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85 lines
3.8 KiB
85 lines
3.8 KiB
# 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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from sklearn.metrics import mean_squared_error, explained_variance_score, max_error, mean_absolute_error, r2_score
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from .. import Component, InstanceFeaturesExtractor, ObjectiveValueExtractor
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from sklearn.linear_model import LinearRegression
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from copy import deepcopy
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import numpy as np
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import logging
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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__(self,
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regressor=LinearRegression()):
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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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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 objective: [%.2f, %.2f]" % (lb, ub))
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def after_solve(self, solver, instance, model, results):
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if self.ub_regressor is not None:
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results["Predicted UB"] = instance.predicted_ub
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results["Predicted LB"] = instance.predicted_lb
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else:
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results["Predicted UB"] = None
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results["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([[inst.lower_bound, inst.upper_bound] for inst in instances])
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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
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