Merge conflict resolution

Author: Sahran Ashoor

setup.cfg renamed to .flake8

@@ -11,8 +11,3 @@ extend-select = D107, D417, D3, D207, D208, D214, D215
 max-line-length = 88
 exclude =
   build, dist, tutorials, website, .eggs
-
-[coverage:report]
-omit =
-    test/*
-    setup.py

.github/workflows/deploy_on_release.yml

@@ -49,39 +49,9 @@ jobs:
       with:
         verbose: true
 
-  check-versions:
-    needs: package-deploy-pypi
-    name: Check if major or minor version changed
-    runs-on: ubuntu-latest
-    outputs:
-      major_minor_changed: ${{ steps.compare.outputs.major_minor_changed }}
-    steps:
-    - uses: actions/checkout@v4
-      with:
-        fetch-depth: 0
-        fetch-tags: true
-        ref: ${{ github.sha }}
-    - name: Check if major or minor version changed
-      id: compare
-      run: |
-        git fetch --tags --force
-        previous_version=$(git describe --tags --abbrev=0 ${{ github.event.release.tag_name }}^)
-        prev=$(cut -d '.' -f 1-2 <<< $previous_version) # remove patch number
-        prev=${prev#v}                                  # remove optional "v" prefix
-        next=$(cut -d '.' -f 1-2 <<< ${{ github.event.release.tag_name }})
-        next=${next#v}
-
-        echo "Updating from version $previous_version to ${{ github.event.release.tag_name }}"
-        if [[ "$prev" == "$next" ]]; then
-          echo "::warning::Major/Minor version was not changed. Skipping website & docs generation step."
-        else
-          echo major_minor_changed=true >> $GITHUB_OUTPUT
-        fi
-
   version-and-publish-website:
-    needs: check-versions
+    needs: package-deploy-pypi
     name: Version and Publish website
-    if: ${{ needs.check-versions.outputs.major_minor_changed == 'true' }}
     uses: ./.github/workflows/publish_website.yml
     with:
       new_version: ${{ github.event.release.tag_name }}

.github/workflows/publish_website.yml

@@ -10,7 +10,19 @@ on:
         required: false
         type: boolean
         default: false
+      dry_run:
+        required: false
+        type: boolean
+        default: false
   workflow_dispatch:
+      run_tutorials:
+        required: true
+        type: boolean
+        default: false
+      dry_run:
+        required: true
+        type: boolean
+        default: false
 
 
 jobs:
@@ -50,6 +62,27 @@ jobs:
       run: |
         uv pip install ."[dev, tutorials]"
     - if: ${{ inputs.new_version }}
+      name: Delete existing similar versions from Docusaurus
+      run: |
+        # Delete existing versions for same Major and Minor version numbers.
+        # We do this to keep only the latest patch for a given major/minor version.
+        MAJOR_MINOR_VERSION=$(cut -d '.' -f 1-2 <<< ${{ inputs.new_version }}) # remove patch number
+        MAJOR_MINOR_VERSION=${MAJOR_MINOR_VERSION#v}                           # remove optional "v" prefix
+        for dir in website/versioned_docs/version-$MAJOR_MINOR_VERSION.*; do
+            if [ -d "$dir" ]; then
+                OLD_VERSION=$(basename "$dir" | sed 's/^version-//') # remove "version-" prefix from the directory name
+                echo "Deleting older version $OLD_VERSION with the same major and minor version numbers as $NEW_VERSION"
+                # Delete version from the three locations Docusaurus uses:
+                #   - versioned_docs/version-X.Y.Z/
+                #   - versioned_sidebars/version-X.Y.Z-sidebars.json
+                #   - versions.json
+                # https://docusaurus.io/docs/versioning#deleting-an-existing-version
+                rm -rf "$dir"
+                rm "website/versioned_sidebars/version-$OLD_VERSION-sidebars.json"
+                sed -i "/\"$OLD_VERSION\"/d" website/versions.json
+            fi
+        done
+    - if: ${{ inputs.new_version && !inputs.dry_run }}
       name: Create new docusaurus version
       run: |
         python3 scripts/convert_ipynb_to_mdx.py --clean
@@ -70,6 +103,7 @@ jobs:
         path: website/build/
 
   deploy-website:
+    if: ${{ !inputs.dry_run }}
     needs: build-website
     permissions:
       pages: write

.github/workflows/test_website.yml

@@ -0,0 +1,26 @@
+name: Test Website
+
+on:
+  push:
+    branches: [ main ]
+  pull_request:
+    branches: [ main ]
+    paths:
+      - "tutorials/**"
+      - "notebooks_community/**"
+      - "docs/**"
+      - "scripts/**"
+      - "website/**"
+  workflow_dispatch:
+
+jobs:
+  website:
+    name: Test building website
+    uses: ./.github/workflows/publish_website.yml
+    with:
+      dry_run: true
+      run_tutorials: false
+    permissions:
+      contents: write
+      pages: write
+      id-token: write

.gitignore

@@ -102,6 +102,10 @@ website/static/files/
 docs/tutorials/*
 !docs/tutorials/index.mdx
 
+# Generated for community noteboooks
+docs/notebooks_community/*
+!docs/notebooks_community/index.mdx
+
 ## Generated for Sphinx
 website/pages/api/
 website/static/js/*

CONTRIBUTING.md

@@ -96,7 +96,7 @@ code can be found in the [website](/website/) folder). It is built using
    [Sphinx](http://www.sphinx-doc.org), and embedded into the Docusaurus
    website. The sphinx .rst source files for this live in
    [sphinx/source](/sphinx/source/).
-3. The Jupyter notebook tutorials, parsed by `nbconvert`, and embedded into the
+3. The Jupyter notebook tutorials, parsed and converted to MDX, and embedded into the
    Docusaurus website. These live in [tutorials](/tutorials/).
 
 To build the documentation you will need [Node](https://nodejs.org/en/) >= 8.x
@@ -108,6 +108,8 @@ The following command will both build the docs and serve the site locally:
 ./scripts/build_docs.sh
 ```
 
+See the [website/README.md](website/README.md) for more details.
+
 ## Pull Requests
 
 We actively welcome your pull requests.

SECURITY.md

@@ -0,0 +1,7 @@
+# Reporting and Fixing Security Issues
+
+Please do not open GitHub issues or pull requests - this makes the problem immediately visible to everyone, including malicious actors. Security issues in this open source project can be safely reported via the Meta Bug Bounty program:
+
+https://bugbounty.meta.com/
+
+Meta's security team will triage your report and determine whether or not is it eligible for a bounty under our program.

botorch/acquisition/__init__.py

@@ -64,6 +64,7 @@
     SampleReducingMCAcquisitionFunction,
 )
 from botorch.acquisition.multi_step_lookahead import qMultiStepLookahead
+from botorch.acquisition.multioutput_acquisition import MultiOutputAcquisitionFunction
 from botorch.acquisition.objective import (
     ConstrainedMCObjective,
     GenericMCObjective,
@@ -136,4 +137,5 @@
     "ScalarizedPosteriorTransform",
     "get_acquisition_function",
     "get_acqf_input_constructor",
+    "MultiOutputAcquisitionFunction",
 ]

botorch/acquisition/active_learning.py

@@ -31,7 +31,11 @@
 from botorch.models.model import Model
 from botorch.sampling.base import MCSampler
 from botorch.sampling.normal import SobolQMCNormalSampler
-from botorch.utils.transforms import concatenate_pending_points, t_batch_mode_transform
+from botorch.utils.transforms import (
+    average_over_ensemble_models,
+    concatenate_pending_points,
+    t_batch_mode_transform,
+)
 from torch import Tensor
 
 
@@ -88,6 +92,7 @@ def __init__(
 
     @concatenate_pending_points
     @t_batch_mode_transform()
+    @average_over_ensemble_models
     def forward(self, X: Tensor) -> Tensor:
         # Construct the fantasy model (we actually do not use the full model,
         # this is just a convenient way of computing fast posterior covariances
@@ -154,6 +159,7 @@ def __init__(
         )
 
     @t_batch_mode_transform()
+    @average_over_ensemble_models
     def forward(self, X: Tensor) -> Tensor:
         r"""Evaluate PairwiseMCPosteriorVariance on the candidate set `X`.
 

botorch/acquisition/analytic.py

@@ -35,7 +35,11 @@
     phi,
 )
 from botorch.utils.safe_math import log1mexp, logmeanexp
-from botorch.utils.transforms import convert_to_target_pre_hook, t_batch_mode_transform
+from botorch.utils.transforms import (
+    average_over_ensemble_models,
+    convert_to_target_pre_hook,
+    t_batch_mode_transform,
+)
 from gpytorch.likelihoods.gaussian_likelihood import FixedNoiseGaussianLikelihood
 from torch import Tensor
 from torch.nn.functional import pad
@@ -154,6 +158,7 @@ def __init__(
         self.maximize = maximize
 
     @t_batch_mode_transform(expected_q=1)
+    @average_over_ensemble_models
     def forward(self, X: Tensor) -> Tensor:
         r"""Evaluate the Log Probability of Improvement on the candidate set X.
 
@@ -208,6 +213,7 @@ def __init__(
         self.maximize = maximize
 
     @t_batch_mode_transform(expected_q=1)
+    @average_over_ensemble_models
     def forward(self, X: Tensor) -> Tensor:
         r"""Evaluate the Probability of Improvement on the candidate set X.
 
@@ -258,6 +264,7 @@ def __init__(
         self.register_buffer("best_f", best_f)
 
     @t_batch_mode_transform()
+    @average_over_ensemble_models
     def forward(self, X: Tensor) -> Tensor:
         """Evaluate approximate qPI on the candidate set X.
 
@@ -334,6 +341,7 @@ def __init__(
         self.maximize = maximize
 
     @t_batch_mode_transform(expected_q=1)
+    @average_over_ensemble_models
     def forward(self, X: Tensor) -> Tensor:
         r"""Evaluate Expected Improvement on the candidate set X.
 
@@ -397,6 +405,7 @@ def __init__(
         self.maximize = maximize
 
     @t_batch_mode_transform(expected_q=1)
+    @average_over_ensemble_models
     def forward(self, X: Tensor) -> Tensor:
         r"""Evaluate logarithm of Expected Improvement on the candidate set X.
 
@@ -578,6 +587,7 @@ def __init__(
         self.register_forward_pre_hook(convert_to_target_pre_hook)
 
     @t_batch_mode_transform(expected_q=1)
+    @average_over_ensemble_models
     def forward(self, X: Tensor) -> Tensor:
         r"""Evaluate Constrained Log Expected Improvement on the candidate set X.
 
@@ -647,6 +657,7 @@ def __init__(
         self.register_forward_pre_hook(convert_to_target_pre_hook)
 
     @t_batch_mode_transform(expected_q=1)
+    @average_over_ensemble_models
     def forward(self, X: Tensor) -> Tensor:
         r"""Evaluate Constrained Log Probability of Feasibility on the candidate set X.
 
@@ -722,6 +733,7 @@ def __init__(
         self.register_forward_pre_hook(convert_to_target_pre_hook)
 
     @t_batch_mode_transform(expected_q=1)
+    @average_over_ensemble_models
     def forward(self, X: Tensor) -> Tensor:
         r"""Evaluate Constrained Expected Improvement on the candidate set X.
 
@@ -810,6 +822,7 @@ def __init__(
         self.best_f, self.maximize = best_f, maximize
 
     @t_batch_mode_transform(expected_q=1)
+    @average_over_ensemble_models
     def forward(self, X: Tensor) -> Tensor:
         r"""Evaluate logarithm of the mean Expected Improvement on the candidate set X.
 
@@ -894,6 +907,7 @@ def __init__(
         super().__init__(model=fantasy_model, best_f=best_f, maximize=maximize)
 
     @t_batch_mode_transform(expected_q=1)
+    @average_over_ensemble_models
     def forward(self, X: Tensor) -> Tensor:
         r"""Evaluate Expected Improvement on the candidate set X.
 
@@ -951,6 +965,7 @@ def __init__(
         self.maximize = maximize
 
     @t_batch_mode_transform(expected_q=1)
+    @average_over_ensemble_models
     def forward(self, X: Tensor) -> Tensor:
         r"""Evaluate the Upper Confidence Bound on the candidate set X.
 
@@ -1000,6 +1015,7 @@ def __init__(
         self.maximize = maximize
 
     @t_batch_mode_transform(expected_q=1)
+    @average_over_ensemble_models
     def forward(self, X: Tensor) -> Tensor:
         r"""Evaluate the posterior mean on the candidate set X.
 
@@ -1041,6 +1057,7 @@ def __init__(
         self.register_buffer("weights", weights)
 
     @t_batch_mode_transform()
+    @average_over_ensemble_models
     def forward(self, X: Tensor) -> Tensor:
         r"""Evaluate the scalarized posterior mean on the candidate set X.
 
@@ -1108,6 +1125,7 @@ def __init__(
         self.maximize = maximize
 
     @t_batch_mode_transform(expected_q=1)
+    @average_over_ensemble_models
     def forward(self, X: Tensor) -> Tensor:
         r"""Evaluate the posterior standard deviation on the candidate set X.