{
   
 "cells": [
  {
   
   "cell_type": "markdown",
   "metadata": {
   },
   "source": [
    "# 基于用户的协同过滤算法"
   ]
  },
  {
   
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
   },
   "outputs": [],
   "source": [
    "# 导入包\n",
    "import random\n",
    "import math\n",
    "import time\n",
    "from tqdm import tqdm"
   ]
  },
  {
   
   "cell_type": "markdown",
   "metadata": {
   },
   "source": [
    "## 一. 通用函数定义"
   ]
  },
  {
   
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
   },
   "outputs": [],
   "source": [
    "# 定义装饰器，监控运行时间\n",
    "def timmer(func):\n",
    "    def wrapper(*args, **kwargs):\n",
    "        start_time = time.time()\n",
    "        res = func(*args, **kwargs)\n",
    "        stop_time = time.time()\n",
    "        print('Func %s, run time: %s' % (func.__name__, stop_time - start_time))\n",
    "        return res\n",
    "    return wrapper"
   ]
  },
  {
   
   "cell_type": "markdown",
   "metadata": {
   },
   "source": [
    "### 1. 数据处理相关\n",
    "1. load data\n",
    "2. split data"
   ]
  },
  {
   
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
   },
   "outputs": [],
   "source": [
    "class Dataset():\n",
    "    \n",
    "    def __init__(self, fp):\n",
    "        # fp: data file path\n",
    "        self.data = self.loadData(fp)\n",
    "    \n",
    "    @timmer\n",
    "    def loadData(self, fp):\n",
    "        data = []\n",
    "        for l in open(fp):\n",
    "            data.append(tuple(map(int, l.strip().split('::')[:2])))\n",
    "        return data\n",
    "    \n",
    "    @timmer\n",
    "    def splitData(self, M, k, seed=1):\n",
    "        '''\n",
    "        :params: data, 加载的所有(user, item)数据条目\n",
    "        :params: M, 划分的数目，最后需要取M折的平均\n",
    "        :params: k, 本次是第几次划分，k~[0, M)\n",
    "        :params: seed, random的种子数，对于不同的k应设置成一样的\n",
    "        :return: train, test\n",
    "        '''\n",
    "        train, test = [], []\n",
    "        random.seed(seed)\n",
    "        for user, item in self.data:\n",
    "            # 这里与书中的不一致，本人认为取M-1较为合理，因randint是左右都覆盖的\n",
    "            if random.randint(0, M-1) == k:  \n",
    "                test.append((user, item))\n",
    "            else:\n",
    "                train.append((user, item))\n",
    "\n",
    "        # 处理成字典的形式，user->set(items)\n",
    "        def convert_dict(data):\n",
    "            data_dict = {}\n",
    "            for user, item in data:\n",
    "                if user not in data_dict:\n",
    "                    data_dict[user] = set()\n",
    "                data_dict[user].add(item)\n",
    "            data_dict = {k: list(data_dict[k]) for k in data_dict}\n",
    "            return data_dict\n",
    "\n",
    "        return convert_dict(train), convert_dict(test)"
   ]
  },
  {
   
   "cell_type": "markdown",
   "metadata": {
   },
   "source": [
    "### 2. 评价指标\n",
    "1. Precision\n",
    "2. Recall\n",
    "3. Coverage\n",
    "4. Popularity(Novelty)"
   ]
  },
  {
   
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
   },
   "outputs": [],
   "source": [
    "class Metric():\n",
    "    \n",
    "    def __init__(self, train, test, GetRecommendation):\n",
    "        '''\n",
    "        :params: train, 训练数据\n",
    "        :params: test, 测试数据\n",
    "        :params: GetRecommendation, 为某个用户获取推荐物品的接口函数\n",
    "        '''\n",
    "        self.train = train\n",
    "        self.test = test\n",
    "        self.GetRecommendation = GetRecommendation\n",
    "        self.recs = self.getRec()\n",
    "        \n",
    "    # 为test中的每个用户进行推荐\n",
    "    def getRec(self):\n",
    "        recs = {}\n",
    "        for user in self.test:\n",
    "            rank = self.GetRecommendation(user)\n",
    "            recs[user] = rank\n",
    "        return recs\n",
    "        \n",
    "    # 定义精确率指标计算方式\n",
    "    def precision(self):\n",
    "        all, hit = 0, 0\n",
    "        for user in self.test:\n",
    "            test_items = set(self.test[user])\n",
    "            rank = self.recs[user]\n",
    "            for item, score in rank:\n",
    "                if item in test_items:\n",
    "                    hit += 1\n",
    "            all += len(rank)\n",
    "        return round(hit / all * 100, 2)\n",
    "    \n",
    "    # 定义召回率指标计算方式\n",
    "    def recall(self):\n",
    "        all, hit = 0, 0\n",
    "        for user in self.test:\n",
    "            test_items = set(self.test[user])\n",
    "            rank = self.recs[user]\n",
    "            for item, score in rank:\n",
    "                if item in test_items:\n",
    "                    hit += 1\n",
    "            all += len(test_items)\n",
    "        return round(hit / all * 100, 2)\n",
    "    \n",
    "    # 定义覆盖率指标计算方式\n",
    "    def coverage(self):\n",
    "        all_item, recom_item = set(), set()\n",
    "        for user in self.test:\n",
    "            for item in self.train[user]:\n",
    "                all_item.add(item)\n",
    "            rank = self.recs[user]\n",
    "            for item, score in rank:\n",
    "                recom_item.add(item)\n",
    "        return round(len(recom_item) / len(all_item) * 100, 2)\n",
    "    \n",
    "    # 定义新颖度指标计算方式\n",
    "    def popularity(self):\n",
    "        # 计算物品的流行度\n",
    "        item_pop = {}\n",
    "        for user in self.train:\n",
    "            for item in self.train[user]:\n",
    "                if item not in item_pop:\n",
    "                    item_pop[item] = 0\n",
    "                item_pop[item] += 1\n",
    "\n",
    "        num, pop = 0, 0\n",
    "        for user in self.test:\n",
    "            rank = self.recs[user]\n",
    "            for item, score in rank:\n",
    "                # 取对数，防止因长尾问题带来的被流行物品所主导\n",
    "                pop += math.log(1 + item_pop[item])\n",
    "                num += 1\n",
    "        return round(pop / num, 6)\n",
    "    \n",
    "    def eval(self):\n",
    "        metric = {'Precision': self.precision(),\n",
    "                  'Recall': self.recall(),\n",
    "                  'Coverage': self.coverage(),\n",
    "                  'Popularity': self.popularity()}\n",
    "        print('Metric:', metric)\n",
    "        return metric"
   ]
  },
  {
   
   "cell_type": "markdown",
   "metadata": {
   },
   "source": [
    "## 二. 算法实现\n",
    "1. Random\n",
    "2. MostPopular\n",
    "3. UserCF\n",
    "4. UserIIF"
   ]
  },
  {
   
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
   },
   "outputs": [],
   "source": [
    "# 1. 随机推荐\n",
    "def Random(train, K, N):\n",
    "    '''\n",
    "    :params: train, 训练数据集\n",
    "    :params: K, 可忽略\n",
    "    :params: N, 超参数，设置取TopN推荐物品数目\n",
    "    :return: GetRecommendation，推荐接口函数\n",
    "    '''\n",
    "    items = {}\n",
    "    for user in train:\n",
    "        for item in train[user]:\n",
    "            items[item] = 1\n",
    "    \n",
    "    def GetRecommendation(user):\n",
    "        # 随机推荐N个未见过的\n",
    "        user_items = set(train[user])\n",
    "        rec_items = {k: items[k] for k in items if k not in user_items}\n",
    "        rec_items = list(rec_items.items())\n",
    "        random.shuffle(rec_items)\n",
    "        return rec_items[:N]\n",
    "    \n",
    "    return GetRecommendation"
   ]
  },
  {
   
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
   },
   "outputs": [],
   "source": [
    "# 2. 热门推荐\n",
    "def MostPopular(train, K, N):\n",
    "    '''\n",
    "    :params: train, 训练数据集\n",
    "    :params: K, 可忽略\n",
    "    :params: N, 超参数，设置取TopN推荐物品数目\n",
    "    :return: GetRecommendation, 推荐接口函数\n",
    "    '''\n",
    "    items = {}\n",
    "    for user in train:\n",
    "        for item in train[user]:\n",
    "            if item not in items:\n",
    "                items[item] = 0\n",
    "            items[item] += 1\n",
    "        \n",
    "    def GetRecommendation(user):\n",
    "        # 随机推荐N个没见过的最热门的\n",
    "        user_items = set(train[user])\n",
    "        rec_items = {k: items[k] for k in items if k not in user_items}\n",
    "        rec_items = list(sorted(rec_items.items(), key=lambda x: x[1], reverse=True))\n",
    "        return rec_items[:N]\n",
    "    \n",
    "    return GetRecommendation"
   ]
  },
  {
   
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
   },
   "outputs": [],
   "source": [
    "# 3. 基于用户余弦相似度的推荐\n",
    "def UserCF(train, K, N):\n",
    "    '''\n",
    "    :params: train, 训练数据集\n",
    "    :params: K, 超参数，设置取TopK相似用户数目\n",
    "    :params: N, 超参数，设置取TopN推荐物品数目\n",
    "    :return: GetRecommendation, 推荐接口函数\n",
    "    '''\n",
    "    # 计算item->user的倒排索引\n",
    "    item_users = {}\n",
    "    for user in train:\n",
    "        for item in train[user]:\n",
    "            if item not in item_users:\n",
    "                item_users[item] = []\n",
    "            item_users[item].append(user)\n",
    "    \n",
    "    # 计算用户相似度矩阵\n",
    "    sim = {}\n",
    "    num = {}\n",
    "    for item in item_users:\n",
    "        users = item_users[item]\n",
    "        for i in range(len(users)):\n",
    "            u = users[i]\n",
    "            if u not in num:\n",
    "                num[u] = 0\n",
    "            num[u] += 1\n",
    "            if u not in sim:\n",
    "                sim[u] = {}\n",
    "            for j in range(len(users)):\n",
    "                if j == i: continue\n",
    "                v = users[j]\n",
    "                if v not in sim[u]:\n",
    "                    sim[u][v] = 0\n",
    "                sim[u][v] += 1\n",
    "    for u in sim:\n",
    "        for v in sim[u]:\n",
    "            sim[u][v] /= math.sqrt(num[u] * num[v])\n",
    "    \n",
    "    # 按照相似度排序\n",
    "    sorted_user_sim = {k: list(sorted(v.items(), \\\n",
    "                               key=lambda x: x[1], reverse=True)) \\\n",
    "                       for k, v in sim.items()}\n",
    "    \n",
    "    # 获取接口函数\n",
    "    def GetRecommendation(user):\n",
    "        items = {}\n",
    "        seen_items = set(train[user])\n",
    "        for u, _ in sorted_user_sim[user][:K]:\n",
    "            for item in train[u]:\n",
    "                # 要去掉用户见过的\n",
    "                if item not in seen_items:\n",
    "                    if item not in items:\n",
    "                        items[item] = 0\n",
    "                    items[item] += sim[user][u]\n",
    "        recs = list(sorted(items.items(), key=lambda x: x[1], reverse=True))[:N]\n",
    "        return recs\n",
    "    \n",
    "    return GetRecommendation"
   ]
  },
  {
   
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
   },
   "outputs": [],
   "source": [
    "# 4. 基于改进的用户余弦相似度的推荐\n",
    "def UserIIF(train, K, N):\n",
    "    '''\n",
    "    :params: train, 训练数据集\n",
    "    :params: K, 超参数，设置取TopK相似用户数目\n",
    "    :params: N, 超参数，设置取TopN推荐物品数目\n",
    "    :return: GetRecommendation, 推荐接口函数\n",
    "    '''\n",
    "    # 计算item->user的倒排索引\n",
    "    item_users = {}\n",
    "    for user in train:\n",
    "        for item in train[user]:\n",
    "            if item not in item_users:\n",
    "                item_users[item] = []\n",
    "            item_users[item].append(user)\n",
    "    \n",
    "    # 计算用户相似度矩阵\n",
    "    sim = {}\n",
    "    num = {}\n",
    "    for item in item_users:\n",
    "        users = item_users[item]\n",
    "        for i in range(len(users)):\n",
    "            u = users[i]\n",
    "            if u not in num:\n",
    "                num[u] = 0\n",
    "            num[u] += 1\n",
    "            if u not in sim:\n",
    "                sim[u] = {}\n",
    "            for j in range(len(users)):\n",
    "                if j == i: continue\n",
    "                v = users[j]\n",
    "                if v not in sim[u]:\n",
    "                    sim[u][v] = 0\n",
    "                # 相比UserCF，主要是改进了这里\n",
    "                sim[u][v] += 1 / math.log(1 + len(users))\n",
    "    for u in sim:\n",
    "        for v in sim[u]:\n",
    "            sim[u][v] /&#