mw-lifecycle-analysis/text_analysis/case3/041525_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": 2,
   "id": "e4f0b3f0-5255-46f1-822f-e455087ba315",
   "metadata": {},
   "outputs": [],
   "source": [
    "phab_path = \"/mmfs1/gscratch/comdata/users/mjilg/mw-repo-lifecycles/case3/0415_http_phab_comments.csv\"\n",
    "phab_df = pd.read_csv(phab_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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\"):\n",
    "                return True\n",
    "    return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 5,
   "id": "d449164e-1d28-4580-9eb1-f0f69978f114",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_79113/456904557.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_79113/456904557.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 04-01-2015 before 10-1-2015\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'] < 1443657600) & (phab_df['date_created'] > 1427846400)]\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": 6,
   "id": "942344db-c8f5-4ed6-a757-c97f8454f18b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique conversation_ids: 975\n",
      "Unique ids: 5657\n",
      "Unique speakers: 429\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": 7,
   "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": 8,
   "id": "3ae40d24-bbe8-49c3-a3a9-70bde1b4d559",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_79113/2783900859.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": 9,
   "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": 10,
   "id": "8b9a12f9-71bf-4bc9-bcfd-c73aab4be920",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_79113/2805711855.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": 11,
   "id": "e3364ab1-1879-4b89-8b3b-6ab5449fccfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1541      After a replace of old instances, it is not po...\n",
       "1546      We've been having the occasional alert flap on...\n",
       "1629      You get a HTTP 400, apparently due to index go...\n",
       "1637      tools.wmflabs.org https certificate expired ce...\n",
       "1649      I'm not sure what brings them in... I'm guessi...\n",
       "                                ...                        \n",
       "237406    https://github.com/GreenSteam/pep257/pull/107 ...\n",
       "237461    Sentry should have unified login via LDAP ([[ ...\n",
       "237483    Everywhere you can logon on a webpage with Med...\n",
       "237727    A bit of a head-scratcher, this one.\\n\\nIf you...\n",
       "238047    It would be great if this extension allowed fo...\n",
       "Name: comment_text, Length: 975, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "task_phab_df['comment_text']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 14,
   "id": "828fb57a-e152-42ef-9c60-660648898532",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_79113/2858732056.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_79113/2858732056.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_79113/2858732056.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_79113/2858732056.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": 15,
   "id": "27e47f6f-0257-4b70-b222-e91ef888c900",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_79113/335308388.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_79113/335308388.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_79113/335308388.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": 16,
   "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": 17,
   "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": 18,
   "id": "82cd9dde-0d14-4de5-8482-5a39de8d2869",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_79113/1984278451.py:7: 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",
    "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 HTTP')\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",
    "\n",
    "#plt.savefig('031825_new_tasks_fig.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9a9b08a7-6c95-4971-b259-8e713c58fbe7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_79113/3304249237.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_79113/3304249237.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_79113/3304249237.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_79113/3304249237.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 06-01-2015 to 08-30-2015\n",
    "unaff_tasks_phab_df = unaff_tasks_phab_df[(unaff_tasks_phab_df['date_created'] < 1440979200) & (unaff_tasks_phab_df['date_created'] > 1433116800)]\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(\"06-01-2015 to 08-30-2015 Weekly Unaffiliated Task Creation by Contributor Tenure\")\n",
    "plt.xlabel('Week')\n",
    "plt.ylabel('Tasks')\n",
    "plt.legend(title=\"Contributor had created # tasks between 04-01-2015 and 06-01-2015:\")\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": 22,
   "id": "b7cfad77-d48a-4708-91f3-89ae1179b90c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_79113/62586942.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_79113/62586942.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_79113/62586942.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_79113/62586942.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                      1642\n",
      "After announcement, before deployment     806\n",
      "After deployment                         3209\n",
      "dtype: int64\n",
      "\n",
      "Number of speakers for each date group:\n",
      "date_group\n",
      "Before announcement                      223\n",
      "After announcement, before deployment    157\n",
      "After deployment                         350\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         1597\n",
      "                                                      True            45\n",
      "After announcement, before deployment  False          False          763\n",
      "                                                      True            43\n",
      "After deployment                       False          False         3088\n",
      "                                                      True           121\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         215\n",
      "                                                      True           21\n",
      "After announcement, before deployment  False          False         148\n",
      "                                                      True           20\n",
      "After deployment                       False          False         339\n",
      "                                                      True           32\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         5448\n",
      "               True           209\n",
      "dtype: int64\n",
      "\n",
      "Number of speakers for each engaged commenter subgroup, and WMF affiliation:\n",
      "est_commenter  meta.affil\n",
      "False          False         416\n",
      "               True           53\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": 22,
     "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": [
    "bins = [\n",
    "    pd.Timestamp('1900-01-01 00:00:01+00:00'),\n",
    "    pd.Timestamp('2015-06-12 00:00:01+00:00'),\n",
    "    pd.Timestamp('2015-07-02 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": 23,
   "id": "5a91a59a-0d1c-48b3-93dd-b9df76ca68e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1502e35c7fd0>"
      ]
     },
     "execution_count": 23,
     "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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