Sumbission dl2

.gitignore

@@ -165,4 +165,9 @@ cython_debug/
 #.idea/
 
 # VS Code
-.vscode/
+.vscode/
+
+jobs/outputs/
+
+*.tex
+*.bib

README.md

@@ -1,78 +1,42 @@
-# ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain
-Official repository for the Topological Deep Learning Challenge 2024, jointly organized by [TAG-DS](https://www.tagds.com) & PyT-Team and hosted by the [Geometry-grounded Representation Learning and Generative Modeling (GRaM) Workshop](https://gram-workshop.github.io) at ICML 2024.
+# DL 2 Project - Reproduction of E(n) Equivariant Simplicial Message Passing Network
 
-## Relevant Information
-- The deadline is **July 12th, 2024 (Anywhere on Earth)**. Participants are welcome to modify their submissions until this time.
-- Please, check out the [main webpage of the challenge](https://pyt-team.github.io/packs/challenge.html) for the full description of the competition (motivation, submission requirements, evaluation, etc.)
+This project is meant to showcase the effort in replication the paper of Eijkelboom et al about message passing along simplices. Our aim was to re-implement the proposed network architecture in the up and comming TopoX package suite. This suite is a newly developed set of packages that facilitate the pipeline of Topological Deep Learning, from lifting techniques to actual models of message passing.
 
-## Brief Description
-The main purpose of the challenge is to further expand the current scope and impact of Topological Deep Learning (TDL), enabling the exploration of its applicability in new contexts and scenarios. To do so, we propose participants to design and implement lifting mappings between different data structures and topological domains (point-clouds, graphs, hypergraphs, simplicial/cell/combinatorial complexes), potentially bridging the gap between TDL and all kinds of existing datasets.
+## Setup
+To setup from a clean environment we will reproduce the steps as mentioned in the `challenge-icml-2024` and an additional one.
 
-
-## General Guidelines
-Everyone can participate and participation is free --only principal PyT-Team developers are excluded. It is sufficient to:
-- Send a valid Pull Request (i.e. passing all tests) before the deadline.
-- Respect Submission Requirements (see below).
-
-Teams are accepted, and there is no restriction on the number of team members. An acceptable Pull Request automatically subscribes a participant/team to the challenge.
-
-We encourage participants to start submitting their Pull Request early on, as this helps addressing potential issues with the code. Moreover, earlier Pull Requests will be given priority consideration in the case of multiple submissions of similar quality implementing the same lifting.
-
-A Pull Request should contain no more than one lifting. However, there is no restriction on the number of submissions (Pull Requests) per participant/team.
-
-## Basic Setup
-To develop on your machine, here are some tips.
-
-First, we recommend using Python 3.11.3, which is the python version used to run the unit-tests.
-
-For example, create a conda environment:
+1. Create the conda environment to be used
    ```bash
    conda create -n topox python=3.11.3
    conda activate topox
    ```
-
-Then:
-
-1. Clone a copy of tmx from source:
-
-   ```bash
-   git clone git@github.com:pyt-team/challenge-icml-2024.git
-   cd challenge-icml-2024
-   ```
-
-2. Install tmx in editable mode:
-
+2. Install the required packages and the TopoX suite including the code reqquiremed from the `challenge-icml-2024` repo
    ```bash
    pip install -e '.[all]'
    ```
-   **Notes:**
-   - Requires pip >= 21.3. Refer: [PEP 660](https://peps.python.org/pep-0660/).
-   - On Windows, use `pip install -e .[all]` instead (without quotes around `[all]`).
-
-4. Install torch, torch-scatter, torch-sparse with or without CUDA depending on your needs.
+3. Install pytorch related libraries
 
       ```bash
-      pip install torch==2.0.1 --extra-index-url https://download.pytorch.org/whl/${CUDA}
-      pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-2.0.1+${CUDA}.html
-      pip install torch-cluster -f https://data.pyg.org/whl/torch-2.0.0+${CUDA}.html
+      pip install torch==2.0.1 --extra-index-url https://download.pytorch.org/whl/cu115
+      pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-2.0.1+cu115.html
+      pip install torch-cluster -f https://data.pyg.org/whl/torch-2.0.0+cu115.html
       ```
+4. Wandb for saving model information
+    ```bash
+    pip install wandb
+    ```
+## Experiments
 
-      where `${CUDA}` should be replaced by either `cpu`, `cu102`, `cu113`, or `cu115` depending on your PyTorch installation (`torch.version.cuda`).
+There are two important directions the experiments follow: execution efficiency, performance of the network.
+First, we will evaluate the efficiency of the lifting procedure for different complexes (Alpha Complex, Vietoris-Rips), the efficiency of pre-computing the invariance relationships prior to the forward pass and the actual
+performance in the QM9 dataset.
 
-5. Ensure that you have a working tmx installation by running the entire test suite with
-
-   ```bash
-   pytest
-   ```
-
-    In case an error occurs, please first check if all sub-packages ([`torch-scatter`](https://github.com/rusty1s/pytorch_scatter), [`torch-sparse`](https://github.com/rusty1s/pytorch_sparse), [`torch-cluster`](https://github.com/rusty1s/pytorch_cluster) and [`torch-spline-conv`](https://github.com/rusty1s/pytorch_spline_conv)) are on its latest reported version.
-
-6. Install pre-commit hooks:
-
-   ```bash
-   pre-commit install
-   ```
+To execute an experiment activate the `topox` environment created above and go into `src`. The file `main.py` will
+run the experiment, some optional paramters are explained below.
 
-## Questions
++ `--lift_type <lift>`: the type of lifting procedure `alpha` or `rips`
++ `--batch_size <size>` the size of each batch
++ `--target_name <name>`: the target molecular property to train/predict for
++ `--debug `: to run a smaller subset of the dataset for testing purpouses
++ `--pre_proc`: wether the invariances should be precomputed during the lift procedure or not (beware it's vary time consuming)
 
-Feel free to contact us through GitHub issues on this repository, or through the [Geometry and Topology in Machine Learning slack](https://tda-in-ml.slack.com/join/shared_invite/enQtOTIyMTIyNTYxMTM2LTA2YmQyZjVjNjgxZWYzMDUyODY5MjlhMGE3ZTI1MzE4NjI2OTY0MmUyMmQ3NGE0MTNmMzNiMTViMjM2MzE4OTc#/). Alternatively, you can contact us via mail at any of these accounts: guillermo.bernardez@upc.edu, lev.telyatnikov@uniroma1.it.

README_backup.md

@@ -0,0 +1,78 @@
+# ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain
+Official repository for the Topological Deep Learning Challenge 2024, jointly organized by [TAG-DS](https://www.tagds.com) & PyT-Team and hosted by the [Geometry-grounded Representation Learning and Generative Modeling (GRaM) Workshop](https://gram-workshop.github.io) at ICML 2024.
+
+## Relevant Information
+- The deadline is **July 12th, 2024 (Anywhere on Earth)**. Participants are welcome to modify their submissions until this time.
+- Please, check out the [main webpage of the challenge](https://pyt-team.github.io/packs/challenge.html) for the full description of the competition (motivation, submission requirements, evaluation, etc.)
+
+## Brief Description
+The main purpose of the challenge is to further expand the current scope and impact of Topological Deep Learning (TDL), enabling the exploration of its applicability in new contexts and scenarios. To do so, we propose participants to design and implement lifting mappings between different data structures and topological domains (point-clouds, graphs, hypergraphs, simplicial/cell/combinatorial complexes), potentially bridging the gap between TDL and all kinds of existing datasets.
+
+
+## General Guidelines
+Everyone can participate and participation is free --only principal PyT-Team developers are excluded. It is sufficient to:
+- Send a valid Pull Request (i.e. passing all tests) before the deadline.
+- Respect Submission Requirements (see below).
+
+Teams are accepted, and there is no restriction on the number of team members. An acceptable Pull Request automatically subscribes a participant/team to the challenge.
+
+We encourage participants to start submitting their Pull Request early on, as this helps addressing potential issues with the code. Moreover, earlier Pull Requests will be given priority consideration in the case of multiple submissions of similar quality implementing the same lifting.
+
+A Pull Request should contain no more than one lifting. However, there is no restriction on the number of submissions (Pull Requests) per participant/team.
+
+## Basic Setup
+To develop on your machine, here are some tips.
+
+First, we recommend using Python 3.11.3, which is the python version used to run the unit-tests.
+
+For example, create a conda environment:
+   ```bash
+   conda create -n topox python=3.11.3
+   conda activate topox
+   ```
+
+Then:
+
+1. Clone a copy of tmx from source:
+
+   ```bash
+   git clone git@github.com:pyt-team/challenge-icml-2024.git
+   cd challenge-icml-2024
+   ```
+
+2. Install tmx in editable mode:
+
+   ```bash
+   pip install -e '.[all]'
+   ```
+   **Notes:**
+   - Requires pip >= 21.3. Refer: [PEP 660](https://peps.python.org/pep-0660/).
+   - On Windows, use `pip install -e .[all]` instead (without quotes around `[all]`).
+
+4. Install torch, torch-scatter, torch-sparse with or without CUDA depending on your needs.
+
+      ```bash
+      pip install torch==2.0.1 --extra-index-url https://download.pytorch.org/whl/${CUDA}
+      pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-2.0.1+${CUDA}.html
+      pip install torch-cluster -f https://data.pyg.org/whl/torch-2.0.0+${CUDA}.html
+      ```
+
+      where `${CUDA}` should be replaced by either `cpu`, `cu102`, `cu113`, or `cu115` depending on your PyTorch installation (`torch.version.cuda`).
+
+5. Ensure that you have a working tmx installation by running the entire test suite with
+
+   ```bash
+   pytest
+   ```
+
+    In case an error occurs, please first check if all sub-packages ([`torch-scatter`](https://github.com/rusty1s/pytorch_scatter), [`torch-sparse`](https://github.com/rusty1s/pytorch_sparse), [`torch-cluster`](https://github.com/rusty1s/pytorch_cluster) and [`torch-spline-conv`](https://github.com/rusty1s/pytorch_spline_conv)) are on its latest reported version.
+
+6. Install pre-commit hooks:
+
+   ```bash
+   pre-commit install
+   ```
+
+## Questions
+
+Feel free to contact us through GitHub issues on this repository, or through the [Geometry and Topology in Machine Learning slack](https://tda-in-ml.slack.com/join/shared_invite/enQtOTIyMTIyNTYxMTM2LTA2YmQyZjVjNjgxZWYzMDUyODY5MjlhMGE3ZTI1MzE4NjI2OTY0MmUyMmQ3NGE0MTNmMzNiMTViMjM2MzE4OTc#/). Alternatively, you can contact us via mail at any of these accounts: guillermo.bernardez@upc.edu, lev.telyatnikov@uniroma1.it.