sample-cf-used-based.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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       "</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",
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       "      <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": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>asin</th>\n",
       "      <th>reviewerID</th>\n",
       "      <th>overall</th>\n",
       "      <th>unixReviewTime</th>\n",
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       "      <th>4</th>\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": 9,
   "id": "9280a043",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total users before filtering:  323489\n",
      "total users  after filtering:  4793\n"
     ]
    },
    {
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      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from itertools import combinations\n",
    "from collections import defaultdict\n",
    "\n",
    "# header: user_id,item_id,rating,timestamp\n",
    "\n",
    "def recommender(training_data, users=[], k=10):\n",
    "\n",
    "    # loading data from dataframe\n",
    "    # user_to_items dict:\n",
    "    # {\n",
    "    #   'user': {\n",
    "    #       'item': ratings...\n",
    "    #   }...\n",
    "    # }\n",
    "    user_to_items = defaultdict(dict)\n",
    "    for _, row in training_data.iterrows():\n",
    "        row = dict(row)\n",
    "        user = row['reviewerID']\n",
    "        item = row['asin']\n",
    "        rating = float(row['overall'])\n",
    "\n",
    "        user_to_items[user][item] = rating\n",
    "\n",
    "    print(\"total users before filtering: \", len(user_to_items))\n",
    "\n",
    "    # remove obscure user to decrease data size\n",
    "    # filtering params\n",
    "    remove_obscure_user = True\n",
    "    user_rating_threshold = 3\n",
    "    all_users = list(user_to_items.keys())\n",
    "    for user in all_users:\n",
    "        ratings = user_to_items[user]\n",
    "        if remove_obscure_user and len(ratings) < user_rating_threshold:\n",
    "            del user_to_items[user]\n",
    "\n",
    "    print(\"total users  after filtering: \", len(user_to_items))\n",
    "\n",
    "    # generate item to user mapping dict\n",
    "    # {\n",
    "    #   'item': {\n",
    "    #       'user': ratings...\n",
    "    #   }...\n",
    "    # }\n",
    "    item_to_users = defaultdict(dict)\n",
    "    for user, items in user_to_items.items():\n",
    "        for item, rating in items.items():\n",
    "            item_to_users[item][user] = rating\n",
    "\n",
    "    # prepare data of computing user similarity \n",
    "    init_sim = lambda: [0 for _ in range(3)]\n",
    "    factory = lambda: defaultdict(init_sim)\n",
    "    pre_user_similarity = defaultdict(factory)\n",
    "    n = len(item_to_users)\n",
    "    index = 0\n",
    "    for item, user_ratings in item_to_users.items():\n",
    "        if len(user_ratings) > 1:\n",
    "            # print(f\"item: {item} have been rated by {len(user_ratings)} users progress: {index}/{n}\")\n",
    "            for user1, user2 in combinations(user_ratings.keys(), 2):\n",
    "                xy = user_ratings[user1] * user_ratings[user2]\n",
    "                xx = user_ratings[user1] ** 2\n",
    "                yy = user_ratings[user2] ** 2\n",
    "                pre_user_similarity[user1][user2][0] += xy\n",
    "                pre_user_similarity[user1][user2][1] += xx\n",
    "                pre_user_similarity[user1][user2][2] += yy\n",
    "\n",
    "                pre_user_similarity[user2][user1][0] += xy\n",
    "                pre_user_similarity[user2][user1][1] += xx\n",
    "                pre_user_similarity[user2][user1][2] += yy\n",
    "        index += 1\n",
    "\n",
    "    user_similarity = {}\n",
    "    for src_user in pre_user_similarity:\n",
    "        user_similarity_order = []\n",
    "        for dst_user, val in pre_user_similarity[src_user].items():\n",
    "            xy = val[0]\n",
    "            xx = val[1]\n",
    "            yy = val[2]\n",
    "            div = ((xx*yy) ** 0.5)\n",
    "            if div == 0:\n",
    "                continue\n",
    "            similarity = xy / div\n",
    "            if similarity < 0:\n",
    "                continue\n",
    "            for i, s in enumerate(user_similarity_order):\n",
    "                target_similarity = s[1]\n",
    "                if target_similarity < similarity:\n",
    "                    user_similarity_order.insert(i, (dst_user, similarity))\n",
    "                    break\n",
    "            else:\n",
    "                user_similarity_order.append((dst_user, similarity))\n",
    "        user_similarity[src_user] = user_similarity_order\n",
    "\n",
    "    recommendation = {}\n",
    "    for user in users:\n",
    "        if user in user_similarity:\n",
    "            sim_users = user_similarity[user]\n",
    "            recommended_items = []\n",
    "            recommended_items_set = set()\n",
    "            user_have_rated = set(user_to_items[user])\n",
    "            stop_recommend = False\n",
    "            for sim_user, _ in sim_users:\n",
    "                items_from_sim_user = sorted(list(user_to_items[sim_user].items()), key=lambda item: item[1])\n",
    "                for item, _ in items_from_sim_user:\n",
    "                    if item not in user_have_rated and item not in recommended_items_set:\n",
    "                        recommended_items.append(item)\n",
    "                        recommended_items_set.add(item)\n",
    "                    if len(recommended_items) >= k:\n",
    "                        stop_recommend = True\n",
    "                        break\n",
    "                if stop_recommend:\n",
    "                    break\n",
    "            recommendation[user] = recommended_items\n",
    "        else:\n",
    "            recommendation[user] = []\n",
    "    return recommendation\n",
    "\n",
    "ratings_by_user = recommender(ratings_trainings, users)\n",
    "ratings_by_user"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04a4ca9e",
   "metadata": {},
   "source": [
    "## 結果評估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ee763747",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 11,
     "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": "000273c4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff0e8005",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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