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</head>
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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.extractors</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.
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import gzip
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import logging
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import pickle
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from abc import ABC, abstractmethod
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import numpy as np
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from tqdm.auto import tqdm
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logger = logging.getLogger(__name__)
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class InstanceIterator:
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def __init__(self, instances):
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self.instances = instances
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self.current = 0
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def __iter__(self):
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return self
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def __next__(self):
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if self.current >= len(self.instances):
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raise StopIteration
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result = self.instances[self.current]
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self.current += 1
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if isinstance(result, str):
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logger.debug("Read: %s" % result)
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try:
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if result.endswith(".gz"):
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with gzip.GzipFile(result, "rb") as file:
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result = pickle.load(file)
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else:
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with open(result, "rb") as file:
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result = pickle.load(file)
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except pickle.UnpicklingError:
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raise Exception(f"Invalid instance file: {result}")
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return result
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class Extractor(ABC):
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@abstractmethod
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def extract(self, instances):
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pass
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@staticmethod
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def split_variables(instance):
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result = {}
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lp_solution = instance.training_data[0]["LP solution"]
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for var_name in lp_solution:
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for index in lp_solution[var_name]:
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category = instance.get_variable_category(var_name, index)
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if category is None:
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continue
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if category not in result:
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result[category] = []
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result[category] += [(var_name, index)]
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return result
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class VariableFeaturesExtractor(Extractor):
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def extract(self, instances):
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result = {}
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for instance in tqdm(
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InstanceIterator(instances),
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desc="Extract (vars)",
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disable=len(instances) < 5,
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):
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instance_features = instance.get_instance_features()
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var_split = self.split_variables(instance)
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lp_solution = instance.training_data[0]["LP solution"]
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for (category, var_index_pairs) in var_split.items():
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if category not in result:
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result[category] = []
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for (var_name, index) in var_index_pairs:
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result[category] += [
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instance_features.tolist()
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+ instance.get_variable_features(var_name, index).tolist()
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+ [lp_solution[var_name][index]]
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]
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for category in result:
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result[category] = np.array(result[category])
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return result
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class SolutionExtractor(Extractor):
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def __init__(self, relaxation=False):
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self.relaxation = relaxation
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def extract(self, instances):
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result = {}
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for instance in tqdm(
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InstanceIterator(instances),
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desc="Extract (solution)",
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disable=len(instances) < 5,
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):
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var_split = self.split_variables(instance)
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if self.relaxation:
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solution = instance.training_data[0]["LP solution"]
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else:
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solution = instance.training_data[0]["Solution"]
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for (category, var_index_pairs) in var_split.items():
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if category not in result:
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result[category] = []
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for (var_name, index) in var_index_pairs:
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v = solution[var_name][index]
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if v is None:
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result[category] += [[0, 0]]
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else:
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result[category] += [[1 - v, v]]
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for category in result:
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result[category] = np.array(result[category])
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return result
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class InstanceFeaturesExtractor(Extractor):
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def extract(self, instances):
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return np.vstack(
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[
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np.hstack(
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[
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instance.get_instance_features(),
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instance.training_data[0]["LP value"],
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]
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)
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for instance in InstanceIterator(instances)
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]
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)
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|
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class ObjectiveValueExtractor(Extractor):
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def __init__(self, kind="lp"):
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assert kind in ["lower bound", "upper bound", "lp"]
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self.kind = kind
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def extract(self, instances):
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if self.kind == "lower bound":
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return np.array(
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[
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[instance.training_data[0]["Lower bound"]]
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for instance in InstanceIterator(instances)
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]
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)
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if self.kind == "upper bound":
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return np.array(
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[
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[instance.training_data[0]["Upper bound"]]
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for instance in InstanceIterator(instances)
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]
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)
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if self.kind == "lp":
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return np.array(
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[
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[instance.training_data[0]["LP value"]]
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for instance in InstanceIterator(instances)
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]
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)</code></pre>
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</details>
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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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</section>
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<section>
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<h2 class="section-title" id="header-classes">Classes</h2>
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<dl>
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<dt id="miplearn.extractors.Extractor"><code class="flex name class">
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<span>class <span class="ident">Extractor</span></span>
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<span>(</span><span>*args, **kwargs)</span>
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</code></dt>
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<dd>
|
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<section class="desc"><p>Helper class that provides a standard way to create an ABC using
|
|
inheritance.</p></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">class Extractor(ABC):
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@abstractmethod
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def extract(self, instances):
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pass
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@staticmethod
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def split_variables(instance):
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result = {}
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lp_solution = instance.training_data[0]["LP solution"]
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for var_name in lp_solution:
|
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for index in lp_solution[var_name]:
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category = instance.get_variable_category(var_name, index)
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if category is None:
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continue
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if category not in result:
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result[category] = []
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result[category] += [(var_name, index)]
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return result</code></pre>
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</details>
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<h3>Ancestors</h3>
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<ul class="hlist">
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|
<li>abc.ABC</li>
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</ul>
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<h3>Subclasses</h3>
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<ul class="hlist">
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<li><a title="miplearn.extractors.VariableFeaturesExtractor" href="#miplearn.extractors.VariableFeaturesExtractor">VariableFeaturesExtractor</a></li>
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<li><a title="miplearn.extractors.SolutionExtractor" href="#miplearn.extractors.SolutionExtractor">SolutionExtractor</a></li>
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<li><a title="miplearn.extractors.InstanceFeaturesExtractor" href="#miplearn.extractors.InstanceFeaturesExtractor">InstanceFeaturesExtractor</a></li>
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<li><a title="miplearn.extractors.ObjectiveValueExtractor" href="#miplearn.extractors.ObjectiveValueExtractor">ObjectiveValueExtractor</a></li>
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</ul>
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<h3>Static methods</h3>
|
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<dl>
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<dt id="miplearn.extractors.Extractor.split_variables"><code class="name flex">
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<span>def <span class="ident">split_variables</span></span>(<span>instance)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">@staticmethod
|
|
def split_variables(instance):
|
|
result = {}
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lp_solution = instance.training_data[0]["LP solution"]
|
|
for var_name in lp_solution:
|
|
for index in lp_solution[var_name]:
|
|
category = instance.get_variable_category(var_name, index)
|
|
if category is None:
|
|
continue
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if category not in result:
|
|
result[category] = []
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result[category] += [(var_name, index)]
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return result</code></pre>
|
|
</details>
|
|
</dd>
|
|
</dl>
|
|
<h3>Methods</h3>
|
|
<dl>
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|
<dt id="miplearn.extractors.Extractor.extract"><code class="name flex">
|
|
<span>def <span class="ident">extract</span></span>(<span>self, instances)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">@abstractmethod
|
|
def extract(self, instances):
|
|
pass</code></pre>
|
|
</details>
|
|
</dd>
|
|
</dl>
|
|
</dd>
|
|
<dt id="miplearn.extractors.InstanceFeaturesExtractor"><code class="flex name class">
|
|
<span>class <span class="ident">InstanceFeaturesExtractor</span></span>
|
|
<span>(</span><span>*args, **kwargs)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"><p>Helper class that provides a standard way to create an ABC using
|
|
inheritance.</p></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">class InstanceFeaturesExtractor(Extractor):
|
|
def extract(self, instances):
|
|
return np.vstack(
|
|
[
|
|
np.hstack(
|
|
[
|
|
instance.get_instance_features(),
|
|
instance.training_data[0]["LP value"],
|
|
]
|
|
)
|
|
for instance in InstanceIterator(instances)
|
|
]
|
|
)</code></pre>
|
|
</details>
|
|
<h3>Ancestors</h3>
|
|
<ul class="hlist">
|
|
<li><a title="miplearn.extractors.Extractor" href="#miplearn.extractors.Extractor">Extractor</a></li>
|
|
<li>abc.ABC</li>
|
|
</ul>
|
|
<h3>Methods</h3>
|
|
<dl>
|
|
<dt id="miplearn.extractors.InstanceFeaturesExtractor.extract"><code class="name flex">
|
|
<span>def <span class="ident">extract</span></span>(<span>self, instances)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def extract(self, instances):
|
|
return np.vstack(
|
|
[
|
|
np.hstack(
|
|
[
|
|
instance.get_instance_features(),
|
|
instance.training_data[0]["LP value"],
|
|
]
|
|
)
|
|
for instance in InstanceIterator(instances)
|
|
]
|
|
)</code></pre>
|
|
</details>
|
|
</dd>
|
|
</dl>
|
|
</dd>
|
|
<dt id="miplearn.extractors.InstanceIterator"><code class="flex name class">
|
|
<span>class <span class="ident">InstanceIterator</span></span>
|
|
<span>(</span><span>instances)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">class InstanceIterator:
|
|
def __init__(self, instances):
|
|
self.instances = instances
|
|
self.current = 0
|
|
|
|
def __iter__(self):
|
|
return self
|
|
|
|
def __next__(self):
|
|
if self.current >= len(self.instances):
|
|
raise StopIteration
|
|
result = self.instances[self.current]
|
|
self.current += 1
|
|
if isinstance(result, str):
|
|
logger.debug("Read: %s" % result)
|
|
try:
|
|
if result.endswith(".gz"):
|
|
with gzip.GzipFile(result, "rb") as file:
|
|
result = pickle.load(file)
|
|
else:
|
|
with open(result, "rb") as file:
|
|
result = pickle.load(file)
|
|
except pickle.UnpicklingError:
|
|
raise Exception(f"Invalid instance file: {result}")
|
|
return result</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.extractors.ObjectiveValueExtractor"><code class="flex name class">
|
|
<span>class <span class="ident">ObjectiveValueExtractor</span></span>
|
|
<span>(</span><span>kind='lp')</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"><p>Helper class that provides a standard way to create an ABC using
|
|
inheritance.</p></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">class ObjectiveValueExtractor(Extractor):
|
|
def __init__(self, kind="lp"):
|
|
assert kind in ["lower bound", "upper bound", "lp"]
|
|
self.kind = kind
|
|
|
|
def extract(self, instances):
|
|
if self.kind == "lower bound":
|
|
return np.array(
|
|
[
|
|
[instance.training_data[0]["Lower bound"]]
|
|
for instance in InstanceIterator(instances)
|
|
]
|
|
)
|
|
if self.kind == "upper bound":
|
|
return np.array(
|
|
[
|
|
[instance.training_data[0]["Upper bound"]]
|
|
for instance in InstanceIterator(instances)
|
|
]
|
|
)
|
|
if self.kind == "lp":
|
|
return np.array(
|
|
[
|
|
[instance.training_data[0]["LP value"]]
|
|
for instance in InstanceIterator(instances)
|
|
]
|
|
)</code></pre>
|
|
</details>
|
|
<h3>Ancestors</h3>
|
|
<ul class="hlist">
|
|
<li><a title="miplearn.extractors.Extractor" href="#miplearn.extractors.Extractor">Extractor</a></li>
|
|
<li>abc.ABC</li>
|
|
</ul>
|
|
<h3>Methods</h3>
|
|
<dl>
|
|
<dt id="miplearn.extractors.ObjectiveValueExtractor.extract"><code class="name flex">
|
|
<span>def <span class="ident">extract</span></span>(<span>self, instances)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def extract(self, instances):
|
|
if self.kind == "lower bound":
|
|
return np.array(
|
|
[
|
|
[instance.training_data[0]["Lower bound"]]
|
|
for instance in InstanceIterator(instances)
|
|
]
|
|
)
|
|
if self.kind == "upper bound":
|
|
return np.array(
|
|
[
|
|
[instance.training_data[0]["Upper bound"]]
|
|
for instance in InstanceIterator(instances)
|
|
]
|
|
)
|
|
if self.kind == "lp":
|
|
return np.array(
|
|
[
|
|
[instance.training_data[0]["LP value"]]
|
|
for instance in InstanceIterator(instances)
|
|
]
|
|
)</code></pre>
|
|
</details>
|
|
</dd>
|
|
</dl>
|
|
</dd>
|
|
<dt id="miplearn.extractors.SolutionExtractor"><code class="flex name class">
|
|
<span>class <span class="ident">SolutionExtractor</span></span>
|
|
<span>(</span><span>relaxation=False)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"><p>Helper class that provides a standard way to create an ABC using
|
|
inheritance.</p></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">class SolutionExtractor(Extractor):
|
|
def __init__(self, relaxation=False):
|
|
self.relaxation = relaxation
|
|
|
|
def extract(self, instances):
|
|
result = {}
|
|
for instance in tqdm(
|
|
InstanceIterator(instances),
|
|
desc="Extract (solution)",
|
|
disable=len(instances) < 5,
|
|
):
|
|
var_split = self.split_variables(instance)
|
|
if self.relaxation:
|
|
solution = instance.training_data[0]["LP solution"]
|
|
else:
|
|
solution = instance.training_data[0]["Solution"]
|
|
for (category, var_index_pairs) in var_split.items():
|
|
if category not in result:
|
|
result[category] = []
|
|
for (var_name, index) in var_index_pairs:
|
|
v = solution[var_name][index]
|
|
if v is None:
|
|
result[category] += [[0, 0]]
|
|
else:
|
|
result[category] += [[1 - v, v]]
|
|
for category in result:
|
|
result[category] = np.array(result[category])
|
|
return result</code></pre>
|
|
</details>
|
|
<h3>Ancestors</h3>
|
|
<ul class="hlist">
|
|
<li><a title="miplearn.extractors.Extractor" href="#miplearn.extractors.Extractor">Extractor</a></li>
|
|
<li>abc.ABC</li>
|
|
</ul>
|
|
<h3>Methods</h3>
|
|
<dl>
|
|
<dt id="miplearn.extractors.SolutionExtractor.extract"><code class="name flex">
|
|
<span>def <span class="ident">extract</span></span>(<span>self, instances)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def extract(self, instances):
|
|
result = {}
|
|
for instance in tqdm(
|
|
InstanceIterator(instances),
|
|
desc="Extract (solution)",
|
|
disable=len(instances) < 5,
|
|
):
|
|
var_split = self.split_variables(instance)
|
|
if self.relaxation:
|
|
solution = instance.training_data[0]["LP solution"]
|
|
else:
|
|
solution = instance.training_data[0]["Solution"]
|
|
for (category, var_index_pairs) in var_split.items():
|
|
if category not in result:
|
|
result[category] = []
|
|
for (var_name, index) in var_index_pairs:
|
|
v = solution[var_name][index]
|
|
if v is None:
|
|
result[category] += [[0, 0]]
|
|
else:
|
|
result[category] += [[1 - v, v]]
|
|
for category in result:
|
|
result[category] = np.array(result[category])
|
|
return result</code></pre>
|
|
</details>
|
|
</dd>
|
|
</dl>
|
|
</dd>
|
|
<dt id="miplearn.extractors.VariableFeaturesExtractor"><code class="flex name class">
|
|
<span>class <span class="ident">VariableFeaturesExtractor</span></span>
|
|
<span>(</span><span>*args, **kwargs)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"><p>Helper class that provides a standard way to create an ABC using
|
|
inheritance.</p></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">class VariableFeaturesExtractor(Extractor):
|
|
def extract(self, instances):
|
|
result = {}
|
|
for instance in tqdm(
|
|
InstanceIterator(instances),
|
|
desc="Extract (vars)",
|
|
disable=len(instances) < 5,
|
|
):
|
|
instance_features = instance.get_instance_features()
|
|
var_split = self.split_variables(instance)
|
|
lp_solution = instance.training_data[0]["LP solution"]
|
|
for (category, var_index_pairs) in var_split.items():
|
|
if category not in result:
|
|
result[category] = []
|
|
for (var_name, index) in var_index_pairs:
|
|
result[category] += [
|
|
instance_features.tolist()
|
|
+ instance.get_variable_features(var_name, index).tolist()
|
|
+ [lp_solution[var_name][index]]
|
|
]
|
|
for category in result:
|
|
result[category] = np.array(result[category])
|
|
return result</code></pre>
|
|
</details>
|
|
<h3>Ancestors</h3>
|
|
<ul class="hlist">
|
|
<li><a title="miplearn.extractors.Extractor" href="#miplearn.extractors.Extractor">Extractor</a></li>
|
|
<li>abc.ABC</li>
|
|
</ul>
|
|
<h3>Methods</h3>
|
|
<dl>
|
|
<dt id="miplearn.extractors.VariableFeaturesExtractor.extract"><code class="name flex">
|
|
<span>def <span class="ident">extract</span></span>(<span>self, instances)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def extract(self, instances):
|
|
result = {}
|
|
for instance in tqdm(
|
|
InstanceIterator(instances),
|
|
desc="Extract (vars)",
|
|
disable=len(instances) < 5,
|
|
):
|
|
instance_features = instance.get_instance_features()
|
|
var_split = self.split_variables(instance)
|
|
lp_solution = instance.training_data[0]["LP solution"]
|
|
for (category, var_index_pairs) in var_split.items():
|
|
if category not in result:
|
|
result[category] = []
|
|
for (var_name, index) in var_index_pairs:
|
|
result[category] += [
|
|
instance_features.tolist()
|
|
+ instance.get_variable_features(var_name, index).tolist()
|
|
+ [lp_solution[var_name][index]]
|
|
]
|
|
for category in result:
|
|
result[category] = np.array(result[category])
|
|
return result</code></pre>
|
|
</details>
|
|
</dd>
|
|
</dl>
|
|
</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" href="index.html">miplearn</a></code></li>
|
|
</ul>
|
|
</li>
|
|
<li><h3><a href="#header-classes">Classes</a></h3>
|
|
<ul>
|
|
<li>
|
|
<h4><code><a title="miplearn.extractors.Extractor" href="#miplearn.extractors.Extractor">Extractor</a></code></h4>
|
|
<ul class="">
|
|
<li><code><a title="miplearn.extractors.Extractor.extract" href="#miplearn.extractors.Extractor.extract">extract</a></code></li>
|
|
<li><code><a title="miplearn.extractors.Extractor.split_variables" href="#miplearn.extractors.Extractor.split_variables">split_variables</a></code></li>
|
|
</ul>
|
|
</li>
|
|
<li>
|
|
<h4><code><a title="miplearn.extractors.InstanceFeaturesExtractor" href="#miplearn.extractors.InstanceFeaturesExtractor">InstanceFeaturesExtractor</a></code></h4>
|
|
<ul class="">
|
|
<li><code><a title="miplearn.extractors.InstanceFeaturesExtractor.extract" href="#miplearn.extractors.InstanceFeaturesExtractor.extract">extract</a></code></li>
|
|
</ul>
|
|
</li>
|
|
<li>
|
|
<h4><code><a title="miplearn.extractors.InstanceIterator" href="#miplearn.extractors.InstanceIterator">InstanceIterator</a></code></h4>
|
|
</li>
|
|
<li>
|
|
<h4><code><a title="miplearn.extractors.ObjectiveValueExtractor" href="#miplearn.extractors.ObjectiveValueExtractor">ObjectiveValueExtractor</a></code></h4>
|
|
<ul class="">
|
|
<li><code><a title="miplearn.extractors.ObjectiveValueExtractor.extract" href="#miplearn.extractors.ObjectiveValueExtractor.extract">extract</a></code></li>
|
|
</ul>
|
|
</li>
|
|
<li>
|
|
<h4><code><a title="miplearn.extractors.SolutionExtractor" href="#miplearn.extractors.SolutionExtractor">SolutionExtractor</a></code></h4>
|
|
<ul class="">
|
|
<li><code><a title="miplearn.extractors.SolutionExtractor.extract" href="#miplearn.extractors.SolutionExtractor.extract">extract</a></code></li>
|
|
</ul>
|
|
</li>
|
|
<li>
|
|
<h4><code><a title="miplearn.extractors.VariableFeaturesExtractor" href="#miplearn.extractors.VariableFeaturesExtractor">VariableFeaturesExtractor</a></code></h4>
|
|
<ul class="">
|
|
<li><code><a title="miplearn.extractors.VariableFeaturesExtractor.extract" href="#miplearn.extractors.VariableFeaturesExtractor.extract">extract</a></code></li>
|
|
</ul>
|
|
</li>
|
|
</ul>
|
|
</li>
|
|
</ul>
|
|
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