GRAPH AUTOENCODER.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LIBRARIES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix,f1_score,precision_score,recall_score\n",
    "import matplotlib.pyplot as plt\n",
    "from typing import Optional,Tuple\n",
    "\n",
    "import torch\n",
    "from torch import Tensor\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "import torch_geometric.nn as pyg_nn\n",
    "from torch_geometric.data import Data\n",
    "import torch_geometric.utils as pyg_utils\n",
    "from torch_geometric.datasets import Planetoid\n",
    "import torch_geometric.transforms as T\n",
    "\n",
    "\n",
    "\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'graph number': 1,\n",
       " 'num_classes': 7,\n",
       " 'num_node_features': 1433,\n",
       " 'graph': (Data(x=[2708, 1433], edge_index=[2, 7392], y=[2708], train_mask=[2708], val_mask=[2708], test_mask=[2708], pos_edge_label=[3696], pos_edge_label_index=[2, 3696]),\n",
       "  Data(x=[2708, 1433], edge_index=[2, 7392], y=[2708], train_mask=[2708], val_mask=[2708], test_mask=[2708], pos_edge_label=[263], pos_edge_label_index=[2, 263], neg_edge_label=[263], neg_edge_label_index=[2, 263]),\n",
       "  Data(x=[2708, 1433], edge_index=[2, 7918], y=[2708], train_mask=[2708], val_mask=[2708], test_mask=[2708], pos_edge_label=[1319], pos_edge_label_index=[2, 1319], neg_edge_label=[1319], neg_edge_label_index=[2, 1319]))}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transform = T.Compose([\n",
    "    T.NormalizeFeatures(),\n",
    "    T.to_device.ToDevice(device),\n",
    "    T.RandomLinkSplit(num_val=0.05, num_test=0.25, is_undirected=True,\n",
    "                      split_labels=True, add_negative_train_samples=False),\n",
    "])\n",
    "dataset = Planetoid(root='/train/Cora', name='Cora',transform=transform)\n",
    "train_data, val_data, test_data = dataset[0]\n",
    "{\n",
    "    'graph number': len(dataset),\n",
    "    'num_classes': dataset.num_classes,\n",
    "    'num_node_features': dataset.num_node_features,\n",
    "    'graph' : dataset[0],\n",
    "}"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GCN_Encoder(nn.Module):\n",
    "    def __init__(self, in_channels, hidden_channels, out_channels):\n",
    "        super(GCN_Encoder, self).__init__()\n",
    "        self.gcn_shared = pyg_nn.GCNConv(in_channels, hidden_channels)\n",
    "        self.gcn_mu = pyg_nn.GCNConv(hidden_channels, out_channels)\n",
    "        self.gcn_logvar = pyg_nn.GCNConv(hidden_channels, out_channels)\n",
    "\n",
    "    def forward(self, x, edge_index):\n",
    "        x = F.relu(self.gcn_shared(x, edge_index))\n",
    "        mu = self.gcn_mu(x, edge_index)\n",
    "        logvar = self.gcn_logvar(x, edge_index)\n",
    "        return mu, logvar\n",
    "    \n",
    "class GCN_Pre_Encoder(nn.Module):\n",
    "    def __init__(self, in_channels, hidden_channels, out_channels):\n",
    "        super(GCN_Pre_Encoder, self).__init__()\n",
    "        self.gcn_shared = pyg_nn.GCNConv(in_channels, hidden_channels)\n",
    "        self.preprocess = nn.Sequential(\n",
    "            nn.Linear(in_channels,2*in_channels),\n",
    "            nn.ReLU(),\n",
    "            nn.BatchNorm1d(2*in_channels),\n",
    "            nn.Linear(2*in_channels,in_channels),\n",
    "            nn.ReLU(),\n",
    "            nn.BatchNorm1d(in_channels),\n",
    "            )\n",
    "        self.gcn_mu = pyg_nn.GCNConv(hidden_channels, out_channels)\n",
    "        self.gcn_logvar = pyg_nn.GCNConv(hidden_channels, out_channels)\n",
    "\n",
    "    def forward(self, x, edge_index):\n",
    "        x = self.preprocess(x)\n",
    "        x = F.relu(self.gcn_shared(x, edge_index))\n",
    "        mu = self.gcn_mu(x, edge_index)\n",
    "        logvar = self.gcn_logvar(x, edge_index)\n",
    "        return mu, logvar\n",
    "\n",
    "class GAT_Encoder(nn.Module):\n",
    "    def __init__(self, in_channels, hidden_channels, out_channels,head=1):\n",
    "        super(GAT_Encoder, self).__init__()\n",
    "        self.preprocess = nn.Sequential(\n",
    "            nn.Linear(in_channels,2*in_channels),\n",
    "            nn.ReLU(),\n",
    "            nn.BatchNorm1d(2*in_channels),\n",
    "            nn.Linear(2*in_channels,in_channels),\n",
    "            nn.ReLU(),\n",
    "            nn.BatchNorm1d(in_channels),\n",
    "            )\n",
    "        self.gcn_shared = pyg_nn.GATv2Conv(in_channels, hidden_channels,head=head)\n",
    "        self.gcn_mu = pyg_nn.GATv2Conv(hidden_channels, out_channels,head=head)\n",
    "        self.gcn_logvar = pyg_nn.GATv2Conv(hidden_channels, out_channels,head=head)\n",
    "        \n",
    "\n",
    "    def forward(self, x, edge_index):\n",
    "        x = self.preprocess(x)\n",
    "        x = F.relu(self.gcn_shared(x, edge_index))\n",
    "        mu = self.gcn_mu(x, edge_index)\n",
    "        logvar = self.gcn_logvar(x, edge_index)\n",
    "        return mu, logvar\n",
    "\n",
    "class Node_Decoder(nn.Module):\n",
    "    def __init__(self, latent_dim, hidden_dim, output_dim):\n",
    "        super(Node_Decoder, self).__init__()\n",
    "        self.fc_encoder = nn.Sequential(\n",
    "            nn.Linear(output_dim,output_dim//4),\n",
    "            nn.ReLU(),\n",
    "            nn.BatchNorm1d(output_dim//4),\n",
    "            nn.Linear(output_dim//4,latent_dim),\n",
    "            nn.ReLU(),\n",
    "        )\n",
    "        self.fc_decoder = nn.Sequential(\n",
    "            nn.Linear(latent_dim + latent_dim,hidden_dim),\n",
    "            nn.ReLU(),\n",
    "            nn.BatchNorm1d(hidden_dim),\n",
    "            nn.Linear(hidden_dim,output_dim),\n",
    "            nn.Sigmoid(),\n",
    "        )\n",
    "\n",
    "    def forward(self,z,x):\n",
    "        x = self.fc_encoder(x)\n",
    "        x = torch.cat((x,z),dim=1)\n",
    "        x = self.fc_decoder(x)\n",
    "        return x\n",
    "\n",
    "in_channels = train_data.num_node_features\n",
    "hidden_channels = in_channels * 2\n",
    "latent_dim = 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "@torch.no_grad()\n",
    "def test_gae(gae_model,node_model,data):\n",
    "    gae_model.eval()\n",
    "    z = gae_model.encode(data.x, data.edge_index)\n",
    "    auc,ap = gae_model.test(z, data.pos_edge_label_index, data.neg_edge_label_index)\n",
    "\n",
    "\n",
    "    x = node_model(z,data.x)\n",
    "    loss = F.mse_loss(x,data.x)\n",
    "    loss = loss + gae_model.recon_loss(z, data.pos_edge_label_index, data.neg_edge_label_index)\n",
    "    loss = loss + (1 / train_data.num_nodes) * gae_model.kl_loss()\n",
    "    return loss.item(),auc,ap\n",
    "\n",
    "# Training loop\n",
    "def update_gae(gae_model,node_model,data,optimizer):\n",
    "    gae_model.train()\n",
    "    z = gae_model.encode(data.x, data.edge_index)\n",
    "\n",
    "    x = node_model(z,data.x)\n",
    "    loss = F.mse_loss(x,data.x)\n",
    "    \n",
    "    loss = loss + gae_model.recon_loss(z, data.edge_index)\n",
    "    loss = loss + (1 / data.num_nodes) * gae_model.kl_loss()\n",
    "    \n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    return loss.item()\n",
    "\n",
    "def train_gae(gae_model,node_model,optimizer,hist,n):\n",
    "    for epoch in range(n):\n",
    "        loss = update_gae(gae_model,node_model,train_data,optimizer)\n",
    "        hist['train']['loss'].append(loss)\n",
    "        \n",
    "        loss,auc,ap = test_gae(gae_model,node_model,test_data)\n",
    "        hist['test']['loss'].append(loss)\n",
    "        hist['test']['auc'].append(auc)\n",
    "        hist['test']['ap'].append(ap)\n",
    "        if (epoch+1) % 10 == 0 :\n",
    "            from IPython.display import clear_output\n",
    "            clear_output()\n",
    "            print(f\"average precision {ap:.4f}\")\n",
    "            plot_loss(hist)\n",
    "\n",
    "def init_hist():\n",
    "    hist = {'train':{},'test':{}}\n",
    "    metrics = \"loss auc ap node_loss\"\n",
    "    for mode in \"train test\".split():\n",
    "        for m in metrics.split():\n",
    "            hist[mode][m]=[]\n",
    "    return hist\n",
    "\n",
    "def plot_loss(hist):\n",
    "    fig,axs = plt.subplots(1,3,figsize=(12,4))\n",
    "    epoch = len(hist['train']['loss'])\n",
    "    fig.suptitle(f'epoch - {epoch}')\n",
    "    for ax,m in zip(axs.flatten(),hist['train'].keys()):\n",
    "        ax.set_title(m)\n",
    "        ax.plot(hist['train'][m],label='train')\n",
    "        ax.plot(hist['test'][m],label='test')\n",
    "        ax.legend()\n",
    "    \n",
    "    plt.show()\n",
    "    \n",
    "def test_node_decoder(gae_model,decoder,data):\n",
    "    gae_model.eval()\n",
    "    decoder.eval()\n",
    "    z = gae_model.encode(data.x, data.edge_index)\n",
    "    x = decoder(z,data.x)\n",
    "    loss = F.mse_loss(x,data.x)\n",
    "    return loss.item()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model = pyg_nn.VGAE(GAT_Encoder(in_channels,hidden_channels,latent_dim))\n",
    "# model = model.to(device)\n",
    "# optimizer = torch.optim.Adam(model.parameters(),lr = 0.001)\n",
    "# hist = init_hist()\n",
    "# train(model,optimizer,hist,150)\n",
    "# hist['test']['ap'][-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model = pyg_nn.VGAE(GCN_Pre_Encoder(in_channels,hidden_channels,latent_dim))\n",
    "# model = model.to(device)\n",
    "# optimizer = torch.optim.Adam(model.parameters(),lr = 0.001)\n",
    "\n",
    "# hist = init_hist()\n",
    "\n",
    "# train(model,optimizer,hist,300)\n",
    "# hist['test']['ap'][-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average precision 0.9228\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.9228118513287371"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gae_model = pyg_nn.VGAE(GCN_Encoder(in_channels,hidden_channels,latent_dim))\n",
    "node_model = Node_Decoder(latent_dim,hidden_channels,in_channels)\n",
    "gae_model = gae_model.to(device)\n",
    "node_model = node_model.to(device)\n",
    "params = list(gae_model.parameters()) + list(node_model.parameters())\n",
    "optimizer = torch.optim.Adam(params,lr = 0.005)\n",
    "hist = init_hist()\n",
    "train_gae(gae_model,node_model,optimizer,hist,150)\n",
    "hist['test']['ap'][-1]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Thresholding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ideal_cm': array([[7325872,       0],\n",
       "        [      0,    7392]], dtype=int64),\n",
       " 'cm': array([[7291823,   34049],\n",
       "        [   3806,    3586]], dtype=int64),\n",
       " 'size_ratio': 5.091314935064935,\n",
       " 'precision': 0.0952836455427129,\n",
       " 'recall': 0.4851190476190476,\n",
       " 'acc': 0.9948379057401997,\n",
       " 'f1': 0.07964110422635308}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def fn(model,data,threshold=None):\n",
    "    model.eval()\n",
    "    z = model.encode(data.x, data.edge_index)\n",
    "    adj = model.decoder.forward_all(z)\n",
    "    adj = adj.long() if threshold is None else adj >= threshold\n",
    "    edge_index = pyg_utils.dense_to_sparse(adj)[0]\n",
    "    \n",
    "    fn = lambda t : t.flatten().cpu().long()\n",
    "    pred,target = fn(adj), fn(pyg_utils.to_dense_adj(val_data.edge_index))\n",
    "    \n",
    "    cm = confusion_matrix(target,pred)\n",
    "    \n",
    "    tn,fp = cm[0,:]\n",
    "    fn,tp = cm[1,:]\n",
    "    \n",
    "    precision = tp / (tp+fp)\n",
    "    recall = tp / (tp+fn)\n",
    "    acc = (tn+tp) / sum([tn,fn, fp, tp])\n",
    "    f1 = (precision * recall)/(precision + recall)\n",
    "    \n",
    "    return {\n",
    "        \"ideal_cm\":confusion_matrix(target,target),\n",
    "        \"cm\": cm,\n",
    "        \"size_ratio\": edge_index.shape[1]/data.edge_index.shape[1],\n",
    "        \"precision\": precision,\n",
    "        \"recall\": recall,\n",
    "        \"acc\" : acc,\n",
    "        \"f1\": f1\n",
    "    }\n",
    "'''\n",
    "[[tn,fp], # n\n",
    " [fn,tp]] # p\n",
    "'''\n",
    "fn(gae_model,train_data,0.95)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x15e5b2bebc0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [0.5,0.9,0.95,0.99,0.995]\n",
    "y = []\n",
    "precision_hist = []\n",
    "recall_hist = []\n",
    "size_hist = []\n",
    "for t in x:\n",
    "    res = fn(gae_model,train_data,t)\n",
    "    precision_hist.append(res['precision'])\n",
    "    recall_hist.append(res['recall'])\n",
    "    size_hist.append(res['size_ratio'])\n",
    "    y.append(res['f1'])\n",
    "\n",
    "[plt.axvline(1 if i is None else i,alpha=0.5,ls='--') for i in x]\n",
    "plt.xlabel('threshold')\n",
    "plt.scatter(x,y,label='f1',marker='x')\n",
    "plt.scatter(x,precision_hist,label='precision',marker='^')\n",
    "plt.scatter(x,recall_hist,label='recall',marker='P')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x15dd6918df0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "plt.axhline(y=np.log10(1))\n",
    "plt.ylabel('log10')\n",
    "plt.xlabel('threshold')\n",
    "[plt.axvline(1 if i is None else i,alpha=0.5,ls='--') for i in x]\n",
    "plt.scatter(x,np.log10(size_hist),label='size_ratio',marker='8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "THRESHOLD = 0.99"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def reconstruct_data(gae_model,node_model,data,threshold=None):\n",
    "    z = gae_model.encode(data.x, data.edge_index)\n",
    "    x = node_model(z,data.x).detach()\n",
    "    adj = gae_model.decoder.forward_all(z).detach()\n",
    "    # adj = adj > threshold\n",
    "    adj = adj.long() if threshold is None else adj > threshold\n",
    "    edge_index = pyg_utils.dense_to_sparse(adj)[0]\n",
    "    recon_data = Data(x=x,edge_index=edge_index,\n",
    "                      y=data.y,\n",
    "                      train_mask = data.train_mask,\n",
    "                      test_mask = data.test_mask,\n",
    "                      val_mask = data.val_mask)\n",
    "    return recon_data.to(device)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CROSS VALIDATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = Planetoid(root='/data/Cora', name='Cora')\n",
    "data = dataset[0].to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GCN(nn.Module):\n",
    "    def __init__(self,in_channels=dataset.num_node_features, \n",
    "                 hidden_channels = 16, out_channels = dataset.num_classes,seed=42):\n",
    "        super().__init__()\n",
    "        torch.manual_seed(seed)\n",
    "        self.conv1 = pyg_nn.GCNConv(in_channels, hidden_channels)\n",
    "        self.conv2 = pyg_nn.GCNConv(hidden_channels, out_channels)\n",
    "        \n",
    "    def forward(self, data):\n",
    "        x, edge_index = data.x, data.edge_index\n",
    "        x = self.conv1(x,edge_index)\n",
    "        x = F.relu(x)\n",
    "        x = F.dropout(x, training=self.training)\n",
    "        x = self.conv2(x, edge_index)\n",
    "        x = F.log_softmax(x, dim=1)\n",
    "        return x\n",
    "\n",
    "def plot_loss(hist):\n",
    "    plt.title(f'epoch - {len(hist[\"train\"])}')\n",
    "    plt.plot(hist['train'],label='train')\n",
    "    plt.plot(hist['test'],label='test')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "def test(model,data):\n",
    "    out = model(data)\n",
    "    pred = model(data).argmax(dim=1)\n",
    "    correct = (pred[data.test_mask] == data.y[data.test_mask]).sum()\n",
    "    acc = int(correct) / int(data.test_mask.sum())\n",
    "    return acc\n",
    "    \n",
    "def train(model,dataset,optimizer, hist, n = 100):\n",
    "    for epoch in range(n):\n",
    "        train_losses = []\n",
    "        test_losses = []\n",
    "        for data in dataset:\n",
    "            model.train()\n",
    "            out = model(data)\n",
    "            loss = F.nll_loss(out[data.train_mask], data.y[data.train_mask])\n",
    "            \n",
    "            \n",
    "            optimizer.zero_grad()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            train_losses.append(loss.item())\n",
    "        \n",
    "        \n",
    "            model.eval()\n",
    "            loss = F.nll_loss(out[data.test_mask], data.y[data.test_mask])\n",
    "            test_losses.append(loss.item())\n",
    "        \n",
    "        mean = lambda xs : torch.tensor(xs).mean()   \n",
    "        hist['train'].append(mean(train_losses))\n",
    "        hist['test'].append(mean(test_losses))\n",
    "    \n",
    "    plot_loss(hist)\n",
    "    pred = model(data).argmax(dim=1)\n",
    "    correct = (pred[data.test_mask] == data.y[data.test_mask]).sum()\n",
    "    acc = int(correct) / int(data.test_mask.sum())\n",
    "    print(f'Accuracy: {acc:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.1440\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.144, 0.78)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier_model = GCN().to(device)\n",
    "hist = {'train':[],'test':[]}\n",
    "optimizer = torch.optim.Adam(classifier_model.parameters(), lr=0.01, weight_decay=5e-4)\n",
    "recon_data = reconstruct_data(gae_model,node_model,data,THRESHOLD)\n",
    "train(classifier_model,[data,recon_data], optimizer, hist, n=200)\n",
    "x1 = test(classifier_model,recon_data)\n",
    "x2 = test(classifier_model,data)\n",
    "x1,x2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.0910\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.091, 0.091)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier_model = GCN().to(device)\n",
    "optimizer = torch.optim.Adam(classifier_model.parameters(), lr=0.01, weight_decay=5e-4)\n",
    "hist = {'train':[],'test':[]}\n",
    "recon_data = reconstruct_data(gae_model,node_model,data)\n",
    "train(classifier_model,[recon_data,recon_data], optimizer,hist,n=200)\n",
    "x1 = test(classifier_model,recon_data)\n",
    "x2 = test(classifier_model,data)\n",
    "x1,x2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8140\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.319, 0.814)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier_model = GCN().to(device)\n",
    "optimizer = torch.optim.Adam(classifier_model.parameters(), lr=0.01, weight_decay=5e-4)\n",
    "hist = {'train':[],'test':[]}\n",
    "train(classifier_model,[data,data], optimizer,hist,n=200)\n",
    "\n",
    "x1 = test(classifier_model,reconstruct_data(gae_model,node_model,data))\n",
    "x2 = test(classifier_model,data)\n",
    "x1,x2"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.10.11"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}