Code/未命名.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import math\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>artist_name</th>\n",
       "      <th>artist_id</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>valence</th>\n",
       "      <th>tempo</th>\n",
       "      <th>loudness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>speechiness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Frank Sinatra</td>\n",
       "      <td>792507</td>\n",
       "      <td>0.384478</td>\n",
       "      <td>0.238017</td>\n",
       "      <td>0.364288</td>\n",
       "      <td>110.181698</td>\n",
       "      <td>-14.271141</td>\n",
       "      <td>0.735648</td>\n",
       "      <td>0.020855</td>\n",
       "      <td>0.232106</td>\n",
       "      <td>0.049614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Vladimir Horowitz</td>\n",
       "      <td>119107</td>\n",
       "      <td>0.343210</td>\n",
       "      <td>0.118844</td>\n",
       "      <td>0.225951</td>\n",
       "      <td>94.900679</td>\n",
       "      <td>-23.193418</td>\n",
       "      <td>0.990070</td>\n",
       "      <td>0.879508</td>\n",
       "      <td>0.183812</td>\n",
       "      <td>0.043360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Johnny Cash</td>\n",
       "      <td>816890</td>\n",
       "      <td>0.619803</td>\n",
       "      <td>0.449381</td>\n",
       "      <td>0.680662</td>\n",
       "      <td>115.037747</td>\n",
       "      <td>-11.593104</td>\n",
       "      <td>0.685637</td>\n",
       "      <td>0.022647</td>\n",
       "      <td>0.242243</td>\n",
       "      <td>0.098216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Billie Holiday</td>\n",
       "      <td>79016</td>\n",
       "      <td>0.572637</td>\n",
       "      <td>0.201368</td>\n",
       "      <td>0.498934</td>\n",
       "      <td>109.912172</td>\n",
       "      <td>-13.225966</td>\n",
       "      <td>0.908499</td>\n",
       "      <td>0.013064</td>\n",
       "      <td>0.217727</td>\n",
       "      <td>0.062432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bob Dylan</td>\n",
       "      <td>66915</td>\n",
       "      <td>0.512598</td>\n",
       "      <td>0.477932</td>\n",
       "      <td>0.551934</td>\n",
       "      <td>126.160149</td>\n",
       "      <td>-11.184330</td>\n",
       "      <td>0.562567</td>\n",
       "      <td>0.034211</td>\n",
       "      <td>0.308978</td>\n",
       "      <td>0.064535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5849</th>\n",
       "      <td>Natalie La Rose</td>\n",
       "      <td>3359519</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.735000</td>\n",
       "      <td>104.990000</td>\n",
       "      <td>-8.714000</td>\n",
       "      <td>0.000792</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.065600</td>\n",
       "      <td>0.037600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5850</th>\n",
       "      <td>Sarah Ross</td>\n",
       "      <td>3381566</td>\n",
       "      <td>0.721000</td>\n",
       "      <td>0.944000</td>\n",
       "      <td>0.626000</td>\n",
       "      <td>85.002000</td>\n",
       "      <td>-5.982000</td>\n",
       "      <td>0.013000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>0.159000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5851</th>\n",
       "      <td>Rotimi</td>\n",
       "      <td>3410250</td>\n",
       "      <td>0.637000</td>\n",
       "      <td>0.501000</td>\n",
       "      <td>0.431000</td>\n",
       "      <td>103.993000</td>\n",
       "      <td>-6.148000</td>\n",
       "      <td>0.229000</td>\n",
       "      <td>0.000059</td>\n",
       "      <td>0.099000</td>\n",
       "      <td>0.187000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5852</th>\n",
       "      <td>Jillian Jacqueline</td>\n",
       "      <td>3455945</td>\n",
       "      <td>0.547000</td>\n",
       "      <td>0.672000</td>\n",
       "      <td>0.283000</td>\n",
       "      <td>155.791000</td>\n",
       "      <td>-5.023000</td>\n",
       "      <td>0.304000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.099600</td>\n",
       "      <td>0.049600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5853</th>\n",
       "      <td>Jaira Burns</td>\n",
       "      <td>3639618</td>\n",
       "      <td>0.566000</td>\n",
       "      <td>0.769000</td>\n",
       "      <td>0.385000</td>\n",
       "      <td>170.036000</td>\n",
       "      <td>-4.342000</td>\n",
       "      <td>0.018300</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.108000</td>\n",
       "      <td>0.087200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5854 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             artist_name  artist_id  danceability    energy   valence  \\\n",
       "0          Frank Sinatra     792507      0.384478  0.238017  0.364288   \n",
       "1      Vladimir Horowitz     119107      0.343210  0.118844  0.225951   \n",
       "2            Johnny Cash     816890      0.619803  0.449381  0.680662   \n",
       "3         Billie Holiday      79016      0.572637  0.201368  0.498934   \n",
       "4              Bob Dylan      66915      0.512598  0.477932  0.551934   \n",
       "...                  ...        ...           ...       ...       ...   \n",
       "5849     Natalie La Rose    3359519      0.830000  0.520000  0.735000   \n",
       "5850          Sarah Ross    3381566      0.721000  0.944000  0.626000   \n",
       "5851              Rotimi    3410250      0.637000  0.501000  0.431000   \n",
       "5852  Jillian Jacqueline    3455945      0.547000  0.672000  0.283000   \n",
       "5853         Jaira Burns    3639618      0.566000  0.769000  0.385000   \n",
       "\n",
       "           tempo   loudness  acousticness  instrumentalness  liveness  \\\n",
       "0     110.181698 -14.271141      0.735648          0.020855  0.232106   \n",
       "1      94.900679 -23.193418      0.990070          0.879508  0.183812   \n",
       "2     115.037747 -11.593104      0.685637          0.022647  0.242243   \n",
       "3     109.912172 -13.225966      0.908499          0.013064  0.217727   \n",
       "4     126.160149 -11.184330      0.562567          0.034211  0.308978   \n",
       "...          ...        ...           ...               ...       ...   \n",
       "5849  104.990000  -8.714000      0.000792          0.000013  0.065600   \n",
       "5850   85.002000  -5.982000      0.013000          0.000000  0.320000   \n",
       "5851  103.993000  -6.148000      0.229000          0.000059  0.099000   \n",
       "5852  155.791000  -5.023000      0.304000          0.000000  0.099600   \n",
       "5853  170.036000  -4.342000      0.018300          0.000000  0.108000   \n",
       "\n",
       "      speechiness  \n",
       "0        0.049614  \n",
       "1        0.043360  \n",
       "2        0.098216  \n",
       "3        0.062432  \n",
       "4        0.064535  \n",
       "...           ...  \n",
       "5849     0.037600  \n",
       "5850     0.159000  \n",
       "5851     0.187000  \n",
       "5852     0.049600  \n",
       "5853     0.087200  \n",
       "\n",
       "[5854 rows x 11 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "influence_pd=pd.read_csv('influence_data.csv')\n",
    "artist_pd=pd.read_csv('data_by_artist.csv')\n",
    "artist_pd=artist_pd.drop(['mode','key','duration_ms','popularity','count'],axis=1)\n",
    "artist_pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in artist_pd.columns:\n",
    "    if(col=='artist_name' or col=='artist_id'): continue\n",
    "    artist_pd[col]=(artist_pd[col]-artist_pd[col].mean())/(artist_pd[col].std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "artist_attr={}\n",
    "for i in range(artist_pd.shape[0]):\n",
    "    artist_attr[artist_pd.loc[i].artist_id]=artist_pd.loc[i].drop('artist_id').to_dict()\n",
    "restore={}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-6-18d2589045c2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     35\u001b[0m     \u001b[1;32mif\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist_attr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;32mcontinue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     36\u001b[0m     \u001b[0mscore\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m+=\u001b[0m\u001b[0mcalc1\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mu\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 37\u001b[1;33m     \u001b[0mscore\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m+=\u001b[0m\u001b[0mcalc2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mu\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-6-18d2589045c2>\u001b[0m in \u001b[0;36mcalc2\u001b[1;34m(u, v)\u001b[0m\n\u001b[0;32m     20\u001b[0m     \u001b[0mcnt\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist_pd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 22\u001b[1;33m         \u001b[0mvid\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0martist_pd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0martist_id\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     23\u001b[0m         \u001b[1;32mif\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvid\u001b[0m\u001b[1;33m==\u001b[0m\u001b[0mu\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mvid\u001b[0m\u001b[1;33m==\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;32mcontinue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     24\u001b[0m         \u001b[0mtmp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcalc1\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mu\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mvid\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\environment_plus\\conda\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    877\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    878\u001b[0m             \u001b[0mmaybe_callable\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 879\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    880\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    881\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mTuple\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\environment_plus\\conda\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1108\u001b[0m         \u001b[1;31m# fall thru to straight lookup\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1109\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_key\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1110\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_label\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1111\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1112\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_slice_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mslice_obj\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mslice\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\environment_plus\\conda\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_get_label\u001b[1;34m(self, label, axis)\u001b[0m\n\u001b[0;32m   1057\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_label\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1058\u001b[0m         \u001b[1;31m# GH#5667 this will fail if the label is not present in the axis.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1059\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1060\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1061\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_handle_lowerdim_multi_index_axis0\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mTuple\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\environment_plus\\conda\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mxs\u001b[1;34m(self, key, axis, level, drop_level)\u001b[0m\n\u001b[0;32m   3515\u001b[0m                 \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3516\u001b[0m                 \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3517\u001b[1;33m                 \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnew_values\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3518\u001b[0m             )\n\u001b[0;32m   3519\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\environment_plus\\conda\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, data, index, dtype, name, copy, fastpath)\u001b[0m\n\u001b[0;32m    327\u001b[0m                 \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msanitize_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mraise_cast_failure\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    328\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 329\u001b[1;33m                 \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSingleBlockManager\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrom_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    330\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    331\u001b[0m         \u001b[0mgeneric\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mNDFrame\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\environment_plus\\conda\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36mfrom_array\u001b[1;34m(cls, array, index)\u001b[0m\n\u001b[0;32m   1567\u001b[0m         \u001b[0mConstructor\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mwe\u001b[0m \u001b[0mhave\u001b[0m \u001b[0man\u001b[0m \u001b[0marray\u001b[0m \u001b[0mthat\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0myet\u001b[0m \u001b[0ma\u001b[0m \u001b[0mBlock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1568\u001b[0m         \"\"\"\n\u001b[1;32m-> 1569\u001b[1;33m         \u001b[0mblock\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmake_block\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mplacement\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mslice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mndim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1570\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1571\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\environment_plus\\conda\\lib\\site-packages\\pandas\\core\\internals\\blocks.py\u001b[0m in \u001b[0;36mmake_block\u001b[1;34m(values, placement, klass, ndim, dtype)\u001b[0m\n\u001b[0;32m   2713\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mklass\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2714\u001b[0m         \u001b[0mdtype\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdtype\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2715\u001b[1;33m         \u001b[0mklass\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_block_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2716\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2717\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mklass\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mDatetimeTZBlock\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mis_datetime64tz_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\environment_plus\\conda\\lib\\site-packages\\pandas\\core\\internals\\blocks.py\u001b[0m in \u001b[0;36mget_block_type\u001b[1;34m(values, dtype)\u001b[0m\n\u001b[0;32m   2679\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mis_datetime64tz_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2680\u001b[0m         \u001b[0mcls\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mDatetimeTZBlock\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2681\u001b[1;33m     \u001b[1;32melif\u001b[0m \u001b[0mis_interval_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mis_period_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2682\u001b[0m         \u001b[0mcls\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mObjectValuesExtensionBlock\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2683\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mis_extension_array_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\environment_plus\\conda\\lib\\site-packages\\pandas\\core\\dtypes\\common.py\u001b[0m in \u001b[0;36mis_period_dtype\u001b[1;34m(arr_or_dtype)\u001b[0m\n\u001b[0;32m    487\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0marr_or_dtype\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    488\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 489\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mPeriodDtype\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marr_or_dtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    490\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    491\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\environment_plus\\conda\\lib\\site-packages\\pandas\\core\\dtypes\\dtypes.py\u001b[0m in \u001b[0;36mis_dtype\u001b[1;34m(cls, dtype)\u001b[0m\n\u001b[0;32m    920\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    921\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 922\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    923\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    924\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\environment_plus\\conda\\lib\\site-packages\\pandas\\core\\dtypes\\base.py\u001b[0m in \u001b[0;36mis_dtype\u001b[1;34m(cls, dtype)\u001b[0m\n\u001b[0;32m    280\u001b[0m         \u001b[0mdtype\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"dtype\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    281\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 282\u001b[1;33m         \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mABCSeries\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mABCIndexClass\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mABCDataFrame\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    283\u001b[0m             \u001b[1;31m# https://github.com/pandas-dev/pandas/issues/22960\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    284\u001b[0m             \u001b[1;31m# avoid passing data to `construct_from_string`. This could\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\environment_plus\\conda\\lib\\site-packages\\pandas\\core\\dtypes\\generic.py\u001b[0m in \u001b[0;36m_check\u001b[1;34m(cls, inst)\u001b[0m\n\u001b[0;32m     10\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mclassmethod\u001b[0m  \u001b[1;31m# type: ignore\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_check\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minst\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mbool\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 12\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minst\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mattr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"_typ\"\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcomp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m     \u001b[0mdct\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m__instancecheck__\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0m_check\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m__subclasscheck__\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0m_check\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "def calc1(u,v):\n",
    "    if(restore.setdefault(u)==None): restore[u]={}\n",
    "    if(restore.setdefault(v)==None): restore[v]={}\n",
    "    if(restore[u].setdefault(v)==None):\n",
    "        atu=artist_attr[u]\n",
    "        atv=artist_attr[v]\n",
    "        n,sum=0,0\n",
    "        for key in atu.keys():\n",
    "            if(key=='artist_name'): continue\n",
    "            n+=1\n",
    "            sum+=math.pow((atu[key]-atv[key]),2)\n",
    "        if(sum==0): return 0\n",
    "        w=1/math.sqrt(sum/n)\n",
    "        restore[u][v]=w\n",
    "        restore[v][u]=w\n",
    "        return w\n",
    "    else:\n",
    "        return restore[u][v]\n",
    "def calc2(u,v):\n",
    "    cnt,w=0,0\n",
    "    for i in range(artist_pd.shape[0]):\n",
    "        vid=artist_pd.loc[i].artist_id\n",
    "        if(vid==u or vid==v): continue\n",
    "        tmp=calc1(u,vid)\n",
    "        if(tmp!=0):\n",
    "            cnt+=1\n",
    "            w+=tmp\n",
    "    return w/cnt\n",
    "\n",
    "score=[0,0]\n",
    "for i in range(influence_pd.shape[0]):\n",
    "    if(i%100==0): print(i)\n",
    "    u=influence_pd.loc[i].influencer_id\n",
    "    v=influence_pd.loc[i].follower_id\n",
    "    if(artist_attr.setdefault(v)==None): continue\n",
    "    score[0]+=calc1(u,v)\n",
    "    score[1]+=calc2(u,v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "artist_pd=artist_pd.drop(['artist_name'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('artist_pd.txt',\"w+\",encoding='utf-8') as f:\n",
    "    f.write(str(artist_pd.shape[0])+\"\\n\")\n",
    "    for i in range(len(artist_pd)):\n",
    "        for col in artist_pd.loc[i].to_list():\n",
    "            f.write(str(col)+\" \")\n",
    "        f.write(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "artist_pd[['artist_id']]=artist_pd[['artist_id']].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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