examples/DKT/DKT.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Deep Knowledge Tracing Model (DKT)\n",
    "This notebook will show you how to train and use the DKT. First, we will show how to get the data (here we use assistment-2009-2010-skill as the dataset). Then we will show how to train a DKT and perform the parameters persistence. At last, we will show how to load the parameters from the file and evaluate on the test dataset.\n",
    "\n",
    "The script version could be found in [DKT.py](DCK.py)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preparation\n",
    "Before we process the data, we need to first acquire the dataset which is shown in this [prepare_dataset.ipynb](prepare_dataset.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "import torch.utils.data as Data\n",
    "from torch.utils.data.dataset import Dataset\n",
    "import tqdm\n",
    "\n",
    "\n",
    "NUM_QUESTIONS = 123\n",
    "BATCH_SIZE = 64\n",
    "HIDDEN_SIZE = 10\n",
    "NUM_LAYERS = 1\n",
    "\n",
    "\n",
    "def get_data_loader(data_path, batch_size, shuffle=False):\n",
    "    data = torch.FloatTensor(np.load(data_path))\n",
    "    data_loader = Data.DataLoader(data, batch_size=batch_size, shuffle=shuffle)\n",
    "    return data_loader\n",
    "\n",
    "\n",
    "train_loader = get_data_loader('../../data/2009_skill_builder_data_corrected/train_data.npy', BATCH_SIZE, True)\n",
    "test_loader = get_data_loader('../../data/2009_skill_builder_data_corrected/test_data.npy', BATCH_SIZE, False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training and Persistence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "logging.getLogger().setLevel(logging.INFO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 0: 100%|██████████| 6/6 [00:00<00:00, 19.09it/s]\n",
      "Epoch 1:  33%|███▎      | 2/6 [00:00<00:00, 17.78it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 0] LogisticLoss: 44.028362\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 1: 100%|██████████| 6/6 [00:00<00:00, 19.07it/s]\n",
      "INFO:root:save parameters to dkt.params\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 1] LogisticLoss: 43.584623\n"
     ]
    }
   ],
   "source": [
    "from EduKTM import DKT\n",
    "\n",
    "dkt = DKT(NUM_QUESTIONS, HIDDEN_SIZE, NUM_LAYERS)\n",
    "dkt.train(train_loader, epoch=2)\n",
    "dkt.save(\"dkt.params\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading and Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:load parameters from dkt.params\n",
      "evaluating: 100%|██████████| 15/15 [00:00<00:00, 56.15it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "auc: 0.944941\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "dkt.load(\"dkt.params\")\n",
    "auc = dkt.eval(test_loader)\n",
    "print(\"auc: %.6f\" % auc)"
   ]
  }
 ],
 "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": "3.9.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}