TopK.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "初始化变量...\n",
      "------ 第1/5组样本 ------\n",
      "载入训练集dataset/ml-100k/u1.base\n",
      "计算训练集各项统计数据...\n",
      "计算相似度矩阵...\n",
      "计算完成\n",
      "在训练集上测试...\n",
      "载入测试集dataset/ml-100k/u1.test\n",
      "测试集规模为 20000\n",
      "在测试集上测试...\n",
      "测试完成\n",
      "------ 第2/5组样本 ------\n",
      "载入训练集dataset/ml-100k/u2.base\n",
      "计算训练集各项统计数据...\n",
      "计算相似度矩阵...\n",
      "计算完成\n",
      "在训练集上测试...\n",
      "载入测试集dataset/ml-100k/u2.test\n",
      "测试集规模为 20000\n",
      "在测试集上测试...\n",
      "测试完成\n",
      "------ 第3/5组样本 ------\n",
      "载入训练集dataset/ml-100k/u3.base\n",
      "计算训练集各项统计数据...\n",
      "计算相似度矩阵...\n",
      "计算完成\n",
      "在训练集上测试...\n",
      "载入测试集dataset/ml-100k/u3.test\n",
      "测试集规模为 20000\n",
      "在测试集上测试...\n",
      "测试完成\n",
      "------ 第4/5组样本 ------\n",
      "载入训练集dataset/ml-100k/u4.base\n",
      "计算训练集各项统计数据...\n",
      "计算相似度矩阵...\n",
      "计算完成\n",
      "在训练集上测试...\n",
      "载入测试集dataset/ml-100k/u4.test\n",
      "测试集规模为 20000\n",
      "在测试集上测试...\n",
      "测试完成\n",
      "------ 第5/5组样本 ------\n",
      "载入训练集dataset/ml-100k/u5.base\n",
      "计算训练集各项统计数据...\n",
      "计算相似度矩阵...\n",
      "计算完成\n",
      "在训练集上测试...\n",
      "载入测试集dataset/ml-100k/u5.test\n",
      "测试集规模为 20000\n",
      "在测试集上测试...\n",
      "测试完成\n",
      "------ 测试结果 ------\n",
      "item-item协同过滤算法中, 各K值在训练集上的RMSE:\n",
      "k =   5:   0.5747\n",
      "k =  10:   0.6845\n",
      "k =  15:   0.7322\n",
      "k =  18:   0.7500\n",
      "k =  20:   0.7596\n",
      "k =  25:   0.7774\n",
      "k =  30:   0.7902\n",
      "k =  40:   0.8073\n",
      "k =  50:   0.8182\n",
      "k = 100:   0.8422\n",
      "k = 200:   0.8546\n",
      "item-item协同过滤算法中, 各K值在测试集上的RMSE:\n",
      "k =   5:   0.9543\n",
      "k =  10:   0.9278\n",
      "k =  15:   0.9223\n",
      "k =  18:   0.9215\n",
      "k =  20:   0.9213\n",
      "k =  25:   0.9217\n",
      "k =  30:   0.9225\n",
      "k =  40:   0.9242\n",
      "k =  50:   0.9258\n",
      "k = 100:   0.9314\n",
      "k = 200:   0.9345\n",
      "user-user协同过滤算法中, 各K值在训练集上的RMSE:\n",
      "k =   5:   0.6101\n",
      "k =  10:   0.7242\n",
      "k =  15:   0.7699\n",
      "k =  18:   0.7866\n",
      "k =  20:   0.7951\n",
      "k =  25:   0.8113\n",
      "k =  30:   0.8225\n",
      "k =  40:   0.8373\n",
      "k =  50:   0.8465\n",
      "k = 100:   0.8660\n",
      "k = 200:   0.8747\n",
      "user-user协同过滤算法中, 各K值在测试集上的RMSE:\n",
      "k =   5:   0.9874\n",
      "k =  10:   0.9573\n",
      "k =  15:   0.9489\n",
      "k =  18:   0.9468\n",
      "k =  20:   0.9458\n",
      "k =  25:   0.9449\n",
      "k =  30:   0.9445\n",
      "k =  40:   0.9447\n",
      "k =  50:   0.9453\n",
      "k = 100:   0.9491\n",
      "k = 200:   0.9526\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import var\n",
    "import predict as pre\n",
    "import utils\n",
    "\n",
    "print('初始化变量...')\n",
    "names = ['user_id', 'item_id', 'rating', 'timestamp']\n",
    "direct = 'dataset/ml-100k/'\n",
    "trainingset_files = (direct + name for name in ('u1.base', 'u2.base', 'u3.base', 'u4.base', 'u5.base'))\n",
    "testset_files = (direct + name for name in ('u1.test', 'u2.test', 'u3.test', 'u4.test', 'u5.test'))\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    \n",
    "    i = 0\n",
    "    nums = 5\n",
    "    k_set = [5, 10, 15, 18, 20, 25, 30, 40, 50, 100, 200]\n",
    "    rmse_topkCF_item_train = {k:[] for k in k_set}\n",
    "    rmse_topkCF_user_train = {k:[] for k in k_set}\n",
    "    rmse_topkCF_item = {k:[] for k in k_set}\n",
    "    rmse_topkCF_user = {k:[] for k in k_set}\n",
    "    for trainingset_file, testset_file in zip(trainingset_files, testset_files):\n",
    "        i += 1\n",
    "        print('------ 第%d/%d组样本 ------' % (i, nums))\n",
    "        df = pd.read_csv(trainingset_file, sep='\\t', names=names)\n",
    "        \n",
    "        var.ratings = np.zeros((var.n_users, var.n_items))\n",
    "        print('载入训练集' + trainingset_file)\n",
    "        for row in df.itertuples():\n",
    "            var.ratings[row[1]-1, row[2]-1] = row[3]\n",
    "          \n",
    "        print('计算训练集各项统计数据...')\n",
    "        utils.cal_mean()\n",
    "\n",
    "        print('计算相似度矩阵...')\n",
    "        var.user_similarity = utils.cal_similarity(kind='user')\n",
    "        var.item_similarity = utils.cal_similarity(kind='item')\n",
    "        print('计算完成')\n",
    "\n",
    "        predictions_topkCF_item_train = {k:[] for k in k_set}\n",
    "        predictions_topkCF_user_train = {k:[] for k in k_set}\n",
    "        targets = []\n",
    "        print('在训练集上测试...')\n",
    "        for row in df.itertuples():\n",
    "            user, item, actual = row[1]-1, row[2]-1, row[3]\n",
    "            for k in k_set:\n",
    "                predictions_topkCF_item_train[k].append(pre.predict_topkCF_item(user, item, k))\n",
    "                predictions_topkCF_user_train[k].append(pre.predict_topkCF_user(user, item, k))\n",
    "            targets.append(actual)\n",
    "        for k in k_set:\n",
    "            rmse_topkCF_item_train[k].append(utils.rmse(np.array(predictions_topkCF_item_train[k]), np.array(targets)))\n",
    "            rmse_topkCF_user_train[k].append(utils.rmse(np.array(predictions_topkCF_user_train[k]), np.array(targets)))    \n",
    "        \n",
    "        print('载入测试集' + testset_file)\n",
    "        test_df = pd.read_csv(testset_file, sep='\\t', names=names)        \n",
    "        predictions_topkCF_item = {k:[] for k in k_set}\n",
    "        predictions_topkCF_user = {k:[] for k in k_set}\n",
    "        targets = []\n",
    "        print('测试集规模为 %d' % len(test_df))\n",
    "        print('在测试集上测试...')\n",
    "        for row in test_df.itertuples():\n",
    "            user, item, actual = row[1]-1, row[2]-1, row[3]\n",
    "            for k in k_set:\n",
    "                predictions_topkCF_item[k].append(pre.predict_topkCF_item(user, item, k))\n",
    "                predictions_topkCF_user[k].append(pre.predict_topkCF_user(user, item, k))\n",
    "            targets.append(actual)\n",
    "        for k in k_set:\n",
    "            rmse_topkCF_item[k].append(utils.rmse(np.array(predictions_topkCF_item[k]), np.array(targets)))\n",
    "            rmse_topkCF_user[k].append(utils.rmse(np.array(predictions_topkCF_user[k]), np.array(targets)))            \n",
    "        print('测试完成')\n",
    "        \n",
    "    print('------ 测试结果 ------')\n",
    "    \n",
    "    print('item-item协同过滤算法中, 各K值在训练集上的RMSE:')\n",
    "    for k in sorted(k_set):\n",
    "        print('k = %3d:   %.4f' % (k, np.mean(rmse_topkCF_item_train[k])))\n",
    "    print('item-item协同过滤算法中, 各K值在测试集上的RMSE:')\n",
    "    for k in sorted(k_set):\n",
    "        print('k = %3d:   %.4f' % (k, np.mean(rmse_topkCF_item[k])))\n",
    "    print('user-user协同过滤算法中, 各K值在训练集上的RMSE:')\n",
    "    for k in sorted(k_set):\n",
    "        print('k = %3d:   %.4f' % (k, np.mean(rmse_topkCF_user_train[k])))\n",
    "    print('user-user协同过滤算法中, 各K值在测试集上的RMSE:')\n",
    "    for k in sorted(k_set):\n",
    "        print('k = %3d:   %.4f' % (k, np.mean(rmse_topkCF_user[k])))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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wEBERUcoxcBAREVHKMXAQERFRyjFwEBERUcoxcBAREVHKMXAQERFRyjFwEBER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\nwQEAABxHcAAAAMcRHAAAwHEEBwAAcNxP1Tn1YiU7iQ4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1167acc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "k_set = [5, 10, 15, 18, 20, 25, 30, 40, 50, 100, 200]\n",
    "rmse_topkCF_item_train = [0.5747, 0.6845, 0.7322, 0.7500, 0.7596, 0.7774, 0.7902, 0.8073, 0.8182, 0.8422, 0.8546]\n",
    "rmse_topkCF_item =       [0.9543, 0.9278, 0.9223, 0.9215, 0.9213, 0.9217, 0.9225, 0.9242, 0.9258, 0.9314, 0.9345]\n",
    "rmse_topkCF_user_train = [0.6101, 0.7242, 0.7699, 0.7866, 0.7951, 0.8113, 0.8225, 0.8373, 0.8465, 0.8660, 0.8747]\n",
    "rmse_topkCF_user =       [0.9874, 0.9573, 0.9489, 0.9468, 0.9458, 0.9449, 0.9445, 0.9447, 0.9453, 0.9491, 0.9526]\n",
    "pal = sns.color_palette(\"Set2\", 2)\n",
    "\n",
    "t = 11\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.plot(k_set[:t], rmse_topkCF_user_train[:t], c=pal[0], label='user-user train', alpha=0.5, linewidth=5)\n",
    "plt.plot(k_set[:t], rmse_topkCF_user[:t], c=pal[0], label='user-user test', linewidth=5)\n",
    "plt.plot(k_set[:t], rmse_topkCF_item_train[:t], c=pal[1], label='item-item train', alpha=0.5, linewidth=5)\n",
    "plt.plot(k_set[:t], rmse_topkCF_item[:t], c=pal[1], label='item-item test', linewidth=5)\n",
    "plt.legend(loc='best', fontsize=18)\n",
    "plt.xticks(fontsize=16);\n",
    "plt.yticks(fontsize=16);\n",
    "plt.xlabel('K', fontsize=25);\n",
    "plt.ylabel('RMSE', fontsize=25);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}