{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ba9e5acd-e17d-4318-9272-04c9f6706186",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import spacy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e4f0b3f0-5255-46f1-822f-e455087ba315",
   "metadata": {},
   "outputs": [],
   "source": [
    "phab_path = \"/mmfs1/gscratch/comdata/users/mjilg/mw-repo-lifecycles/case1/0312_resolved_ve_phab_comments.csv\"\n",
    "phab_df = pd.read_csv(phab_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d449164e-1d28-4580-9eb1-f0f69978f114",
   "metadata": {},
   "outputs": [],
   "source": [
    "#find gerrit phab PHID: PHID-USER-idceizaw6elwiwm5xshb\n",
    "phab_df['isGerrit'] = phab_df['AuthorPHID'] == 'PHID-USER-idceizaw6elwiwm5xshb'\n",
    "#cleaning df\n",
    "phab_df['id'] = phab_df.index + 1\n",
    "#may have to build out the reply_to column \n",
    "phab_df['reply_to'] = phab_df.groupby('TaskPHID')['id'].shift()\n",
    "phab_df['reply_to'] = phab_df['reply_to'].where(pd.notnull(phab_df['reply_to']), None)\n",
    "\n",
    "phab_df = phab_df.rename(columns={\n",
    "    'AuthorPHID': 'speaker',\n",
    "    'TaskPHID': 'conversation_id',\n",
    "    'WMFaffil':'meta.affil',\n",
    "    'isGerrit': 'meta.gerrit'\n",
    "})\n",
    "\n",
    "# after 11-1-2012 before 11-1-2013\n",
    "phab_df['timestamp'] = pd.to_datetime(phab_df['date_created'], unit='s', origin='unix', utc=True)\n",
    "filtered_phab_df = phab_df[(phab_df['date_created'] < 1383264000) & (phab_df['date_created'] > 1351728000)]\n",
    "\n",
    "#removing headless conversations\n",
    "task_phab_df = filtered_phab_df[filtered_phab_df['comment_type']==\"task_description\"]\n",
    "headed_task_phids = task_phab_df['conversation_id'].unique()\n",
    "filtered_phab_df = filtered_phab_df[filtered_phab_df['conversation_id'].isin(headed_task_phids)]\n",
    "\n",
    "#removing gerrit comments \n",
    "comment_phab_df = filtered_phab_df[filtered_phab_df['meta.gerrit'] != True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "942344db-c8f5-4ed6-a757-c97f8454f18b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique conversation_ids: 2081\n",
      "Unique ids: 8804\n",
      "Unique speakers: 230\n"
     ]
    }
   ],
   "source": [
    "unique_conversation_ids = len(comment_phab_df['conversation_id'].unique())\n",
    "unique_ids = len(comment_phab_df['id'].unique())\n",
    "unique_speakers = len(comment_phab_df['speaker'].unique())\n",
    "\n",
    "print(f\"Unique conversation_ids: {unique_conversation_ids}\")\n",
    "print(f\"Unique ids: {unique_ids}\")\n",
    "print(f\"Unique speakers: {unique_speakers}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c0aade6b-f425-4f9b-ae2a-721ea49712ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .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>task_title</th>\n",
       "      <th>comment_text</th>\n",
       "      <th>date_created</th>\n",
       "      <th>speaker</th>\n",
       "      <th>meta.affil</th>\n",
       "      <th>conversation_id</th>\n",
       "      <th>comment_type</th>\n",
       "      <th>status</th>\n",
       "      <th>text</th>\n",
       "      <th>resolved_text</th>\n",
       "      <th>meta.gerrit</th>\n",
       "      <th>id</th>\n",
       "      <th>reply_to</th>\n",
       "      <th>first_comment</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>708</th>\n",
       "      <td>VisualEditor: [Regression] \"More\" menu gets sh...</td>\n",
       "      <td>Tested on both the Italian and the English Wik...</td>\n",
       "      <td>1380976920</td>\n",
       "      <td>PHID-USER-wil4b5lylrvf3krixlkl</td>\n",
       "      <td>True</td>\n",
       "      <td>PHID-TASK-64s56xzrc22ustp2z7wx</td>\n",
       "      <td>task_description</td>\n",
       "      <td>resolved</td>\n",
       "      <td>Tested on both the Italian and the English Wik...</td>\n",
       "      <td>Tested on both the Italian and the English Wik...</td>\n",
       "      <td>False</td>\n",
       "      <td>709</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>2013-10-05 12:42:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>709</th>\n",
       "      <td>VisualEditor: [Regression] \"More\" menu gets sh...</td>\n",
       "      <td>Note that this is fixed and has been deployed ...</td>\n",
       "      <td>1381281033</td>\n",
       "      <td>PHID-USER-ydswvwhh5pm4lshahjje</td>\n",
       "      <td>True</td>\n",
       "      <td>PHID-TASK-64s56xzrc22ustp2z7wx</td>\n",
       "      <td>task_subcomment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Note that this is fixed and has been deployed ...</td>\n",
       "      <td>Note that this is fixed and has been deployed ...</td>\n",
       "      <td>False</td>\n",
       "      <td>710</td>\n",
       "      <td>709.0</td>\n",
       "      <td>False</td>\n",
       "      <td>2013-10-09 01:10:33+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>712</th>\n",
       "      <td>VisualEditor: [Regression] \"More\" menu gets sh...</td>\n",
       "      <td>*** Bug 55362 has been marked as a duplicate o...</td>\n",
       "      <td>1381267451</td>\n",
       "      <td>PHID-USER-ydswvwhh5pm4lshahjje</td>\n",
       "      <td>True</td>\n",
       "      <td>PHID-TASK-64s56xzrc22ustp2z7wx</td>\n",
       "      <td>task_subcomment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>*** Bug 55362 has been marked as a duplicate o...</td>\n",
       "      <td>*** Bug 55362 has been marked as a duplicate o...</td>\n",
       "      <td>False</td>\n",
       "      <td>713</td>\n",
       "      <td>712.0</td>\n",
       "      <td>False</td>\n",
       "      <td>2013-10-08 21:24:11+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>VisualEditor: [Regression] \"More\" menu gets sh...</td>\n",
       "      <td>(In reply to comment #6)\\n&gt; Krinkle, do I need...</td>\n",
       "      <td>1381168024</td>\n",
       "      <td>PHID-USER-sai77mtxmpqnm6pycyvz</td>\n",
       "      <td>True</td>\n",
       "      <td>PHID-TASK-64s56xzrc22ustp2z7wx</td>\n",
       "      <td>task_subcomment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(In reply to comment #6)\\n&gt; Krinkle, do I need...</td>\n",
       "      <td>(In reply to comment #6)\\n&gt; Krinkle, do I need...</td>\n",
       "      <td>False</td>\n",
       "      <td>718</td>\n",
       "      <td>717.0</td>\n",
       "      <td>False</td>\n",
       "      <td>2013-10-07 17:47:04+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>VisualEditor: [Regression] \"More\" menu gets sh...</td>\n",
       "      <td>Krinkle, do I need to file a different bug for...</td>\n",
       "      <td>1381142922</td>\n",
       "      <td>PHID-USER-wil4b5lylrvf3krixlkl</td>\n",
       "      <td>True</td>\n",
       "      <td>PHID-TASK-64s56xzrc22ustp2z7wx</td>\n",
       "      <td>task_subcomment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Krinkle, do I need to file a different bug for...</td>\n",
       "      <td>Krinkle, do Krinkle need to file a different b...</td>\n",
       "      <td>False</td>\n",
       "      <td>719</td>\n",
       "      <td>718.0</td>\n",
       "      <td>False</td>\n",
       "      <td>2013-10-07 10:48:42+00:00</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32172</th>\n",
       "      <td>Setup wikibugs and gerrit-wm for #mediawiki-vi...</td>\n",
       "      <td>Puppet config for wikibugs:\\n\\nhttps://gerrit....</td>\n",
       "      <td>1354738560</td>\n",
       "      <td>PHID-USER-ydswvwhh5pm4lshahjje</td>\n",
       "      <td>True</td>\n",
       "      <td>PHID-TASK-ciosa56mnibqn4lx27ub</td>\n",
       "      <td>task_description</td>\n",
       "      <td>resolved</td>\n",
       "      <td>Puppet config for wikibugs:\\n\\nhttps://gerrit....</td>\n",
       "      <td>Puppet config for wikibugs:\\n\\nhttps://gerrit....</td>\n",
       "      <td>False</td>\n",
       "      <td>32173</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>2012-12-05 20:16:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32178</th>\n",
       "      <td>Setup wikibugs and gerrit-wm for #mediawiki-vi...</td>\n",
       "      <td>gerrit-wm is done, but wikibugs is \"an almight...</td>\n",
       "      <td>1360206228</td>\n",
       "      <td>PHID-USER-ydswvwhh5pm4lshahjje</td>\n",
       "      <td>True</td>\n",
       "      <td>PHID-TASK-ciosa56mnibqn4lx27ub</td>\n",
       "      <td>task_subcomment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gerrit-wm is done, but wikibugs is \"an almight...</td>\n",
       "      <td>gerrit-wm is done, but wikibugs is \"an almight...</td>\n",
       "      <td>False</td>\n",
       "      <td>32179</td>\n",
       "      <td>32178.0</td>\n",
       "      <td>False</td>\n",
       "      <td>2013-02-07 03:03:48+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32179</th>\n",
       "      <td>Setup wikibugs and gerrit-wm for #mediawiki-vi...</td>\n",
       "      <td>Attempted fixes in Gerrit 37566 and Gerrit 37570.</td>\n",
       "      <td>1354926921</td>\n",
       "      <td>PHID-USER-ydswvwhh5pm4lshahjje</td>\n",
       "      <td>True</td>\n",
       "      <td>PHID-TASK-ciosa56mnibqn4lx27ub</td>\n",
       "      <td>task_subcomment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attempted fixes in Gerrit 37566 and Gerrit 37570.</td>\n",
       "      <td>Attempted fixes in Gerrit 37566 and Gerrit 37570.</td>\n",
       "      <td>False</td>\n",
       "      <td>32180</td>\n",
       "      <td>32179.0</td>\n",
       "      <td>False</td>\n",
       "      <td>2012-12-08 00:35:21+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32180</th>\n",
       "      <td>VisualEditor: Two replacements within the same...</td>\n",
       "      <td>Test case:\\n\\n+                       'removin...</td>\n",
       "      <td>1353134520</td>\n",
       "      <td>PHID-USER-fovtl67ew4l4cc3oeypc</td>\n",
       "      <td>False</td>\n",
       "      <td>PHID-TASK-guukovmsjsnlpphgujcv</td>\n",
       "      <td>task_description</td>\n",
       "      <td>invalid</td>\n",
       "      <td>Test case:\\n\\n+                       'removin...</td>\n",
       "      <td>Test case:\\n\\n+                       'removin...</td>\n",
       "      <td>False</td>\n",
       "      <td>32181</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>2012-11-17 06:42:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32181</th>\n",
       "      <td>VisualEditor: Two replacements within the same...</td>\n",
       "      <td>With bug 45061 all change marker code has been...</td>\n",
       "      <td>1360975473</td>\n",
       "      <td>PHID-USER-it53o2f2kyryqyj33uzt</td>\n",
       "      <td>False</td>\n",
       "      <td>PHID-TASK-guukovmsjsnlpphgujcv</td>\n",
       "      <td>task_subcomment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>With bug 45061 all change marker code has been...</td>\n",
       "      <td>With bug 45061 all change marker code has been...</td>\n",
       "      <td>False</td>\n",
       "      <td>32182</td>\n",
       "      <td>32181.0</td>\n",
       "      <td>False</td>\n",
       "      <td>2013-02-16 00:44:33+00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8804 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              task_title  \\\n",
       "708    VisualEditor: [Regression] \"More\" menu gets sh...   \n",
       "709    VisualEditor: [Regression] \"More\" menu gets sh...   \n",
       "712    VisualEditor: [Regression] \"More\" menu gets sh...   \n",
       "717    VisualEditor: [Regression] \"More\" menu gets sh...   \n",
       "718    VisualEditor: [Regression] \"More\" menu gets sh...   \n",
       "...                                                  ...   \n",
       "32172  Setup wikibugs and gerrit-wm for #mediawiki-vi...   \n",
       "32178  Setup wikibugs and gerrit-wm for #mediawiki-vi...   \n",
       "32179  Setup wikibugs and gerrit-wm for #mediawiki-vi...   \n",
       "32180  VisualEditor: Two replacements within the same...   \n",
       "32181  VisualEditor: Two replacements within the same...   \n",
       "\n",
       "                                            comment_text  date_created  \\\n",
       "708    Tested on both the Italian and the English Wik...    1380976920   \n",
       "709    Note that this is fixed and has been deployed ...    1381281033   \n",
       "712    *** Bug 55362 has been marked as a duplicate o...    1381267451   \n",
       "717    (In reply to comment #6)\\n> Krinkle, do I need...    1381168024   \n",
       "718    Krinkle, do I need to file a different bug for...    1381142922   \n",
       "...                                                  ...           ...   \n",
       "32172  Puppet config for wikibugs:\\n\\nhttps://gerrit....    1354738560   \n",
       "32178  gerrit-wm is done, but wikibugs is \"an almight...    1360206228   \n",
       "32179  Attempted fixes in Gerrit 37566 and Gerrit 37570.    1354926921   \n",
       "32180  Test case:\\n\\n+                       'removin...    1353134520   \n",
       "32181  With bug 45061 all change marker code has been...    1360975473   \n",
       "\n",
       "                              speaker  meta.affil  \\\n",
       "708    PHID-USER-wil4b5lylrvf3krixlkl        True   \n",
       "709    PHID-USER-ydswvwhh5pm4lshahjje        True   \n",
       "712    PHID-USER-ydswvwhh5pm4lshahjje        True   \n",
       "717    PHID-USER-sai77mtxmpqnm6pycyvz        True   \n",
       "718    PHID-USER-wil4b5lylrvf3krixlkl        True   \n",
       "...                               ...         ...   \n",
       "32172  PHID-USER-ydswvwhh5pm4lshahjje        True   \n",
       "32178  PHID-USER-ydswvwhh5pm4lshahjje        True   \n",
       "32179  PHID-USER-ydswvwhh5pm4lshahjje        True   \n",
       "32180  PHID-USER-fovtl67ew4l4cc3oeypc       False   \n",
       "32181  PHID-USER-it53o2f2kyryqyj33uzt       False   \n",
       "\n",
       "                      conversation_id      comment_type    status  \\\n",
       "708    PHID-TASK-64s56xzrc22ustp2z7wx  task_description  resolved   \n",
       "709    PHID-TASK-64s56xzrc22ustp2z7wx   task_subcomment       NaN   \n",
       "712    PHID-TASK-64s56xzrc22ustp2z7wx   task_subcomment       NaN   \n",
       "717    PHID-TASK-64s56xzrc22ustp2z7wx   task_subcomment       NaN   \n",
       "718    PHID-TASK-64s56xzrc22ustp2z7wx   task_subcomment       NaN   \n",
       "...                               ...               ...       ...   \n",
       "32172  PHID-TASK-ciosa56mnibqn4lx27ub  task_description  resolved   \n",
       "32178  PHID-TASK-ciosa56mnibqn4lx27ub   task_subcomment       NaN   \n",
       "32179  PHID-TASK-ciosa56mnibqn4lx27ub   task_subcomment       NaN   \n",
       "32180  PHID-TASK-guukovmsjsnlpphgujcv  task_description   invalid   \n",
       "32181  PHID-TASK-guukovmsjsnlpphgujcv   task_subcomment       NaN   \n",
       "\n",
       "                                                    text  \\\n",
       "708    Tested on both the Italian and the English Wik...   \n",
       "709    Note that this is fixed and has been deployed ...   \n",
       "712    *** Bug 55362 has been marked as a duplicate o...   \n",
       "717    (In reply to comment #6)\\n> Krinkle, do I need...   \n",
       "718    Krinkle, do I need to file a different bug for...   \n",
       "...                                                  ...   \n",
       "32172  Puppet config for wikibugs:\\n\\nhttps://gerrit....   \n",
       "32178  gerrit-wm is done, but wikibugs is \"an almight...   \n",
       "32179  Attempted fixes in Gerrit 37566 and Gerrit 37570.   \n",
       "32180  Test case:\\n\\n+                       'removin...   \n",
       "32181  With bug 45061 all change marker code has been...   \n",
       "\n",
       "                                           resolved_text  meta.gerrit     id  \\\n",
       "708    Tested on both the Italian and the English Wik...        False    709   \n",
       "709    Note that this is fixed and has been deployed ...        False    710   \n",
       "712    *** Bug 55362 has been marked as a duplicate o...        False    713   \n",
       "717    (In reply to comment #6)\\n> Krinkle, do I need...        False    718   \n",
       "718    Krinkle, do Krinkle need to file a different b...        False    719   \n",
       "...                                                  ...          ...    ...   \n",
       "32172  Puppet config for wikibugs:\\n\\nhttps://gerrit....        False  32173   \n",
       "32178  gerrit-wm is done, but wikibugs is \"an almight...        False  32179   \n",
       "32179  Attempted fixes in Gerrit 37566 and Gerrit 37570.        False  32180   \n",
       "32180  Test case:\\n\\n+                       'removin...        False  32181   \n",
       "32181  With bug 45061 all change marker code has been...        False  32182   \n",
       "\n",
       "       reply_to first_comment                 timestamp  \n",
       "708         NaN         False 2013-10-05 12:42:00+00:00  \n",
       "709       709.0         False 2013-10-09 01:10:33+00:00  \n",
       "712       712.0         False 2013-10-08 21:24:11+00:00  \n",
       "717       717.0         False 2013-10-07 17:47:04+00:00  \n",
       "718       718.0         False 2013-10-07 10:48:42+00:00  \n",
       "...         ...           ...                       ...  \n",
       "32172       NaN         False 2012-12-05 20:16:00+00:00  \n",
       "32178   32178.0         False 2013-02-07 03:03:48+00:00  \n",
       "32179   32179.0         False 2012-12-08 00:35:21+00:00  \n",
       "32180       NaN         False 2012-11-17 06:42:00+00:00  \n",
       "32181   32181.0         False 2013-02-16 00:44:33+00:00  \n",
       "\n",
       "[8804 rows x 15 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comment_phab_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d226d781-b002-4842-a3ae-92d4851a5878",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "def preprocess_text(text):\n",
    "    text = str(text)\n",
    "    text = text.replace('*', ' ')\n",
    "    text = text.replace('-', ' ')\n",
    "    text = re.sub(r'http\\S+', '', text)\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3ae40d24-bbe8-49c3-a3a9-70bde1b4d559",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_49967/3649688126.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  comment_phab_df['processed_text'] = comment_phab_df['text'].apply(preprocess_text)\n"
     ]
    }
   ],
   "source": [
    "comment_phab_df['processed_text'] = comment_phab_df['text'].apply(preprocess_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b8eddf40-1fe2-4fce-be74-b32552b40c57",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_49967/1316816771.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  comment_phab_df['processed_resolved_text'] = comment_phab_df['resolved_text'].apply(preprocess_text)\n"
     ]
    }
   ],
   "source": [
    "comment_phab_df['processed_resolved_text'] = comment_phab_df['resolved_text'].apply(preprocess_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a8469b16-4ae6-4b06-bf1b-1f2f6c736cab",
   "metadata": {},
   "outputs": [],
   "source": [
    "nlp = spacy.load(\"en_core_web_sm\")\n",
    "\n",
    "def extract_dependency_tree(sentence):\n",
    "    doc = nlp(sentence)\n",
    "    return [(token.text, token.lemma_, token.dep_, token.head.text, token.ancestors, token.subtree, token.children) for token in doc]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8b9a12f9-71bf-4bc9-bcfd-c73aab4be920",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_49967/2805711855.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  comment_phab_df['dependency_tree'] = comment_phab_df['processed_text'].apply(extract_dependency_tree)\n"
     ]
    }
   ],
   "source": [
    "comment_phab_df['dependency_tree'] = comment_phab_df['processed_text'].apply(extract_dependency_tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "337a528a-5667-4e1f-ac9a-37caabc03a18",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_49967/2117289791.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  comment_phab_df['resolved_dependency_tree'] = comment_phab_df['processed_resolved_text'].apply(extract_dependency_tree)\n"
     ]
    }
   ],
   "source": [
    "comment_phab_df['resolved_dependency_tree'] = comment_phab_df['processed_resolved_text'].apply(extract_dependency_tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a3f5d40b-f56e-4e31-a7f9-40b7ddb4d2a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#get VAD scores\n",
    "import numpy as np\n",
    "#https://saifmohammad.com/WebPages/nrc-vad.html\n",
    "column_headings = ['Word', 'Valence', 'Arousal', 'Domination']\n",
    "vad_lexicon = pd.read_csv('NRC-VAD-Lexicon.txt', delimiter='\\t', header=None, names=column_headings)\n",
    "vad_dict = vad_lexicon.set_index('Word').T.to_dict()\n",
    "\n",
    "def vad_scoring(dependency_tree):\n",
    "    valence = []\n",
    "    arousal = []\n",
    "    dominance = []\n",
    "    for token, lemma, dep, head, ancestors, subtree, children in dependency_tree:\n",
    "        if lemma in vad_dict:\n",
    "            valence.append(vad_dict[lemma]['Valence'])\n",
    "            arousal.append(vad_dict[lemma]['Arousal'])\n",
    "            dominance.append(vad_dict[lemma]['Domination'])\n",
    "\n",
    "    # Compute average scores across the comment\n",
    "    avg_valence = np.mean(valence) if valence else 0\n",
    "    avg_arousal = np.mean(arousal) if arousal else 0\n",
    "    avg_dominance = np.mean(dominance) if dominance else 0\n",
    "\n",
    "    return [avg_valence, avg_arousal, avg_dominance]\n",
    "\n",
    "def dominance_prevail(dependency_tree):\n",
    "    dominant_words = 0 \n",
    "    for token, lemma, dep, head, ancestors, subtree, children in dependency_tree:\n",
    "        if lemma in vad_dict:\n",
    "            if vad_dict[lemma]['Domination'] >= 0.75:\n",
    "                dominant_words += 1\n",
    "    return dominant_words\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "828fb57a-e152-42ef-9c60-660648898532",
   "metadata": {},
   "outputs": [],
   "source": [
    "#establishing per-comment VAD scores \n",
    "comment_phab_df['avg_vad_scores'] = comment_phab_df['dependency_tree'].apply(vad_scoring)\n",
    "comment_phab_df['dominant_wc'] = comment_phab_df['dependency_tree'].apply(dominance_prevail)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "27e47f6f-0257-4b70-b222-e91ef888c900",
   "metadata": {},
   "outputs": [
    {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>...</th>\n",
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       "      <th>average_v_score</th>\n",
       "      <th>average_a_score</th>\n",
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       "      <td>Note that this is fixed and has been deployed ...</td>\n",
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       "      <td>VisualEditor: [Regression] \"More\" menu gets sh...</td>\n",
       "      <td>*** Bug 55362 has been marked as a duplicate o...</td>\n",
       "      <td>1381267451</td>\n",
       "      <td>PHID-USER-ydswvwhh5pm4lshahjje</td>\n",
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       "      <td>False</td>\n",
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       "      <td>(In reply to comment #6)\\n&gt; Krinkle, do I need...</td>\n",
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       "      <th>32172</th>\n",
       "      <td>Setup wikibugs and gerrit-wm for #mediawiki-vi...</td>\n",
       "      <td>Puppet config for wikibugs:\\n\\nhttps://gerrit....</td>\n",
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       "      <td>Puppet config for wikibugs:\\n\\n\\n\\nPuppet conf...</td>\n",
       "      <td>Puppet config for wikibugs:\\n\\n\\n\\nPuppet conf...</td>\n",
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       "    <tr>\n",
       "      <th>32178</th>\n",
       "      <td>Setup wikibugs and gerrit-wm for #mediawiki-vi...</td>\n",
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       "      <td>gerrit-wm is done, but wikibugs is \"an almight...</td>\n",
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       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>2013-02-07 03:03:48+00:00</td>\n",
       "      <td>gerrit wm is done, but wikibugs is \"an almight...</td>\n",
       "      <td>gerrit wm is done, but wikibugs is \"an almight...</td>\n",
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       "    <tr>\n",
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       "      <td>Setup wikibugs and gerrit-wm for #mediawiki-vi...</td>\n",
       "      <td>Attempted fixes in Gerrit 37566 and Gerrit 37570.</td>\n",
       "      <td>1354926921</td>\n",
       "      <td>PHID-USER-ydswvwhh5pm4lshahjje</td>\n",
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       "      <td>task_subcomment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attempted fixes in Gerrit 37566 and Gerrit 37570.</td>\n",
       "      <td>Attempted fixes in Gerrit 37566 and Gerrit 37570.</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>2012-12-08 00:35:21+00:00</td>\n",
       "      <td>Attempted fixes in Gerrit 37566 and Gerrit 37570.</td>\n",
       "      <td>Attempted fixes in Gerrit 37566 and Gerrit 37570.</td>\n",
       "      <td>[(Attempted, attempt, amod, fixes, &lt;generator ...</td>\n",
       "      <td>[(Attempted, attempt, amod, fixes, &lt;generator ...</td>\n",
       "      <td>0.692500</td>\n",
       "      <td>0.514500</td>\n",
       "      <td>0.475000</td>\n",
       "      <td>0</td>\n",
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       "      <th>32180</th>\n",
       "      <td>VisualEditor: Two replacements within the same...</td>\n",
       "      <td>Test case:\\n\\n+                       'removin...</td>\n",
       "      <td>1353134520</td>\n",
       "      <td>PHID-USER-fovtl67ew4l4cc3oeypc</td>\n",
       "      <td>False</td>\n",
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       "      <td>task_description</td>\n",
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       "      <td>Test case:\\n\\n+                       'removin...</td>\n",
       "      <td>Test case:\\n\\n+                       'removin...</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>2012-11-17 06:42:00+00:00</td>\n",
       "      <td>Test case:\\n\\n+                       'removin...</td>\n",
       "      <td>Test case:\\n\\n+                       'removin...</td>\n",
       "      <td>[(Test, test, compound, case, &lt;generator objec...</td>\n",
       "      <td>[(Test, test, compound, case, &lt;generator objec...</td>\n",
       "      <td>0.567509</td>\n",
       "      <td>0.448561</td>\n",
       "      <td>0.535053</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32181</th>\n",
       "      <td>VisualEditor: Two replacements within the same...</td>\n",
       "      <td>With bug 45061 all change marker code has been...</td>\n",
       "      <td>1360975473</td>\n",
       "      <td>PHID-USER-it53o2f2kyryqyj33uzt</td>\n",
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       "      <td>task_subcomment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>With bug 45061 all change marker code has been...</td>\n",
       "      <td>With bug 45061 all change marker code has been...</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>2013-02-16 00:44:33+00:00</td>\n",
       "      <td>With bug 45061 all change marker code has been...</td>\n",
       "      <td>With bug 45061 all change marker code has been...</td>\n",
       "      <td>[(With, with, prep, change, &lt;generator object ...</td>\n",
       "      <td>[(With, with, prep, change, &lt;generator object ...</td>\n",
       "      <td>0.530429</td>\n",
       "      <td>0.412000</td>\n",
       "      <td>0.509571</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8804 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              task_title  \\\n",
       "708    VisualEditor: [Regression] \"More\" menu gets sh...   \n",
       "709    VisualEditor: [Regression] \"More\" menu gets sh...   \n",
       "712    VisualEditor: [Regression] \"More\" menu gets sh...   \n",
       "717    VisualEditor: [Regression] \"More\" menu gets sh...   \n",
       "718    VisualEditor: [Regression] \"More\" menu gets sh...   \n",
       "...                                                  ...   \n",
       "32172  Setup wikibugs and gerrit-wm for #mediawiki-vi...   \n",
       "32178  Setup wikibugs and gerrit-wm for #mediawiki-vi...   \n",
       "32179  Setup wikibugs and gerrit-wm for #mediawiki-vi...   \n",
       "32180  VisualEditor: Two replacements within the same...   \n",
       "32181  VisualEditor: Two replacements within the same...   \n",
       "\n",
       "                                            comment_text  date_created  \\\n",
       "708    Tested on both the Italian and the English Wik...    1380976920   \n",
       "709    Note that this is fixed and has been deployed ...    1381281033   \n",
       "712    *** Bug 55362 has been marked as a duplicate o...    1381267451   \n",
       "717    (In reply to comment #6)\\n> Krinkle, do I need...    1381168024   \n",
       "718    Krinkle, do I need to file a different bug for...    1381142922   \n",
       "...                                                  ...           ...   \n",
       "32172  Puppet config for wikibugs:\\n\\nhttps://gerrit....    1354738560   \n",
       "32178  gerrit-wm is done, but wikibugs is \"an almight...    1360206228   \n",
       "32179  Attempted fixes in Gerrit 37566 and Gerrit 37570.    1354926921   \n",
       "32180  Test case:\\n\\n+                       'removin...    1353134520   \n",
       "32181  With bug 45061 all change marker code has been...    1360975473   \n",
       "\n",
       "                              speaker  meta.affil  \\\n",
       "708    PHID-USER-wil4b5lylrvf3krixlkl        True   \n",
       "709    PHID-USER-ydswvwhh5pm4lshahjje        True   \n",
       "712    PHID-USER-ydswvwhh5pm4lshahjje        True   \n",
       "717    PHID-USER-sai77mtxmpqnm6pycyvz        True   \n",
       "718    PHID-USER-wil4b5lylrvf3krixlkl        True   \n",
       "...                               ...         ...   \n",
       "32172  PHID-USER-ydswvwhh5pm4lshahjje        True   \n",
       "32178  PHID-USER-ydswvwhh5pm4lshahjje        True   \n",
       "32179  PHID-USER-ydswvwhh5pm4lshahjje        True   \n",
       "32180  PHID-USER-fovtl67ew4l4cc3oeypc       False   \n",
       "32181  PHID-USER-it53o2f2kyryqyj33uzt       False   \n",
       "\n",
       "                      conversation_id      comment_type    status  \\\n",
       "708    PHID-TASK-64s56xzrc22ustp2z7wx  task_description  resolved   \n",
       "709    PHID-TASK-64s56xzrc22ustp2z7wx   task_subcomment       NaN   \n",
       "712    PHID-TASK-64s56xzrc22ustp2z7wx   task_subcomment       NaN   \n",
       "717    PHID-TASK-64s56xzrc22ustp2z7wx   task_subcomment       NaN   \n",
       "718    PHID-TASK-64s56xzrc22ustp2z7wx   task_subcomment       NaN   \n",
       "...                               ...               ...       ...   \n",
       "32172  PHID-TASK-ciosa56mnibqn4lx27ub  task_description  resolved   \n",
       "32178  PHID-TASK-ciosa56mnibqn4lx27ub   task_subcomment       NaN   \n",
       "32179  PHID-TASK-ciosa56mnibqn4lx27ub   task_subcomment       NaN   \n",
       "32180  PHID-TASK-guukovmsjsnlpphgujcv  task_description   invalid   \n",
       "32181  PHID-TASK-guukovmsjsnlpphgujcv   task_subcomment       NaN   \n",
       "\n",
       "                                                    text  \\\n",
       "708    Tested on both the Italian and the English Wik...   \n",
       "709    Note that this is fixed and has been deployed ...   \n",
       "712    *** Bug 55362 has been marked as a duplicate o...   \n",
       "717    (In reply to comment #6)\\n> Krinkle, do I need...   \n",
       "718    Krinkle, do I need to file a different bug for...   \n",
       "...                                                  ...   \n",
       "32172  Puppet config for wikibugs:\\n\\nhttps://gerrit....   \n",
       "32178  gerrit-wm is done, but wikibugs is \"an almight...   \n",
       "32179  Attempted fixes in Gerrit 37566 and Gerrit 37570.   \n",
       "32180  Test case:\\n\\n+                       'removin...   \n",
       "32181  With bug 45061 all change marker code has been...   \n",
       "\n",
       "                                           resolved_text  ...  first_comment  \\\n",
       "708    Tested on both the Italian and the English Wik...  ...          False   \n",
       "709    Note that this is fixed and has been deployed ...  ...          False   \n",
       "712    *** Bug 55362 has been marked as a duplicate o...  ...          False   \n",
       "717    (In reply to comment #6)\\n> Krinkle, do I need...  ...          False   \n",
       "718    Krinkle, do Krinkle need to file a different b...  ...          False   \n",
       "...                                                  ...  ...            ...   \n",
       "32172  Puppet config for wikibugs:\\n\\nhttps://gerrit....  ...          False   \n",
       "32178  gerrit-wm is done, but wikibugs is \"an almight...  ...          False   \n",
       "32179  Attempted fixes in Gerrit 37566 and Gerrit 37570.  ...          False   \n",
       "32180  Test case:\\n\\n+                       'removin...  ...          False   \n",
       "32181  With bug 45061 all change marker code has been...  ...          False   \n",
       "\n",
       "                      timestamp  \\\n",
       "708   2013-10-05 12:42:00+00:00   \n",
       "709   2013-10-09 01:10:33+00:00   \n",
       "712   2013-10-08 21:24:11+00:00   \n",
       "717   2013-10-07 17:47:04+00:00   \n",
       "718   2013-10-07 10:48:42+00:00   \n",
       "...                         ...   \n",
       "32172 2012-12-05 20:16:00+00:00   \n",
       "32178 2013-02-07 03:03:48+00:00   \n",
       "32179 2012-12-08 00:35:21+00:00   \n",
       "32180 2012-11-17 06:42:00+00:00   \n",
       "32181 2013-02-16 00:44:33+00:00   \n",
       "\n",
       "                                          processed_text  \\\n",
       "708    Tested on both the Italian and the English Wik...   \n",
       "709    Note that this is fixed and has been deployed ...   \n",
       "712        Bug 55362 has been marked as a duplicate o...   \n",
       "717    (In reply to comment #6)\\n> Krinkle, do I need...   \n",
       "718    Krinkle, do I need to file a different bug for...   \n",
       "...                                                  ...   \n",
       "32172  Puppet config for wikibugs:\\n\\n\\n\\nPuppet conf...   \n",
       "32178  gerrit wm is done, but wikibugs is \"an almight...   \n",
       "32179  Attempted fixes in Gerrit 37566 and Gerrit 37570.   \n",
       "32180  Test case:\\n\\n+                       'removin...   \n",
       "32181  With bug 45061 all change marker code has been...   \n",
       "\n",
       "                                 processed_resolved_text  \\\n",
       "708    Tested on both the Italian and the English Wik...   \n",
       "709    Note that this is fixed and has been deployed ...   \n",
       "712        Bug 55362 has been marked as a duplicate o...   \n",
       "717    (In reply to comment #6)\\n> Krinkle, do I need...   \n",
       "718    Krinkle, do Krinkle need to file a different b...   \n",
       "...                                                  ...   \n",
       "32172  Puppet config for wikibugs:\\n\\n\\n\\nPuppet conf...   \n",
       "32178  gerrit wm is done, but wikibugs is \"an almight...   \n",
       "32179  Attempted fixes in Gerrit 37566 and Gerrit 37570.   \n",
       "32180  Test case:\\n\\n+                       'removin...   \n",
       "32181  With bug 45061 all change marker code has been...   \n",
       "\n",
       "                                         dependency_tree  \\\n",
       "708    [(Tested, test, advcl, Reach, <generator objec...   \n",
       "709    [(Note, note, ROOT, Note, <generator object at...   \n",
       "712    [(    ,     , dep, Bug, <generator object at 0...   \n",
       "717    [((, (, punct, comment, <generator object at 0...   \n",
       "718    [(Krinkle, Krinkle, npadvmod, need, <generator...   \n",
       "...                                                  ...   \n",
       "32172  [(Puppet, puppet, compound, config, <generator...   \n",
       "32178  [(gerrit, gerrit, compound, wm, <generator obj...   \n",
       "32179  [(Attempted, attempt, amod, fixes, <generator ...   \n",
       "32180  [(Test, test, compound, case, <generator objec...   \n",
       "32181  [(With, with, prep, change, <generator object ...   \n",
       "\n",
       "                                resolved_dependency_tree average_v_score  \\\n",
       "708    [(Tested, test, advcl, Reach, <generator objec...        0.575304   \n",
       "709    [(Note, note, ROOT, Note, <generator object at...        0.623100   \n",
       "712    [(    ,     , dep, Bug, <generator object at 0...        0.501833   \n",
       "717    [((, (, punct, comment, <generator object at 0...        0.569450   \n",
       "718    [(Krinkle, Krinkle, npadvmod, need, <generator...        0.614556   \n",
       "...                                                  ...             ...   \n",
       "32172  [(Puppet, puppet, compound, config, <generator...        0.525333   \n",
       "32178  [(gerrit, gerrit, compound, wm, <generator obj...        0.595818   \n",
       "32179  [(Attempted, attempt, amod, fixes, <generator ...        0.692500   \n",
       "32180  [(Test, test, compound, case, <generator objec...        0.567509   \n",
       "32181  [(With, with, prep, change, <generator object ...        0.530429   \n",
       "\n",
       "      average_a_score average_d_score  dominant_wc  \n",
       "708          0.397913        0.475913            2  \n",
       "709          0.422900        0.543500            0  \n",
       "712          0.391667        0.429500            0  \n",
       "717          0.405600        0.437650            1  \n",
       "718          0.432444        0.437667            1  \n",
       "...               ...             ...          ...  \n",
       "32172        0.429333        0.401333            0  \n",
       "32178        0.512091        0.566273            3  \n",
       "32179        0.514500        0.475000            0  \n",
       "32180        0.448561        0.535053            4  \n",
       "32181        0.412000        0.509571            0  \n",
       "\n",
       "[8804 rows x 23 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comment_phab_df[['average_v_score', 'average_a_score', 'average_d_score']] = pd.DataFrame(comment_phab_df['avg_vad_scores'].tolist(), index=comment_phab_df.index)\n",
    "comment_phab_df = comment_phab_df.drop(columns=['avg_vad_scores'])\n",
    "comment_phab_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "184ccbe6-0a7a-41b8-9b02-bc439ff975d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# expand the dependency parser \n",
    "dependency_relations = []\n",
    "resolved_dependency_relations = []\n",
    "\n",
    "for index, row in comment_phab_df.iterrows():\n",
    "    text = row['text']\n",
    "    timestamp = row['timestamp']\n",
    "    comment_id = row['id']\n",
    "    conversation_id = row['conversation_id']\n",
    "    WMFaffil = row['meta.affil']\n",
    "    \n",
    "    for token, lemma, dep, head, ancestors, subtree, children in row['dependency_tree']:\n",
    "        if re.search(r'\\b(visualeditor|VE|ve|VisualEditor)\\b', token, re.IGNORECASE):\n",
    "            dependency_relations.append({\n",
    "                'comment_id': comment_id,\n",
    "                'timestamp': timestamp,\n",
    "                'wmfAffil':WMFaffil,\n",
    "                'token': token,\n",
    "                'dependency': dep,\n",
    "                'head': head,\n",
    "                'depth': len(list(ancestors)), \n",
    "                'children': len(list(children)) \n",
    "            })\n",
    "    for token, lemma, dep, head, ancestors, subtree, children in row['resolved_dependency_tree']:\n",
    "        if re.search(r'\\b(visualeditor|VE|ve|VisualEditor)\\b', token, re.IGNORECASE):\n",
    "            resolved_dependency_relations.append({\n",
    "                'comment_id': comment_id,\n",
    "                'timestamp': timestamp,\n",
    "                'wmfAffil':WMFaffil,\n",
    "                'token': token,\n",
    "                'dependency': dep,\n",
    "                'head': head,\n",
    "                'depth': len(list(ancestors)), \n",
    "                'children': len(list(children)) \n",
    "            })\n",
    "\n",
    "resolved_dependency_relations_df = pd.DataFrame(resolved_dependency_relations)            \n",
    "dependency_relations_df = pd.DataFrame(dependency_relations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "82498686-14f4-40c8-9e33-27b31f115b47",
   "metadata": {},
   "outputs": [],
   "source": [
    "#now analysis/plotting \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from matplotlib.gridspec import GridSpec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "82cd9dde-0d14-4de5-8482-5a39de8d2869",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_49967/3248547585.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  task_phab_df['first_comment'] = task_phab_df.groupby('speaker')['timestamp'].rank(method='first') == 1\n",
      "/tmp/ipykernel_49967/3248547585.py:6: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.\n",
      "  task_phab_df['week'] = task_phab_df['timestamp'].dt.to_period('W').dt.start_time\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "task_phab_df = phab_df[phab_df['comment_type']==\"task_description\"]\n",
    "task_phab_df['first_comment'] = task_phab_df.groupby('speaker')['timestamp'].rank(method='first') == 1\n",
    "task_phab_df = task_phab_df[(task_phab_df['date_created'] < 1383264000) & (task_phab_df['date_created'] > 1351728000)]\n",
    "\n",
    "task_phab_df['week'] = task_phab_df['timestamp'].dt.to_period('W').dt.start_time\n",
    "unique_taskPHIDs = task_phab_df.groupby('week')['conversation_id'].nunique()\n",
    "\n",
    "wmf_task_phab_df = task_phab_df[task_phab_df['meta.affil'] == True]\n",
    "wmf_tasks = wmf_task_phab_df.groupby('week')['conversation_id'].nunique()\n",
    "\n",
    "other_task_phab_df = task_phab_df[task_phab_df['meta.affil'] != True]\n",
    "other_tasks = other_task_phab_df.groupby('week')['conversation_id'].nunique()\n",
    "\n",
    "new_tasks_phab_df = task_phab_df[task_phab_df['first_comment'] == True]\n",
    "new_tasks = new_tasks_phab_df.groupby('week')['conversation_id'].nunique()\n",
    "\n",
    "sns.lineplot(x=unique_taskPHIDs.index, y=unique_taskPHIDs.values, color='black', label='Total', marker='o')\n",
    "sns.lineplot(x=wmf_tasks.index, y=wmf_tasks.values, color='#c7756a', label='WMF-affiliated authors', marker='o')\n",
    "sns.lineplot(x=other_tasks.index, y=other_tasks.values, color='#5da2d8', label='Nonaffiliated authors', marker='o')\n",
    "sns.lineplot(x=new_tasks.index, y=new_tasks.values, color=\"green\", label=\"first-timers\", marker='o')\n",
    "\n",
    "plt.title('New Phabricator Tasks Indexed with \"VisualEditor\"')\n",
    "plt.xlabel('Timestamp')\n",
    "plt.ylabel('Unique taskPHIDs')\n",
    "plt.xticks(rotation=45)\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b7cfad77-d48a-4708-91f3-89ae1179b90c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1096.11x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "comment_phab_df['before_after'] = comment_phab_df['timestamp'] > pd.Timestamp('2013-07-01 00:00:01+00:00')\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "sns.lmplot(data=comment_phab_df, x=\"date_created\", y=\"dominant_wc\", hue=\"before_after\", col=\"meta.affil\", scatter=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "d2d67d38-f005-4c94-be3c-39eb6b22686f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_49967/3455565877.py:2: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.\n",
      "  filtered_dependencies = dependency_relations_df[dependency_relations_df['token'].str.contains(pattern, regex=True)]\n",
      "/tmp/ipykernel_49967/3455565877.py:3: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.\n",
      "  resolved_filtered_dependencies = resolved_dependency_relations_df[resolved_dependency_relations_df['token'].str.contains(pattern, regex=True)]\n",
      "/tmp/ipykernel_49967/3455565877.py:18: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.\n",
      "  filtered_dependencies['week'] = filtered_dependencies['timestamp'].dt.to_period('W').dt.start_time\n",
      "/tmp/ipykernel_49967/3455565877.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  filtered_dependencies['week'] = filtered_dependencies['timestamp'].dt.to_period('W').dt.start_time\n",
      "/tmp/ipykernel_49967/3455565877.py:37: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.\n",
      "  resolved_filtered_dependencies['week'] = resolved_filtered_dependencies['timestamp'].dt.to_period('W').dt.start_time\n",
      "/tmp/ipykernel_49967/3455565877.py:37: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  resolved_filtered_dependencies['week'] = resolved_filtered_dependencies['timestamp'].dt.to_period('W').dt.start_time\n",
      "/tmp/ipykernel_49967/3455565877.py:40: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  resolved_wmf_filtered_dependencies = resolved_filtered_dependencies[filtered_dependencies['wmfAffil'] == True]\n"
     ]
    },
    {
     "ename": "IndexingError",
     "evalue": "Unalignable boolean Series provided as indexer (index of the boolean Series and of the indexed object do not match).",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexingError\u001b[0m                             Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[53], line 40\u001b[0m\n\u001b[1;32m     37\u001b[0m resolved_filtered_dependencies[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mweek\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m resolved_filtered_dependencies[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtimestamp\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mdt\u001b[38;5;241m.\u001b[39mto_period(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mW\u001b[39m\u001b[38;5;124m'\u001b[39m)\u001b[38;5;241m.\u001b[39mdt\u001b[38;5;241m.\u001b[39mstart_time\n\u001b[1;32m     38\u001b[0m resolved_median_depth \u001b[38;5;241m=\u001b[39m resolved_filtered_dependencies\u001b[38;5;241m.\u001b[39mgroupby(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mweek\u001b[39m\u001b[38;5;124m'\u001b[39m)[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdepth\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mmedian()\u001b[38;5;241m.\u001b[39mreset_index()\n\u001b[0;32m---> 40\u001b[0m resolved_wmf_filtered_dependencies \u001b[38;5;241m=\u001b[39m \u001b[43mresolved_filtered_dependencies\u001b[49m\u001b[43m[\u001b[49m\u001b[43mfiltered_dependencies\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mwmfAffil\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m]\u001b[49m\n\u001b[1;32m     41\u001b[0m resolved_wmf_median_depth \u001b[38;5;241m=\u001b[39m resolved_wmf_filtered_dependencies\u001b[38;5;241m.\u001b[39mgroupby(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mweek\u001b[39m\u001b[38;5;124m'\u001b[39m)[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdepth\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mmedian()\u001b[38;5;241m.\u001b[39mreset_index()\n\u001b[1;32m     43\u001b[0m resolved_other_filtered_dependencies \u001b[38;5;241m=\u001b[39m resolved_filtered_dependencies[filtered_dependencies[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwmfAffil\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m]\n",
      "File \u001b[0;32m/gscratch/scrubbed/mjilg/jupyter-notebook/lib/python3.9/site-packages/pandas/core/frame.py:4093\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   4091\u001b[0m \u001b[38;5;66;03m# Do we have a (boolean) 1d indexer?\u001b[39;00m\n\u001b[1;32m   4092\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m com\u001b[38;5;241m.\u001b[39mis_bool_indexer(key):\n\u001b[0;32m-> 4093\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_bool_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4095\u001b[0m \u001b[38;5;66;03m# We are left with two options: a single key, and a collection of keys,\u001b[39;00m\n\u001b[1;32m   4096\u001b[0m \u001b[38;5;66;03m# We interpret tuples as collections only for non-MultiIndex\u001b[39;00m\n\u001b[1;32m   4097\u001b[0m is_single_key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28misinstance\u001b[39m(key, \u001b[38;5;28mtuple\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_list_like(key)\n",
      "File \u001b[0;32m/gscratch/scrubbed/mjilg/jupyter-notebook/lib/python3.9/site-packages/pandas/core/frame.py:4149\u001b[0m, in \u001b[0;36mDataFrame._getitem_bool_array\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   4143\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m   4144\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mItem wrong length \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(key)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m instead of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindex)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   4145\u001b[0m     )\n\u001b[1;32m   4147\u001b[0m \u001b[38;5;66;03m# check_bool_indexer will throw exception if Series key cannot\u001b[39;00m\n\u001b[1;32m   4148\u001b[0m \u001b[38;5;66;03m# be reindexed to match DataFrame rows\u001b[39;00m\n\u001b[0;32m-> 4149\u001b[0m key \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_bool_indexer\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4151\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m key\u001b[38;5;241m.\u001b[39mall():\n\u001b[1;32m   4152\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcopy(deep\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m)\n",
      "File \u001b[0;32m/gscratch/scrubbed/mjilg/jupyter-notebook/lib/python3.9/site-packages/pandas/core/indexing.py:2662\u001b[0m, in \u001b[0;36mcheck_bool_indexer\u001b[0;34m(index, key)\u001b[0m\n\u001b[1;32m   2660\u001b[0m indexer \u001b[38;5;241m=\u001b[39m result\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39mget_indexer_for(index)\n\u001b[1;32m   2661\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m \u001b[38;5;129;01min\u001b[39;00m indexer:\n\u001b[0;32m-> 2662\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m IndexingError(\n\u001b[1;32m   2663\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnalignable boolean Series provided as \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   2664\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mindexer (index of the boolean Series and of \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   2665\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mthe indexed object do not match).\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   2666\u001b[0m     )\n\u001b[1;32m   2668\u001b[0m result \u001b[38;5;241m=\u001b[39m result\u001b[38;5;241m.\u001b[39mtake(indexer)\n\u001b[1;32m   2670\u001b[0m \u001b[38;5;66;03m# fall through for boolean\u001b[39;00m\n",
      "\u001b[0;31mIndexingError\u001b[0m: Unalignable boolean Series provided as indexer (index of the boolean Series and of the indexed object do not match)."
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pattern = r'\\b(ve|VE|visualeditor|VisualEditor)\\b'\n",
    "filtered_dependencies = dependency_relations_df[dependency_relations_df['token'].str.contains(pattern, regex=True)]\n",
    "resolved_filtered_dependencies = resolved_dependency_relations_df[resolved_dependency_relations_df['token'].str.contains(pattern, regex=True)]\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "gs = GridSpec(2, 1, height_ratios=[6, 6])\n",
    "\n",
    "# Main plot: Token depth by timestamp\n",
    "'''\n",
    "ax0 = plt.subplot(gs[0])\n",
    "sns.scatterplot(data=filtered_dependencies, x='timestamp', y='dependency', hue='wmfAffil', style='dependency', markers=True, s=100, ax=ax0)\n",
    "ax0.set_title('VE Depth by Timestamp w/o URLS')\n",
    "ax0.set_xlabel('')\n",
    "ax0.set_ylabel('Dependency Type')\n",
    "ax0.legend().set_visible(False)\n",
    "'''\n",
    "# Calculate the median depth over time\n",
    "filtered_dependencies['week'] = filtered_dependencies['timestamp'].dt.to_period('W').dt.start_time\n",
    "median_depth = filtered_dependencies.groupby('week')['depth'].median().reset_index()\n",
    "\n",
    "wmf_filtered_dependencies = filtered_dependencies[filtered_dependencies['wmfAffil'] == True]\n",
    "wmf_median_depth = wmf_filtered_dependencies.groupby('week')['depth'].median().reset_index()\n",
    "\n",
    "other_filtered_dependencies = filtered_dependencies[filtered_dependencies['wmfAffil'] != True]\n",
    "other_median_depth = other_filtered_dependencies.groupby('week')['depth'].median().reset_index()\n",
    "\n",
    "# Plot the median depth over time\n",
    "ax0 = plt.subplot(gs[0])\n",
    "sns.lineplot(data=median_depth, x='week', y='depth', ax=ax0, color='black', label='Median Depth', marker='o')\n",
    "sns.lineplot(data=wmf_median_depth, x='week', y='depth', ax=ax0, color='#c7756a', label='WMF-affiliated authors', marker='x')\n",
    "sns.lineplot(data=other_median_depth, x='week', y='depth', ax=ax0, color='#5da2d8', label='Nonaffiliated authors', marker='x')\n",
    "ax0.set_title('Median Depth of VE in Phabricator Sentence Dependency Trees')\n",
    "ax0.set_ylabel('Median Depth')\n",
    "ax0.set_xlabel('')\n",
    "\n",
    "# Calculate the median depth over time\n",
    "resolved_filtered_dependencies['week'] = resolved_filtered_dependencies['timestamp'].dt.to_period('W').dt.start_time\n",
    "resolved_median_depth = resolved_filtered_dependencies.groupby('week')['depth'].median().reset_index()\n",
    "\n",
    "resolved_wmf_filtered_dependencies = resolved_filtered_dependencies[filtered_dependencies['wmfAffil'] == True]\n",
    "resolved_wmf_median_depth = resolved_wmf_filtered_dependencies.groupby('week')['depth'].median().reset_index()\n",
    "\n",
    "resolved_other_filtered_dependencies = resolved_filtered_dependencies[filtered_dependencies['wmfAffil'] != True]\n",
    "resolved_other_median_depth = resolved_other_filtered_dependencies.groupby('week')['depth'].median().reset_index()\n",
    "\n",
    "# Plot the median depth over time\n",
    "ax1 = plt.subplot(gs[1])\n",
    "sns.lineplot(data=resolved_median_depth, x='week', y='depth', ax=ax1, color='black', label='Median Depth', marker='o')\n",
    "sns.lineplot(data=resolved_wmf_median_depth, x='week', y='depth', ax=ax1, color='#c7756a', label='WMF-affiliated authors', marker='x')\n",
    "sns.lineplot(data=resolved_other_median_depth, x='week', y='depth', ax=ax1, color='#5da2d8', label='Nonaffiliated authors', marker='x')\n",
    "ax1.set_title('Median Depth of VE in Coreference-resolved Phabricator Sentence Dependency Trees')\n",
    "ax1.set_ylabel('Median Depth')\n",
    "ax1.set_xlabel('')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.21"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}