Update jupyter-book workflow

.github/workflows/jupyter-book.yml

@@ -9,27 +9,25 @@ on:
 # This job installs dependencies, build the book, and pushes it to `gh-pages`
 jobs:
   deploy-book:
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-20.04
     steps:
-    # Install dependencies
+    # Install dependencies and build the book
     - uses: actions/checkout@v3
     - uses: actions/setup-python@v3
       with:
         python-version: 3.7
-    - run: pip install jupyter-book==0.15.1, docutils==0.17.1
-
-    # Build the book
-    - name: Build the book
-      run: |
+    - run: |
+        cd docs
+        pip install -r requirements.txt
         jupyter-book build .
 
     # Push the book's HTML to github-pages
     - name: Deploy github-pages
       uses: peaceiris/actions-gh-pages@v3
       with:
         personal_token: ${{ secrets.PERSONAL_TOKEN }}
-        publish_dir: ./_build/html
-        exclude_assets: '_sources'
         external_repository: openbenchmark/openbenchmark.github.io
         publish_branch: main
+        publish_dir: ./docs/_build/html
+        exclude_assets: '_sources'
         destination_dir: BARS

README.md

@@ -22,7 +22,7 @@ If you find our benchmarks helpful in your research, please kindly cite the foll
 
 ## Contributing
 
-We welcome any contribution that could help improve the BARS benchmark. Check the [start guide on how to contribute](./contribution.md).
+We welcome any contribution that could help improve the BARS benchmark. Check the [start guide on how to contribute](./docs/contribution.md).
 
 ## Discussion
 

docs/CTR/leaderboard/avazu_x4.md

@@ -31,7 +31,7 @@ Please use the following evaluation settings for this benchmark:
 ```{admonition} Avazu_x4_002
 :class: note
 Please use the following evaluation settings for this benchmark:
-+ Dataset split: [avazu_x4](https://github.com/reczoo/Datasets/tree/main/Avazu/Avazu_x4)
++ Dataset split: [Avazu_x4](https://github.com/reczoo/Datasets/tree/main/Avazu/Avazu_x4)
 + Rare features filtering: min_categr_count=1
 + Embedding size: 40
 ```

docs/CTR/leaderboard/movielenslatest_x1.csv

@@ -17,7 +17,7 @@ Year,Publication,Model,Paper URL,AUC,Logloss,Running Steps,Contributor
 2021,WWW'21,DCN-V2,https://arxiv.org/abs/2008.13535,0.9691,0.2147,https://github.com/reczoo/BARS/tree/main/ranking/ctr/DCNv2/DCNv2_movielenslatest_x1,"<a href=""https://arxiv.org/abs/2205.09626"">Zhu et al.</a>"
 2018,KDD'18,CIN,https://arxiv.org/pdf/1803.05170.pdf,0.9674,0.2085,https://github.com/reczoo/BARS/tree/main/ranking/ctr/xDeepFM/CIN_movielenslatest_x1,"<a href=""https://arxiv.org/abs/2205.09626"">Zhu et al.</a>"
 2018,KDD'18,xDeepFM,https://arxiv.org/pdf/1803.05170.pdf,0.9697,0.2409,https://github.com/reczoo/BARS/tree/main/ranking/ctr/xDeepFM/xDeepFM_movielenslatest_x1,"<a href=""https://arxiv.org/abs/2205.09626"">Zhu et al.</a>"
-2019,CIKM'19,AutoInt,https://arxiv.org/abs/1810.11921,0.9662,0.2228,https://github.com/reczoo/BARS/tree/main/ranking/ctr/AutoInt/AutoInt_movielenslatest_x1,"<a href=""https://arxiv.org/abs/2205.09626"">Zhu et al.</a>"
+2019,CIKM'19,AutoInt,https://arxiv.org/abs/1810.11921,0.9663,0.2228,https://github.com/reczoo/BARS/tree/main/ranking/ctr/AutoInt/AutoInt_movielenslatest_x1,"<a href=""https://arxiv.org/abs/2205.09626"">Zhu et al.</a>"
 2019,CIKM'19,AutoInt+,https://arxiv.org/abs/1810.11921,0.9692,0.2148,https://github.com/reczoo/BARS/tree/main/ranking/ctr/AutoInt/AutoInt+_movielenslatest_x1,"<a href=""https://arxiv.org/abs/2205.09626"">Zhu et al.</a>"
 2019,CIKM'19,FiGNN,https://arxiv.org/abs/1910.05552,0.9525,0.2559,https://github.com/reczoo/BARS/tree/main/ranking/ctr/FiGNN/FiGNN_movielenslatest_x1,"<a href=""https://arxiv.org/abs/2205.09626"">Zhu et al.</a>"
 2019,RecSys'19,FiBiNET,https://arxiv.org/abs/1905.09433,0.9576,0.2518,https://github.com/reczoo/BARS/tree/main/ranking/ctr/FiBiNET/FiBiNET_movielenslatest_x1,"<a href=""https://arxiv.org/abs/2205.09626"">Zhu et al.</a>"

docs/CTR/leaderboard/plots.py

@@ -12,6 +12,7 @@ def show_table(csv_path):
     df["Running Steps"] = df["Running Steps"].map(lambda x: f"<a href={x}>🔗</a>")
     df = df.sort_values(by=["AUC"], ascending=False).reset_index(drop=True)
     df.insert(0, "Rank", range(1, len(df) + 1))
+    df[['AUC', 'Logloss']] = df[['AUC', 'Logloss']].applymap('{:.4f}'.format)
     show(df, lengthMenu=[10, 20, 50, 100], classes="display")
 
 def show_plot(csv_path):

docs/Matching/leaderboard/plots.py

@@ -6,13 +6,14 @@
 opt.columnDefs = [{"className": "dt-center", "targets": "_all"}]
 
 def show_table(csv_path):
-    df = pd.read_csv(csv_path)
-    df = df.fillna("")
+    df = pd.read_csv(csv_path).fillna("")
     df["Model"] = df.apply(lambda x: f"<a href={x['Paper URL']}>{x['Model']}</a>", axis=1)
     del df["Paper URL"], df["Recall@50"], df["NDCG@50"], df["HitRate@50"]
     df["Running Steps"] = df["Running Steps"].map(lambda x: f"<a href={x}>🔗</a>")
     df = df.sort_values(by=["Recall@20"], ascending=False).reset_index(drop=True)
     df.insert(0, "Rank", range(1, len(df) + 1))
+    df[['Recall@20', 'NDCG@20', 'HitRate@20']] = df[['Recall@20', 'NDCG@20', 'HitRate@20']].applymap(
+        lambda x: '{:.4f}'.format(x) if x else "")
     show(df, lengthMenu=[10, 20, 50, 100], classes="display")
 
 def show_plot(csv_path):

docs/_toc.yml

@@ -23,12 +23,13 @@ parts:
     - file: CTR/leaderboard/criteo_x1.md
     - file: CTR/leaderboard/criteo_x4.md
     - file: CTR/leaderboard/frappe_x1.md
+    - file: CTR/leaderboard/movielenslatest_x1.md
     - file: CTR/leaderboard/kkbox_x1.md
+    - file: CTR/leaderboard/amazonelectronics_x1.md
+    - file: CTR/leaderboard/taobaoad_x1.md
     - file: CTR/leaderboard/kuaivideo_x1.md
     - file: CTR/leaderboard/microvideo1.7m_x1.md
-    - file: CTR/leaderboard/movielenslatest_x1.md
-    - file: CTR/leaderboard/taobaoad_x1.md
-
+
 - caption: "CANDIDATE MATCHING"
   chapters:
   - file: Matching/index.md

docs/requirements.txt

@@ -0,0 +1,5 @@
+jupyter-book==0.15.1
+docutils==0.17.1
+itables==1.6.3
+pandas==1.0.1
+plotly==5.18.0