FM_Demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#http://files.grouplens.org/datasets/movielens/ml-1m.zip "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "path='/Users/luyao/Downloads/ml-1m/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/luyao/anaconda/envs/tfpy3/lib/python3.7/site-packages/ipykernel_launcher.py:2: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "  \n"
     ]
    },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>M</td>\n",
       "      <td>56</td>\n",
       "      <td>16</td>\n",
       "      <td>70072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
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       "      <td>25</td>\n",
       "      <td>15</td>\n",
       "      <td>55117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>M</td>\n",
       "      <td>45</td>\n",
       "      <td>7</td>\n",
       "      <td>02460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>M</td>\n",
       "      <td>25</td>\n",
       "      <td>20</td>\n",
       "      <td>55455</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   user_id gender  age  occupation    zip\n",
       "0        1      F    1          10  48067\n",
       "1        2      M   56          16  70072\n",
       "2        3      M   25          15  55117\n",
       "3        4      M   45           7  02460\n",
       "4        5      M   25          20  55455"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unames = ['user_id', 'gender', 'age', 'occupation', 'zip']\n",
    "users = pd.read_csv(path+'users.dat', sep='::', header=None, names=unames)\n",
    "users.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6040, 5)"
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     },
     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "users.shape"
   ]
  },
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   "execution_count": 6,
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   "outputs": [
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       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1193</td>\n",
       "      <td>5</td>\n",
       "      <td>978300760</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>661</td>\n",
       "      <td>3</td>\n",
       "      <td>978302109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>914</td>\n",
       "      <td>3</td>\n",
       "      <td>978301968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3408</td>\n",
       "      <td>4</td>\n",
       "      <td>978300275</td>\n",
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       "      <td>1</td>\n",
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       "      <td>5</td>\n",
       "      <td>978824291</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   user_id  movie_id  rating  timestamp\n",
       "0        1      1193       5  978300760\n",
       "1        1       661       3  978302109\n",
       "2        1       914       3  978301968\n",
       "3        1      3408       4  978300275\n",
       "4        1      2355       5  978824291"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnames = ['user_id', 'movie_id', 'rating', 'timestamp']\n",
    "ratings = pd.read_table(path+'ratings.dat', sep='::', header=None, names=rnames, engine='python')\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000209, 4)"
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "ratings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <th>movie_id</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children's|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   movie_id                               title                        genres\n",
       "0         1                    Toy Story (1995)   Animation|Children's|Comedy\n",
       "1         2                      Jumanji (1995)  Adventure|Children's|Fantasy\n",
       "2         3             Grumpier Old Men (1995)                Comedy|Romance\n",
       "3         4            Waiting to Exhale (1995)                  Comedy|Drama\n",
       "4         5  Father of the Bride Part II (1995)                        Comedy"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnames = ['movie_id', 'title', 'genres']\n",
    "movies = pd.read_table(path+'movies.dat', sep='::', header=None, names=mnames, engine='python')\n",
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3883, 3)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <th>user_id</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
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       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td>Drama</td>\n",
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       "      <th>1</th>\n",
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       "      <td>16</td>\n",
       "      <td>70072</td>\n",
       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td>Drama</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>1193</td>\n",
       "      <td>4</td>\n",
       "      <td>978220179</td>\n",
       "      <td>M</td>\n",
       "      <td>25</td>\n",
       "      <td>12</td>\n",
       "      <td>32793</td>\n",
       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td>Drama</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>1193</td>\n",
       "      <td>4</td>\n",
       "      <td>978199279</td>\n",
       "      <td>M</td>\n",
       "      <td>25</td>\n",
       "      <td>7</td>\n",
       "      <td>22903</td>\n",
       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>1193</td>\n",
       "      <td>5</td>\n",
       "      <td>978158471</td>\n",
       "      <td>M</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>95350</td>\n",
       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   user_id  movie_id  rating  timestamp gender  age  occupation    zip  \\\n",
       "0        1      1193       5  978300760      F    1          10  48067   \n",
       "1        2      1193       5  978298413      M   56          16  70072   \n",
       "2       12      1193       4  978220179      M   25          12  32793   \n",
       "3       15      1193       4  978199279      M   25           7  22903   \n",
       "4       17      1193       5  978158471      M   50           1  95350   \n",
       "\n",
       "                                    title genres  \n",
       "0  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "1  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "2  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "3  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "4  One Flew Over the Cuckoo's Nest (1975)  Drama  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#合并数据\n",
    "df = pd.merge(pd.merge(ratings, users, on='user_id'), movies, on='movie_id')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000209, 10)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    348971\n",
       "3    261197\n",
       "5    226310\n",
       "2    107557\n",
       "1     56174\n",
       "Name: rating, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rating'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(739012, 10)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#构造2分类数据集\n",
    "df=df[df['rating']!=3]\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "322"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据量比较大，单纯为了验证FM的效果，这里挑选有20～35次打分的movie_id\n",
    "movie_group=df.groupby('movie_id').size()\n",
    "movie_group=pd.Series(movie_group).where(lambda x : x<35).dropna()\n",
    "movie_list=pd.Series(movie_group).where(lambda x : x>20).dropna().index.values\n",
    "len(movie_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8635, 10)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df[df['movie_id'].isin(movie_list)]\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#这里挑选有>20次打分行为的user_id\n",
    "user_group=df.groupby('user_id').size()\n",
    "user_list=pd.Series(user_group).where(lambda x : x>20).dropna().index.values\n",
    "len(user_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1251, 10)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df[df['user_id'].isin(user_list)]\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(37, 317)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['user_id'].nunique(), df['movie_id'].nunique() #一共37个人，评分的总电影数是317部"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>occupation</th>\n",
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       "      <td>498</td>\n",
       "      <td>2</td>\n",
       "      <td>975254346</td>\n",
       "      <td>M</td>\n",
       "      <td>45</td>\n",
       "      <td>20</td>\n",
       "      <td>10024</td>\n",
       "      <td>Mr. Jones (1993)</td>\n",
       "      <td>Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169419</th>\n",
       "      <td>1150</td>\n",
       "      <td>498</td>\n",
       "      <td>1</td>\n",
       "      <td>974875561</td>\n",
       "      <td>F</td>\n",
       "      <td>25</td>\n",
       "      <td>20</td>\n",
       "      <td>75226</td>\n",
       "      <td>Mr. Jones (1993)</td>\n",
       "      <td>Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169421</th>\n",
       "      <td>1448</td>\n",
       "      <td>498</td>\n",
       "      <td>4</td>\n",
       "      <td>976552318</td>\n",
       "      <td>F</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>17522</td>\n",
       "      <td>Mr. Jones (1993)</td>\n",
       "      <td>Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169443</th>\n",
       "      <td>4169</td>\n",
       "      <td>498</td>\n",
       "      <td>2</td>\n",
       "      <td>976588339</td>\n",
       "      <td>M</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>66048</td>\n",
       "      <td>Mr. Jones (1993)</td>\n",
       "      <td>Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169455</th>\n",
       "      <td>5812</td>\n",
       "      <td>498</td>\n",
       "      <td>2</td>\n",
       "      <td>1041034564</td>\n",
       "      <td>F</td>\n",
       "      <td>25</td>\n",
       "      <td>7</td>\n",
       "      <td>92120</td>\n",
       "      <td>Mr. Jones (1993)</td>\n",
       "      <td>Drama|Romance</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        user_id  movie_id  rating   timestamp gender  age  occupation    zip  \\\n",
       "169417      889       498       2   975254346      M   45          20  10024   \n",
       "169419     1150       498       1   974875561      F   25          20  75226   \n",
       "169421     1448       498       4   976552318      F   25           3  17522   \n",
       "169443     4169       498       2   976588339      M   50           0  66048   \n",
       "169455     5812       498       2  1041034564      F   25           7  92120   \n",
       "\n",
       "                   title         genres  \n",
       "169417  Mr. Jones (1993)  Drama|Romance  \n",
       "169419  Mr. Jones (1993)  Drama|Romance  \n",
       "169421  Mr. Jones (1993)  Drama|Romance  \n",
       "169443  Mr. Jones (1993)  Drama|Romance  \n",
       "169455  Mr. Jones (1993)  Drama|Romance  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    443\n",
       "2    359\n",
       "1    276\n",
       "5    173\n",
       "Name: rating, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rating'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['rating']=df['rating'].map(lambda x: -1 if x>3 else 1) #1,2是label=1  4,5是label=0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>1%</th>\n",
       "      <th>5%</th>\n",
       "      <th>10%</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>90%</th>\n",
       "      <th>99%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1251.0</td>\n",
       "      <td>30.801759</td>\n",
       "      <td>10.550785</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      count       mean        std  min   1%    5%   10%   25%   50%   75%  \\\n",
       "age  1251.0  30.801759  10.550785  1.0  1.0  18.0  18.0  25.0  25.0  35.0   \n",
       "\n",
       "      90%   99%   max  \n",
       "age  50.0  50.0  50.0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#异常处理\n",
    "df[['age']].describe([0.01,0.05,0.1,0.25,0.5,0.75,0.9,0.99]).T #age是被分段后的，没有异常值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# - Age is chosen from the following ranges:\n",
    "\n",
    "# \t*  1:  \"Under 18\"\n",
    "# \t* 18:  \"18-24\"\n",
    "# \t* 25:  \"25-34\"\n",
    "# \t* 35:  \"35-44\"\n",
    "# \t* 45:  \"45-49\"\n",
    "# \t* 50:  \"50-55\"\n",
    "# \t* 56:  \"56+\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#时间处理\n",
    "# df['timestamp'] = pd.to_datetime(df['timestamp'], unit='s')\n",
    "# df['date'] = df['timestamp'].apply(lambda x: x.strftime('%Y%m%d'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1251 entries, 169417 to 999103\n",
      "Data columns (total 10 columns):\n",
      "user_id       1251 non-null int64\n",
      "movie_id      1251 non-null int64\n",
      "rating        1251 non-null int64\n",
      "timestamp     1251 non-null int64\n",
      "gender        1251 non-null object\n",
      "age           1251 non-null int64\n",
      "occupation    1251 non-null int64\n",
      "zip           1251 non-null object\n",
      "title         1251 non-null object\n",
      "genres        1251 non-null object\n",
      "dtypes: int64(6), object(4)\n",
      "memory usage: 107.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
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       "      <th>201</th>\n",
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       "      <th>214</th>\n",
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       "      <th>240</th>\n",
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       "      <th>347</th>\n",
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       "      <th>386</th>\n",
       "      <th>391</th>\n",
       "      <th>409</th>\n",
       "      <th>418</th>\n",
       "      <th>422</th>\n",
       "      <th>433</th>\n",
       "      <th>443</th>\n",
       "      <th>452</th>\n",
       "      <th>453</th>\n",
       "      <th>460</th>\n",
       "      <th>463</th>\n",
       "      <th>470</th>\n",
       "      <th>472</th>\n",
       "      <th>476</th>\n",
       "      <th>488</th>\n",
       "      <th>495</th>\n",
       "      <th>496</th>\n",
       "      <th>498</th>\n",
       "      <th>499</th>\n",
       "      <th>510</th>\n",
       "      <th>568</th>\n",
       "      <th>571</th>\n",
       "      <th>583</th>\n",
       "      <th>600</th>\n",
       "      <th>602</th>\n",
       "      <th>619</th>\n",
       "      <th>621</th>\n",
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       "      <th>665</th>\n",
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       "      <th>841</th>\n",
       "      <th>846</th>\n",
       "      <th>862</th>\n",
       "      <th>869</th>\n",
       "      <th>875</th>\n",
       "      <th>885</th>\n",
       "      <th>956</th>\n",
       "      <th>957</th>\n",
       "      <th>964</th>\n",
       "      <th>970</th>\n",
       "      <th>984</th>\n",
       "      <th>990</th>\n",
       "      <th>1044</th>\n",
       "      <th>1053</th>\n",
       "      <th>1068</th>\n",
       "      <th>1112</th>\n",
       "      <th>1113</th>\n",
       "      <th>1119</th>\n",
       "      <th>1123</th>\n",
       "      <th>1151</th>\n",
       "      <th>1169</th>\n",
       "      <th>1310</th>\n",
       "      <th>1312</th>\n",
       "      <th>1317</th>\n",
       "      <th>1322</th>\n",
       "      <th>1325</th>\n",
       "      <th>1329</th>\n",
       "      <th>1331</th>\n",
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       "      <th>1415</th>\n",
       "      <th>1417</th>\n",
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       "      <th>1473</th>\n",
       "      <th>1489</th>\n",
       "      <th>1490</th>\n",
       "      <th>1507</th>\n",
       "      <th>1508</th>\n",
       "      <th>1529</th>\n",
       "      <th>1547</th>\n",
       "      <th>1551</th>\n",
       "      <th>1563</th>\n",
       "      <th>1564</th>\n",
       "      <th>1585</th>\n",
       "      <th>1596</th>\n",
       "      <th>1602</th>\n",
       "      <th>1609</th>\n",
       "      <th>1650</th>\n",
       "      <th>1668</th>\n",
       "      <th>1669</th>\n",
       "      <th>1677</th>\n",
       "      <th>1695</th>\n",
       "      <th>1696</th>\n",
       "      <th>1755</th>\n",
       "      <th>1771</th>\n",
       "      <th>1791</th>\n",
       "      <th>1798</th>\n",
       "      <th>1809</th>\n",
       "      <th>1812</th>\n",
       "      <th>1826</th>\n",
       "      <th>1829</th>\n",
       "      <th>1844</th>\n",
       "      <th>1854</th>\n",
       "      <th>1859</th>\n",
       "      <th>1867</th>\n",
       "      <th>1869</th>\n",
       "      <th>1896</th>\n",
       "      <th>1902</th>\n",
       "      <th>1910</th>\n",
       "      <th>1922</th>\n",
       "      <th>1926</th>\n",
       "      <th>1932</th>\n",
       "      <th>2007</th>\n",
       "      <th>2031</th>\n",
       "      <th>2032</th>\n",
       "      <th>2044</th>\n",
       "      <th>2045</th>\n",
       "      <th>2049</th>\n",
       "      <th>2103</th>\n",
       "      <th>2104</th>\n",
       "      <th>2131</th>\n",
       "      <th>2179</th>\n",
       "      <th>2190</th>\n",
       "      <th>2205</th>\n",
       "      <th>2237</th>\n",
       "      <th>2284</th>\n",
       "      <th>2286</th>\n",
       "      <th>2307</th>\n",
       "      <th>2323</th>\n",
       "      <th>2341</th>\n",
       "      <th>2345</th>\n",
       "      <th>2400</th>\n",
       "      <th>2426</th>\n",
       "      <th>2449</th>\n",
       "      <th>2465</th>\n",
       "      <th>2477</th>\n",
       "      <th>2479</th>\n",
       "      <th>2481</th>\n",
       "      <th>2483</th>\n",
       "      <th>2493</th>\n",
       "      <th>2494</th>\n",
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       "      <th>2579</th>\n",
       "      <th>2586</th>\n",
       "      <th>2593</th>\n",
       "      <th>2610</th>\n",
       "      <th>2620</th>\n",
       "      <th>2626</th>\n",
       "      <th>2630</th>\n",
       "      <th>2635</th>\n",
       "      <th>2638</th>\n",
       "      <th>2646</th>\n",
       "      <th>2653</th>\n",
       "      <th>2659</th>\n",
       "      <th>2663</th>\n",
       "      <th>2666</th>\n",
       "      <th>2681</th>\n",
       "      <th>2689</th>\n",
       "      <th>2704</th>\n",
       "      <th>2711</th>\n",
       "      <th>2744</th>\n",
       "      <th>2758</th>\n",
       "      <th>2766</th>\n",
       "      <th>2810</th>\n",
       "      <th>2812</th>\n",
       "      <th>2813</th>\n",
       "      <th>2835</th>\n",
       "      <th>2847</th>\n",
       "      <th>2849</th>\n",
       "      <th>2864</th>\n",
       "      <th>2876</th>\n",
       "      <th>2878</th>\n",
       "      <th>2884</th>\n",
       "      <th>2893</th>\n",
       "      <th>2938</th>\n",
       "      <th>2939</th>\n",
       "      <th>2977</th>\n",
       "      <th>2979</th>\n",
       "      <th>2982</th>\n",
       "      <th>2992</th>\n",
       "      <th>3002</th>\n",
       "      <th>3010</th>\n",
       "      <th>3014</th>\n",
       "      <th>3021</th>\n",
       "      <th>3073</th>\n",
       "      <th>3109</th>\n",
       "      <th>3124</th>\n",
       "      <th>3128</th>\n",
       "      <th>3132</th>\n",
       "      <th>3133</th>\n",
       "      <th>3143</th>\n",
       "      <th>3144</th>\n",
       "      <th>3158</th>\n",
       "      <th>3192</th>\n",
       "      <th>3218</th>\n",
       "      <th>3222</th>\n",
       "      <th>3223</th>\n",
       "      <th>3239</th>\n",
       "      <th>3276</th>\n",
       "      <th>3306</th>\n",
       "      <th>3311</th>\n",
       "      <th>3318</th>\n",
       "      <th>3329</th>\n",
       "      <th>3331</th>\n",
       "      <th>3338</th>\n",
       "      <th>3339</th>\n",
       "      <th>3343</th>\n",
       "      <th>3367</th>\n",
       "      <th>3389</th>\n",
       "      <th>3390</th>\n",
       "      <th>3395</th>\n",
       "      <th>3414</th>\n",
       "      <th>3434</th>\n",
       "      <th>3442</th>\n",
       "      <th>3446</th>\n",
       "      <th>3454</th>\n",
       "      <th>3496</th>\n",
       "      <th>3518</th>\n",
       "      <th>3520</th>\n",
       "      <th>3539</th>\n",
       "      <th>3540</th>\n",
       "      <th>3559</th>\n",
       "      <th>3569</th>\n",
       "      <th>3573</th>\n",
       "      <th>3586</th>\n",
       "      <th>3588</th>\n",
       "      <th>3597</th>\n",
       "      <th>3602</th>\n",
       "      <th>3605</th>\n",
       "      <th>3612</th>\n",
       "      <th>3620</th>\n",
       "      <th>3632</th>\n",
       "      <th>3659</th>\n",
       "      <th>3665</th>\n",
       "      <th>3667</th>\n",
       "      <th>3673</th>\n",
       "      <th>3674</th>\n",
       "      <th>3691</th>\n",
       "      <th>3711</th>\n",
       "      <th>3719</th>\n",
       "      <th>3731</th>\n",
       "      <th>3736</th>\n",
       "      <th>3739</th>\n",
       "      <th>3765</th>\n",
       "      <th>3780</th>\n",
       "      <th>3781</th>\n",
       "      <th>3782</th>\n",
       "      <th>3797</th>\n",
       "      <th>3805</th>\n",
       "      <th>3806</th>\n",
       "      <th>3816</th>\n",
       "      <th>3820</th>\n",
       "      <th>3838</th>\n",
       "      <th>3851</th>\n",
       "      <th>3855</th>\n",
       "      <th>3870</th>\n",
       "      <th>3884</th>\n",
       "      <th>3885</th>\n",
       "      <th>3886</th>\n",
       "      <th>3898</th>\n",
       "      <th>3900</th>\n",
       "      <th>3912</th>\n",
       "      <th>3913</th>\n",
       "      <th>3921</th>\n",
       "      <th>3922</th>\n",
       "      <th>3931</th>\n",
       "      <th>3938</th>\n",
       "      <th>3942</th>\n",
       "      <th>3951</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>169417</th>\n",
       "      <td>1</td>\n",
       "      <td>975254346</td>\n",
       "      <td>M</td>\n",
       "      <td>45</td>\n",
       "      <td>20</td>\n",
       "      <td>10024</td>\n",
       "      <td>Mr. Jones (1993)</td>\n",
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       "    <tr>\n",
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       "      <td>F</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169421</th>\n",
       "      <td>-1</td>\n",
       "      <td>976552318</td>\n",
       "      <td>F</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>17522</td>\n",
       "      <td>Mr. Jones (1993)</td>\n",
       "      <td>Drama|Romance</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169443</th>\n",
       "      <td>1</td>\n",
       "      <td>976588339</td>\n",
       "      <td>M</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        rating   timestamp gender  age  occupation    zip             title  \\\n",
       "169417       1   975254346      M   45          20  10024  Mr. Jones (1993)   \n",
       "169419       1   974875561      F   25          20  75226  Mr. Jones (1993)   \n",
       "169421      -1   976552318      F   25           3  17522  Mr. Jones (1993)   \n",
       "169443       1   976588339      M   50           0  66048  Mr. Jones (1993)   \n",
       "169455       1  1041034564      F   25           7  92120  Mr. Jones (1993)   \n",
       "\n",
       "               genres  195  752  850  889  1069  1150  1181  1242  1266  1448  \\\n",
       "169417  Drama|Romance    0    0    0    1     0     0     0     0     0     0   \n",
       "169419  Drama|Romance    0    0    0    0     0     1     0     0     0     0   \n",
       "169421  Drama|Romance    0    0    0    0     0     0     0     0     0     1   \n",
       "169443  Drama|Romance    0    0    0    0     0     0     0     0     0     0   \n",
       "169455  Drama|Romance    0    0    0    0     0     0     0     0     0     0   \n",
       "\n",
       "        1470  1680  1698  1733  1941  2106  2665  2909  3032  3224  3391  \\\n",
       "169417     0     0     0     0     0     0     0     0     0     0     0   \n",
       "169419     0     0     0     0     0     0     0     0     0     0     0   \n",
       "169421     0     0     0     0     0     0     0     0     0     0     0   \n",
       "169443     0     0     0     0     0     0     0     0     0     0     0   \n",
       "169455     0     0     0     0     0     0     0     0     0     0     0   \n",
       "\n",
       "        3476  3526  3539  3610  3829  4161  4169  4227  4277  4344  4510  \\\n",
       "169417     0     0     0     0     0     0     0     0     0     0     0   \n",
       "169419     0     0     0     0     0     0     0     0     0     0     0   \n",
       "169421     0     0     0     0     0     0     0     0     0     0     0   \n",
       "169443     0     0     0     0     0     0     1     0     0     0     0   \n",
       "169455     0     0     0     0     0     0     0     0     0     0     0   \n",
       "\n",
       "        4647  5074  5111  5795  5812  27  38  40  54  55  77  83  99  103  \\\n",
       "169417     0     0     0     0     0   0   0   0   0   0   0   0   0    0   \n",
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       "        269  278  294  297  301  304  320  324  335  341  343  347  384  386  \\\n",
       "169417    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
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       "\n",
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       "        1053  1068  1112  1113  1119  1123  1151  1169  1310  1312  1317  \\\n",
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       "        1322  1325  1329  1331  1413  1415  1417  1428  1439  1447  1463  \\\n",
       "169417     0     0     0     0     0     0     0     0     0     0     0   \n",
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       "        1472  1473  1489  1490  1507  1508  1529  1547  1551  1563  1564  \\\n",
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       "\n",
       "        1869  1896  1902  1910  1922  1926  1932  2007  2031  2032  2044  \\\n",
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       "        3659  3665  3667  3673  3674  3691  3711  3719  3731  3736  3739  \\\n",
       "169417     0     0     0     0     0     0     0     0     0     0     0   \n",
       "169419     0     0     0     0     0     0     0     0     0     0     0   \n",
       "169421     0     0     0     0     0     0     0     0     0     0     0   \n",
       "169443     0     0     0     0     0     0     0     0     0     0     0   \n",
       "169455     0     0     0     0     0     0     0     0     0     0     0   \n",
       "\n",
       "        3765  3780  3781  3782  3797  3805  3806  3816  3820  3838  3851  \\\n",
       "169417     0     0     0     0     0     0     0     0     0     0     0   \n",
       "169419     0     0     0     0     0     0     0     0     0     0     0   \n",
       "169421     0     0     0     0     0     0     0     0     0     0     0   \n",
       "169443     0     0     0     0     0     0     0     0     0     0     0   \n",
       "169455     0     0     0     0     0     0     0     0     0     0     0   \n",
       "\n",
       "        3855  3870  3884  3885  3886  3898  3900  3912  3913  3921  3922  \\\n",
       "169417     0     0     0     0     0     0     0     0     0     0     0   \n",
       "169419     0     0     0     0     0     0     0     0     0     0     0   \n",
       "169421     0     0     0     0     0     0     0     0     0     0     0   \n",
       "169443     0     0     0     0     0     0     0     0     0     0     0   \n",
       "169455     0     0     0     0     0     0     0     0     0     0     0   \n",
       "\n",
       "        3931  3938  3942  3951  \n",
       "169417     0     0     0     0  \n",
       "169419     0     0     0     0  \n",
       "169421     0     0     0     0  \n",
       "169443     0     0     0     0  \n",
       "169455     0     0     0     0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#one-hot encoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "columns=['user_id', 'movie_id']\n",
    "\n",
    "for i in columns:\n",
    "    get_dummy_feature=pd.get_dummies(df[i])\n",
    "    df=pd.concat([df, get_dummy_feature],axis=1)\n",
    "    df=df.drop(i, axis=1)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1251, 362)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#label encoder\n",
    "# from sklearn.preprocessing import LabelEncoder\n",
    "# columns =['gender','zip','genres']\n",
    "\n",
    "# for i in columns:\n",
    "#     le = LabelEncoder()\n",
    "#     df[i]=le.fit_transform(df[i])\n",
    "\n",
    "# df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop(['timestamp','gender','age','occupation','zip','title','genres'], axis=1) \n",
    "#这些特征可以进一步挖掘。这里都不要了，只保留one-hot特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((875, 354), (376, 354))"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X=df.drop('rating', axis=1)\n",
    "Y=df['rating']\n",
    "\n",
    "X_train,X_val,Y_train,Y_val=train_test_split(X, Y, test_size=0.3, random_state=123)\n",
    "X_train.shape, X_val.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 1    635\n",
       "-1    616\n",
       "Name: rating, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.value_counts()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FM model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid(x):\n",
    "    return 1 / (1 + np.exp(-x))\n",
    "\n",
    "def logit(y, y_hat): #对每一个样本计算损失\n",
    "    return np.log(1 + np.exp(-y * y_hat))\n",
    "\n",
    "def df_logit(y, y_hat):\n",
    "    return sigmoid(-y * y_hat) * (-y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator,ClassifierMixin\n",
    "from collections import Counter\n",
    "\n",
    "\n",
    "class FactorizationMachine(BaseEstimator):\n",
    "    def __init__(self, k=5, learning_rate=0.01, iternum=100): \n",
    "        self.w0 = None \n",
    "        self.W = None \n",
    "        self.V = None \n",
    "        self.k = k \n",
    "        self.alpha = learning_rate\n",
    "        self.iternum = iternum\n",
    "        \n",
    "        \n",
    "    def _FM(self, Xi):\n",
    "        interaction = np.sum((Xi.dot(self.V)) ** 2 - (Xi ** 2).dot(self.V ** 2)) \n",
    "        y_hat = self.w0 + Xi.dot(self.W) + interaction / 2  \n",
    "        return y_hat[0]\n",
    "        \n",
    "        \n",
    "    def _FM_SGD(self, X, y):\n",
    "        m, n = np.shape(X) \n",
    "        #初始化参数\n",
    "        self.w0 = 0\n",
    "        self.W = np.random.uniform(size=(n, 1))\n",
    "        self.V = np.random.uniform(size=(n, self.k)) #Vj是第j个特征的隐向量  Vjf是第j个特征的隐向量表示中的第f维\n",
    "        \n",
    "        for it in range(self.iternum):\n",
    "            total_loss = 0  \n",
    "            for i in range(m):  # 遍历训练集\n",
    "                y_hat = self._FM(Xi=X[i])  #X[i]是第i个样本  X[i,j]是第i个样本的第j个特征\n",
    "\n",
    "                total_loss += logit(y=y[i], y_hat=y_hat)  # 计算logit损失函数值\n",
    "                dloss = df_logit(y=y[i], y_hat=y_hat)  # 计算logit损失函数的外层偏导\n",
    "\n",
    "                dloss_w0 = dloss * 1   # 公式中的w0求导，计算复杂度O(1)\n",
    "                self.w0 = self.w0 - self.alpha * dloss_w0 \n",
    "\n",
    "                for j in range(n): \n",
    "                    if X[i, j] != 0:\n",
    "                        dloss_Wj = dloss * X[i, j]  # 公式中的wi求导，计算复杂度O(n)\n",
    "                        self.W[j] = self.W[j] - self.alpha * dloss_Wj \n",
    "                        for f in range(self.k):  # 公式中的vif求导，计算复杂度O(kn)\n",
    "                            dloss_Vjf = dloss * (X[i, j] * (X[i].dot(self.V[:, f])) - self.V[j, f] * X[i, j] ** 2)\n",
    "                            self.V[j, f] = self.V[j, f] - self.alpha * dloss_Vjf \n",
    "\n",
    "            if it %20==0:\n",
    "                print('iter={}, loss={:.4f}'.format(it, total_loss / m))\n",
    "\n",
    "        return self\n",
    "    \n",
    "    \n",
    "    def _FM_predict(self, X):\n",
    "        predicts, threshold = [], 0.5  # sigmoid阈值设置\n",
    "        for i in range(X.shape[0]):  # 遍历测试集\n",
    "            y_hat = self._FM(Xi=X[i])  # FM的模型方程 \n",
    "            predicts.append(-1 if sigmoid(y_hat) < threshold else 1) \n",
    "        return np.array(predicts)        \n",
    "        \n",
    "        \n",
    "    def fit(self, X, y):\n",
    "        if isinstance(X, pd.DataFrame):\n",
    "            X = np.array(X)\n",
    "            y = np.array(y)\n",
    "            \n",
    "        return self._FM_SGD(X, y)\n",
    "\n",
    "    \n",
    "    def predict(self, X):\n",
    "        if isinstance(X, pd.DataFrame):\n",
    "            X = np.array(X)\n",
    "        \n",
    "        return self._FM_predict(X)\n",
    "    \n",
    "    \n",
    "    def predict_proba(self, X):\n",
    "        pass\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train & Predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter=0, loss=1.6656\n",
      "iter=20, loss=0.6065\n",
      "iter=40, loss=0.5190\n",
      "iter=60, loss=0.4686\n",
      "iter=80, loss=0.4349\n",
      "iter=100, loss=0.4099\n",
      "iter=120, loss=0.3899\n",
      "iter=140, loss=0.3730\n",
      "CPU times: user 2min 23s, sys: 1.92 s, total: 2min 25s\n",
      "Wall time: 2min 26s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "FactorizationMachine(iternum=150, k=10, learning_rate=None)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "model=FactorizationMachine(k=10, learning_rate=0.001, iternum=150)\n",
    "model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集roc: 84.81%\n",
      "混淆矩阵: \n",
      " [[361  63]\n",
      " [ 70 381]]\n"
     ]
    }
   ],
   "source": [
    "y_pred=model.predict(X_train)\n",
    "\n",
    "print('训练集roc: {:.2%}'.format(roc_auc_score(Y_train.values, y_pred)))\n",
    "print('混淆矩阵: \\n',confusion_matrix(Y_train.values, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "验证集roc: 73.79%\n",
      "混淆矩阵: \n",
      " [[131  61]\n",
      " [ 38 146]]\n"
     ]
    }
   ],
   "source": [
    "y_pred=model.predict(X_val)\n",
    "\n",
    "print('验证集roc: {:.2%}'.format(roc_auc_score(Y_val.values, y_pred)))\n",
    "print('混淆矩阵: \\n',confusion_matrix(Y_val.values, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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