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https://github.com/ANL-CEEESA/MIPLearn.git
synced 2025-12-06 01:18:52 -06:00
Objective: Refactoring
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@@ -38,11 +38,8 @@ class ObjectiveValueComponent(Component):
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regressor: Regressor = ScikitLearnRegressor(LinearRegression()),
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) -> None:
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assert isinstance(regressor, Regressor)
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self.ub_regressor: Optional[Regressor] = None
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self.lb_regressor: Optional[Regressor] = None
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self.regressors: Dict[str, Regressor] = {}
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self.regressor_prototype = regressor
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self._predicted_ub: Optional[float] = None
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self._predicted_lb: Optional[float] = None
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def before_solve_mip(
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self,
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@@ -55,63 +52,19 @@ class ObjectiveValueComponent(Component):
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) -> None:
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logger.info("Predicting optimal value...")
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pred = self.sample_predict(features, training_data)
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if "Upper bound" in pred:
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ub = pred["Upper bound"]
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logger.info("Predicted upper bound: %.6e" % ub)
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stats["Objective: Predicted UB"] = ub
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if "Lower bound" in pred:
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lb = pred["Lower bound"]
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logger.info("Predicted lower bound: %.6e" % lb)
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stats["Objective: Predicted LB"] = lb
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for (c, v) in pred.items():
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logger.info(f"Predicted {c.lower()}: %.6e" % v)
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stats[f"Objective: Predicted {c.lower()}"] = v # type: ignore
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def fit_xy(
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self,
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x: Dict[str, np.ndarray],
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y: Dict[str, np.ndarray],
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) -> None:
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if "Lower bound" in y:
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self.lb_regressor = self.regressor_prototype.clone()
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self.lb_regressor.fit(x["Lower bound"], y["Lower bound"])
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if "Upper bound" in y:
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self.ub_regressor = self.regressor_prototype.clone()
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self.ub_regressor.fit(x["Upper bound"], y["Upper bound"])
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# def evaluate(
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# self,
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# instances: Union[List[str], List[Instance]],
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# ) -> Dict[str, Dict[str, float]]:
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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 InstanceIterator(instances)
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# ]
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# )
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# y_pred_lb = y_pred["Lower bound"]
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# y_pred_ub = y_pred["Upper bound"]
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# y_true_lb, y_true_ub = y_true[:, 1], y_true[:, 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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for c in ["Upper bound", "Lower bound"]:
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if c in y:
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self.regressors[c] = self.regressor_prototype.clone()
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self.regressors[c].fit(x[c], y[c])
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def sample_predict(
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self,
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@@ -120,16 +73,11 @@ class ObjectiveValueComponent(Component):
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) -> Dict[str, float]:
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pred: Dict[str, float] = {}
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x, _ = self.sample_xy(features, sample)
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if self.lb_regressor is not None:
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lb_pred = self.lb_regressor.predict(np.array(x["Lower bound"]))
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pred["Lower bound"] = lb_pred[0, 0]
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else:
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logger.info("Lower bound regressor not fitted. Skipping.")
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if self.ub_regressor is not None:
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ub_pred = self.ub_regressor.predict(np.array(x["Upper bound"]))
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pred["Upper bound"] = ub_pred[0, 0]
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else:
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logger.info("Upper bound regressor not fitted. Skipping.")
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for c in ["Upper bound", "Lower bound"]:
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if c in self.regressors is not None:
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pred[c] = self.regressors[c].predict(np.array(x[c]))[0, 0]
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else:
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logger.info(f"{c} regressor not fitted. Skipping.")
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return pred
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@staticmethod
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@@ -142,12 +90,10 @@ class ObjectiveValueComponent(Component):
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f = list(features["Instance"]["User features"])
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if "LP value" in sample and sample["LP value"] is not None:
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f += [sample["LP value"]]
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x["Lower bound"] = [f]
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x["Upper bound"] = [f]
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if "Lower bound" in sample and sample["Lower bound"] is not None:
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y["Lower bound"] = [[sample["Lower bound"]]]
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if "Upper bound" in sample and sample["Upper bound"] is not None:
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y["Upper bound"] = [[sample["Upper bound"]]]
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for c in ["Upper bound", "Lower bound"]:
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x[c] = [f]
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if c in sample and sample[c] is not None: # type: ignore
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y[c] = [[sample[c]]] # type: ignore
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return x, y
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def sample_evaluate(
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@@ -166,8 +112,7 @@ class ObjectiveValueComponent(Component):
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result: Dict[Hashable, Dict[str, float]] = {}
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pred = self.sample_predict(features, sample)
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if "Upper bound" in sample and sample["Upper bound"] is not None:
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result["Upper bound"] = compare(pred["Upper bound"], sample["Upper bound"])
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if "Lower bound" in sample and sample["Lower bound"] is not None:
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result["Lower bound"] = compare(pred["Lower bound"], sample["Lower bound"])
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for c in ["Upper bound", "Lower bound"]:
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if c in sample and sample[c] is not None: # type: ignore
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result[c] = compare(pred[c], sample[c]) # type: ignore
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return result
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