sample-mf-lightfm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "84de56a8",
   "metadata": {},
   "source": [
    "# Sample Code"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "688675f5",
   "metadata": {},
   "source": [
    "## 基礎建設"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b32226db",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wei/.virtualenvs/py39/lib/python3.9/site-packages/pandas/compat/__init__.py:97: UserWarning: Could not import the lzma module. Your installed Python is incomplete. Attempting to use lzma compression will result in a RuntimeError.\n",
      "  warnings.warn(msg)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import gzip, json\n",
    "\n",
    "def parse(path):\n",
    "    g = gzip.open(path, 'rb')\n",
    "    for l in g:\n",
    "        yield json.loads(l)\n",
    "\n",
    "def getDF(path):\n",
    "    i = 0\n",
    "    df = {}\n",
    "    for d in parse(path):\n",
    "        df[i] = d\n",
    "        i += 1\n",
    "    return pd.DataFrame.from_dict(df, orient='index')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84ff0f9d",
   "metadata": {},
   "source": [
    "## 載入資料"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9402e9c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "!wget http://deepyeti.ucsd.edu/jianmo/amazon/categoryFilesSmall/All_Beauty.csv\n",
    "!wget http://deepyeti.ucsd.edu/jianmo/amazon/metaFiles2/meta_All_Beauty.json.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "84b66cc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata = getDF('/content/meta_All_Beauty.json.gz')\n",
    "ratings = pd.read_csv('/content/All_Beauty.csv', names=['asin', 'reviewerID', 'overall', 'unixReviewTime'], header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9fd77203",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>tech1</th>\n",
       "      <th>description</th>\n",
       "      <th>fit</th>\n",
       "      <th>title</th>\n",
       "      <th>also_buy</th>\n",
       "      <th>tech2</th>\n",
       "      <th>brand</th>\n",
       "      <th>feature</th>\n",
       "      <th>rank</th>\n",
       "      <th>also_view</th>\n",
       "      <th>details</th>\n",
       "      <th>main_cat</th>\n",
       "      <th>similar_item</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>asin</th>\n",
       "      <th>imageURL</th>\n",
       "      <th>imageURLHighRes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[]</td>\n",
       "      <td></td>\n",
       "      <td>[Loud 'N Clear Personal Sound Amplifier allows...</td>\n",
       "      <td></td>\n",
       "      <td>Loud 'N Clear&amp;trade; Personal Sound Amplifier</td>\n",
       "      <td>[]</td>\n",
       "      <td></td>\n",
       "      <td>idea village</td>\n",
       "      <td>[]</td>\n",
       "      <td>2,938,573 in Beauty &amp; Personal Care (</td>\n",
       "      <td>[]</td>\n",
       "      <td>{'ASIN: ': '6546546450'}</td>\n",
       "      <td>All Beauty</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>6546546450</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[]</td>\n",
       "      <td></td>\n",
       "      <td>[No7 Lift &amp; Luminate Triple Action Serum 50ml ...</td>\n",
       "      <td></td>\n",
       "      <td>No7 Lift &amp;amp; Luminate Triple Action Serum 50...</td>\n",
       "      <td>[B01E7LCSL6, B008X5RVME]</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>872,854 in Beauty &amp; Personal Care (</td>\n",
       "      <td>[]</td>\n",
       "      <td>{'Shipping Weight:': '0.3 ounces (', 'ASIN: ':...</td>\n",
       "      <td>All Beauty</td>\n",
       "      <td>class=\"a-bordered a-horizontal-stripes  a-spa...</td>\n",
       "      <td></td>\n",
       "      <td>$44.99</td>\n",
       "      <td>7178680776</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[]</td>\n",
       "      <td></td>\n",
       "      <td>[No7 Stay Perfect Foundation now stays perfect...</td>\n",
       "      <td></td>\n",
       "      <td>No7 Stay Perfect Foundation Cool Vanilla by No7</td>\n",
       "      <td>[]</td>\n",
       "      <td></td>\n",
       "      <td>No7</td>\n",
       "      <td>[]</td>\n",
       "      <td>956,696 in Beauty &amp; Personal Care (</td>\n",
       "      <td>[B01B8BR0O8, B01B8BR0NO, B014MHXXM8]</td>\n",
       "      <td>{'Shipping Weight:': '3.5 ounces (', 'ASIN: ':...</td>\n",
       "      <td>All Beauty</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>$28.76</td>\n",
       "      <td>7250468162</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[]</td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td></td>\n",
       "      <td>Wella Koleston Perfect Hair Colour 44/44 Mediu...</td>\n",
       "      <td>[B0041PBXX8]</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>1,870,258 in Beauty &amp; Personal Care (</td>\n",
       "      <td>[]</td>\n",
       "      <td>{'\n",
       "    Item Weight: \n",
       "    ': '1.76 ounces', 'Sh...</td>\n",
       "      <td>All Beauty</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>7367905066</td>\n",
       "      <td>[https://images-na.ssl-images-amazon.com/image...</td>\n",
       "      <td>[https://images-na.ssl-images-amazon.com/image...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[]</td>\n",
       "      <td></td>\n",
       "      <td>[Lacto Calamine Skin Balance Daily Nourishing ...</td>\n",
       "      <td></td>\n",
       "      <td>Lacto Calamine Skin Balance Oil control 120 ml...</td>\n",
       "      <td>[]</td>\n",
       "      <td></td>\n",
       "      <td>Pirmal Healthcare</td>\n",
       "      <td>[]</td>\n",
       "      <td>67,701 in Beauty &amp; Personal Care (</td>\n",
       "      <td>[3254895630, B007VL1D9S, B00EH9A0RI, B0773MBG4...</td>\n",
       "      <td>{'Shipping Weight:': '12 ounces (', 'ASIN: ': ...</td>\n",
       "      <td>All Beauty</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>$12.15</td>\n",
       "      <td>7414204790</td>\n",
       "      <td>[https://images-na.ssl-images-amazon.com/image...</td>\n",
       "      <td>[https://images-na.ssl-images-amazon.com/image...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  category tech1                                        description fit  \\\n",
       "0       []        [Loud 'N Clear Personal Sound Amplifier allows...       \n",
       "1       []        [No7 Lift & Luminate Triple Action Serum 50ml ...       \n",
       "2       []        [No7 Stay Perfect Foundation now stays perfect...       \n",
       "3       []                                                       []       \n",
       "4       []        [Lacto Calamine Skin Balance Daily Nourishing ...       \n",
       "\n",
       "                                               title  \\\n",
       "0      Loud 'N Clear&trade; Personal Sound Amplifier   \n",
       "1  No7 Lift &amp; Luminate Triple Action Serum 50...   \n",
       "2    No7 Stay Perfect Foundation Cool Vanilla by No7   \n",
       "3  Wella Koleston Perfect Hair Colour 44/44 Mediu...   \n",
       "4  Lacto Calamine Skin Balance Oil control 120 ml...   \n",
       "\n",
       "                   also_buy tech2              brand feature  \\\n",
       "0                        []             idea village      []   \n",
       "1  [B01E7LCSL6, B008X5RVME]                               []   \n",
       "2                        []                      No7      []   \n",
       "3              [B0041PBXX8]                               []   \n",
       "4                        []        Pirmal Healthcare      []   \n",
       "\n",
       "                                    rank  \\\n",
       "0  2,938,573 in Beauty & Personal Care (   \n",
       "1    872,854 in Beauty & Personal Care (   \n",
       "2    956,696 in Beauty & Personal Care (   \n",
       "3  1,870,258 in Beauty & Personal Care (   \n",
       "4     67,701 in Beauty & Personal Care (   \n",
       "\n",
       "                                           also_view  \\\n",
       "0                                                 []   \n",
       "1                                                 []   \n",
       "2               [B01B8BR0O8, B01B8BR0NO, B014MHXXM8]   \n",
       "3                                                 []   \n",
       "4  [3254895630, B007VL1D9S, B00EH9A0RI, B0773MBG4...   \n",
       "\n",
       "                                             details    main_cat  \\\n",
       "0                           {'ASIN: ': '6546546450'}  All Beauty   \n",
       "1  {'Shipping Weight:': '0.3 ounces (', 'ASIN: ':...  All Beauty   \n",
       "2  {'Shipping Weight:': '3.5 ounces (', 'ASIN: ':...  All Beauty   \n",
       "3  {'\n",
       "    Item Weight: \n",
       "    ': '1.76 ounces', 'Sh...  All Beauty   \n",
       "4  {'Shipping Weight:': '12 ounces (', 'ASIN: ': ...  All Beauty   \n",
       "\n",
       "                                        similar_item date   price        asin  \\\n",
       "0                                                                  6546546450   \n",
       "1   class=\"a-bordered a-horizontal-stripes  a-spa...       $44.99  7178680776   \n",
       "2                                                          $28.76  7250468162   \n",
       "3                                                                  7367905066   \n",
       "4                                                          $12.15  7414204790   \n",
       "\n",
       "                                            imageURL  \\\n",
       "0                                                 []   \n",
       "1                                                 []   \n",
       "2                                                 []   \n",
       "3  [https://images-na.ssl-images-amazon.com/image...   \n",
       "4  [https://images-na.ssl-images-amazon.com/image...   \n",
       "\n",
       "                                     imageURLHighRes  \n",
       "0                                                 []  \n",
       "1                                                 []  \n",
       "2                                                 []  \n",
       "3  [https://images-na.ssl-images-amazon.com/image...  \n",
       "4  [https://images-na.ssl-images-amazon.com/image...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b402792c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>asin</th>\n",
       "      <th>reviewerID</th>\n",
       "      <th>overall</th>\n",
       "      <th>unixReviewTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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      "text/plain": [
       "         asin      reviewerID  overall  unixReviewTime\n",
       "0  0143026860  A1V6B6TNIC10QE      1.0      1424304000\n",
       "1  0143026860  A2F5GHSXFQ0W6J      4.0      1418860800\n",
       "2  0143026860  A1572GUYS7DGSR      4.0      1407628800\n",
       "3  0143026860   A1PSGLFK1NSVO      5.0      1362960000\n",
       "4  0143026860   A6IKXKZMTKGSC      5.0      1324771200"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "695362e3",
   "metadata": {},
   "source": [
    "## 資料整理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b552fb23",
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings['DATE'] = pd.to_datetime(ratings['unixReviewTime'], unit='s')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "054eeba4",
   "metadata": {},
   "source": [
    "## 資料切分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "284b14a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings_trainings = ratings[\n",
    "    (ratings['DATE'] < '2018-09-01')\n",
    "]\n",
    "ratings_testings = ratings[\n",
    "    (ratings['DATE'] >= '2018-09-01') & \n",
    "    (ratings['DATE'] <= '2018-09-30')\n",
    "]\n",
    "ratings_testings_by_user = ratings_testings.groupby('reviewerID').agg(list).reset_index()[['reviewerID', 'asin']].to_dict('records')\n",
    "ratings_testings_by_user = { rating['reviewerID']: rating['asin'] for rating in ratings_testings_by_user }\n",
    "users = list(ratings_testings_by_user.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6c76061",
   "metadata": {},
   "source": [
    "## 產生推薦"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "71cb693d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting lightfm\n",
      "  Downloading lightfm-1.16.tar.gz (310 kB)\n",
      "\u001b[K     |████████████████████████████████| 310 kB 1.1 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: numpy in /Users/wei/.virtualenvs/py39/lib/python3.9/site-packages (from lightfm) (1.20.2)\n",
      "Requirement already satisfied: scipy>=0.17.0 in /Users/wei/.virtualenvs/py39/lib/python3.9/site-packages (from lightfm) (1.6.3)\n",
      "Requirement already satisfied: requests in /Users/wei/.virtualenvs/py39/lib/python3.9/site-packages (from lightfm) (2.25.1)\n",
      "Requirement already satisfied: scikit-learn in /Users/wei/.virtualenvs/py39/lib/python3.9/site-packages (from lightfm) (0.24.2)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /Users/wei/.virtualenvs/py39/lib/python3.9/site-packages (from requests->lightfm) (2020.12.5)\n",
      "Requirement already satisfied: chardet<5,>=3.0.2 in /Users/wei/.virtualenvs/py39/lib/python3.9/site-packages (from requests->lightfm) (4.0.0)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /Users/wei/.virtualenvs/py39/lib/python3.9/site-packages (from requests->lightfm) (2.10)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /Users/wei/.virtualenvs/py39/lib/python3.9/site-packages (from requests->lightfm) (1.26.4)\n",
      "Requirement already satisfied: joblib>=0.11 in /Users/wei/.virtualenvs/py39/lib/python3.9/site-packages (from scikit-learn->lightfm) (1.0.1)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /Users/wei/.virtualenvs/py39/lib/python3.9/site-packages (from scikit-learn->lightfm) (2.1.0)\n",
      "Building wheels for collected packages: lightfm\n",
      "  Building wheel for lightfm (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for lightfm: filename=lightfm-1.16-cp39-cp39-macosx_11_0_x86_64.whl size=448292 sha256=da0b043b20fc1e5437c90f76099ae376055db62341dab84e4431127b9011258f\n",
      "  Stored in directory: /Users/wei/Library/Caches/pip/wheels/d7/75/52/e42e5f9cd86d4902a352aff4dadde75ec041af713ffcf3ed05\n",
      "Successfully built lightfm\n",
      "Installing collected packages: lightfm\n",
      "Successfully installed lightfm-1.16\n",
      "\u001b[33mWARNING: You are using pip version 21.0.1; however, version 21.3.1 is available.\n",
      "You should consider upgrading via the '/Users/wei/.virtualenvs/py39/bin/python -m pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install lightfm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "61e7487b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wei/.virtualenvs/py39/lib/python3.9/site-packages/lightfm/_lightfm_fast.py:9: UserWarning: LightFM was compiled without OpenMP support. Only a single thread will be used.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from lightfm import LightFM\n",
    "import numpy as np\n",
    "\n",
    "def recommend_items(model, data, user_id, item_list, k):\n",
    "    \n",
    "    # 取得item數量\n",
    "    n_users, n_items = data.shape\n",
    "\n",
    "    # 預測user對所有item的評分並進行推薦\n",
    "    scores = model.predict(user_id, np.arange(n_items))\n",
    "    top_items = item_list[np.argsort(-scores)]\n",
    "    return top_items[:k].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9280a043",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " 'A103T1QOGFCSEH': ['B000FOI48G',\n",
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       "  'B0067F28ZW'],\n",
       " 'AZHS42HV4FKKA': ['B000FOI48G',\n",
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       "  'B00VF344X0',\n",
       "  'B0067F28ZW']}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from lightfm.data import Dataset\n",
    "\n",
    "def recommender(training_data, users=[], k=10):\n",
    "    '''\n",
    "    * training_data: dataframe 輸入的訓練資料集（2018-09-01 以前資料）\n",
    "    * users: [] 需要被推薦的使用者\n",
    "    * k: int 每個使用者需要推薦的商品數\n",
    "    * recommendations: dict\n",
    "      {\n",
    "          使用者一： [推薦商品一, 推薦商品二, ...],\n",
    "          使用者二： [...], ...\n",
    "      }\n",
    "    '''\n",
    "    recommendations = {}\n",
    "    '''\n",
    "    mf-based, lightFM\n",
    "    '''\n",
    "    \n",
    "    # 取出評分矩陣所需的訓練資料並以lightfm的Dataset形式存取\n",
    "    training_data = training_data[[\"reviewerID\",\"asin\",\"overall\"]]\n",
    "    data = Dataset()\n",
    "    data.fit(np.append(training_data.reviewerID.unique(), users, 0), training_data.asin.unique())\n",
    "    interactions_matrix, weights_matrix = data.build_interactions([tuple(i) for i in training_data.values])\n",
    "\n",
    "    # 取得user與item的mapping資料\n",
    "    user_id_map, user_feature_map, item_id_map, item_feature_map = data.mapping()\n",
    "    item_list = np.array(list(item_id_map.keys())).astype(object)\n",
    "\n",
    "    # 訓練模型\n",
    "    model = LightFM(loss='warp', item_alpha=1e-6, no_components=30)\n",
    "    model = model.fit(interactions_matrix, sample_weight = weights_matrix, epochs=3, num_threads=2)\n",
    "\n",
    "    # 訓練混合模型（Hybrid Model）加入 user / item feature\n",
    "    # model_hybrid = LightFM(loss='warp', item_alpha=1e-6, no_components=30)\n",
    "    # model_hybrid = model_hybrid.fit(interactions_matrix, sample_weight = weights_matrix, item_features=item_features, epochs=3, num_threads=2)\n",
    "\n",
    "    # 推薦\n",
    "    recommendations = {user: recommend_items(model, interactions_matrix, user_id_map[user], item_list, k) for user in users}\n",
    "\n",
    "    return recommendations\n",
    "\n",
    "# 推薦成果：可看出冷啟動問題嚴重，針對未曾出現的使用者無法有鑑別的個別推薦，實務上可能會主動請使用者先評分再訓練進行推薦\n",
    "#         除此之外也能嘗試使用混合模型（Hybrid Model）加入user / item feature進行訓練\n",
    "ratings_by_user = recommender(ratings_trainings, users)\n",
    "ratings_by_user\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04a4ca9e",
   "metadata": {},
   "source": [
    "## 結果評估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ee763747",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08135593220338982"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def evaluate(ratings_testings_by_user={}, ratings_by_user={}, method=None):\n",
    "    '''\n",
    "    * ratings_testings_by_user: dict 真實被購買的商品資料（2018-09-01 以後資料）\n",
    "    * ratings_by_user: dict 利用訓練資料學習的推薦商品\n",
    "    * method: str\n",
    "    * score: float\n",
    "    '''\n",
    "    total = 0\n",
    "    for d in ratings_testings_by_user:\n",
    "        if d in ratings_by_user:\n",
    "            total += len(set(ratings_by_user[d]) & set(ratings_testings_by_user[d]))\n",
    "\n",
    "    score = total / len(ratings_testings)\n",
    "    return score\n",
    "\n",
    "evaluate(ratings_testings_by_user, ratings_by_user)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53055ac2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d723bfd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
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