1_movie_KNN.ipynb · 刘亚翔/movie_recommendation_APP_question

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1_movie_KNN.ipynb 42.90 KB

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**问题：**\n",
    "假设你正在为一个电影推荐系统设计一个简单的KNN算法。我们有以下一些用户的电影评分数据，数据由两个特征组成：用户对电影A和电影B的评分，分别在1-5之间。用户的标签（电影类型偏好）是动作片（标签0）或者是喜剧片（标签1）。我们有一个新用户，他给电影A评分为3，电影B评分为4。请问这个用户可能偏好哪种类型的电影？\n",
    "\n",
    "**数据：**\n",
    "\n",
    "| 用户   | 电影A评分 | 电影B评分 | 偏好类型 |\n",
    "| ------ | --------- | --------- | -------- |\n",
    "| 用户1  | 5         | 1         | 动作片   |\n",
    "| 用户2  | 4         | 2         | 动作片   |\n",
    "| 用户3  | 2         | 5         | 喜剧片   |\n",
    "| 用户4  | 1         | 4         | 喜剧片   |\n",
    "| 用户5  | 3         | 2         | 动作片   |\n",
    "| 用户6  | 2         | 5         | 喜剧片   |\n",
    "\n",
    "你需要做以下步骤：\n",
    "1. 构造数据\n",
    "2. 创建KNN模型\n",
    "3. 使用数据训练模型\n",
    "4. 预测新用户的喜好"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 0. 引入核心包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array([[5, 1],\n",
    "              [4, 2],\n",
    "              [2, 5],\n",
    "              [1, 4],\n",
    "              [3, 2],\n",
    "              [2, 5]])#TODO\n",
    "\n",
    "y =  np.array([0, 0, 1, 1, 0, 1])#TODO  # 0表示动作片，1表示喜剧片"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. 创建 KNN 模型\n",
    "k = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=1)#TODO"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. 训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=1)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(X, y)# TODO"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. 用模型推理(预测)用户的喜好"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_user = np.array([[3, 4]])\n",
    "prediction = knn.predict(new_user)#TODO"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. 数据可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"User Movie Preference\", size=15) \n",
    "plt.xlabel(\"Movie A rating\")\n",
    "plt.ylabel(\"Movie B rating\")\n",
    "plt.grid()\n",
    "\n",
    "# 绘制原始样本点，要求不同的影片喜好类别用不同的颜色标记\n",
    "# 提示: 用scatter散点图绘制，用它的参数c实现不同的类别用不同的颜色标记\n",
    "colors = ['red' if label == 0 else 'blue' for label in y]\n",
    "plt.scatter(X[:, 0], X[:, 1], c=colors, label='Existing Users')#TODO\n",
    "\n",
    "# 绘制新数据点，用红色x标记，大小为8\n",
    "# 提示：用plt.plot()绘制，用它的参数marker实现不同的标记符号\n",
    "plt.scatter(new_user[0, 0], new_user[0, 1], color='green', marker='x', s=100, label='New User')#TODO\n",
    "\n",
    "# 新数据最近邻索引为第一个最近邻的索引\n",
    "dist, idx = knn.kneighbors(new_user)#TODO\n",
    "nearest = X[idx[0][0]]#TODO # 获取最近邻点的坐标，这是一个列表，第一个元素是x坐标，第二个元素是y坐标\n",
    "\n",
    "# 用红线标记新数据点与最近邻点的连接线\n",
    "# 提示：用plt.plot()绘制，用 r-- 实现红色虚线\n",
    "plt.plot([new_user[0, 0], nearest[0]], [new_user[0, 1], nearest[1]], 'r--', label='Nearest Neighbor')#TODO\n",
    "\n",
    "# 为每个点添加坐标文本  \n",
    "for x, y in zip(X[:, 0], X[:, 1]):\n",
    "    plt.text(x, y+0.1, f'({x}, {y})') \n",
    "\n",
    "# 为新数据点添加坐标文本\n",
    "plt.text(new_user[0,0], new_user[0,1]+0.1, f'({new_user[0,0]}, {new_user[0,1]})')\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.11.5"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

一键复制编辑原始数据按行查看历史

提交于 2024-09-03 17:46 . added a file

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