Lexical_Alignment_ms.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12998 entries, 0 to 12997\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   Timestamp    12998 non-null  object \n",
      " 1   minute       12998 non-null  float64\n",
      " 2   seconds      12998 non-null  float64\n",
      " 3   TotalSecond  12998 non-null  float64\n",
      " 4   Speaker1     12997 non-null  object \n",
      " 5   Speaker      12997 non-null  object \n",
      " 6   Text         12995 non-null  object \n",
      " 7   group        12998 non-null  object \n",
      "dtypes: float64(3), object(5)\n",
      "memory usage: 812.5+ KB\n",
      "None\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11402 entries, 0 to 11401\n",
      "Data columns (total 4 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   Speaker    11402 non-null  object\n",
      " 1   Role       11402 non-null  object\n",
      " 2   Utterance  11402 non-null  object\n",
      " 3   group      11402 non-null  object\n",
      "dtypes: object(4)\n",
      "memory usage: 356.4+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "import itertools\n",
    "import collections\n",
    "from collections import Counter\n",
    "import pandas as pd\n",
    "import regex as re\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "\n",
    "# Middle School Data\n",
    "df_ms = pd.read_pickle(\"Middle_School/ms_df\")\n",
    "print(df_ms.info())\n",
    "\n",
    "# Undergrad Data\n",
    "df_ug = pd.read_pickle(\"Undergrad/ug_df\")\n",
    "print(df_ug.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "make new dataframes with only relevant columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "ms_df = df_ms[['Speaker', 'Text', 'group']]\n",
    "\n",
    "ug_df = df_ug[['Speaker', 'Utterance', 'group']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convert text to lowercase\n",
    "Will convert text to lowercase so there is no issue with word comparisons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3417: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\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",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0                           to, um, to get all of the-\n",
      "1    no, you have to do the project. then you save ...\n",
      "2            we'll just read the instructions for now.\n",
      "3    yeah, i did. number two. are [inaudible 00:02:...\n",
      "4    the original one? the one we have on the scree...\n",
      "5                             did you run the program?\n",
      "6                                                  oh.\n",
      "7             you have to remake all of the variables?\n",
      "8    i don't think so. i don't know what to do. i d...\n",
      "9    go back on the directions. yeah. so, we've ope...\n",
      "Name: Text, dtype: object\n"
     ]
    }
   ],
   "source": [
    "for i,j in enumerate(ms_df['Text']):\n",
    "    ms_df['Text'][i] = str(j).lower()\n",
    "\n",
    "print(ms_df['Text'][0:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Remove the text in [], which includes things like \"[Student calls teacher to look at their assignment.]\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0                           to, um, to get all of the-\n",
      "1    no, you have to do the project. then you save ...\n",
      "2            we'll just read the instructions for now.\n",
      "3                       yeah, i did. number two. are .\n",
      "4    the original one? the one we have on the scree...\n",
      "5                             did you run the program?\n",
      "6                                                  oh.\n",
      "7             you have to remake all of the variables?\n",
      "8    i don't think so. i don't know what to do. i d...\n",
      "9    go back on the directions. yeah. so, we've ope...\n",
      "Name: Text, dtype: object\n"
     ]
    }
   ],
   "source": [
    "for i,j in enumerate(ms_df['Text']):\n",
    "    ms_df['Text'][i] = re.sub(\"[\\(\\[].*?[\\)\\]]\", '', str(j))\n",
    "\n",
    "print(ms_df['Text'][0:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tokenize and Remove punctuation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "for i, j in enumerate(ms_df['Text']):\n",
    "    # print('j:', j)\n",
    "    words = nltk.word_tokenize(str(j))\n",
    "    ms_df['Text'][i] = [word for word in words if word.isalnum()]\n",
    "    # print(\"after:\",ms_df['Text'][i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "ms_df.to_pickle('Middle_School/before_stop_remove')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove Stop Words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "stopword = stopwords.words('english')\n",
    "for i, j in enumerate(ms_df['Text']):\n",
    "    # print(j)\n",
    "    ms_df['Text'][i] = [word for word in j if word not in stopword]\n",
    "    # print(ms_df['Text'][i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get dictionary of words in entire corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(1759, 'okay'), (1470, 'oh'), (1285, 'yeah'), (1279, 'wait'), (985, 'one'), (939, 'like'), (657, 'right'), (630, 'go'), (520, 'need'), (482, 'say'), (479, 'clone'), (436, 'know'), (413, 'think'), (391, 'make'), (381, 'put'), (373, 'um'), (371, 'change'), (353, 'let'), (327, 'see'), (327, 'beak'), (310, 'amplitude'), (303, 'set'), (299, 'water'), (298, 'two'), (253, 'click'), (249, 'going'), (242, 'part'), (242, 'level'), (242, 'eight'), (229, 'variable'), (222, 'thing'), (219, 'wavelength'), (213, 'uh'), (205, 'na'), (205, 'create'), (199, 'three'), (185, 'less'), (184, 'seconds'), (184, 'says'), (184, 'first'), (181, 'supposed'), (180, 'would'), (176, 'want'), (176, 'back'), (174, 'get'), (173, 'well'), (161, 'maybe'), (157, 'new'), (156, 'length'), (156, 'gon'), (154, 'yes'), (144, 'good'), (140, 'forever'), (139, 'look'), (139, 'got'), (133, 'size'), (132, 'stop'), (132, 'guys'), (131, 'second'), (131, 'really'), (129, 'time'), (128, 'variables'), (126, 'something'), (123, 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(1, '43'), (1, '38'), (1, '350'), (1, '35'), (1, '33'), (1, '300'), (1, '255'), (1, '222'), (1, '22'), (1, '2021'), (1, '200'), (1, '19'), (1, '18'), (1, '166'), (1, '136'), (1, '13'), (1, '127'), (1, '109'), (1, '103')]\n",
      "2591\n"
     ]
    }
   ],
   "source": [
    "corpus_freq = {}\n",
    "for i in ms_df['Text']:\n",
    "    for word in i:\n",
    "        if word in corpus_freq:\n",
    "            corpus_freq[word] += 1\n",
    "        else:\n",
    "            corpus_freq[word] = 1\n",
    "\n",
    "print(sorted( ((v,k) for k,v in corpus_freq.items()), reverse=True))\n",
    "print(len(corpus_freq))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Get dictionary of words for each group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Feb2019_G02.xlsx': {'um': 23, 'get': 6, 'project': 1, 'save': 1, 'beak': 65, 'length': 41, 'understand': 1, 'part': 8, 'right': 35, 'read': 3, 'instructions': 5, 'yeah': 42, 'number': 14, 'two': 21, 'original': 5, 'one': 59, 'screen': 5, 'run': 1, 'program': 1, 'oh': 25, 'remake': 1, 'variables': 7, 'think': 6, 'know': 16, 'supposed': 3, 'wait': 39, 'go': 24, 'back': 5, 'directions': 2, 'opened': 1, 'thing': 3, 'says': 6, 'images': 1, 'bird': 9, 'make': 18, 'clones': 7, 'mean': 2, 'background': 1, 'live': 1, 'carefully': 1, 'guys': 7, 'spring': 1, 'create': 16, 'variable': 14, 'second': 7, 'delete': 11, 'give': 1, 'small': 1, 'hint': 1, 'first': 12, 'animal': 2, 'going': 5, 'like': 43, 'click': 19, 'another': 4, 'resets': 1, 'timer': 9, 'hold': 1, 'set': 13, 'find': 5, 'already': 1, 'done': 3, 'also': 2, 'want': 9, 'show': 3, 'final': 1, 'version': 1, 'made': 1, 'start': 13, 'clone': 69, 'generations': 1, '0': 1, '1': 1, 'new': 19, 'every': 9, 'time': 11, 'open': 1, 'break': 1, 'size': 20, 'increase': 4, 'decrease': 1, 'uh': 3, 'either': 4, 'ca': 4, 'see': 6, 'generation': 14, 'three': 10, 'four': 3, 'third': 2, 'cycle': 1, 'many': 4, 'seconds': 13, 'alive': 4, 'air': 1, 'string': 1, 'long': 4, 'five': 9, 'probably': 2, 'good': 5, 'let': 15, 'try': 11, 'need': 14, 'less': 8, 'okay': 26, 'putting': 1, 'creates': 1, 'whenever': 1, 'starts': 1, 'creating': 2, 'amount': 1, 'way': 6, 'reason': 1, 'started': 1, 'replicates': 1, 'duplicates': 2, 'deletes': 1, 'say': 12, 'old': 3, 'disappearing': 1, 'figure': 4, 'counter': 24, 'push': 1, 'restart': 1, 'button': 1, 'sec': 3, 'dies': 2, 'longer': 3, 'work': 5, 'could': 2, 'created': 3, 'well': 2, 'count': 2, 'tell': 2, 'us': 1, 'code': 1, 'use': 1, 'born': 1, 'needs': 2, 'increased': 1, 'seeing': 1, 'actually': 1, 'send': 3, 'move': 4, 'around': 1, 'worry': 1, 'check': 1, 'larger': 6, 'eight': 35, 'something': 7, 'gon': 6, 'na': 7, 'based': 1, 'rules': 1, 'happened': 1, 'switch': 1, 'put': 9, 'would': 11, 'shorter': 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'saving': 1, 'ha': 1, 'perfect': 4, 'show': 2, 'uf': 3, 'argue': 1, 'showing': 2, 'accept': 1, 'random': 2, 'shoot': 1, 'crazy': 1, 'dang': 1, 'tired': 1, 'wo': 1, 'though': 4, 'great': 2, 'blank': 2, 'leave': 1, 'guess': 1, 'smart': 3, 'writing': 2, 'word': 1, 'way': 1, 'proud': 3, 'done': 2, 'saying': 2, 'every': 2, 'code': 4, 'ends': 1, 'time': 2, 'work': 4, 'delete': 2, 'maybe': 1, 'rules': 1, 'color': 12, 'depends': 6, 'need': 3, 'watching': 4, 'judge': 1, 'key': 1, 'pressed': 1, 'everyone': 1, 'pressing': 1, 'numbers': 1, 'thank': 2, 'love': 2, 'feel': 2, 'speed': 4, 'move': 1, 'boom': 2, 'dad': 6, 'sees': 1, 'daughter': 1, 'offense': 1, 'fly': 1, 'orlando': 1, 'become': 1, 'movie': 1, 'star': 1, 'money': 1, 'visual': 2, 'arrange': 1, 'worst': 1, 'scream': 1, 'ever': 2, 'red': 1, 'longest': 1, 'shortest': 1, 'following': 1, 'table': 1, 'shows': 1, 'keep': 3, 'piece': 1, 'project': 1, 'disconnect': 1, 'block': 1, 'previous': 1, 'run': 1, 'anymore': 2, 'understand': 2, 'trying': 1, 'enough': 1, 'mouth': 1, 'knew': 1, 'clicking': 1, 'slide': 1, 'nano': 2, 'happy': 1, 'day': 1, 'makes': 1, 'eyes': 1, 'hurt': 1, 'speeds': 1, 'high': 2, 'poop': 3, 'emoji': 5, 'win': 1, 'siri': 4, 'creepy': 1, 'drag': 3, 'drop': 2, 'missed': 1, 'clearly': 1, 'nice': 1, 'transparent': 1, 'tools': 1, 'modern': 1, 'best': 1, 'alright': 3, 'copy': 1, 'fault': 1, 'rewind': 1, 'video': 1, 'roll': 1, 'clip': 1, 'kidding': 1, 'loop': 5, 'another': 3, 'duh': 1, 'oops': 2, 'broke': 4, 'switch': 1, 'lost': 1, 'footage': 1, 'deleted': 2, 'ta': 3, 'three': 11, 'productive': 1, 'navigator': 1, 'visible': 3, 'deleting': 2, 'adding': 1, 'rid': 1, 'left': 1, 'costumes': 1, 'third': 1, 'shhh': 1, 'mmhmm': 1, 'wavelengths': 1, 'range': 1, 'whatever': 1, 'seven': 2, 's2': 2, 'anyway': 1, 'started': 1, 'copied': 1, 'image': 1, 'control': 1, 'c': 1, 'gets': 1, 'lower': 1, 'wow': 1, 'took': 1, 'away': 1, 'mouse': 1, 'obligated': 1, 'order': 1, 'properly': 1, 'randomly': 1, 'thousand': 1, 'forever': 1, 'four': 9, 'fifty': 9, 'exact': 1, 'eight': 2, 'surprised': 1, 'snap': 1, 'violet': 1, 'conflicting': 1, 'colors': 1, 'orange': 1, 'inside': 1, 'press': 1, 'shut': 1, 'likes': 1, 'telling': 1, 'truth': 1, 'ahead': 1, 'cowboy': 1, 'instead': 1, 'never': 2, 'expect': 1, 'suffer': 1, 'slow': 1, 'low': 1, 'possible': 1, 'good': 1, 'e': 5, 'plus': 3, 'twenty': 5, 'five': 3, 'exponent': 1, 'fifth': 2, 'power': 1, 'advanced': 1, 'able': 1, 'crying': 1, 'thinks': 1, 'heck': 1, 'fluctuating': 1}}\n"
     ]
    }
   ],
   "source": [
    "group_freq = {}\n",
    "\n",
    "# loop through each utterance\n",
    "for i,j in enumerate(ms_df['Text']):\n",
    "    # get group\n",
    "    group = ms_df['group'][i]\n",
    "    # loop through each word in each utterance\n",
    "    for word in j:\n",
    "        # check if group is in dictionary\n",
    "        if group in group_freq:\n",
    "            # check if word is in group's entry of dictionary\n",
    "            if word in group_freq[group]:\n",
    "                group_freq[group][word] += 1\n",
    "            else:\n",
    "                group_freq[group][word] = 1\n",
    "        # add group to dictionary\n",
    "        else:\n",
    "            group_freq[group] = {}\n",
    "            group_freq[group][word] = 1\n",
    "print(group_freq)\n",
    "unique_by_group = []\n",
    "for k, v in group_freq.items():\n",
    "    unique_by_group.append(len([item for item in v if item]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[282, 368, 227, 113, 314, 204, 565, 300, 338, 322, 274, 363, 368, 316, 358, 260, 356, 306, 207, 205, 274, 324, 323, 195, 403, 454, 329, 201, 275, 316, 139, 218, 364, 367, 389, 462]\n"
     ]
    }
   ],
   "source": [
    "print(unique_by_group)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get dictionary for each member of each group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Feb2019_G02.xlsx': {'S1': {'um': 14, 'get': 5, 'remake': 1, 'variables': 5, 'go': 12, 'back': 3, 'directions': 2, 'yeah': 26, 'opened': 1, 'thing': 1, 'says': 3, 'images': 1, 'bird': 4, 'right': 13, 'make': 10, 'two': 11, 'one': 15, 'clones': 3, 'beak': 23, 'length': 20, 'part': 1, 'second': 2, 'delete': 5, 'another': 1, 'variable': 7, 'timer': 3, 'find': 4, 'alive': 1, 'create': 2, 'clone': 19, 'like': 8, 'air': 1, 'string': 1, 'uh': 1, 'probably': 2, 'three': 6, 'four': 1, 'oh': 15, 'wait': 15, 'five': 6, 'seconds': 5, 'good': 3, 'many': 3, 'long': 1, 'need': 7, 'less': 3, 'try': 7, 'putting': 1, 'done': 1, 'creating': 2, 'time': 5, 'amount': 1, 'way': 5, 'reason': 1, 'okay': 17, 'say': 3, 'old': 1, 'know': 6, 'disappearing': 1, 'figure': 2, 'counter': 12, 'push': 1, 'restart': 1, 'button': 1, 'longer': 2, 'work': 4, 'could': 1, 'well': 2, 'count': 2, 'every': 3, 'let': 4, 'tell': 1, 'us': 1, 'code': 1, 'use': 1, 'size': 5, 'change': 10, 'would': 7, 'maybe': 5, 'nope': 1, 'getting': 1, 'bigger': 1, 'tool': 1, 'gon': 2, 'na': 3, 'rid': 2, 'simpler': 1, 'wo': 2, 'want': 3, 'making': 2, 'shorter': 1, 'disconnect': 1, 'see': 3, 'makes': 1, 'able': 2, 'reset': 7, 'inherit': 1, 'c': 1, 'feel': 1, 'set': 5, 'clicked': 2, 'zero': 4, 'also': 1, 'needs': 1, 'eight': 11, 'start': 2, 'think': 1, 'working': 4, 'generation': 4, 'click': 3, 'bottom': 1, 'forever': 4, 'wan': 1, 'pick': 1, 'random': 1, 'number': 2, 'ten': 2, 'larger': 1, 'lower': 1, 'else': 1, 'sorry': 1, 'operators': 1, 'six': 4, 'put': 2, 'looks': 1, 'drag': 1, 'greater': 1, 'die': 2, 'move': 2, 'end': 1, 'negative': 3, 'either': 1, 'seven': 3, 'eleven': 1, 'keep': 1, 'starting': 5, 'going': 1, 'everything': 1, 'except': 1, 'something': 1, 'messing': 1, 'new': 3, 'resize': 1, 'birds': 1, 'ca': 2, 'anymore': 1, 'dead': 1, 'alright': 1, 'fine': 1, 'setting': 1, 'showing': 1, 'deactivate': 1, 'supposed': 1, 'combine': 2, 'ones': 1}, 'S2': {'project': 1, 'save': 1, 'beak': 24, 'length': 16, 'understand': 1, 'part': 2, 'right': 10, 'yeah': 15, 'number': 2, 'two': 5, 'oh': 8, 'think': 5, 'know': 5, 'supposed': 2, 'wait': 23, 'mean': 1, 'background': 1, 'go': 7, 'live': 1, 'spring': 1, 'create': 9, 'variable': 6, 'bird': 2, 'timer': 6, 'hold': 1, 'one': 27, 'set': 4, 'already': 1, 'done': 2, 'many': 1, 'seconds': 8, 'want': 2, 'clone': 34, 'alive': 3, 'like': 4, 'long': 3, 'five': 3, 'three': 2, 'let': 6, 'try': 4, 'start': 9, 'okay': 2, 'creates': 1, 'says': 3, 'whenever': 1, 'starts': 1, 'another': 1, 'started': 1, 'replicates': 1, 'duplicates': 2, 'deletes': 1, 'say': 2, 'old': 2, 'sec': 3, 'dies': 1, 'get': 1, 'longer': 1, 'need': 3, 'figure': 2, 'make': 7, 'counter': 12, 'click': 12, 'new': 2, 'created': 3, 'um': 9, 'born': 1, 'increase': 2, 'needs': 1, 'increased': 1, 'happened': 1, 'ca': 1, 'clones': 2, 'gon': 1, 'na': 1, 'put': 6, 'find': 1, 'would': 4, 'shorter': 1, 'size': 3, 'change': 15, 'ah': 1, 'must': 1, 'delete': 4, 'first': 6, 'normal': 1, 'reset': 1, 'zero': 2, 'somewhere': 1, 'withdraw': 1, 'inherit': 3, 'script': 4, 'called': 1, 'different': 2, 'thing': 2, 'every': 1, 'second': 3, 'control': 2, 'guess': 1, 'way': 1, 'top': 1, 'flag': 2, 'clicked': 2, 'variables': 1, 'forgot': 1, 'repeat': 1, 'forever': 2, 'generation': 2, 'going': 2, '12': 1, 'good': 1, 'got': 1, 'basically': 1, 'everything': 1, 'else': 4, 'picking': 1, 'eight': 5, 'lengths': 2, 'operators': 1, 'seven': 1, 'wo': 2, 'work': 1, 'six': 1, 'greater': 1, 'less': 1, 'take': 1, 'back': 1, 'blank': 2, 'fix': 1, 'far': 1, 'time': 1, 'great': 1, 'trouble': 1, 'four': 1, 'takes': 1, 'short': 1, 'none': 1, 'numbers': 1, 'locked': 1, 'juno': 1, 'even': 1, 'turn': 1}, 'Other': {'read': 3, 'instructions': 5, 'original': 5, 'one': 17, 'screen': 5, 'right': 12, 'run': 1, 'program': 1, 'carefully': 1, 'guys': 7, 'give': 1, 'small': 1, 'hint': 1, 'create': 5, 'first': 6, 'animal': 2, 'going': 2, 'like': 31, 'clones': 2, 'click': 4, 'another': 2, 'resets': 1, 'also': 1, 'want': 4, 'show': 3, 'final': 1, 'version': 1, 'made': 1, 'start': 2, 'clone': 16, 'generations': 1, '0': 1, '1': 1, 'new': 14, 'beak': 18, 'every': 5, 'time': 5, 'open': 1, 'break': 1, 'size': 12, 'increase': 2, 'decrease': 1, 'uh': 2, 'either': 3, 'ca': 1, 'see': 3, 'number': 10, 'generation': 8, 'bird': 3, 'know': 5, 'go': 5, 'back': 1, 'second': 2, 'three': 2, 'four': 1, 'third': 2, 'cycle': 1, 'seeing': 1, 'actually': 1, 'send': 3, 'part': 5, 'make': 1, 'move': 2, 'around': 1, 'need': 4, 'worry': 1, 'check': 1, 'larger': 5, 'eight': 19, 'something': 6, 'less': 4, 'gon': 3, 'na': 3, 'delete': 2, 'based': 1, 'rules': 1, 'let': 5, 'switch': 1, 'basically': 1, 'wo': 2, 'change': 8, 'physically': 1, 'oh': 2, 'mean': 1, 'variable': 1, 'okay': 7, 'length': 5, 'beginning': 5, 'yes': 1, 'good': 1, 'similar': 1, 'choose': 1, 'rule': 2, 'look': 2, 'tell': 1, 'combine': 1, 'two': 5, 'working': 1, 'set': 4, 'say': 7, 'pick': 5, 'random': 2, 'could': 1, 'variables': 1, 'minus': 4, 'plus': 1, 'case': 1, 'take': 1, 'peak': 1, 'seven': 2, 'survive': 2, 'die': 5, 'else': 1, 'picks': 1, 'nine': 7, 'fine': 1, 'always': 1, 'everyone': 1, 'watch': 1, 'side': 2, 'picked': 1, 'sent': 1, 'example': 1, 'put': 1, 'six': 3, 'ten': 1, 'eleven': 2, 'folks': 1, 'mine': 1, 'though': 1, 'survives': 1, 'group': 2, 'alright': 1, 'inside': 1, 'dies': 1, 'makes': 1, 'questions': 1, 'far': 1, 'solution': 2, 'wait': 1, 'stop': 1, 'generated': 1, 'zero': 1, 'said': 1, 'type': 1, 'sorry': 1, 'short': 1, 'yeah': 1, 'became': 1, 'anyway': 1, 'survey': 1}}, 'Dec11-2019 - t040 t085.xlsx': {'S2': {'let': 10, 'see': 7, 'right': 9, 'want': 5, 'going': 5, 'need': 8, 'patience': 4, 'already': 5, 'two': 3, 'sprites': 3, 'remember': 2, 'oh': 19, 'yeah': 10, 'opening': 1, 'project': 1, 'excersice': 1, 'level': 28, 'wow': 4, 'impressive': 1, 'exercise': 3, 'die': 1, 'would': 3, 'died': 1, 'know': 1, 'yes': 19, 'boom': 6, 'teacherer': 3, 'signed': 1, 'save': 1, 'homeostasis': 9, 'homeo': 1, 'look': 3, 'says': 3, 'wo': 1, 'making': 1, 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'come': 6, 'still': 2, 'stuff': 2, 'put': 7, 'together': 1, 'okay': 23, 'set': 4, 'home': 1, 'control': 2, 'anything': 2, 'oh': 16, 'go': 17, 'changes': 1, 'sprite': 13, 'later': 1, 'apparently': 2, 'first': 2, 'create': 5, 'water': 21, 'level': 13, 'whatever': 2, 'means': 3, 'decrease': 2, 'times': 1, 'exercise': 3, 'changing': 2, 'restart': 1, 'attached': 1, 'one': 5, 'flag': 1, 'variable': 4, 'change': 7, 'example': 1, 'read': 2, 'say': 10, 'press': 3, 'new': 6, 'spread': 1, 'call': 1, 'image': 3, 'costumes': 3, 'images': 1, 'let': 6, 'back': 7, 'smart': 1, 'turtle': 3, 'student': 5, 'resource': 2, 'done': 8, 'page': 1, 'got': 4, 'keep': 3, 'moving': 2, 'trying': 1, 'download': 2, 'big': 3, 'supposed': 2, 'another': 2, 'shrink': 2, 'costume': 1, 'delete': 2, 'add': 3, 'little': 1, 'yes': 2, 'please': 1, 'switch': 1, 'use': 2, 'fine': 10, 'sorry': 4, 'moved': 1, 'chair': 1, 'guy': 2, 'homeostasis': 4, 'putting': 1, 'also': 2, 'operators': 1, 'top': 1, 'makes': 2, 'sense': 2, 'work': 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     ]
    }
   ],
   "source": [
    "speaker_freq = {}\n",
    "\n",
    "for i,j in enumerate(ms_df['Text']):\n",
    "    # get group\n",
    "    group = ms_df['group'][i]\n",
    "    # get speaker\n",
    "    speaker = ms_df['Speaker'][i]\n",
    "    # loop through each word in each utterance\n",
    "    for word in j:\n",
    "        # check if group is in dictionary\n",
    "        if group in speaker_freq:\n",
    "            # check if speaker is in group's dictionary\n",
    "            if speaker in speaker_freq[group]:\n",
    "                # check if word is in speaker's entry of dictionary\n",
    "                if word in speaker_freq[group][speaker]:\n",
    "                    speaker_freq[group][speaker][word] += 1\n",
    "                else:\n",
    "                    speaker_freq[group][speaker][word] = 1\n",
    "            # add speaker to group entry\n",
    "            else:\n",
    "                speaker_freq[group][speaker] = {}\n",
    "                speaker_freq[group][speaker][word] = 1\n",
    "        # add group to dictionary\n",
    "        else:\n",
    "            speaker_freq[group] = {}\n",
    "            speaker_freq[group][speaker] = {}\n",
    "            speaker_freq[group][speaker][word] = 1\n",
    "print(speaker_freq)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "See how many words each speaker says"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feb2019_G02.xlsx\n",
      "speaker 1 unique words: 155\n",
      "speaker 2 unique words: 141\n",
      "Dec11-2019 - t040 t085.xlsx\n",
      "speaker 1 unique words: 213\n",
      "speaker 2 unique words: 250\n",
      "Feb2019_G43.xlsx\n",
      "speaker 1 unique words: 64\n",
      "speaker 2 unique words: 140\n",
      "Dec2018_G2.xlsx\n",
      "speaker 1 unique words: 94\n",
      "speaker 2 unique words: 33\n",
      "Dec4-2019 - t040 t085.xlsx\n",
      "speaker 1 unique words: 160\n",
      "speaker 2 unique words: 240\n",
      "Dec11-2019 - t060 t105.xlsx\n",
      "speaker 1 unique words: 143\n",
      "speaker 2 unique words: 118\n",
      "Feb2019_G18.xlsx\n",
      "speaker 1 unique words: 409\n",
      "speaker 2 unique words: 272\n",
      "Dec4-2019 - t031 t070.xlsx\n",
      "speaker 1 unique words: 96\n",
      "speaker 2 unique words: 242\n",
      "Feb2019_G19.xlsx\n",
      "speaker 1 unique words: 225\n",
      "speaker 2 unique words: 136\n",
      "Dec11-2019 - t031 t070.xlsx\n",
      "speaker 1 unique words: 136\n",
      "speaker 2 unique words: 231\n",
      "Dec11-2019 - t007 t091.xlsx\n",
      "speaker 1 unique words: 158\n",
      "speaker 2 unique words: 194\n",
      "Dec4-2019 - t099 t107.xlsx\n",
      "speaker 1 unique words: 255\n",
      "speaker 2 unique words: 234\n",
      "Dec4-2019 - t007 t091.xlsx\n",
      "speaker 1 unique words: 170\n",
      "speaker 2 unique words: 244\n",
      "Feb2019_G42.xlsx\n",
      "speaker 1 unique words: 257\n",
      "speaker 2 unique words: 83\n",
      "Dec2018_G22.xlsx\n",
      "speaker 1 unique words: 255\n",
      "speaker 2 unique words: 197\n",
      "Feb2019_G08.xlsx\n",
      "speaker 1 unique words: 124\n",
      "speaker 2 unique words: 118\n",
      "Feb2019_G04.xlsx\n",
      "speaker 1 unique words: 170\n",
      "speaker 2 unique words: 141\n",
      "Dec4-2019 - t012 t065.xlsx\n",
      "speaker 1 unique words: 93\n",
      "speaker 2 unique words: 242\n",
      "Dec2018_G14.xlsx\n",
      "speaker 1 unique words: 108\n",
      "speaker 2 unique words: 160\n",
      "Dec2018_G4.xlsx\n",
      "speaker 1 unique words: 172\n",
      "speaker 2 unique words: 74\n",
      "Dec11-2019 - t012 t065.xlsx\n",
      "speaker 1 unique words: 147\n",
      "speaker 2 unique words: 201\n",
      "Feb2019_G29.xlsx\n",
      "speaker 1 unique words: 173\n",
      "speaker 2 unique words: 192\n",
      "Dec2018_G5.xlsx\n",
      "speaker 1 unique words: 224\n",
      "speaker 2 unique words: 161\n",
      "Dec2018_G15.xlsx\n",
      "speaker 1 unique words: 124\n",
      "speaker 2 unique words: 109\n",
      "Dec11-2019 - t99 t107.xlsx\n",
      "speaker 1 unique words: 296\n",
      "speaker 2 unique words: 224\n",
      "Feb2019_G05.xlsx\n",
      "speaker 1 unique words: 226\n",
      "speaker 2 unique words: 77\n",
      "Feb2019_G25.xlsx\n",
      "speaker 1 unique words: 124\n",
      "speaker 2 unique words: 207\n",
      "Dec2018_G9.xlsx\n",
      "speaker 1 unique words: 109\n",
      "speaker 2 unique words: 144\n",
      "Dec4-2019 - t105 t060.xlsx\n",
      "speaker 1 unique words: 121\n",
      "speaker 2 unique words: 207\n",
      "Feb2019_G26.xlsx\n",
      "speaker 1 unique words: 156\n",
      "speaker 2 unique words: 135\n",
      "Dec2018_G20.xlsx\n",
      "speaker 1 unique words: 85\n",
      "speaker 2 unique words: 71\n",
      "Dec2018_G6.xlsx\n",
      "speaker 1 unique words: 127\n",
      "speaker 2 unique words: 69\n",
      "Dec2018_G10.xlsx\n",
      "speaker 1 unique words: 206\n",
      "speaker 2 unique words: 200\n",
      "Dec11-2019 - t022 t075.xlsx\n",
      "speaker 1 unique words: 306\n",
      "speaker 2 unique words: 132\n",
      "Dec4-2019 - t022 t075.xlsx\n",
      "speaker 1 unique words: 293\n",
      "speaker 2 unique words: 161\n",
      "Dec2018_G1.xlsx\n",
      "speaker 1 unique words: 336\n",
      "speaker 2 unique words: 266\n"
     ]
    }
   ],
   "source": [
    "groups = ms_df['group'].unique()\n",
    "for g in groups:\n",
    "    print(g)\n",
    "    print('speaker 1 unique words:', len(speaker_freq[g]['S1']))\n",
    "    print('speaker 2 unique words:', len(speaker_freq[g]['S2']))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Feb2019_G02.xlsx' 'Dec11-2019 - t040 t085.xlsx' 'Feb2019_G43.xlsx'\n",
      " 'Dec2018_G2.xlsx' 'Dec4-2019 - t040 t085.xlsx'\n",
      " 'Dec11-2019 - t060 t105.xlsx' 'Feb2019_G18.xlsx'\n",
      " 'Dec4-2019 - t031 t070.xlsx' 'Feb2019_G19.xlsx'\n",
      " 'Dec11-2019 - t031 t070.xlsx' 'Dec11-2019 - t007 t091.xlsx'\n",
      " 'Dec4-2019 - t099 t107.xlsx' 'Dec4-2019 - t007 t091.xlsx'\n",
      " 'Feb2019_G42.xlsx' 'Dec2018_G22.xlsx' 'Feb2019_G08.xlsx'\n",
      " 'Feb2019_G04.xlsx' 'Dec4-2019 - t012 t065.xlsx' 'Dec2018_G14.xlsx'\n",
      " 'Dec2018_G4.xlsx' 'Dec11-2019 - t012 t065.xlsx' 'Feb2019_G29.xlsx'\n",
      " 'Dec2018_G5.xlsx' 'Dec2018_G15.xlsx' 'Dec11-2019 - t99 t107.xlsx'\n",
      " 'Feb2019_G05.xlsx' 'Feb2019_G25.xlsx' 'Dec2018_G9.xlsx'\n",
      " 'Dec4-2019 - t105 t060.xlsx' 'Feb2019_G26.xlsx' 'Dec2018_G20.xlsx'\n",
      " 'Dec2018_G6.xlsx' 'Dec2018_G10.xlsx' 'Dec11-2019 - t022 t075.xlsx'\n",
      " 'Dec4-2019 - t022 t075.xlsx' 'Dec2018_G1.xlsx']\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "groups = ms_df['group'].unique()\n",
    "print(groups)\n",
    "\n",
    "global_alignment = {}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Feb2019_G02.xlsx': {'entr_1c': -0.8053640769392831, 'entr_2c': -0.4081237911025145}, 'Dec11-2019 - t040 t085.xlsx': {'entr_1c': -1.0115880473515622, 'entr_2c': -0.5065123010130246}, 'Feb2019_G43.xlsx': {'entr_1c': -1.3042243826894941, 'entr_2c': -0.703125}, 'Dec2018_G2.xlsx': {'entr_1c': -1.5999999999999979, 'entr_2c': -0.7656903765690377}, 'Dec4-2019 - t040 t085.xlsx': {'entr_1c': -0.9521447721179638, 'entr_2c': -0.48903878583473864}, 'Dec11-2019 - t060 t105.xlsx': {'entr_1c': -0.8246471335208204, 'entr_2c': -0.5146341463414634}, 'Feb2019_G18.xlsx': {'entr_1c': -0.9495133819951441, 'entr_2c': -0.4692387904066736}, 'Dec4-2019 - t031 t070.xlsx': {'entr_1c': -1.0849335748792313, 'entr_2c': -0.7257926306769494}, 'Feb2019_G19.xlsx': {'entr_1c': -0.9312141905748531, 'entr_2c': -0.5590433482810164}, 'Dec11-2019 - t031 t070.xlsx': {'entr_1c': -1.000206681724908, 'entr_2c': -0.5915863277826467}, 'Dec11-2019 - t007 t091.xlsx': {'entr_1c': -0.8499585970308189, 'entr_2c': -0.42120343839541546}, 'Dec4-2019 - t099 t107.xlsx': {'entr_1c': -0.7589838593891145, 'entr_2c': -0.42770352369380316}, 'Dec4-2019 - t007 t091.xlsx': {'entr_1c': -1.147739841857489, 'entr_2c': -0.5787278415015641}, 'Feb2019_G42.xlsx': {'entr_1c': -1.122183395589836, 'entr_2c': -0.7502198768689534}, 'Dec2018_G22.xlsx': {'entr_1c': -1.2030776340110907, 'entr_2c': -0.594048884165781}, 'Feb2019_G08.xlsx': {'entr_1c': -0.8705930901173363, 'entr_2c': -0.4325581395348837}, 'Feb2019_G04.xlsx': {'entr_1c': -1.2231855654730215, 'entr_2c': -0.6117455138662317}, 'Dec4-2019 - t012 t065.xlsx': {'entr_1c': -1.0353204172876302, 'entr_2c': -0.5914702581369248}, 'Dec2018_G14.xlsx': {'entr_1c': -0.9987112504353873, 'entr_2c': -0.478134110787172}, 'Dec2018_G4.xlsx': {'entr_1c': -1.2936759457530365, 'entr_2c': -0.6920583468395461}, 'Dec11-2019 - t012 t065.xlsx': {'entr_1c': -0.8880884362191037, 'entr_2c': -0.47718631178707227}, 'Feb2019_G29.xlsx': {'entr_1c': -1.1539673291469752, 'entr_2c': -0.6018099547511312}, 'Dec2018_G5.xlsx': {'entr_1c': -1.2477693891557968, 'entr_2c': -0.6229946524064172}, 'Dec2018_G15.xlsx': {'entr_1c': -1.2167919799498723, 'entr_2c': -0.6072874493927125}, 'Dec11-2019 - t99 t107.xlsx': {'entr_1c': -0.7554475853945837, 'entr_2c': -0.3863157894736842}, 'Feb2019_G05.xlsx': {'entr_1c': -1.1468513755747787, 'entr_2c': -0.7445339470655926}, 'Feb2019_G25.xlsx': {'entr_1c': -1.275593066908067, 'entr_2c': -0.7088948787061995}, 'Dec2018_G9.xlsx': {'entr_1c': -1.1441944040386747, 'entr_2c': -0.5691411935953421}, 'Dec4-2019 - t105 t060.xlsx': {'entr_1c': -1.2095848211160154, 'entr_2c': -0.6037514654161782}, 'Feb2019_G26.xlsx': {'entr_1c': -1.3583304823811146, 'entr_2c': -0.7115072933549432}, 'Dec2018_G20.xlsx': {'entr_1c': -1.183900257487464, 'entr_2c': -0.6122448979591837}, 'Dec2018_G6.xlsx': {'entr_1c': -1.3124107726183896, 'entr_2c': -0.6666666666666666}, 'Dec2018_G10.xlsx': {'entr_1c': -0.9233990507484512, 'entr_2c': -0.4654130288784419}, 'Dec11-2019 - t022 t075.xlsx': {'entr_1c': -1.1293504183677583, 'entr_2c': -0.5961395694135115}, 'Dec4-2019 - t022 t075.xlsx': {'entr_1c': -1.0256145638807872, 'entr_2c': -0.5864150943396227}, 'Dec2018_G1.xlsx': {'entr_1c': -1.1223611671372886, 'entr_2c': -0.6014580801944107}}\n"
     ]
    }
   ],
   "source": [
    "for g in groups:\n",
    "    global_alignment[g] = {}\n",
    "    # get speakers\n",
    "    speaker1 = speaker_freq[g]['S1']\n",
    "    speaker2 = speaker_freq[g]['S2']\n",
    "    # count of all words used by speakers\n",
    "    all_s1 = sum(speaker1.values())\n",
    "    all_s2 = sum(speaker2.values())\n",
    "    # do math\n",
    "    # list to hold entr(w) values\n",
    "    entr_w = []\n",
    "    entr_2c_top = []\n",
    "    entr_2c_bottom = []\n",
    "    for w in group_freq[g].keys():\n",
    "        wc1 = 0\n",
    "        if w in speaker1.keys():\n",
    "        # value of word for speaker 1\n",
    "            wc1 = speaker1[w]\n",
    "        # speaker 1 value\n",
    "        s1_w = wc1/all_s1\n",
    "        # value of word for speaker 2\n",
    "        wc2 = 0\n",
    "        if w in speaker2.keys():\n",
    "            wc2 = speaker2[w]\n",
    "        s2_w = wc2/all_s2\n",
    "        entr_w.append(-(abs(s1_w-s2_w)))\n",
    "        entr_2c_top.append(abs(wc1-wc2))\n",
    "        entr_2c_bottom.append(wc1+wc2)\n",
    "    global_alignment[g]['entr_1c'] = sum(entr_w)\n",
    "    global_alignment[g]['entr_2c'] = -(sum(entr_2c_top)/sum(entr_2c_bottom))\n",
    "\n",
    "print(global_alignment)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Because a speaker in speaker 2 in Dec2018_G2 only has 33 unique words after cleaning, we'll compare the top 25 words\n",
    "for all groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Feb2019_G02.xlsx': {'entr_1c': -0.4818311270502611, 'entr_2c': -0.2413793103448276}, 'Dec11-2019 - t040 t085.xlsx': {'entr_1c': -0.6333861409376284, 'entr_2c': -0.3344594594594595}, 'Feb2019_G43.xlsx': {'entr_1c': -1.0263385894992203, 'entr_2c': -0.6186770428015564}, 'Dec2018_G2.xlsx': {'entr_1c': -1.468085106382979, 'entr_2c': -0.712}, 'Dec4-2019 - t040 t085.xlsx': {'entr_1c': -0.643060643060643, 'entr_2c': -0.3620689655172414}, 'Dec11-2019 - t060 t105.xlsx': {'entr_1c': -0.48040268456375845, 'entr_2c': -0.39420935412026725}, 'Feb2019_G18.xlsx': {'entr_1c': -0.5917656464138875, 'entr_2c': -0.296}, 'Dec4-2019 - t031 t070.xlsx': {'entr_1c': -0.6757700280010184, 'entr_2c': -0.6553945249597424}, 'Feb2019_G19.xlsx': {'entr_1c': -0.634114066428477, 'entr_2c': -0.4384949348769899}, 'Dec11-2019 - t031 t070.xlsx': {'entr_1c': -0.6159110350727117, 'entr_2c': -0.47643979057591623}, 'Dec11-2019 - t007 t091.xlsx': {'entr_1c': -0.46921096921096916, 'entr_2c': -0.23642732049036777}, 'Dec4-2019 - t099 t107.xlsx': {'entr_1c': -0.36174939891043, 'entr_2c': -0.2805755395683453}, 'Dec4-2019 - t007 t091.xlsx': {'entr_1c': -0.6084780615910611, 'entr_2c': -0.30198019801980197}, 'Feb2019_G42.xlsx': {'entr_1c': -0.7755411255411256, 'entr_2c': -0.6793893129770993}, 'Dec2018_G22.xlsx': {'entr_1c': -0.8100840336134454, 'entr_2c': -0.4049586776859504}, 'Feb2019_G08.xlsx': {'entr_1c': -0.5886630155636005, 'entr_2c': -0.2966601178781925}, 'Feb2019_G04.xlsx': {'entr_1c': -0.702504222662848, 'entr_2c': -0.4049586776859504}, 'Dec4-2019 - t012 t065.xlsx': {'entr_1c': -0.5654545454545457, 'entr_2c': -0.43187066974595845}, 'Dec2018_G14.xlsx': {'entr_1c': -0.5694174307678058, 'entr_2c': -0.2833333333333333}, 'Dec2018_G4.xlsx': {'entr_1c': -0.9358922008983256, 'entr_2c': -0.5405405405405406}, 'Dec11-2019 - t012 t065.xlsx': {'entr_1c': -0.4971380114479543, 'entr_2c': -0.30985915492957744}, 'Feb2019_G29.xlsx': {'entr_1c': -0.6129858558859561, 'entr_2c': -0.38011695906432746}, 'Dec2018_G5.xlsx': {'entr_1c': -0.7487804878048782, 'entr_2c': -0.4233576642335766}, 'Dec2018_G15.xlsx': {'entr_1c': -0.7237974326421217, 'entr_2c': -0.3775933609958506}, 'Dec11-2019 - t99 t107.xlsx': {'entr_1c': -0.3713649815344729, 'entr_2c': -0.2074235807860262}, 'Feb2019_G05.xlsx': {'entr_1c': -0.7611706795894227, 'entr_2c': -0.6712962962962963}, 'Feb2019_G25.xlsx': {'entr_1c': -0.8060606060606061, 'entr_2c': -0.6082474226804123}, 'Dec2018_G9.xlsx': {'entr_1c': -0.774698242933537, 'entr_2c': -0.3867403314917127}, 'Dec4-2019 - t105 t060.xlsx': {'entr_1c': -0.8335585585585585, 'entr_2c': -0.4211764705882353}, 'Feb2019_G26.xlsx': {'entr_1c': -0.7023809523809523, 'entr_2c': -0.48917748917748916}, 'Dec2018_G20.xlsx': {'entr_1c': -0.8025039123630672, 'entr_2c': -0.4482758620689655}, 'Dec2018_G6.xlsx': {'entr_1c': -0.9314516129032256, 'entr_2c': -0.4803921568627451}, 'Dec2018_G10.xlsx': {'entr_1c': -0.4679045092838197, 'entr_2c': -0.2413793103448276}, 'Dec11-2019 - t022 t075.xlsx': {'entr_1c': -0.8207935039572412, 'entr_2c': -0.4152823920265781}, 'Dec4-2019 - t022 t075.xlsx': {'entr_1c': -0.5694419441944195, 'entr_2c': -0.4205607476635514}, 'Dec2018_G1.xlsx': {'entr_1c': -0.806270652424499, 'entr_2c': -0.5023771790808241}}\n"
     ]
    }
   ],
   "source": [
    "global_alignment_25 = {}\n",
    "\n",
    "for g in groups:\n",
    "    global_alignment_25[g] = {}\n",
    "    speaker1 = speaker_freq[g]['S1']\n",
    "    speaker2 = speaker_freq[g]['S2']\n",
    "    # count of all words used by speakers\n",
    "    entr_w = []\n",
    "    entr_2c_top = []\n",
    "    entr_2c_bottom = []\n",
    "    top_25 = []\n",
    "    for i in range(0,25):\n",
    "        sort = sorted(((v,k) for k,v in group_freq[g].items()), reverse=True)\n",
    "        top_25.append(sort[i][1])\n",
    "    # do math\n",
    "    all_s1_li = []\n",
    "    for i in top_25:\n",
    "        if i in speaker1:\n",
    "            all_s1_li.append(speaker1[i])\n",
    "    all_s1 = sum(all_s1_li)\n",
    "    all_s2_li = []\n",
    "    for i in top_25:\n",
    "        if i in speaker2:\n",
    "            all_s2_li.append(speaker2[i])\n",
    "    all_s2 = sum(all_s2_li)\n",
    "    for i in top_25:\n",
    "        wc1 = 0\n",
    "        if i in speaker1.keys():\n",
    "            wc1 = speaker1[i]\n",
    "        # speaker 1 value\n",
    "        s1_w = wc1/all_s1\n",
    "        # value of word for speaker 2\n",
    "        wc2 = 0\n",
    "        if i in speaker2.keys():\n",
    "            wc2 = speaker2[i]\n",
    "        s2_w = wc2/all_s2\n",
    "        entr_w.append(-(abs(s1_w-s2_w)))\n",
    "        entr_2c_top.append(abs(wc1-wc2))\n",
    "        entr_2c_bottom.append(wc1+wc2)\n",
    "    global_alignment_25[g]['entr_1c'] = sum(entr_w)\n",
    "    global_alignment_25[g]['entr_2c'] = -(sum(entr_2c_top)/sum(entr_2c_bottom))\n",
    "\n",
    "print(global_alignment_25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 36 entries, 0 to 35\n",
      "Data columns (total 5 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   group        36 non-null     object \n",
      " 1   entr_1c_all  36 non-null     float64\n",
      " 2   entr_2c_all  36 non-null     float64\n",
      " 3   entr_1c_25   36 non-null     float64\n",
      " 4   entr_2c_25   36 non-null     float64\n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 1.7+ KB\n",
      "None\n",
      "Feb2019_G02.xlsx , -0.8053640769392831 , -0.4081237911025145 , -0.4818311270502611 , -0.2413793103448276\n",
      "Dec11-2019 - t040 t085.xlsx , -1.0115880473515622 , -0.5065123010130246 , -0.6333861409376284 , -0.3344594594594595\n",
      "Feb2019_G43.xlsx , -1.3042243826894941 , -0.703125 , -1.0263385894992203 , -0.6186770428015564\n",
      "Dec2018_G2.xlsx , -1.5999999999999979 , -0.7656903765690377 , -1.468085106382979 , -0.712\n",
      "Dec4-2019 - t040 t085.xlsx , -0.9521447721179638 , -0.48903878583473864 , -0.643060643060643 , -0.3620689655172414\n",
      "Dec11-2019 - t060 t105.xlsx , -0.8246471335208204 , -0.5146341463414634 , -0.48040268456375845 , -0.39420935412026725\n",
      "Feb2019_G18.xlsx , -0.9495133819951441 , -0.4692387904066736 , -0.5917656464138875 , -0.296\n",
      "Dec4-2019 - t031 t070.xlsx , -1.0849335748792313 , -0.7257926306769494 , -0.6757700280010184 , -0.6553945249597424\n",
      "Feb2019_G19.xlsx , -0.9312141905748531 , -0.5590433482810164 , -0.634114066428477 , -0.4384949348769899\n",
      "Dec11-2019 - t031 t070.xlsx , -1.000206681724908 , -0.5915863277826467 , -0.6159110350727117 , -0.47643979057591623\n",
      "Dec11-2019 - t007 t091.xlsx , -0.8499585970308189 , -0.42120343839541546 , -0.46921096921096916 , -0.23642732049036777\n",
      "Dec4-2019 - t099 t107.xlsx , -0.7589838593891145 , -0.42770352369380316 , -0.36174939891043 , -0.2805755395683453\n",
      "Dec4-2019 - t007 t091.xlsx , -1.147739841857489 , -0.5787278415015641 , -0.6084780615910611 , -0.30198019801980197\n",
      "Feb2019_G42.xlsx , -1.122183395589836 , -0.7502198768689534 , -0.7755411255411256 , -0.6793893129770993\n",
      "Dec2018_G22.xlsx , -1.2030776340110907 , -0.594048884165781 , -0.8100840336134454 , -0.4049586776859504\n",
      "Feb2019_G08.xlsx , -0.8705930901173363 , -0.4325581395348837 , -0.5886630155636005 , -0.2966601178781925\n",
      "Feb2019_G04.xlsx , -1.2231855654730215 , -0.6117455138662317 , -0.702504222662848 , -0.4049586776859504\n",
      "Dec4-2019 - t012 t065.xlsx , -1.0353204172876302 , -0.5914702581369248 , -0.5654545454545457 , -0.43187066974595845\n",
      "Dec2018_G14.xlsx , -0.9987112504353873 , -0.478134110787172 , -0.5694174307678058 , -0.2833333333333333\n",
      "Dec2018_G4.xlsx , -1.2936759457530365 , -0.6920583468395461 , -0.9358922008983256 , -0.5405405405405406\n",
      "Dec11-2019 - t012 t065.xlsx , -0.8880884362191037 , -0.47718631178707227 , -0.4971380114479543 , -0.30985915492957744\n",
      "Feb2019_G29.xlsx , -1.1539673291469752 , -0.6018099547511312 , -0.6129858558859561 , -0.38011695906432746\n",
      "Dec2018_G5.xlsx , -1.2477693891557968 , -0.6229946524064172 , -0.7487804878048782 , -0.4233576642335766\n",
      "Dec2018_G15.xlsx , -1.2167919799498723 , -0.6072874493927125 , -0.7237974326421217 , -0.3775933609958506\n",
      "Dec11-2019 - t99 t107.xlsx , -0.7554475853945837 , -0.3863157894736842 , -0.3713649815344729 , -0.2074235807860262\n",
      "Feb2019_G05.xlsx , -1.1468513755747787 , -0.7445339470655926 , -0.7611706795894227 , -0.6712962962962963\n",
      "Feb2019_G25.xlsx , -1.275593066908067 , -0.7088948787061995 , -0.8060606060606061 , -0.6082474226804123\n",
      "Dec2018_G9.xlsx , -1.1441944040386747 , -0.5691411935953421 , -0.774698242933537 , -0.3867403314917127\n",
      "Dec4-2019 - t105 t060.xlsx , -1.2095848211160154 , -0.6037514654161782 , -0.8335585585585585 , -0.4211764705882353\n",
      "Feb2019_G26.xlsx , -1.3583304823811146 , -0.7115072933549432 , -0.7023809523809523 , -0.48917748917748916\n",
      "Dec2018_G20.xlsx , -1.183900257487464 , -0.6122448979591837 , -0.8025039123630672 , -0.4482758620689655\n",
      "Dec2018_G6.xlsx , -1.3124107726183896 , -0.6666666666666666 , -0.9314516129032256 , -0.4803921568627451\n",
      "Dec2018_G10.xlsx , -0.9233990507484512 , -0.4654130288784419 , -0.4679045092838197 , -0.2413793103448276\n",
      "Dec11-2019 - t022 t075.xlsx , -1.1293504183677583 , -0.5961395694135115 , -0.8207935039572412 , -0.4152823920265781\n",
      "Dec4-2019 - t022 t075.xlsx , -1.0256145638807872 , -0.5864150943396227 , -0.5694419441944195 , -0.4205607476635514\n",
      "Dec2018_G1.xlsx , -1.1223611671372886 , -0.6014580801944107 , -0.806270652424499 , -0.5023771790808241\n"
     ]
    }
   ],
   "source": [
    "global_1c = []\n",
    "global_2c = []\n",
    "top_1c = []\n",
    "top_2c = []\n",
    "\n",
    "for g in groups:\n",
    "    # print(g)\n",
    "    # print('all words entr1c:', global_alignment[g]['entr_1c'])\n",
    "    global_1c.append(global_alignment[g]['entr_1c'])\n",
    "    # print('top 25 words entr1c:', global_alignment_25[g]['entr_1c'])\n",
    "    top_1c.append(global_alignment_25[g]['entr_1c'])\n",
    "    # print('all words entr2c:', global_alignment[g]['entr_2c'])\n",
    "    global_2c.append(global_alignment[g]['entr_2c'])\n",
    "    # print('top 25 words entr2c:', global_alignment_25[g]['entr_2c'])\n",
    "    top_2c.append(global_alignment_25[g]['entr_2c'])\n",
    "\n",
    "# print(global_alignment)\n",
    "entr_ms_df = pd.DataFrame.from_dict(global_alignment, 'index')\n",
    "entr_ms_df.reset_index(inplace=True)\n",
    "entr_ms_df.rename(columns={'index':'group', 'entr_1c':'entr_1c_all', 'entr_2c':'entr_2c_all'}, inplace=True)\n",
    "entr_ms_df25 = pd.DataFrame.from_dict(global_alignment_25, 'index')\n",
    "entr_ms_df25.reset_index(inplace=True)\n",
    "entr_ms_df25.rename(columns={'index':'group', 'entr_1c':'entr_1c_25', 'entr_2c':'entr_2c_25'}, inplace=True)\n",
    "entr_ms_df = entr_ms_df.merge(entr_ms_df25)\n",
    "print(entr_ms_df.info())\n",
    "\n",
    "for i, j in enumerate(entr_ms_df['group']):\n",
    "    print(j, \",\", entr_ms_df['entr_1c_all'][i], \",\", entr_ms_df['entr_2c_all'][i], \",\", entr_ms_df['entr_1c_25'][i], \",\", entr_ms_df['entr_2c_25'][i])\n",
    "\n",
    "entr_ms_df.to_pickle('Middle_School/entr')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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q1Kjh7HwiImIChz/iV6lShRo1alCmTBmOHTvGjBkzmDNnjjOziZNlZN/770kUxTJEpPg5tGbwyiuv8NNPP9GjRw/efPNNqlSpAkBgYKBTw4lzlXF3pVbEZ/e0jFOzuhZRGhEpTg6VQd++fWncuDFeXl5cvHjRfn1MTIzTgomIiHkc2kx04MAB3n//fQCmT59OdHQ0wB1/v1hEREoXh8pg+/btjB49GoAFCxawbds2p4YSERFzOVQGFouFrKwsALKzszEMw6mhRETEXA7tMwgJCaF79+7UrVuXEydOMGjQIGfnEhEREzlUBkFBQbRv356zZ89So0YNfH19nZ1LRERM5FAZWK1WYmNjyczMtF83c+ZMp4USERFzOVQGERERPP/881StWtXZeUREpBg4VAYVK1YkKCjI2VlERKSYOFQG1apVIzo6mgYNGmCxWABo1aqVU4OJiIh5HCqD7OxsTp48ycmTJ+3XqQxERP48HCqDmTNncvLkSc6cOUO9evWoXLmys3OJiIiJHCqDVatWsWXLFn777TcCAgI4ffo0U6ZMcXY2ERExiUNHIH/22WcsX74cb29vBgwYwKFDhwo12Pvvv09wcDCBgYGsWbOmUMsQEZGi59CagWEYWCwW+85jDw+PAg+0Z88eDh48SExMDNevX2fZsmUFXoaIiDiHQ2XQrVs3+vfvz4ULFxg8eDAdOnQo8ED//e9/qVu3LsOGDSMtLY3x48cXeBkiIuIcFsPBs84lJSVx7Ngx/P39qV+/foEHmjx5MhcuXGDx4sWcO3eOIUOGsGnTJvvaxh8lJiZStmzZAo9jtoyMDMqUKWPqmH61auN1X9GcPrwoftzGarUWSRYonsezMJSzaJWGnKUh400NGjQo8DwOrRksWrTI/ndSUhJbt25l+PDhBRrIx8eH2rVr4+HhQe3atfH09CQ1NZUKFSrkOb2Li0uh7pDZrFZrseS81zdxKLpfKSvK+19cj2dBKWfRKg05S0NGoNAfzhzagVyxYkUqVqxIhQoVSElJITk5ucADNWvWjISEBAzDICUlhevXr+Pj41Pg5YiISNFz+BTWv1eYU1g/88wz7Nu3jz59+mAYBlOmTMHV1bXAyxERkaLnUBn8/sjjS5cuceHChUINpp3GIiIlk0Nl8PsDzDw9PQkPD3daIBERMZ9DZbBy5Upn5xARkWLkUBn06NGD9PR0PD097T9wc/NAtK+++sqpAUVExPkcKoMmTZrQq1cvmjRpwo8//sjSpUuZPn26s7OJiIhJHCqDpKQkmjRpAkC9evVITk4u1CkpRESkZHKoDLy9vZk/fz6NGjVi//79PPjgg87OJSIiJnLooLO5c+dSrlw5EhIS8PPzY8aMGc7OJSIiJnKoDDw9Pbn//vvx8fHB39+fK1euODuXiIiYyKEymDJlChcuXGDXrl2kp6frOAMRkT8Zh8rgzJkzvPLKK3h4eNCuXTuuXr3q7FwiImIih8ogNzeX1NRULBYLaWlpuLg4NJuIiJQSDn2b6NVXX6Vfv35cunSJ4OBgJk2a5OxcIiJiIofKIDk5mc2bN5OamsoDDzxwxx+kERGR0smh7T2rV68GwNfXV0UgTpGRnQvc+w/l3FyOiBSMQ2sGWVlZ9OrVC39/f/v+grlz5zo1mPy1lHF3LVG/3ibyV5NvGbz77rsMHTqUsWPHkpKSQpUqVczKJSIiJsp3M9G3334LQMuWLVmzZg0tW7a0/xMRkT+PfMvAMIw8/xYRkT+XfMvg9zuLteNYROTPK999BkePHiUkJATDMDh+/Lj9b4vFwscff2xWRhERcbJ8y2D9+vVm5RARkWKUbxlUq1bNrBwiIlKMdJIhERFRGYiIiMpARERQGYiICCoDERFBZSAiIqgMRESEYiiDX375haeffpqkpCSzhxYRkTswtQyys7OZMmUKZcqUMXNYERG5C4d+3KaozJ49m5CQEKKjo+86rc1mw2q1mpDq3mRkZDic069Wbbzu83RyInNlZOdSxt21uGPYFVWe61k53Odx55eHI7/Iln49kzOnTtxzlntRkOdncSoNOUtDxnthWhnExcXh6+tL69atHSoDFxeXe/4JRDNYrdYC5fyz/ZpXSfuFsqLMc6/LOTWra7E/hwv6/CwupSFnacgIFLqwTNtMtHbtWnbt2kVYWBhWq5Xw8HAuXbpk1vAiIpIP09YMPvzwQ/vfYWFhTJ06lUqVKpk1vIiI5ENfLRUREXN3IN+0cuXK4hhWRETuQGsGIiKiMhAREZWBiIigMhAREVQGIiKCykBERFAZiIgIKgMREUFlICIiqAxERASVgYiIoDIQERFUBiIigspARERQGYiICCoDERFBZSAiIqgMREQElYGIiKAyEBERVAYiIoLKQEREUBmIiAgqAxERQWUgIiKoDEREBJWBiIigMhAREcDNrIGys7OZOHEi58+fJysriyFDhtC+fXuzhhcRkXyYVgbr16/Hx8eHN998k19//ZVevXqpDERESgjTyqBTp0507NgRAMMwcHV1NWtoERG5C9PKwMvLC4C0tDRGjhzJqFGj8p3eZrNhtVrNiHZPMjIyHM7ZoEEDJ6eRkqa4n8MFeX4Wp9KQszRkvBemlQFAcnIyw4YNIzQ0lO7du+c7rYuLS6l487RaraUipxSP4n5ulJbnZ2nIWRoyQuE/gJhWBj///DP//Oc/mTJlCk8++aRZw4qIiANM+2rp4sWLuXLlCu+++y5hYWGEhYWRkZFh1vAiIpIP09YMJk+ezOTJk80aTkRECkAHnYmIiMpARERUBiIigspARERQGYiICCoDERFBZSAiIqgMREQElYGIiKAyEBERVAYiIoLKQEREUBmIiAgluAw8PDyKZDkZ2blOXU5p+LELEZG7MfWXzgrCxcWFWhGf3fNyTs3qWgRpoIy76z3nKaosIiJFrcSuGYiIiHlUBiIiojIQERGVgYiIoDIQERFUBiIigspARERQGYiICCoDERFBZSAiIqgMREQElYGIiKAyEBERTDxrqc1mY+rUqfz44494eHgwffp0atasadbwIiKSD9PWDLZu3UpWVhaxsbGMGTOGWbNmmTW0iIjchWllkJiYSOvWrQFo3LgxR44cMWtoERG5C4thGIYZA02aNInnnnuOp59+GoC2bduydetW3Nzy3lL13Xff4enpaUY0EZE/jczMTBo3blzg+UzbZ1CuXDnS09Ptl2022x2LACjUnRERkcIxbTNR06ZN2blzJ3DjU3/dunXNGlpERO7CtM1EN79NdOzYMQzDICoqijp16pgxtIiI3IVpZSAiIiWXDjoTERGVgYiIqAxERIQSWAZbtmxhzJgxed62Y8cO+vbtS1BQEFOnTqU4d3fklxNu7DAfNGgQMTExJqa6XX45V6xYQVBQEEFBQSxatMjkZLfKL+fq1asJDAykb9++bN++3eRkN2RkZDBixAhCQ0MZPHgwqampt00zc+ZM+vTpQ9++fUlMTCyRGePi4ggKCiIwMJB33nnH9IzgWE6A69ev07NnT/u3EM3mSM7Zs2cTHBxM7969Wb16tan5bDYbU6ZMITg4mLCwME6fPn3L7QV+3RglSGRkpNGxY0dj1KhRt9129epVo2vXrsYvv/xiGIZhREdH2/82W345b5o7d64RFBRkfPTRRyYmu1V+Oc+cOWMEBAQYOTk5hs1mM4KDgw2r1VoMKfPPefHiRaNbt25GZmamceXKFfvfZlu2bJmxYMECwzAMY+PGjUZkZOQtt1utViMoKMiw2WzGyZMnjYCAgBKX8fTp00afPn2M69evG7m5ucZbb71lZGVllbicN0VERBg9e/Y0duzYYWY8u7vl3L17tzF06FDDMAwjMzPT6NChg/Hrr7+alm/z5s1GeHi4YRiGcfDgQePll1+231aY102JWjNo2rQpU6dOzfO2gwcPUrduXWbPnk1oaCgVK1bE19fX3ID/X345ATZt2oTFYrGffqO45JezatWq/Oc//8HV1RWLxUJOTk6xHfGdX87vv/+eJk2a4OHhgbe3N35+fvzwww/mBuTW06m0adOG3bt333J75cqVKVOmDFlZWaSlpeV7QGVxZdy1axcNGzYkPDyc559/nqZNm+Lu7l7icgIsXbqUJk2aUL9+fbPj2d0tZ5MmTYiKirJfzs3NNfX/Pb9T/BTmdWP+MxZYs2YNH3zwwS3XRUVF0aVLF/bs2ZPnPJcvX2bPnj3Ex8dTtmxZ+vfvT+PGjfH39y9ROY8dO8bGjRtZsGCBaavhhcnp7u6Or68vhmHwxhtv8Mgjjzj1sSxszrS0NLy9ve2Xvby8SEtLMz1nhQoV7Dm8vLy4evXqLbe7ubnh4uJC586duXr1KpGRkSUu4+XLl9m/fz8xMTFkZmYSGhpK48aNKV++fInKuXv3bk6fPs20adM4cOCA07Lda05PT088PT3Jzs4mIiKC4OBgvLy8TMkLN14b5cqVs192dXUlJycHNze3Qr1uiqUMbm6nLggfHx8ee+wxKlWqBEDz5s2xWq1OfQMrTM74+HhSUlIYMGAA58+fx93dnWrVqtGmTRsnpSxcTrhxDpOJEyfi5eXFa6+95oRktypMzj+exiQ9Pf2WJ7kz5JVz+PDh9hzp6em3vYHGx8dTsWJFli5dSnp6uv2NtmrVqiUmo4+PDy1btqRcuXKUK1eO2rVrc+rUKRo1auSUjIXN+cknn3D+/HnCwsI4ceIER48epVKlSjRo0KBE5QT47bffGDlyJC1btuSll15yWr685HeKn8K8bkrUZqL8PProoxw7dozU1FRycnI4dOgQDz30UHHHus348eNZs2YNK1euJCAggIEDBzq1CArLMAyGDh1KvXr1mDZtGq6ursUdKU+NGjUiMTGRzMxMrl69SlJSUrGcyqRp06bs2LEDgJ07d9KsWbNbbi9fvjxly5bF1dUVLy8vPDw8uHbtWonK2LRpU/bu3UtmZibXrl0jKSkJPz8/UzM6knPu3Ll8/PHHrFy5ktatWzNu3DinFkFhc2ZkZDBw4EB69+7NsGHDiiXfnU7xU5jXTbGsGRTE8uXL8fPzo3379owZM4ZBgwYB0KlTpxJ1fqPf5yzJbua02Wzs3buXrKwsEhISABg9ejRNmjQp5oQ3/P7xDAsLIzQ0FMMwePXVV4tl30a/fv0IDw+nX79+uLu7M3fuXADeeOMNOnXqRPfu3Tlw4AAhISHk5ubSvXt3ateuXaIyNmrUiN69e9OvXz/7hwEfHx9TMzqasyS4W84DBw5w9uxZ1qxZw5o1a4Abmz1r1KhhSr5nn32Wb775hpCQEPspfu7ldaPTUYiISOnZTCQiIs6jMhAREZWBiIioDEREBJWBiIhQCr5aKuJM0dHR7Nq1i5ycHCwWC+Hh4TRs2LC4Y4mYTmUgf1nHjx9n27ZtxMTEYLFYsFqthIeHs379+uKOJmI6HWcgf1kpKSkEBQUxYsQI2rRpQ5UqVcjKysJqtRIVFYXNZqNKlSrMmTOHEydOEBkZiaurK56enkRGRmKz2RgyZAg+Pj60adOGNm3aMH36dODGqR+ioqLIzs5m1KhRGIZBZmYmr7/+erEcTStyV0V/YlWR0uPIkSNGRESE8fTTTxsdO3Y0Nm3aZPTo0cM4fvy4YRiGsXr1auPIkSNGQECA8b///c8wDMPYsmWLMWLECOPs2bPGE088YT81cFBQkPHTTz/Z55s3b56xfft2Y8SIEcb169eNw4cPG/v37y+eOypyF9pMJH9Zp0+fply5csycOROAw4cPM3jwYNLS0qhTpw6A/eRlFy9etH+ib9Gihf3UBNWrV8fDwwOApKQkXn/9dQCys7OpVasWbdq04dSpUwwdOhQ3NzeGDBli6n0UcZTKQP6yfvzxR2JjY3nvvffw8PDA39+f8uXLU7VqVU6dOkWtWrWIjo7G39+fypUr88MPP1C/fn327dtHrVq1AHBx+b8v5Pn7+zN79mwefPBBEhMTuXTpEnv27KFy5cosW7aMgwcPMm/ePFauXFlM91jkzlQG8pf13HPPkZSURJ8+fShbtiyGYTB+/HgqV67MxIkTcXFxoVKlSgwcOJBq1aoRGRmJYRi4urre8qMmN02dOpXw8HD7N5NmzJiBj48Po0ePJiYmhpycnGI5uz0oJ78AAAAwSURBVKWII7QDWUREdNCZiIioDEREBJWBiIigMhAREVQGIiKCykBERFAZiIgI8P8AD0PA/3HM+M8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('whitegrid')\n",
    "plt.hist(global_1c)\n",
    "plt.ylim([0,12])\n",
    "plt.xlim([-1.6, 0])\n",
    "plt.xlabel('Scores')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Middle School Pairs All 1(c)')\n",
    "plt.show()\n",
    "\n",
    "plt.hist(top_1c)\n",
    "plt.ylim([0,12])\n",
    "plt.xlim([-1.6, 0])\n",
    "plt.xlabel('Scores')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Middle School Pairs Top 25 1(c)')\n",
    "plt.show()\n",
    "\n",
    "plt.hist(global_2c)\n",
    "plt.ylim([0,12])\n",
    "plt.xlim([-1.6, 0])\n",
    "plt.xlabel('Scores')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Middle School Pairs All 2(c)')\n",
    "plt.show()\n",
    "\n",
    "plt.hist(top_2c)\n",
    "plt.ylim([0,12])\n",
    "plt.xlim([-1.6, 0])\n",
    "plt.xlabel('Scores')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Middle School Pairs Top 25 2(c)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## entr1c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "entr = {'all1c':global_1c, 'top251c':top_1c, 'all2c': global_2c, 'top252c': top_2c}\n",
    "entr_df = pd.DataFrame(entr)\n",
    "entr_df.describe()\n",
    "\n",
    "\n",
    "entr_df.to_pickle('Middle_School/entr', )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 15 entries, 0 to 14\n",
      "Data columns (total 4 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   entr_1c_all  15 non-null     float64\n",
      " 1   entr_2c_all  15 non-null     float64\n",
      " 2   entr_1c_25   15 non-null     float64\n",
      " 3   entr_2c_25   15 non-null     float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 600.0 bytes\n",
      "None\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 36 entries, 0 to 35\n",
      "Data columns (total 4 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   all1c    36 non-null     float64\n",
      " 1   top251c  36 non-null     float64\n",
      " 2   all2c    36 non-null     float64\n",
      " 3   top252c  36 non-null     float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 1.2 KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "df_ug_entr = pd.read_pickle(\"Undergrad/entr\")\n",
    "df_ug_entr.drop(columns=['group'], inplace=True)\n",
    "df_ug_group_word_unique = [556, 460, 370, 410, 217, 450, 507, 350, 337, 570, 379, 629, 516, 606, 270]\n",
    "print(df_ug_entr.info())\n",
    "print(entr_df.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MannwhitneyuResult(statistic=225.0, pvalue=0.17880616450069253)\n",
      "MannwhitneyuResult(statistic=131.0, pvalue=0.00209735817115814)\n",
      "MannwhitneyuResult(statistic=227.0, pvalue=0.18981469275794255)\n",
      "MannwhitneyuResult(statistic=246.0, pvalue=0.3135457322450721)\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import mannwhitneyu\n",
    "\n",
    "print(mannwhitneyu(entr_df['all1c'], df_ug_entr['entr_1c_all']))\n",
    "print(mannwhitneyu(entr_df['all2c'], df_ug_entr['entr_2c_all']))\n",
    "print(mannwhitneyu(entr_df['top251c'], df_ug_entr['entr_1c_25']))\n",
    "print(mannwhitneyu(entr_df['top252c'], df_ug_entr['entr_2c_25']))\n",
    "# print(sum(entr['all2c'])/len(entr['all2c']))\n",
    "# print(ttest_ind(entr_df, df_ug_entr))\n",
    "# print(ttest_ind(entr_df, df_ug_entr, equal_var=False))\n",
    "# print(ttest_ind(unique_by_group, df_ug_group_word_unique))\n",
    "# print(ttest_ind(unique_by_group, df_ug_group_word_unique, equal_var=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "307.75\n",
      "565\n",
      "113\n",
      "91.65661849689695\n",
      "316.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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qKiry5bHRE7/eT2bMmKHq6mrNmTNHgUBAK1euVEZGhl5++WV1dnaqpKRE9913n+655x798ssvmj17dtfbdPgJl8cDgEFcpAMABhFvADCIeAOAQcQbAAwi3gBgEPEGAIOINwAY9D+n9TRHYQy7pgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import statistics\n",
    "\n",
    "plt.hist(unique_by_group)\n",
    "print(sum(unique_by_group)/len(unique_by_group))\n",
    "print(max(unique_by_group))\n",
    "print(min(unique_by_group))\n",
    "print(statistics.stdev(unique_by_group))\n",
    "print(statistics.median(unique_by_group))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "441.8\n",
      "629\n",
      "217\n",
      "122.8263582693645\n",
      "450\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_ug_group_word_unique)\n",
    "print(sum(df_ug_group_word_unique)/len(df_ug_group_word_unique))\n",
    "print(max(df_ug_group_word_unique))\n",
    "print(min(df_ug_group_word_unique))\n",
    "print(statistics.stdev(df_ug_group_word_unique))\n",
    "print(statistics.median(df_ug_group_word_unique))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: [all1c, top251c, all2c, top252c]\n",
      "Index: []\n",
      "-1.1222722813635624\n",
      "-0.6594153355308308\n",
      "-0.5928176059742138\n",
      "-0.4101205348562642\n"
     ]
    }
   ],
   "source": [
    "print(entr_df[:0])\n",
    "print(statistics.median(entr_df['all1c']))\n",
    "print(statistics.median(entr_df['top251c']))\n",
    "print(statistics.median(entr_df['all2c']))\n",
    "print(statistics.median(entr_df['top252c']))\n",
    "# print(statistics.median(df_ug_entr[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# group1shuffle=random.sample(range(0,36),36)\n",
    "# group2shuffle=random.sample(range(0,36),36)\n",
    "# Saving the previous shuffle\n",
    "group1shuffle=[17, 5, 11, 4, 30, 33, 35, 18, 14, 0, 25, 1, 26, 24, 23, 9, 10, 6, 29, 31, 8, 32, 19, 2, 13, 7, 20, 21, 16, 3, 15, 12, 34, 22, 27, 28]\n",
    "group2shuffle=[5, 12, 21, 26, 27, 29, 10, 6, 17, 2, 23, 18, 15, 11, 0, 34, 19, 1, 28, 4, 22, 20, 33, 7, 24, 35, 3, 14, 9, 25, 16, 30, 8, 13, 31, 32]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[17, 5, 11, 4, 30, 33, 35, 18, 14, 0, 25, 1, 26, 24, 23, 9, 10, 6, 29, 31, 8, 32, 19, 2, 13, 7, 20, 21, 16, 3, 15, 12, 34, 22, 27, 28]\n",
      "[5, 12, 21, 26, 27, 29, 10, 6, 17, 2, 23, 18, 15, 11, 0, 34, 19, 1, 28, 4, 22, 20, 33, 7, 24, 35, 3, 14, 9, 25, 16, 30, 8, 13, 31, 32]\n"
     ]
    }
   ],
   "source": [
    "print(group1shuffle)\n",
    "print(group2shuffle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# groups = unique list of groups\n",
    "global_alignment_baseline = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dec4-2019 - t012 t065.xlsx , Dec11-2019 - t060 t105.xlsx , -1.380144404332133 , -0.670020120724346\n",
      "Dec11-2019 - t060 t105.xlsx , Dec4-2019 - t007 t091.xlsx , -1.4634367474146452 , -0.7304964539007093\n",
      "Dec4-2019 - t099 t107.xlsx , Feb2019_G29.xlsx , -1.3898496170891448 , -0.7293428757319453\n",
      "Dec4-2019 - t040 t085.xlsx , Feb2019_G25.xlsx , -1.348650970957213 , -0.6594427244582043\n",
      "Dec2018_G20.xlsx , Dec2018_G9.xlsx , -1.1435408318753888 , -0.5915032679738562\n",
      "Dec11-2019 - t022 t075.xlsx , Feb2019_G26.xlsx , -1.5761715761715709 , -0.8609975470155355\n",
      "Dec2018_G1.xlsx , Dec11-2019 - t007 t091.xlsx , -1.3748983975398992 , -0.7046662728883638\n",
      "Dec2018_G14.xlsx , Feb2019_G18.xlsx , -1.1864585955197684 , -0.6960431654676259\n",
      "Dec2018_G22.xlsx , Dec4-2019 - t012 t065.xlsx , -1.3853244116569592 , -0.693069306930693\n",
      "Feb2019_G02.xlsx , Feb2019_G43.xlsx , -1.0370302742577493 , -0.5347368421052632\n",
      "Feb2019_G05.xlsx , Dec2018_G15.xlsx , -1.273857721226139 , -0.7062374245472837\n",
      "Dec11-2019 - t040 t085.xlsx , Dec2018_G14.xlsx , -1.5110474698519933 , -0.741871921182266\n",
      "Feb2019_G25.xlsx , Feb2019_G08.xlsx , -1.5042346718857358 , -0.7922077922077922\n",
      "Dec11-2019 - t99 t107.xlsx , Dec4-2019 - t099 t107.xlsx , -1.0884722063908245 , -0.5535614133482895\n",
      "Dec2018_G15.xlsx , Feb2019_G02.xlsx , -1.497540923438301 , -0.7818696883852692\n",
      "Dec11-2019 - t031 t070.xlsx , Dec4-2019 - t022 t075.xlsx , -1.2224613224001593 , -0.6246575342465753\n",
      "Dec11-2019 - t007 t091.xlsx , Dec2018_G4.xlsx , -1.391609195402301 , -0.7654723127035831\n",
      "Feb2019_G18.xlsx , Dec11-2019 - t040 t085.xlsx , -1.4074532913681213 , -0.6987627756858527\n",
      "Feb2019_G26.xlsx , Dec4-2019 - t105 t060.xlsx , -1.4405972577827815 , -0.7186147186147186\n",
      "Dec2018_G6.xlsx , Dec4-2019 - t040 t085.xlsx , -1.253457888438445 , -0.7120772946859903\n",
      "Feb2019_G19.xlsx , Dec2018_G5.xlsx , -1.2708477548571673 , -0.691717791411043\n",
      "Dec2018_G10.xlsx , Dec11-2019 - t012 t065.xlsx , -1.2948229280759445 , -0.6574882471457354\n",
      "Dec2018_G4.xlsx , Dec11-2019 - t022 t075.xlsx , -1.1793021499919534 , -0.5842558425584256\n",
      "Feb2019_G43.xlsx , Dec4-2019 - t031 t070.xlsx , -1.4013418079096038 , -0.8571428571428571\n",
      "Feb2019_G42.xlsx , Dec11-2019 - t99 t107.xlsx , -1.320884607081173 , -0.6468551644547028\n",
      "Dec4-2019 - t031 t070.xlsx , Dec2018_G1.xlsx , -1.4402264042108375 , -0.7200538358008075\n",
      "Dec11-2019 - t012 t065.xlsx , Dec2018_G2.xlsx , -1.6110431591822447 , -0.8702460850111857\n",
      "Feb2019_G29.xlsx , Dec2018_G22.xlsx , -1.3181871345029164 , -0.6630727762803235\n",
      "Feb2019_G04.xlsx , Dec11-2019 - t031 t070.xlsx , -1.4085592106357885 , -0.7697189483227561\n",
      "Dec2018_G2.xlsx , Feb2019_G05.xlsx , -1.6044917257683162 , -0.8006230529595015\n",
      "Feb2019_G08.xlsx , Feb2019_G04.xlsx , -1.112907496968601 , -0.5719844357976653\n",
      "Dec4-2019 - t007 t091.xlsx , Dec2018_G20.xlsx , -1.4050206069689037 , -0.751412429378531\n",
      "Dec4-2019 - t022 t075.xlsx , Feb2019_G19.xlsx , -1.3537178375864205 , -0.7144992526158446\n",
      "Dec2018_G5.xlsx , Feb2019_G42.xlsx , -1.5509648688767919 , -0.7737226277372263\n",
      "Dec2018_G9.xlsx , Dec2018_G6.xlsx , -1.4031130160162433 , -0.7306843267108167\n",
      "Dec4-2019 - t105 t060.xlsx , Dec2018_G10.xlsx , -1.3863602558381711 , -0.7004048582995951\n",
      "{0: {'entr_1c': -1.380144404332133, 'entr_2c': -0.670020120724346}, 1: {'entr_1c': -1.4634367474146452, 'entr_2c': -0.7304964539007093}, 2: {'entr_1c': -1.3898496170891448, 'entr_2c': -0.7293428757319453}, 3: {'entr_1c': -1.348650970957213, 'entr_2c': -0.6594427244582043}, 4: {'entr_1c': -1.1435408318753888, 'entr_2c': -0.5915032679738562}, 5: {'entr_1c': -1.5761715761715709, 'entr_2c': -0.8609975470155355}, 6: {'entr_1c': -1.3748983975398992, 'entr_2c': -0.7046662728883638}, 7: {'entr_1c': -1.1864585955197684, 'entr_2c': -0.6960431654676259}, 8: {'entr_1c': -1.3853244116569592, 'entr_2c': -0.693069306930693}, 9: {'entr_1c': -1.0370302742577493, 'entr_2c': -0.5347368421052632}, 10: {'entr_1c': -1.273857721226139, 'entr_2c': -0.7062374245472837}, 11: {'entr_1c': -1.5110474698519933, 'entr_2c': -0.741871921182266}, 12: {'entr_1c': -1.5042346718857358, 'entr_2c': -0.7922077922077922}, 13: {'entr_1c': -1.0884722063908245, 'entr_2c': -0.5535614133482895}, 14: {'entr_1c': -1.497540923438301, 'entr_2c': -0.7818696883852692}, 15: {'entr_1c': -1.2224613224001593, 'entr_2c': -0.6246575342465753}, 16: {'entr_1c': -1.391609195402301, 'entr_2c': -0.7654723127035831}, 17: {'entr_1c': -1.4074532913681213, 'entr_2c': -0.6987627756858527}, 18: {'entr_1c': -1.4405972577827815, 'entr_2c': -0.7186147186147186}, 19: {'entr_1c': -1.253457888438445, 'entr_2c': -0.7120772946859903}, 20: {'entr_1c': -1.2708477548571673, 'entr_2c': -0.691717791411043}, 21: {'entr_1c': -1.2948229280759445, 'entr_2c': -0.6574882471457354}, 22: {'entr_1c': -1.1793021499919534, 'entr_2c': -0.5842558425584256}, 23: {'entr_1c': -1.4013418079096038, 'entr_2c': -0.8571428571428571}, 24: {'entr_1c': -1.320884607081173, 'entr_2c': -0.6468551644547028}, 25: {'entr_1c': -1.4402264042108375, 'entr_2c': -0.7200538358008075}, 26: {'entr_1c': -1.6110431591822447, 'entr_2c': -0.8702460850111857}, 27: {'entr_1c': -1.3181871345029164, 'entr_2c': -0.6630727762803235}, 28: {'entr_1c': -1.4085592106357885, 'entr_2c': -0.7697189483227561}, 29: {'entr_1c': -1.6044917257683162, 'entr_2c': -0.8006230529595015}, 30: {'entr_1c': -1.112907496968601, 'entr_2c': -0.5719844357976653}, 31: {'entr_1c': -1.4050206069689037, 'entr_2c': -0.751412429378531}, 32: {'entr_1c': -1.3537178375864205, 'entr_2c': -0.7144992526158446}, 33: {'entr_1c': -1.5509648688767919, 'entr_2c': -0.7737226277372263}, 34: {'entr_1c': -1.4031130160162433, 'entr_2c': -0.7306843267108167}, 35: {'entr_1c': -1.3863602558381711, 'entr_2c': -0.7004048582995951}}\n"
     ]
    }
   ],
   "source": [
    "for i in range(0,36):\n",
    "    global_alignment_baseline[i] = {}\n",
    "    # get speakers\n",
    "    group1 = groups[group1shuffle[i]]\n",
    "    group2 = groups[group2shuffle[i]]\n",
    "    speaker1 = speaker_freq[group1]['S1']\n",
    "    speaker2 = speaker_freq[group2]['S2']\n",
    "    # count of all words used by speakers\n",
    "    all_s1 = sum(speaker1.values())\n",
    "    all_s2 = sum(speaker2.values())\n",
    "    # do math\n",
    "    # list to hold entr(w) values\n",
    "    entr_w = []\n",
    "    entr_2c_top = []\n",
    "    entr_2c_bottom = []\n",
    "    group_counter = Counter(speaker1) + Counter(speaker2)\n",
    "    group_dict = dict(group_counter)\n",
    "    # print(group_dict.keys())\n",
    "    for w in group_dict.keys():\n",
    "        wc1= 0\n",
    "        if w in speaker1.keys():\n",
    "            # value of word for speaker 1\n",
    "            wc1 = speaker1[w]\n",
    "        # speaker 1 value\n",
    "        s1_w = wc1/all_s1\n",
    "        # value of word for speaker 2\n",
    "        wc2 = 0\n",
    "        if w in speaker2.keys():\n",
    "            wc2 = speaker2[w]\n",
    "        s2_w = wc2/all_s2\n",
    "        entr_w.append(-(abs(s1_w-s2_w)))\n",
    "        entr_2c_top.append(abs(wc1-wc2))\n",
    "        entr_2c_bottom.append(wc1+wc2)\n",
    "    global_alignment_baseline[i]['entr_1c'] = sum(entr_w)\n",
    "    global_alignment_baseline[i]['entr_2c'] = -(sum(entr_2c_top)/sum(entr_2c_bottom))\n",
    "    print(group1, \",\", group2, \",\", sum(entr_w), \",\", -(sum(entr_2c_top)/sum(entr_2c_bottom)))\n",
    "\n",
    "print(global_alignment_baseline)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dec4-2019 - t012 t065.xlsx , Dec11-2019 - t060 t105.xlsx , -0.9416666666666667 , -0.44696969696969696\n",
      "Dec11-2019 - t060 t105.xlsx , Dec4-2019 - t007 t091.xlsx , -1.1226437795780861 , -0.5701754385964912\n",
      "Dec4-2019 - t099 t107.xlsx , Feb2019_G29.xlsx , -0.9479883581578497 , -0.6339285714285714\n",
      "Dec4-2019 - t040 t085.xlsx , Feb2019_G25.xlsx , -0.973875290066622 , -0.4527363184079602\n",
      "Dec2018_G20.xlsx , Dec2018_G9.xlsx , -0.8413793103448275 , -0.43573667711598746\n",
      "Dec11-2019 - t022 t075.xlsx , Feb2019_G26.xlsx , -1.2725294858582386 , -0.8244111349036403\n",
      "Dec2018_G1.xlsx , Dec11-2019 - t007 t091.xlsx , -0.9587619801283742 , -0.5064194008559201\n",
      "Dec2018_G14.xlsx , Feb2019_G18.xlsx , -0.6154934823091249 , -0.5\n",
      "Dec2018_G22.xlsx , Dec4-2019 - t012 t065.xlsx , -1.02813752698119 , -0.5278969957081545\n",
      "Feb2019_G02.xlsx , Feb2019_G43.xlsx , -0.616724336793541 , -0.34328358208955223\n",
      "Feb2019_G05.xlsx , Dec2018_G15.xlsx , -0.8481864040355512 , -0.5765199161425576\n",
      "Dec11-2019 - t040 t085.xlsx , Dec2018_G14.xlsx , -1.1494817239498092 , -0.5511221945137157\n",
      "Feb2019_G25.xlsx , Feb2019_G08.xlsx , -1.0607287449392715 , -0.6989966555183946\n",
      "Dec11-2019 - t99 t107.xlsx , Dec4-2019 - t099 t107.xlsx , -0.6921359633002547 , -0.35990037359900373\n",
      "Dec2018_G15.xlsx , Feb2019_G02.xlsx , -1.2239681938659597 , -0.7401129943502824\n",
      "Dec11-2019 - t031 t070.xlsx , Dec4-2019 - t022 t075.xlsx , -0.7365138947750802 , -0.40273972602739727\n",
      "Dec11-2019 - t007 t091.xlsx , Dec2018_G4.xlsx , -0.9128304397798077 , -0.6325301204819277\n",
      "Feb2019_G18.xlsx , Dec11-2019 - t040 t085.xlsx , -0.9740978854658293 , -0.49019607843137253\n",
      "Feb2019_G26.xlsx , Dec4-2019 - t105 t060.xlsx , -1.148634796384307 , -0.5683060109289617\n",
      "Dec2018_G6.xlsx , Dec4-2019 - t040 t085.xlsx , -0.7285318559556788 , -0.5705263157894737\n",
      "Feb2019_G19.xlsx , Dec2018_G5.xlsx , -0.8759578544061302 , -0.5592105263157895\n",
      "Dec2018_G10.xlsx , Dec11-2019 - t012 t065.xlsx , -0.9696319018404909 , -0.48606811145510836\n",
      "Dec2018_G4.xlsx , Dec11-2019 - t022 t075.xlsx , -0.7619094343325321 , -0.37777777777777777\n",
      "Feb2019_G43.xlsx , Dec4-2019 - t031 t070.xlsx , -0.9825918762088974 , -0.8333333333333334\n",
      "Feb2019_G42.xlsx , Dec11-2019 - t99 t107.xlsx , -1.1084439338513168 , -0.5390946502057613\n",
      "Dec4-2019 - t031 t070.xlsx , Dec2018_G1.xlsx , -1.0144867118290777 , -0.5051546391752577\n",
      "Dec11-2019 - t012 t065.xlsx , Dec2018_G2.xlsx , -1.3289557975656636 , -0.8247011952191236\n",
      "Feb2019_G29.xlsx , Dec2018_G22.xlsx , -0.8201327775204569 , -0.4357142857142857\n",
      "Feb2019_G04.xlsx , Dec11-2019 - t031 t070.xlsx , -0.9186674669867946 , -0.6640316205533597\n",
      "Dec2018_G2.xlsx , Feb2019_G05.xlsx , -1.401032702237521 , -0.6993464052287581\n",
      "Feb2019_G08.xlsx , Feb2019_G04.xlsx , -0.7506508205998866 , -0.40454545454545454\n",
      "Dec4-2019 - t007 t091.xlsx , Dec2018_G20.xlsx , -0.9880681426305261 , -0.5923076923076923\n",
      "Dec4-2019 - t022 t075.xlsx , Feb2019_G19.xlsx , -1.0150943396226415 , -0.5769230769230769\n",
      "Dec2018_G5.xlsx , Feb2019_G42.xlsx , -1.0984234234234236 , -0.5602094240837696\n",
      "Dec2018_G9.xlsx , Dec2018_G6.xlsx , -1.2634008338296605 , -0.688715953307393\n",
      "Dec4-2019 - t105 t060.xlsx , Dec2018_G10.xlsx , -1.0910394265232974 , -0.5508474576271186\n",
      "{0: {'entr_1c': -0.9416666666666667, 'entr_2c': -0.44696969696969696}, 1: {'entr_1c': -1.1226437795780861, 'entr_2c': -0.5701754385964912}, 2: {'entr_1c': -0.9479883581578497, 'entr_2c': -0.6339285714285714}, 3: {'entr_1c': -0.973875290066622, 'entr_2c': -0.4527363184079602}, 4: {'entr_1c': -0.8413793103448275, 'entr_2c': -0.43573667711598746}, 5: {'entr_1c': -1.2725294858582386, 'entr_2c': -0.8244111349036403}, 6: {'entr_1c': -0.9587619801283742, 'entr_2c': -0.5064194008559201}, 7: {'entr_1c': -0.6154934823091249, 'entr_2c': -0.5}, 8: {'entr_1c': -1.02813752698119, 'entr_2c': -0.5278969957081545}, 9: {'entr_1c': -0.616724336793541, 'entr_2c': -0.34328358208955223}, 10: {'entr_1c': -0.8481864040355512, 'entr_2c': -0.5765199161425576}, 11: {'entr_1c': -1.1494817239498092, 'entr_2c': -0.5511221945137157}, 12: {'entr_1c': -1.0607287449392715, 'entr_2c': -0.6989966555183946}, 13: {'entr_1c': -0.6921359633002547, 'entr_2c': -0.35990037359900373}, 14: {'entr_1c': -1.2239681938659597, 'entr_2c': -0.7401129943502824}, 15: {'entr_1c': -0.7365138947750802, 'entr_2c': -0.40273972602739727}, 16: {'entr_1c': -0.9128304397798077, 'entr_2c': -0.6325301204819277}, 17: {'entr_1c': -0.9740978854658293, 'entr_2c': -0.49019607843137253}, 18: {'entr_1c': -1.148634796384307, 'entr_2c': -0.5683060109289617}, 19: {'entr_1c': -0.7285318559556788, 'entr_2c': -0.5705263157894737}, 20: {'entr_1c': -0.8759578544061302, 'entr_2c': -0.5592105263157895}, 21: {'entr_1c': -0.9696319018404909, 'entr_2c': -0.48606811145510836}, 22: {'entr_1c': -0.7619094343325321, 'entr_2c': -0.37777777777777777}, 23: {'entr_1c': -0.9825918762088974, 'entr_2c': -0.8333333333333334}, 24: {'entr_1c': -1.1084439338513168, 'entr_2c': -0.5390946502057613}, 25: {'entr_1c': -1.0144867118290777, 'entr_2c': -0.5051546391752577}, 26: {'entr_1c': -1.3289557975656636, 'entr_2c': -0.8247011952191236}, 27: {'entr_1c': -0.8201327775204569, 'entr_2c': -0.4357142857142857}, 28: {'entr_1c': -0.9186674669867946, 'entr_2c': -0.6640316205533597}, 29: {'entr_1c': -1.401032702237521, 'entr_2c': -0.6993464052287581}, 30: {'entr_1c': -0.7506508205998866, 'entr_2c': -0.40454545454545454}, 31: {'entr_1c': -0.9880681426305261, 'entr_2c': -0.5923076923076923}, 32: {'entr_1c': -1.0150943396226415, 'entr_2c': -0.5769230769230769}, 33: {'entr_1c': -1.0984234234234236, 'entr_2c': -0.5602094240837696}, 34: {'entr_1c': -1.2634008338296605, 'entr_2c': -0.688715953307393}, 35: {'entr_1c': -1.0910394265232974, 'entr_2c': -0.5508474576271186}}\n"
     ]
    }
   ],
   "source": [
    "global_alignment_25_baseline = {}\n",
    "\n",
    "for i in range(0,36):\n",
    "    global_alignment_25_baseline[i] = {}\n",
    "    group1 = groups[group1shuffle[i]]\n",
    "    group2 = groups[group2shuffle[i]]\n",
    "    speaker1 = speaker_freq[group1]['S1']\n",
    "    speaker2 = speaker_freq[group2]['S2']\n",
    "    group_dict = Counter(speaker1) + Counter(speaker2)\n",
    "    top_25 = list(group_dict.most_common(25))\n",
    "    # print(len(top_25))\n",
    "    # count of all words used by speakers\n",
    "    entr_w = []\n",
    "    entr_2c_top = []\n",
    "    entr_2c_bottom = []\n",
    "    # top_25 = []\n",
    "    # for i in range(0,25):\n",
    "    #     sort = sorted(((v,k) for k,v in group_freq[g].items()), reverse=True)\n",
    "    #     top_25.append(sort[i][1])\n",
    "    # do math\n",
    "    all_s1_li = []\n",
    "    for j in top_25:\n",
    "        # (print(i[0]))\n",
    "        if j[0] in speaker1:\n",
    "            all_s1_li.append(speaker1[j[0]])\n",
    "    all_s1 = sum(all_s1_li)\n",
    "    all_s2_li = []\n",
    "    for j in top_25:\n",
    "        if j[0] in speaker2:\n",
    "            all_s2_li.append(speaker2[j[0]])\n",
    "    all_s2 = sum(all_s2_li)\n",
    "    for j in top_25:\n",
    "        wc1 = 0\n",
    "        if j[0] in speaker1.keys():\n",
    "            wc1 = speaker1[j[0]]\n",
    "        # speaker 1 value\n",
    "        s1_w = wc1/all_s1\n",
    "        # value of word for speaker 2\n",
    "        wc2 = 0\n",
    "        if j[0] in speaker2.keys():\n",
    "            wc2 = speaker2[j[0]]\n",
    "        s2_w = wc2/all_s2\n",
    "        entr_w.append(-(abs(s1_w-s2_w)))\n",
    "        entr_2c_top.append(abs(wc1-wc2))\n",
    "        entr_2c_bottom.append(wc1+wc2)\n",
    "    global_alignment_25_baseline[i]['entr_1c'] = sum(entr_w)\n",
    "    global_alignment_25_baseline[i]['entr_2c'] = -(sum(entr_2c_top)/sum(entr_2c_bottom))\n",
    "    print(group1, \",\", group2, \",\", sum(entr_w), \",\", -(sum(entr_2c_top)/sum(entr_2c_bottom)))\n",
    "\n",
    "print(global_alignment_25_baseline)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "global_1c_base = []\n",
    "global_2c_base = []\n",
    "top_1c_base = []\n",
    "top_2c_base = []\n",
    "\n",
    "for i in range(0,36):\n",
    "    print(i)\n",
    "    print('all words entr1c:', global_alignment_baseline[i]['entr_1c'])\n",
    "    global_1c_base.append(global_alignment_baseline[i]['entr_1c'])\n",
    "    print('top 25 words entr1c:', global_alignment_25_baseline[i]['entr_1c'])\n",
    "    top_1c_base.append(global_alignment_25_baseline[i]['entr_1c'])\n",
    "    print('all words entr2c:', global_alignment_baseline[i]['entr_2c'])\n",
    "    global_2c_base.append(global_alignment_baseline[i]['entr_2c'])\n",
    "    print('top 25 words entr2c:', global_alignment_25_baseline[i]['entr_2c'])\n",
    "    top_2c_base.append(global_alignment_25_baseline[i]['entr_2c'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('whitegrid')\n",
    "plt.hist(global_1c_base)\n",
    "plt.title('Global 1(c)')\n",
    "plt.show()\n",
    "plt.hist(top_1c_base)\n",
    "plt.title('Top 1(c)')\n",
    "plt.show()\n",
    "plt.hist(global_2c_base)\n",
    "plt.title('Global 2(c)')\n",
    "plt.show()\n",
    "plt.hist(top_2c_base)\n",
    "plt.title('Top 2(c)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "entr_base = {'all1c':global_1c_base, 'top251c':top_1c_base, 'all2c': global_2c_base, 'top252c': top_2c_base}\n",
    "entr_df_base = pd.DataFrame(entr_base)\n",
    "entr_df_base.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "for i in sorted(groups):\n",
    "    print(i)\n",
    "\n",
    "print(len(groups))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from scipy.stats import ttest_ind\n",
    "# print(sum(entr['all2c'])/len(entr['all2c']))\n",
    "print(ttest_ind(entr_df, entr_df_base))\n",
    "print(ttest_ind(entr_df, entr_df_base, equal_var=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ttest_relResult(statistic=11.155079991302642, pvalue=4.2479486149632064e-17)\n",
      "Ttest_relResult(statistic=3.171543575909581, pvalue=0.0022632342653586893)\n",
      "Ttest_relResult(statistic=9.802222537145203, pvalue=1.056064592486445e-14)\n",
      "Ttest_relResult(statistic=7.104404488333645, pvalue=8.601732855806657e-10)\n"
     ]
    }
   ],
   "source": [
    "orig = pd.read_csv('Original_Scores.csv', sep=',', header=None)\n",
    "base = pd.read_csv('Baseline_Scores.csv', sep=',', header=None)\n",
    "\n",
    "from scipy.stats import ttest_rel\n",
    "print(ttest_rel(orig[0], base[0]))\n",
    "print(ttest_rel(orig[1], base[1]))\n",
    "print(ttest_rel(orig[2], base[2]))\n",
    "print(ttest_rel(orig[3], base[3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WilcoxonResult(statistic=2406.0, pvalue=4.913479453988451e-12)\n",
      "WilcoxonResult(statistic=1955.0, pvalue=1.525137327580763e-05)\n",
      "WilcoxonResult(statistic=2349.0, pvalue=4.7268001929942344e-11)\n",
      "WilcoxonResult(statistic=2194.0, pvalue=1.2858013404704076e-08)\n"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "print(stats.wilcoxon(orig[0], base[0], alternative='greater'))\n",
    "print(stats.wilcoxon(orig[1], base[1], alternative='greater'))\n",
    "print(stats.wilcoxon(orig[2], base[2], alternative='greater'))\n",
    "print(stats.wilcoxon(orig[3], base[3], alternative='greater'))"
   ]
  }
 ],
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