Handle minimization/maximization conversion with the scaler

src/ert/run_models/everest_run_model.py

@@ -552,8 +552,7 @@ def _get_objectives_and_constraints(
         results: list[dict[str, NDArray[np.float64]]],
         cached_results: dict[int, Any],
     ) -> tuple[NDArray[np.float64], NDArray[np.float64] | None]:
-        # We minimize the negative of the objectives:
-        objectives = -self._get_simulation_results(
+        objectives = self._get_simulation_results(
             results, self._everest_config.objective_names, control_values, batch_data
         )
 

src/everest/everest_storage.py

@@ -13,7 +13,7 @@
 import polars as pl
 from ropt.enums import EventType
 from ropt.plan import BasicOptimizer, Event
-from ropt.results import FunctionResults, GradientResults, convert_to_maximize
+from ropt.results import FunctionResults, GradientResults
 
 from everest.config import EverestConfig
 from everest.strings import EVEREST
@@ -661,14 +661,11 @@ def _store_gradient_results(self, results: GradientResults) -> _GradientResults:
     def _on_batch_evaluation_finished(self, event: Event) -> None:
         logger.debug("Storing batch results dataframes")
 
-        converted_results = tuple(
-            convert_to_maximize(result) for result in event.data.get("results", [])
-        )
         results: list[FunctionResults | GradientResults] = []
 
         best_value = -np.inf
         best_results = None
-        for item in converted_results:
+        for item in event.data.get("results", []):
             if isinstance(item, GradientResults):
                 results.append(item)
             if (

src/everest/optimizer/opt_model_transforms.py

@@ -56,11 +56,17 @@ def __init__(
         self._auto_scales = np.asarray(auto_scales, dtype=np.bool_)
         self._weights = np.asarray(weights, dtype=np.float64)
 
+    # The transform methods below all return the negative of the objectives.
+    # This is because Everest maximizes the objectives, while ropt is a minimizer.
+
     def forward(self, objectives: NDArray[np.float64]) -> NDArray[np.float64]:
-        return objectives / self._scales
+        return -objectives / self._scales
 
     def backward(self, objectives: NDArray[np.float64]) -> NDArray[np.float64]:
-        return objectives * self._scales
+        return -objectives * self._scales
+
+    def transform_weighted_objective(self, weighted_objective):
+        return -weighted_objective
 
     def calculate_auto_scales(self, objectives: NDArray[np.float64]) -> None:
         auto_scales = np.abs(