graph-transe.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Translational embeddings (TransE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameters\n",
    "Define hyper-parameters inclduing the evaluation information:\n",
    "\n",
    "This is the basic set of parameters for a TransE model\n",
    " \n",
    "Unless you have a powerful GPU or use a different dataset than the one provided, set the number of epochs to a low number (5 or 10) for this tutorial; when you want to use and apply the model for a prediction task later, train for more epochs (ideally, on a good GPU)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "org_id = '9606' # 4932 - yeast, 9606 - human\n",
    "\n",
    "embeddings_size = 50\n",
    "batch_size = 32\n",
    "margin = 0.1\n",
    "reg_norm = 1\n",
    "optimizer = 'adam'\n",
    "loss_function = 'mse'\n",
    "epochs = 100\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kulmanm/KAUST/CBRC/ontology-tutorial/venv/lib/python3.6/site-packages/tensorflow/python/ops/gradients_impl.py:112: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
      "  \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "26023/26023 [==============================] - 291s 11ms/step - loss: 114.0954 - val_loss: 18.6479\n",
      "Epoch 2/100\n",
      "26023/26023 [==============================] - 265s 10ms/step - loss: 21.8254 - val_loss: 4.5449\n",
      "Epoch 3/100\n",
      "26023/26023 [==============================] - 257s 10ms/step - loss: 5.7751 - val_loss: 1.1735\n",
      "Epoch 4/100\n",
      "26023/26023 [==============================] - 249s 10ms/step - loss: 1.5106 - val_loss: 0.4180\n",
      "Epoch 5/100\n",
      "26023/26023 [==============================] - 246s 9ms/step - loss: 0.7002 - val_loss: 0.2671\n",
      "Epoch 6/100\n",
      "26023/26023 [==============================] - 241s 9ms/step - loss: 0.5357 - val_loss: 0.2256\n",
      "Epoch 7/100\n",
      "26023/26023 [==============================] - 243s 9ms/step - loss: 0.3710 - val_loss: 0.2029\n",
      "Epoch 8/100\n",
      "26023/26023 [==============================] - 244s 9ms/step - loss: 0.2403 - val_loss: 0.1865\n",
      "Epoch 9/100\n",
      "26023/26023 [==============================] - 246s 9ms/step - loss: 0.2016 - val_loss: 0.1826\n",
      "Epoch 10/100\n",
      "26023/26023 [==============================] - 244s 9ms/step - loss: 0.1744 - val_loss: 0.1659\n",
      "Epoch 11/100\n",
      "26023/26023 [==============================] - 253s 10ms/step - loss: 0.1606 - val_loss: 0.1716\n",
      "Epoch 12/100\n",
      "26023/26023 [==============================] - 250s 10ms/step - loss: 0.1478 - val_loss: 0.1606\n",
      "Epoch 13/100\n",
      "26023/26023 [==============================] - 248s 10ms/step - loss: 0.1374 - val_loss: 0.1585\n",
      "Epoch 14/100\n",
      "26023/26023 [==============================] - 251s 10ms/step - loss: 0.1292 - val_loss: 0.1526\n",
      "Epoch 15/100\n",
      "26023/26023 [==============================] - 249s 10ms/step - loss: 0.1248 - val_loss: 0.1452\n",
      "Epoch 16/100\n",
      "26023/26023 [==============================] - 250s 10ms/step - loss: 0.1192 - val_loss: 0.1451\n",
      "Epoch 17/100\n",
      "26023/26023 [==============================] - 245s 9ms/step - loss: 0.1159 - val_loss: 0.1392\n",
      "Epoch 18/100\n",
      "26023/26023 [==============================] - 254s 10ms/step - loss: 0.1117 - val_loss: 0.1362\n",
      "Epoch 19/100\n",
      "26023/26023 [==============================] - 243s 9ms/step - loss: 0.1084 - val_loss: 0.1430\n",
      "Epoch 20/100\n",
      "26023/26023 [==============================] - 242s 9ms/step - loss: 0.1063 - val_loss: 0.1303\n",
      "Epoch 21/100\n",
      "26023/26023 [==============================] - 241s 9ms/step - loss: 0.1027 - val_loss: 0.1310\n",
      "Epoch 22/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.1005 - val_loss: 0.1301\n",
      "Epoch 23/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0972 - val_loss: 0.1293\n",
      "Epoch 24/100\n",
      "26023/26023 [==============================] - 239s 9ms/step - loss: 0.0967 - val_loss: 0.1260\n",
      "Epoch 25/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0952 - val_loss: 0.1284\n",
      "Epoch 26/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0933 - val_loss: 0.1253\n",
      "Epoch 27/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0923 - val_loss: 0.1241\n",
      "Epoch 28/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0911 - val_loss: 0.1248\n",
      "Epoch 29/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0903 - val_loss: 0.1251\n",
      "Epoch 30/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0894 - val_loss: 0.1236\n",
      "Epoch 31/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0869 - val_loss: 0.1202\n",
      "Epoch 32/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0868 - val_loss: 0.1275\n",
      "Epoch 33/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0845 - val_loss: 0.1199\n",
      "Epoch 34/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0838 - val_loss: 0.1222\n",
      "Epoch 35/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0836 - val_loss: 0.1241\n",
      "Epoch 36/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0824 - val_loss: 0.1200\n",
      "Epoch 37/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0814 - val_loss: 0.1178\n",
      "Epoch 38/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0813 - val_loss: 0.1193\n",
      "Epoch 39/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0807 - val_loss: 0.1190\n",
      "Epoch 40/100\n",
      "26023/26023 [==============================] - 237s 9ms/step - loss: 0.0786 - val_loss: 0.1232\n",
      "Epoch 41/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0790 - val_loss: 0.1181\n",
      "Epoch 42/100\n",
      "26023/26023 [==============================] - 237s 9ms/step - loss: 0.0780 - val_loss: 0.1187\n",
      "Epoch 43/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0778 - val_loss: 0.1189\n",
      "Epoch 44/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0773 - val_loss: 0.1141\n",
      "Epoch 45/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0755 - val_loss: 0.1182\n",
      "Epoch 46/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0765 - val_loss: 0.1200\n",
      "Epoch 47/100\n",
      "26023/26023 [==============================] - 237s 9ms/step - loss: 0.0739 - val_loss: 0.1122\n",
      "Epoch 48/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0755 - val_loss: 0.1175\n",
      "Epoch 49/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0753 - val_loss: 0.1155\n",
      "Epoch 50/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0746 - val_loss: 0.1156\n",
      "Epoch 51/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0740 - val_loss: 0.1166\n",
      "Epoch 52/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0728 - val_loss: 0.1139\n",
      "Epoch 53/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0735 - val_loss: 0.1127\n",
      "Epoch 54/100\n",
      "26023/26023 [==============================] - 239s 9ms/step - loss: 0.0723 - val_loss: 0.1092\n",
      "Epoch 55/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0726 - val_loss: 0.1086\n",
      "Epoch 56/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0730 - val_loss: 0.1132\n",
      "Epoch 57/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0716 - val_loss: 0.1139\n",
      "Epoch 58/100\n",
      "26023/26023 [==============================] - 239s 9ms/step - loss: 0.0706 - val_loss: 0.1095\n",
      "Epoch 59/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0706 - val_loss: 0.1115\n",
      "Epoch 60/100\n",
      "26023/26023 [==============================] - 239s 9ms/step - loss: 0.0708 - val_loss: 0.1083\n",
      "Epoch 61/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0701 - val_loss: 0.1095\n",
      "Epoch 62/100\n",
      "26023/26023 [==============================] - 239s 9ms/step - loss: 0.0713 - val_loss: 0.1093\n",
      "Epoch 63/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0693 - val_loss: 0.1097\n",
      "Epoch 64/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0697 - val_loss: 0.1096\n",
      "Epoch 65/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0689 - val_loss: 0.1111\n",
      "Epoch 66/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0678 - val_loss: 0.1131\n",
      "Epoch 67/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0691 - val_loss: 0.1123\n",
      "Epoch 68/100\n",
      "26023/26023 [==============================] - 237s 9ms/step - loss: 0.0681 - val_loss: 0.1139\n",
      "Epoch 69/100\n",
      "26023/26023 [==============================] - 237s 9ms/step - loss: 0.0681 - val_loss: 0.1119\n",
      "Epoch 70/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0677 - val_loss: 0.1099\n",
      "Epoch 71/100\n",
      "26023/26023 [==============================] - 237s 9ms/step - loss: 0.0677 - val_loss: 0.1092\n",
      "Epoch 72/100\n",
      "26023/26023 [==============================] - 237s 9ms/step - loss: 0.0675 - val_loss: 0.1105\n",
      "Epoch 73/100\n",
      "26023/26023 [==============================] - 240s 9ms/step - loss: 0.0672 - val_loss: 0.1119\n",
      "Epoch 74/100\n",
      "26023/26023 [==============================] - 240s 9ms/step - loss: 0.0660 - val_loss: 0.1090\n",
      "Epoch 75/100\n",
      "26023/26023 [==============================] - 243s 9ms/step - loss: 0.0665 - val_loss: 0.1057\n",
      "Epoch 76/100\n",
      "26023/26023 [==============================] - 240s 9ms/step - loss: 0.0662 - val_loss: 0.1100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 77/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0661 - val_loss: 0.1110\n",
      "Epoch 78/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0671 - val_loss: 0.1129\n",
      "Epoch 79/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0663 - val_loss: 0.1056\n",
      "Epoch 80/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0658 - val_loss: 0.1070\n",
      "Epoch 81/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0662 - val_loss: 0.1098\n",
      "Epoch 82/100\n",
      "26023/26023 [==============================] - 237s 9ms/step - loss: 0.0656 - val_loss: 0.1081\n",
      "Epoch 83/100\n",
      "26023/26023 [==============================] - 237s 9ms/step - loss: 0.0663 - val_loss: 0.1077\n",
      "Epoch 84/100\n",
      "26023/26023 [==============================] - 237s 9ms/step - loss: 0.0657 - val_loss: 0.1054\n",
      "Epoch 85/100\n",
      "26023/26023 [==============================] - 237s 9ms/step - loss: 0.0648 - val_loss: 0.1060\n",
      "Epoch 86/100\n",
      "26023/26023 [==============================] - 237s 9ms/step - loss: 0.0649 - val_loss: 0.1063\n",
      "Epoch 87/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0646 - val_loss: 0.1039\n",
      "Epoch 88/100\n",
      "26023/26023 [==============================] - 240s 9ms/step - loss: 0.0637 - val_loss: 0.1075\n",
      "Epoch 89/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0656 - val_loss: 0.1061\n",
      "Epoch 90/100\n",
      "26023/26023 [==============================] - 240s 9ms/step - loss: 0.0639 - val_loss: 0.1073\n",
      "Epoch 91/100\n",
      "26023/26023 [==============================] - 236s 9ms/step - loss: 0.0649 - val_loss: 0.1061\n",
      "Epoch 92/100\n",
      "26023/26023 [==============================] - 237s 9ms/step - loss: 0.0632 - val_loss: 0.1111\n",
      "Epoch 93/100\n",
      "26023/26023 [==============================] - 237s 9ms/step - loss: 0.0642 - val_loss: 0.1079\n",
      "Epoch 94/100\n",
      "26023/26023 [==============================] - 237s 9ms/step - loss: 0.0640 - val_loss: 0.1063\n",
      "Epoch 95/100\n",
      "26023/26023 [==============================] - 237s 9ms/step - loss: 0.0640 - val_loss: 0.1053\n",
      "Epoch 96/100\n",
      "26023/26023 [==============================] - 238s 9ms/step - loss: 0.0640 - val_loss: 0.1035\n",
      "Epoch 97/100\n",
      "26023/26023 [==============================] - 237s 9ms/step - loss: 0.0635 - val_loss: 0.1069\n",
      "Epoch 98/100\n",
      "26023/26023 [==============================] - 237s 9ms/step - loss: 0.0641 - val_loss: 0.1054\n",
      "Epoch 99/100\n",
      "26023/26023 [==============================] - 237s 9ms/step - loss: 0.0633 - val_loss: 0.1083\n",
      "Epoch 100/100\n",
      "26023/26023 [==============================] - 237s 9ms/step - loss: 0.0628 - val_loss: 0.1053\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f1c74245a20>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers import Input\n",
    "from tensorflow.keras import optimizers\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, CSVLogger\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from rdflib import Graph\n",
    "import math\n",
    "\n",
    "\n",
    "class TransE(tf.keras.Model):\n",
    "    \n",
    "    def __init__(self, nb_classes, nb_relations, embedding_size, margin=0.1, reg_norm=1):\n",
    "        super(TransE, self).__init__()\n",
    "        self.nb_classes = nb_classes\n",
    "        self.nb_relations = nb_relations\n",
    "        self.margin = margin\n",
    "        self.reg_norm = 1\n",
    "        bound = 6 / math.sqrt(embedding_size)\n",
    "        cls_weights = np.random.uniform(low=-bound, high=bound, size=(nb_classes, embedding_size))\n",
    "        rel_weights = np.random.uniform(low=-bound, high=bound, size=(nb_relations, embedding_size))\n",
    "        self.cls_embeddings = tf.keras.layers.Embedding(\n",
    "            nb_classes,\n",
    "            embedding_size,\n",
    "            input_length=1,\n",
    "            weights=[cls_weights,])\n",
    "        self.rel_embeddings = tf.keras.layers.Embedding(\n",
    "            nb_relations,\n",
    "            embedding_size,\n",
    "            input_length=1,\n",
    "            weights=[rel_weights,])\n",
    "    \n",
    "    def reg(self, x):\n",
    "        res = tf.abs(tf.norm(x, axis=1) - self.reg_norm)\n",
    "        res = tf.reshape(res, [-1, 1])\n",
    "        return res\n",
    "    \n",
    "        \n",
    "    def call(self, input):\n",
    "        pos, neg = input\n",
    "        ph, pl, pt = pos[:, 0], pos[:, 1], pos[:, 2]\n",
    "        nh, nl, nt = neg[:, 0], neg[:, 1], neg[:, 2]\n",
    "        nh = self.cls_embeddings(nh)\n",
    "        nl = self.rel_embeddings(nl)\n",
    "        nt = self.cls_embeddings(nt)\n",
    "        ph = self.cls_embeddings(ph)\n",
    "        pl = self.rel_embeddings(pl)\n",
    "        pt = self.cls_embeddings(pt)\n",
    "        \n",
    "        pos = tf.subtract(tf.add(ph, pl), pt)\n",
    "        neg = tf.subtract(tf.add(nh, nl), nt)\n",
    "        \n",
    "        self._loss = tf.norm(pos, axis=1) - tf.norm(neg, axis=1) + self.margin\n",
    "        self._loss = tf.reduce_sum(tf.nn.relu(self._loss))\n",
    "        self._loss = (tf.reshape(self._loss, [-1, 1]) + self.reg(ph)\n",
    "                      + self.reg(ph) + self.reg(nh) + self.reg(nh))\n",
    "        return self._loss\n",
    "\n",
    "    \n",
    "class Generator(object):\n",
    "\n",
    "    def __init__(self, data, triple_set, nb_classes, batch_size):\n",
    "        self.data = data\n",
    "        self.triple_set = triple_set\n",
    "        self.nb_classes = nb_classes\n",
    "        self.size = len(data)\n",
    "        self.start = 0\n",
    "        self.batch_size = batch_size\n",
    "\n",
    "    def __iter__(self):\n",
    "        return self\n",
    "\n",
    "    def __next__(self):\n",
    "        if self.start < self.size:\n",
    "            end = self.start + self.batch_size\n",
    "            batch = self.data[self.start:end]\n",
    "            neg_triples = []\n",
    "            for h, l, t in batch:\n",
    "                while True:\n",
    "                    hn = h\n",
    "                    tn = t\n",
    "                    if np.random.choice([False, True]):\n",
    "                        hn = np.random.randint(0, self.nb_classes)\n",
    "                    else:\n",
    "                        tn = np.random.randint(0, self.nb_classes)\n",
    "                    if (hn, l, tn) not in self.triple_set:\n",
    "                        neg_triples.append((hn, l, tn))\n",
    "                        break\n",
    "            self.start = end\n",
    "            batch = np.hstack(batch).reshape(len(batch), 3)\n",
    "            neg_triples = np.hstack(neg_triples).reshape(len(neg_triples), 3)\n",
    "            labels = np.zeros((len(batch), 1), dtype='float32')\n",
    "            return ([batch, neg_triples], labels)\n",
    "        else:\n",
    "            self.start = 0\n",
    "            return self.__next__()\n",
    "            #raise StopIteration()\n",
    "\n",
    "\n",
    "def load_data(filename=f'data/train/{org_id}.plain.nt', format='nt'):\n",
    "    g = Graph()\n",
    "    g.parse(filename, format=format)\n",
    "    classes = {}\n",
    "    relations = {}\n",
    "    data = []\n",
    "    for h, l, t in g:\n",
    "        h, l, t = str(h), str(l), str(t)\n",
    "        if h not in classes:\n",
    "            classes[h] = len(classes)\n",
    "        if t not in classes:\n",
    "            classes[t] = len(classes)\n",
    "        if l not in relations:\n",
    "            relations[l] = len(relations)\n",
    "        data.append((classes[h], relations[l], classes[t]))\n",
    "    return data, classes, relations\n",
    "\n",
    "def load_valid_data(data_file, classes, relations):\n",
    "    data = []\n",
    "    rel = f'http://interacts'\n",
    "    with open(data_file, 'r') as f:\n",
    "        for line in f:\n",
    "            it = line.strip().split()\n",
    "            id1 = f'http://{it[0]}'\n",
    "            id2 = f'http://{it[1]}'\n",
    "            if id1 not in classes or id2 not in classes or rel not in relations:\n",
    "                continue\n",
    "            data.append((classes[id1], relations[rel], classes[id2]))\n",
    "    return data\n",
    "\n",
    "data, classes, relations = load_data()\n",
    "train_steps = int(math.ceil(len(data) / batch_size))\n",
    "train_generator = Generator(data, set(data), len(classes), batch_size)\n",
    "\n",
    "valid_data = load_valid_data(f'data/valid/{org_id}.protein.links.v11.0.txt', classes, relations)\n",
    "valid_generator = Generator(valid_data, set(data), len(classes), batch_size)\n",
    "valid_steps = int(math.ceil(len(valid_data) / batch_size))\n",
    "\n",
    "pos_input = Input(shape=(3,), dtype='int32')\n",
    "neg_input = Input(shape=(3,), dtype='int32')\n",
    "transe_model = TransE(len(classes), len(relations), embeddings_size)\n",
    "out = transe_model([pos_input, neg_input])\n",
    "model = tf.keras.Model(inputs=[pos_input, neg_input], outputs=out)\n",
    "model.compile(optimizer=optimizer, loss=loss_function)\n",
    "\n",
    "model.fit_generator(\n",
    "    train_generator,\n",
    "    steps_per_epoch=train_steps,\n",
    "    epochs=epochs,\n",
    "    validation_data=valid_generator,\n",
    "    validation_steps=valid_steps,\n",
    "    workers=4,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save embeddings:\n",
    "\n",
    "The embeddings provided in the data package have been trained for 100 epochs and may provide better results; you may want to skip this step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "cls_embeddings = transe_model.cls_embeddings.get_weights()[0]\n",
    "rel_embeddings = transe_model.rel_embeddings.get_weights()[0]\n",
    "cls_embeddings = list(cls_embeddings)\n",
    "rel_embeddings = list(rel_embeddings)\n",
    "\n",
    "df = pd.DataFrame({'classes': list(classes.keys()), 'embeddings': cls_embeddings})\n",
    "df.to_pickle(f'data/transe/{org_id}_cls_embeddings.pkl')\n",
    "\n",
    "df = pd.DataFrame({'relations': list(relations.keys()), 'embeddings': rel_embeddings})\n",
    "df.to_pickle(f'data/transe/{org_id}_rel_embeddings.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation results on test set:\n",
    " * Hits@k, k $\\in$ {10, 100}\n",
    " * Mean rank\n",
    " * ROC AUC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proteins:  6229\n",
      "0.06 0.32 1125.44 0.82\n",
      "0.13 0.40 1074.69 0.83\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import rankdata\n",
    "org_id = '4932'\n",
    "\n",
    "def load_test_data(data_file, classes, relations):\n",
    "    data = []\n",
    "    rel = f'http://interacts'\n",
    "    with open(data_file, 'r') as f:\n",
    "        for line in f:\n",
    "            it = line.strip().split()\n",
    "            id1 = f'http://{it[0]}'\n",
    "            id2 = f'http://{it[1]}'\n",
    "            if id1 not in classes or id2 not in classes or rel not in relations:\n",
    "                continue\n",
    "            data.append((id1, rel, id2))\n",
    "    return data\n",
    "\n",
    "def compute_rank_roc(ranks, n_prots):\n",
    "    auc_x = list(ranks.keys())\n",
    "    auc_x.sort()\n",
    "    auc_y = []\n",
    "    tpr = 0\n",
    "    sum_rank = sum(ranks.values())\n",
    "    for x in auc_x:\n",
    "        tpr += ranks[x]\n",
    "        auc_y.append(tpr / sum_rank)\n",
    "    auc_x.append(n_prots)\n",
    "    auc_y.append(1)\n",
    "    auc = np.trapz(auc_y, auc_x) / n_prots\n",
    "    return auc\n",
    "\n",
    "# Load embeddings from saved files\n",
    "cls_df = pd.read_pickle(f'data/transe/{org_id}_cls_embeddings.pkl')\n",
    "rel_df = pd.read_pickle(f'data/transe/{org_id}_rel_embeddings.pkl')\n",
    "classes = {v: k for k, v in enumerate(cls_df['classes'])}\n",
    "relations = {v: k for k, v in enumerate(rel_df['relations'])}\n",
    "\n",
    "nb_classes = len(cls_df)\n",
    "nb_relations = len(rel_df)\n",
    "embeds_list = cls_df['embeddings'].values\n",
    "rembeds_list = rel_df['embeddings'].values\n",
    "size = len(embeds_list[0])\n",
    "embeds = np.zeros((nb_classes, size), dtype=np.float32)\n",
    "for i, emb in enumerate(embeds_list):\n",
    "    embeds[i, :] = emb\n",
    "\n",
    "proteins = {}\n",
    "for k, v in classes.items():\n",
    "    if not k.startswith('http://GO:'):\n",
    "        proteins[k] = v\n",
    "\n",
    "print('Proteins: ', len(proteins))\n",
    "\n",
    "prot_index = list(proteins.values())\n",
    "prot_embeds = embeds[prot_index, :]\n",
    "prot_dict = {v: k for k, v in enumerate(prot_index)}\n",
    "    \n",
    "rsize = len(rembeds_list[0])\n",
    "rembeds = np.zeros((nb_relations, rsize), dtype=np.float32)\n",
    "for i, emb in enumerate(rembeds_list):\n",
    "    rembeds[i, :] = emb\n",
    "\n",
    "# Load training data to computed filtered rank\n",
    "train_data = load_test_data(f'data/train/{org_id}.protein.links.v11.0.txt', classes, relations)\n",
    "valid_data = load_test_data(f'data/valid/{org_id}.protein.links.v11.0.txt', classes, relations)\n",
    "trlabels = {}\n",
    "for c, r, d in train_data:\n",
    "    c, r, d = prot_dict[classes[c]], relations[r], prot_dict[classes[d]]\n",
    "    if r not in trlabels:\n",
    "        trlabels[r] = np.ones((len(prot_embeds), len(prot_embeds)), dtype=np.int32)\n",
    "    trlabels[r][c, d] = 1000\n",
    "for c, r, d in valid_data:\n",
    "    c, r, d = prot_dict[classes[c]], relations[r], prot_dict[classes[d]]\n",
    "    if r not in trlabels:\n",
    "        trlabels[r] = np.ones((len(prot_embeds), len(prot_embeds)), dtype=np.int32)\n",
    "    trlabels[r][c, d] = 1000\n",
    "\n",
    "\n",
    "# Load test data and compute ranks for each protein\n",
    "test_data = load_test_data(f'data/test/{org_id}.protein.links.v11.0.txt', classes, relations)\n",
    "top1 = 0\n",
    "top10 = 0\n",
    "top100 = 0\n",
    "mean_rank = 0\n",
    "ftop1 = 0\n",
    "ftop10 = 0\n",
    "ftop100 = 0\n",
    "fmean_rank = 0\n",
    "labels = {}\n",
    "preds = {}\n",
    "ranks = {}\n",
    "franks = {}\n",
    "eval_data = test_data\n",
    "n = len(eval_data)\n",
    "for c, r, d in eval_data:\n",
    "    c, r, d = prot_dict[classes[c]], relations[r], prot_dict[classes[d]]\n",
    "    if r not in labels:\n",
    "        labels[r] = np.zeros((len(prot_embeds), len(prot_embeds)), dtype=np.int32)\n",
    "    if r not in preds:\n",
    "        preds[r] = np.zeros((len(prot_embeds), len(prot_embeds)), dtype=np.float32)\n",
    "    labels[r][c, d] = 1\n",
    "    ec = prot_embeds[c, :]\n",
    "    er = rembeds[r, :]\n",
    "    ec += er\n",
    "\n",
    "    # Compute distance\n",
    "    dst = np.linalg.norm(prot_embeds - ec.reshape(1, -1), axis=1)\n",
    "    res = dst.flatten()\n",
    "\n",
    "    preds[r][c, :] = res\n",
    "    index = rankdata(res, method='average')\n",
    "    rank = index[d]\n",
    "    if rank == 1:\n",
    "        top1 += 1\n",
    "    if rank <= 10:\n",
    "        top10 += 1\n",
    "    if rank <= 100:\n",
    "        top100 += 1\n",
    "    mean_rank += rank\n",
    "    if rank not in ranks:\n",
    "        ranks[rank] = 0\n",
    "    ranks[rank] += 1\n",
    "\n",
    "    # Filtered rank\n",
    "    index = rankdata((res * trlabels[r][c, :]), method='average')\n",
    "    rank = index[d]\n",
    "    if rank == 1:\n",
    "        ftop1 += 1\n",
    "    if rank <= 10:\n",
    "        ftop10 += 1\n",
    "    if rank <= 100:\n",
    "        ftop100 += 1\n",
    "    fmean_rank += rank\n",
    "\n",
    "    if rank not in franks:\n",
    "        franks[rank] = 0\n",
    "    franks[rank] += 1\n",
    "top1 /= n\n",
    "top10 /= n\n",
    "top100 /= n\n",
    "mean_rank /= n\n",
    "ftop1 /= n\n",
    "ftop10 /= n\n",
    "ftop100 /= n\n",
    "fmean_rank /= n\n",
    "\n",
    "rank_auc = compute_rank_roc(ranks, len(proteins))\n",
    "frank_auc = compute_rank_roc(franks, len(proteins))\n",
    "\n",
    "print(f'{top10:.2f} {top100:.2f} {mean_rank:.2f} {rank_auc:.2f}')\n",
    "print(f'{ftop10:.2f} {ftop100:.2f} {fmean_rank:.2f} {frank_auc:.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "from MulticoreTSNE import MulticoreTSNE as TSNE\n",
    "X = TSNE(n_components=2, verbose=1, n_iter=2500, n_jobs=8).fit_transform(prot_embeds)\n",
    "\n",
    "ec_numbers = {}\n",
    "with open('data/yeast_ec.tab') as f:\n",
    "    next(f)\n",
    "    for line in f:\n",
    "        it = line.strip().split('\\t', -1)\n",
    "        if len(it) < 5:\n",
    "            continue\n",
    "        if it[3]:\n",
    "            prot_id = it[3].split(';')[0]\n",
    "            prot_id = 'http://{0}'.format(prot_id)    \n",
    "            ec_numbers[prot_id] = it[4]\n",
    "ec_classes = {'0': [[], []]}\n",
    "for i, item in enumerate(proteins.items()):\n",
    "    k, v = item\n",
    "    if k in ec_numbers:\n",
    "        ec = ec_numbers[k].split('.')[0]\n",
    "        if ec not in ec_classes:\n",
    "            ec_classes[ec] = [[], []]\n",
    "        ec_classes[ec][0].append(X[i, 0])\n",
    "        ec_classes[ec][1].append(X[i, 1])\n",
    "colors = iter(plt.cm.rainbow(np.linspace(0, 1, len(ec_classes))))\n",
    "fig, ax = plt.subplots(figsize=(20, 10))\n",
    "\n",
    "for ec, items in ec_classes.items():\n",
    "    if ec == '0':\n",
    "        continue\n",
    "    color = next(colors)\n",
    "    ax.scatter(items[0], items[1], color=color, label=ec)\n",
    "\n",
    "ax.legend()\n",
    "ax.grid(True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 2: Infer top 10 functions for a query protein"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "query = 'http://4932.YOR234C'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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