Cora_gcn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "51773a67",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a9093799",
   "metadata": {},
   "outputs": [],
   "source": [
    "zip_file = keras.utils.get_file(\n",
    "    fname=\"cora.tgz\",\n",
    "    origin=\"https://linqs-data.soe.ucsc.edu/public/lbc/cora.tgz\",\n",
    "    extract=True,\n",
    ")\n",
    "data_dir = os.path.join(os.path.dirname(zip_file), \"cora\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5d958505",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Citations shape: (5429, 2)\n"
     ]
    }
   ],
   "source": [
    "citations = pd.read_csv(\n",
    "    os.path.join(data_dir, \"cora.cites\"),\n",
    "    sep=\"\\t\",\n",
    "    header=None,\n",
    "    names=[\"target\", \"source\"],\n",
    ")\n",
    "print(\"Citations shape:\", citations.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8b976fa5",
   "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>target</th>\n",
       "      <th>source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1823</th>\n",
       "      <td>12210</td>\n",
       "      <td>6125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2955</th>\n",
       "      <td>37483</td>\n",
       "      <td>63931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3322</th>\n",
       "      <td>55968</td>\n",
       "      <td>892139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3451</th>\n",
       "      <td>63915</td>\n",
       "      <td>145215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1434</th>\n",
       "      <td>6818</td>\n",
       "      <td>50337</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      target  source\n",
       "1823   12210    6125\n",
       "2955   37483   63931\n",
       "3322   55968  892139\n",
       "3451   63915  145215\n",
       "1434    6818   50337"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "citations.sample(frac=1).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ec29c509",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Papers shape: (2708, 1435)\n"
     ]
    }
   ],
   "source": [
    "column_names = [\"paper_id\"] + [f\"term_{idx}\" for idx in range(1433)] + [\"subject\"]\n",
    "papers = pd.read_csv(\n",
    "    os.path.join(data_dir, \"cora.content\"), sep=\"\\t\", header=None, names=column_names,\n",
    ")\n",
    "print(\"Papers shape:\", papers.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f7781379",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             35               680              1520  \\\n",
      "paper_id                    13966          1114352           230879   \n",
      "term_0                          0                0                0   \n",
      "term_1                          0                0                0   \n",
      "term_2                          0                0                0   \n",
      "term_3                          0                0                0   \n",
      "...                           ...              ...              ...   \n",
      "term_1429                       0                0                0   \n",
      "term_1430                       0                0                0   \n",
      "term_1431                       0                0                0   \n",
      "term_1432                       0                0                0   \n",
      "subject    Reinforcement_Learning  Neural_Networks  Neural_Networks   \n",
      "\n",
      "                            117                    292   \n",
      "paper_id                 1115291                 642847  \n",
      "term_0                         0                      0  \n",
      "term_1                         0                      0  \n",
      "term_2                         0                      0  \n",
      "term_3                         0                      0  \n",
      "...                          ...                    ...  \n",
      "term_1429                      0                      0  \n",
      "term_1430                      0                      0  \n",
      "term_1431                      0                      0  \n",
      "term_1432                      0                      0  \n",
      "subject    Probabilistic_Methods  Probabilistic_Methods  \n",
      "\n",
      "[1435 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "print(papers.sample(5).T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "45b76257",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neural_Networks           818\n",
      "Probabilistic_Methods     426\n",
      "Genetic_Algorithms        418\n",
      "Theory                    351\n",
      "Case_Based                298\n",
      "Reinforcement_Learning    217\n",
      "Rule_Learning             180\n",
      "Name: subject, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(papers.subject.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "64a13d82",
   "metadata": {},
   "outputs": [],
   "source": [
    "class_values = sorted(papers[\"subject\"].unique())\n",
    "class_idx = {name: id for id, name in enumerate(class_values)}\n",
    "paper_idx = {name: idx for idx, name in enumerate(sorted(papers[\"paper_id\"].unique()))}\n",
    "\n",
    "papers[\"paper_id\"] = papers[\"paper_id\"].apply(lambda name: paper_idx[name])\n",
    "citations[\"source\"] = citations[\"source\"].apply(lambda name: paper_idx[name])\n",
    "citations[\"target\"] = citations[\"target\"].apply(lambda name: paper_idx[name])\n",
    "papers[\"subject\"] = papers[\"subject\"].apply(lambda value: class_idx[value])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "89cc85b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "colors = papers[\"subject\"].tolist()\n",
    "cora_graph = nx.from_pandas_edgelist(citations.sample(n=1500))\n",
    "subjects = list(papers[papers[\"paper_id\"].isin(list(cora_graph.nodes))][\"subject\"])\n",
    "nx.draw_spring(cora_graph, node_size=15, node_color=subjects)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6b790de6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape: (1332, 1435)\n",
      "Test data shape: (1376, 1435)\n"
     ]
    }
   ],
   "source": [
    "train_data, test_data = [], []\n",
    "\n",
    "for _, group_data in papers.groupby(\"subject\"):\n",
    "    # Select around 50% of the dataset for training.\n",
    "    random_selection = np.random.rand(len(group_data.index)) <= 0.5\n",
    "    train_data.append(group_data[random_selection])\n",
    "    test_data.append(group_data[~random_selection])\n",
    "\n",
    "train_data = pd.concat(train_data).sample(frac=1)\n",
    "test_data = pd.concat(test_data).sample(frac=1)\n",
    "\n",
    "print(\"Train data shape:\", train_data.shape)\n",
    "print(\"Test data shape:\", test_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "582be078",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>paper_id</th>\n",
       "      <th>term_0</th>\n",
       "      <th>term_1</th>\n",
       "      <th>term_2</th>\n",
       "      <th>term_3</th>\n",
       "      <th>term_4</th>\n",
       "      <th>term_5</th>\n",
       "      <th>term_6</th>\n",
       "      <th>term_7</th>\n",
       "      <th>term_8</th>\n",
       "      <th>...</th>\n",
       "      <th>term_1424</th>\n",
       "      <th>term_1425</th>\n",
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       "      <th>term_1428</th>\n",
       "      <th>term_1429</th>\n",
       "      <th>term_1430</th>\n",
       "      <th>term_1431</th>\n",
       "      <th>term_1432</th>\n",
       "      <th>subject</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1272</th>\n",
       "      <td>1378</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1722</th>\n",
       "      <td>1537</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2034</th>\n",
       "      <td>859</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>834</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1586</th>\n",
       "      <td>587</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1127</th>\n",
       "      <td>2681</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025</th>\n",
       "      <td>1703</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2094</th>\n",
       "      <td>2234</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2095</th>\n",
       "      <td>697</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1376 rows × 1435 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      paper_id  term_0  term_1  term_2  term_3  term_4  term_5  term_6  \\\n",
       "1272      1378       0       0       0       0       0       0       0   \n",
       "693       1146       0       0       0       0       0       0       0   \n",
       "1722      1537       0       0       0       0       0       0       0   \n",
       "2034       859       0       0       0       0       0       0       0   \n",
       "401        834       0       0       0       0       0       0       0   \n",
       "...        ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "1586       587       0       0       0       0       0       0       0   \n",
       "1127      2681       0       0       0       0       0       0       0   \n",
       "2025      1703       0       0       0       0       0       0       0   \n",
       "2094      2234       0       0       0       0       0       0       0   \n",
       "2095       697       0       0       0       0       0       0       0   \n",
       "\n",
       "      term_7  term_8  ...  term_1424  term_1425  term_1426  term_1427  \\\n",
       "1272       0       0  ...          0          0          0          0   \n",
       "693        0       0  ...          0          0          0          0   \n",
       "1722       0       0  ...          0          0          0          0   \n",
       "2034       0       0  ...          0          0          0          0   \n",
       "401        0       0  ...          0          0          0          0   \n",
       "...      ...     ...  ...        ...        ...        ...        ...   \n",
       "1586       0       0  ...          0          0          0          0   \n",
       "1127       0       0  ...          0          0          0          0   \n",
       "2025       0       0  ...          0          0          0          0   \n",
       "2094       0       0  ...          0          0          0          0   \n",
       "2095       0       0  ...          0          0          0          0   \n",
       "\n",
       "      term_1428  term_1429  term_1430  term_1431  term_1432  subject  \n",
       "1272          0          0          0          0          0        2  \n",
       "693           0          0          0          0          0        2  \n",
       "1722          0          0          0          0          0        3  \n",
       "2034          0          0          0          0          0        3  \n",
       "401           0          0          0          0          0        3  \n",
       "...         ...        ...        ...        ...        ...      ...  \n",
       "1586          0          0          0          0          0        6  \n",
       "1127          0          0          0          0          0        1  \n",
       "2025          0          0          0          0          0        3  \n",
       "2094          0          0          0          0          0        3  \n",
       "2095          0          0          0          0          0        2  \n",
       "\n",
       "[1376 rows x 1435 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cbf75933",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_ffn(hidden_units, dropout_rate, name=None):\n",
    "    fnn_layers = []\n",
    "\n",
    "    for units in hidden_units:\n",
    "        fnn_layers.append(layers.BatchNormalization())\n",
    "        fnn_layers.append(layers.Dropout(dropout_rate))\n",
    "        fnn_layers.append(layers.Dense(units, activation=tf.nn.relu)) # Maybe Change\n",
    "\n",
    "    return keras.Sequential(fnn_layers, name=name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d464f79b",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_names = set(papers.columns) - {\"paper_id\", \"subject\"}\n",
    "num_features = len(feature_names)\n",
    "num_classes = len(class_idx)\n",
    "\n",
    "# Create train and test features as a numpy array.\n",
    "x_train = train_data[feature_names].to_numpy()\n",
    "x_test = test_data[feature_names].to_numpy()\n",
    "# Create train and test targets as a numpy array.\n",
    "y_train = train_data[\"subject\"]\n",
    "y_test = test_data[\"subject\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "58b67dae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "375     6\n",
       "1827    6\n",
       "1653    0\n",
       "1046    0\n",
       "2282    5\n",
       "       ..\n",
       "2306    2\n",
       "342     3\n",
       "357     1\n",
       "1463    3\n",
       "44      0\n",
       "Name: subject, Length: 1332, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a1698312",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Edges shape: (2, 5429)\n",
      "Nodes shape: (2708, 1433)\n"
     ]
    }
   ],
   "source": [
    "# Create an edges array (sparse adjacency matrix) of shape [2, num_edges].\n",
    "edges = citations[[\"source\", \"target\"]].to_numpy().T\n",
    "# Create an edge weights array of ones.\n",
    "edge_weights = tf.ones(shape=edges.shape[1])\n",
    "# Create a node features array of shape [num_nodes, num_features].\n",
    "node_features = tf.cast(\n",
    "    papers.sort_values(\"paper_id\")[feature_names].to_numpy(), dtype=tf.dtypes.float32\n",
    ")\n",
    "# Create graph info tuple with node_features, edges, and edge_weights.\n",
    "graph_info = (node_features, edges, edge_weights)\n",
    "\n",
    "print(\"Edges shape:\", edges.shape)\n",
    "print(\"Nodes shape:\", node_features.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7daaed53",
   "metadata": {},
   "outputs": [],
   "source": [
    "class GraphConvLayer(layers.Layer):\n",
    "    def __init__(\n",
    "        self,\n",
    "        hidden_units,\n",
    "        dropout_rate=0.2,\n",
    "        normalize=False,\n",
    "        *args,\n",
    "        **kwargs,\n",
    "    ):\n",
    "        super(GraphConvLayer, self).__init__(*args, **kwargs)\n",
    "\n",
    "        self.ffn_prepare = create_ffn(hidden_units, dropout_rate)\n",
    "        self.update_fn = create_ffn(hidden_units, dropout_rate)\n",
    "        self.normalize = normalize\n",
    "            \n",
    "    def prepare(self, node_repesentations, weights=None):\n",
    "\n",
    "        messages = self.ffn_prepare(node_repesentations)\n",
    "        if weights is not None:\n",
    "            messages = messages * tf.expand_dims(weights, -1)\n",
    "        return messages\n",
    "\n",
    "    def aggregate(self, node_indices, neighbour_messages):\n",
    "\n",
    "        num_nodes = tf.math.reduce_max(node_indices) + 1\n",
    "        \n",
    "        aggregated_message = tf.math.unsorted_segment_mean(neighbour_messages, node_indices, num_segments=num_nodes)\n",
    "\n",
    "        return aggregated_message\n",
    "\n",
    "    def update(self, node_repesentations, aggregated_messages):\n",
    "        \n",
    "        h = tf.concat([node_repesentations, aggregated_messages], axis=1)\n",
    "        node_embeddings = self.update_fn(h)\n",
    "        if self.normalize:\n",
    "            node_embeddings = tf.nn.l2_normalize(node_embeddings, axis=-1)\n",
    "        \n",
    "        return node_embeddings\n",
    "\n",
    "    def call(self, inputs):\n",
    "\n",
    "        node_repesentations, edges, edge_weights = inputs\n",
    "        node_indices, neighbour_indices = edges[0], edges[1]\n",
    "        neighbour_repesentations = tf.gather(node_repesentations, neighbour_indices)\n",
    "        neighbour_messages = self.prepare(neighbour_repesentations, edge_weights)\n",
    "        aggregated_messages = self.aggregate(node_indices, neighbour_messages)\n",
    "        \n",
    "        return self.update(node_repesentations, aggregated_messages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "75259168",
   "metadata": {},
   "outputs": [],
   "source": [
    "class GNNNodeClassifier(tf.keras.Model):\n",
    "    def __init__(\n",
    "        self,\n",
    "        graph_info,\n",
    "        num_classes,\n",
    "        hidden_units,\n",
    "        dropout_rate=0.2,\n",
    "        normalize=True,\n",
    "        *args,\n",
    "        **kwargs,\n",
    "    ):\n",
    "        super(GNNNodeClassifier, self).__init__(*args, **kwargs)\n",
    "\n",
    "        # Unpack graph_info to three elements: node_features, edges, and edge_weight.\n",
    "        node_features, edges, edge_weights = graph_info\n",
    "        self.node_features = node_features\n",
    "        self.edges = edges\n",
    "        self.edge_weights = edge_weights\n",
    "        # Set edge_weights to ones if not provided.\n",
    "        if self.edge_weights is None:\n",
    "            self.edge_weights = tf.ones(shape=edges.shape[1])\n",
    "        # Scale edge_weights to sum to 1.\n",
    "        self.edge_weights = self.edge_weights / tf.math.reduce_sum(self.edge_weights)\n",
    "\n",
    "        # Create a process layer.\n",
    "        self.preprocess = create_ffn(hidden_units, dropout_rate, name=\"preprocess\")\n",
    "        # Create the first GraphConv layer.\n",
    "        self.conv1 = GraphConvLayer(\n",
    "            hidden_units,\n",
    "            dropout_rate,\n",
    "            normalize,\n",
    "            name=\"graph_conv1\",\n",
    "        )\n",
    "        # Create the second GraphConv layer.\n",
    "        self.conv2 = GraphConvLayer(\n",
    "            hidden_units,\n",
    "            dropout_rate,\n",
    "            normalize,\n",
    "            name=\"graph_conv2\",\n",
    "        )\n",
    "        # Create a postprocess layer.\n",
    "        self.postprocess = create_ffn(hidden_units, dropout_rate, name=\"postprocess\")\n",
    "        # Create a compute logits layer.\n",
    "        self.compute_logits = layers.Dense(units=num_classes, name=\"logits\")\n",
    "\n",
    "    def call(self, input_node_indices):\n",
    "        # Preprocess the node_features to produce node representations.\n",
    "        x = self.preprocess(self.node_features)\n",
    "        # Apply the first graph conv layer.\n",
    "        x1 = self.conv1((x, self.edges, self.edge_weights))\n",
    "        # Skip connection.\n",
    "        x = x1 + x\n",
    "        # Apply the second graph conv layer.\n",
    "        x2 = self.conv2((x, self.edges, self.edge_weights))\n",
    "        # Skip connection.\n",
    "        x = x2 + x\n",
    "        # Postprocess node embedding.\n",
    "        x = self.postprocess(x)\n",
    "        # Fetch node embeddings for the input node_indices.\n",
    "        node_embeddings = tf.gather(x, input_node_indices)\n",
    "        # Compute logits\n",
    "        return self.compute_logits(node_embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b9b31074",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GNN output shape: tf.Tensor(\n",
      "[[ 0.12213348 -0.04998919  0.13695551 -0.17981656 -0.37935397  0.02384088\n",
      "  -0.15028745]\n",
      " [ 0.24244504 -0.08599614  0.0608013  -0.16799368 -0.36297342  0.14068095\n",
      "  -0.01822183]\n",
      " [-0.03210312 -0.13179825  0.24338028 -0.22524327 -0.22751032  0.15350536\n",
      "  -0.03073245]], shape=(3, 7), dtype=float32)\n",
      "Model: \"gnn_model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " preprocess (Sequential)     (2708, 32)                52804     \n",
      "                                                                 \n",
      " graph_conv1 (GraphConvLayer  multiple                 5888      \n",
      " )                                                               \n",
      "                                                                 \n",
      " graph_conv2 (GraphConvLayer  multiple                 5888      \n",
      " )                                                               \n",
      "                                                                 \n",
      " postprocess (Sequential)    (2708, 32)                2368      \n",
      "                                                                 \n",
      " logits (Dense)              multiple                  231       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 67,179\n",
      "Trainable params: 63,481\n",
      "Non-trainable params: 3,698\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "hidden_units = [32, 32]\n",
    "learning_rate = 0.01\n",
    "dropout_rate = 0.5\n",
    "num_epochs = 300\n",
    "batch_size = 256\n",
    "\n",
    "\n",
    "gnn_model = GNNNodeClassifier(\n",
    "    graph_info=graph_info,\n",
    "    num_classes=num_classes,\n",
    "    hidden_units=hidden_units,\n",
    "    dropout_rate=dropout_rate,\n",
    "    name=\"gnn_model\",\n",
    ")\n",
    "\n",
    "print(\"GNN output shape:\", gnn_model([1, 10, 100]))\n",
    "\n",
    "gnn_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "968fe740",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def run_experiment(model, x_train, y_train):\n",
    "    # Compile the model.\n",
    "    model.compile(\n",
    "        optimizer=keras.optimizers.Adam(learning_rate),\n",
    "        loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "        metrics=[keras.metrics.SparseCategoricalAccuracy(name=\"acc\")],\n",
    "    )\n",
    "    # Create an early stopping callback.\n",
    "    early_stopping = keras.callbacks.EarlyStopping(\n",
    "        monitor=\"val_acc\", patience=50, restore_best_weights=True\n",
    "    )\n",
    "    # Fit the model.\n",
    "    history = model.fit(\n",
    "        x=x_train,\n",
    "        y=y_train,\n",
    "        epochs=num_epochs,\n",
    "        batch_size=batch_size,\n",
    "        validation_split=0.15,\n",
    "        callbacks=[early_stopping],\n",
    "    )\n",
    "\n",
    "    return history\n",
    "\n",
    "\n",
    "def display_learning_curves(history):\n",
    "    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))\n",
    "\n",
    "    ax1.plot(history.history[\"loss\"])\n",
    "    ax1.plot(history.history[\"val_loss\"])\n",
    "    ax1.legend([\"train\", \"test\"], loc=\"upper right\")\n",
    "    ax1.set_xlabel(\"Epochs\")\n",
    "    ax1.set_ylabel(\"Loss\")\n",
    "\n",
    "    ax2.plot(history.history[\"acc\"])\n",
    "    ax2.plot(history.history[\"val_acc\"])\n",
    "    ax2.legend([\"train\", \"test\"], loc=\"upper right\")\n",
    "    ax2.set_xlabel(\"Epochs\")\n",
    "    ax2.set_ylabel(\"Accuracy\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "38c196f8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/300\n",
      "5/5 [==============================] - 2s 108ms/step - loss: 2.2999 - acc: 0.1908 - val_loss: 1.9242 - val_acc: 0.2700\n",
      "Epoch 2/300\n",
      "5/5 [==============================] - 0s 49ms/step - loss: 2.0006 - acc: 0.2571 - val_loss: 1.9452 - val_acc: 0.1700\n",
      "Epoch 3/300\n",
      "5/5 [==============================] - 0s 53ms/step - loss: 1.9220 - acc: 0.2606 - val_loss: 1.9387 - val_acc: 0.2350\n",
      "Epoch 4/300\n",
      "5/5 [==============================] - 0s 52ms/step - loss: 1.9011 - acc: 0.2650 - val_loss: 1.9215 - val_acc: 0.2850\n",
      "Epoch 5/300\n",
      "5/5 [==============================] - 0s 47ms/step - loss: 1.8801 - acc: 0.2703 - val_loss: 1.9065 - val_acc: 0.2700\n",
      "Epoch 6/300\n",
      "5/5 [==============================] - 0s 53ms/step - loss: 1.8606 - acc: 0.2845 - val_loss: 1.8993 - val_acc: 0.2700\n",
      "Epoch 7/300\n",
      "5/5 [==============================] - 0s 47ms/step - loss: 1.8702 - acc: 0.2898 - val_loss: 1.8932 - val_acc: 0.2700\n",
      "Epoch 8/300\n",
      "5/5 [==============================] - 0s 52ms/step - loss: 1.8318 - acc: 0.2898 - val_loss: 1.8842 - val_acc: 0.2700\n",
      "Epoch 9/300\n",
      "5/5 [==============================] - 0s 48ms/step - loss: 1.8340 - acc: 0.2818 - val_loss: 1.8661 - val_acc: 0.2700\n",
      "Epoch 10/300\n",
      "5/5 [==============================] - 0s 54ms/step - loss: 1.8217 - acc: 0.3021 - val_loss: 1.8511 - val_acc: 0.2700\n",
      "Epoch 11/300\n",
      "5/5 [==============================] - 0s 47ms/step - loss: 1.8076 - acc: 0.3057 - val_loss: 1.8350 - val_acc: 0.2700\n",
      "Epoch 12/300\n",
      "5/5 [==============================] - 0s 52ms/step - loss: 1.7881 - acc: 0.3110 - val_loss: 1.8196 - val_acc: 0.2700\n",
      "Epoch 13/300\n",
      "5/5 [==============================] - 0s 47ms/step - loss: 1.7764 - acc: 0.3021 - val_loss: 1.7981 - val_acc: 0.2800\n",
      "Epoch 14/300\n",
      "5/5 [==============================] - 0s 52ms/step - loss: 1.7383 - acc: 0.3233 - val_loss: 1.7646 - val_acc: 0.3050\n",
      "Epoch 15/300\n",
      "5/5 [==============================] - 0s 49ms/step - loss: 1.7479 - acc: 0.3269 - val_loss: 1.7241 - val_acc: 0.3250\n",
      "Epoch 16/300\n",
      "5/5 [==============================] - 0s 53ms/step - loss: 1.7075 - acc: 0.3348 - val_loss: 1.6747 - val_acc: 0.3350\n",
      "Epoch 17/300\n",
      "5/5 [==============================] - 0s 47ms/step - loss: 1.6548 - acc: 0.3551 - val_loss: 1.6143 - val_acc: 0.3350\n",
      "Epoch 18/300\n",
      "5/5 [==============================] - 0s 53ms/step - loss: 1.6038 - acc: 0.3834 - val_loss: 1.5567 - val_acc: 0.3550\n",
      "Epoch 19/300\n",
      "5/5 [==============================] - 0s 50ms/step - loss: 1.5608 - acc: 0.3825 - val_loss: 1.4954 - val_acc: 0.3850\n",
      "Epoch 20/300\n",
      "5/5 [==============================] - 0s 56ms/step - loss: 1.5485 - acc: 0.4072 - val_loss: 1.4616 - val_acc: 0.4300\n",
      "Epoch 21/300\n",
      "5/5 [==============================] - 0s 47ms/step - loss: 1.4665 - acc: 0.4435 - val_loss: 1.4664 - val_acc: 0.4300\n",
      "Epoch 22/300\n",
      "5/5 [==============================] - 0s 51ms/step - loss: 1.4531 - acc: 0.4602 - val_loss: 1.6121 - val_acc: 0.3900\n",
      "Epoch 23/300\n",
      "5/5 [==============================] - 0s 49ms/step - loss: 1.3813 - acc: 0.4744 - val_loss: 2.0559 - val_acc: 0.3700\n",
      "Epoch 24/300\n",
      "5/5 [==============================] - 0s 52ms/step - loss: 1.3573 - acc: 0.5035 - val_loss: 2.5215 - val_acc: 0.3600\n",
      "Epoch 25/300\n",
      "5/5 [==============================] - 0s 48ms/step - loss: 1.3215 - acc: 0.5141 - val_loss: 2.4767 - val_acc: 0.3750\n",
      "Epoch 26/300\n",
      "5/5 [==============================] - 0s 51ms/step - loss: 1.2446 - acc: 0.5380 - val_loss: 2.0742 - val_acc: 0.4200\n",
      "Epoch 27/300\n",
      "5/5 [==============================] - 0s 48ms/step - loss: 1.2076 - acc: 0.5451 - val_loss: 2.1078 - val_acc: 0.4250\n",
      "Epoch 28/300\n",
      "5/5 [==============================] - 0s 55ms/step - loss: 1.1734 - acc: 0.5698 - val_loss: 2.0577 - val_acc: 0.4500\n",
      "Epoch 29/300\n",
      "5/5 [==============================] - 0s 48ms/step - loss: 1.1462 - acc: 0.5963 - val_loss: 1.7025 - val_acc: 0.5150\n",
      "Epoch 30/300\n",
      "5/5 [==============================] - 0s 52ms/step - loss: 1.1830 - acc: 0.5636 - val_loss: 1.3271 - val_acc: 0.5800\n",
      "Epoch 31/300\n",
      "5/5 [==============================] - 0s 47ms/step - loss: 1.0521 - acc: 0.6254 - val_loss: 1.4015 - val_acc: 0.5550\n",
      "Epoch 32/300\n",
      "5/5 [==============================] - 0s 51ms/step - loss: 1.0724 - acc: 0.6334 - val_loss: 1.4957 - val_acc: 0.5550\n",
      "Epoch 33/300\n",
      "5/5 [==============================] - 0s 50ms/step - loss: 1.0398 - acc: 0.6228 - val_loss: 1.5067 - val_acc: 0.5500\n",
      "Epoch 34/300\n",
      "5/5 [==============================] - 0s 50ms/step - loss: 0.9848 - acc: 0.6519 - val_loss: 1.3981 - val_acc: 0.5600\n",
      "Epoch 35/300\n",
      "5/5 [==============================] - 0s 49ms/step - loss: 0.9834 - acc: 0.6634 - val_loss: 1.3360 - val_acc: 0.5550\n",
      "Epoch 36/300\n",
      "5/5 [==============================] - 0s 53ms/step - loss: 1.0124 - acc: 0.6475 - val_loss: 1.3298 - val_acc: 0.5850\n",
      "Epoch 37/300\n",
      "5/5 [==============================] - 0s 49ms/step - loss: 0.9250 - acc: 0.6731 - val_loss: 1.1728 - val_acc: 0.6350\n",
      "Epoch 38/300\n",
      "5/5 [==============================] - 0s 47ms/step - loss: 0.9512 - acc: 0.6758 - val_loss: 1.1520 - val_acc: 0.6450\n",
      "Epoch 39/300\n",
      "5/5 [==============================] - 0s 50ms/step - loss: 0.9628 - acc: 0.6793 - val_loss: 1.3751 - val_acc: 0.5750\n",
      "Epoch 40/300\n",
      "5/5 [==============================] - 0s 51ms/step - loss: 0.9118 - acc: 0.6820 - val_loss: 1.5016 - val_acc: 0.5600\n",
      "Epoch 41/300\n",
      "5/5 [==============================] - 0s 48ms/step - loss: 0.8617 - acc: 0.7138 - val_loss: 1.4529 - val_acc: 0.5750\n",
      "Epoch 42/300\n",
      "5/5 [==============================] - 0s 46ms/step - loss: 0.9033 - acc: 0.7049 - val_loss: 1.3648 - val_acc: 0.5900\n",
      "Epoch 43/300\n",
      "5/5 [==============================] - 0s 49ms/step - loss: 0.8507 - acc: 0.7173 - val_loss: 1.2649 - val_acc: 0.6050\n",
      "Epoch 44/300\n",
      "5/5 [==============================] - 0s 52ms/step - loss: 0.7853 - acc: 0.7279 - val_loss: 1.2778 - val_acc: 0.6300\n",
      "Epoch 45/300\n",
      "5/5 [==============================] - 0s 48ms/step - loss: 0.8475 - acc: 0.7200 - val_loss: 1.2785 - val_acc: 0.6250\n",
      "Epoch 46/300\n",
      "5/5 [==============================] - 0s 47ms/step - loss: 0.8759 - acc: 0.6996 - val_loss: 1.2808 - val_acc: 0.6350\n",
      "Epoch 47/300\n",
      "5/5 [==============================] - 0s 52ms/step - loss: 0.7903 - acc: 0.7394 - val_loss: 1.3425 - val_acc: 0.6100\n",
      "Epoch 48/300\n",
      "5/5 [==============================] - 0s 50ms/step - loss: 0.7990 - acc: 0.7332 - val_loss: 1.4280 - val_acc: 0.5850\n",
      "Epoch 49/300\n",
      "5/5 [==============================] - 0s 50ms/step - loss: 0.7634 - acc: 0.7376 - val_loss: 1.3632 - val_acc: 0.6200\n",
      "Epoch 50/300\n",
      "5/5 [==============================] - 0s 48ms/step - loss: 0.7373 - acc: 0.7465 - val_loss: 1.0889 - val_acc: 0.6750\n",
      "Epoch 51/300\n",
      "5/5 [==============================] - 0s 53ms/step - loss: 0.7173 - acc: 0.7580 - val_loss: 0.9471 - val_acc: 0.7150\n",
      "Epoch 52/300\n",
      "5/5 [==============================] - 0s 49ms/step - loss: 0.7410 - acc: 0.7491 - val_loss: 0.8946 - val_acc: 0.7400\n",
      "Epoch 53/300\n",
      "5/5 [==============================] - 0s 50ms/step - loss: 0.7436 - acc: 0.7359 - val_loss: 0.9530 - val_acc: 0.7150\n",
      "Epoch 54/300\n",
      "5/5 [==============================] - 0s 49ms/step - loss: 0.6697 - acc: 0.7898 - val_loss: 1.1136 - val_acc: 0.6750\n",
      "Epoch 55/300\n",
      "5/5 [==============================] - 0s 55ms/step - loss: 0.6805 - acc: 0.7606 - val_loss: 1.1889 - val_acc: 0.6550\n",
      "Epoch 56/300\n",
      "5/5 [==============================] - 0s 47ms/step - loss: 0.7280 - acc: 0.7606 - val_loss: 1.1975 - val_acc: 0.6500\n",
      "Epoch 57/300\n",
      "5/5 [==============================] - 0s 56ms/step - loss: 0.6836 - acc: 0.7597 - val_loss: 1.1736 - val_acc: 0.6400\n",
      "Epoch 58/300\n",
      "5/5 [==============================] - 0s 48ms/step - loss: 0.7671 - acc: 0.7562 - val_loss: 0.9568 - val_acc: 0.6800\n",
      "Epoch 59/300\n",
      "5/5 [==============================] - 0s 56ms/step - loss: 0.6965 - acc: 0.7633 - val_loss: 0.7316 - val_acc: 0.7750\n",
      "Epoch 60/300\n",
      "5/5 [==============================] - 0s 47ms/step - loss: 0.6813 - acc: 0.7721 - val_loss: 0.7418 - val_acc: 0.7550\n",
      "Epoch 61/300\n",
      "5/5 [==============================] - 0s 54ms/step - loss: 0.6705 - acc: 0.7747 - val_loss: 0.7881 - val_acc: 0.7200\n",
      "Epoch 62/300\n",
      "5/5 [==============================] - 0s 48ms/step - loss: 0.7323 - acc: 0.7588 - val_loss: 0.7121 - val_acc: 0.7600\n",
      "Epoch 63/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 53ms/step - loss: 0.6748 - acc: 0.7845 - val_loss: 0.6627 - val_acc: 0.7900\n",
      "Epoch 64/300\n",
      "5/5 [==============================] - 0s 46ms/step - loss: 0.6280 - acc: 0.7906 - val_loss: 0.6496 - val_acc: 0.7750\n",
      "Epoch 65/300\n",
      "5/5 [==============================] - 0s 51ms/step - loss: 0.6824 - acc: 0.7783 - val_loss: 0.7105 - val_acc: 0.7800\n",
      "Epoch 66/300\n",
      "5/5 [==============================] - 0s 48ms/step - loss: 0.6266 - acc: 0.7906 - val_loss: 0.6697 - val_acc: 0.8150\n",
      "Epoch 67/300\n",
      "5/5 [==============================] - 0s 52ms/step - loss: 0.6404 - acc: 0.7951 - val_loss: 0.6556 - val_acc: 0.8050\n",
      "Epoch 68/300\n",
      "5/5 [==============================] - 0s 46ms/step - loss: 0.6176 - acc: 0.7977 - val_loss: 0.6958 - val_acc: 0.7800\n",
      "Epoch 69/300\n",
      "5/5 [==============================] - 0s 53ms/step - loss: 0.5594 - acc: 0.8180 - val_loss: 0.6798 - val_acc: 0.7850\n",
      "Epoch 70/300\n",
      "5/5 [==============================] - 0s 47ms/step - loss: 0.6860 - acc: 0.7818 - val_loss: 0.7840 - val_acc: 0.7500\n",
      "Epoch 71/300\n",
      "5/5 [==============================] - 0s 52ms/step - loss: 0.5787 - acc: 0.8163 - val_loss: 0.7998 - val_acc: 0.7500\n",
      "Epoch 72/300\n",
      "5/5 [==============================] - 0s 48ms/step - loss: 0.5816 - acc: 0.8180 - val_loss: 0.7256 - val_acc: 0.7500\n",
      "Epoch 73/300\n",
      "5/5 [==============================] - 0s 52ms/step - loss: 0.6087 - acc: 0.8083 - val_loss: 0.7180 - val_acc: 0.7600\n",
      "Epoch 74/300\n",
      "5/5 [==============================] - 0s 47ms/step - loss: 0.5825 - acc: 0.8198 - val_loss: 0.6869 - val_acc: 0.7950\n",
      "Epoch 75/300\n",
      "5/5 [==============================] - 0s 50ms/step - loss: 0.5601 - acc: 0.8154 - val_loss: 0.7080 - val_acc: 0.7750\n",
      "Epoch 76/300\n",
      "5/5 [==============================] - 0s 49ms/step - loss: 0.5488 - acc: 0.8260 - val_loss: 0.6714 - val_acc: 0.7800\n",
      "Epoch 77/300\n",
      "5/5 [==============================] - 0s 51ms/step - loss: 0.5551 - acc: 0.8260 - val_loss: 0.6373 - val_acc: 0.8050\n",
      "Epoch 78/300\n",
      "5/5 [==============================] - 0s 48ms/step - loss: 0.5953 - acc: 0.7986 - val_loss: 0.6511 - val_acc: 0.8150\n",
      "Epoch 79/300\n",
      "5/5 [==============================] - 0s 51ms/step - loss: 0.5081 - acc: 0.8277 - val_loss: 0.6971 - val_acc: 0.7800\n",
      "Epoch 80/300\n",
      "5/5 [==============================] - 0s 49ms/step - loss: 0.5276 - acc: 0.8383 - val_loss: 0.6702 - val_acc: 0.7950\n",
      "Epoch 81/300\n",
      "5/5 [==============================] - 0s 52ms/step - loss: 0.5632 - acc: 0.8216 - val_loss: 0.6698 - val_acc: 0.8050\n",
      "Epoch 82/300\n",
      "5/5 [==============================] - 0s 49ms/step - loss: 0.5544 - acc: 0.8101 - val_loss: 0.6814 - val_acc: 0.8000\n",
      "Epoch 83/300\n",
      "5/5 [==============================] - 0s 48ms/step - loss: 0.5558 - acc: 0.8216 - val_loss: 0.6733 - val_acc: 0.8100\n",
      "Epoch 84/300\n",
      "5/5 [==============================] - 0s 51ms/step - loss: 0.4977 - acc: 0.8516 - val_loss: 0.6659 - val_acc: 0.8200\n",
      "Epoch 85/300\n",
      "5/5 [==============================] - 0s 53ms/step - loss: 0.5787 - acc: 0.8216 - val_loss: 0.6490 - val_acc: 0.8150\n",
      "Epoch 86/300\n",
      "5/5 [==============================] - 0s 48ms/step - loss: 0.5177 - acc: 0.8277 - val_loss: 0.6609 - val_acc: 0.8100\n",
      "Epoch 87/300\n",
      "5/5 [==============================] - 0s 47ms/step - loss: 0.5136 - acc: 0.8251 - val_loss: 0.6344 - val_acc: 0.8100\n",
      "Epoch 88/300\n",
      "5/5 [==============================] - 0s 49ms/step - loss: 0.4938 - acc: 0.8330 - val_loss: 0.7431 - val_acc: 0.7800\n",
      "Epoch 89/300\n",
      "5/5 [==============================] - 0s 51ms/step - loss: 0.5321 - acc: 0.8295 - val_loss: 0.8380 - val_acc: 0.7450\n",
      "Epoch 90/300\n",
      "5/5 [==============================] - 0s 48ms/step - loss: 0.5402 - acc: 0.8322 - val_loss: 0.8332 - val_acc: 0.7450\n",
      "Epoch 91/300\n",
      "5/5 [==============================] - 0s 47ms/step - loss: 0.5072 - acc: 0.8401 - val_loss: 0.8003 - val_acc: 0.7750\n",
      "Epoch 92/300\n",
      "5/5 [==============================] - 0s 51ms/step - loss: 0.5271 - acc: 0.8357 - val_loss: 0.7484 - val_acc: 0.8050\n",
      "Epoch 93/300\n",
      "5/5 [==============================] - 0s 51ms/step - loss: 0.4927 - acc: 0.8489 - val_loss: 0.7020 - val_acc: 0.7950\n",
      "Epoch 94/300\n",
      "5/5 [==============================] - 0s 49ms/step - loss: 0.4978 - acc: 0.8445 - val_loss: 0.6908 - val_acc: 0.7850\n",
      "Epoch 95/300\n",
      "5/5 [==============================] - 0s 48ms/step - loss: 0.4955 - acc: 0.8322 - val_loss: 0.6787 - val_acc: 0.7800\n",
      "Epoch 96/300\n",
      "5/5 [==============================] - 0s 55ms/step - loss: 0.5112 - acc: 0.8401 - val_loss: 0.6082 - val_acc: 0.8000\n",
      "Epoch 97/300\n",
      "5/5 [==============================] - 0s 47ms/step - loss: 0.4700 - acc: 0.8578 - val_loss: 0.6532 - val_acc: 0.8100\n",
      "Epoch 98/300\n",
      "5/5 [==============================] - 0s 52ms/step - loss: 0.4921 - acc: 0.8428 - val_loss: 0.7085 - val_acc: 0.8050\n",
      "Epoch 99/300\n",
      "5/5 [==============================] - 0s 49ms/step - loss: 0.4910 - acc: 0.8498 - val_loss: 0.7310 - val_acc: 0.8000\n",
      "Epoch 100/300\n",
      "5/5 [==============================] - 0s 54ms/step - loss: 0.5015 - acc: 0.8366 - val_loss: 0.6851 - val_acc: 0.8150\n",
      "Epoch 101/300\n",
      "5/5 [==============================] - 0s 48ms/step - loss: 0.5544 - acc: 0.8286 - val_loss: 0.6357 - val_acc: 0.8200\n",
      "Epoch 102/300\n",
      "5/5 [==============================] - 0s 52ms/step - loss: 0.4629 - acc: 0.8595 - val_loss: 0.6489 - val_acc: 0.8050\n",
      "Epoch 103/300\n",
      "5/5 [==============================] - 0s 47ms/step - loss: 0.4665 - acc: 0.8604 - val_loss: 0.6738 - val_acc: 0.8100\n",
      "Epoch 104/300\n",
      "5/5 [==============================] - 0s 52ms/step - loss: 0.4919 - acc: 0.8463 - val_loss: 0.6678 - val_acc: 0.8100\n",
      "Epoch 105/300\n",
      "5/5 [==============================] - 0s 47ms/step - loss: 0.4825 - acc: 0.8578 - val_loss: 0.6329 - val_acc: 0.8100\n",
      "Epoch 106/300\n",
      "5/5 [==============================] - 0s 52ms/step - loss: 0.4882 - acc: 0.8454 - val_loss: 0.6383 - val_acc: 0.8150\n",
      "Epoch 107/300\n",
      "5/5 [==============================] - 0s 48ms/step - loss: 0.4789 - acc: 0.8454 - val_loss: 0.6202 - val_acc: 0.8200\n",
      "Epoch 108/300\n",
      "5/5 [==============================] - 0s 55ms/step - loss: 0.4892 - acc: 0.8516 - val_loss: 0.6481 - val_acc: 0.8200\n",
      "Epoch 109/300\n",
      "5/5 [==============================] - 0s 47ms/step - loss: 0.4743 - acc: 0.8525 - val_loss: 0.6491 - val_acc: 0.8050\n",
      "Epoch 110/300\n",
      "5/5 [==============================] - 0s 54ms/step - loss: 0.4527 - acc: 0.8428 - val_loss: 0.6464 - val_acc: 0.8200\n",
      "Epoch 111/300\n",
      "5/5 [==============================] - 0s 48ms/step - loss: 0.4784 - acc: 0.8507 - val_loss: 0.6537 - val_acc: 0.8100\n",
      "Epoch 112/300\n",
      "5/5 [==============================] - 0s 55ms/step - loss: 0.4991 - acc: 0.8516 - val_loss: 0.6535 - val_acc: 0.8050\n",
      "Epoch 113/300\n",
      "5/5 [==============================] - 0s 47ms/step - loss: 0.4818 - acc: 0.8489 - val_loss: 0.6495 - val_acc: 0.7950\n",
      "Epoch 114/300\n",
      "5/5 [==============================] - 0s 52ms/step - loss: 0.4316 - acc: 0.8701 - val_loss: 0.6673 - val_acc: 0.8150\n",
      "Epoch 115/300\n",
      "5/5 [==============================] - 0s 49ms/step - loss: 0.4697 - acc: 0.8481 - val_loss: 0.6910 - val_acc: 0.7900\n",
      "Epoch 116/300\n",
      "5/5 [==============================] - 0s 53ms/step - loss: 0.4415 - acc: 0.8595 - val_loss: 0.7131 - val_acc: 0.7800\n",
      "Epoch 117/300\n",
      "5/5 [==============================] - 0s 48ms/step - loss: 0.4738 - acc: 0.8551 - val_loss: 0.6666 - val_acc: 0.8050\n",
      "Epoch 118/300\n",
      "5/5 [==============================] - 0s 53ms/step - loss: 0.4179 - acc: 0.8719 - val_loss: 0.6628 - val_acc: 0.8150\n",
      "Epoch 119/300\n",
      "5/5 [==============================] - 0s 48ms/step - loss: 0.4253 - acc: 0.8781 - val_loss: 0.6749 - val_acc: 0.8050\n",
      "Epoch 120/300\n",
      "5/5 [==============================] - 0s 52ms/step - loss: 0.4428 - acc: 0.8604 - val_loss: 0.6833 - val_acc: 0.7900\n",
      "Epoch 121/300\n",
      "5/5 [==============================] - 0s 48ms/step - loss: 0.4896 - acc: 0.8463 - val_loss: 0.6791 - val_acc: 0.7950\n",
      "Epoch 122/300\n",
      "5/5 [==============================] - 0s 52ms/step - loss: 0.4793 - acc: 0.8534 - val_loss: 0.6627 - val_acc: 0.8050\n",
      "Epoch 123/300\n",
      "5/5 [==============================] - 0s 48ms/step - loss: 0.3972 - acc: 0.8763 - val_loss: 0.6593 - val_acc: 0.8050\n",
      "Epoch 124/300\n",
      "5/5 [==============================] - 0s 52ms/step - loss: 0.4695 - acc: 0.8578 - val_loss: 0.6692 - val_acc: 0.8000\n",
      "Epoch 125/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 48ms/step - loss: 0.4712 - acc: 0.8454 - val_loss: 0.6539 - val_acc: 0.8150\n",
      "Epoch 126/300\n",
      "5/5 [==============================] - 0s 52ms/step - loss: 0.4667 - acc: 0.8648 - val_loss: 0.6382 - val_acc: 0.8150\n",
      "Epoch 127/300\n",
      "5/5 [==============================] - 0s 48ms/step - loss: 0.4607 - acc: 0.8454 - val_loss: 0.6392 - val_acc: 0.8150\n",
      "Epoch 128/300\n",
      "5/5 [==============================] - 0s 51ms/step - loss: 0.4530 - acc: 0.8569 - val_loss: 0.6541 - val_acc: 0.8150\n",
      "Epoch 129/300\n",
      "5/5 [==============================] - 0s 47ms/step - loss: 0.4135 - acc: 0.8728 - val_loss: 0.6936 - val_acc: 0.8000\n",
      "Epoch 130/300\n",
      "5/5 [==============================] - 0s 53ms/step - loss: 0.4168 - acc: 0.8763 - val_loss: 0.6838 - val_acc: 0.8000\n",
      "Epoch 131/300\n",
      "5/5 [==============================] - 0s 47ms/step - loss: 0.4087 - acc: 0.8746 - val_loss: 0.7210 - val_acc: 0.7950\n",
      "Epoch 132/300\n",
      "5/5 [==============================] - 0s 51ms/step - loss: 0.4260 - acc: 0.8657 - val_loss: 0.7401 - val_acc: 0.7800\n",
      "Epoch 133/300\n",
      "5/5 [==============================] - 0s 48ms/step - loss: 0.4200 - acc: 0.8754 - val_loss: 0.6839 - val_acc: 0.8100\n",
      "Epoch 134/300\n",
      "5/5 [==============================] - 0s 53ms/step - loss: 0.4405 - acc: 0.8640 - val_loss: 0.6783 - val_acc: 0.8200\n"
     ]
    }
   ],
   "source": [
    "x_train = train_data.paper_id.to_numpy()\n",
    "history = run_experiment(gnn_model, x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f43375d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_learning_curves(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4392f992",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 77.62%\n"
     ]
    }
   ],
   "source": [
    "x_test = test_data.paper_id.to_numpy()\n",
    "_, test_accuracy = gnn_model.evaluate(x=x_test, y=y_test, verbose=0)\n",
    "print(f\"Test accuracy: {round(test_accuracy * 100, 2)}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "784cd1ee",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  },
  "varInspector": {
   "cols": {
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    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
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    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
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   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
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    "_Feature"
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   "window_display": false
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 "nbformat": 4,
 "nbformat_minor": 5
}