[MCBO]: add support for RDUCB.

MCBO/.gitignore

@@ -1,18 +1,12 @@
 .idea/
-/mcbo/acq_optimizers/old/
-/mcbo/old
-/boils/old
 /mcbo/tasks/eda_seq_opt/scripts/main_eda_seq_opt_34_level.sh
 /mcbo/tasks/eda_seq_opt/scripts/main_eda_seq_opt_34_lut.sh
 /libs/mockturtle
 /mcbo/optimizers/old_bo/
 /mcbo/optimizers/utils/tlbo_utils/
 /mcbo/tasks/eda_seq_opt/scripts/eda_results_checker.*.py
-/perso.md
 /mcbo/tasks/nn_transfer/
 /mcbo/tasks/data/nn_transfer/
-/cocabo/
-/hebo/
 /mcbo/tasks/antibody_design/path_to_AbsolutNoLib.txt
 /mcbo/tasks/eda_seq_opt/utils/utils_design_groups_perso.py
 /mcbo/tasks/eda_seq_opt/scripts/eda_results_checker_34.py

MCBO/README.md

@@ -2,7 +2,7 @@
 
 This library provides an easy way to mix-and-match Bayesian optimization components in order to run new
 and existing mixed-variable or combinatorial Bayesian optimization. Motivations and principles are described in 
-[this paper](https://arxiv.org/pdf/2306.09803.pdf)
+[this paper](https://arxiv.org/pdf/2306.09803.pdf).
 
 <p align="center">
 <img src="./paper/images/all_mix_match.PNG" width="750"/>
@@ -74,6 +74,7 @@ search_space = task.get_search_space()
 - `gp_hed`: GP with the Hamming embedding via dictionary kernel.
 - `gp_ssk`: GP with string subsequence kernel. 
 - `gp_diff`: GP with diffusion kernel.
+- `gp_rd`:  GP with random tree decomposition additive kernel (for very high dimension).
 - `lr_sparse_hs`: Bayesian linear regression with Hoorseshoe prior.
 
 
@@ -84,6 +85,7 @@ search_space = task.get_search_space()
 - `ls`: Exhaustive Local Search.
 - `is`: Interleaved search with Hill-Climbing and Gradient-Descent.
 - `mab`: Multi-Armed Bandit for categorical and Gradient-Descent for numerical.
+- `mp`: Message passing (compatible with `gp_rd` model)
 
 ### Acquisition functions
 
@@ -189,3 +191,4 @@ some of our primitives, we refer to
 - BOCS: https://github.com/baptistar/BOCS
 - BOiLS: https://github.com/huawei-noah/HEBO/tree/master/BOiLS
 - BODi: https://github.com/aryandeshwal/BODi/
+- RDUCB: https://github.com/huawei-noah/HEBO/tree/master/RDUCB

MCBO/THIRD PARTY OPEN SOURCE SOFTWARE NOTICE.txt

@@ -9,6 +9,8 @@ THE OPEN SOURCE SOFTWARE IN THIS SOFTWARE IS DISTRIBUTED IN THE HOPE THAT IT WIL
 BUT WITHOUT ANY WARRANTY, WITHOUT EVEN THE IMPLIED WARRANTY OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE.
 SEE THE APPLICABLE LICENSES FOR MORE DETAILS.
 
+------------------------------------------------------------------------------------------------------------------------
+
 Copyright Notice and License Texts
 
 Software: Mockturtle (https://mockturtle.readthedocs.io/)
@@ -32,6 +34,7 @@ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 SOFTWARE.
 
+---
 
 Software: ABC: System for Sequential Synthesis and Verification (http://www.eecs.berkeley.edu/~alanmi/abc/)
 Copyright notice: Copyright (c) 2018-2020
@@ -52,5 +55,31 @@ A PARTICULAR PURPOSE. THE SOFTWARE PROVIDED HEREUNDER IS ON AN "AS IS" BASIS,
 AND THE UNIVERSITY OF CALIFORNIA HAS NO OBLIGATION TO PROVIDE MAINTENANCE,
 SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
 
+---
+
+Software: https://github.com/eric-vader/HD-BO-Additive-Models/blob/master/hdbo/acquisition_optimizer.py
+
+MIT License
+
+Copyright (c) 2020 Eric Han
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
 
 

MCBO/mcbo/acq_funcs/__init__.py

@@ -11,3 +11,5 @@
 from mcbo.acq_funcs.ei import EI
 from mcbo.acq_funcs.factory import acq_factory
 from mcbo.acq_funcs.lcb import LCB
+from mcbo.acq_funcs.additive_lcb import AddLCB
+from mcbo.acq_funcs.additive_lcb import AddLCB

MCBO/mcbo/acq_funcs/additive_lcb.py

@@ -0,0 +1,42 @@
+# Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
+
+# This program is free software; you can redistribute it and/or modify it under
+# the terms of the MIT license.
+
+# This program is distributed in the hope that it will be useful, but WITHOUT ANY
+# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
+# PARTICULAR PURPOSE. See the MIT License for more details.
+
+from typing import Tuple
+import torch
+
+from mcbo.acq_funcs.lcb import LCB
+from mcbo.models.gp.rand_decomposition_gp import RandDecompositionGP
+
+
+class AddLCB(LCB):
+    def __init__(self, beta: float = 1.96):
+        super().__init__(beta)
+
+    def evaluate(self,
+                 x: torch.Tensor,
+                 model: RandDecompositionGP,
+                 **kwargs
+                 ) -> torch.Tensor:
+        val = 0
+        for clique in model.graph:
+            val += self.partial_evaluate(x, model, clique, **kwargs)
+
+        return val
+
+    def partial_evaluate(self,
+                 x: torch.Tensor,
+                 model: RandDecompositionGP,
+                 clique: Tuple[int],
+                 **kwargs
+                 ) -> torch.Tensor:
+        mean, var = model.partial_predict(x, clique)
+        mean = mean.flatten()
+        std = var.clamp_min(1e-9).sqrt().flatten()
+
+        return mean - self.beta * std

MCBO/mcbo/acq_funcs/factory.py

@@ -10,6 +10,7 @@
 from mcbo.acq_funcs.ei import EI
 from mcbo.acq_funcs.lcb import LCB
 from mcbo.acq_funcs.pi import PI
+from mcbo.acq_funcs.additive_lcb import AddLCB
 from mcbo.acq_funcs.thompson_sampling import ThompsonSampling
 
 
@@ -18,6 +19,10 @@ def acq_factory(acq_func_id: str, **kwargs):
         beta = kwargs.get('beta', 1.96)
         acq_func = LCB(beta)
 
+    elif acq_func_id == "addlcb":
+        beta = kwargs.get('beta', 1.96)
+        acq_func = AddLCB(beta)
+
     elif acq_func_id == 'ei':
         acq_func = EI(augmented_ei=False)