add tutorial

Tutorial.ipynb

@@ -0,0 +1,159 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "premier-closing",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import torch\n",
+    "\n",
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "documented-viewer",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import drGAT"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "protected-regular",
+   "metadata": {},
+   "source": [
+    "# model evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "869740ff-e2fc-43b8-9a0f-49e637522ec4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test = torch.load('test.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "favorite-saturday",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "tmp = !ls | grep pt\n",
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "model = torch.load('sample.pt', map_location=device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "subject-allen",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Accuracy</th>\n",
+       "      <th>Precision</th>\n",
+       "      <th>Recall</th>\n",
+       "      <th>F1 Score</th>\n",
+       "      <th>True Positive</th>\n",
+       "      <th>True Negative</th>\n",
+       "      <th>False Positive</th>\n",
+       "      <th>False Negative</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.771375</td>\n",
+       "      <td>0.740881</td>\n",
+       "      <td>0.783245</td>\n",
+       "      <td>0.761474</td>\n",
+       "      <td>1178</td>\n",
+       "      <td>1312</td>\n",
+       "      <td>412</td>\n",
+       "      <td>326</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Accuracy  Precision    Recall  F1 Score  True Positive  True Negative  \\\n",
+       "0  0.771375   0.740881  0.783245  0.761474           1178           1312   \n",
+       "\n",
+       "   False Positive  False Negative  \n",
+       "0             412             326  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "drGAT.eval(model, test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "korean-recipe",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "drGAT",
+   "language": "python",
+   "name": "drgat"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}