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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.components.objective</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 logging
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from copy import deepcopy
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import numpy as np
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from sklearn.linear_model import LinearRegression
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from sklearn.metrics import (
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mean_squared_error,
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explained_variance_score,
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max_error,
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mean_absolute_error,
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r2_score,
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)
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from miplearn.classifiers import Regressor
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from miplearn.components.component import Component
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from miplearn.extractors import InstanceFeaturesExtractor, ObjectiveValueExtractor
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logger = logging.getLogger(__name__)
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class ObjectiveValueComponent(Component):
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"""
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A Component which predicts the optimal objective value of the problem.
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"""
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def __init__(
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self,
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regressor: Regressor = LinearRegression(),
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) -> None:
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self.ub_regressor = None
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self.lb_regressor = None
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self.regressor_prototype = regressor
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def before_solve(self, solver, instance, model):
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if self.ub_regressor is not None:
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logger.info("Predicting optimal value...")
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lb, ub = self.predict([instance])[0]
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instance.predicted_ub = ub
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instance.predicted_lb = lb
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logger.info("Predicted values: lb=%.2f, ub=%.2f" % (lb, ub))
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def after_solve(
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self,
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solver,
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instance,
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model,
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stats,
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training_data,
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):
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if self.ub_regressor is not None:
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stats["Predicted UB"] = instance.predicted_ub
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stats["Predicted LB"] = instance.predicted_lb
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else:
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stats["Predicted UB"] = None
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stats["Predicted LB"] = None
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def fit(self, training_instances):
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logger.debug("Extracting features...")
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features = InstanceFeaturesExtractor().extract(training_instances)
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ub = ObjectiveValueExtractor(kind="upper bound").extract(training_instances)
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lb = ObjectiveValueExtractor(kind="lower bound").extract(training_instances)
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assert ub.shape == (len(training_instances), 1)
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assert lb.shape == (len(training_instances), 1)
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self.ub_regressor = deepcopy(self.regressor_prototype)
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self.lb_regressor = deepcopy(self.regressor_prototype)
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logger.debug("Fitting ub_regressor...")
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self.ub_regressor.fit(features, ub.ravel())
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logger.debug("Fitting ub_regressor...")
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self.lb_regressor.fit(features, lb.ravel())
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def predict(self, instances):
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features = InstanceFeaturesExtractor().extract(instances)
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lb = self.lb_regressor.predict(features)
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ub = self.ub_regressor.predict(features)
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assert lb.shape == (len(instances),)
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assert ub.shape == (len(instances),)
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return np.array([lb, ub]).T
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def evaluate(self, instances):
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y_pred = self.predict(instances)
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y_true = np.array(
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[
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[
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inst.training_data[0]["Lower bound"],
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inst.training_data[0]["Upper bound"],
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]
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for inst in instances
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]
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)
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y_true_lb, y_true_ub = y_true[:, 0], y_true[:, 1]
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y_pred_lb, y_pred_ub = y_pred[:, 1], y_pred[:, 1]
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ev = {
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"Lower bound": {
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"Mean squared error": mean_squared_error(y_true_lb, y_pred_lb),
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"Explained variance": explained_variance_score(y_true_lb, y_pred_lb),
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"Max error": max_error(y_true_lb, y_pred_lb),
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"Mean absolute error": mean_absolute_error(y_true_lb, y_pred_lb),
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"R2": r2_score(y_true_lb, y_pred_lb),
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"Median absolute error": mean_absolute_error(y_true_lb, y_pred_lb),
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},
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"Upper bound": {
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"Mean squared error": mean_squared_error(y_true_ub, y_pred_ub),
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"Explained variance": explained_variance_score(y_true_ub, y_pred_ub),
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"Max error": max_error(y_true_ub, y_pred_ub),
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"Mean absolute error": mean_absolute_error(y_true_ub, y_pred_ub),
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"R2": r2_score(y_true_ub, y_pred_ub),
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"Median absolute error": mean_absolute_error(y_true_ub, y_pred_ub),
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},
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}
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return ev</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.components.objective.ObjectiveValueComponent"><code class="flex name class">
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<span>class <span class="ident">ObjectiveValueComponent</span></span>
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<span>(</span><span>regressor=LinearRegression())</span>
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</code></dt>
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<dd>
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<section class="desc"><p>A Component which predicts the optimal objective value of the problem.</p></section>
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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">class ObjectiveValueComponent(Component):
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"""
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A Component which predicts the optimal objective value of the problem.
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"""
|
|
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def __init__(
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self,
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regressor: Regressor = LinearRegression(),
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) -> None:
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self.ub_regressor = None
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|
self.lb_regressor = None
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self.regressor_prototype = regressor
|
|
|
|
def before_solve(self, solver, instance, model):
|
|
if self.ub_regressor is not None:
|
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logger.info("Predicting optimal value...")
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lb, ub = self.predict([instance])[0]
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instance.predicted_ub = ub
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instance.predicted_lb = lb
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logger.info("Predicted values: lb=%.2f, ub=%.2f" % (lb, ub))
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|
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|
def after_solve(
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self,
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solver,
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instance,
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model,
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stats,
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training_data,
|
|
):
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if self.ub_regressor is not None:
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stats["Predicted UB"] = instance.predicted_ub
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stats["Predicted LB"] = instance.predicted_lb
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else:
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stats["Predicted UB"] = None
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stats["Predicted LB"] = None
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|
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def fit(self, training_instances):
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logger.debug("Extracting features...")
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features = InstanceFeaturesExtractor().extract(training_instances)
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ub = ObjectiveValueExtractor(kind="upper bound").extract(training_instances)
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lb = ObjectiveValueExtractor(kind="lower bound").extract(training_instances)
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assert ub.shape == (len(training_instances), 1)
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assert lb.shape == (len(training_instances), 1)
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self.ub_regressor = deepcopy(self.regressor_prototype)
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self.lb_regressor = deepcopy(self.regressor_prototype)
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logger.debug("Fitting ub_regressor...")
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self.ub_regressor.fit(features, ub.ravel())
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logger.debug("Fitting ub_regressor...")
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self.lb_regressor.fit(features, lb.ravel())
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|
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def predict(self, instances):
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features = InstanceFeaturesExtractor().extract(instances)
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lb = self.lb_regressor.predict(features)
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ub = self.ub_regressor.predict(features)
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assert lb.shape == (len(instances),)
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assert ub.shape == (len(instances),)
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return np.array([lb, ub]).T
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def evaluate(self, instances):
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y_pred = self.predict(instances)
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y_true = np.array(
|
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[
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[
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inst.training_data[0]["Lower bound"],
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|
inst.training_data[0]["Upper bound"],
|
|
]
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for inst in instances
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|
]
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)
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y_true_lb, y_true_ub = y_true[:, 0], y_true[:, 1]
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y_pred_lb, y_pred_ub = y_pred[:, 1], y_pred[:, 1]
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ev = {
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"Lower bound": {
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"Mean squared error": mean_squared_error(y_true_lb, y_pred_lb),
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"Explained variance": explained_variance_score(y_true_lb, y_pred_lb),
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"Max error": max_error(y_true_lb, y_pred_lb),
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"Mean absolute error": mean_absolute_error(y_true_lb, y_pred_lb),
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"R2": r2_score(y_true_lb, y_pred_lb),
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"Median absolute error": mean_absolute_error(y_true_lb, y_pred_lb),
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},
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"Upper bound": {
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"Mean squared error": mean_squared_error(y_true_ub, y_pred_ub),
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"Explained variance": explained_variance_score(y_true_ub, y_pred_ub),
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"Max error": max_error(y_true_ub, y_pred_ub),
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"Mean absolute error": mean_absolute_error(y_true_ub, y_pred_ub),
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"R2": r2_score(y_true_ub, y_pred_ub),
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"Median absolute error": mean_absolute_error(y_true_ub, y_pred_ub),
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},
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}
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return ev</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><a title="miplearn.components.component.Component" href="component.html#miplearn.components.component.Component">Component</a></li>
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<li>abc.ABC</li>
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</ul>
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<h3>Methods</h3>
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<dl>
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<dt id="miplearn.components.objective.ObjectiveValueComponent.evaluate"><code class="name flex">
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<span>def <span class="ident">evaluate</span></span>(<span>self, instances)</span>
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</code></dt>
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<dd>
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<section class="desc"></section>
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<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def evaluate(self, instances):
|
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y_pred = self.predict(instances)
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|
y_true = np.array(
|
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[
|
|
[
|
|
inst.training_data[0]["Lower bound"],
|
|
inst.training_data[0]["Upper bound"],
|
|
]
|
|
for inst in instances
|
|
]
|
|
)
|
|
y_true_lb, y_true_ub = y_true[:, 0], y_true[:, 1]
|
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y_pred_lb, y_pred_ub = y_pred[:, 1], y_pred[:, 1]
|
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ev = {
|
|
"Lower bound": {
|
|
"Mean squared error": mean_squared_error(y_true_lb, y_pred_lb),
|
|
"Explained variance": explained_variance_score(y_true_lb, y_pred_lb),
|
|
"Max error": max_error(y_true_lb, y_pred_lb),
|
|
"Mean absolute error": mean_absolute_error(y_true_lb, y_pred_lb),
|
|
"R2": r2_score(y_true_lb, y_pred_lb),
|
|
"Median absolute error": mean_absolute_error(y_true_lb, y_pred_lb),
|
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},
|
|
"Upper bound": {
|
|
"Mean squared error": mean_squared_error(y_true_ub, y_pred_ub),
|
|
"Explained variance": explained_variance_score(y_true_ub, y_pred_ub),
|
|
"Max error": max_error(y_true_ub, y_pred_ub),
|
|
"Mean absolute error": mean_absolute_error(y_true_ub, y_pred_ub),
|
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"R2": r2_score(y_true_ub, y_pred_ub),
|
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"Median absolute error": mean_absolute_error(y_true_ub, y_pred_ub),
|
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},
|
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}
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return ev</code></pre>
|
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</details>
|
|
</dd>
|
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<dt id="miplearn.components.objective.ObjectiveValueComponent.fit"><code class="name flex">
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<span>def <span class="ident">fit</span></span>(<span>self, training_instances)</span>
|
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</code></dt>
|
|
<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def fit(self, training_instances):
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logger.debug("Extracting features...")
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features = InstanceFeaturesExtractor().extract(training_instances)
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ub = ObjectiveValueExtractor(kind="upper bound").extract(training_instances)
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lb = ObjectiveValueExtractor(kind="lower bound").extract(training_instances)
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assert ub.shape == (len(training_instances), 1)
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assert lb.shape == (len(training_instances), 1)
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self.ub_regressor = deepcopy(self.regressor_prototype)
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self.lb_regressor = deepcopy(self.regressor_prototype)
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logger.debug("Fitting ub_regressor...")
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self.ub_regressor.fit(features, ub.ravel())
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logger.debug("Fitting ub_regressor...")
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self.lb_regressor.fit(features, lb.ravel())</code></pre>
|
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</details>
|
|
</dd>
|
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<dt id="miplearn.components.objective.ObjectiveValueComponent.predict"><code class="name flex">
|
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<span>def <span class="ident">predict</span></span>(<span>self, instances)</span>
|
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</code></dt>
|
|
<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def predict(self, instances):
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features = InstanceFeaturesExtractor().extract(instances)
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lb = self.lb_regressor.predict(features)
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ub = self.ub_regressor.predict(features)
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assert lb.shape == (len(instances),)
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assert ub.shape == (len(instances),)
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return np.array([lb, ub]).T</code></pre>
|
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</details>
|
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</dd>
|
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</dl>
|
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<h3>Inherited members</h3>
|
|
<ul class="hlist">
|
|
<li><code><b><a title="miplearn.components.component.Component" href="component.html#miplearn.components.component.Component">Component</a></b></code>:
|
|
<ul class="hlist">
|
|
<li><code><a title="miplearn.components.component.Component.after_solve" href="component.html#miplearn.components.component.Component.after_solve">after_solve</a></code></li>
|
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<li><code><a title="miplearn.components.component.Component.before_solve" href="component.html#miplearn.components.component.Component.before_solve">before_solve</a></code></li>
|
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<li><code><a title="miplearn.components.component.Component.iteration_cb" href="component.html#miplearn.components.component.Component.iteration_cb">iteration_cb</a></code></li>
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</ul>
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</li>
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</ul>
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</dd>
|
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</dl>
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</section>
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<h1>Index</h1>
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<div class="toc">
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<ul></ul>
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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.components" href="index.html">miplearn.components</a></code></li>
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</ul>
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</li>
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<li><h3><a href="#header-classes">Classes</a></h3>
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<ul>
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<li>
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<h4><code><a title="miplearn.components.objective.ObjectiveValueComponent" href="#miplearn.components.objective.ObjectiveValueComponent">ObjectiveValueComponent</a></code></h4>
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<ul class="">
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<li><code><a title="miplearn.components.objective.ObjectiveValueComponent.evaluate" href="#miplearn.components.objective.ObjectiveValueComponent.evaluate">evaluate</a></code></li>
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<li><code><a title="miplearn.components.objective.ObjectiveValueComponent.fit" href="#miplearn.components.objective.ObjectiveValueComponent.fit">fit</a></code></li>
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<li><code><a title="miplearn.components.objective.ObjectiveValueComponent.predict" href="#miplearn.components.objective.ObjectiveValueComponent.predict">predict</a></code></li>
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</ul>
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</li>
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