Add SoftKNNClassifierModel (#3243)

botorch/models/__init__.py

@@ -8,6 +8,7 @@
     ApproximateGPyTorchModel,
     SingleTaskVariationalGP,
 )
+from botorch.models.classifier import SoftKNNClassifierModel
 from botorch.models.cost import AffineFidelityCostModel
 from botorch.models.deterministic import (
     AffineDeterministicModel,
@@ -52,4 +53,5 @@
     "SingleTaskGP",
     "SingleTaskMultiFidelityGP",
     "SingleTaskVariationalGP",
+    "SoftKNNClassifierModel",
 ]

botorch/models/classifier.py

@@ -0,0 +1,218 @@
+#!/usr/bin/env python3
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Classifier-based models for constraint boundaries and deterministic feasibility.
+
+These models wrap classifiers as BoTorch deterministic models,
+enabling them to be used for modeling binary constraints, feasibility, and other
+discontinuous outputs where traditional GP models fail due to smoothness assumptions.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+import torch
+from botorch.models.deterministic import GenericDeterministicModel
+from botorch.models.transforms.input import InputTransform
+from botorch.utils.datasets import SupervisedDataset
+from torch import Tensor
+
+
+class SoftKNNClassifierModel(GenericDeterministicModel):
+    """
+    Soft K-Nearest Neighbors classifier wrapped as a BoTorch deterministic model.
+
+    This model uses Gaussian kernel weighting to compute soft class probabilities.
+    Supports both fixed scalar sigma and learnable per-dimension sigma trained via
+    leave-one-out (LOO) cross-validation.
+
+    Example:
+        >>> from botorch.models.classifier import SoftKNNClassifierModel
+        >>> from botorch.utils.datasets import SupervisedDataset
+        >>> import torch
+        >>>
+        >>> X = torch.randn(100, 5)
+        >>> y = torch.randint(0, 2, (100, 1), dtype=torch.float64)
+        >>> dataset = SupervisedDataset(X=X, Y=y)
+        >>>
+        >>> # Fixed sigma
+        >>> model_inputs = SoftKNNClassifierModel.construct_inputs(
+        ...     training_data=dataset,
+        ...     sigma=0.3
+        ... )
+        >>> model = SoftKNNClassifierModel(**model_inputs)
+        >>>
+        >>> # Learnable per-dimension sigma
+        >>> model_inputs = SoftKNNClassifierModel.construct_inputs(
+        ...     training_data=dataset,
+        ...     learnable_sigma=True,
+        ...     sigma_epochs=100
+        ... )
+        >>> model = SoftKNNClassifierModel(**model_inputs)
+    """
+
+    def __init__(
+        self,
+        train_X: Tensor,
+        train_Y: Tensor,
+        sigma: float = 0.1,
+        learnable_sigma: bool = False,
+        sigma_lr: float = 0.1,
+        sigma_epochs: int = 100,
+        input_transform: InputTransform | None = None,
+        **kwargs: Any,
+    ) -> None:
+        """Initialize SoftKNNClassifierModel.
+
+        Args:
+            train_X: Training features tensor of shape (n, d).
+            train_Y: Training labels tensor of shape (n,) or (n, 1), binary (0 or 1).
+            sigma: Initial Gaussian kernel bandwidth (default: 0.1).
+            learnable_sigma: If True, learn per-dimension sigma via LOO
+                cross-validation (default: False).
+            sigma_lr: Learning rate for sigma optimization (default: 0.1).
+            sigma_epochs: Training epochs for sigma (default: 100).
+            input_transform: Optional InputTransform applied to both training
+                and test inputs before distance computation.
+            **kwargs: Additional arguments (ignored).
+        """
+        # Ensure train_Y is 1D
+        train_Y = train_Y.view(-1)
+
+        # Apply input transform to training data if provided
+        # This ensures train_X_t is in the same space as test inputs
+        # (which are transformed via Model.transform_inputs in posterior())
+        if input_transform is not None:
+            train_X_t = input_transform(train_X)
+        else:
+            train_X_t = train_X
+
+        # Learn or use fixed sigma
+        learned_sigma_tensor: Tensor | None = None
+        if learnable_sigma:
+            # Learn per-dimension sigma via LOO cross-validation
+            d = train_X_t.shape[-1]
+            log_sigma = torch.nn.Parameter(
+                torch.full(
+                    (d,),
+                    torch.log(torch.tensor(sigma, dtype=train_X_t.dtype)),
+                    device=train_X_t.device,
+                    dtype=train_X_t.dtype,
+                )
+            )
+
+            optimizer = torch.optim.Adam([log_sigma], lr=sigma_lr, foreach=True)
+            N = train_X_t.shape[0]
+            train_Y_float = train_Y.to(dtype=train_X_t.dtype)
+
+            for _ in range(sigma_epochs):
+                optimizer.zero_grad()
+                sigma_vec = log_sigma.exp()  # [d]
+
+                # Pairwise distances with per-dim sigma: sum((x_i - x_j)^2 / sigma_j^2)
+                diffs = train_X_t.unsqueeze(1) - train_X_t.unsqueeze(0)  # [N, N, d]
+                dists = torch.sum((diffs**2) / (sigma_vec**2), dim=2)  # [N, N]
+
+                # LOO: exclude self (diagonal)
+                mask = ~torch.eye(N, dtype=torch.bool, device=train_X_t.device)
+                weights = torch.exp(-dists / 2) * mask
+
+                weighted_class1 = torch.sum(
+                    weights * (train_Y_float == 1.0).to(dtype=train_X_t.dtype), dim=1
+                )
+                total_weights = torch.sum(weights, dim=1)
+                prob_class1 = weighted_class1 / (total_weights + 1e-12)
+
+                # Binary cross-entropy loss
+                eps = 1e-7
+                prob_class1_clamped = prob_class1.clamp(eps, 1 - eps)
+                loss = -torch.mean(
+                    train_Y_float * torch.log(prob_class1_clamped)
+                    + (1 - train_Y_float) * torch.log(1 - prob_class1_clamped)
+                )
+                loss.backward()
+                optimizer.step()
+
+            # Detach learned sigma for inference
+            sigma_final: Tensor | float = log_sigma.exp().detach()  # [d]
+            learned_sigma_tensor = sigma_final
+        else:
+            sigma_final = sigma  # scalar
+
+        # Create prediction closure with transformed training data
+        def predict_proba_fn(X: Tensor) -> Tensor:
+            original_shape = X.shape[:-1]
+            # Already transformed via Model.transform_inputs if set
+            X_flat = X.reshape(-1, X.shape[-1])
+
+            diffs = X_flat.unsqueeze(1) - train_X_t.to(X_flat).unsqueeze(0)
+
+            if isinstance(sigma_final, Tensor):
+                # Per-dimension sigma
+                dists = torch.sum((diffs**2) / (sigma_final.to(X_flat) ** 2), dim=2)
+                weights = torch.exp(-dists / 2)
+            else:
+                # Scalar sigma
+                dists = torch.sum(diffs**2, dim=2)
+                weights = torch.exp(-dists / (2 * sigma_final**2))
+
+            mask_class1 = train_Y.to(X_flat) == 1.0
+            mask_class1 = mask_class1.to(dtype=X_flat.dtype)
+
+            weighted_class1 = torch.matmul(weights, mask_class1)
+            total_weights = torch.sum(weights, dim=1)
+            probs_flat = weighted_class1 / (total_weights + 1e-12)
+
+            return probs_flat.reshape(*original_shape, 1)
+
+        # Initialize parent with the prediction function
+        super().__init__(f=predict_proba_fn, num_outputs=1)
+
+        # Register input_transform as a submodule so posterior() applies it
+        if input_transform is not None:
+            self.input_transform = input_transform
+
+        # Expose learned sigma (if any) for inspection
+        self.learned_sigma = learned_sigma_tensor
+
+    @classmethod
+    def construct_inputs(
+        cls,
+        training_data: SupervisedDataset,
+        **kwargs: Any,
+    ) -> dict[str, Any]:
+        """
+        Construct inputs for SoftKNNClassifierModel from training data.
+
+        This method extracts training data and parameters that will be passed
+        to __init__, where the input_transform is applied and the prediction
+        closure is created. This ensures compatibility with Ax's model bridge,
+        which adds input_transform after calling construct_inputs.
+
+        Args:
+            training_data: SupervisedDataset with X (features) and Y (labels).
+            sigma: Initial Gaussian kernel bandwidth (default: 0.1).
+            learnable_sigma: If True, learn per-dimension sigma via LOO
+                cross-validation (default: False).
+            sigma_lr: Learning rate for sigma optimization (default: 0.1).
+            sigma_epochs: Training epochs for sigma (default: 100).
+            input_transform: Optional InputTransform applied to both training
+                and test inputs before distance computation.
+
+        Returns:
+            Dictionary with training data and model parameters.
+        """
+        return {
+            "train_X": training_data.X.detach().clone(),
+            "train_Y": training_data.Y.detach().clone(),
+            "sigma": kwargs.get("sigma", 0.1),
+            "learnable_sigma": kwargs.get("learnable_sigma", False),
+            "sigma_lr": kwargs.get("sigma_lr", 0.1),
+            "sigma_epochs": kwargs.get("sigma_epochs", 100),
+            "input_transform": kwargs.get("input_transform", None),
+        }

sphinx/source/models.rst

@@ -39,6 +39,11 @@ Additive GP Models
 .. automodule:: botorch.models.additive_gp
     :members:
 
+Classifier Models
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. automodule:: botorch.models.classifier
+    :members:
+
 Cost Models (for cost-aware optimization)
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 .. automodule:: botorch.models.cost