Use a scaler object to re-implement control scaling

src/ert/run_models/everest_run_model.py

@@ -30,7 +30,7 @@
 from ert.storage import open_storage
 from everest.config import ControlConfig, ControlVariableGuessListConfig, EverestConfig
 from everest.everest_storage import EverestStorage, OptimalResult
-from everest.optimizer.everest2ropt import everest2ropt
+from everest.optimizer.everest2ropt import everest2ropt, get_ropt_transforms
 from everest.simulator.everest_to_ert import everest_to_ert_config
 from everest.strings import EVEREST
 
@@ -96,7 +96,10 @@ def __init__(
         )
 
         self._everest_config = everest_config
-        self._ropt_config = everest2ropt(everest_config)
+        self._ropt_transforms = get_ropt_transforms(everest_config)
+        self._ropt_config = everest2ropt(
+            everest_config, transforms=self._ropt_transforms
+        )
 
         self._sim_callback = simulation_callback
         self._opt_callback = optimization_callback
@@ -191,7 +194,7 @@ def run_experiment(
             output_dir=Path(self._everest_config.optimization_output_dir),
         )
         self.ever_storage.init(self._everest_config)
-        self.ever_storage.observe_optimizer(optimizer)
+        self.ever_storage.observe_optimizer(optimizer, self._ropt_transforms)
 
         # Run the optimization:
         optimizer_exit_code = optimizer.run().exit_code

src/everest/everest_storage.py

@@ -5,6 +5,7 @@
 import os
 import traceback
 from dataclasses import dataclass, field
+from functools import partial
 from pathlib import Path
 from typing import Any, TypedDict, cast
 
@@ -13,6 +14,7 @@
 from ropt.enums import EventType
 from ropt.plan import BasicOptimizer, Event
 from ropt.results import FunctionResults, GradientResults, convert_to_maximize
+from ropt.transforms import Transforms
 
 from everest.config import EverestConfig
 from everest.strings import EVEREST
@@ -387,9 +389,12 @@ def read_from_output_dir(self) -> None:
         exp = _OptimizerOnlyExperiment(self._output_dir)
         self.data.read_from_experiment(exp)
 
-    def observe_optimizer(self, optimizer: BasicOptimizer) -> None:
+    def observe_optimizer(
+        self, optimizer: BasicOptimizer, transforms: Transforms
+    ) -> None:
         optimizer.add_observer(
-            EventType.FINISHED_EVALUATION, self._on_batch_evaluation_finished
+            EventType.FINISHED_EVALUATION,
+            partial(self._on_batch_evaluation_finished, transforms=transforms),
         )
         optimizer.add_observer(
             EventType.FINISHED_OPTIMIZER_STEP, self._on_optimization_finished
@@ -658,11 +663,14 @@ def _store_gradient_results(self, results: GradientResults) -> _GradientResults:
             "perturbation_constraints": perturbation_constraints,
         }
 
-    def _on_batch_evaluation_finished(self, event: Event) -> None:
+    def _on_batch_evaluation_finished(
+        self, event: Event, transforms: Transforms
+    ) -> None:
         logger.debug("Storing batch results dataframes")
 
         converted_results = tuple(
-            convert_to_maximize(result) for result in event.data.get("results", [])
+            convert_to_maximize(result).transform_back(transforms)
+            for result in event.data.get("results", [])
         )
         results: list[FunctionResults | GradientResults] = []
 

src/everest/optimizer/everest2ropt.py

@@ -2,8 +2,9 @@
 import os
 from typing import Any
 
-from ropt.config.enopt import EnOptConfig
+from ropt.config.enopt import EnOptConfig, EnOptContext
 from ropt.enums import ConstraintType, PerturbationType, VariableType
+from ropt.transforms import Transforms
 
 from everest.config import (
     EverestConfig,
@@ -16,44 +17,20 @@
 from everest.config.utils import FlattenedControls
 from everest.strings import EVEREST
 
+from .transforms import ControlScaler
+
 
 def _parse_controls(controls: FlattenedControls, ropt_config):
     control_types = [
         None if type_ is None else VariableType[type_.upper()]
         for type_ in controls.types
     ]
-    if all(item is None for item in controls.auto_scales):
-        offsets = None
-        scales = None
-    else:
-        scales = [
-            (ub - lb) / (sr[1] - sr[0]) if au else 1.0
-            for au, lb, ub, sr in zip(
-                controls.auto_scales,
-                controls.lower_bounds,
-                controls.upper_bounds,
-                controls.scaled_ranges,
-                strict=True,
-            )
-        ]
-        offsets = [
-            lb - sr[0] * sc if au else 0.0
-            for au, lb, sc, sr in zip(
-                controls.auto_scales,
-                controls.lower_bounds,
-                scales,
-                controls.scaled_ranges,
-                strict=True,
-            )
-        ]
     indices = [idx for idx, is_enabled in enumerate(controls.enabled) if is_enabled]
     ropt_config["variables"] = {
         "types": None if all(item is None for item in control_types) else control_types,
         "initial_values": controls.initial_guesses,
         "lower_bounds": controls.lower_bounds,
         "upper_bounds": controls.upper_bounds,
-        "offsets": offsets,
-        "scales": scales,
         "indices": indices if indices else None,
     }
 
@@ -367,7 +344,9 @@ def _parse_environment(
         ropt_config["gradient"]["seed"] = random_seed
 
 
-def everest2ropt(ever_config: EverestConfig) -> EnOptConfig:
+def everest2ropt(
+    ever_config: EverestConfig, transforms: Transforms | None = None
+) -> EnOptConfig:
     """Generate a ropt configuration from an Everest one
 
     NOTE: This method is a work in progress. So far only the some of
@@ -402,4 +381,21 @@ def everest2ropt(ever_config: EverestConfig) -> EnOptConfig:
         ropt_config=ropt_config,
     )
 
-    return EnOptConfig.model_validate(ropt_config)
+    return EnOptConfig.model_validate(
+        ropt_config,
+        context=None if transforms is None else EnOptContext(transforms=transforms),
+    )
+
+
+def get_ropt_transforms(ever_config: EverestConfig) -> Transforms:
+    controls = FlattenedControls(ever_config.controls)
+    if any(item is not None for item in controls.auto_scales):
+        variable_scaler = ControlScaler(
+            controls.lower_bounds,
+            controls.upper_bounds,
+            controls.scaled_ranges,
+            controls.auto_scales,
+        )
+    else:
+        variable_scaler = None
+    return Transforms(variables=variable_scaler)

src/everest/optimizer/transforms.py

@@ -0,0 +1,27 @@
+from collections.abc import Sequence
+
+import numpy as np
+from ropt.transforms import VariableScaler
+
+
+class ControlScaler(VariableScaler):
+    def __init__(
+        self,
+        lower_bounds: Sequence[float],
+        upper_bounds: Sequence[float],
+        scaled_ranges: Sequence[tuple[float, float]],
+        auto_scales: Sequence[bool],
+    ) -> None:
+        scales = [
+            (ub - lb) / (sr[1] - sr[0]) if au else 1.0
+            for au, lb, ub, sr in zip(
+                auto_scales, lower_bounds, upper_bounds, scaled_ranges, strict=True
+            )
+        ]
+        offsets = [
+            lb - sr[0] * sc if au else 0.0
+            for au, lb, sc, sr in zip(
+                auto_scales, lower_bounds, scales, scaled_ranges, strict=True
+            )
+        ]
+        super().__init__(np.array(scales), np.array(offsets))

tests/everest/test_ropt_initialization.py

@@ -7,7 +7,7 @@
 
 from everest.config import EverestConfig
 from everest.config_file_loader import yaml_file_to_substituted_config_dict
-from everest.optimizer.everest2ropt import everest2ropt
+from everest.optimizer.everest2ropt import everest2ropt, get_ropt_transforms
 from tests.everest.utils import relpath
 
 _CONFIG_DIR = relpath("test_data/mocked_test_case")
@@ -41,7 +41,7 @@ def test_everest2ropt_controls_auto_scale():
     controls = config.controls
     controls[0].auto_scale = True
     controls[0].scaled_range = [0.3, 0.7]
-    ropt_config = everest2ropt(config)
+    ropt_config = everest2ropt(config, transforms=get_ropt_transforms(config))
     assert numpy.allclose(ropt_config.variables.lower_bounds, 0.3)
     assert numpy.allclose(ropt_config.variables.upper_bounds, 0.7)
 
@@ -51,7 +51,7 @@ def test_everest2ropt_variables_auto_scale():
     controls = config.controls
     controls[0].variables[1].auto_scale = True
     controls[0].variables[1].scaled_range = [0.3, 0.7]
-    ropt_config = everest2ropt(config)
+    ropt_config = everest2ropt(config, transforms=get_ropt_transforms(config))
     assert ropt_config.variables.lower_bounds[0] == 0.0
     assert ropt_config.variables.upper_bounds[0] == 0.1
     assert ropt_config.variables.lower_bounds[1] == 0.3
@@ -115,7 +115,7 @@ def test_everest2ropt_controls_input_constraint_auto_scale():
     scaled_coefficients = coefficients * (max_values - min_values) / 0.4
     scaled_coefficients[:2, 1] = coefficients[:2, 1] * 2.0 / 0.4
 
-    ropt_config = everest2ropt(config)
+    ropt_config = everest2ropt(config, transforms=get_ropt_transforms(config))
     assert numpy.allclose(
         ropt_config.linear_constraints.coefficients,
         scaled_coefficients,