Add SoftKNNClassifierModel (#3243)#3243
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Summary: X-link: meta-pytorch/botorch#3243 Add a differentiable Soft K-Nearest Neighbors classifier model for failure-aware Bayesian optimization. Unlike tree-based classifiers (RF, XGBoost), SoftKNN is fully differentiable, enabling gradient-based acquisition function optimization. The model uses Gaussian kernel weights: P(y=1|x) = sum(w_i * y_i) / sum(w_i) where w_i = exp(-||x - x_i||^2 / (2 * sigma^2)) Implements construct_inputs classmethod for seamless Ax integration. Differential Revision: D90894389
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Summary: X-link: facebook/Ax#5072 Add a differentiable Soft K-Nearest Neighbors classifier model for failure-aware Bayesian optimization. Unlike tree-based classifiers (RF, XGBoost), SoftKNN is fully differentiable, enabling gradient-based acquisition function optimization. The model uses Gaussian kernel weights: P(y=1|x) = sum(w_i * y_i) / sum(w_i) where w_i = exp(-||x - x_i||^2 / (2 * sigma^2)) Implements construct_inputs classmethod for seamless Ax integration. Differential Revision: D90894389
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Summary: X-link: meta-pytorch/botorch#3243 Add a differentiable Soft K-Nearest Neighbors classifier model for failure-aware Bayesian optimization. Unlike tree-based classifiers (RF, XGBoost), SoftKNN is fully differentiable, enabling gradient-based acquisition function optimization. The model uses Gaussian kernel weights: P(y=1|x) = sum(w_i * y_i) / sum(w_i) where w_i = exp(-||x - x_i||^2 / (2 * sigma^2)) Implements construct_inputs classmethod for seamless Ax integration. Differential Revision: D90894389
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Summary: X-link: meta-pytorch/botorch#3243 Add a differentiable Soft K-Nearest Neighbors classifier model for failure-aware Bayesian optimization. Unlike tree-based classifiers (RF, XGBoost), SoftKNN is fully differentiable, enabling gradient-based acquisition function optimization. The model uses Gaussian kernel weights: P(y=1|x) = sum(w_i * y_i) / sum(w_i) where w_i = exp(-||x - x_i||^2 / (2 * sigma^2)) Implements construct_inputs classmethod for seamless Ax integration. Differential Revision: D90894389
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Summary: X-link: facebook/Ax#5072 Add a differentiable Soft K-Nearest Neighbors classifier model for failure-aware Bayesian optimization. Unlike tree-based classifiers (RF, XGBoost), SoftKNN is fully differentiable, enabling gradient-based acquisition function optimization. The model uses Gaussian kernel weights: P(y=1|x) = sum(w_i * y_i) / sum(w_i) where w_i = exp(-||x - x_i||^2 / (2 * sigma^2)) Implements construct_inputs classmethod for seamless Ax integration. Differential Revision: D90894389
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Summary: X-link: meta-pytorch/botorch#3243 Add a differentiable Soft K-Nearest Neighbors classifier model for failure-aware Bayesian optimization. Unlike tree-based classifiers (RF, XGBoost), SoftKNN is fully differentiable, enabling gradient-based acquisition function optimization. The model uses Gaussian kernel weights: P(y=1|x) = sum(w_i * y_i) / sum(w_i) where w_i = exp(-||x - x_i||^2 / (2 * sigma^2)) Implements construct_inputs classmethod for seamless Ax integration. Differential Revision: D90894389
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Summary: X-link: meta-pytorch/botorch#3243 Add a differentiable Soft K-Nearest Neighbors classifier model for failure-aware Bayesian optimization. Unlike tree-based classifiers (RF, XGBoost), SoftKNN is fully differentiable, enabling gradient-based acquisition function optimization. The model uses Gaussian kernel weights: P(y=1|x) = sum(w_i * y_i) / sum(w_i) where w_i = exp(-||x - x_i||^2 / (2 * sigma^2)) Implements construct_inputs classmethod for seamless Ax integration. Differential Revision: D90894389
Summary: X-link: facebook/Ax#5072 Add a differentiable Soft K-Nearest Neighbors classifier model for failure-aware Bayesian optimization. Unlike tree-based classifiers (RF, XGBoost), SoftKNN is fully differentiable, enabling gradient-based acquisition function optimization. The model uses Gaussian kernel weights: P(y=1|x) = sum(w_i * y_i) / sum(w_i) where w_i = exp(-||x - x_i||^2 / (2 * sigma^2)) Implements construct_inputs classmethod for seamless Ax integration. Differential Revision: D90894389
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Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
## main #3243 +/- ##
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Coverage 99.98% 99.98%
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Files 220 221 +1
Lines 21844 21905 +61
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+ Hits 21840 21901 +61
Misses 4 4 ☔ View full report in Codecov by Sentry. 🚀 New features to boost your workflow:
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Summary:
X-link: facebook/Ax#5072
Add a differentiable Soft K-Nearest Neighbors classifier model for failure-aware
Bayesian optimization. Unlike tree-based classifiers (RF, XGBoost), SoftKNN is
fully differentiable, enabling gradient-based acquisition function optimization.
The model uses Gaussian kernel weights:
P(y=1|x) = sum(w_i * y_i) / sum(w_i)
where w_i = exp(-||x - x_i||^2 / (2 * sigma^2))
Implements construct_inputs classmethod for seamless Ax integration.
Differential Revision: D90894389