02_disjoint_linucb_param_tuning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "from striatum.bandit import linucb\n",
    "from utils.data import *\n",
    "from utils.policy import *\n",
    "plt.style.use('seaborn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_DIR = os.path.abspath('../../Data/yahoo!_front_page_today_module_user_click_log/R6')\n",
    "DATA_FILE_NAMES = ['ydata-fp-td-clicks-v1_0.20090501.gz']\n",
    "MODEL_DIR = os.path.abspath('models')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_paths = []\n",
    "for data_file in DATA_FILE_NAMES:\n",
    "    data_paths.append(os.path.join(DATA_DIR, data_file))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "policy_learner = PolicyLearner(linucb.LinUCB, context_dimension=6, seed=0, reset_freq=1000000)\n",
    "param_grid = {'alpha': [0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters: {'alpha': 0.0} | Average Reward: 4.5860% | Computation Time: 0:27:31\n",
      "Parameters: {'alpha': 0.2} | Average Reward: 6.9742% | Computation Time: 0:27:59\n",
      "Parameters: {'alpha': 0.4} | Average Reward: 6.5563% | Computation Time: 0:27:32\n",
      "Parameters: {'alpha': 0.6} | Average Reward: 6.4192% | Computation Time: 0:27:22\n",
      "Parameters: {'alpha': 0.8} | Average Reward: 6.4236% | Computation Time: 0:27:19\n",
      "Parameters: {'alpha': 1.0} | Average Reward: 6.2404% | Computation Time: 0:28:23\n",
      "Parameters: {'alpha': 1.2} | Average Reward: 6.0043% | Computation Time: 0:28:31\n",
      "Parameters: {'alpha': 1.4} | Average Reward: 6.0415% | Computation Time: 0:27:25\n"
     ]
    }
   ],
   "source": [
    "res = search_param(policy_learner, param_grid, parse_data, data_paths=data_paths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.isdir(MODEL_DIR):\n",
    "    os.mkdir(MODEL_DIR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(os.path.join(MODEL_DIR, 'param_disjoint_linucb.pkl'), 'wb') as file:\n",
    "    pickle.dump(res, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(os.path.join(MODEL_DIR, 'param_disjoint_linucb.pkl'), 'rb') as file:\n",
    "    res = pickle.load(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameter: {'alpha': 0.2}\n",
      "Cumulative CTR: 6.9742%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_param = res['param'][np.argmax(res['avg_reward'])]\n",
    "cum_ctr = np.max(res['avg_reward'])\n",
    "print('Best Parameter: {0}\\nCumulative CTR: {1:0.4%}'.format(best_param, cum_ctr))\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "_ = ax.plot(res['seq_avg_reward'][np.argmax(res['avg_reward'])], color='mediumblue', \n",
    "            label='LinUCB with Disjoint Linear Models', linewidth=1)\n",
    "ax.axhline(y=cum_ctr, color='black', linestyle='--', linewidth=1)\n",
    "ax.set_xlabel('Num. of Events')\n",
    "ax.set_ylabel('CTR')\n",
    "ax.set_ylim([0.0, 0.1])\n",
    "ax.legend(loc='best')\n",
    "_ = ax.set_title('Average Reward', fontsize=12, fontweight='bold')"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}