link_pred.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HeteroData(\n",
      "  author={\n",
      "    x=[4057, 334],\n",
      "    y=[4057],\n",
      "    train_mask=[4057],\n",
      "    val_mask=[4057],\n",
      "    test_mask=[4057],\n",
      "  },\n",
      "  paper={ x=[14328, 4231] },\n",
      "  term={ x=[7723, 50] },\n",
      "  conference={\n",
      "    num_nodes=20,\n",
      "    x=[20, 1],\n",
      "  },\n",
      "  (author, to, paper)={ edge_index=[2, 19645] },\n",
      "  (paper, to, author)={ edge_index=[2, 19645] },\n",
      "  (paper, to, term)={ edge_index=[2, 85810] },\n",
      "  (paper, to, conference)={ edge_index=[2, 14328] },\n",
      "  (term, to, paper)={ edge_index=[2, 85810] },\n",
      "  (conference, to, paper)={ edge_index=[2, 14328] }\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn.functional as F\n",
    "\n",
    "import torch_geometric.transforms as T\n",
    "from torch_geometric.datasets import DBLP\n",
    "\n",
    "import numpy as np\n",
    "import random\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "##from modules.heteroGNN import HeteroGNN\n",
    "#import graph_polluters\n",
    "from tqdm import tqdm\n",
    "\n",
    "# We initialize conference node features with a single one-vector as feature:\n",
    "dataset = DBLP('./data/dblp', transform=T.Constant(node_types='conference'))\n",
    "data = dataset[0]\n",
    "print(data)\n",
    "\n",
    "\n",
    "def set_seed(seed=42):\n",
    "    #torch.backends.cudnn.benchmark = False\n",
    "    torch.backends.cudnn.deterministic = True\n",
    "    #torch.use_deterministic_algorithms(True)\n",
    "    np.random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    random.seed(seed)\n",
    "    torch.cuda.manual_seed_all(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch_geometric.transforms as T\n",
    "from modules.basic_link_pred import Model\n",
    "\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "model = Model(data.metadata(), hidden_channels=50, num_layers=10)\n",
    "\n",
    "from_type, to_type = 'author', 'paper'\n",
    "edge_type = ('author', 'to', 'paper')\n",
    "\n",
    "\n",
    "transform = T.RandomLinkSplit(\n",
    "    num_val=0.1,\n",
    "    num_test=0.1,\n",
    "    disjoint_train_ratio=0.99,\n",
    "    neg_sampling_ratio=2.0,\n",
    "    add_negative_train_samples=True,\n",
    "    edge_types=(\"author\", \"to\", \"paper\"),\n",
    "    rev_edge_types=(\"paper\", \"to\", \"author\")\n",
    ")\n",
    "\n",
    "train_data, val_data, test_data = transform(data)\n",
    "\n",
    "model = model.to(device)\n",
    "train_data = train_data.to(device)\n",
    "val_data =  val_data.to(device)\n",
    "with torch.no_grad():  # Initialize lazy modules.\n",
    "    out = model(train_data, edge_type)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.005, weight_decay=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HeteroData(\n",
       "  author={\n",
       "    x=[4057, 334],\n",
       "    y=[4057],\n",
       "    train_mask=[4057],\n",
       "    val_mask=[4057],\n",
       "    test_mask=[4057],\n",
       "  },\n",
       "  paper={ x=[14328, 4231] },\n",
       "  term={ x=[7723, 50] },\n",
       "  conference={\n",
       "    num_nodes=20,\n",
       "    x=[20, 1],\n",
       "  },\n",
       "  (author, to, paper)={ edge_index=[2, 19645] },\n",
       "  (paper, to, author)={ edge_index=[2, 19645] },\n",
       "  (paper, to, term)={ edge_index=[2, 85810] },\n",
       "  (paper, to, conference)={ edge_index=[2, 14328] },\n",
       "  (term, to, paper)={ edge_index=[2, 85810] },\n",
       "  (conference, to, paper)={ edge_index=[2, 14328] }\n",
       ")"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HeteroData(\n",
       "  author={\n",
       "    x=[4057, 334],\n",
       "    y=[4057],\n",
       "    train_mask=[4057],\n",
       "    val_mask=[4057],\n",
       "    test_mask=[4057],\n",
       "  },\n",
       "  paper={ x=[14328, 4231] },\n",
       "  term={ x=[7723, 50] },\n",
       "  conference={\n",
       "    num_nodes=20,\n",
       "    x=[20, 1],\n",
       "  },\n",
       "  (author, to, paper)={\n",
       "    edge_index=[2, 158],\n",
       "    edge_label=[46677],\n",
       "    edge_label_index=[2, 46677],\n",
       "  },\n",
       "  (paper, to, author)={ edge_index=[2, 158] },\n",
       "  (paper, to, term)={ edge_index=[2, 85810] },\n",
       "  (paper, to, conference)={ edge_index=[2, 14328] },\n",
       "  (term, to, paper)={ edge_index=[2, 85810] },\n",
       "  (conference, to, paper)={ edge_index=[2, 14328] }\n",
       ")"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 2080,  1800,   318,  ...,  1602,  3203,  1814],\n",
       "        [13275, 12873,  5183,  ...,  4733, 14095,  6505]], device='cuda:0')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['author','to','paper'].edge_label_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1., 1., 1.,  ..., 0., 0., 0.], device='cuda:0')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['author','to','paper'].edge_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HeteroData(\n",
       "  author={\n",
       "    x=[4057, 334],\n",
       "    y=[4057],\n",
       "    train_mask=[4057],\n",
       "    val_mask=[4057],\n",
       "    test_mask=[4057],\n",
       "  },\n",
       "  paper={ x=[14328, 4231] },\n",
       "  term={ x=[7723, 50] },\n",
       "  conference={\n",
       "    num_nodes=20,\n",
       "    x=[20, 1],\n",
       "  },\n",
       "  (author, to, paper)={\n",
       "    edge_index=[2, 17681],\n",
       "    edge_label=[5892],\n",
       "    edge_label_index=[2, 5892],\n",
       "  },\n",
       "  (paper, to, author)={ edge_index=[2, 17681] },\n",
       "  (paper, to, term)={ edge_index=[2, 85810] },\n",
       "  (paper, to, conference)={ edge_index=[2, 14328] },\n",
       "  (term, to, paper)={ edge_index=[2, 85810] },\n",
       "  (conference, to, paper)={ edge_index=[2, 14328] }\n",
       ")"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:32<00:00, 15.48it/s]\n"
     ]
    }
   ],
   "source": [
    "import tqdm\n",
    "import torch.nn.functional as F\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from utils.save_model import save_model\n",
    "from datetime import datetime\n",
    "import os\n",
    "\n",
    "epochs = 500\n",
    "\n",
    "def train_epoch(data, optimizer):\n",
    "    optimizer.zero_grad()\n",
    "    model.train()\n",
    "    pred = model(data, edge_type)\n",
    "    ground_truth = data[edge_type].edge_label\n",
    "    loss = F.binary_cross_entropy_with_logits(pred, ground_truth)\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    return float(loss)\n",
    "\n",
    "@torch.no_grad\n",
    "def eval_epoch(data):\n",
    "    model.eval()\n",
    "    pred = model(data, edge_type)\n",
    "    ground_truth = data[edge_type].edge_label\n",
    "    score = roc_auc_score(ground_truth.cpu().numpy(), pred.cpu().numpy())\n",
    "    return score\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "train_loss_history, val_loss_history = [],[]\n",
    "best_val_score = 0\n",
    "directory = './saved_models/link_predictor'\n",
    "model_name = 'link_prediction_model'\n",
    "for epoch in tqdm.trange(epochs):\n",
    "\n",
    "    train_loss_history.append(train_epoch(train_data, optimizer))\n",
    "    val_score = eval_epoch(val_data)\n",
    "    if val_score > best_val_score:\n",
    "        \n",
    "        save_model(model,  directory, file_name=model_name)\n",
    "        best_val_score = val_score\n",
    "    val_loss_history.append(val_score)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor(-9.2162, device='cuda:0', grad_fn=<MinBackward1>),\n",
       " tensor(-0.2446, device='cuda:0', grad_fn=<MaxBackward1>))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "pred = model(test_data, edge_type)\n",
    "\n",
    "pred.min(), pred.max()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.8936, 0.9359, 0.7284,  ..., 0.1928, 0.9283, 0.3511], device='cuda:0',\n",
       "       grad_fn=<DivBackward0>)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8637768530079103"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = os.path.join(directory, model_name+'.pt')\n",
    "model.load_state_dict(torch.load(path))\n",
    "test_data.to(device)\n",
    "\n",
    "pred = model(test_data, edge_type)\n",
    "ground_truth = test_data[edge_type].edge_label\n",
    "score = roc_auc_score(ground_truth.cpu().numpy(), pred.cpu().detach().numpy())\n",
    "\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "pred = model(test_data, edge_type)\n",
    "ground_truth = test_data[edge_type].edge_label\n",
    "pred_norm = (pred - pred.min() ) / ( pred.max() - pred.min())\n",
    "fpr_norm, tpr_norm, thresholds_norm = roc_curve(ground_truth.cpu().numpy(), pred_norm.cpu().detach().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr, tpr, thresholds = roc_curve(ground_truth.cpu().numpy(), pred.cpu().detach().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00000000e+00, 5.09164969e-04, 3.56415479e-03, ...,\n",
       "       9.99490835e-01, 9.99490835e-01, 1.00000000e+00])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tpr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([       inf, 1.        , 0.9987454 , ..., 0.12469155, 0.01497363,\n",
       "       0.        ], dtype=float32)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thresholds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import RocCurveDisplay\n",
    "\n",
    "display = RocCurveDisplay(fpr=fpr_norm,tpr=tpr_norm)\n",
    "display.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import RocCurveDisplay\n",
    "\n",
    "display = RocCurveDisplay(fpr=fpr,tpr=tpr)\n",
    "display.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HeteroData(\n",
       "  author={\n",
       "    x=[4057, 334],\n",
       "    y=[4057],\n",
       "    train_mask=[4057],\n",
       "    val_mask=[4057],\n",
       "    test_mask=[4057],\n",
       "  },\n",
       "  paper={ x=[14328, 4231] },\n",
       "  term={ x=[7723, 50] },\n",
       "  conference={\n",
       "    num_nodes=20,\n",
       "    x=[20, 1],\n",
       "  },\n",
       "  (author, to, paper)={\n",
       "    edge_index=[2, 17681],\n",
       "    edge_label=[5892],\n",
       "    edge_label_index=[2, 5892],\n",
       "  },\n",
       "  (paper, to, author)={ edge_index=[2, 17681] },\n",
       "  (paper, to, term)={ edge_index=[2, 85810] },\n",
       "  (paper, to, conference)={ edge_index=[2, 14328] },\n",
       "  (term, to, paper)={ edge_index=[2, 85810] },\n",
       "  (conference, to, paper)={ edge_index=[2, 14328] }\n",
       ")"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(epochs), train_loss_history)\n",
    "plt.ylabel('Train Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(epochs), val_loss_history)\n",
    "plt.ylabel('AOC')\n",
    "plt.xlabel('Epoch')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "my-env",
   "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": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}