Reorganize directories

miplearn/components/objective.py

@@ -1,54 +0,0 @@
-#  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.
-
-from .. import Component, InstanceFeaturesExtractor, ObjectiveValueExtractor
-from sklearn.linear_model import LinearRegression
-from copy import deepcopy
-import numpy as np
-import logging
-logger = logging.getLogger(__name__)
-
-
-class ObjectiveValueComponent(Component):
-    """
-    A Component which predicts the optimal objective value of the problem.
-    """
-    def __init__(self,
-                 regressor=LinearRegression()):
-        self.ub_regressor = None
-        self.lb_regressor = None
-        self.regressor_prototype = regressor
-    
-    def before_solve(self, solver, instance, model):
-        if self.ub_regressor is not None:
-            lb, ub = self.predict([instance])[0]
-            instance.predicted_ub = ub
-            instance.predicted_lb = lb
-            logger.info("Predicted objective: [%.2f, %.2f]" % (lb, ub))
-    
-    def after_solve(self, solver, instance, model, results):
-        if self.ub_regressor is not None:
-            results["Predicted UB"] = instance.predicted_ub
-            results["Predicted LB"] = instance.predicted_lb
-        else:
-            results["Predicted UB"] = None
-            results["Predicted LB"] = None
-    
-    def fit(self, training_instances):
-        logger.debug("Extracting features...")
-        features = InstanceFeaturesExtractor().extract(training_instances)
-        ub = ObjectiveValueExtractor(kind="upper bound").extract(training_instances)
-        lb = ObjectiveValueExtractor(kind="lower bound").extract(training_instances)
-        self.ub_regressor = deepcopy(self.regressor_prototype)
-        self.lb_regressor = deepcopy(self.regressor_prototype)
-        logger.debug("Fitting ub_regressor...")
-        self.ub_regressor.fit(features, ub)
-        logger.debug("Fitting ub_regressor...")
-        self.lb_regressor.fit(features, lb)
-        
-    def predict(self, instances):
-        features = InstanceFeaturesExtractor().extract(instances)
-        lb = self.lb_regressor.predict(features)
-        ub = self.ub_regressor.predict(features)
-        return np.hstack([lb, ub])