From e3f0cd44b433d885bd554830f348604bd4112584 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lu=C3=ADs=20F=2E=20Pereira?= Date: Thu, 23 Jan 2025 11:40:35 -0800 Subject: [PATCH] Move icml2024-pr6 files --- .../test_NeighborhoodComplexLifting.py} | 0 .../graph2simplicial/neighborhood_lifting.py | 0 .../neighborhood_lifting.ipynb | 358 ------------------ 3 files changed, 358 deletions(-) rename test/transforms/liftings/{graph2simplicial/test_neighborhood_lifting.py => simplicial/test_NeighborhoodComplexLifting.py} (100%) rename {modules => topobenchmark}/transforms/liftings/graph2simplicial/neighborhood_lifting.py (100%) delete mode 100644 tutorials/graph2simplicial/neighborhood_lifting.ipynb diff --git a/test/transforms/liftings/graph2simplicial/test_neighborhood_lifting.py b/test/transforms/liftings/simplicial/test_NeighborhoodComplexLifting.py similarity index 100% rename from test/transforms/liftings/graph2simplicial/test_neighborhood_lifting.py rename to test/transforms/liftings/simplicial/test_NeighborhoodComplexLifting.py diff --git a/modules/transforms/liftings/graph2simplicial/neighborhood_lifting.py b/topobenchmark/transforms/liftings/graph2simplicial/neighborhood_lifting.py similarity index 100% rename from modules/transforms/liftings/graph2simplicial/neighborhood_lifting.py rename to topobenchmark/transforms/liftings/graph2simplicial/neighborhood_lifting.py diff --git a/tutorials/graph2simplicial/neighborhood_lifting.ipynb b/tutorials/graph2simplicial/neighborhood_lifting.ipynb deleted file mode 100644 index fa5c7ceb..00000000 --- a/tutorials/graph2simplicial/neighborhood_lifting.ipynb +++ /dev/null @@ -1,358 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Graph-to-Simplicial Neighborhood Lifting Tutorial" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "***\n", - "This notebook shows how to import a dataset, with the desired lifting, and how to run a neural network using the loaded data.\n", - "\n", - "The notebook is divided into sections:\n", - "\n", - "- [Loading the dataset](#loading-the-dataset) loads the config files for the data and the desired tranformation, createsa a dataset object and visualizes it.\n", - "- [Loading and applying the lifting](#loading-and-applying-the-lifting) defines a simple neural network to test that the lifting creates the expected incidence matrices.\n", - "- [Create and run a simplicial nn model](#create-and-run-a-simplicial-nn-model) simply runs a forward pass of the model to check that everything is working as expected.\n", - "\n", - "***\n", - "***\n", - "\n", - "Note that for simplicity the notebook is setup to use a simple graph. However, there is a set of available datasets that you can play with.\n", - "\n", - "To switch to one of the available datasets, simply change the *dataset_name* variable in [Dataset config](#dataset-config) to one of the following names:\n", - "\n", - "* cocitation_cora\n", - "* cocitation_citeseer\n", - "* cocitation_pubmed\n", - "* MUTAG\n", - "* NCI1\n", - "* NCI109\n", - "* PROTEINS_TU\n", - "* AQSOL\n", - "* ZINC\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Imports and utilities" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# With this cell any imported module is reloaded before each cell execution\n", - "%load_ext autoreload\n", - "%autoreload 2\n", - "from modules.data.load.loaders import GraphLoader\n", - "from modules.data.preprocess.preprocessor import PreProcessor\n", - "from modules.utils.utils import (\n", - " describe_data,\n", - " load_dataset_config,\n", - " load_model_config,\n", - " load_transform_config,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Loading the Dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we just need to spicify the name of the available dataset that we want to load. First, the dataset config is read from the corresponding yaml file (located at `/configs/datasets/` directory), and then the data is loaded via the implemented `Loaders`.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Dataset configuration for manual_dataset:\n", - "\n", - "{'data_domain': 'graph',\n", - " 'data_type': 'toy_dataset',\n", - " 'data_name': 'manual',\n", - " 'data_dir': 'datasets/graph/toy_dataset',\n", - " 'num_features': 1,\n", - " 'num_classes': 2,\n", - " 'task': 'classification',\n", - " 'loss_type': 'cross_entropy',\n", - " 'monitor_metric': 'accuracy',\n", - " 'task_level': 'node'}\n" - ] - } - ], - "source": [ - "dataset_name = \"manual_dataset\" # \"manual_dataset\"#\"PROTEINS_TU\"\n", - "dataset_config = load_dataset_config(dataset_name)\n", - "loader = GraphLoader(dataset_config)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can then access to the data through the `load()`method:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Dataset only contains 1 sample:\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " - Graph with 8 vertices and 13 edges.\n", - " - Features dimensions: [1, 0]\n", - " - There are 0 isolated nodes.\n", - "\n" - ] - } - ], - "source": [ - "dataset = loader.load()\n", - "describe_data(dataset)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Loading and Applying the Lifting" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this section we will instantiate the neighborhood lifting. This lifting constructs a simplicial complex, called the **Neighborhood complex**, as it is usually defined in the field of **topological combinatorics**. Let me briefly describe this construction, for more details please see [[1]](https://doi.org/10.1007/978-3-540-76649-0).\n", - "\n", - "Consider a graph $G=(V,E)$. Its neighborhood complex $N(G)$ is a simplicial complex with the vertex set $V$ and simplices given by subsets $A\\subseteq V$ such, that $\\forall a\\in A\\; \\exists v: (a,v)\\in E$. That is, say, 3 vertices form a simplex iff there's another vertex which is adjacent to each of these 3 vertices.\n", - "\n", - "This complex in fact can be seen as a special case of **Dowker's complex** [[2]](https://www.jstor.org/stable/1969768) (or see [this nLab page](https://ncatlab.org/nlab/show/Dowker%27s+theorem) for more details): given a graph $G=(V,E)$, consider the following symmetric relation $R$ on the set $V$ of vertices: $$ xRy \\iff (x,y)\\in E. $$ The Dowker's complex consists of simplices $\\{x_0, ..., x_n\\}$ such that $\\exists y: \\forall i: x_iRy$.\n", - "\n", - "Then, just following the Dowker's construction, one can obtain a neighborhood complex: indeed, an existence of a simplex $\\sigma$ in a neighborhood complex on the set of vertices $\\{v_0, ..., v_n\\}$ constitutes the existence of a vertex $w$ such that $\\forall i: (v_i, w)\\in E$. But then $\\forall i: v_iRw$ hence there's a simplex on the vertices $\\{v_0, ..., v_n\\}$ in the Dowker's complex. Converse holds as well.\n", - "\n", - "***\n", - "[[1]](https://doi.org/10.1007/978-3-540-76649-0) Matoušek, J. (2008). Using the Borsuk–Ulam Theorem. Springer Berlin Heidelberg.\n", - "\n", - "[[2]](https://www.jstor.org/stable/1969768) C. H. Dowker, Homology Groups of Relations, Annals of Math. 56 (1952), 84–95.\n", - "***" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Transform configuration for graph2simplicial/neighborhood_lifting:\n", - "\n", - "{'transform_type': 'lifting',\n", - " 'transform_name': 'NeighborhoodComplexLifting',\n", - " 'preserve_edge_attr': False,\n", - " 'signed': True,\n", - " 'feature_lifting': 'ProjectionSum'}\n" - ] - } - ], - "source": [ - "# Define transformation type and id\n", - "transform_type = \"liftings\"\n", - "# If the transform is a topological lifting, it should include both the type of the lifting and the identifier\n", - "transform_id = \"graph2simplicial/neighborhood_lifting\"\n", - "\n", - "# Read yaml file\n", - "transform_config = {\n", - " \"lifting\": load_transform_config(transform_type, transform_id)\n", - " # other transforms (e.g. data manipulations, feature liftings) can be added here\n", - "}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We than apply the transform via our `PreProcessor`:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Transform parameters are the same, using existing data_dir: /Users/snopoff/git_repos/challenge-icml-2024/datasets/graph/toy_dataset/manual/lifting/2172744449\n", - "\n", - "Dataset only contains 1 sample:\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " - The complex has 8 0-cells.\n", - " - The 0-cells have features dimension 1\n", - " - The complex has 22 1-cells.\n", - " - The 1-cells have features dimension 1\n", - " - The complex has 27 2-cells.\n", - " - The 2-cells have features dimension 1\n", - " - The complex has 16 3-cells.\n", - " - The 3-cells have features dimension 1\n", - " - The complex has 6 4-cells.\n", - " - The 4-cells have features dimension 1\n", - " - The complex has 1 5-cells.\n", - " - The 5-cells have features dimension 1\n", - "\n" - ] - } - ], - "source": [ - "lifted_dataset = PreProcessor(dataset, transform_config, loader.data_dir)\n", - "describe_data(lifted_dataset)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create and Run a Simplicial NN Model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this section a simple model is created to test that the used lifting works as intended. In this case the model uses the `up_laplacian_1` and the `down_laplacian_1` so the lifting should make sure to add them to the data." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Model configuration for simplicial SAN:\n", - "\n", - "{'in_channels': None,\n", - " 'hidden_channels': 32,\n", - " 'out_channels': None,\n", - " 'n_layers': 2,\n", - " 'n_filters': 2,\n", - " 'order_harmonic': 5,\n", - " 'epsilon_harmonic': 0.1}\n" - ] - } - ], - "source": [ - "from modules.models.simplicial.san import SANModel\n", - "\n", - "model_type = \"simplicial\"\n", - "model_id = \"san\"\n", - "model_config = load_model_config(model_type, model_id)\n", - "\n", - "model = SANModel(model_config, dataset_config)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "y_hat = model(lifted_dataset.get(0))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If everything is correct the cell above should execute without errors. " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv_topox", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -}