{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "judicial-clearing",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "defensive-liver",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('MediaSearch_20210127.tsv',sep='\\t',header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "brave-cylinder",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[1] = data[1].apply(lambda x: int(x[x.find(':')+1:]))\n",
    "data.iloc[:,2:17] = data.iloc[:,2:17].applymap(lambda x: float(x[x.find(':')+1:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "unable-shore",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <th>1</th>\n",
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       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7431</th>\n",
       "      <td>-1</td>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>18.349998</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td># Patterson's General Store Ledger - DPLA - 00...</td>\n",
       "      <td>Miss James</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7432</th>\n",
       "      <td>-1</td>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.072510</td>\n",
       "      <td>18.349998</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td># Patterson's General Store Ledger - DPLA - 00...</td>\n",
       "      <td>Miss James</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7433</th>\n",
       "      <td>-1</td>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>5.587692</td>\n",
       "      <td>4.949943</td>\n",
       "      <td>10.363359</td>\n",
       "      <td>10.363359</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.291962</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.986000</td>\n",
       "      <td>12.289614</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td># James Meredith's Letter to the Registrar, Un...</td>\n",
       "      <td>Miss James</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7434</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>15.927404</td>\n",
       "      <td>16.88351</td>\n",
       "      <td>16.058949</td>\n",
       "      <td>15.291920</td>\n",
       "      <td>12.418142</td>\n",
       "      <td>12.418142</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.127102</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.741373</td>\n",
       "      <td>18.958454</td>\n",
       "      <td>17.161888</td>\n",
       "      <td>17.941786</td>\n",
       "      <td>0.0</td>\n",
       "      <td># Miss James by Camille Silvy (cropped).jpg</td>\n",
       "      <td>Miss James</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7435</th>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>15.977814</td>\n",
       "      <td>16.052890</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.987890</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.306084</td>\n",
       "      <td>17.825270</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td># Hindhead Tunnel Miss James Bridge.JPG</td>\n",
       "      <td>Miss James</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7436</th>\n",
       "      <td>-1</td>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.746199</td>\n",
       "      <td>20.056736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td># Herstmonceux Place - geograph.org.uk - 15863...</td>\n",
       "      <td>Miss James</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7437</th>\n",
       "      <td>-1</td>\n",
       "      <td>8</td>\n",
       "      <td>17.795383</td>\n",
       "      <td>16.75280</td>\n",
       "      <td>15.976525</td>\n",
       "      <td>16.070520</td>\n",
       "      <td>14.965977</td>\n",
       "      <td>14.965977</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.022842</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.441265</td>\n",
       "      <td>15.371144</td>\n",
       "      <td>14.034350</td>\n",
       "      <td>14.117758</td>\n",
       "      <td>0.0</td>\n",
       "      <td># Miss James' Walk NT sign.jpg</td>\n",
       "      <td>Miss James</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      0   1          2         3          4          5          6          7   \\\n",
       "7431  -1   8   0.000000   0.00000   0.000000   0.000000   0.000000   0.000000   \n",
       "7432  -1   8   0.000000   0.00000   0.000000   0.000000   0.000000   0.000000   \n",
       "7433  -1   8   0.000000   0.00000   5.587692   4.949943  10.363359  10.363359   \n",
       "7434   1   8  15.927404  16.88351  16.058949  15.291920  12.418142  12.418142   \n",
       "7435   0   8   0.000000   0.00000  15.977814  16.052890   0.000000   0.000000   \n",
       "7436  -1   8   0.000000   0.00000   0.000000   0.000000   0.000000   0.000000   \n",
       "7437  -1   8  17.795383  16.75280  15.976525  16.070520  14.965977  14.965977   \n",
       "\n",
       "       8    9          10   11         12         13         14         15  \\\n",
       "7431  0.0  0.0   0.000000  0.0  17.072510  18.349998   0.000000   0.000000   \n",
       "7432  0.0  0.0   0.000000  0.0  17.072510  18.349998   0.000000   0.000000   \n",
       "7433  0.0  0.0   7.291962  0.0  11.986000  12.289614   0.000000   0.000000   \n",
       "7434  0.0  0.0  22.127102  0.0  19.741373  18.958454  17.161888  17.941786   \n",
       "7435  0.0  0.0  22.987890  0.0  17.306084  17.825270   0.000000   0.000000   \n",
       "7436  0.0  0.0   0.000000  0.0  19.746199  20.056736   0.000000   0.000000   \n",
       "7437  0.0  0.0  23.022842  0.0  17.441265  15.371144  14.034350  14.117758   \n",
       "\n",
       "       16                                                 17          18  \n",
       "7431  0.0  # Patterson's General Store Ledger - DPLA - 00...  Miss James  \n",
       "7432  0.0  # Patterson's General Store Ledger - DPLA - 00...  Miss James  \n",
       "7433  0.0  # James Meredith's Letter to the Registrar, Un...  Miss James  \n",
       "7434  0.0        # Miss James by Camille Silvy (cropped).jpg  Miss James  \n",
       "7435  0.0            # Hindhead Tunnel Miss James Bridge.JPG  Miss James  \n",
       "7436  0.0  # Herstmonceux Place - geograph.org.uk - 15863...  Miss James  \n",
       "7437  0.0                     # Miss James' Walk NT sign.jpg  Miss James  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data[1] == 8] # select qid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "recreational-leonard",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature = data.iloc[:,2:17].copy()\n",
    "feature.columns = [str(x) for x in range(1, 16)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "martial-luxembourg",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
       "      <th>2</th>\n",
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       "      <th>5</th>\n",
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       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8783.000000</td>\n",
       "      <td>8783.000000</td>\n",
       "      <td>8783.000000</td>\n",
       "      <td>8783.000000</td>\n",
       "      <td>8783.000000</td>\n",
       "      <td>8783.000000</td>\n",
       "      <td>8783.000000</td>\n",
       "      <td>8783.000000</td>\n",
       "      <td>8783.000000</td>\n",
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       "      <td>8783.000000</td>\n",
       "      <td>8783.000000</td>\n",
       "      <td>8783.000000</td>\n",
       "      <td>8783.000000</td>\n",
       "      <td>8783.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.873118</td>\n",
       "      <td>1.919654</td>\n",
       "      <td>9.729096</td>\n",
       "      <td>9.315373</td>\n",
       "      <td>8.179032</td>\n",
       "      <td>8.179032</td>\n",
       "      <td>1.960876</td>\n",
       "      <td>1.882697</td>\n",
       "      <td>11.608655</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.281795</td>\n",
       "      <td>12.398102</td>\n",
       "      <td>3.987170</td>\n",
       "      <td>3.797345</td>\n",
       "      <td>1.606014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.656279</td>\n",
       "      <td>4.651493</td>\n",
       "      <td>8.046713</td>\n",
       "      <td>8.189460</td>\n",
       "      <td>8.141790</td>\n",
       "      <td>8.141790</td>\n",
       "      <td>4.905001</td>\n",
       "      <td>4.862315</td>\n",
       "      <td>11.170136</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.856893</td>\n",
       "      <td>8.978599</td>\n",
       "      <td>7.209812</td>\n",
       "      <td>7.109495</td>\n",
       "      <td>4.235872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.360486</td>\n",
       "      <td>6.230877</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.889608</td>\n",
       "      <td>9.537116</td>\n",
       "      <td>7.436558</td>\n",
       "      <td>7.436558</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.490162</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.593857</td>\n",
       "      <td>11.631214</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.970320</td>\n",
       "      <td>14.613925</td>\n",
       "      <td>13.459657</td>\n",
       "      <td>13.459657</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.813028</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.695495</td>\n",
       "      <td>17.750853</td>\n",
       "      <td>7.326907</td>\n",
       "      <td>6.376496</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>31.278097</td>\n",
       "      <td>30.218302</td>\n",
       "      <td>43.350067</td>\n",
       "      <td>44.527184</td>\n",
       "      <td>47.128155</td>\n",
       "      <td>47.128155</td>\n",
       "      <td>30.328350</td>\n",
       "      <td>30.142078</td>\n",
       "      <td>73.689400</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54.403400</td>\n",
       "      <td>55.522293</td>\n",
       "      <td>47.798890</td>\n",
       "      <td>45.360588</td>\n",
       "      <td>14.294704</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 1            2            3            4            5  \\\n",
       "count  8783.000000  8783.000000  8783.000000  8783.000000  8783.000000   \n",
       "mean      1.873118     1.919654     9.729096     9.315373     8.179032   \n",
       "std       4.656279     4.651493     8.046713     8.189460     8.141790   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "50%       0.000000     0.000000     9.889608     9.537116     7.436558   \n",
       "75%       0.000000     0.000000    14.970320    14.613925    13.459657   \n",
       "max      31.278097    30.218302    43.350067    44.527184    47.128155   \n",
       "\n",
       "                 6            7            8            9      10  \\\n",
       "count  8783.000000  8783.000000  8783.000000  8783.000000  8783.0   \n",
       "mean      8.179032     1.960876     1.882697    11.608655     0.0   \n",
       "std       8.141790     4.905001     4.862315    11.170136     0.0   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.0   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.0   \n",
       "50%       7.436558     0.000000     0.000000    11.490162     0.0   \n",
       "75%      13.459657     0.000000     0.000000    17.813028     0.0   \n",
       "max      47.128155    30.328350    30.142078    73.689400     0.0   \n",
       "\n",
       "                11           12           13           14           15  \n",
       "count  8783.000000  8783.000000  8783.000000  8783.000000  8783.000000  \n",
       "mean     13.281795    12.398102     3.987170     3.797345     1.606014  \n",
       "std       8.856893     8.978599     7.209812     7.109495     4.235872  \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "25%       7.360486     6.230877     0.000000     0.000000     0.000000  \n",
       "50%      12.593857    11.631214     0.000000     0.000000     0.000000  \n",
       "75%      18.695495    17.750853     7.326907     6.376496     0.000000  \n",
       "max      54.403400    55.522293    47.798890    45.360588    14.294704  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "different-british",
   "metadata": {},
   "outputs": [
    {
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       "              1          2          3          4          5          6  \\\n",
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       "...         ...        ...        ...        ...        ...        ...   \n",
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       "\n",
       "              7          8          9   10         11         12         13  \\\n",
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       "...         ...        ...        ...  ...        ...        ...        ...   \n",
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       "8779   8.934017   9.085819  13.406617  0.0  11.307065  11.244468   0.000000   \n",
       "8780   0.000000   0.000000   0.000000  0.0  10.166705   9.703100   0.000000   \n",
       "8781   0.000000   0.000000  14.847295  0.0  11.634139  11.382575  13.589712   \n",
       "8782   0.000000   0.000000   0.000000  0.0  10.399742  10.222792   0.000000   \n",
       "\n",
       "             14         15  \n",
       "0     14.834125   0.000000  \n",
       "1      0.000000   0.000000  \n",
       "2      0.000000  13.733603  \n",
       "3      0.000000  14.262909  \n",
       "4      0.000000   0.000000  \n",
       "...         ...        ...  \n",
       "8778   0.000000  11.901220  \n",
       "8779   0.000000   0.000000  \n",
       "8780   0.000000   0.000000  \n",
       "8781  13.659342   0.000000  \n",
       "8782   0.000000   0.000000  \n",
       "\n",
       "[8783 rows x 15 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "electronic-provider",
   "metadata": {},
   "outputs": [],
   "source": [
    "rating = data[0].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "chicken-munich",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame({'feature':list(range(1,16)), 'rate of nonzero score':((feature > 0).sum(axis=0)/8783).values})\n",
    "ax = df.plot.bar(x='feature', y='rate of nonzero score', rot=0, grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "quick-watson",
   "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>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21.599610</td>\n",
       "      <td>20.682966</td>\n",
       "      <td>16.616700</td>\n",
       "      <td>16.738070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>23.066238</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.571686</td>\n",
       "      <td>16.565067</td>\n",
       "      <td>14.098110</td>\n",
       "      <td>14.834125</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
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       "[8783 rows x 15 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "verbal-cleaning",
   "metadata": {},
   "outputs": [
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       "\n",
       "              7          8          9   10         11         12        13  \\\n",
       "0      0.000000   0.000000  23.066238  0.0  18.571686  16.565067  14.09811   \n",
       "1      0.000000   0.000000  23.244255  0.0  20.544075  19.795902   0.00000   \n",
       "2      0.000000   0.000000   0.000000  0.0   0.000000   0.000000   0.00000   \n",
       "3      0.000000   0.000000  21.310215  0.0  17.368101  17.481064   0.00000   \n",
       "4     14.340542  14.655287  23.026300  0.0  12.062780  12.286295   0.00000   \n",
       "...         ...        ...        ...  ...        ...        ...       ...   \n",
       "8777   0.000000   0.000000   0.000000  0.0  10.924480  10.995590   0.00000   \n",
       "8778   0.000000   0.000000   0.000000  0.0  10.714787  10.621951   0.00000   \n",
       "8779   8.934017   9.085819  13.406617  0.0  11.307065  11.244468   0.00000   \n",
       "8780   0.000000   0.000000   0.000000  0.0  10.166705   9.703100   0.00000   \n",
       "8782   0.000000   0.000000   0.000000  0.0  10.399742  10.222792   0.00000   \n",
       "\n",
       "             14         15  \n",
       "0     14.834125   0.000000  \n",
       "1      0.000000   0.000000  \n",
       "2      0.000000  13.733603  \n",
       "3      0.000000  14.262909  \n",
       "4      0.000000   0.000000  \n",
       "...         ...        ...  \n",
       "8777   0.000000  11.798944  \n",
       "8778   0.000000  11.901220  \n",
       "8779   0.000000   0.000000  \n",
       "8780   0.000000   0.000000  \n",
       "8782   0.000000   0.000000  \n",
       "\n",
       "[7459 rows x 15 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature.iloc[rating[rating != 0].index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "hidden-nitrogen",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1\n",
       "1       1\n",
       "2       1\n",
       "3       1\n",
       "4      -1\n",
       "       ..\n",
       "8777   -1\n",
       "8778    1\n",
       "8779   -1\n",
       "8780    1\n",
       "8782   -1\n",
       "Name: 0, Length: 7459, dtype: int64"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating[rating != 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "medical-athens",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([   0,    1,    2,    3,    4,    5,    6,    7,    8,    9,\n",
       "            ...\n",
       "            8772, 8773, 8774, 8775, 8776, 8777, 8778, 8779, 8780, 8782],\n",
       "           dtype='int64', length=7459)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating[rating != 0].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "contemporary-pasta",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.42344681012562146"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(feature > 0).sum(axis=0).sum() / (feature.shape[0]*feature.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "received-running",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3907 1324 3552\n"
     ]
    }
   ],
   "source": [
    "print((rating == 1).sum(), (rating == 0).sum(), (rating == -1).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "supported-hardware",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.15074575885232835"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1324 / 8783"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "abroad-tiger",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3907 4876\n"
     ]
    }
   ],
   "source": [
    "numbinrating = rating.apply(lambda x: 1 if x > 0 else 0)\n",
    "print((numbinrating == 1).sum(), (numbinrating == 0).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "leading-backing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1\n",
       "1       1\n",
       "2       1\n",
       "3       1\n",
       "4       0\n",
       "       ..\n",
       "8777    0\n",
       "8778    1\n",
       "8779    0\n",
       "8780    1\n",
       "8782    0\n",
       "Name: 0, Length: 7459, dtype: int64"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numbinrating.iloc[rating[rating != 0].index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "careful-louis",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics, preprocessing\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "refined-ethiopia",
   "metadata": {},
   "outputs": [],
   "source": [
    "balanced_accuracy = []\n",
    "avg_precision_score = []\n",
    "brier_loss = []\n",
    "f1_score = []\n",
    "coefs = []\n",
    "intercepts = []\n",
    "\n",
    "for rs in range(10):\n",
    "    X_train,X_test,y_train,y_test = train_test_split(feature.iloc[rating[rating != 0].index], \n",
    "                                                     numbinrating.iloc[rating[rating != 0].index],\n",
    "                                                     test_size=0.2,random_state=rs)\n",
    "    # standardization using min-max scaler\n",
    "    #min_max_scaler = preprocessing.MinMaxScaler()\n",
    "    #X_train_minmax = min_max_scaler.fit_transform(X_train)\n",
    "    #X_test_minmax = min_max_scaler.transform(X_test)\n",
    "    # logistic regression\n",
    "    logit=LogisticRegression(fit_intercept=True,solver='liblinear')\n",
    "    fitted=logit.fit(X_train,y_train)\n",
    "    y_pred=logit.predict(X_test)\n",
    "    balanced_accuracy.append(metrics.balanced_accuracy_score(y_test, y_pred))\n",
    "    y_pred_p=logit.predict_proba(X_test)\n",
    "    avg_precision_score.append(metrics.average_precision_score(y_test, y_pred_p.T[1]))\n",
    "    brier_loss.append(metrics.brier_score_loss(y_test, y_pred_p.T[1]))\n",
    "    f1_score.append(metrics.average_precision_score(y_test, y_pred))\n",
    "    coefs.append(fitted.coef_[0])\n",
    "    intercepts.append(fitted.intercept_[0])\n",
    "    #print('balanced accuracy: {:.4f}'.format(metrics.balanced_accuracy_score(y_test, y_pred)))\n",
    "    #print('average precision score: {:.4f}'.format(metrics.average_precision_score(y_test, y_pred_p.T[1])))\n",
    "    #print('brier score loss: {:.4f}'.format(metrics.brier_score_loss(y_test, y_pred_p.T[1]))) # The smaller, the better\n",
    "    #print('f1 score: {:.4f}'.format(metrics.average_precision_score(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "friendly-gnome",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "balanced accuracy: 0.6624\n",
      "average precision score: 0.7551\n",
      "brier score loss: 0.2073\n",
      "f1 score: 0.6293\n"
     ]
    }
   ],
   "source": [
    "# no standardization\n",
    "print('balanced accuracy: {:.4f}'.format(np.mean(balanced_accuracy)))\n",
    "print('average precision score: {:.4f}'.format(np.mean(avg_precision_score)))\n",
    "print('brier score loss: {:.4f}'.format(np.mean(brier_loss))) # The smaller, the better\n",
    "print('f1 score: {:.4f}'.format(np.mean(f1_score)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "printable-singer",
   "metadata": {},
   "outputs": [],
   "source": [
    "balanced_accuracy = []\n",
    "avg_precision_score = []\n",
    "brier_loss = []\n",
    "f1_score = []\n",
    "coefs = []\n",
    "intercepts = []\n",
    "\n",
    "for rs in range(10):\n",
    "    X_train,X_test,y_train,y_test = train_test_split(feature,numbinrating,test_size=0.2,random_state=rs)\n",
    "    # standardization using min-max scaler\n",
    "    #min_max_scaler = preprocessing.MinMaxScaler()\n",
    "    #X_train_minmax = min_max_scaler.fit_transform(X_train)\n",
    "    #X_test_minmax = min_max_scaler.transform(X_test)\n",
    "    # logistic regression\n",
    "    logit=LogisticRegression(fit_intercept=True,solver='liblinear')\n",
    "    fitted=logit.fit(X_train,y_train)\n",
    "    y_pred=logit.predict(X_test)\n",
    "    balanced_accuracy.append(metrics.balanced_accuracy_score(y_test, y_pred))\n",
    "    y_pred_p=logit.predict_proba(X_test)\n",
    "    avg_precision_score.append(metrics.average_precision_score(y_test, y_pred_p.T[1]))\n",
    "    brier_loss.append(metrics.brier_score_loss(y_test, y_pred_p.T[1]))\n",
    "    f1_score.append(metrics.average_precision_score(y_test, y_pred))\n",
    "    coefs.append(fitted.coef_[0])\n",
    "    intercepts.append(fitted.intercept_[0])\n",
    "    #print('balanced accuracy: {:.4f}'.format(metrics.balanced_accuracy_score(y_test, y_pred)))\n",
    "    #print('average precision score: {:.4f}'.format(metrics.average_precision_score(y_test, y_pred_p.T[1])))\n",
    "    #print('brier score loss: {:.4f}'.format(metrics.brier_score_loss(y_test, y_pred_p.T[1]))) # The smaller, the better\n",
    "    #print('f1 score: {:.4f}'.format(metrics.average_precision_score(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "thorough-shopper",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "balanced accuracy: 0.6306\n",
      "average precision score: 0.6506\n",
      "brier score loss: 0.2153\n",
      "f1 score: 0.5318\n"
     ]
    }
   ],
   "source": [
    "# no standardization\n",
    "print('balanced accuracy: {:.4f}'.format(np.mean(balanced_accuracy)))\n",
    "print('average precision score: {:.4f}'.format(np.mean(avg_precision_score)))\n",
    "print('brier score loss: {:.4f}'.format(np.mean(brier_loss))) # The smaller, the better\n",
    "print('f1 score: {:.4f}'.format(np.mean(f1_score)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "literary-conversation",
   "metadata": {},
   "outputs": [],
   "source": [
    "# best\n",
    "balanced accuracy: 0.6342\n",
    "average precision score: 0.6821\n",
    "brier score loss: 0.2137\n",
    "f1 score: 0.5533"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "joined-destination",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# no standardization\n",
    "df = pd.DataFrame({'feature':list(range(1,16)), 'coefficients':np.stack(coefs).mean(axis=0)})\n",
    "ax = df.plot(x='feature', y='coefficients', rot=0, kind='bar', grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "broken-taylor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.02159655,  0.04977869,  0.05276279,  0.04668993,  0.02615154,\n",
       "        0.02615154,  0.00636762,  0.01282327, -0.02016103,  0.        ,\n",
       "       -0.02192281,  0.02003289, -0.04702737,  0.03902806,  0.10792615])"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.stack(coefs).mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "blond-middle",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.02422864, 0.0258807 , 0.00526667, 0.00627771, 0.00087469,\n",
       "       0.00087469, 0.01432958, 0.01491875, 0.00288218, 0.        ,\n",
       "       0.0042263 , 0.00431502, 0.00625123, 0.00565562, 0.00302476])"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.stack(coefs).std(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "stylish-prague",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.172818154019393"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(intercepts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "anticipated-bibliography",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# no standardization\n",
    "df = pd.DataFrame({'feature':list(range(1,16)), 'coefficients':np.stack(coefs).mean(axis=0)})\n",
    "ax = df.plot(x='feature', y='coefficients', rot=0, kind='bar', grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "filled-intervention",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.00722761,  0.021936  ,  0.04056063,  0.03559395,  0.02052407,\n",
       "        0.02052407,  0.01702212,  0.00057635, -0.01129512,  0.        ,\n",
       "       -0.01127191,  0.0138696 , -0.03131791,  0.02770065,  0.09263194])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.stack(coefs).mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "international-reward",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00777849, 0.00867834, 0.00309854, 0.00398819, 0.00066269,\n",
       "       0.00066269, 0.01262642, 0.01286337, 0.00246544, 0.        ,\n",
       "       0.00341984, 0.00401186, 0.00823876, 0.00863262, 0.00237907])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.stack(coefs).std(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "precious-reader",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.3638519776835112"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(intercepts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "isolated-implementation",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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