examples/aifb.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rrgcn import RRGCNEmbedder\n",
    "from torch_geometric.datasets import Entities\n",
    "import torch\n",
    "from catboost import CatBoostClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "dataset = Entities('./', 'aifb')\n",
    "data = dataset[0].to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedder = RRGCNEmbedder(num_nodes=data.num_nodes, num_relations=dataset.num_relations, num_layers=1, emb_size=512, device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00,  5.65it/s]\n"
     ]
    }
   ],
   "source": [
    "embs = embedder.embeddings(data.edge_index, data.edge_type, idx=torch.hstack((data.train_idx, data.test_idx)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_embs, val_embs, y_train, y_val = train_test_split(embs[:len(data.train_idx)], data.train_y, stratify=data.train_y.cpu().numpy(), test_size=0.1, random_state=42)\n",
    "test_embs = embs[len(data.train_idx):]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: less than 75% gpu memory available for training. Free: 8148.4375 Total: 11178.5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate set to 0.085304\n",
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      "127:\tlearn: 0.0478158\ttest: 0.6336537\tbest: 0.6281488 (120)\ttotal: 1.54s\tremaining: 10.5s\n",
      "128:\tlearn: 0.0472445\ttest: 0.6331554\tbest: 0.6281488 (120)\ttotal: 1.55s\tremaining: 10.5s\n",
      "129:\tlearn: 0.0467508\ttest: 0.6319422\tbest: 0.6281488 (120)\ttotal: 1.56s\tremaining: 10.4s\n",
      "130:\tlearn: 0.0462970\ttest: 0.6311057\tbest: 0.6281488 (120)\ttotal: 1.57s\tremaining: 10.4s\n",
      "bestTest = 0.6281487965\n",
      "bestIteration = 120\n",
      "Shrink model to first 121 iterations.\n"
     ]
    }
   ],
   "source": [
    "task_type = 'GPU' if torch.cuda.is_available() else 'CPU'\n",
    "clf = CatBoostClassifier(iterations=1000, early_stopping_rounds=10, task_type=task_type, random_seed=42, auto_class_weights=\"Balanced\")\n",
    "clf = clf.fit(train_embs.cpu().numpy(), y_train.cpu().numpy(), eval_set=(val_embs.cpu().numpy(), y_val.cpu().numpy()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.88      0.90        16\n",
      "           1       0.92      1.00      0.96        11\n",
      "           2       1.00      1.00      1.00         3\n",
      "           3       0.83      0.83      0.83         6\n",
      "\n",
      "    accuracy                           0.92        36\n",
      "   macro avg       0.92      0.93      0.92        36\n",
      "weighted avg       0.92      0.92      0.92        36\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(clf.predict(test_embs.cpu().numpy()), data.test_y.cpu().numpy()))"
   ]
  }
 ],
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