scripts/train.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('..')\n",
    "import CAT\n",
    "import json\n",
    "import logging\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def setuplogger():\n",
    "    root = logging.getLogger()\n",
    "    root.setLevel(logging.INFO)\n",
    "    handler = logging.StreamHandler(sys.stdout)\n",
    "    handler.setLevel(logging.INFO)\n",
    "    formatter = logging.Formatter(\"[%(levelname)s %(asctime)s] %(message)s\")\n",
    "    handler.setFormatter(formatter)\n",
    "    root.addHandler(handler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "setuplogger()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# choose dataset here\n",
    "dataset = 'assistment'\n",
    "# modify config here\n",
    "config = {\n",
    "    'learning_rate': 0.002,\n",
    "    'batch_size': 2048,\n",
    "    'num_epochs': 10,\n",
    "    'num_dim': 1, # for IRT or MIRT\n",
    "    'device': 'cpu',\n",
    "    # for NeuralCD\n",
    "    'prednet_len1': 128,\n",
    "    'prednet_len2': 64,\n",
    "    'betas': (0.9, 0.999),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read datasets\n",
    "train_triplets = pd.read_csv(f'../data/{dataset}/train_triples.csv', encoding='utf-8').to_records(index=False)\n",
    "concept_map = json.load(open(f'../data/{dataset}/concept_map.json', 'r'))\n",
    "concept_map = {int(k):v for k,v in concept_map.items()}\n",
    "metadata = json.load(open(f'../data/{dataset}/metadata.json', 'r'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = CAT.dataset.TrainDataset(train_triplets, concept_map,\n",
    "                                      metadata['num_train_students'], \n",
    "                                      metadata['num_questions'], \n",
    "                                      metadata['num_concepts'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'betas'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_27436\\1368016865.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCAT\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mIRTModel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;31m# train model\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minit_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlog_step\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\workplace\\tmp\\EduCAT\\CAT\\model\\IRT.py\u001b[0m in \u001b[0;36minit_model\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m     54\u001b[0m         \u001b[0mpolicy_lr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.0005\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     55\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mIRT\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnum_students\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnum_questions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'num_dim'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 56\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpolicy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mStraightThrough\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnum_questions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnum_questions\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mpolicy_lr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     57\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_q\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnum_questions\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     58\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\workplace\\tmp\\EduCAT\\CAT\\model\\utils.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, state_dim, action_dim, lr, config)\u001b[0m\n\u001b[0;32m     34\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     35\u001b[0m         \u001b[0mdevice\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconfig\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'device'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 36\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbetas\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconfig\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'betas'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     37\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpolicy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mActor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstate_dim\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maction_dim\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     38\u001b[0m         self.optimizer = torch.optim.Adam(\n",
      "\u001b[1;31mKeyError\u001b[0m: 'betas'"
     ]
    }
   ],
   "source": [
    "# define model here\n",
    "model = CAT.model.IRTModel(**config)\n",
    "# train model\n",
    "model.init_model(train_data)\n",
    "model.train(train_data, log_step=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save model\n",
    "model.adaptest_save('../ckpt/irt.pt')"
   ]
  }
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