Merge pull request #316 from geometric-intelligence/guille/2026_TDL_challenge

Fix tests (pyg-lib related)

Author: gbg141

.gitignore

@@ -89,6 +89,9 @@ tutorials/lightning_logs/*
 tutorials/checkpoints
 tutorials/checkpoints/*
 
+# GraphUniverse challenge notebook outputs (JSON + figures)
+2026_tdl_challenge/outputs/
+
 # IPython
 profile_default/
 ipython_config.py

2026_tdl_challenge/__init__.py

@@ -0,0 +1,174 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "021b3a4c",
+      "metadata": {},
+      "source": [
+        "# 2026 TDL Challenge: GraphUniverse\n",
+        "\n",
+        "This notebook evaluates your model across a grid of GraphUniverse's synthetic graph distributions:\n",
+        "- **Homophily** (low, mid, high)\n",
+        "- **Average degree** (low, high)  \n",
+        "- **Power-law exponent** (low, high)\n",
+        "\n",
+        "Each configuration is trained with multiple random seeds. The best checkpoint from each run is then evaluated on all other grid settings (out-of-distribution evaluation).\n",
+        "\n",
+        "## Setup Requirements\n",
+        "\n",
+        "**Make sure Weights & Biases is configured:**\n",
+        "```bash\n",
+        "wandb login\n",
+        "```\n",
+        "\n",
+        "## How to Use\n",
+        "\n",
+        "1. Set your `MODEL_CONFIG` (path to your model yaml)\n",
+        "2. Run the evaluation\n",
+        "3. Results will be saved in:\n",
+        "   - `results.json` with detailed metrics\n",
+        "   - Heatmap visualizations showing performance across the grid\n",
+        "   - OOD performance delta plots"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "53c1d2fb",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "from pathlib import Path\n",
+        "\n",
+        "# Setup paths\n",
+        "_ROOT = Path.cwd().resolve()\n",
+        "_REPO = _ROOT if (_ROOT / \"configs\" / \"run.yaml\").exists() else _ROOT.parent\n",
+        "if str(_REPO) not in sys.path:\n",
+        "    sys.path.insert(0, str(_REPO))\n",
+        "\n",
+        "from utils import (\n",
+        "    resolve_project_root,\n",
+        "    run_challenge_grid,\n",
+        "    save_challenge_artifacts,\n",
+        ")\n",
+        "\n",
+        "PROJECT_ROOT = resolve_project_root(_REPO)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "config_section",
+      "metadata": {},
+      "source": [
+        "## Configuration\n",
+        "\n",
+        "**Set your model config path here:**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "config_cell",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Your model configuration (e.g., \"graph/gcn\", \"graph/gin\", \"graph/gat\")\n",
+        "MODEL_CONFIG = \"graph/gin\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "run_section",
+      "metadata": {},
+      "source": [
+        "## Run Evaluation\n",
+        "\n",
+        "This will train and evaluate your model across all grid configurations."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "93a4bb1f",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "results, study_id = run_challenge_grid(\n",
+        "    project_root=PROJECT_ROOT,\n",
+        "    model_config=MODEL_CONFIG,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "save_section",
+      "metadata": {},
+      "source": [
+        "## Save Results\n",
+        "\n",
+        "Generate JSON output and visualization plots."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "save_cell",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "output_paths = save_challenge_artifacts(\n",
+        "    results,\n",
+        "    model_config=MODEL_CONFIG,\n",
+        "    study_id=study_id,\n",
+        ")\n",
+        "\n",
+        "print(f\"\\nResults saved to: {output_paths['dir']}\")\n",
+        "print(f\"JSON: {output_paths['json']}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "inspect_section",
+      "metadata": {},
+      "source": [
+        "## Inspect Results\n",
+        "\n",
+        "View results as a DataFrame for quick inspection."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "inspect_cell",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "pd.DataFrame(results)"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "tb",
+      "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": 5
+}