Move python files to root folder; remove built docs

miplearn/components/objective.py

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+#  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 sklearn.metrics import mean_squared_error, explained_variance_score, max_error, mean_absolute_error, r2_score
+
+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)
+        assert ub.shape == (len(training_instances), 1)
+        assert lb.shape == (len(training_instances), 1)
+        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.ravel())
+        logger.debug("Fitting ub_regressor...")
+        self.lb_regressor.fit(features, lb.ravel())
+        
+    def predict(self, instances):
+        features = InstanceFeaturesExtractor().extract(instances)
+        lb = self.lb_regressor.predict(features)
+        ub = self.ub_regressor.predict(features)
+        assert lb.shape == (len(instances),)
+        assert ub.shape == (len(instances),)
+        return np.array([lb, ub]).T
+
+    def evaluate(self, instances):
+        y_pred = self.predict(instances)
+        y_true = np.array([[inst.lower_bound, inst.upper_bound] for inst in instances])
+        y_true_lb, y_true_ub = y_true[:, 0], y_true[:, 1]
+        y_pred_lb, y_pred_ub = y_pred[:, 1], y_pred[:, 1]
+        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),
+            },
+            "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),
+                "R2": r2_score(y_true_ub, y_pred_ub),
+                "Median absolute error": mean_absolute_error(y_true_ub, y_pred_ub),
+            },
+        }
+        return ev