i think i've done this shit RMSE: 0.64 !!!!

lab4/gp/fitness.py

@@ -77,32 +77,6 @@ class MAEFitness(BaseFitness):
         return float(np.mean(np.abs(predicted - true_values)))
 
 
-class HuberFitness(BaseFitness):
-    """Huber Loss (компромисс между MSE и MAE)"""
-
-    def __init__(
-        self,
-        target_fn: TargetFunction,
-        test_points_fn: TestPointsFn,
-        delta: float = 1.0,
-    ):
-        super().__init__(target_fn, test_points_fn)
-        self.delta = delta
-
-    def fitness_fn(
-        self,
-        chromosome: Chromosome,
-        predicted: NDArray[np.float64],
-        true_values: NDArray[np.float64],
-    ) -> float:
-        error = predicted - true_values
-        mask = np.abs(error) <= self.delta
-        squared = 0.5 * (error[mask] ** 2)
-        linear = self.delta * (np.abs(error[~mask]) - 0.5 * self.delta)
-        huber = np.concatenate([squared, linear])
-        return float(np.mean(huber))
-
-
 class NRMSEFitness(BaseFitness):
     """Нормализованный RMSE (масштаб-инвариантен)"""
 
@@ -128,12 +102,18 @@ class PenalizedFitness(BaseFitness):
         base_fitness: BaseFitness,
         lambda_: float = 0.001,
         depth_weight: float = 0.2,
+        scale_penalty: bool | None = None,
     ):
         super().__init__(target_fn, test_points_fn)
         self.base_fitness = base_fitness
         self.lambda_ = lambda_
         self.depth_weight = depth_weight
 
+        # Масштабировать штраф необязательно, если функция фитнеса нормализована
+        if scale_penalty is None:
+            scale_penalty = not isinstance(base_fitness, NRMSEFitness)
+        self.scale_penalty = scale_penalty
+
     def fitness_fn(
         self,
         chromosome: Chromosome,
@@ -146,4 +126,8 @@ class PenalizedFitness(BaseFitness):
         depth = chromosome.root.get_depth()
 
         penalty = self.lambda_ * (size + self.depth_weight * depth)
+
+        if self.scale_penalty:
+            penalty *= base
+
         return float(base + penalty)