{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b270bd36-529e-4595-a780-ef6c8151c31f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/gscratch/scrubbed/mjilg/envs/coref2-notebook/lib/python3.7/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "/gscratch/scrubbed/mjilg/envs/coref2-notebook/lib/python3.7/site-packages/torch/cuda/__init__.py:497: UserWarning: Can't initialize NVML\n",
      "  warnings.warn(\"Can't initialize NVML\")\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd \n",
    "import spacy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f6448c6f-2b5d-45f5-a32e-b3b47c16ef85",
   "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": "e30e81ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "#because of compute issues, need to do the sampling before the coreference resolution\n",
    "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\n",
    "\n",
    "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": 4,
   "id": "f359805f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/gscratch/scrubbed/mjilg/envs/coref2-notebook/lib/python3.7/site-packages/ipykernel_launcher.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",
      "/gscratch/scrubbed/mjilg/envs/coref2-notebook/lib/python3.7/site-packages/ipykernel_launcher.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"
     ]
    }
   ],
   "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": 5,
   "id": "4241cb0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5657"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(comment_phab_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f32f6eed-3aeb-4b05-8d40-7ed85e7235c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<spacy_experimental.coref.span_resolver_component.SpanResolver at 0x14e629c449f0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nlp = spacy.load(\"en_core_web_trf\")\n",
    "nlp_coref = spacy.load(\"en_coreference_web_trf\")\n",
    "\n",
    "# use replace_listeners for the coref components\n",
    "nlp_coref.replace_listeners(\"transformer\", \"coref\", [\"model.tok2vec\"])\n",
    "nlp_coref.replace_listeners(\"transformer\", \"span_resolver\", [\"model.tok2vec\"])\n",
    "\n",
    "# we won't copy over the span cleaner - this keeps the head cluster information, which we want\n",
    "nlp.add_pipe(\"merge_entities\")\n",
    "nlp.add_pipe(\"coref\", source=nlp_coref)\n",
    "nlp.add_pipe(\"span_resolver\", source=nlp_coref)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5b062d8-2d26-4a3e-a84c-ba0eaf6eb436",
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://github.com/explosion/spaCy/discussions/13572\n",
    "# https://github.com/explosion/spaCy/issues/13111 \n",
    "# https://explosion.ai/blog/coref\n",
    "# https://gist.github.com/thomashacker/b5dd6042c092e0a22c2b9243a64a2466\n",
    "doc = nlp(\"John is frustrated with the VisualEditor project, he thinks it doesn't work.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "424d35e0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "999e1656-0036-4ba2-bedf-f54493f67790",
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://gist.github.com/thomashacker/b5dd6042c092e0a22c2b9243a64a2466\n",
    "from spacy.tokens import Doc\n",
    "# Define lightweight function for resolving references in text\n",
    "def resolve_references(doc: Doc) -> str:\n",
    "    \"\"\"Function for resolving references with the coref ouput\n",
    "    doc (Doc): The Doc object processed by the coref pipeline\n",
    "    RETURNS (str): The Doc string with resolved references\n",
    "    \"\"\"\n",
    "    # token.idx : token.text\n",
    "    token_mention_mapper = {}\n",
    "    output_string = \"\"\n",
    "    clusters = [\n",
    "        val for key, val in doc.spans.items() if key.startswith(\"coref_cluster\")\n",
    "    ]\n",
    "\n",
    "    # Iterate through every found cluster\n",
    "    for cluster in clusters:\n",
    "        first_mention = cluster[0]\n",
    "        # Iterate through every other span in the cluster\n",
    "        for mention_span in list(cluster)[1:]:\n",
    "            # Set first_mention as value for the first token in mention_span in the token_mention_mapper\n",
    "            token_mention_mapper[mention_span[0].idx] = first_mention.text + mention_span[0].whitespace_\n",
    "            \n",
    "            for token in mention_span[1:]:\n",
    "                # Set empty string for all the other tokens in mention_span\n",
    "                token_mention_mapper[token.idx] = \"\"\n",
    "\n",
    "    # Iterate through every token in the Doc\n",
    "    for token in doc:\n",
    "        # Check if token exists in token_mention_mapper\n",
    "        if token.idx in token_mention_mapper:\n",
    "            output_string += token_mention_mapper[token.idx]\n",
    "        # Else add original token text\n",
    "        else:\n",
    "            output_string += token.text + token.whitespace_\n",
    "\n",
    "    return output_string\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "be476647-624b-4e95-ab62-9c6b08f85368",
   "metadata": {},
   "outputs": [],
   "source": [
    "def resolving_comment(text):\n",
    "    doc = nlp(text)\n",
    "    resolved_text = resolve_references(doc)\n",
    "    return resolved_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a9628b54-a1df-49cd-a365-9cba59de3421",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'i hate ve.interface, ve.interface always messes up i browser'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resolving_comment(\"i hate ve.interface, it always messes up my browser\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "46873641-8e88-4829-9e24-4dd5e6749bd1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/gscratch/scrubbed/mjilg/envs/coref2-notebook/lib/python3.7/site-packages/ipykernel_launcher.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",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "comment_phab_df['text'] = comment_phab_df['comment_text'].apply(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79e3f7e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Token indices sequence length is longer than the specified maximum sequence length for this model (712 > 512). Running this sequence through the model will result in indexing errors\n",
      "Token indices sequence length is longer than the specified maximum sequence length for this model (712 > 512). Running this sequence through the model will result in indexing errors\n",
      "Token indices sequence length is longer than the specified maximum sequence length for this model (572 > 512). Running this sequence through the model will result in indexing errors\n"
     ]
    }
   ],
   "source": [
    "comment_phab_df['resolved_text'] = comment_phab_df['text'].apply(resolving_comment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b583feb-1c62-4c96-9ba0-2996d72e70d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "comment_phab_df['resolved_text'][46088]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92bf47ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "comment_phab_df.to_csv(\"/mmfs1/gscratch/comdata/users/mjilg/mw-repo-lifecycles/case3/041525_coref_rel_phab_comments.csv\", index=False)"
   ]
  }
 ],
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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