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examples/train_gnn.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Training a GNN on the Mantra Dataset\n",
+    "\n",
+    "In this quick tutorial, we provide an example use-case for the mantra dataset.\n",
+    "After a quick overview and preliminary exploratory analysis we train a GNN\n",
+    "to predict the orientability. \n",
+    "\n",
+    "Our dataset can be installed via the pip command line with the command \n",
+    "```{python}\n",
+    "pip install mantra\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exploratory analysis of the dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load all required packages. \n",
+    "\n",
+    "import torch \n",
+    "import numpy as np \n",
+    "import pandas as pd \n",
+    "import seaborn as sns \n",
+    "\n",
+    "# Load the mantra dataset\n",
+    "from mantra.datasets import ManifoldTriangulations\n",
+    "\n",
+    "# Instantiate the dataset. Following the `torch-geometric` API, we download the \n",
+    "# dataset into the root directory. \n",
+    "dataset = ManifoldTriangulations(root=\"../data\", manifold=\"2\", version=\"latest\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exploratory analysis\n",
+    "\n",
+    "As a first step we aim to have a throrough understanding of the dataset. In particular we are interested \n",
+    "in the distribution of the various properties in the dataset, such as the betti \n",
+    "numbers, types of manifolds and distribution of orientability. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
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+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
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