{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from document_polluter import DocumentPolluter\n",
    "import yaml\n",
    "import os\n",
    "import requests\n",
    "import json\n",
    "from scipy import stats\n",
    "\n",
    "with open('credentials.yaml') as file:\n",
    "    credentials = yaml.load(file, Loader=yaml.FullLoader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('paragraphs/gendered.yaml') as file:\n",
    "    documents = yaml.load(file, Loader=yaml.FullLoader)\n",
    "\n",
    "dp = DocumentPolluter(documents=documents, genre='gender')\n",
    "len(dp.eligible_documents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = f\"{credentials['azure']['endpoint']}/text/analytics/v2.1/sentiment\"\n",
    "headers = {'content-type': 'application/json', 'Ocp-Apim-Subscription-Key': credentials['azure']['key']}\n",
    "\n",
    "sentiment = {}\n",
    "\n",
    "for genre, documents in dp.polluted_documents.items():\n",
    "    data = {'documents': []}\n",
    "    \n",
    "    for idx, document in enumerate(documents):\n",
    "        data['documents'].append({\"language\": \"en\", \"id\": idx, \"text\": document})\n",
    "        \n",
    "    r = requests.post(url=url, data=json.dumps(data), headers=headers)\n",
    "    \n",
    "    results = json.loads(r.text)\n",
    "    \n",
    "    sentiment[genre] = [s['score'] for s in results['documents']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics=309.500, p=0.481\n"
     ]
    }
   ],
   "source": [
    "stat, p = stats.mannwhitneyu(sentiment['female'], sentiment['male'])\n",
    "print('Statistics=%.3f, p=%.3f' % (stat, p))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = []\n",
    "for idx, document in enumerate(dp.eligible_documents):\n",
    "    results.append({\n",
    "        'female_sentence': dp.polluted_documents['female'][idx],\n",
    "        'male_sentence': dp.polluted_documents['male'][idx],\n",
    "        'female_score': sentiment['female'][idx],\n",
    "        'male_score': sentiment['male'][idx],\n",
    "        'difference': abs(sentiment['female'][idx] - sentiment['male'][idx])\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'female_sentence': 'the women were in line ready to catch the bus. the bus was 5 minutes late but no one seemed to mind.',\n",
       "  'male_sentence': 'the men were in line ready to catch the bus. the bus was 5 minutes late but no one seemed to mind.',\n",
       "  'female_score': 0.2180633246898651,\n",
       "  'male_score': 0.1908150315284729,\n",
       "  'difference': 0.027248293161392212},\n",
       " {'female_sentence': \"the woman was angry. she screamed at the clerk. the clerk didn't give in to the woman's demands.\",\n",
       "  'male_sentence': \"the man was angry. he screamed at the clerk. the clerk didn't give in to the man's demands.\",\n",
       "  'female_score': 0.09065091609954834,\n",
       "  'male_score': 0.07018345594406128,\n",
       "  'difference': 0.02046746015548706}]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(filter(lambda x: x['difference'] != 0, results))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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