KNN代码实现.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>电影名称</th>\n",
       "      <th>打斗镜头</th>\n",
       "      <th>接吻镜头</th>\n",
       "      <th>电影类型</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>无问西东</td>\n",
       "      <td>1</td>\n",
       "      <td>101</td>\n",
       "      <td>爱情</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>后来的我们</td>\n",
       "      <td>3</td>\n",
       "      <td>89</td>\n",
       "      <td>爱情</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>前任3</td>\n",
       "      <td>5</td>\n",
       "      <td>97</td>\n",
       "      <td>爱情</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>红海行动</td>\n",
       "      <td>100</td>\n",
       "      <td>3</td>\n",
       "      <td>动作</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>战狼2</td>\n",
       "      <td>103</td>\n",
       "      <td>5</td>\n",
       "      <td>动作</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>唐人街探案</td>\n",
       "      <td>67</td>\n",
       "      <td>7</td>\n",
       "      <td>动作</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "    电影名称  打斗镜头  接吻镜头 电影类型\n",
       "0   无问西东     1   101   爱情\n",
       "1  后来的我们     3    89   爱情\n",
       "2    前任3     5    97   爱情\n",
       "3   红海行动   100     3   动作\n",
       "4    战狼2   103     5   动作\n",
       "5  唐人街探案    67     7   动作"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rowdata = {'电影名称':['无问西东','后来的我们','前任3','红海行动','战狼2','唐人街探案'],\n",
    "           '打斗镜头':[1,3,5,100,103,67],\n",
    "           '接吻镜头':[101,89,97,3,5,7],\n",
    "           '电影类型':['爱情','爱情','爱情','动作','动作','动作']\n",
    "          }\n",
    "movie_data = pd.DataFrame(rowdata)\n",
    "movie_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data = [24,67]  #dist = list((((movie_data.iloc[:6,1:3]-new_data)**2).sum(1))**0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[41.048751503547585,\n",
       " 30.4138126514911,\n",
       " 35.510561809129406,\n",
       " 99.35793878699377,\n",
       " 100.42410069301094,\n",
       " 73.81734213584231]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#计算已知类别数据集中点与当前点的距离 计算欧式距离\n",
    "dist = list ((((movie_data .iloc[:6,1:3]-new_data)**2).sum(1))**0.5)\n",
    "dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dist</th>\n",
       "      <th>labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30.413813</td>\n",
       "      <td>爱情</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35.510562</td>\n",
       "      <td>爱情</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41.048752</td>\n",
       "      <td>爱情</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>73.817342</td>\n",
       "      <td>动作</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        dist labels\n",
       "1  30.413813     爱情\n",
       "2  35.510562     爱情\n",
       "0  41.048752     爱情\n",
       "5  73.817342     动作"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#将距离升序排列,然后选取距离最小的k个点\n",
    "#dist_l = pd.DataFrame({'dist': dist, 'labels': (movie_data.iloc[:6, 3])})\n",
    "dist_l = pd.DataFrame({'dist':dist,'labels':(movie_data.iloc[:6,3])})#form DataFrame \n",
    "# dr = dist_l.sort_values(by = 'dist')[: 4]sort \n",
    "dr = dist_l.sort_values(by = 'dist')[:4] #show \n",
    "dr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "爱情    3\n",
       "动作    1\n",
       "Name: labels, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 确定前k个点所在类别的出现频率\n",
    "re = dr.loc[:,'labels'].value_counts()\n",
    "re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['爱情']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 选择频率最高的类别作为当前点的预测类别\n",
    "result = []\n",
    "result.append(re.index[0])\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将KNN 算法封装成一个函数，方便后面调用\n",
    "import pandas as pd\n",
    "\"\"\"\n",
    "函数功能：KNN 算法分类器\n",
    "参数说明：\n",
    "dataset:训练集\n",
    "new_data: 需要预测的数据集合\n",
    "k：KNN算法中选择距离最近的几个数据\n",
    "\n",
    "返回：\n",
    "result:分类结果\n",
    "\"\"\"\n",
    "def classify(new,k,dataSet):\n",
    "    result = []\n",
    "    dist = list((((dataSet.iloc[:6,1:3]-new)**2).sum(1))**0.5)#计算欧式距离\n",
    "    dist_l = pd.DataFrame({'dist':dist,'label':dataSet.iloc[:6,3]})#将欧式距离和标签形成一个表格\n",
    "    dr = dist_l.sort_values(by='dist')[:k]#根据距离进行排序，然后选取前k个点\n",
    "    re = dr.loc[:,'label'].value_counts()#统计标签频率\n",
    "    result.append(re.index[0])\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['爱情']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new = new_data\n",
    "k = 5\n",
    "dataSet = movie_data\n",
    "classify(new,k,dataSet)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###准备数据，约会网站的KNN算法实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40920</td>\n",
       "      <td>8.326976</td>\n",
       "      <td>0.953952</td>\n",
       "      <td>largeDoses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14488</td>\n",
       "      <td>7.153469</td>\n",
       "      <td>1.673904</td>\n",
       "      <td>smallDoses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26052</td>\n",
       "      <td>1.441871</td>\n",
       "      <td>0.805124</td>\n",
       "      <td>didntLike</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>75136</td>\n",
       "      <td>13.147394</td>\n",
       "      <td>0.428964</td>\n",
       "      <td>didntLike</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>38344</td>\n",
       "      <td>1.669788</td>\n",
       "      <td>0.134296</td>\n",
       "      <td>didntLike</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       0          1         2           3\n",
       "0  40920   8.326976  0.953952  largeDoses\n",
       "1  14488   7.153469  1.673904  smallDoses\n",
       "2  26052   1.441871  0.805124   didntLike\n",
       "3  75136  13.147394  0.428964   didntLike\n",
       "4  38344   1.669788  0.134296   didntLike"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datingTest = pd.read_table('datingTestSet.txt',header = None)#获取数据\n",
    "datingTest.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
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       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40920</td>\n",
       "      <td>8.326976</td>\n",
       "      <td>0.953952</td>\n",
       "      <td>largeDoses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14488</td>\n",
       "      <td>7.153469</td>\n",
       "      <td>1.673904</td>\n",
       "      <td>smallDoses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26052</td>\n",
       "      <td>1.441871</td>\n",
       "      <td>0.805124</td>\n",
       "      <td>didntLike</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>75136</td>\n",
       "      <td>13.147394</td>\n",
       "      <td>0.428964</td>\n",
       "      <td>didntLike</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>38344</td>\n",
       "      <td>1.669788</td>\n",
       "      <td>0.134296</td>\n",
       "      <td>didntLike</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>11145</td>\n",
       "      <td>3.410627</td>\n",
       "      <td>0.631838</td>\n",
       "      <td>smallDoses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>68846</td>\n",
       "      <td>9.974715</td>\n",
       "      <td>0.669787</td>\n",
       "      <td>didntLike</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>26575</td>\n",
       "      <td>10.650102</td>\n",
       "      <td>0.866627</td>\n",
       "      <td>largeDoses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>48111</td>\n",
       "      <td>9.134528</td>\n",
       "      <td>0.728045</td>\n",
       "      <td>largeDoses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>43757</td>\n",
       "      <td>7.882601</td>\n",
       "      <td>1.332446</td>\n",
       "      <td>largeDoses</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         0          1         2           3\n",
       "0    40920   8.326976  0.953952  largeDoses\n",
       "1    14488   7.153469  1.673904  smallDoses\n",
       "2    26052   1.441871  0.805124   didntLike\n",
       "3    75136  13.147394  0.428964   didntLike\n",
       "4    38344   1.669788  0.134296   didntLike\n",
       "..     ...        ...       ...         ...\n",
       "995  11145   3.410627  0.631838  smallDoses\n",
       "996  68846   9.974715  0.669787   didntLike\n",
       "997  26575  10.650102  0.866627  largeDoses\n",
       "998  48111   9.134528  0.728045  largeDoses\n",
       "999  43757   7.882601  1.332446  largeDoses\n",
       "\n",
       "[1000 rows x 4 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datingTest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 4)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datingTest.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datingTest.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 4 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   0       1000 non-null   int64  \n",
      " 1   1       1000 non-null   float64\n",
      " 2   2       1000 non-null   float64\n",
      " 3   3       1000 non-null   object \n",
      "dtypes: float64(2), int64(1), object(1)\n",
      "memory usage: 31.4+ KB\n"
     ]
    }
   ],
   "source": [
    "datingTest.info()#展示详细信息提前了解"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      largeDoses\n",
       "1      smallDoses\n",
       "2       didntLike\n",
       "3       didntLike\n",
       "4       didntLike\n",
       "          ...    \n",
       "995    smallDoses\n",
       "996     didntLike\n",
       "997    largeDoses\n",
       "998    largeDoses\n",
       "999    largeDoses\n",
       "Name: 3, Length: 1000, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datingTest.iloc[:,-1]#最后一行"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用 Matplotlib 创建散点图,查看各数据的分布情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 29609 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 28216 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 25103 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 35270 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 39057 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 25152 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 21344 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 26102 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 38388 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 27604 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 27599 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 21608 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 28040 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 36153 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 20912 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 28103 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 28107 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 20844 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 21319 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 25968 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 24180 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 39134 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 34892 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 24120 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 23458 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 37324 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 31243 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 29609 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 28216 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 25103 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 35270 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 39057 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 25152 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 21344 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 26102 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 38388 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 27604 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 27599 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 21608 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 28040 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 36153 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 20912 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 28103 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 28107 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 20844 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 21319 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 25968 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 24180 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 39134 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 34892 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 24120 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 23458 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 37324 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/home/hhl/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 31243 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib  inline\n",
    "import  matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#用不同的颜色将不同的标签区分\n",
    "Colors = []\n",
    "for i in range(datingTest.shape[0]): #遍历一遍\n",
    "    m = datingTest.iloc[i,-1]#获取当前列的喜好\n",
    "    if m=='didntLike':\n",
    "        Colors.append('black')\n",
    "    if m=='smallDoses':\n",
    "        Colors.append('orange')\n",
    "    if m=='largeDoses':\n",
    "        Colors.append('red')\n",
    "        \n",
    "#绘制两两特征之间的散点图\n",
    "\n",
    "plt.rcParams['font.sans-serif']=['Simhei']#将图中文字设置为黑色\n",
    "pl=plt.figure(figsize=(12,8))#设置一个画布\n",
    "\n",
    "fig1 = pl.add_subplot(221)#两行两列的第一个图\n",
    "plt.scatter(datingTest.iloc[:,1],datingTest.iloc[:,2],marker = '.',c=Colors)\n",
    "plt.xlabel('玩游戏视频所占时间比')\n",
    "plt.ylabel('每周消费冰淇淋公升数')\n",
    "\n",
    "fig2=pl.add_subplot(222)\n",
    "plt.scatter(datingTest.iloc[:,0],datingTest.iloc[:,1],marker='.',c=Colors)\n",
    "plt.xlabel('每年飞行常客里程')\n",
    "plt.ylabel('玩游戏视频所占时间比')\n",
    "\n",
    "fig3=pl.add_subplot(223)\n",
    "plt.scatter(datingTest.iloc[:,0],datingTest.iloc[:,2],marker='.',c=Colors)\n",
    "plt.xlabel('每年飞行常客里程')\n",
    "plt.ylabel('每周消费冰淇淋公升数')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##因为不同的参数之间的权重不一样，在这里三个参数的权重应该是一致的，所以要对所有的数据进行归一化处理，这里用最简单的0-1归一化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "函数功能：归一化\n",
    "参数说明：\n",
    "dataSet: 原始数据集\n",
    "返回：返回0-1标准化以后的数据集\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "def minmax(dataSet):\n",
    "    minDf = dataSet.min()\n",
    "    maxDf = dataSet.max()\n",
    "    normSet = (dataSet-minDf)/(maxDf-minDf)#归一化处理\n",
    "    return normSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.448325</td>\n",
       "      <td>0.398051</td>\n",
       "      <td>0.562334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.158733</td>\n",
       "      <td>0.341955</td>\n",
       "      <td>0.987244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.285429</td>\n",
       "      <td>0.068925</td>\n",
       "      <td>0.474496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.823201</td>\n",
       "      <td>0.628480</td>\n",
       "      <td>0.252489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.420102</td>\n",
       "      <td>0.079820</td>\n",
       "      <td>0.078578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>0.122106</td>\n",
       "      <td>0.163037</td>\n",
       "      <td>0.372224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>0.754287</td>\n",
       "      <td>0.476818</td>\n",
       "      <td>0.394621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>0.291159</td>\n",
       "      <td>0.509103</td>\n",
       "      <td>0.510795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>0.527111</td>\n",
       "      <td>0.436655</td>\n",
       "      <td>0.429005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>0.479408</td>\n",
       "      <td>0.376809</td>\n",
       "      <td>0.785718</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2\n",
       "0    0.448325  0.398051  0.562334\n",
       "1    0.158733  0.341955  0.987244\n",
       "2    0.285429  0.068925  0.474496\n",
       "3    0.823201  0.628480  0.252489\n",
       "4    0.420102  0.079820  0.078578\n",
       "..        ...       ...       ...\n",
       "995  0.122106  0.163037  0.372224\n",
       "996  0.754287  0.476818  0.394621\n",
       "997  0.291159  0.509103  0.510795\n",
       "998  0.527111  0.436655  0.429005\n",
       "999  0.479408  0.376809  0.785718\n",
       "\n",
       "[1000 rows x 3 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minmax(datingTest.iloc[:,:3])#归一化以后的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      largeDoses\n",
       "1      smallDoses\n",
       "2       didntLike\n",
       "3       didntLike\n",
       "4       didntLike\n",
       "          ...    \n",
       "995    smallDoses\n",
       "996     didntLike\n",
       "997    largeDoses\n",
       "998    largeDoses\n",
       "999    largeDoses\n",
       "Name: 3, Length: 1000, dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datingTest.iloc[:,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.448325</td>\n",
       "      <td>0.398051</td>\n",
       "      <td>0.562334</td>\n",
       "      <td>largeDoses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.158733</td>\n",
       "      <td>0.341955</td>\n",
       "      <td>0.987244</td>\n",
       "      <td>smallDoses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.285429</td>\n",
       "      <td>0.068925</td>\n",
       "      <td>0.474496</td>\n",
       "      <td>didntLike</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.823201</td>\n",
       "      <td>0.628480</td>\n",
       "      <td>0.252489</td>\n",
       "      <td>didntLike</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.420102</td>\n",
       "      <td>0.079820</td>\n",
       "      <td>0.078578</td>\n",
       "      <td>didntLike</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>0.122106</td>\n",
       "      <td>0.163037</td>\n",
       "      <td>0.372224</td>\n",
       "      <td>smallDoses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>0.754287</td>\n",
       "      <td>0.476818</td>\n",
       "      <td>0.394621</td>\n",
       "      <td>didntLike</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>0.291159</td>\n",
       "      <td>0.509103</td>\n",
       "      <td>0.510795</td>\n",
       "      <td>largeDoses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>0.527111</td>\n",
       "      <td>0.436655</td>\n",
       "      <td>0.429005</td>\n",
       "      <td>largeDoses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>0.479408</td>\n",
       "      <td>0.376809</td>\n",
       "      <td>0.785718</td>\n",
       "      <td>largeDoses</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2           3\n",
       "0    0.448325  0.398051  0.562334  largeDoses\n",
       "1    0.158733  0.341955  0.987244  smallDoses\n",
       "2    0.285429  0.068925  0.474496   didntLike\n",
       "3    0.823201  0.628480  0.252489   didntLike\n",
       "4    0.420102  0.079820  0.078578   didntLike\n",
       "..        ...       ...       ...         ...\n",
       "995  0.122106  0.163037  0.372224  smallDoses\n",
       "996  0.754287  0.476818  0.394621   didntLike\n",
       "997  0.291159  0.509103  0.510795  largeDoses\n",
       "998  0.527111  0.436655  0.429005  largeDoses\n",
       "999  0.479408  0.376809  0.785718  largeDoses\n",
       "\n",
       "[1000 rows x 4 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datingT = pd.concat([minmax(datingTest.iloc[:,:3]),datingTest.iloc[:,3]],axis=1)\n",
    "#pd.concat将归一化以后的数据和标签进行连接\n",
    "datingT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 将数据随机切割为测试集和训练集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "函数功能:切分训练集和测试集\n",
    "参数说明:\n",
    "dataSet:原始数据集\n",
    "rate:训练集所占比例\n",
    "返回:切分好的训练集和测试集\n",
    "\"\"\"\n",
    "def randSplit(dataSet,rate=0.9):\n",
    "    n = dataSet.shape[0]\n",
    "    m = int(n*rate)\n",
    "    train = dataSet.iloc[:m,:]\n",
    "    test = dataSet.iloc[m:,:]\n",
    "    test.index = range(test.shape[0])#将序号重新排\n",
    "    return train,test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "train,test = randSplit(datingT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.448325</td>\n",
       "      <td>0.398051</td>\n",
       "      <td>0.562334</td>\n",
       "      <td>largeDoses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.158733</td>\n",
       "      <td>0.341955</td>\n",
       "      <td>0.987244</td>\n",
       "      <td>smallDoses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.285429</td>\n",
       "      <td>0.068925</td>\n",
       "      <td>0.474496</td>\n",
       "      <td>didntLike</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.823201</td>\n",
       "      <td>0.628480</td>\n",
       "      <td>0.252489</td>\n",
       "      <td>didntLike</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.420102</td>\n",
       "      <td>0.079820</td>\n",
       "      <td>0.078578</td>\n",
       "      <td>didntLike</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>0.243665</td>\n",
       "      <td>0.486131</td>\n",
       "      <td>0.979099</td>\n",
       "      <td>largeDoses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>896</th>\n",
       "      <td>0.165350</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.808206</td>\n",
       "      <td>smallDoses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>897</th>\n",
       "      <td>0.054967</td>\n",
       "      <td>0.359158</td>\n",
       "      <td>0.080380</td>\n",
       "      <td>smallDoses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>898</th>\n",
       "      <td>0.111106</td>\n",
       "      <td>0.393932</td>\n",
       "      <td>0.058181</td>\n",
       "      <td>smallDoses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>899</th>\n",
       "      <td>0.389710</td>\n",
       "      <td>0.698530</td>\n",
       "      <td>0.735519</td>\n",
       "      <td>largeDoses</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>900 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2           3\n",
       "0    0.448325  0.398051  0.562334  largeDoses\n",
       "1    0.158733  0.341955  0.987244  smallDoses\n",
       "2    0.285429  0.068925  0.474496   didntLike\n",
       "3    0.823201  0.628480  0.252489   didntLike\n",
       "4    0.420102  0.079820  0.078578   didntLike\n",
       "..        ...       ...       ...         ...\n",
       "895  0.243665  0.486131  0.979099  largeDoses\n",
       "896  0.165350  0.000000  0.808206  smallDoses\n",
       "897  0.054967  0.359158  0.080380  smallDoses\n",
       "898  0.111106  0.393932  0.058181  smallDoses\n",
       "899  0.389710  0.698530  0.735519  largeDoses\n",
       "\n",
       "[900 rows x 4 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.513766</td>\n",
       "      <td>0.170320</td>\n",
       "      <td>0.262181</td>\n",
       "      <td>didntLike</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.089599</td>\n",
       "      <td>0.154426</td>\n",
       "      <td>0.785277</td>\n",
       "      <td>smallDoses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.611167</td>\n",
       "      <td>0.172689</td>\n",
       "      <td>0.915245</td>\n",
       "      <td>didntLike</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.012578</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.195477</td>\n",
       "      <td>smallDoses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.110241</td>\n",
       "      <td>0.187926</td>\n",
       "      <td>0.287082</td>\n",
       "      <td>smallDoses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>0.122106</td>\n",
       "      <td>0.163037</td>\n",
       "      <td>0.372224</td>\n",
       "      <td>smallDoses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0.754287</td>\n",
       "      <td>0.476818</td>\n",
       "      <td>0.394621</td>\n",
       "      <td>didntLike</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>0.291159</td>\n",
       "      <td>0.509103</td>\n",
       "      <td>0.510795</td>\n",
       "      <td>largeDoses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0.527111</td>\n",
       "      <td>0.436655</td>\n",
       "      <td>0.429005</td>\n",
       "      <td>largeDoses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0.479408</td>\n",
       "      <td>0.376809</td>\n",
       "      <td>0.785718</td>\n",
       "      <td>largeDoses</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1         2           3\n",
       "0   0.513766  0.170320  0.262181   didntLike\n",
       "1   0.089599  0.154426  0.785277  smallDoses\n",
       "2   0.611167  0.172689  0.915245   didntLike\n",
       "3   0.012578  0.000000  0.195477  smallDoses\n",
       "4   0.110241  0.187926  0.287082  smallDoses\n",
       "..       ...       ...       ...         ...\n",
       "95  0.122106  0.163037  0.372224  smallDoses\n",
       "96  0.754287  0.476818  0.394621   didntLike\n",
       "97  0.291159  0.509103  0.510795  largeDoses\n",
       "98  0.527111  0.436655  0.429005  largeDoses\n",
       "99  0.479408  0.376809  0.785718  largeDoses\n",
       "\n",
       "[100 rows x 4 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(900, 4)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape #说明长宽"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 分类器针对约会网站进行测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "函数功能：KNN 算法分类器\n",
    "参数说明：\n",
    "返回结果：预测好分类的测试集\n",
    "\"\"\"\n",
    "def datingClass(train,test,k):\n",
    "    n = train.shape[1]-1 #列数减1把那个分类的去掉\n",
    "    m = test.shape[0]\n",
    "    result = []\n",
    "    for i in range(m):\n",
    "        dist = list((((train.iloc[:,:n]-test.iloc[i,:n])**2).sum(1))**0.5)\n",
    "        dist_l = pd.DataFrame({'dist':dist,'labels':train.iloc[:,n]})\n",
    "        dr = dist_l.sort_values(by = 'dist')[: k]\n",
    "        re = dr.loc[:, 'labels'].value_counts()\n",
    "        result.append(re.index[0])#将预测的结果连接上去\n",
    "        \n",
    "    result = pd.Series(result)\n",
    "    test['predict'] = result\n",
    "    acc = (test.iloc[:,-1]==test.iloc[:,-2]).mean()#是对所有的数据取均值，得出准确率\n",
    "    print('模型预测准确率为{acc}')\n",
    "    return test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型预测准确率为0.95\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-33-9e04e0e9fc58>:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test['predict'] = result\n"
     ]
    },
    {
     "data": {
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       "      <th>0</th>\n",
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       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>predict</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.513766</td>\n",
       "      <td>0.170320</td>\n",
       "      <td>0.262181</td>\n",
       "      <td>didntLike</td>\n",
       "      <td>didntLike</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.089599</td>\n",
       "      <td>0.154426</td>\n",
       "      <td>0.785277</td>\n",
       "      <td>smallDoses</td>\n",
       "      <td>smallDoses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.611167</td>\n",
       "      <td>0.172689</td>\n",
       "      <td>0.915245</td>\n",
       "      <td>didntLike</td>\n",
       "      <td>didntLike</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.012578</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.195477</td>\n",
       "      <td>smallDoses</td>\n",
       "      <td>smallDoses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.110241</td>\n",
       "      <td>0.187926</td>\n",
       "      <td>0.287082</td>\n",
       "      <td>smallDoses</td>\n",
       "      <td>smallDoses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>0.122106</td>\n",
       "      <td>0.163037</td>\n",
       "      <td>0.372224</td>\n",
       "      <td>smallDoses</td>\n",
       "      <td>smallDoses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0.754287</td>\n",
       "      <td>0.476818</td>\n",
       "      <td>0.394621</td>\n",
       "      <td>didntLike</td>\n",
       "      <td>didntLike</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>0.291159</td>\n",
       "      <td>0.509103</td>\n",
       "      <td>0.510795</td>\n",
       "      <td>largeDoses</td>\n",
       "      <td>largeDoses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0.527111</td>\n",
       "      <td>0.436655</td>\n",
       "      <td>0.429005</td>\n",
       "      <td>largeDoses</td>\n",
       "      <td>largeDoses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0.479408</td>\n",
       "      <td>0.376809</td>\n",
       "      <td>0.785718</td>\n",
       "      <td>largeDoses</td>\n",
       "      <td>largeDoses</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1         2           3     predict\n",
       "0   0.513766  0.170320  0.262181   didntLike   didntLike\n",
       "1   0.089599  0.154426  0.785277  smallDoses  smallDoses\n",
       "2   0.611167  0.172689  0.915245   didntLike   didntLike\n",
       "3   0.012578  0.000000  0.195477  smallDoses  smallDoses\n",
       "4   0.110241  0.187926  0.287082  smallDoses  smallDoses\n",
       "..       ...       ...       ...         ...         ...\n",
       "95  0.122106  0.163037  0.372224  smallDoses  smallDoses\n",
       "96  0.754287  0.476818  0.394621   didntLike   didntLike\n",
       "97  0.291159  0.509103  0.510795  largeDoses  largeDoses\n",
       "98  0.527111  0.436655  0.429005  largeDoses  largeDoses\n",
       "99  0.479408  0.376809  0.785718  largeDoses  largeDoses\n",
       "\n",
       "[100 rows x 5 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datingClass(train,test,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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