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models/base.py

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+"""Abstract base classes for surrogate models."""
+
+from abc import ABC, abstractmethod
+from typing import Optional, Tuple
+
+import torch
+from torch import Tensor
+
+
+class SurrogateModel(ABC):
+    """Abstract base class for all surrogate models in the platform.
+
+    A surrogate model provides predictions (mean + uncertainty) and can be
+    updated with new observations.
+    """
+
+    @abstractmethod
+    def predict(self, X: Tensor) -> Tuple[Tensor, Tensor]:
+        """Return posterior mean and variance at input locations X.
+
+        Args:
+            X: Input tensor of shape (n, d).
+
+        Returns:
+            mean: Predicted mean of shape (n, 1).
+            variance: Predicted variance of shape (n, 1).
+        """
+
+    @abstractmethod
+    def fit(self, X: Tensor, y: Tensor) -> None:
+        """Fit/update the surrogate model with observed data.
+
+        Args:
+            X: Training inputs of shape (n, d).
+            y: Training targets of shape (n, 1).
+        """
+
+    @abstractmethod
+    def posterior(self, X: Tensor):
+        """Return the full posterior distribution at X (for BoTorch compatibility).
+
+        Args:
+            X: Input tensor of shape (batch, n, d).
+        """
+
+    def condition_on_observations(self, X: Tensor, y: Tensor) -> "SurrogateModel":
+        """Return a new model conditioned on additional observations.
+
+        Default implementation refits the model. Subclasses can override
+        for fantasy-based conditioning.
+        """
+        raise NotImplementedError(
+            "Fantasy conditioning not implemented for this model. Use fit() instead."
+        )