lab_item_based_cf.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = './data'\n",
    "\n",
    "# define file path\n",
    "train_file = os.path.join(data_path, 'review_train.csv')\n",
    "test_file = os.path.join(data_path, 'review_test.csv')\n",
    "weight_matrix_file = os.path.join(data_path, 'weight_matrix.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read datasets\n",
    "train_data = pd.read_csv(train_file)\n",
    "test_data = pd.read_csv(test_file)\n",
    "weight_matrix = pd.read_csv(weight_matrix_file)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Item-based CF Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert the review data into a dictionary {(uid, bid): rating}\n",
    "review_dict = {(row[0], row[1]): row[2] for row in train_data.values.tolist()}\n",
    "\n",
    "# map each user_id to a list of business {user_id: list[business_ids]}\n",
    "user_groups = train_data.groupby('uid')\n",
    "user_business_dict = {uid: list(user_groups.get_group(uid)['bid']) for uid in user_groups.groups}\n",
    "\n",
    "# transfer weight_matrix to a dictionary {(bid1, bid2): weight}\n",
    "weight_matrix_dict = {(row[0], row[1]): row[2] for row in weight_matrix.values.tolist()}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find the most N correlated business according to the weight matrix\n",
    "\n",
    "def find_n_nearest_business(target_bid, neighbor_business, n=3):\n",
    "    \n",
    "    ### pseudocode ###\n",
    "    \n",
    "    # for bid in neighbor_business:\n",
    "    #     get the correlation between the target_bid and bid\n",
    "    \n",
    "    neighbor_business_weight = {}\n",
    "\n",
    "    for bid in neighbor_business:\n",
    "        b_pair = tuple(sorted([target_bid, bid]))\n",
    "        if weight_matrix_dict.get(b_pair):\n",
    "            neighbor_business_weight[bid] = weight_matrix_dict[b_pair]\n",
    "\n",
    "    sorted_neighbor_business_weight = sorted(neighbor_business_weight.items(), key=lambda kv: kv[1], reverse=True)\n",
    "    \n",
    "    if len(sorted_neighbor_business_weight) > n:\n",
    "        return sorted_neighbor_business_weight[:n]\n",
    "    else:\n",
    "        return sorted_neighbor_business_weight\n",
    "\n",
    "    # find the most N correlated business\n",
    "    # return top N business\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute weighted average over neighborhood set\n",
    "\n",
    "def weighted_average_prediction(target_user, target_business, nearest_business_weight):\n",
    "    \n",
    "    ### pseudocode ###\n",
    "    \n",
    "    weighted_sum, sum_weight = 0.0, 0.0\n",
    "    \n",
    "    w_list, r_list = [], []\n",
    "    \n",
    "    for item in nearest_business_weight:  \n",
    "        bid, weight = item[0], item[1]\n",
    "        rating = review_dict[(target_user, bid)]\n",
    "        w_list.append(weight)\n",
    "        r_list.append(rating)\n",
    "\n",
    "    weighted_sum = sum([w_list[i] * r for i, r in enumerate(r_list)]) if len(r_list) > 0 else 0.0\n",
    "    sum_weight = sum(w_list) if len(w_list) > 0 else 0.0\n",
    "    \n",
    "    prediction = weighted_sum / sum_weight if sum_weight != 0.0 else 0.0\n",
    "    \n",
    "    #     rating = the rating of (target_user, bid)\n",
    "    #     weighted_sum += rating * weight\n",
    "    #     sum_weight += weight\n",
    "    \n",
    "    return prediction\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['JxKWfZ6hG8iRnK8QL65jTw', 'PQER4ba8Q0zqB1G2QiXamQ']\n",
      "[('JxKWfZ6hG8iRnK8QL65jTw', 1.0)]\n"
     ]
    }
   ],
   "source": [
    "# make prediction for each given (user, business) pair in the testing data\n",
    "\n",
    "results = []\n",
    "NEIGHBOR_THRE = 3\n",
    "\n",
    "for pair in test_data.values.tolist():\n",
    "    \n",
    "    target_user, target_business = pair[0], pair[1]\n",
    "    \n",
    "    if target_user != 't3RHt1j91D8GP5XD5Mvxow' or target_business != 'mDR12Hafvr84ctpsV6YLag':\n",
    "        continue\n",
    "\n",
    "    neighbor_business = user_business_dict.get(target_user)\n",
    "    print(neighbor_business)\n",
    "    \n",
    "    if neighbor_business is None:\n",
    "        results.append([target_user, target_business, 0.0])\n",
    "        continue\n",
    "\n",
    "    ### pseudocode ###\n",
    "    n_nearest_business = find_n_nearest_business(target_business, neighbor_business, n=3)\n",
    "    print(n_nearest_business)\n",
    "\n",
    "    prediction =  weighted_average_prediction(target_user, target_business, n_nearest_business)\n",
    "    \n",
    "    results.append([target_user, target_business, prediction])\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
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       " ['niKu4se6xtf-ctlm0udr1w', 'RESDUcs7fIiihp38-d6_6g', 3.0],\n",
       " ['FKyQWTe-yrm1tRTRveurkA', 'f4x1YBxkLrZg652xt2KR5g', 4.0],\n",
       " ['wfK7oukwc90fVqFG2lw87w', 'V-aCFCkkRyakP6SeIfG9-A', 4.47766925198709],\n",
       " ['Y3PQ5P_R7jcgiIS_1dIv-w', 'W1Yr6c2XDx_RBjb6WsV-aQ', 3.3118858116620182],\n",
       " ['vhDL0RHifIgEdWytB1yrSg', 'DkYS3arLOhA8si5uUEmHOw', 4.47748604820627],\n",
       " ['3KK7L2Q6NMfJA1Ky4Ntmdg', '3fPIRTnBhd81MixgS9YoMQ', 4.017403259951116],\n",
       " ['t_sjtqBjKhxedjUxoVJzrg', 'ksuxxeP_V73Y4iKPeLq77A', 4.0],\n",
       " ['-xDW3gYiYaoeVASXywTPgw', 'f4x1YBxkLrZg652xt2KR5g', 4.333333333333333],\n",
       " ['8cX_Pen0uzuhmJXFt4-wUg', '4jUj7xHIhdHpTrb0ZXSToA', 3.0],\n",
       " ['vWP8-aQX0rYJszqkWVL7Ew', 'ytk8XVFegCSdSBXLDZutpw', 4.0],\n",
       " ['p_azadim_uWFOXAhhKB3ag', 'IMLrj2klosTFvPRLv56cng', 2.3215765061520215],\n",
       " ['XvSr0CG8nBq2IcUIJ7HohA', 'ybq76JnmovyueBmrxef-aQ', 4.0],\n",
       " ['Mv55A30nEcAl3vk4qow5dA', 'S_5VQYMMa4aD3NzgYLkvhg', 4.316373258327123],\n",
       " ['77lef7kKkoOVupY-7AmieQ', 'E67gGK8yOn9kkNzx8FzQKA', 2.6666666666666665],\n",
       " ['cKJ0r01eF7E-tYp2FQOyRA', 'BcW7Z9lPmOB_8eS2lEuOqQ', 3.699493363815538],\n",
       " ['pZ6P_1V-bTSoYG1fg1TXzw', '7zMwR4FkvTbSGY0vRT5Ljg', 0.0],\n",
       " ['nixQjPlfVhMEhrNkmp5FDw', 'XQJr9_jZR1vOoZVcZfDDwQ', 4.0],\n",
       " ['Kp5KHBSmV-Htc9NeSjJfPA', 'xxAGaqYnboeR0SilS7DbAg', 3.3333333333333335],\n",
       " ['VpqKcpEq2rgoS7Jlb_Aj1Q', 'gBfPyzPRmeOaj3SdcIj0Rw', 3.6666666666666665],\n",
       " ['iuRpRASIRqFaDd_B_IEDbQ', 'K7lWdNUhCbcnEvI0NhGewg', 3.0],\n",
       " ['aT8XPrYOWORUwMR9jlBnpQ', 'sqRX-XLlhx4rs2c1TpBf8A', 4.0],\n",
       " ['CiJat5Up2jAO3B0qTbiN5Q', 'xkVMIk_Vqh17f48ZQ_6b0w', 4.190398836254904],\n",
       " ['TGSi0e7-ovol_PBIQgodOg', 'hihud--QRriCYZw1zZvW4g', 3.747401225448866],\n",
       " ['C2C0GPKvzWWnP57Os9eQ0w', 'YkWT4n-pPDlPJKfkfXVL1w', 3.6666666666666665],\n",
       " ['ZsjuEgOdvx9l5GKZdtwYIw', 'nMaIIKEAW2cTEWbEyeFyXQ', 3.3333333333333335],\n",
       " ['zyFN0KU7Y9MfTZIROrsKhA', '1CR2ddUcjYYwRd5JLtiRrw', 5.0],\n",
       " ['qKUEhJUl0Z9MFW7R7BTvRQ', 'WQyGqfFKd-baBTVfZWzeTw', 3.2],\n",
       " ['kl_yCsj4ESbUphmpCfVvRA', 'm-xsH8VkcYtTTvp2Fa6Ajw', 3.6666666666666665],\n",
       " ['59GSwMflQFllOAWvw0F5mw', 'XQETjKH84gxDrAiz2lH7Wg', 4.0],\n",
       " ['ZcFB7xOpJufgX9hHPFh9mQ', 'XXW_OFaYQkkGOGniujZFHg', 4.565150494188077],\n",
       " ['7KlKDah4NtWNfD7OY4Bnog', '0q_BHpxbikVtPRRLRu-U0g', 5.0],\n",
       " ['qYpoq2wwCDdIBfcSSBYqww', '7-Ba9uSmYYpXgnQRoIPxMQ', 0.0],\n",
       " ['HcT3BZLc25R9eekoHBA1MQ', 'ZrS5rhJ4OvXQG_38wPEWlA', 3.4546038557267766],\n",
       " ['Zf3JtxPFxt0OyJ7TyEqPwA', 'fHM09_y3QX3n4a_bIFbk_w', 2.7931489301311956],\n",
       " ['f5k5UJgCqcYQ99h9R0iphA', 'Ls89UV4aSRE8eW2Pj_LaWg', 0.0],\n",
       " ['hYWwocw-J4KdyGLrUlOYXA', 'Cu5icjH_psCatgvN4fU1-A', 4.0],\n",
       " ['_pIG1_s6Bzymn1zeyLjBHQ', 'j5nPiTwWEFr-VsePew7Sjg', 3.3536637344877716],\n",
       " ['Se3XL5Opy0e2cHbxxjs2ZQ', 'YZGSNhgTS6YeyUYoivD-Ww', 3.660865580016295],\n",
       " ['1NbFusAiFMOLT1vOUyVr8Q', 'qAz6G72Xnrgxnep6mKUnWg', 1.0],\n",
       " ['7eHHg9xRGzYeJucvxZxueg', 'cYwJA2A6I12KNkm2rtXd5g', 3.0],\n",
       " ['ADIMCaJqpUy97l8LMOtHYA', '-U7tvCtaraTQ9b0zBhpBMA', 3.0733102100214715],\n",
       " ['6pzA8EDhHgW3A5-rBObhBA', 'sba0fwlKAZFGWWtsf5Jbwg', 4.666666666666667],\n",
       " ['e-1X-FLmRhAy-itsSbOdjA', 'xkVMIk_Vqh17f48ZQ_6b0w', 5.0],\n",
       " ['aXpiHi7FW_wOIe7nGFRSDQ', 'A6lKCuTrDSJ_eFKyumZCJQ', 3.540034829362886],\n",
       " ['j-hMlTnPgB8TsyFp4kRC1g', 'K7lWdNUhCbcnEvI0NhGewg', 5.0],\n",
       " ['sa5zd7bwBQzajQB48Y9SUw', 'u3nwislXMKKwkpHAXAArbQ', 3.3333333333333335],\n",
       " ['lgxCqDMnowyx1Q5fLdLPCA', 'pomGBqfbxcqPv14c3XH-ZQ', 1.954845230823088],\n",
       " ['Xsyw11gZiltqWL5RvWGKJg', 'y8d90Pt16Nip-B5UXWBP-w', 4.3630752648095825],\n",
       " ['AL3lnSNH9NeCSSlXTSl0ng', 'yfxDa8RFOvJPQh0rNtakHA', 0.0],\n",
       " ['UySLQuZU6SOuuMO4J74x7w', 'bpRo8L8dkhgbJhdIKa9mwA', 0.0],\n",
       " ['LNPyE5X1K5B_flcTCH9StQ', 'sOYsxYYFl03PhHmz_rBDZQ', 3.6666666666666665],\n",
       " ['vxKTtij9eIGgzDK5kDNegA', 'RESDUcs7fIiihp38-d6_6g', 2.8490591552302647],\n",
       " ['NplEVcB7S4qElE7eMresfQ', 'ZCQa7CJxZ-53Zxd_pobWug', 4.0],\n",
       " ['wfK7oukwc90fVqFG2lw87w', 'aS1qJzTGyluRT2tJ09Jbaw', 4.267949192431123],\n",
       " ['7weuSPSSqYLUFga6IYP4pg', 'hroo5nOO8b9QhHX0GLg7oA', 4.017581282850524],\n",
       " ['Pinnqw9LFBBlrtpFL7tQxg', 'yfxDa8RFOvJPQh0rNtakHA', 4.387700956305545],\n",
       " ['BIcfTJw4-76b9NuZUgpKIA', 'zdE82PiD6wquvjYLyhOJNA', 2.9550284333694203],\n",
       " ['ELcQDlf69kb-ihJfxZyL0A', '9wxxjUKdaiWeK0fGxSYhzQ', 2.976965329093323],\n",
       " ['PKEzKWv_FktMm2mGPjwd0Q', 'vPWvyl9gxmuOIXDBL6lSpw', 3.3333333333333335],\n",
       " ['gGXpE3n723jNBS9BiC_55g', 'XWxYHSld0T2TfANwt1KmqA', 0.0],\n",
       " ['huXqrSaGyNO1aZKiM55EUg', 'jWv5GUtEp30OD5L5C8c2DQ', 5.0],\n",
       " ['w40aqRQmpqg4MLWIIV-0NQ', 'uh7DUWtPoZkuEE05fghJ_w', 0.0],\n",
       " ['SHEziY_V4G60mVyLNvjLSQ', 'azIZRqX_1qbp0_6GKSgSAA', 3.248706790145853],\n",
       " ['76crp1vXF1A3LOoNP6zcUQ', 'jLxeBgWhLRbII2ACkgH1Sg', 3.308573031431603],\n",
       " ['3ZCS3niZj8GjbZY6Dx7uqw', 'A5Rkh7UymKm0_Rxm9K2PJw', 4.999999999999999],\n",
       " ['yQiFb3kzronkldMDomOAWg', 'B_WggEKFq-ZFNui8CHPYvA', 3.812915922681569],\n",
       " ['xFSLb_pxXta5G4oaRB1ylQ', 'NLEe-RzDSU-5BN6xp_WWCw', 4.28434080462529],\n",
       " ['EzXDaJjdCwMtWdBJiRcC6Q', 'GI-CAiZ_Gg3h21PwrANB4Q', 1.0],\n",
       " ['C2C0GPKvzWWnP57Os9eQ0w', 'e13SEvJud_vgeDR_doL4sQ', 3.666666666666667],\n",
       " ['wV4uhvJGok8nOR56Ex5mAg', 'OPxWcHK96_cbmiF7legDnA', 3.672163286941679],\n",
       " ['ajMm_rBqbKYALu-I8-__Kg', 'DXYhrg9EXuW8n5QMnCoHAg', 0.0],\n",
       " ['6DbvYyAvTtXTmVOAG32Djw', 'Rb2s2-2kIvbPXnPEuM_OBg', 4.0],\n",
       " ['_ir8NHMtV7KPTwmBmlkmQw', 'UNI1agsPX2k3eJSJVB91nw', 4.663125826250464],\n",
       " ['wZEDZtOye6QrQo1wCOHNmA', 'HhVmDybpU7L50Kb5A0jXTg', 4.113830909335463],\n",
       " ['vpSruwUEfZw_TyaTDHEFDA', 'QiuTcA0bKQMCyOFBFJZ58w', 4.666666666666667],\n",
       " ...]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# finalize the results\n",
    "# fill the missing predictions with average values\n",
    "\n",
    "avg_rating = sum(list(train_data['ratings'])) / len(train_data)\n",
    "\n",
    "def quick_check(x):\n",
    "    if x > 5:\n",
    "        return 5.0\n",
    "    elif x < 1:\n",
    "        return 1.0\n",
    "    else:\n",
    "        return x\n",
    "\n",
    "final_results = []\n",
    "\n",
    "for result in results:\n",
    "    uid, bid, prediction = result[0], result[1], result[2]\n",
    "    prediction = avg_rating if prediction == 0.0 else prediction\n",
    "    prediction = quick_check(prediction)\n",
    "    final_results.append([uid, bid, prediction])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36480"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(final_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36480"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# write the results to a CSV file\n",
    "\n",
    "results_df = pd.DataFrame(final_results, columns=['uid', 'bid', 'prediction'])\n",
    "results_file_path = os.path.join(data_path, 'review_prediction.csv')\n",
    "results_df.to_csv(results_file_path, index=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load ground truth for the testing data\n",
    "\n",
    "test_ground_truth_file = os.path.join(data_path, 'review_test_ground_truth.csv')\n",
    "test_ground_truth_data = pd.read_csv(test_ground_truth_file)\n",
    "\n",
    "evaluation = test_ground_truth_data.merge(results_df, on=['uid', 'bid'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      uid                     bid  ratings  prediction\n",
      "0  HEvyblFw4I-UsMqgPGYY_Q  iA8Ve2sZKN5Vz3mYKrtCaQ      3.0    3.664495\n",
      "1  HEvyblFw4I-UsMqgPGYY_Q  iA8Ve2sZKN5Vz3mYKrtCaQ      3.0    3.664495\n",
      "2  HEvyblFw4I-UsMqgPGYY_Q  iA8Ve2sZKN5Vz3mYKrtCaQ      1.0    3.664495\n",
      "3  HEvyblFw4I-UsMqgPGYY_Q  iA8Ve2sZKN5Vz3mYKrtCaQ      1.0    3.664495\n",
      "4  DNNkLmbwfI0ufKGqQfmvKQ  faPVqws-x-5k2CQKDNtHxw      5.0    4.426148\n",
      "5  F_5_UNX-wrAFCXuAkBZRDw  UPIYuRaZvknINOd1w8kqRQ      4.0    3.982551\n",
      "6  ldqh2aWLTW6D2RHDCj_2TA  DHUAQ4pzH9KKzGZDm1jZLg      3.0    3.666667\n",
      "7  f17-l69K0G7WAeTmPHtptw  HYKTKG3X7jtLe6elxp63JQ      4.0    3.764051\n",
      "8  FAjCZoxiGw9HJKueB8YWTg  O7ot_LMlCfLpOP9tBqeNfw      4.0    3.541475\n",
      "9  leyNDNVu09Ldbg5ujPWMhQ  RESDUcs7fIiihp38-d6_6g      3.0    3.666667\n"
     ]
    }
   ],
   "source": [
    "print(evaluation[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: 28147, 1: 7838, 2: 1449, 3: 258, 4: 60}\n"
     ]
    }
   ],
   "source": [
    "# method 1: count the segments\n",
    "\n",
    "segments = {0:0, 1:0, 2:0, 3:0, 4:0}\n",
    "\n",
    "for _, row in evaluation.iterrows():\n",
    "    if row['delta'] < 1.0:\n",
    "        segments[0] += 1\n",
    "    elif 1.0 <= row['delta'] < 2.0:\n",
    "        segments[1] += 1\n",
    "    elif 2.0 <= row['delta'] < 3.0:\n",
    "        segments[2] += 1\n",
    "    elif 3.0 <= row['delta'] < 4.0:\n",
    "        segments[3] += 1\n",
    "    elif row['delta'] >= 4.0:\n",
    "        segments[4] += 1\n",
    "\n",
    "print(segments)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['t3RHt1j91D8GP5XD5Mvxow', 'mDR12Hafvr84ctpsV6YLag', 5.0, 1.0, 4.0],\n",
       " ['qr06IGEdVEIQmyv-NZe-og', 'hihud--QRriCYZw1zZvW4g', 5.0, 1.0, 4.0],\n",
       " ['EzXDaJjdCwMtWdBJiRcC6Q', 'GI-CAiZ_Gg3h21PwrANB4Q', 5.0, 1.0, 4.0],\n",
       " ['ZjHrVjxHDFiGv_5ufMYs6Q', 'DkYS3arLOhA8si5uUEmHOw', 5.0, 1.0, 4.0],\n",
       " ['H8k77jyHPfl38P-5o525WA', '16Fplxu-OwVmTEFxQAUP4g', 5.0, 1.0, 4.0],\n",
       " ['mi-Wx6DGzx6loUWbGRW0-A', 'pH0BLkL4cbxKzu471VZnuA', 5.0, 1.0, 4.0],\n",
       " ['0uU-AkvZMbzTMSLRtm1m3A', 'Z0YOHYA6YtW131xULbnMzQ', 5.0, 1.0, 4.0],\n",
       " ['N4K69ZdyU-B8NzVYxwjK0A', 'EAwh1OmG6t6p3nRaZOW_AA', 5.0, 1.0, 4.0],\n",
       " ['DQ5Szn4T14ddEd6F5jHHpg', '364hhL5st0LV16UcBHRJ3A', 5.0, 1.0, 4.0],\n",
       " ['fBEwo1ogY45H85oRW86L_w', 'JDZ6_yycNQFTpUZzLIKHUg', 5.0, 1.0, 4.0],\n",
       " ['fBEwo1ogY45H85oRW86L_w', 'JDZ6_yycNQFTpUZzLIKHUg', 5.0, 1.0, 4.0],\n",
       " ['fBEwo1ogY45H85oRW86L_w', 'JDZ6_yycNQFTpUZzLIKHUg', 5.0, 1.0, 4.0],\n",
       " ['fBEwo1ogY45H85oRW86L_w', 'JDZ6_yycNQFTpUZzLIKHUg', 5.0, 1.0, 4.0],\n",
       " ['HQnfONrTQKBdHPV7iebtxQ', 'iLlPXAA6NYTgWaG4DgCNEA', 5.0, 1.0, 4.0],\n",
       " ['3zczu4YDXk5M27xMQl-Few', 'Rj-7ymdw8aNZBRqGRAjR3Q', 5.0, 1.0, 4.0],\n",
       " ['jXWDygjndn69XKChSa2mtA', '3BCsAgo_1i4xMuTyLKMLRQ', 5.0, 1.0, 4.0],\n",
       " ['tZ7guKlHjEg0ZhBHt6fCyg', 'dJF4C707KVhFx1qcRJhlEA', 5.0, 1.0, 4.0],\n",
       " ['npsvJP7IU8naAYzBy_G6zA', 'faPVqws-x-5k2CQKDNtHxw', 5.0, 1.0, 4.0],\n",
       " ['2FoXDAv7tZyRNGbjt3zUEQ', 'ysv6yhVYOoH9Pf7PlMyD0g', 5.0, 1.0, 4.0],\n",
       " ['2FoXDAv7tZyRNGbjt3zUEQ', 'ysv6yhVYOoH9Pf7PlMyD0g', 5.0, 1.0, 4.0],\n",
       " ['-XvB5TvlGahyamqIsJO-fg', '3vxIqhm2jCVjkUWtmDvTsw', 5.0, 1.0, 4.0],\n",
       " ['r7QUt2jxea7SjeeqZdZwJA', 'zU9w_xRlQSRIYXxGo-HSOA', 5.0, 1.0, 4.0],\n",
       " ['qKUEhJUl0Z9MFW7R7BTvRQ', 'UCpUOtvqR-NBWBNVMzJleA', 5.0, 1.0, 4.0],\n",
       " ['W3suKmjNmD9qLzfJln5bWg', '0AQnRQw34IQW9-1gJkYnMA', 5.0, 1.0, 4.0],\n",
       " ['iuoWUmvleAusz0_T5iHNHg', 'jY7Ql_Qk8duv7jZrMZahaw', 5.0, 1.0, 4.0],\n",
       " ['pF7uEfsYCT_7AIKqBel2Xg', '4JNXUYY8wbaaDmk3BPzlWw', 5.0, 1.0, 4.0],\n",
       " ['WRWvu_McOkXguooZftZJmw', 'V1EdIokE0YYN__dNYfXRdA', 5.0, 1.0, 4.0],\n",
       " ['ZFAtjSBQDA3kDExHwFz4Iw', 'qmh6zxtJ8C8-YiUPv7ySlw', 5.0, 1.0, 4.0],\n",
       " ['nixQjPlfVhMEhrNkmp5FDw', 'FhIeCF6QrsLaRvAeu0oEPQ', 5.0, 1.0, 4.0],\n",
       " ['8LRRYce7kIV4pqXNs_lL6A', '2iTsRqUsPGRH1li1WVRvKQ', 5.0, 1.0, 4.0],\n",
       " ['gxlxakxEgvE1PYMRvPYgHQ', '2PS9kBbuJcmBhcNp-D62uA', 5.0, 1.0, 4.0],\n",
       " ['Q17Yh-Zsb4VBWd7-ltNh4A', 'AtD6B83S4Mbmq0t7iDnUVA', 5.0, 1.0, 4.0],\n",
       " ['xPSXDnjJ_oXoVSlkvmDItA', 'X075p7boJU1j1JNHUj5UQQ', 5.0, 1.0, 4.0],\n",
       " ['O9Dbdo2LZut3e8VqIY9gtg', 'MohTx0z3LnzAnr0bIsstHQ', 5.0, 1.0, 4.0],\n",
       " ['HI3CVYpg_GXNl0P-dC8KDA', 'NvKNe9DnQavC9GstglcBJQ', 5.0, 1.0, 4.0],\n",
       " ['0KeT9NKimYkHN0wlrij-dg', 'Z6E2Syzo_IYLIGpI5tSpRg', 5.0, 1.0, 4.0],\n",
       " ['v2EEqpCJyV00Tz2QFNkZSw', 'rcaPajgKOJC2vo_l3xa42A', 5.0, 1.0, 4.0],\n",
       " ['l46RFzV1zwUFlUlDC5fnBw', '3ZHyw0d8mk0HOEP7v4fW1A', 5.0, 1.0, 4.0],\n",
       " ['hspfHiY5uEA2xHBIb5o8qw', 'DkYS3arLOhA8si5uUEmHOw', 5.0, 1.0, 4.0],\n",
       " ['h9oGTdr9uhNWQvu0iS7oDA', 'DkYS3arLOhA8si5uUEmHOw', 5.0, 1.0, 4.0],\n",
       " ['1LdXqpTv0bcKQVkQB6Lttg', 'VHZPbCbIPHEkKOotdCTiow', 5.0, 1.0, 4.0],\n",
       " ['W7tKBCQ1XugMEDjB8ZBXqg', 'pKk7jCFIm96qDdk0laVT2w', 5.0, 1.0, 4.0],\n",
       " ['YkIeScjUjGTVY_SXLEBSHg', 'KNTjauGqmib3sG1HWJyKEQ', 5.0, 1.0, 4.0],\n",
       " ['PcDnLiV-FN_rw0VDncX-yg', 'f_eiOrEcMnkHB7GvQVOHkQ', 5.0, 1.0, 4.0],\n",
       " ['oOoJ1-FcGHfwmvjmQrJp3w', 'g8OnV26ywJlZpezdBnOWUQ', 5.0, 1.0, 4.0],\n",
       " ['0uU-AkvZMbzTMSLRtm1m3A', 'g8OnV26ywJlZpezdBnOWUQ', 5.0, 1.0, 4.0],\n",
       " ['vKsoRog51j2820WF8LV9YA', '6fz0hnNIVpLF5v2NqJfA9w', 5.0, 1.0, 4.0],\n",
       " ['-v21yktIGgm4O2CHMk8e7g', 'UUGoM4q4i8rK2CBRS0xDAw', 5.0, 1.0, 4.0],\n",
       " ['Nf1lASbJ_TZy2zuCgTepjQ', 'XXW_OFaYQkkGOGniujZFHg', 5.0, 1.0, 4.0],\n",
       " ['wLh4YepDaBzQ1JMrj5fDzQ', 'vOfz55MmWgjNQ3BA2PnhgQ', 5.0, 1.0, 4.0],\n",
       " ['2GpsORXt8RIs5fUfS4ujmQ', 'cYwJA2A6I12KNkm2rtXd5g', 5.0, 1.0, 4.0],\n",
       " ['JMg2J3Z23o623Wn9bn4wpg', 'oDNfrdvMV2Fq0R6vd2XwNw', 5.0, 1.0, 4.0],\n",
       " ['H8k77jyHPfl38P-5o525WA', 'IWN2heYitkg-D4UdqfxcMA', 5.0, 1.0, 4.0],\n",
       " ['HURHW-bsdjbFxi46tDvKDQ', 'bpRo8L8dkhgbJhdIKa9mwA', 5.0, 1.0, 4.0],\n",
       " ['8w9EalPi60iI_0XgQShalw', 'w12x7UBSSRsdbp3ZqguG3Q', 5.0, 1.0, 4.0],\n",
       " ['QpCLG8Ciuh0H3GzutpJtJQ', 'ujHiaprwCQ5ewziu0Vi9rw', 5.0, 1.0, 4.0],\n",
       " ['hizGc5W1tBHPghM5YKCAtg', '0EgYXYjt2XJL4hlsKnzrcw', 5.0, 1.0, 4.0],\n",
       " ['1BuQV4vMW3ZI3ukZUIAGyg', 'ujHiaprwCQ5ewziu0Vi9rw', 5.0, 1.0, 4.0],\n",
       " ['6ambPUQU_mgUr7PQpYWmmw', '2dCnTMv3Hh-fM8bOjYS4Xw', 5.0, 1.0, 4.0],\n",
       " ['xNKjIktegMyHVSXFznJTRg', 'Sovgwq-E-n6wLqNh3X_rXg', 5.0, 1.0, 4.0]]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "segments = []\n",
    "\n",
    "for _, row in evaluation.iterrows():\n",
    "    if row['delta'] >= 4.0:\n",
    "        segments.append(row.values.tolist())\n",
    "    \n",
    "segments\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "segments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE = 1.2209066307426057.\n"
     ]
    }
   ],
   "source": [
    "# method 2: compute RMSE\n",
    "\n",
    "evaluation['delta'] = evaluation['ratings'] - evaluation['prediction']\n",
    "\n",
    "RMSE = (sum(evaluation['delta'] ** 2) / len(evaluation)) ** 0.5\n",
    "print('RMSE = {}.'.format(RMSE))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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