mw-lifecycle-analysis/text_analysis/case2/040425_phab_comments.ipynb

{
 "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": 8,
   "id": "e4f0b3f0-5255-46f1-822f-e455087ba315",
   "metadata": {},
   "outputs": [],
   "source": [
    "phab_path = \"/mmfs1/gscratch/comdata/users/mjilg/mw-repo-lifecycles/case2/0512_https_phab_comments.csv\"\n",
    "phab_df = pd.read_csv(phab_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ac5e624b-08a4-4ede-bc96-cfc26c3edac3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def http_relevant(text):\n",
    "    if pd.isnull(text):\n",
    "        return False\n",
    "    # expanded dictionary for relevancy\n",
    "    # http, login, SSL, TLS, certificate \n",
    "    for word in text.split():\n",
    "        if \"://\" not in word.lower():\n",
    "            #http\n",
    "            if \"http\" in word.lower():\n",
    "                return True\n",
    "            #login\n",
    "            if \"login\" in word.lower():\n",
    "                return True\n",
    "            #ssl\n",
    "            if \"ssl\" in word.lower():\n",
    "                return True\n",
    "            #tls\n",
    "            if \"tls\" in word.lower():\n",
    "                return True\n",
    "            #cert\n",
    "            if word.lower().startswith(\"cert\") and not word.lower().startswith(\"certain\"):\n",
    "                return True\n",
    "    return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d5925c49-ea1d-4813-98aa-eae10d5879ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_migrated(comment_text):\n",
    "    if pd.isnull(comment_text):\n",
    "        return False\n",
    "    text = comment_text.strip()\n",
    "    if text.startswith(\"Originally from: http://sourceforge.net\"):\n",
    "        return True \n",
    "    return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d449164e-1d28-4580-9eb1-f0f69978f114",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_59130/3758790231.py:41: 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",
      "  mid_comment_phab_df['is_relevant'] = mid_comment_phab_df['conversation_id'].isin(relevant_conversation_ids)\n",
      "/tmp/ipykernel_59130/3758790231.py:44: 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",
      "  mid_comment_phab_df['is_migrated'] = mid_comment_phab_df['conversation_id'].isin(migrated_conversation_ids)\n"
     ]
    }
   ],
   "source": [
    "#find gerrit phab PHID: PHID-USER-idceizaw6elwiwm5xshb\n",
    "phab_df['isGerrit'] = phab_df['AuthorPHID'] == 'PHID-USER-idceizaw6elwiwm5xshb'\n",
    "\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 9-3-2011 before 11-27-2013\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'] <  1385596799) & (phab_df['date_created'] > 1315008000)]\n",
    "#filtered_phab_df = phab_df[(phab_df['date_created'] < 1381691276) & (phab_df['date_created'] > 1379975444)]\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",
    "mid_comment_phab_df = filtered_phab_df[filtered_phab_df['meta.gerrit'] != True]\n",
    "\n",
    "# filter out the sourceforge migration \n",
    "# Originally from: http://sourceforge.net in the task task_summary\n",
    "migrated_conversation_ids = task_phab_df[task_phab_df['comment_text'].apply(is_migrated)]['conversation_id'].unique()\n",
    "\n",
    "#cut down to only the data that is relevant (mentions http)\n",
    "relevant_conversation_ids = task_phab_df[\n",
    "    task_phab_df['comment_text'].apply(http_relevant) |\n",
    "    task_phab_df['task_title'].apply(http_relevant)\n",
    "]['conversation_id'].unique()\n",
    "\n",
    "task_phab_df['is_relevant'] = task_phab_df['conversation_id'].isin(relevant_conversation_ids)\n",
    "mid_comment_phab_df['is_relevant'] = mid_comment_phab_df['conversation_id'].isin(relevant_conversation_ids)\n",
    "\n",
    "task_phab_df['is_migrated'] = task_phab_df['conversation_id'].isin(migrated_conversation_ids)\n",
    "mid_comment_phab_df['is_migrated'] = mid_comment_phab_df['conversation_id'].isin(migrated_conversation_ids)\n",
    "\n",
    "comment_phab_df = mid_comment_phab_df[(mid_comment_phab_df['is_relevant'] == True) & (mid_comment_phab_df['is_migrated'] != True)]\n",
    "task_phab_df = task_phab_df[(task_phab_df['is_relevant'] == True) & (task_phab_df['is_migrated'] != True)]\n",
    "#comment_phab_df = mid_comment_phab_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "942344db-c8f5-4ed6-a757-c97f8454f18b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique conversation_ids: 1021\n",
      "Unique ids: 6282\n",
      "Unique speakers: 293\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": 12,
   "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": 13,
   "id": "3ae40d24-bbe8-49c3-a3a9-70bde1b4d559",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_59130/2783900859.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['comment_text'].apply(preprocess_text)\n"
     ]
    }
   ],
   "source": [
    "comment_phab_df['processed_text'] = comment_phab_df['comment_text'].apply(preprocess_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8eddf40-1fe2-4fce-be74-b32552b40c57",
   "metadata": {},
   "outputs": [],
   "source": [
    "#comment_phab_df['processed_resolved_text'] = comment_phab_df['resolved_text'].apply(preprocess_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a8469b16-4ae6-4b06-bf1b-1f2f6c736cab",
   "metadata": {},
   "outputs": [],
   "source": [
    "nlp = spacy.load(\"en_core_web_sm\")\n",
    "\n",
    "def extract_dependency_tree(text):\n",
    "    doc = nlp(text)\n",
    "    dependency_trees = []\n",
    "    \n",
    "    for sentence in doc.sents:\n",
    "        for token in sentence:\n",
    "            token_info = (\n",
    "                token.text, \n",
    "                token.lemma_, \n",
    "                token.dep_, \n",
    "                token.head.text, \n",
    "                list(token.ancestors), \n",
    "                list(token.subtree), \n",
    "                list(token.children)\n",
    "            )\n",
    "            dependency_trees.append(token_info)\n",
    "        \n",
    "    return dependency_trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8b9a12f9-71bf-4bc9-bcfd-c73aab4be920",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_59130/2805711855.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": null,
   "id": "337a528a-5667-4e1f-ac9a-37caabc03a18",
   "metadata": {},
   "outputs": [],
   "source": [
    "#comment_phab_df['resolved_dependency_tree'] = comment_phab_df['processed_resolved_text'].apply(extract_dependency_tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "e3364ab1-1879-4b89-8b3b-6ab5449fccfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "114      After last update via SVN bot does not work, s...\n",
       "156      Timestamp has been changed since 27th septembe...\n",
       "176      **Author:** `happy.melon.wiki`\\n\\n**Descriptio...\n",
       "246      Steps to reproduce\\n1) Login to translatewiki....\n",
       "370      Recently, several refs are not accessible thro...\n",
       "                               ...                        \n",
       "45008    We have reports that since the HTTPS enabling,...\n",
       "45245    ssh is quite painful over a slow and/or lossy ...\n",
       "45299    The problem:\\nEspecially during lightning depl...\n",
       "45372    There are many pages for which VisualEditor is...\n",
       "46077    **Author:** `ka.hing.chan`\\n\\n**Description:**...\n",
       "Name: comment_text, Length: 382, dtype: object"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "task_phab_df['comment_text']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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",
    "            if vad_dict[lemma]['Domination'] <= 0.25:\n",
    "                dominant_words += 1\n",
    "    return dominant_words\n",
    "\n",
    "def arousal_prevail(dependency_tree):\n",
    "    arousal_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]['Arousal'] >= 0.75:\n",
    "                arousal_words += 1\n",
    "            if vad_dict[lemma]['Arousal'] <= 0.25:\n",
    "                arousal_words += 1\n",
    "    return arousal_words\n",
    "\n",
    "def valence_prevail(dependency_tree):\n",
    "    valence_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]['Valence'] >= 0.75:\n",
    "                valence_words += 1\n",
    "            if vad_dict[lemma]['Valence'] <= 0.25:\n",
    "                valence_words += 1\n",
    "    return valence_words\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "828fb57a-e152-42ef-9c60-660648898532",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_59130/2858732056.py:2: 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['avg_vad_scores'] = comment_phab_df['dependency_tree'].apply(vad_scoring)\n",
      "/tmp/ipykernel_59130/2858732056.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",
      "  comment_phab_df['dominant_wc'] = comment_phab_df['dependency_tree'].apply(dominance_prevail)\n",
      "/tmp/ipykernel_59130/2858732056.py:4: 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['arousal_wc'] = comment_phab_df['dependency_tree'].apply(arousal_prevail)\n",
      "/tmp/ipykernel_59130/2858732056.py:5: 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['valence_wc'] = comment_phab_df['dependency_tree'].apply(valence_prevail)\n"
     ]
    }
   ],
   "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)\n",
    "comment_phab_df['arousal_wc'] = comment_phab_df['dependency_tree'].apply(arousal_prevail)\n",
    "comment_phab_df['valence_wc'] = comment_phab_df['dependency_tree'].apply(valence_prevail)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "27e47f6f-0257-4b70-b222-e91ef888c900",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_59130/335308388.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[['average_v_score', 'average_a_score', 'average_d_score']] = pd.DataFrame(comment_phab_df['avg_vad_scores'].tolist(), index=comment_phab_df.index)\n",
      "/tmp/ipykernel_59130/335308388.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[['average_v_score', 'average_a_score', 'average_d_score']] = pd.DataFrame(comment_phab_df['avg_vad_scores'].tolist(), index=comment_phab_df.index)\n",
      "/tmp/ipykernel_59130/335308388.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[['average_v_score', 'average_a_score', 'average_d_score']] = pd.DataFrame(comment_phab_df['avg_vad_scores'].tolist(), index=comment_phab_df.index)\n"
     ]
    }
   ],
   "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'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "184ccbe6-0a7a-41b8-9b02-bc439ff975d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# expand the dependency parser \n",
    "\n",
    "#pattern = r'\\b(ve|VE|visualeditor|VisualEditor)\\b'\n",
    "#pattern = r'\\b(WMF|Foundation)\\b'\n",
    "#pattern = r'\\b(bots|scripts|gadgets)\\b'\n",
    "pattern = r'\\b(http|https)\\b'\n",
    "\n",
    "dependency_relations = []\n",
    "resolved_dependency_relations = []\n",
    "\n",
    "for index, row in comment_phab_df.iterrows():\n",
    "    text = row['comment_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(pattern, 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",
    "    '''        \n",
    "    for token, lemma, dep, head, ancestors, subtree, children in row['resolved_dependency_tree']:\n",
    "        if re.search(pattern, 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": 20,
   "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": 22,
   "id": "82cd9dde-0d14-4de5-8482-5a39de8d2869",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_59130/1194677655.py:37: 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 = task_phab_df[task_phab_df['is_relevant'] == True]\n",
    "task_phab_df['first_comment'] = task_phab_df.groupby('speaker')['timestamp'].rank(method='first') <= 5\n",
    "#task_phab_df = task_phab_df[(task_phab_df['date_created'] < 1383264000) & (task_phab_df['date_created'] > 1351728000)]\n",
    "\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",
    "unaff_new_tasks_phab_df = task_phab_df[(task_phab_df['first_comment'] == True) & (task_phab_df['meta.affil'] != True)]\n",
    "unaff_new_tasks = unaff_new_tasks_phab_df.groupby('week')['conversation_id'].nunique()\n",
    "\n",
    "aff_new_tasks_phab_df = task_phab_df[(task_phab_df['first_comment'] == True) & (task_phab_df['meta.affil'] == True)]\n",
    "aff_new_tasks = aff_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=aff_new_tasks.index, y=aff_new_tasks.values, color='#c7756a',linestyle=\"dotted\", label=\"WMF-affiliated new authors\", marker='x')\n",
    "#sns.lineplot(x=unaff_new_tasks.index, y=unaff_new_tasks.values, color='#5da2d8', linestyle=\"dotted\", label=\"Nonaffiliated new authors\", marker='x')\n",
    "\n",
    "plt.title('New Relevant Phabricator Tasks Indexed with HTTPS')\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",
    "'''\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",
    "sns.barplot(x=other_tasks.index, y=other_tasks.values, color='#5da2d8', label='Nonaffiliated authors')\n",
    "sns.barplot(x=wmf_tasks.index, y=-wmf_tasks.values, color='#c7756a', label='WMF-affiliated authors')\n",
    "\n",
    "plt.title('New Relevant Phabricator Tasks Indexed with HTTP')\n",
    "plt.xlabel('Timestamp')\n",
    "plt.ylabel('Unique taskPHIDs')\n",
    "plt.xticks(rotation=90)\n",
    "# Customize the x-axis for weekly labels\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "#plt.savefig('031825_new_tasks_fig.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9a9b08a7-6c95-4971-b259-8e713c58fbe7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_59130/3939123449.py:4: 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",
      "  unaff_tasks_phab_df['speakers_task'] = unaff_tasks_phab_df.groupby('speaker')['timestamp'].rank(method='first').astype(int)\n",
      "/tmp/ipykernel_59130/3939123449.py:17: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.\n",
      "  unaff_tasks_phab_df['week'] = unaff_tasks_phab_df['timestamp'].dt.to_period('W').dt.start_time\n",
      "/tmp/ipykernel_59130/3939123449.py:18: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  weekly_breakdown = unaff_tasks_phab_df.groupby(['week', 'task_bins']).size().unstack(fill_value=0)\n",
      "/tmp/ipykernel_59130/3939123449.py:20: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  speaker_breakdown = unaff_tasks_phab_df.groupby(['week', 'task_bins']).nunique()['speaker'].unstack(fill_value=0)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#task_phab_df = phab_df[phab_df['comment_type'] == \"task_description\"]\n",
    "unaff_tasks_phab_df = task_phab_df[task_phab_df['meta.affil'] != True]\n",
    "# Rank speaker's task values within each group\n",
    "unaff_tasks_phab_df['speakers_task'] = unaff_tasks_phab_df.groupby('speaker')['timestamp'].rank(method='first').astype(int)\n",
    "\n",
    "# Filter dates 08-01-2013 to 11-27-2013\n",
    "unaff_tasks_phab_df = unaff_tasks_phab_df[(unaff_tasks_phab_df['date_created'] < 1385596799) & (unaff_tasks_phab_df['date_created'] > 1375315200)]\n",
    "# Bin the speakers based on the number of tasks they created\n",
    "bins = [0, 6, 26, 51, float('inf')]\n",
    "labels = ['0-5', '6-25', '26-50', '51+']\n",
    "min_speakers_task = unaff_tasks_phab_df.groupby('speaker')['speakers_task'].min().reset_index()\n",
    "min_speakers_task = min_speakers_task.rename(columns={'speakers_task': 'min_speakers_task'})\n",
    "unaff_tasks_phab_df = unaff_tasks_phab_df.merge(min_speakers_task, on='speaker', how='left')\n",
    "unaff_tasks_phab_df['task_bins'] = pd.cut(unaff_tasks_phab_df['min_speakers_task'], bins=bins, labels=labels, right=False)\n",
    "\n",
    "# Calculate the weekly breakdown of binned speakers_task values\n",
    "unaff_tasks_phab_df['week'] = unaff_tasks_phab_df['timestamp'].dt.to_period('W').dt.start_time\n",
    "weekly_breakdown = unaff_tasks_phab_df.groupby(['week', 'task_bins']).size().unstack(fill_value=0)\n",
    "\n",
    "speaker_breakdown = unaff_tasks_phab_df.groupby(['week', 'task_bins']).nunique()['speaker'].unstack(fill_value=0)\n",
    "\n",
    "# Reshape the DataFrame for use with Seaborn\n",
    "weekly_breakdown = weekly_breakdown.reset_index().melt(id_vars='week', value_vars=labels, var_name='task_bins', value_name='count')\n",
    "speaker_breakdown = speaker_breakdown.reset_index().melt(id_vars='week', value_vars=labels, var_name='task_bins', value_name='speakers')\n",
    "\n",
    "# Plot the stacked bar plot using Seaborn\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.barplot(data=weekly_breakdown, x='week', y='count', hue='task_bins', palette='colorblind')\n",
    "#sns.barplot(data=speaker_breakdown, x='week', y='speakers', hue='task_bins', palette='colorblind')\n",
    "plt.title(\"08-01-2013 to 11-27-2013 Weekly Unaffiliated Task Creation by Contributor Tenure\")\n",
    "plt.xlabel('Week')\n",
    "plt.ylabel('Tasks')\n",
    "plt.legend(title=\"Contributor had created # tasks between 09-03-2011 and 08-01-2013:\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "#plt.savefig('031625_weekly_tasks_by_history.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b7cfad77-d48a-4708-91f3-89ae1179b90c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_59130/2708736932.py:27: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  comment_counts = affective_comment_phab_df.groupby('date_group').size()\n",
      "/tmp/ipykernel_59130/2708736932.py:28: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  speaker_counts = affective_comment_phab_df.groupby('date_group')['speaker'].nunique()\n",
      "/tmp/ipykernel_59130/2708736932.py:35: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  comment_counts_engaged = affective_comment_phab_df.groupby(['date_group', 'est_commenter', 'meta.affil']).size()\n",
      "/tmp/ipykernel_59130/2708736932.py:36: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  speaker_counts_engaged = affective_comment_phab_df.groupby(['date_group', 'est_commenter', 'meta.affil'])['speaker'].nunique()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of comments for each date group:\n",
      "date_group\n",
      "Before announcement                      4890\n",
      "After announcement, before deployment     328\n",
      "After deployment                         1064\n",
      "dtype: int64\n",
      "\n",
      "Number of speakers for each date group:\n",
      "date_group\n",
      "Before announcement                      243\n",
      "After announcement, before deployment     73\n",
      "After deployment                         145\n",
      "Name: speaker, dtype: int64\n",
      "\n",
      "Number of comments for each date group and engaged commenter subgroup:\n",
      "date_group                             est_commenter  meta.affil\n",
      "Before announcement                    False          False         1927\n",
      "                                                      True            38\n",
      "                                       True           False         2832\n",
      "                                                      True            93\n",
      "After announcement, before deployment  False          False          134\n",
      "                                                      True             4\n",
      "                                       True           False          170\n",
      "                                                      True            20\n",
      "After deployment                       False          False          539\n",
      "                                                      True             9\n",
      "                                       True           False          503\n",
      "                                                      True            13\n",
      "dtype: int64\n",
      "\n",
      "Number of speakers for each date group and engaged commenter subgroup:\n",
      "date_group                             est_commenter  meta.affil\n",
      "Before announcement                    False          False         221\n",
      "                                                      True           14\n",
      "                                       True           False          20\n",
      "                                                      True           15\n",
      "After announcement, before deployment  False          False          52\n",
      "                                                      True            4\n",
      "                                       True           False          18\n",
      "                                                      True            9\n",
      "After deployment                       False          False         126\n",
      "                                                      True            8\n",
      "                                       True           False          17\n",
      "                                                      True            5\n",
      "Name: speaker, dtype: int64\n",
      "\n",
      "Number of comments for each engaged commenter subgroup, and WMF affiliation:\n",
      "est_commenter  meta.affil\n",
      "False          False         2600\n",
      "               True            51\n",
      "True           False         3505\n",
      "               True           126\n",
      "dtype: int64\n",
      "\n",
      "Number of speakers for each engaged commenter subgroup, and WMF affiliation:\n",
      "est_commenter  meta.affil\n",
      "False          False         269\n",
      "               True           24\n",
      "True           False          20\n",
      "               True           16\n",
      "Name: speaker, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\nplot1 = sns.lmplot(data=comment_phab_df, x=\"date_created\", y=\"dominant_wc\", hue=\"date_group\", col=\"meta.affil\", row=\\'new_commenter\\', scatter=False, legend=False, palette=palette)\\nplot1.set_axis_labels(\"Timestamp\", \"Count of Dominance Polarized Words\")\\nplot1.set_titles(row_template=\"Author\\'s 100+ Comment: {row_name}\",col_template=\"WMF Affiliation: {col_name}\")\\nplot1.fig.subplots_adjust(top=0.9)  # Adjust subplots to make room for the title\\nplot1.add_legend(title=\"Comment publication timestamp:\")\\nfig1 = plot1.fig\\n# Plot for arousal_wc\\nplot2 = sns.lmplot(data=comment_phab_df, x=\"date_created\", y=\"arousal_wc\", hue=\"date_group\", col=\"meta.affil\", row=\\'engaged_commenter\\', scatter=False, legend=False, palette=palette)\\nplot2.set_axis_labels(\"Timestamp\", \"Count of Arousal Polarized Words\")\\nplot2.set_titles(row_template=\"Author\\'s 100+ Comment: {row_name}\",col_template=\"WMF Affiliation: {col_name}\")\\nplot2.add_legend(title=\"Comment publication timestamp:\")\\n#plot2.add_legend(title=\"Before/After 07/01/2013 Wide Release\")\\n\\nplot3 = sns.lmplot(data=comment_phab_df, x=\"date_created\", y=\"valence_wc\", hue=\"date_group\", col=\"meta.affil\", row=\\'engaged_commenter\\', scatter=False, legend=False, palette=palette)\\nplot3.set_axis_labels(\"Timestamp\", \"Count of Valence Polarized Words\")\\nplot3.set_titles(row_template=\"Author\\'s 100+ Comment: {row_name}\",col_template=\"WMF Affiliation: {col_name}\")\\nplot3.add_legend(title=\"Comment publication timestamp:\")\\n'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1333.5x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = [\n",
    "    pd.Timestamp('1900-01-01 00:00:01+00:00'),\n",
    "    pd.Timestamp('2013-08-01 00:00:01+00:00'),\n",
    "    pd.Timestamp('2013-08-28 00:00:01+00:00'),\n",
    "    pd.Timestamp('2100-08-28 00:00:01+00:00')\n",
    "]\n",
    "labels = ['Before announcement', 'After announcement, before deployment', 'After deployment']\n",
    "\n",
    "#creating variables of interest\n",
    "affective_comment_phab_df = comment_phab_df\n",
    "affective_comment_phab_df['date_group'] = pd.cut(affective_comment_phab_df['timestamp'], bins=bins, labels=labels, right=False)\n",
    "affective_comment_phab_df['speakers_comment'] = affective_comment_phab_df.groupby('speaker')['timestamp'].rank(method='first').astype(int)\n",
    "#all comments prior to june 1 2013\n",
    "subset_comment_phab_df = affective_comment_phab_df[affective_comment_phab_df['date_created'] <= 1370044800]\n",
    "#getting counts \n",
    "comment_counts = subset_comment_phab_df.groupby('speaker')['speakers_comment'].max().reset_index()\n",
    "comment_counts = comment_counts.rename(columns={'speakers_comment': 'pre_june_2013_comments'})\n",
    "#merge back \n",
    "affective_comment_phab_df = affective_comment_phab_df.merge(comment_counts, on='speaker', how='left')\n",
    "affective_comment_phab_df['pre_june_2013_comments'] = affective_comment_phab_df['pre_june_2013_comments'].fillna(0)\n",
    "\n",
    "affective_comment_phab_df['new_commenter'] = affective_comment_phab_df['pre_june_2013_comments'] <= 10\n",
    "affective_comment_phab_df['est_commenter'] = affective_comment_phab_df['pre_june_2013_comments'] > 50\n",
    "\n",
    "palette = ['#31449c', '#4a7c85', '#c5db68']\n",
    "\n",
    "comment_counts = affective_comment_phab_df.groupby('date_group').size()\n",
    "speaker_counts = affective_comment_phab_df.groupby('date_group')['speaker'].nunique()\n",
    "\n",
    "print(\"Number of comments for each date group:\")\n",
    "print(comment_counts)\n",
    "print(\"\\nNumber of speakers for each date group:\")\n",
    "print(speaker_counts)\n",
    "\n",
    "comment_counts_engaged = affective_comment_phab_df.groupby(['date_group', 'est_commenter', 'meta.affil']).size()\n",
    "speaker_counts_engaged = affective_comment_phab_df.groupby(['date_group', 'est_commenter', 'meta.affil'])['speaker'].nunique()\n",
    "\n",
    "print(\"\\nNumber of comments for each date group and engaged commenter subgroup:\")\n",
    "print(comment_counts_engaged)\n",
    "print(\"\\nNumber of speakers for each date group and engaged commenter subgroup:\")\n",
    "print(speaker_counts_engaged)\n",
    "\n",
    "comment_counts_wmf = affective_comment_phab_df.groupby(['est_commenter', 'meta.affil']).size()\n",
    "speaker_counts_wmf = affective_comment_phab_df.groupby(['est_commenter', 'meta.affil'])['speaker'].nunique()\n",
    "\n",
    "print(\"\\nNumber of comments for each engaged commenter subgroup, and WMF affiliation:\")\n",
    "print(comment_counts_wmf)\n",
    "print(\"\\nNumber of speakers for each engaged commenter subgroup, and WMF affiliation:\")\n",
    "print(speaker_counts_wmf)\n",
    "\n",
    "#comment_phab_df['before_after'] = comment_phab_df['timestamp'] > pd.Timestamp('2013-07-01 00:00:01+00:00')\n",
    "#fig, axes = plt.subplots(2, 1, figsize=(10, 12), sharex=True)\n",
    "affective_comment_phab_df['polarized_wc'] = affective_comment_phab_df['dominant_wc'] + affective_comment_phab_df['valence_wc'] + affective_comment_phab_df['arousal_wc'] \n",
    "plot1 = sns.lmplot(data=affective_comment_phab_df, x=\"date_created\", y=\"polarized_wc\", hue=\"date_group\", col=\"meta.affil\", row='est_commenter', scatter=False, legend=False, palette=palette)\n",
    "plot1.set_axis_labels(\"Timestamp\", \"Count of Polarized Words\")\n",
    "plot1.set_titles(row_template=\"Established Author: {row_name}\", col_template=\"WMF Affiliation: {col_name}\")\n",
    "plot1.fig.subplots_adjust(top=0.9)  # Adjust subplots to make room for the title\n",
    "plot1.add_legend(title=\"Comment publication timestamp:\")\n",
    "fig1 = plot1.fig\n",
    "'''\n",
    "plot1 = sns.lmplot(data=comment_phab_df, x=\"date_created\", y=\"dominant_wc\", hue=\"date_group\", col=\"meta.affil\", row='new_commenter', scatter=False, legend=False, palette=palette)\n",
    "plot1.set_axis_labels(\"Timestamp\", \"Count of Dominance Polarized Words\")\n",
    "plot1.set_titles(row_template=\"Author's 100+ Comment: {row_name}\",col_template=\"WMF Affiliation: {col_name}\")\n",
    "plot1.fig.subplots_adjust(top=0.9)  # Adjust subplots to make room for the title\n",
    "plot1.add_legend(title=\"Comment publication timestamp:\")\n",
    "fig1 = plot1.fig\n",
    "# Plot for arousal_wc\n",
    "plot2 = sns.lmplot(data=comment_phab_df, x=\"date_created\", y=\"arousal_wc\", hue=\"date_group\", col=\"meta.affil\", row='engaged_commenter', scatter=False, legend=False, palette=palette)\n",
    "plot2.set_axis_labels(\"Timestamp\", \"Count of Arousal Polarized Words\")\n",
    "plot2.set_titles(row_template=\"Author's 100+ Comment: {row_name}\",col_template=\"WMF Affiliation: {col_name}\")\n",
    "plot2.add_legend(title=\"Comment publication timestamp:\")\n",
    "#plot2.add_legend(title=\"Before/After 07/01/2013 Wide Release\")\n",
    "\n",
    "plot3 = sns.lmplot(data=comment_phab_df, x=\"date_created\", y=\"valence_wc\", hue=\"date_group\", col=\"meta.affil\", row='engaged_commenter', scatter=False, legend=False, palette=palette)\n",
    "plot3.set_axis_labels(\"Timestamp\", \"Count of Valence Polarized Words\")\n",
    "plot3.set_titles(row_template=\"Author's 100+ Comment: {row_name}\",col_template=\"WMF Affiliation: {col_name}\")\n",
    "plot3.add_legend(title=\"Comment publication timestamp:\")\n",
    "'''\n",
    "# Show plots\n",
    "#fig1.savefig('031725_engaged_commenter_D_scoring_fig.png')\n",
    "#plot2.fig.savefig('031725_engaged_commenter_A_scoring_fig.png')\n",
    "#plot3.fig.savefig('031725_engaged_commenter_V_scoring_fig.png')\n",
    "#plt.savefig('031625_engaged_commenter_VAD_scoring_fig.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5a91a59a-0d1c-48b3-93dd-b9df76ca68e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x14fc59025cd0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1333.5x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot2 = sns.lmplot(data=affective_comment_phab_df, x=\"speakers_comment\", y=\"polarized_wc\", hue=\"date_group\", col=\"meta.affil\", scatter=False, legend=False, palette=palette)\n",
    "plot2.set_axis_labels(\"Index of Speaker's Comment\", \"Count of Polarized Words\")\n",
    "plot2.set_titles(col_template=\"WMF Affiliation: {col_name}\")\n",
    "plot2.fig.subplots_adjust(top=0.9)  # Adjust subplots to make room for the title\n",
    "plot2.add_legend(title=\"Comment publication timestamp:\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d2d67d38-f005-4c94-be3c-39eb6b22686f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_19468/1559616732.py:4: 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"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'resolved_dependency_relations_df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[20], line 5\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m#pattern = r'\\b(ve|VE|visualeditor|VisualEditor)\\b'\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m#pattern = r'\\b(WMF|Foundation)\\b'\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m#pattern = r'\\b(bots)\\b'\u001b[39;00m\n\u001b[1;32m      4\u001b[0m filtered_dependencies \u001b[38;5;241m=\u001b[39m dependency_relations_df[dependency_relations_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtoken\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mstr\u001b[38;5;241m.\u001b[39mcontains(pattern, regex\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)]\n\u001b[0;32m----> 5\u001b[0m resolved_filtered_dependencies \u001b[38;5;241m=\u001b[39m \u001b[43mresolved_dependency_relations_df\u001b[49m[resolved_dependency_relations_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtoken\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mstr\u001b[38;5;241m.\u001b[39mcontains(pattern, regex\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)]\n\u001b[1;32m      7\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m12\u001b[39m, \u001b[38;5;241m8\u001b[39m))\n\u001b[1;32m      8\u001b[0m gs \u001b[38;5;241m=\u001b[39m GridSpec(\u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m1\u001b[39m, height_ratios\u001b[38;5;241m=\u001b[39m[\u001b[38;5;241m6\u001b[39m, \u001b[38;5;241m6\u001b[39m])\n",
      "\u001b[0;31mNameError\u001b[0m: name 'resolved_dependency_relations_df' is not defined"
     ]
    }
   ],
   "source": [
    "#pattern = r'\\b(ve|VE|visualeditor|VisualEditor)\\b'\n",
    "#pattern = r'\\b(WMF|Foundation)\\b'\n",
    "#pattern = r'\\b(bots)\\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[resolved_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[resolved_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()\n",
    "\n",
    "#plt.savefig('031625_VE_depth_fig.png')"
   ]
  }
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