revlog_clear.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import timedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>id</th>\n",
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      "text/plain": [
       "              id            cid  ease  lastIvl  type\n",
       "0  1381391111515  1381391091222     1      -60     0\n",
       "1  1381391118078  1381391091222     1      -60     0\n",
       "2  1381392124223  1381391091222     3      -60     0\n",
       "3  1381392128210  1381391091222     3     -600     0\n",
       "4  1381444213315  1381391091223     1      -60     0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('anki_revlog.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(df[df['ease']==0].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
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       "      <td>2013-10-11 06:30:13.315000+08:00</td>\n",
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      "text/plain": [
       "              id            cid  ease  lastIvl  type  \\\n",
       "0  1381391111515  1381391091222     1      -60     0   \n",
       "1  1381391118078  1381391091222     1      -60     0   \n",
       "2  1381392124223  1381391091222     3      -60     0   \n",
       "3  1381392128210  1381391091222     3     -600     0   \n",
       "4  1381444213315  1381391091223     1      -60     0   \n",
       "\n",
       "                          raw_date  \n",
       "0 2013-10-10 15:45:11.515000+08:00  \n",
       "1 2013-10-10 15:45:18.078000+08:00  \n",
       "2 2013-10-10 16:02:04.223000+08:00  \n",
       "3 2013-10-10 16:02:08.210000+08:00  \n",
       "4 2013-10-11 06:30:13.315000+08:00  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['raw_date'] = pd.to_datetime(df['id'],unit='ms')\n",
    "df['raw_date'] = df['raw_date'].dt.tz_localize('UTC').dt.tz_convert('Asia/Shanghai')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2017-04-22 12:54:31.729000+08:00</td>\n",
       "      <td>2017-04-22 00:00:00+08:00</td>\n",
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       "      <th>1731</th>\n",
       "      <td>1492836876449</td>\n",
       "      <td>1482392528727</td>\n",
       "      <td>4</td>\n",
       "      <td>-60</td>\n",
       "      <td>0</td>\n",
       "      <td>2017-04-22 12:54:36.449000+08:00</td>\n",
       "      <td>2017-04-22 00:00:00+08:00</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "                 id            cid  ease  lastIvl  type  \\\n",
       "1727  1492836849841  1482314752476     4      -60     0   \n",
       "1728  1492836858505  1482399896125     4      -60     0   \n",
       "1729  1492836863457  1482394049701     4      -60     0   \n",
       "1730  1492836871729  1482316716328     1      -60     0   \n",
       "1731  1492836876449  1482392528727     4      -60     0   \n",
       "\n",
       "                             raw_date                      date  \n",
       "1727 2017-04-22 12:54:09.841000+08:00 2017-04-22 00:00:00+08:00  \n",
       "1728 2017-04-22 12:54:18.505000+08:00 2017-04-22 00:00:00+08:00  \n",
       "1729 2017-04-22 12:54:23.457000+08:00 2017-04-22 00:00:00+08:00  \n",
       "1730 2017-04-22 12:54:31.729000+08:00 2017-04-22 00:00:00+08:00  \n",
       "1731 2017-04-22 12:54:36.449000+08:00 2017-04-22 00:00:00+08:00  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['date'] = df['raw_date'].map(lambda x: x-timedelta(days=1) if x.hour < 5 else x)\n",
    "df['date'] = df['date'].dt.floor('D')\n",
    "df.drop(df[df['date'].dt.year < 2017].index, inplace=True)\n",
    "df.drop_duplicates(['cid', 'date'], keep='first', inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-01-02 06:28:23.943000+08:00</td>\n",
       "      <td>2018-01-02 00:00:00+08:00</td>\n",
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       "      <td>2</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-02-16 13:58:14.671000+08:00</td>\n",
       "      <td>2018-02-16 00:00:00+08:00</td>\n",
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       "      <td>2</td>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-04-14 07:48:11.664000+08:00</td>\n",
       "      <td>2018-04-14 00:00:00+08:00</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "                   id            cid  ease  lastIvl  type  \\\n",
       "49826   1509771867443  1492849449112     3      -60     0   \n",
       "50931   1510029646781  1492849449112     3      -60     0   \n",
       "57735   1512291706979  1492849449112     3      -60     0   \n",
       "58006   1512339235482  1492849449112     3        1     1   \n",
       "59058   1512597928962  1492849449112     3        3     1   \n",
       "61669   1513289227663  1492849449112     3        8     1   \n",
       "69078   1514845703943  1492849449112     3       18     1   \n",
       "90059   1518760694671  1492849449112     2       45     1   \n",
       "118153  1523663291664  1492849449112     2       57     1   \n",
       "\n",
       "                               raw_date                      date  \n",
       "49826  2017-11-04 13:04:27.443000+08:00 2017-11-04 00:00:00+08:00  \n",
       "50931  2017-11-07 12:40:46.781000+08:00 2017-11-07 00:00:00+08:00  \n",
       "57735  2017-12-03 17:01:46.979000+08:00 2017-12-03 00:00:00+08:00  \n",
       "58006  2017-12-04 06:13:55.482000+08:00 2017-12-04 00:00:00+08:00  \n",
       "59058  2017-12-07 06:05:28.962000+08:00 2017-12-07 00:00:00+08:00  \n",
       "61669  2017-12-15 06:07:07.663000+08:00 2017-12-15 00:00:00+08:00  \n",
       "69078  2018-01-02 06:28:23.943000+08:00 2018-01-02 00:00:00+08:00  \n",
       "90059  2018-02-16 13:58:14.671000+08:00 2018-02-16 00:00:00+08:00  \n",
       "118153 2018-04-14 07:48:11.664000+08:00 2018-04-14 00:00:00+08:00  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.cid == 1492849449112)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.sort_values(by=['cid', 'id'], inplace=True)\n",
    "df['used_interval'] = df.date.diff().dt.days\n",
    "df.loc[df['type']==0, 'used_interval'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop_duplicates(['cid', 'used_interval'], keep='first', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "for cid in df.loc[df['used_interval']<0, 'cid']:\n",
    "    df.drop(df[df.cid == cid].index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['p'] = df['ease'].map({1:0, 2:1, 3:1, 4:1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "del df['id']\n",
    "del df['raw_date']\n",
    "del df['date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "                cid  ease  lastIvl  type  used_interval  p  repeat_long  \\\n",
       "3390  1321762260687     4      -60     0            0.0  1            1   \n",
       "3391  1321762260688     4      -60     0            0.0  1            1   \n",
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       "3431  1321762260709     3      -60     0            0.0  1            1   \n",
       "3680  1321762260729     4      -60     0            0.0  1            1   \n",
       "\n",
       "     fb_history ivl_history  \n",
       "3390                         \n",
       "3391                         \n",
       "3606                         \n",
       "3431                         \n",
       "3680                         "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df['acc_rep'] = 0\n",
    "# df['acc_lapses'] = 0\n",
    "# df['con_rep'] = 0\n",
    "# df['con_lapses'] = 0\n",
    "df['repeat_long'] = 1\n",
    "df['fb_history'] = \"\"\n",
    "df['ivl_history'] = \"\"\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
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       "      <td>-60</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 cid  type  repeat_long  acc_lapses  con_lapses  acc_rep  \\\n",
       "14516  1361008615067     0            1           0           0        0   \n",
       "14475  1361029158853     0            1           0           0        0   \n",
       "14519  1361029234689     0            1           0           0        0   \n",
       "14481  1361091677114     0            1           0           0        0   \n",
       "14480  1361126950488     0            1           0           0        0   \n",
       "\n",
       "       con_rep  used_interval  lastIvl  ease  p  \n",
       "14516        0            0.0      -60     3  1  \n",
       "14475        0            0.0      -60     3  1  \n",
       "14519        0            0.0      -60     2  1  \n",
       "14481        0            0.0      -60     2  1  \n",
       "14480        0            0.0      -60     3  1  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cols = ['cid', 'type', 'repeat_long', 'acc_lapses', 'con_lapses', 'acc_rep', 'con_rep', 'used_interval', 'lastIvl', 'ease', 'p']\n",
    "# df = df[cols]\n",
    "# df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
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       "      <td>4</td>\n",
       "      <td>-60</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                cid  ease  lastIvl  type  used_interval  p  repeat_long  \\\n",
       "3390  1321762260687     4      -60     0            0.0  1            1   \n",
       "3391  1321762260688     4      -60     0            0.0  1            1   \n",
       "3606  1321762260705     1      -60     0            0.0  0            1   \n",
       "3431  1321762260709     3      -60     0            0.0  1            1   \n",
       "3680  1321762260729     4      -60     0            0.0  1            1   \n",
       "\n",
       "     fb_history ivl_history  \n",
       "3390                         \n",
       "3391                         \n",
       "3606                         \n",
       "3431                         \n",
       "3680                         "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def get_feature(x):\n",
    "#     for idx, log in enumerate(x.itertuples()):\n",
    "#         if idx == x.shape[0] - 1:\n",
    "#             break\n",
    "#         x.iloc[idx + 1, 2] = x.iloc[idx, 2] + 1\n",
    "#         if log.p == 1:\n",
    "#             x.iloc[idx + 1, 3] = x.iloc[idx, 3]\n",
    "#             x.iloc[idx + 1, 4] = 0\n",
    "#             x.iloc[idx + 1, 5] = x.iloc[idx, 5] + 1\n",
    "#             x.iloc[idx + 1, 6] = x.iloc[idx, 6] + 1\n",
    "#         else:\n",
    "#             x.iloc[idx + 1, 3] = x.iloc[idx, 3] + 1\n",
    "#             x.iloc[idx + 1, 4] = x.iloc[idx, 4] + 1\n",
    "#             x.iloc[idx + 1, 5] = x.iloc[idx, 5]\n",
    "#             x.iloc[idx + 1, 6] = 0\n",
    "#     return x\n",
    "def get_feature(x):\n",
    "    for idx, log in enumerate(x.itertuples()):\n",
    "        if idx == x.shape[0] - 1:\n",
    "            break\n",
    "        x.iloc[idx + 1, 6] = x.iloc[idx, 6] + 1\n",
    "        x.iloc[idx + 1, 8] = x.iloc[idx, 8] + str(int(x.iloc[idx, 4])) + \",\"\n",
    "        if log.p == 1:\n",
    "            x.iloc[idx + 1, 7] = x.iloc[idx, 7] + \"1\"\n",
    "        else:\n",
    "            x.iloc[idx + 1, 7] = x.iloc[idx, 7] + \"0\"\n",
    "    return x\n",
    "df = df.groupby('cid').apply(get_feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [cid, ease, lastIvl, type, used_interval, p, repeat_long, fb_history, ivl_history]\n",
       "Index: []"
      ]
     },
     "execution_count": 28,
     "metadata": {},
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    }
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   "source": [
    "df[(df.used_interval == 0) & (df.repeat_long == 2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('revlog_history.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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