chore: prepare for update of genetic algorithm (#1190)

Andreas will update the genetic algorithm for 15-minutes optimization
intervals.

Copy the current GENETIC optimization algorithm to GENETIC0 to enable
to keep the algorithm with the current functionality. Also copy resources
like the load interpolator to the GENETIC0 algorithm to keep them despite
possible later changes to the interpolator.

Make the deprecated legacy /optimize endpoint use the GENETIC0 optimization
algorithm to in-fact behave the same way even if there will later be changes
to the GENETIC algorithm by Andreas. Add a new REST endpoint to provide
the unprocessed optimisation results of the GENETIC and GENETIC0 algorithm
in case one wants to use them as done with the deprecated /optimize endpoint.

Adapt the optimization configuration to have distinct configurations for the
GENETIC and the GENETIC0 algorithm.

Create a copy of the current tests for the GENETIC algorithm to be used
for the GENETIC0 algorithm. This avoids the tests for the GENETIC0
algorithm to be influenced by later changes by Andreas.

Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>

src/akkudoktoreos/optimization/optimization.py

@@ -1,4 +1,4 @@
-from typing import Optional, Union
+from typing import Optional
 
 from pydantic import Field, computed_field
 
@@ -8,92 +8,54 @@ from akkudoktoreos.core.pydantic import (
     PydanticBaseModel,
     PydanticDateTimeDataFrame,
 )
+from akkudoktoreos.optimization.genetic0.genetic0settings import Genetic0CommonSettings
+from akkudoktoreos.optimization.genetic.geneticsettings import GeneticCommonSettings
 from akkudoktoreos.utils.datetimeutil import DateTime
 
 
-class GeneticCommonSettings(SettingsBaseModel):
-    """General Genetic Optimization Algorithm Configuration."""
-
-    individuals: Optional[int] = Field(
-        default=300,
-        ge=10,
-        json_schema_extra={
-            "description": "Number of individuals (solutions) in the population [>= 10]. Defaults to 300.",
-            "examples": [300],
-        },
-    )
-
-    generations: Optional[int] = Field(
-        default=400,
-        ge=10,
-        json_schema_extra={
-            "description": "Number of generations to evolve [>= 10]. Defaults to 400.",
-            "examples": [400],
-        },
-    )
-
-    seed: Optional[int] = Field(
-        default=None,
-        ge=0,
-        json_schema_extra={
-            "description": "Random seed for reproducibility. None = random.",
-            "examples": [None, 42],
-        },
-    )
-
-    # --- Penalties (existing) -------------------------------------------------
-
-    penalties: dict[str, Union[float, int, str]] = Field(
-        default_factory=lambda: {
-            "ev_soc_miss": 10,
-            "ac_charge_break_even": 1.0,
-        },
-        json_schema_extra={
-            "description": "Penalty parameters used in fitness evaluation.",
-            "examples": [{"ev_soc_miss": 10}],
-        },
-    )
+def optimization_algorithms() -> list[str]:
+    """Valid optimization algorithms."""
+    # Return static built-in optimization algorithms.
+    return [
+        "GENETIC",
+        "GENETIC0",
+    ]
 
 
 class OptimizationCommonSettings(SettingsBaseModel):
     """General Optimization Configuration."""
 
-    horizon_hours: int = Field(
-        default=24,
-        ge=0,
-        json_schema_extra={
-            "description": "The general time window within which the energy optimization goal shall be achieved [h]. Defaults to 24 hours.",
-            "examples": [24],
-        },
-    )
-
-    interval: int = Field(
-        default=3600,
-        ge=15 * 60,
-        le=60 * 60,
-        json_schema_extra={
-            "description": "The optimization interval [sec]. Defaults to 3600 seconds (1 hour)",
-            "examples": [60 * 60, 15 * 60],
-        },
-    )
-
     algorithm: str = Field(
         default="GENETIC",
         json_schema_extra={
             "description": "The optimization algorithm. Defaults to GENETIC",
-            "examples": ["GENETIC"],
+            "examples": ["GENETIC", "GENETIC0"],
         },
     )
 
     genetic: GeneticCommonSettings = Field(
         default_factory=GeneticCommonSettings,
         json_schema_extra={
-            "description": "Genetic optimization algorithm configuration.",
+            "description": "GENETIC optimization algorithm configuration.",
+            "examples": [{"individuals": 400, "seed": None, "penalties": {"ev_soc_miss": 10}}],
+        },
+    )
+
+    genetic0: Genetic0CommonSettings = Field(
+        default_factory=Genetic0CommonSettings,
+        json_schema_extra={
+            "description": "GENETIC0 optimization algorithm configuration.",
             "examples": [{"individuals": 400, "seed": None, "penalties": {"ev_soc_miss": 10}}],
         },
     )
 
     # Computed fields
+    @computed_field  # type: ignore[prop-decorator]
+    @property
+    def algorithms(self) -> list[str]:
+        """Available optimization algorithms."""
+        return optimization_algorithms()
+
     @computed_field  # type: ignore[prop-decorator]
     @property
     def keys(self) -> list[str]:
@@ -112,15 +74,6 @@ class OptimizationCommonSettings(SettingsBaseModel):
             key_list = df.columns.tolist()
         return sorted(set(key_list))
 
-    @computed_field  # type: ignore[prop-decorator]
-    @property
-    def horizon(self) -> int:
-        """Number of optimization steps."""
-        if self.interval is None or self.interval == 0 or self.horizon_hours is None:
-            return 0
-        num_steps = int(float(self.horizon_hours * 3600) / self.interval)
-        return num_steps
-
 
 class OptimizationSolution(PydanticBaseModel):
     """General Optimization Solution."""