance.build_lazy_constraint">build_lazy_constraint</a></code></li>
|
||||
<li><code><a title="miplearn.problems.knapsack.KnapsackInstance.find_violated_lazy_constraints" href="../instance.html#miplearn.instance.Instance.find_violated_lazy_constraints">find_violated_lazy_constraints</a></code></li>
|
||||
<li><code><a title="miplearn.problems.knapsack.KnapsackInstance.get_instance_features" href="../instance.html#miplearn.instance.Instance.get_instance_features">get_instance_features</a></code></li>
|
||||
<li><code><a title="miplearn.problems.knapsack.KnapsackInstance.get_variable_category" href="../instance.html#miplearn.instance.Instance.get_variable_category">get_variable_category</a></code></li>
|
||||
<li><code><a title="miplearn.problems.knapsack.KnapsackInstance.get_variable_features" href="../instance.html#miplearn.instance.Instance.get_variable_features">get_variable_features</a></code></li>
|
||||
<li><code><a title="miplearn.problems.knapsack.KnapsackInstance.to_model" href="../instance.html#miplearn.instance.Instance.to_model">to_model</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</dd>
|
||||
<dt id="miplearn.problems.knapsack.KnapsackInstance"><code class="flex name class">
|
||||
<span>class <span class="ident">KnapsackInstance</span></span>
|
||||
<span>(</span><span>weights, prices, capacity)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Simpler (one-dimensional) Knapsack Problem, used for testing.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class KnapsackInstance(Instance):
|
||||
"""
|
||||
Simpler (one-dimensional) Knapsack Problem, used for testing.
|
||||
"""
|
||||
|
||||
def __init__(self, weights, prices, capacity):
|
||||
super().__init__()
|
||||
self.weights = weights
|
||||
self.prices = prices
|
||||
self.capacity = capacity
|
||||
|
||||
def to_model(self):
|
||||
model = pe.ConcreteModel()
|
||||
items = range(len(self.weights))
|
||||
model.x = pe.Var(items, domain=pe.Binary)
|
||||
model.OBJ = pe.Objective(
|
||||
expr=sum(model.x[v] * self.prices[v] for v in items), sense=pe.maximize
|
||||
)
|
||||
model.eq_capacity = pe.Constraint(
|
||||
expr=sum(model.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],
|
||||
]
|
||||
)</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.instance.Instance" href="../instance.html#miplearn.instance.Instance">Instance</a></li>
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Subclasses</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.problems.knapsack.GurobiKnapsackInstance" href="#miplearn.problems.knapsack.GurobiKnapsackInstance">GurobiKnapsackInstance</a></li>
|
||||
</ul>
|
||||
<h3>Inherited members</h3>
|
||||
<ul class="hlist">
|
||||
<li><code><b><a title="miplearn.instance.Instance" href="../instance.html#miplearn.instance.Instance">Instance</a></b></code>:
|
||||
<ul class="hlist">
|
||||
<li><code><a title="miplearn.instance.Instance.build_lazy_constraint" href="../instance.html#miplearn.instance.Instance.build_lazy_constraint">build_lazy_constraint</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.find_violated_lazy_constraints" href="../instance.html#miplearn.instance.Instance.find_violated_lazy_constraints">find_violated_lazy_constraints</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.get_instance_features" href="../instance.html#miplearn.instance.Instance.get_instance_features">get_instance_features</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.get_variable_category" href="../instance.html#miplearn.instance.Instance.get_variable_category">get_variable_category</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.get_variable_features" href="../instance.html#miplearn.instance.Instance.get_variable_features">get_variable_features</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.to_model" href="../instance.html#miplearn.instance.Instance.to_model">to_model</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</dd>
|
||||
<dt id="miplearn.problems.knapsack.MultiKnapsackGenerator"><code class="flex name class">
|
||||
<span>class <span class="ident">MultiKnapsackGenerator</span></span>
|
||||
<span>(</span><span>n=<scipy.stats._distn_infrastructure.rv_frozen object>, m=<scipy.stats._distn_infrastructure.rv_frozen object>, w=<scipy.stats._distn_infrastructure.rv_frozen object>, K=<scipy.stats._distn_infrastructure.rv_frozen object>, u=<scipy.stats._distn_infrastructure.rv_frozen object>, alpha=<scipy.stats._distn_infrastructure.rv_frozen object>, fix_w=False, w_jitter=<scipy.stats._distn_infrastructure.rv_frozen object>, round=True)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Initialize the problem generator.</p>
|
||||
<p>Instances have a random number of items (or variables) and a random number of knapsacks
|
||||
(or constraints), as specified by the provided probability distributions <code>n</code> and <code>m</code>,
|
||||
respectively. The weight of each item <code>i</code> on knapsack <code>j</code> is sampled independently from
|
||||
the provided distribution <code>w</code>. The capacity of knapsack <code>j</code> is set to:</p>
|
||||
<pre><code>alpha_j * sum(w[i,j] for i in range(n)),
|
||||
</code></pre>
|
||||
<p>where <code>alpha_j</code>, the tightness ratio, is sampled from the provided probability
|
||||
distribution <code>alpha</code>. To make the instances more challenging, the costs of the items
|
||||
are linearly correlated to their average weights. More specifically, the weight of each
|
||||
item <code>i</code> is set to:</p>
|
||||
<pre><code>sum(w[i,j]/m for j in range(m)) + K * u_i,
|
||||
</code></pre>
|
||||
<p>where <code>K</code>, the correlation coefficient, and <code>u_i</code>, the correlation multiplier, are sampled
|
||||
from the provided probability distributions. Note that <code>K</code> is only sample once for the
|
||||
entire instance.</p>
|
||||
<p>If fix_w=True is provided, then w[i,j] are kept the same in all generated instances. This
|
||||
also implies that n and m are kept fixed. Although the prices and capacities are derived
|
||||
from w[i,j], as long as u and K are not constants, the generated instances will still not
|
||||
be completely identical.</p>
|
||||
<p>If a probability distribution w_jitter is provided, then item weights will be set to
|
||||
w[i,j] * gamma[i,j] where gamma[i,j] is sampled from w_jitter. When combined with
|
||||
fix_w=True, this argument may be used to generate instances where the weight of each item
|
||||
is roughly the same, but not exactly identical, across all instances. The prices of the
|
||||
items and the capacities of the knapsacks will be calculated as above, but using these
|
||||
perturbed weights instead.</p>
|
||||
<p>By default, all generated prices, weights and capacities are rounded to the nearest integer
|
||||
number. If <code>round=False</code> is provided, this rounding will be disabled.</p>
|
||||
<h2 id="parameters">Parameters</h2>
|
||||
<dl>
|
||||
<dt><strong><code>n</code></strong> : <code>rv_discrete</code></dt>
|
||||
<dd>Probability distribution for the number of items (or variables)</dd>
|
||||
<dt><strong><code>m</code></strong> : <code>rv_discrete</code></dt>
|
||||
<dd>Probability distribution for the number of knapsacks (or constraints)</dd>
|
||||
<dt><strong><code>w</code></strong> : <code>rv_continuous</code></dt>
|
||||
<dd>Probability distribution for the item weights</dd>
|
||||
<dt><strong><code>K</code></strong> : <code>rv_continuous</code></dt>
|
||||
<dd>Probability distribution for the profit correlation coefficient</dd>
|
||||
<dt><strong><code>u</code></strong> : <code>rv_continuous</code></dt>
|
||||
<dd>Probability distribution for the profit multiplier</dd>
|
||||
<dt><strong><code>alpha</code></strong> : <code>rv_continuous</code></dt>
|
||||
<dd>Probability distribution for the tightness ratio</dd>
|
||||
<dt><strong><code>fix_w</code></strong> : <code>boolean</code></dt>
|
||||
<dd>If true, weights are kept the same (minus the noise from w_jitter) in all instances</dd>
|
||||
<dt><strong><code>w_jitter</code></strong> : <code>rv_continuous</code></dt>
|
||||
<dd>Probability distribution for random noise added to the weights</dd>
|
||||
<dt><strong><code>round</code></strong> : <code>boolean</code></dt>
|
||||
<dd>If true, all prices, weights and capacities are rounded to the nearest integer</dd>
|
||||
</dl></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class MultiKnapsackGenerator:
|
||||
def __init__(
|
||||
self,
|
||||
n=randint(low=100, high=101),
|
||||
m=randint(low=30, high=31),
|
||||
w=randint(low=0, high=1000),
|
||||
K=randint(low=500, high=500),
|
||||
u=uniform(loc=0.0, scale=1.0),
|
||||
alpha=uniform(loc=0.25, scale=0.0),
|
||||
fix_w=False,
|
||||
w_jitter=uniform(loc=1.0, scale=0.0),
|
||||
round=True,
|
||||
):
|
||||
"""Initialize the problem generator.
|
||||
|
||||
Instances have a random number of items (or variables) and a random number of knapsacks
|
||||
(or constraints), as specified by the provided probability distributions `n` and `m`,
|
||||
respectively. The weight of each item `i` on knapsack `j` is sampled independently from
|
||||
the provided distribution `w`. The capacity of knapsack `j` is set to:
|
||||
|
||||
alpha_j * sum(w[i,j] for i in range(n)),
|
||||
|
||||
where `alpha_j`, the tightness ratio, is sampled from the provided probability
|
||||
distribution `alpha`. To make the instances more challenging, the costs of the items
|
||||
are linearly correlated to their average weights. More specifically, the weight of each
|
||||
item `i` is set to:
|
||||
|
||||
sum(w[i,j]/m for j in range(m)) + K * u_i,
|
||||
|
||||
where `K`, the correlation coefficient, and `u_i`, the correlation multiplier, are sampled
|
||||
from the provided probability distributions. Note that `K` is only sample once for the
|
||||
entire instance.
|
||||
|
||||
If fix_w=True is provided, then w[i,j] are kept the same in all generated instances. This
|
||||
also implies that n and m are kept fixed. Although the prices and capacities are derived
|
||||
from w[i,j], as long as u and K are not constants, the generated instances will still not
|
||||
be completely identical.
|
||||
|
||||
If a probability distribution w_jitter is provided, then item weights will be set to
|
||||
w[i,j] * gamma[i,j] where gamma[i,j] is sampled from w_jitter. When combined with
|
||||
fix_w=True, this argument may be used to generate instances where the weight of each item
|
||||
is roughly the same, but not exactly identical, across all instances. The prices of the
|
||||
items and the capacities of the knapsacks will be calculated as above, but using these
|
||||
perturbed weights instead.
|
||||
|
||||
By default, all generated prices, weights and capacities are rounded to the nearest integer
|
||||
number. If `round=False` is provided, this rounding will be disabled.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
n: rv_discrete
|
||||
Probability distribution for the number of items (or variables)
|
||||
m: rv_discrete
|
||||
Probability distribution for the number of knapsacks (or constraints)
|
||||
w: rv_continuous
|
||||
Probability distribution for the item weights
|
||||
K: rv_continuous
|
||||
Probability distribution for the profit correlation coefficient
|
||||
u: rv_continuous
|
||||
Probability distribution for the profit multiplier
|
||||
alpha: rv_continuous
|
||||
Probability distribution for the tightness ratio
|
||||
fix_w: boolean
|
||||
If true, weights are kept the same (minus the noise from w_jitter) in all instances
|
||||
w_jitter: rv_continuous
|
||||
Probability distribution for random noise added to the weights
|
||||
round: boolean
|
||||
If true, all prices, weights and capacities are rounded to the nearest integer
|
||||
"""
|
||||
assert isinstance(n, rv_frozen), "n should be a SciPy probability distribution"
|
||||
assert isinstance(m, rv_frozen), "m should be a SciPy probability distribution"
|
||||
assert isinstance(w, rv_frozen), "w should be a SciPy probability distribution"
|
||||
assert isinstance(K, rv_frozen), "K should be a SciPy probability distribution"
|
||||
assert isinstance(u, rv_frozen), "u should be a SciPy probability distribution"
|
||||
assert isinstance(
|
||||
alpha, rv_frozen
|
||||
), "alpha should be a SciPy probability distribution"
|
||||
assert isinstance(fix_w, bool), "fix_w should be boolean"
|
||||
assert isinstance(
|
||||
w_jitter, rv_frozen
|
||||
), "w_jitter should be a SciPy probability distribution"
|
||||
|
||||
self.n = n
|
||||
self.m = m
|
||||
self.w = w
|
||||
self.K = K
|
||||
self.u = u
|
||||
self.alpha = alpha
|
||||
self.w_jitter = w_jitter
|
||||
self.round = round
|
||||
|
||||
if fix_w:
|
||||
self.fix_n = self.n.rvs()
|
||||
self.fix_m = self.m.rvs()
|
||||
self.fix_w = np.array([self.w.rvs(self.fix_n) for _ in range(self.fix_m)])
|
||||
self.fix_u = self.u.rvs(self.fix_n)
|
||||
self.fix_K = self.K.rvs()
|
||||
else:
|
||||
self.fix_n = None
|
||||
self.fix_m = None
|
||||
self.fix_w = None
|
||||
self.fix_u = None
|
||||
self.fix_K = None
|
||||
|
||||
def generate(self, n_samples):
|
||||
def _sample():
|
||||
if self.fix_w is not None:
|
||||
n = self.fix_n
|
||||
m = self.fix_m
|
||||
w = self.fix_w
|
||||
u = self.fix_u
|
||||
K = self.fix_K
|
||||
else:
|
||||
n = self.n.rvs()
|
||||
m = self.m.rvs()
|
||||
w = np.array([self.w.rvs(n) for _ in range(m)])
|
||||
u = self.u.rvs(n)
|
||||
K = self.K.rvs()
|
||||
w = w * np.array([self.w_jitter.rvs(n) for _ in range(m)])
|
||||
alpha = self.alpha.rvs(m)
|
||||
p = np.array([w[:, j].sum() / m + K * u[j] for j in range(n)])
|
||||
b = np.array([w[i, :].sum() * alpha[i] for i in range(m)])
|
||||
if self.round:
|
||||
p = p.round()
|
||||
b = b.round()
|
||||
w = w.round()
|
||||
return MultiKnapsackInstance(p, b, w)
|
||||
|
||||
return [_sample() for _ in range(n_samples)]</code></pre>
|
||||
</details>
|
||||
<h3>Methods</h3>
|
||||
<dl>
|
||||
<dt id="miplearn.problems.knapsack.MultiKnapsackGenerator.generate"><code class="name flex">
|
||||
<span>def <span class="ident">generate</span></span>(<span>self, n_samples)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def generate(self, n_samples):
|
||||
def _sample():
|
||||
if self.fix_w is not None:
|
||||
n = self.fix_n
|
||||
m = self.fix_m
|
||||
w = self.fix_w
|
||||
u = self.fix_u
|
||||
K = self.fix_K
|
||||
else:
|
||||
n = self.n.rvs()
|
||||
m = self.m.rvs()
|
||||
w = np.array([self.w.rvs(n) for _ in range(m)])
|
||||
u = self.u.rvs(n)
|
||||
K = self.K.rvs()
|
||||
w = w * np.array([self.w_jitter.rvs(n) for _ in range(m)])
|
||||
alpha = self.alpha.rvs(m)
|
||||
p = np.array([w[:, j].sum() / m + K * u[j] for j in range(n)])
|
||||
b = np.array([w[i, :].sum() * alpha[i] for i in range(m)])
|
||||
if self.round:
|
||||
p = p.round()
|
||||
b = b.round()
|
||||
w = w.round()
|
||||
return MultiKnapsackInstance(p, b, w)
|
||||
|
||||
return [_sample() for _ in range(n_samples)]</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd>
|
||||
<dt id="miplearn.problems.knapsack.MultiKnapsackInstance"><code class="flex name class">
|
||||
<span>class <span class="ident">MultiKnapsackInstance</span></span>
|
||||
<span>(</span><span>prices, capacities, weights)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Representation of the Multidimensional 0-1 Knapsack Problem.</p>
|
||||
<p>Given a set of n items and m knapsacks, the problem is to find a subset of items S maximizing
|
||||
sum(prices[i] for i in S). If selected, each item i occupies weights[i,j] units of space in
|
||||
each knapsack j. Furthermore, each knapsack j has limited storage space, given by capacities[j].</p>
|
||||
<p>This implementation assigns a different category for each decision variable, and therefore
|
||||
trains one ML model per variable. It is only suitable when training and test instances have
|
||||
same size and items don't shuffle around.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class MultiKnapsackInstance(Instance):
|
||||
"""Representation of the Multidimensional 0-1 Knapsack Problem.
|
||||
|
||||
Given a set of n items and m knapsacks, the problem is to find a subset of items S maximizing
|
||||
sum(prices[i] for i in S). If selected, each item i occupies weights[i,j] units of space in
|
||||
each knapsack j. Furthermore, each knapsack j has limited storage space, given by capacities[j].
|
||||
|
||||
This implementation assigns a different category for each decision variable, and therefore
|
||||
trains one ML model per variable. It is only suitable when training and test instances have
|
||||
same size and items don't shuffle around.
|
||||
"""
|
||||
|
||||
def __init__(self, prices, capacities, weights):
|
||||
super().__init__()
|
||||
assert isinstance(prices, np.ndarray)
|
||||
assert isinstance(capacities, np.ndarray)
|
||||
assert isinstance(weights, np.ndarray)
|
||||
assert len(weights.shape) == 2
|
||||
self.m, self.n = weights.shape
|
||||
assert prices.shape == (self.n,)
|
||||
assert capacities.shape == (self.m,)
|
||||
self.prices = prices
|
||||
self.capacities = capacities
|
||||
self.weights = weights
|
||||
|
||||
def to_model(self):
|
||||
model = pe.ConcreteModel()
|
||||
model.x = pe.Var(range(self.n), domain=pe.Binary)
|
||||
model.OBJ = pe.Objective(
|
||||
rule=lambda model: sum(model.x[j] * self.prices[j] for j in range(self.n)),
|
||||
sense=pe.maximize,
|
||||
)
|
||||
model.eq_capacity = pe.ConstraintList()
|
||||
for i in range(self.m):
|
||||
model.eq_capacity.add(
|
||||
sum(model.x[j] * self.weights[i, j] for j in range(self.n))
|
||||
<= self.capacities[i]
|
||||
)
|
||||
|
||||
return model
|
||||
|
||||
def get_instance_features(self):
|
||||
return np.hstack(
|
||||
[
|
||||
np.mean(self.prices),
|
||||
self.capacities,
|
||||
]
|
||||
)
|
||||
|
||||
def get_variable_features(self, var, index):
|
||||
return np.hstack(
|
||||
[
|
||||
self.prices[index],
|
||||
self.weights[:, index],
|
||||
]
|
||||
)</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.instance.Instance" href="../instance.html#miplearn.instance.Instance">Instance</a></li>
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Inherited members</h3>
|
||||
<ul class="hlist">
|
||||
<li><code><b><a title="miplearn.instance.Instance" href="../instance.html#miplearn.instance.Instance">Instance</a></b></code>:
|
||||
<ul class="hlist">
|
||||
<li><code><a title="miplearn.instance.Instance.build_lazy_constraint" href="../instance.html#miplearn.instance.Instance.build_lazy_constraint">build_lazy_constraint</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.find_violated_lazy_constraints" href="../instance.html#miplearn.instance.Instance.find_violated_lazy_constraints">find_violated_lazy_constraints</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.get_instance_features" href="../instance.html#miplearn.instance.Instance.get_instance_features">get_instance_features</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.get_variable_category" href="../instance.html#miplearn.instance.Instance.get_variable_category">get_variable_category</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.get_variable_features" href="../instance.html#miplearn.instance.Instance.get_variable_features">get_variable_features</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.to_model" href="../instance.html#miplearn.instance.Instance.to_model">to_model</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.problems" href="index.html">miplearn.problems</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||||
<ul>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.problems.knapsack.ChallengeA" href="#miplearn.problems.knapsack.ChallengeA">ChallengeA</a></code></h4>
|
||||
</li>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.problems.knapsack.GurobiKnapsackInstance" href="#miplearn.problems.knapsack.GurobiKnapsackInstance">GurobiKnapsackInstance</a></code></h4>
|
||||
</li>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.problems.knapsack.KnapsackInstance" href="#miplearn.problems.knapsack.KnapsackInstance">KnapsackInstance</a></code></h4>
|
||||
</li>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.problems.knapsack.MultiKnapsackGenerator" href="#miplearn.problems.knapsack.MultiKnapsackGenerator">MultiKnapsackGenerator</a></code></h4>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.problems.knapsack.MultiKnapsackGenerator.generate" href="#miplearn.problems.knapsack.MultiKnapsackGenerator.generate">generate</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.problems.knapsack.MultiKnapsackInstance" href="#miplearn.problems.knapsack.MultiKnapsackInstance">MultiKnapsackInstance</a></code></h4>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
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|
||||
<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.item .name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul{padding-left:1.5em}.toc > ul > li{margin-top:.5em}}</style>
|
||||
<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
|
||||
</head>
|
||||
<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.problems.stab</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># 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 networkx as nx
|
||||
import numpy as np
|
||||
import pyomo.environ as pe
|
||||
from scipy.stats import uniform, randint
|
||||
from scipy.stats.distributions import rv_frozen
|
||||
|
||||
from miplearn.instance import Instance
|
||||
|
||||
|
||||
class ChallengeA:
|
||||
def __init__(
|
||||
self,
|
||||
seed=42,
|
||||
n_training_instances=500,
|
||||
n_test_instances=50,
|
||||
):
|
||||
|
||||
np.random.seed(seed)
|
||||
self.generator = MaxWeightStableSetGenerator(
|
||||
w=uniform(loc=100.0, scale=50.0),
|
||||
n=randint(low=200, high=201),
|
||||
p=uniform(loc=0.05, scale=0.0),
|
||||
fix_graph=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 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
|
||||
Erdős-Rényi graph $G_{n,p}$ is generated in the constructor, where $n$ and $p$ are sampled
|
||||
from user-provided probability distributions `n` and `p`. To 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
|
||||
parameters are sampled in the same way.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
w=uniform(loc=10.0, scale=1.0),
|
||||
n=randint(low=250, high=251),
|
||||
p=uniform(loc=0.05, scale=0.0),
|
||||
fix_graph=True,
|
||||
):
|
||||
"""Initialize the problem generator.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
w: rv_continuous
|
||||
Probability distribution for vertex weights.
|
||||
n: rv_discrete
|
||||
Probability distribution for parameter $n$ in Erdős-Rényi model.
|
||||
p: rv_continuous
|
||||
Probability distribution for parameter $p$ in Erdős-Rényi model.
|
||||
"""
|
||||
assert isinstance(w, rv_frozen), "w should be a SciPy probability distribution"
|
||||
assert isinstance(n, rv_frozen), "n should be a SciPy probability distribution"
|
||||
assert isinstance(p, rv_frozen), "p should be a SciPy probability distribution"
|
||||
self.w = w
|
||||
self.n = n
|
||||
self.p = p
|
||||
self.fix_graph = fix_graph
|
||||
self.graph = None
|
||||
if fix_graph:
|
||||
self.graph = self._generate_graph()
|
||||
|
||||
def generate(self, n_samples):
|
||||
def _sample():
|
||||
if self.graph is not None:
|
||||
graph = self.graph
|
||||
else:
|
||||
graph = self._generate_graph()
|
||||
weights = self.w.rvs(graph.number_of_nodes())
|
||||
return MaxWeightStableSetInstance(graph, weights)
|
||||
|
||||
return [_sample() for _ in range(n_samples)]
|
||||
|
||||
def _generate_graph(self):
|
||||
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
|
||||
|
||||
def to_model(self):
|
||||
nodes = list(self.graph.nodes)
|
||||
model = pe.ConcreteModel()
|
||||
model.x = pe.Var(nodes, domain=pe.Binary)
|
||||
model.OBJ = pe.Objective(
|
||||
expr=sum(model.x[v] * self.weights[v] for v in nodes), sense=pe.maximize
|
||||
)
|
||||
model.clique_eqs = pe.ConstraintList()
|
||||
for clique in nx.find_cliques(self.graph):
|
||||
model.clique_eqs.add(sum(model.x[i] for i in clique) <= 1)
|
||||
return model
|
||||
|
||||
def get_instance_features(self):
|
||||
return np.ones(0)
|
||||
|
||||
def get_variable_features(self, var, index):
|
||||
neighbor_weights = [0] * 15
|
||||
neighbor_degrees = [100] * 15
|
||||
for n in self.graph.neighbors(index):
|
||||
neighbor_weights += [self.weights[n] / self.weights[index]]
|
||||
neighbor_degrees += [self.graph.degree(n) / self.graph.degree(index)]
|
||||
neighbor_weights.sort(reverse=True)
|
||||
neighbor_degrees.sort()
|
||||
features = []
|
||||
features += neighbor_weights[:5]
|
||||
features += neighbor_degrees[:5]
|
||||
features += [self.graph.degree(index)]
|
||||
return np.array(features)
|
||||
|
||||
def get_variable_category(self, var, index):
|
||||
return "default"</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-classes">Classes</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.problems.stab.ChallengeA"><code class="flex name class">
|
||||
<span>class <span class="ident">ChallengeA</span></span>
|
||||
<span>(</span><span>seed=42, n_training_instances=500, n_test_instances=50)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class ChallengeA:
|
||||
def __init__(
|
||||
self,
|
||||
seed=42,
|
||||
n_training_instances=500,
|
||||
n_test_instances=50,
|
||||
):
|
||||
|
||||
np.random.seed(seed)
|
||||
self.generator = MaxWeightStableSetGenerator(
|
||||
w=uniform(loc=100.0, scale=50.0),
|
||||
n=randint(low=200, high=201),
|
||||
p=uniform(loc=0.05, scale=0.0),
|
||||
fix_graph=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)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.problems.stab.MaxWeightStableSetGenerator"><code class="flex name class">
|
||||
<span>class <span class="ident">MaxWeightStableSetGenerator</span></span>
|
||||
<span>(</span><span>w=<scipy.stats._distn_infrastructure.rv_frozen object>, n=<scipy.stats._distn_infrastructure.rv_frozen object>, p=<scipy.stats._distn_infrastructure.rv_frozen object>, fix_graph=True)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Random instance generator for the Maximum-Weight Stable Set Problem.</p>
|
||||
<p>The generator has two modes of operation. When <code>fix_graph=True</code> is provided, one random
|
||||
Erdős-Rényi graph $G_{n,p}$ is generated in the constructor, where $n$ and $p$ are sampled
|
||||
from user-provided probability distributions <code>n</code> and <code>p</code>. To generate each instance, the
|
||||
generator independently samples each $w_v$ from the user-provided probability distribution <code>w</code>.</p>
|
||||
<p>When <code>fix_graph=False</code>, a new random graph is generated for each instance; the remaining
|
||||
parameters are sampled in the same way.</p>
|
||||
<p>Initialize the problem generator.</p>
|
||||
<h2 id="parameters">Parameters</h2>
|
||||
<dl>
|
||||
<dt><strong><code>w</code></strong> : <code>rv_continuous</code></dt>
|
||||
<dd>Probability distribution for vertex weights.</dd>
|
||||
<dt><strong><code>n</code></strong> : <code>rv_discrete</code></dt>
|
||||
<dd>Probability distribution for parameter $n$ in Erdős-Rényi model.</dd>
|
||||
<dt><strong><code>p</code></strong> : <code>rv_continuous</code></dt>
|
||||
<dd>Probability distribution for parameter $p$ in Erdős-Rényi model.</dd>
|
||||
</dl></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">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
|
||||
Erdős-Rényi graph $G_{n,p}$ is generated in the constructor, where $n$ and $p$ are sampled
|
||||
from user-provided probability distributions `n` and `p`. To 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
|
||||
parameters are sampled in the same way.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
w=uniform(loc=10.0, scale=1.0),
|
||||
n=randint(low=250, high=251),
|
||||
p=uniform(loc=0.05, scale=0.0),
|
||||
fix_graph=True,
|
||||
):
|
||||
"""Initialize the problem generator.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
w: rv_continuous
|
||||
Probability distribution for vertex weights.
|
||||
n: rv_discrete
|
||||
Probability distribution for parameter $n$ in Erdős-Rényi model.
|
||||
p: rv_continuous
|
||||
Probability distribution for parameter $p$ in Erdős-Rényi model.
|
||||
"""
|
||||
assert isinstance(w, rv_frozen), "w should be a SciPy probability distribution"
|
||||
assert isinstance(n, rv_frozen), "n should be a SciPy probability distribution"
|
||||
assert isinstance(p, rv_frozen), "p should be a SciPy probability distribution"
|
||||
self.w = w
|
||||
self.n = n
|
||||
self.p = p
|
||||
self.fix_graph = fix_graph
|
||||
self.graph = None
|
||||
if fix_graph:
|
||||
self.graph = self._generate_graph()
|
||||
|
||||
def generate(self, n_samples):
|
||||
def _sample():
|
||||
if self.graph is not None:
|
||||
graph = self.graph
|
||||
else:
|
||||
graph = self._generate_graph()
|
||||
weights = self.w.rvs(graph.number_of_nodes())
|
||||
return MaxWeightStableSetInstance(graph, weights)
|
||||
|
||||
return [_sample() for _ in range(n_samples)]
|
||||
|
||||
def _generate_graph(self):
|
||||
return nx.generators.random_graphs.binomial_graph(self.n.rvs(), self.p.rvs())</code></pre>
|
||||
</details>
|
||||
<h3>Methods</h3>
|
||||
<dl>
|
||||
<dt id="miplearn.problems.stab.MaxWeightStableSetGenerator.generate"><code class="name flex">
|
||||
<span>def <span class="ident">generate</span></span>(<span>self, n_samples)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def generate(self, n_samples):
|
||||
def _sample():
|
||||
if self.graph is not None:
|
||||
graph = self.graph
|
||||
else:
|
||||
graph = self._generate_graph()
|
||||
weights = self.w.rvs(graph.number_of_nodes())
|
||||
return MaxWeightStableSetInstance(graph, weights)
|
||||
|
||||
return [_sample() for _ in range(n_samples)]</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd>
|
||||
<dt id="miplearn.problems.stab.MaxWeightStableSetInstance"><code class="flex name class">
|
||||
<span>class <span class="ident">MaxWeightStableSetInstance</span></span>
|
||||
<span>(</span><span>graph, weights)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>An instance of the Maximum-Weight Stable Set Problem.</p>
|
||||
<p>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.</p>
|
||||
<p>This is one of Karp's 21 NP-complete problems.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">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
|
||||
|
||||
def to_model(self):
|
||||
nodes = list(self.graph.nodes)
|
||||
model = pe.ConcreteModel()
|
||||
model.x = pe.Var(nodes, domain=pe.Binary)
|
||||
model.OBJ = pe.Objective(
|
||||
expr=sum(model.x[v] * self.weights[v] for v in nodes), sense=pe.maximize
|
||||
)
|
||||
model.clique_eqs = pe.ConstraintList()
|
||||
for clique in nx.find_cliques(self.graph):
|
||||
model.clique_eqs.add(sum(model.x[i] for i in clique) <= 1)
|
||||
return model
|
||||
|
||||
def get_instance_features(self):
|
||||
return np.ones(0)
|
||||
|
||||
def get_variable_features(self, var, index):
|
||||
neighbor_weights = [0] * 15
|
||||
neighbor_degrees = [100] * 15
|
||||
for n in self.graph.neighbors(index):
|
||||
neighbor_weights += [self.weights[n] / self.weights[index]]
|
||||
neighbor_degrees += [self.graph.degree(n) / self.graph.degree(index)]
|
||||
neighbor_weights.sort(reverse=True)
|
||||
neighbor_degrees.sort()
|
||||
features = []
|
||||
features += neighbor_weights[:5]
|
||||
features += neighbor_degrees[:5]
|
||||
features += [self.graph.degree(index)]
|
||||
return np.array(features)
|
||||
|
||||
def get_variable_category(self, var, index):
|
||||
return "default"</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.instance.Instance" href="../instance.html#miplearn.instance.Instance">Instance</a></li>
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Inherited members</h3>
|
||||
<ul class="hlist">
|
||||
<li><code><b><a title="miplearn.instance.Instance" href="../instance.html#miplearn.instance.Instance">Instance</a></b></code>:
|
||||
<ul class="hlist">
|
||||
<li><code><a title="miplearn.instance.Instance.build_lazy_constraint" href="../instance.html#miplearn.instance.Instance.build_lazy_constraint">build_lazy_constraint</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.find_violated_lazy_constraints" href="../instance.html#miplearn.instance.Instance.find_violated_lazy_constraints">find_violated_lazy_constraints</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.get_instance_features" href="../instance.html#miplearn.instance.Instance.get_instance_features">get_instance_features</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.get_variable_category" href="../instance.html#miplearn.instance.Instance.get_variable_category">get_variable_category</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.get_variable_features" href="../instance.html#miplearn.instance.Instance.get_variable_features">get_variable_features</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.to_model" href="../instance.html#miplearn.instance.Instance.to_model">to_model</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.problems" href="index.html">miplearn.problems</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||||
<ul>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.problems.stab.ChallengeA" href="#miplearn.problems.stab.ChallengeA">ChallengeA</a></code></h4>
|
||||
</li>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.problems.stab.MaxWeightStableSetGenerator" href="#miplearn.problems.stab.MaxWeightStableSetGenerator">MaxWeightStableSetGenerator</a></code></h4>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.problems.stab.MaxWeightStableSetGenerator.generate" href="#miplearn.problems.stab.MaxWeightStableSetGenerator.generate">generate</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.problems.stab.MaxWeightStableSetInstance" href="#miplearn.problems.stab.MaxWeightStableSetInstance">MaxWeightStableSetInstance</a></code></h4>
|
||||
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|
||||
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|
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|
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<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># 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.</code></pre>
|
||||
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|
||||
<h2 class="section-title" id="header-submodules">Sub-modules</h2>
|
||||
<dl>
|
||||
<dt><code class="name"><a title="miplearn.problems.tests.test_knapsack" href="test_knapsack.html">miplearn.problems.tests.test_knapsack</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.problems.tests.test_stab" href="test_stab.html">miplearn.problems.tests.test_stab</a></code></dt>
|
||||
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|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.problems.tests.test_tsp" href="test_tsp.html">miplearn.problems.tests.test_tsp</a></code></dt>
|
||||
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|
||||
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|
||||
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|
||||
<li><code><a title="miplearn.problems" href="../index.html">miplearn.problems</a></code></li>
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||||
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|
||||
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|
||||
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|
||||
<li><code><a title="miplearn.problems.tests.test_knapsack" href="test_knapsack.html">miplearn.problems.tests.test_knapsack</a></code></li>
|
||||
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|
||||
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115
0.2/api/miplearn/problems/tests/test_knapsack.html
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|
||||
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||||
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|
||||
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|
||||
<h1 class="title">Module <code>miplearn.problems.tests.test_knapsack</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># 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
|
||||
from scipy.stats import uniform, randint
|
||||
|
||||
from miplearn.problems.knapsack import MultiKnapsackGenerator
|
||||
|
||||
|
||||
def test_knapsack_generator():
|
||||
gen = MultiKnapsackGenerator(
|
||||
n=randint(low=100, high=101),
|
||||
m=randint(low=30, high=31),
|
||||
w=randint(low=0, high=1000),
|
||||
K=randint(low=500, high=501),
|
||||
u=uniform(loc=1.0, scale=1.0),
|
||||
alpha=uniform(loc=0.50, scale=0.0),
|
||||
)
|
||||
instances = gen.generate(100)
|
||||
w_sum = sum(instance.weights for instance in instances) / len(instances)
|
||||
b_sum = sum(instance.capacities for instance in instances) / len(instances)
|
||||
assert round(np.mean(w_sum), -1) == 500.0
|
||||
assert round(np.mean(b_sum), -3) == 25000.0</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-functions">Functions</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.problems.tests.test_knapsack.test_knapsack_generator"><code class="name flex">
|
||||
<span>def <span class="ident">test_knapsack_generator</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_knapsack_generator():
|
||||
gen = MultiKnapsackGenerator(
|
||||
n=randint(low=100, high=101),
|
||||
m=randint(low=30, high=31),
|
||||
w=randint(low=0, high=1000),
|
||||
K=randint(low=500, high=501),
|
||||
u=uniform(loc=1.0, scale=1.0),
|
||||
alpha=uniform(loc=0.50, scale=0.0),
|
||||
)
|
||||
instances = gen.generate(100)
|
||||
w_sum = sum(instance.weights for instance in instances) / len(instances)
|
||||
b_sum = sum(instance.capacities for instance in instances) / len(instances)
|
||||
assert round(np.mean(w_sum), -1) == 500.0
|
||||
assert round(np.mean(b_sum), -3) == 25000.0</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
</article>
|
||||
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|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.problems.tests" href="index.html">miplearn.problems.tests</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.problems.tests.test_knapsack.test_knapsack_generator" href="#miplearn.problems.tests.test_knapsack.test_knapsack_generator">test_knapsack_generator</a></code></li>
|
||||
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|
||||
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191
0.2/api/miplearn/problems/tests/test_stab.html
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191
0.2/api/miplearn/problems/tests/test_stab.html
Normal file
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<style>.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:30px;overflow:hidden}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:1em 0 .50em 0}h3{font-size:1.4em;margin:25px 0 10px 0}h4{margin:0;font-size:105%}a{color:#058;text-decoration:none;transition:color .3s ease-in-out}a:hover{color:#e82}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900}pre code{background:#f8f8f8;font-size:.8em;line-height:1.4em}code{background:#f2f2f1;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{background:#f8f8f8;border:0;border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0;padding:1ex}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{font-weight:bold}#index h4 + ul{margin-bottom:.6em}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-weight:bold;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source summary,.git-link-div{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase}.source summary > *{white-space:nowrap;cursor:pointer}.git-link{color:inherit;margin-left:1em}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}.admonition{padding:.1em .5em;margin-bottom:1em}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style>
|
||||
<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.item .name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul{padding-left:1.5em}.toc > ul > li{margin-top:.5em}}</style>
|
||||
<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
|
||||
</head>
|
||||
<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.problems.tests.test_stab</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># 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 networkx as nx
|
||||
import numpy as np
|
||||
from scipy.stats import uniform, randint
|
||||
|
||||
from miplearn.problems.stab import MaxWeightStableSetInstance
|
||||
from miplearn.solvers.learning import LearningSolver
|
||||
|
||||
|
||||
def test_stab():
|
||||
graph = nx.cycle_graph(5)
|
||||
weights = [1.0, 1.0, 1.0, 1.0, 1.0]
|
||||
instance = MaxWeightStableSetInstance(graph, weights)
|
||||
solver = LearningSolver()
|
||||
stats = solver.solve(instance)
|
||||
assert stats["Lower bound"] == 2.0
|
||||
|
||||
|
||||
def test_stab_generator_fixed_graph():
|
||||
np.random.seed(42)
|
||||
from miplearn.problems.stab import MaxWeightStableSetGenerator
|
||||
|
||||
gen = MaxWeightStableSetGenerator(
|
||||
w=uniform(loc=50.0, scale=10.0),
|
||||
n=randint(low=10, high=11),
|
||||
p=uniform(loc=0.05, scale=0.0),
|
||||
fix_graph=True,
|
||||
)
|
||||
instances = gen.generate(1_000)
|
||||
weights = np.array([instance.weights for instance in instances])
|
||||
weights_avg_actual = np.round(np.average(weights, axis=0))
|
||||
weights_avg_expected = [55.0] * 10
|
||||
assert list(weights_avg_actual) == weights_avg_expected
|
||||
|
||||
|
||||
def test_stab_generator_random_graph():
|
||||
np.random.seed(42)
|
||||
from miplearn.problems.stab import MaxWeightStableSetGenerator
|
||||
|
||||
gen = MaxWeightStableSetGenerator(
|
||||
w=uniform(loc=50.0, scale=10.0),
|
||||
n=randint(low=30, high=41),
|
||||
p=uniform(loc=0.5, scale=0.0),
|
||||
fix_graph=False,
|
||||
)
|
||||
instances = gen.generate(1_000)
|
||||
n_nodes = [instance.graph.number_of_nodes() for instance in instances]
|
||||
n_edges = [instance.graph.number_of_edges() for instance in instances]
|
||||
assert np.round(np.mean(n_nodes)) == 35.0
|
||||
assert np.round(np.mean(n_edges), -1) == 300.0</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-functions">Functions</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.problems.tests.test_stab.test_stab"><code class="name flex">
|
||||
<span>def <span class="ident">test_stab</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_stab():
|
||||
graph = nx.cycle_graph(5)
|
||||
weights = [1.0, 1.0, 1.0, 1.0, 1.0]
|
||||
instance = MaxWeightStableSetInstance(graph, weights)
|
||||
solver = LearningSolver()
|
||||
stats = solver.solve(instance)
|
||||
assert stats["Lower bound"] == 2.0</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.problems.tests.test_stab.test_stab_generator_fixed_graph"><code class="name flex">
|
||||
<span>def <span class="ident">test_stab_generator_fixed_graph</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_stab_generator_fixed_graph():
|
||||
np.random.seed(42)
|
||||
from miplearn.problems.stab import MaxWeightStableSetGenerator
|
||||
|
||||
gen = MaxWeightStableSetGenerator(
|
||||
w=uniform(loc=50.0, scale=10.0),
|
||||
n=randint(low=10, high=11),
|
||||
p=uniform(loc=0.05, scale=0.0),
|
||||
fix_graph=True,
|
||||
)
|
||||
instances = gen.generate(1_000)
|
||||
weights = np.array([instance.weights for instance in instances])
|
||||
weights_avg_actual = np.round(np.average(weights, axis=0))
|
||||
weights_avg_expected = [55.0] * 10
|
||||
assert list(weights_avg_actual) == weights_avg_expected</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.problems.tests.test_stab.test_stab_generator_random_graph"><code class="name flex">
|
||||
<span>def <span class="ident">test_stab_generator_random_graph</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_stab_generator_random_graph():
|
||||
np.random.seed(42)
|
||||
from miplearn.problems.stab import MaxWeightStableSetGenerator
|
||||
|
||||
gen = MaxWeightStableSetGenerator(
|
||||
w=uniform(loc=50.0, scale=10.0),
|
||||
n=randint(low=30, high=41),
|
||||
p=uniform(loc=0.5, scale=0.0),
|
||||
fix_graph=False,
|
||||
)
|
||||
instances = gen.generate(1_000)
|
||||
n_nodes = [instance.graph.number_of_nodes() for instance in instances]
|
||||
n_edges = [instance.graph.number_of_edges() for instance in instances]
|
||||
assert np.round(np.mean(n_nodes)) == 35.0
|
||||
assert np.round(np.mean(n_edges), -1) == 300.0</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.problems.tests" href="index.html">miplearn.problems.tests</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.problems.tests.test_stab.test_stab" href="#miplearn.problems.tests.test_stab.test_stab">test_stab</a></code></li>
|
||||
<li><code><a title="miplearn.problems.tests.test_stab.test_stab_generator_fixed_graph" href="#miplearn.problems.tests.test_stab.test_stab_generator_fixed_graph">test_stab_generator_fixed_graph</a></code></li>
|
||||
<li><code><a title="miplearn.problems.tests.test_stab.test_stab_generator_random_graph" href="#miplearn.problems.tests.test_stab.test_stab_generator_random_graph">test_stab_generator_random_graph</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</nav>
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242
0.2/api/miplearn/problems/tests/test_tsp.html
Normal file
242
0.2/api/miplearn/problems/tests/test_tsp.html
Normal file
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|
||||
<!doctype html>
|
||||
<html lang="en">
|
||||
<head>
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<meta charset="utf-8">
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<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
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<title>miplearn.problems.tests.test_tsp API documentation</title>
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<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
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<link href='https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/8.0.0/sanitize.min.css' rel='stylesheet'>
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<link href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/styles/github.min.css" rel="stylesheet">
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<style>.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:30px;overflow:hidden}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:1em 0 .50em 0}h3{font-size:1.4em;margin:25px 0 10px 0}h4{margin:0;font-size:105%}a{color:#058;text-decoration:none;transition:color .3s ease-in-out}a:hover{color:#e82}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900}pre code{background:#f8f8f8;font-size:.8em;line-height:1.4em}code{background:#f2f2f1;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{background:#f8f8f8;border:0;border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0;padding:1ex}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{font-weight:bold}#index h4 + ul{margin-bottom:.6em}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-weight:bold;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source summary,.git-link-div{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase}.source summary > *{white-space:nowrap;cursor:pointer}.git-link{color:inherit;margin-left:1em}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}.admonition{padding:.1em .5em;margin-bottom:1em}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style>
|
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<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.item .name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul{padding-left:1.5em}.toc > ul > li{margin-top:.5em}}</style>
|
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<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
|
||||
</head>
|
||||
<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.problems.tests.test_tsp</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># 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
|
||||
from numpy.linalg import norm
|
||||
from scipy.spatial.distance import pdist, squareform
|
||||
from scipy.stats import uniform, randint
|
||||
|
||||
from miplearn.problems.tsp import TravelingSalesmanGenerator, TravelingSalesmanInstance
|
||||
from miplearn.solvers.learning import LearningSolver
|
||||
|
||||
|
||||
def test_generator():
|
||||
instances = TravelingSalesmanGenerator(
|
||||
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=0.95, scale=0.1),
|
||||
fix_cities=True,
|
||||
).generate(100)
|
||||
assert len(instances) == 100
|
||||
assert instances[0].n_cities == 100
|
||||
assert norm(instances[0].distances - instances[0].distances.T) < 1e-6
|
||||
d = [instance.distances[0, 1] for instance in instances]
|
||||
assert np.std(d) > 0
|
||||
|
||||
|
||||
def test_instance():
|
||||
n_cities = 4
|
||||
distances = np.array(
|
||||
[
|
||||
[0.0, 1.0, 2.0, 1.0],
|
||||
[1.0, 0.0, 1.0, 2.0],
|
||||
[2.0, 1.0, 0.0, 1.0],
|
||||
[1.0, 2.0, 1.0, 0.0],
|
||||
]
|
||||
)
|
||||
instance = TravelingSalesmanInstance(n_cities, distances)
|
||||
solver = LearningSolver()
|
||||
stats = solver.solve(instance)
|
||||
x = instance.training_data[0]["Solution"]["x"]
|
||||
assert x[0, 1] == 1.0
|
||||
assert x[0, 2] == 0.0
|
||||
assert x[0, 3] == 1.0
|
||||
assert x[1, 2] == 1.0
|
||||
assert x[1, 3] == 0.0
|
||||
assert x[2, 3] == 1.0
|
||||
assert stats["Lower bound"] == 4.0
|
||||
assert stats["Upper bound"] == 4.0
|
||||
|
||||
|
||||
def test_subtour():
|
||||
n_cities = 6
|
||||
cities = np.array(
|
||||
[
|
||||
[0.0, 0.0],
|
||||
[1.0, 0.0],
|
||||
[2.0, 0.0],
|
||||
[3.0, 0.0],
|
||||
[0.0, 1.0],
|
||||
[3.0, 1.0],
|
||||
]
|
||||
)
|
||||
distances = squareform(pdist(cities))
|
||||
instance = TravelingSalesmanInstance(n_cities, distances)
|
||||
solver = LearningSolver()
|
||||
solver.solve(instance)
|
||||
assert hasattr(instance, "found_violated_lazy_constraints")
|
||||
assert hasattr(instance, "found_violated_user_cuts")
|
||||
x = instance.training_data[0]["Solution"]["x"]
|
||||
assert x[0, 1] == 1.0
|
||||
assert x[0, 4] == 1.0
|
||||
assert x[1, 2] == 1.0
|
||||
assert x[2, 3] == 1.0
|
||||
assert x[3, 5] == 1.0
|
||||
assert x[4, 5] == 1.0
|
||||
solver.fit([instance])
|
||||
solver.solve(instance)</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-functions">Functions</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.problems.tests.test_tsp.test_generator"><code class="name flex">
|
||||
<span>def <span class="ident">test_generator</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_generator():
|
||||
instances = TravelingSalesmanGenerator(
|
||||
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=0.95, scale=0.1),
|
||||
fix_cities=True,
|
||||
).generate(100)
|
||||
assert len(instances) == 100
|
||||
assert instances[0].n_cities == 100
|
||||
assert norm(instances[0].distances - instances[0].distances.T) < 1e-6
|
||||
d = [instance.distances[0, 1] for instance in instances]
|
||||
assert np.std(d) > 0</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.problems.tests.test_tsp.test_instance"><code class="name flex">
|
||||
<span>def <span class="ident">test_instance</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_instance():
|
||||
n_cities = 4
|
||||
distances = np.array(
|
||||
[
|
||||
[0.0, 1.0, 2.0, 1.0],
|
||||
[1.0, 0.0, 1.0, 2.0],
|
||||
[2.0, 1.0, 0.0, 1.0],
|
||||
[1.0, 2.0, 1.0, 0.0],
|
||||
]
|
||||
)
|
||||
instance = TravelingSalesmanInstance(n_cities, distances)
|
||||
solver = LearningSolver()
|
||||
stats = solver.solve(instance)
|
||||
x = instance.training_data[0]["Solution"]["x"]
|
||||
assert x[0, 1] == 1.0
|
||||
assert x[0, 2] == 0.0
|
||||
assert x[0, 3] == 1.0
|
||||
assert x[1, 2] == 1.0
|
||||
assert x[1, 3] == 0.0
|
||||
assert x[2, 3] == 1.0
|
||||
assert stats["Lower bound"] == 4.0
|
||||
assert stats["Upper bound"] == 4.0</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.problems.tests.test_tsp.test_subtour"><code class="name flex">
|
||||
<span>def <span class="ident">test_subtour</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_subtour():
|
||||
n_cities = 6
|
||||
cities = np.array(
|
||||
[
|
||||
[0.0, 0.0],
|
||||
[1.0, 0.0],
|
||||
[2.0, 0.0],
|
||||
[3.0, 0.0],
|
||||
[0.0, 1.0],
|
||||
[3.0, 1.0],
|
||||
]
|
||||
)
|
||||
distances = squareform(pdist(cities))
|
||||
instance = TravelingSalesmanInstance(n_cities, distances)
|
||||
solver = LearningSolver()
|
||||
solver.solve(instance)
|
||||
assert hasattr(instance, "found_violated_lazy_constraints")
|
||||
assert hasattr(instance, "found_violated_user_cuts")
|
||||
x = instance.training_data[0]["Solution"]["x"]
|
||||
assert x[0, 1] == 1.0
|
||||
assert x[0, 4] == 1.0
|
||||
assert x[1, 2] == 1.0
|
||||
assert x[2, 3] == 1.0
|
||||
assert x[3, 5] == 1.0
|
||||
assert x[4, 5] == 1.0
|
||||
solver.fit([instance])
|
||||
solver.solve(instance)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.problems.tests" href="index.html">miplearn.problems.tests</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.problems.tests.test_tsp.test_generator" href="#miplearn.problems.tests.test_tsp.test_generator">test_generator</a></code></li>
|
||||
<li><code><a title="miplearn.problems.tests.test_tsp.test_instance" href="#miplearn.problems.tests.test_tsp.test_instance">test_instance</a></code></li>
|
||||
<li><code><a title="miplearn.problems.tests.test_tsp.test_subtour" href="#miplearn.problems.tests.test_tsp.test_subtour">test_subtour</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</nav>
|
||||
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<p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.7.0</a>.</p>
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<script>hljs.initHighlightingOnLoad()</script>
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|
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</html>
|
||||
587
0.2/api/miplearn/problems/tsp.html
Normal file
587
0.2/api/miplearn/problems/tsp.html
Normal file
@@ -0,0 +1,587 @@
|
||||
<!doctype html>
|
||||
<html lang="en">
|
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<head>
|
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<meta charset="utf-8">
|
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<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
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<title>miplearn.problems.tsp API documentation</title>
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<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
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|
||||
<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.item .name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul{padding-left:1.5em}.toc > ul > li{margin-top:.5em}}</style>
|
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<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
|
||||
</head>
|
||||
<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.problems.tsp</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># 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 networkx as nx
|
||||
import numpy as np
|
||||
import pyomo.environ as pe
|
||||
from scipy.spatial.distance import pdist, squareform
|
||||
from scipy.stats import uniform, randint
|
||||
from scipy.stats.distributions import rv_frozen
|
||||
|
||||
from miplearn.instance import Instance
|
||||
|
||||
|
||||
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)</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-classes">Classes</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.problems.tsp.ChallengeA"><code class="flex name class">
|
||||
<span>class <span class="ident">ChallengeA</span></span>
|
||||
<span>(</span><span>seed=42, n_training_instances=500, n_test_instances=50)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">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)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.problems.tsp.TravelingSalesmanGenerator"><code class="flex name class">
|
||||
<span>class <span class="ident">TravelingSalesmanGenerator</span></span>
|
||||
<span>(</span><span>x=<scipy.stats._distn_infrastructure.rv_frozen object>, y=<scipy.stats._distn_infrastructure.rv_frozen object>, n=<scipy.stats._distn_infrastructure.rv_frozen object>, gamma=<scipy.stats._distn_infrastructure.rv_frozen object>, fix_cities=True, round=True)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Random generator for the Traveling Salesman Problem.</p>
|
||||
<p>Initializes the problem generator.</p>
|
||||
<p>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 <code>n</code>, <code>x</code> and <code>y</code>. For each
|
||||
(unordered) pair of cities (i,j), the distance d[i,j] between them is set to:</p>
|
||||
<pre><code>d[i,j] = gamma[i,j] \sqrt{(x_i - x_j)^2 + (y_i - y_j)^2}
|
||||
</code></pre>
|
||||
<p>where gamma is sampled from the provided probability distribution <code>gamma</code>.</p>
|
||||
<p>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.</p>
|
||||
<p>By default, all distances d[i,j] are rounded to the nearest integer.
|
||||
If <code>round=False</code>
|
||||
is provided, this rounding will be disabled.</p>
|
||||
<h2 id="arguments">Arguments</h2>
|
||||
<dl>
|
||||
<dt><strong><code>x</code></strong> : <code>rv_continuous</code></dt>
|
||||
<dd>Probability distribution for the x-coordinate of each city.</dd>
|
||||
<dt><strong><code>y</code></strong> : <code>rv_continuous</code></dt>
|
||||
<dd>Probability distribution for the y-coordinate of each city.</dd>
|
||||
<dt><strong><code>n</code></strong> : <code>rv_discrete</code></dt>
|
||||
<dd>Probability distribution for the number of cities.</dd>
|
||||
<dt><strong><code>fix_cities</code></strong> : <code>bool</code></dt>
|
||||
<dd>If False, cities will be resampled for every generated instance. Otherwise, list of
|
||||
cities will be computed once, during the constructor.</dd>
|
||||
<dt><strong><code>round</code></strong> : <code>bool</code></dt>
|
||||
<dd>If True, distances are rounded to the nearest integer.</dd>
|
||||
</dl></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">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</code></pre>
|
||||
</details>
|
||||
<h3>Methods</h3>
|
||||
<dl>
|
||||
<dt id="miplearn.problems.tsp.TravelingSalesmanGenerator.generate"><code class="name flex">
|
||||
<span>def <span class="ident">generate</span></span>(<span>self, n_samples)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">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)]</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd>
|
||||
<dt id="miplearn.problems.tsp.TravelingSalesmanInstance"><code class="flex name class">
|
||||
<span>class <span class="ident">TravelingSalesmanInstance</span></span>
|
||||
<span>(</span><span>n_cities, distances)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>An instance ot the Traveling Salesman Problem.</p>
|
||||
<p>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.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">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)</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.instance.Instance" href="../instance.html#miplearn.instance.Instance">Instance</a></li>
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Methods</h3>
|
||||
<dl>
|
||||
<dt id="miplearn.problems.tsp.TravelingSalesmanInstance.build_user_cut"><code class="name flex">
|
||||
<span>def <span class="ident">build_user_cut</span></span>(<span>self, model, violation)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def build_user_cut(self, model, violation):
|
||||
return self.build_lazy_constraint(model, violation)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.problems.tsp.TravelingSalesmanInstance.find_violated_user_cuts"><code class="name flex">
|
||||
<span>def <span class="ident">find_violated_user_cuts</span></span>(<span>self, model)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def find_violated_user_cuts(self, model):
|
||||
return self.find_violated_lazy_constraints(model)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
<h3>Inherited members</h3>
|
||||
<ul class="hlist">
|
||||
<li><code><b><a title="miplearn.instance.Instance" href="../instance.html#miplearn.instance.Instance">Instance</a></b></code>:
|
||||
<ul class="hlist">
|
||||
<li><code><a title="miplearn.instance.Instance.build_lazy_constraint" href="../instance.html#miplearn.instance.Instance.build_lazy_constraint">build_lazy_constraint</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.find_violated_lazy_constraints" href="../instance.html#miplearn.instance.Instance.find_violated_lazy_constraints">find_violated_lazy_constraints</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.get_instance_features" href="../instance.html#miplearn.instance.Instance.get_instance_features">get_instance_features</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.get_variable_category" href="../instance.html#miplearn.instance.Instance.get_variable_category">get_variable_category</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.get_variable_features" href="../instance.html#miplearn.instance.Instance.get_variable_features">get_variable_features</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.to_model" href="../instance.html#miplearn.instance.Instance.to_model">to_model</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.problems" href="index.html">miplearn.problems</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||||
<ul>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.problems.tsp.ChallengeA" href="#miplearn.problems.tsp.ChallengeA">ChallengeA</a></code></h4>
|
||||
</li>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.problems.tsp.TravelingSalesmanGenerator" href="#miplearn.problems.tsp.TravelingSalesmanGenerator">TravelingSalesmanGenerator</a></code></h4>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.problems.tsp.TravelingSalesmanGenerator.generate" href="#miplearn.problems.tsp.TravelingSalesmanGenerator.generate">generate</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.problems.tsp.TravelingSalesmanInstance" href="#miplearn.problems.tsp.TravelingSalesmanInstance">TravelingSalesmanInstance</a></code></h4>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.problems.tsp.TravelingSalesmanInstance.build_user_cut" href="#miplearn.problems.tsp.TravelingSalesmanInstance.build_user_cut">build_user_cut</a></code></li>
|
||||
<li><code><a title="miplearn.problems.tsp.TravelingSalesmanInstance.find_violated_user_cuts" href="#miplearn.problems.tsp.TravelingSalesmanInstance.find_violated_user_cuts">find_violated_user_cuts</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</nav>
|
||||
</main>
|
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<footer id="footer">
|
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<p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.7.0</a>.</p>
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|
||||
Reference in New Issue
Block a user