Merge branch 'main' into graphuniverse-dataset

Author: gbg141

.gitattributes

@@ -1 +1 @@
-*.ipynb linguist-vendored
+*.ipynb linguist-vendored

.github/CODE_OF_CONDUCT.md

@@ -73,4 +73,4 @@ available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.ht
 [homepage]: https://www.contributor-covenant.org
 
 For answers to common questions about this code of conduct, see
-https://www.contributor-covenant.org/faq
+https://www.contributor-covenant.org/faq

.github/workflows/docs.yml

@@ -59,4 +59,4 @@ jobs:
           token: ${{ secrets.DOCUMENTATION_KEY }}
           repository-name: geometric-intelligence/geometric-intelligence.github.io
           target-folder: topobench
-          clean: true
+          clean: true

.github/workflows/lint.yml

@@ -22,4 +22,4 @@ jobs:
           # This ensures the ruff version matches what you have in pyproject.toml
           version-file: "pyproject.toml"
           # Optional: fails the build if the code changes (e.g. formatting)
-          changed-files: "true"
+          changed-files: "true"

.github/workflows/test.yml

@@ -44,4 +44,4 @@ jobs:
         with:
           token: ${{ secrets.CODECOV_TOKEN }}
           file: coverage.xml
-          fail_ci_if_error: false
+          fail_ci_if_error: false

.gitignore

@@ -195,4 +195,4 @@ wandb/
 
 # test temporary
 .test_tmp/
-uv.lock
+uv.lock

.pre-commit-config.yaml

@@ -5,10 +5,17 @@ repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
     rev: v5.0.0
     hooks:
+      - id: end-of-file-fixer
+      - id: trailing-whitespace
+        args:
+          - --markdown-linebreak-ext=md
       - id: fix-byte-order-marker
       - id: check-case-conflict
       - id: check-merge-conflict
+      - id: check-ast
+      - id: check-json
       - id: check-yaml
+      - id: check-symlinks
       - id: mixed-line-ending
         args:
           - --fix=no

README.md

@@ -30,19 +30,19 @@ Assess how your model compares against state-of-the-art topological neural netwo
   <a href="#gear-neural-networks">Neural Networks</a> •
   <a href="#rocket-liftings-and-transforms">Liftings and Transforms</a> •
   <a href="#books-datasets">Datasets</a> •
-  <a href="#mag-references">References</a> 
+  <a href="#mag-references">References</a>
 </p>
 
 
 ---
 
-> 🏆 The **TAG-DS Topological Deep Learning Challenge 2025** has concluded! A huge shotout to all participants. Check out the winners and honorable mentions on [`the challenge website`](https://geometric-intelligence.github.io/topobench/tdl-challenge/index.html).
+> 🏆 The **TAG-DS Topological Deep Learning Challenge 2025** has concluded! A huge shout-out to all participants. Check out the winners and honorable mentions on [`the challenge website`](https://geometric-intelligence.github.io/topobench/tdl-challenge/index.html).
 
 ---
 
 ## :pushpin: Overview
 
-`TopoBench` (TB) is a modular Python library designed to standardize benchmarking and accelerate research in Topological Deep Learning (TDL). In particular, TB allows to train and compare the performances of all sorts of Topological Neural Networks (TNNs) across the different topological domains, where by _topological domain_ we refer to a graph, a simplicial complex, a cellular complex, or a hypergraph. For detailed information, please refer to the [`TopoBench: A Framework for Benchmarking Topological Deep Learning`](https://arxiv.org/pdf/2406.06642) paper.
+`TopoBench` (TB) is a modular Python library designed to standardize benchmarking and accelerate research in Topological Deep Learning (TDL). In particular, TB allows training and comparing the performances of all sorts of Topological Neural Networks (TNNs) across the different topological domains, where by _topological domain_ we refer to a graph, a simplicial complex, a cellular complex, or a hypergraph. For detailed information, please refer to the [`TopoBench: A Framework for Benchmarking Topological Deep Learning`](https://arxiv.org/pdf/2406.06642) paper.
 
 <p align="center">
   <img src="resources/workflow.jpg" width="700">
@@ -94,7 +94,7 @@ uv sync --all-extras
 Once the environment is active, you can launch the TopoBench pipeline:
 ```bash
 # Using the activated virtual environment
-python -m topobench 
+python -m topobench
 
 # Or execute directly via uv without manual activation
 uv run python -m topobench
@@ -201,7 +201,7 @@ bash scripts/reproduce.sh
 
 ## :anchor: Tutorials
 
-Explore our [tutorials](https://github.com/geometric-intelligence/TopoBench/tree/main/tutorials) for further details on how to add new datasets, transforms/liftings, and benchmark tasks. 
+Explore our [tutorials](https://github.com/geometric-intelligence/TopoBench/tree/main/tutorials) for further details on how to add new datasets, transforms/liftings, and benchmark tasks.
 
 ## :gear: Neural Networks
 
@@ -264,13 +264,13 @@ We list the neural networks trained and evaluated by `TopoBench`, organized by t
 
 ## :rocket: Liftings and Transforms
 
-We list the liftings used in `TopoBench` to transform datasets. Here, a _lifting_ refers to a function that transforms a dataset defined on a topological domain (_e.g._, on a graph) into the same dataset but supported on a different topological domain (_e.g._, on a simplicial complex). 
+We list the liftings used in `TopoBench` to transform datasets. Here, a _lifting_ refers to a function that transforms a dataset defined on a topological domain (_e.g._, on a graph) into the same dataset but supported on a different topological domain (_e.g._, on a simplicial complex).
 
 ### <a name="structural_liftings"></a> Structural Liftings
 
 The structural lifting is responsible for the transformation of the underlying relationships or elements of the data. For instance, it might determine how nodes and edges in a graph are mapped into triangles and tetrahedra in a simplicial complex. This structural transformation can be further categorized into connectivity-based, where the mapping relies solely on the existing connections within the data, and feature-based, where the data's inherent properties or features guide the new structure.
 
-We enumerate below the structural liftings currently implemented in `TopoBench`; please check out the provided description links for further details. 
+We enumerate below the structural liftings currently implemented in `TopoBench`; please check out the provided description links for further details.
 
 **Remark:**: Most of these liftings are adaptations of winner submissions of the ICML TDL Challenge 2024 ([paper](https://proceedings.mlr.press/v251/bernardez24a.html) | [repo](https://github.com/pyt-team/challenge-icml-2024)); see the [Structural Liftings wiki](https://github.com/geometric-intelligence/TopoBench/wiki/Structural-Liftings) for a complete list of compatible liftings.
 
@@ -338,11 +338,11 @@ Feature liftings address the transfer of data attributes or features during mapp
 | ProjectionSum       | Projects r-cell features of a graph to r+1-cell structures utilizing incidence matrices \(B_{r}\). | All  |
 | ConcatenationLifting | Concatenate r-cell features to obtain r+1-cell features.                   | Simplicial        |
 
-### Data Transformations 
+### Data Transformations
 
 Specially useful in pre-processing steps, these are the general data manipulations currently implemented in `TopoBench`:
 
-| Transform | Description | 
+| Transform | Description |
 | --- | --- |
 | OneHotDegreeFeatures | Adds the node degree as one hot encodings to the node features. |
 | NodeFeaturesToFloat |Converts the node features of the input graph to float. |
@@ -473,12 +473,12 @@ If you find `TopoBench` useful, we would appreciate if you cite us!
 
 <details>
 <summary><b> More information regarding Topological Deep Learning </b></summary>
-  
+
   [Topological Graph Signal Compression](https://arxiv.org/pdf/2308.11068)
-  
+
   [Architectures of Topological Deep Learning: A Survey on Topological Neural Networks](https://par.nsf.gov/servlets/purl/10477141)
-  
-  [TopoX: a suite of Python packages for machine learning on topological domains](https://arxiv.org/pdf/2402.02441)	
+
+  [TopoX: a suite of Python packages for machine learning on topological domains](https://arxiv.org/pdf/2402.02441)
 </details>
 
 ---
@@ -490,4 +490,3 @@ Feel free to reach out via email if you want to collaborate, do your thesis with
 
 📧 **Contact Email:** [topological.intelligence@gmail.com](mailto:topological.intelligence@gmail.com)  
 ▶️ **YouTube Channel:** [Topological Intelligence](https://www.youtube.com/@TopologicalIntelligence)
-

codecov.yml

@@ -5,4 +5,4 @@ coverage:
   precision: 2
 ignore:
   - "test/"
-  - "topobench/run.py"
+  - "topobench/run.py"

configs/callbacks/default.yaml

@@ -25,4 +25,4 @@ model_summary:
 
 learning_rate_monitor:
   _target_: lightning.pytorch.callbacks.LearningRateMonitor
-  logging_interval: "epoch"
+  logging_interval: "epoch"