word_graph_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Word Graph Analysis\n",
    "## as introduced by Mota et al. 2012\n",
    " make sure that all excel tables are in the same folder as this jupyter notebook if you intend to execute cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports packages, read in all excel tables, define classifiers and computation of results/report\n",
    "import re\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pandas import ExcelWriter\n",
    "from pandas import ExcelFile\n",
    "from sklearn import svm\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.model_selection import train_test_split, KFold, cross_val_score, LeaveOneOut, ShuffleSplit\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "df_movingwindows = pd.read_excel(\"df_movingwindows.xlsx\")\n",
    "df_parameters = pd.read_excel(\"df_parameters.xlsx\")\n",
    "df_parameters_task1 = pd.read_excel(\"df_parameters_task1.xlsx\")\n",
    "df_parameters_task2 = pd.read_excel(\"df_parameters_task2.xlsx\")\n",
    "df_parameters_windows_task1 = pd.read_excel(\"df_parameters_windows_task1.xlsx\")\n",
    "df_parameters_windows_task2 = pd.read_excel(\"df_parameters_windows_task2.xlsx\")\n",
    "df_parameters_windows_both = df_parameters_windows_task1 + df_parameters_windows_task2\n",
    "\n",
    "df_movingwindows = df_movingwindows.drop(\"Unnamed: 0\", axis=1)\n",
    "df_parameters = df_parameters.drop(\"Unnamed: 0\", axis=1)\n",
    "df_parameters_task1 = df_parameters_task1.drop(\"Unnamed: 0\", axis=1)\n",
    "df_parameters_task2 = df_parameters_task2.drop(\"Unnamed: 0\", axis=1)\n",
    "df_parameters_windows_task1 = df_parameters_windows_task1.drop(\"Unnamed: 0\", axis=1)\n",
    "df_parameters_windows_task2 = df_parameters_windows_task2.drop(\"Unnamed: 0\", axis=1)\n",
    "df_parameters_windows_both = df_parameters_windows_both.drop(\"Unnamed: 0\", axis=1)\n",
    "\n",
    "df = pd.read_excel(\"df_with_lemmata.xlsx\")\n",
    "df = df.set_index('ID')\n",
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'57C372_0', '57C392_0', '57C402_0', '57C442_0', '57C502_0', '57C522_0', '57C742_0', '57P071_1', '57P132_2', '57P412_2', '57P542_2', '58C091_0', '58C152_0', '58C162_0', '58C272_0', '58C302_0', '58C312_0', '58C372_0', '58C392_0', '58C402_0', '58C442_0', '58C502_0', '58C522_0', '58C742_0', '58P071_1', '58P132_2', '58P412_2', '58P542_2', '59C091_0', '59C152_0', '59C162_0', '59C272_0', '59C302_0', '59C312_0', '59C372_0', '59C392_0', '59C402_0', '59C442_0', '59C502_0', '59C522_0', '59C742_0', '59P071_1', '59P132_2', '59P412_2', '59P542_2', '5C022_0', '5C041_0', '5C072_0', '5C082_0', '5C091_0', '5C101_0', '5C131_0', '5C152_0', '5C162_0', '5C172_0', '5C182_0', '5C202_0', '5C212_0', '5C222_0', '5C272_0', '5C282_0', '5C292_0', '5C302_0', '5C312_0', '5C322_0', '5C342_0', '5C372_0', '5C382_0', '5C392_0', '5C402_0', '5C412_0', '5C422_0', '5C432_0', '5C442_0', '5C502_0', '5C522_0', '5C562_0', '5C582_0', '5C602_0', '5C612_0', '5C622_0', '5C632_0', '5C652_0', '5C672_0', '5C682_0', '5C712_0', '5C732_0', '5C742_0', '5P042_2', '5P052_2', '5P071_1', '5P092_2', '5P111_1', '5P132_2', '5P141_1', '5P162_2', '5P201_1', '5P212_2', '5P332_2', '5P352_2', '5P412_2', '5P422_2', '5P432_2', '5P542_2', '5P642_2', '5P672_2', '5P712_2', '60C091_0', '60C152_0', '60C162_0', '60C272_0', '60C302_0', '60C312_0', '60C372_0', '60C392_0', '60C402_0', '60C442_0', '60C502_0', '60C522_0', '60C742_0', '60P071_1', '60P132_2', '60P412_2', '60P542_2', '61C091_0', '61C152_0', '61C162_0', '61C272_0', '61C302_0', '61C312_0', '61C372_0', '61C392_0', '61C402_0', '61C442_0', '61C502_0', '61C522_0', '61C742_0', '61P071_1', '61P132_2', '61P412_2', '61P542_2', '62C091_0', '62C152_0', '62C162_0', '62C272_0', '62C302_0', '62C312_0', '62C372_0', '62C392_0', '62C402_0', '62C442_0', '62C502_0', '62C522_0', '62C742_0', '62P071_1', '62P132_2', '62P412_2', '62P542_2', '63C091_0', '63C152_0', '63C162_0', '63C272_0', '63C302_0', '63C312_0', '63C372_0', '63C392_0', '63C402_0', '63C442_0', '63C502_0', '63C522_0', '63C742_0', '63P071_1', '63P132_2', '63P412_2', '63P542_2', '64C091_0', '64C152_0', '64C162_0', '64C272_0', '64C302_0', '64C312_0', '64C372_0', '64C392_0', '64C402_0', '64C442_0', '64C502_0', '64C522_0', '64C742_0', '64P071_1', '64P132_2', '64P412_2', '64P542_2', '65C091_0', '65C152_0', '65C162_0', '65C272_0', '65C302_0', '65C312_0', '65C372_0', '65C392_0', '65C402_0', '65C442_0', '65C502_0', '65C522_0', '65C742_0', '65P071_1', '65P132_2', '65P412_2', '65P542_2', '66C091_0', '66C152_0', '66C162_0', '66C272_0', '66C302_0', '66C312_0', '66C372_0', '66C392_0', '66C402_0', '66C442_0', '66C502_0', '66C522_0', '66C742_0', '66P071_1', '66P132_2', '66P412_2', '66P542_2', '67C091_0', '67C152_0', '67C162_0', '67C272_0', '67C302_0', '67C312_0', '67C372_0', '67C392_0', '67C402_0', '67C442_0', '67C502_0', '67C522_0', '67C742_0', '67P071_1', '67P132_2', '67P412_2', '67P542_2', '68C091_0', '68C152_0', '68C162_0', '68C272_0', '68C302_0', '68C312_0', '68C372_0', '68C392_0', '68C402_0', '68C442_0', '68C502_0', '68C522_0', '68C742_0', '68P071_1', '68P132_2', '68P412_2', '68P542_2', '69C091_0', '69C152_0', '69C162_0', '69C272_0', '69C302_0', '69C312_0', '69C372_0', '69C392_0', '69C402_0', '69C442_0', '69C502_0', '69C522_0', '69C742_0', '69P071_1', '69P132_2', '69P412_2', '6C022_0', '6C041_0', '6C072_0', '6C082_0', '6C091_0', '6C101_0', '6C131_0', '6C152_0', '6C162_0', '6C172_0', '6C182_0', '6C202_0', '6C212_0', '6C222_0', '6C272_0', '6C282_0', '6C292_0', '6C302_0', '6C312_0', '6C322_0', '6C342_0', '6C372_0', '6C382_0', '6C392_0', '6C402_0', '6C412_0', '6C422_0', '6C432_0', '6C442_0', '6C502_0', '6C522_0', '6C562_0', '6C582_0', '6C602_0', '6C612_0', '6C622_0', '6C652_0', '6C672_0', '6C682_0', '6C712_0', '6C732_0', '6C742_0', '6P042_2', '6P052_2', '6P071_1', '6P092_2', '6P111_1', '6P132_2', '6P141_1', '6P162_2', '6P201_1', '6P212_2', '6P332_2', '6P352_2', '6P412_2', '6P422_2', '6P432_2', '6P542_2', '6P642_2', '6P672_2', '6P712_2', '70C091_0', '70C152_0', '70C162_0', '70C272_0', '70C302_0', '70C312_0', '70C372_0', '70C392_0', '70C402_0', '70C442_0', '70C502_0', '70C522_0', '70C742_0', '70P071_1', '70P132_2', '70P412_2', '71C091_0', '71C152_0', '71C162_0', '71C272_0', '71C302_0', '71C312_0', '71C372_0', '71C392_0', '71C402_0', '71C442_0', '71C502_0', '71C522_0', '71C742_0', '71P071_1', '71P132_2', '71P412_2', '72C091_0', '72C152_0', '72C162_0', '72C272_0', '72C302_0', '72C312_0', '72C372_0', '72C392_0', '72C402_0', '72C442_0', '72C502_0', '72C522_0', '72C742_0', '72P071_1', '72P132_2', '72P412_2', '73C091_0', '73C152_0', '73C162_0', '73C272_0', '73C302_0', '73C312_0', '73C372_0', '73C392_0', '73C402_0', '73C442_0', '73C502_0', '73C522_0', '73C742_0', '73P071_1', '73P132_2', '73P412_2', '74C091_0', '74C152_0', '74C162_0', '74C272_0', '74C302_0', '74C312_0', '74C372_0', '74C392_0', '74C402_0', '74C442_0', '74C502_0', '74C522_0', '74C742_0', '74P071_1', '74P132_2', '74P412_2', '75C091_0', '75C152_0', '75C162_0', '75C272_0', '75C302_0', '75C312_0', '75C392_0', '75C402_0', '75C442_0', '75C502_0', '75C522_0', '75C742_0', '75P071_1', '75P132_2', '75P412_2', '76C091_0', '76C152_0', '76C162_0', '76C272_0', '76C302_0', '76C312_0', '76C392_0', '76C402_0', '76C442_0', '76C502_0', '76C522_0', '76C742_0', '76P071_1', '76P132_2', '76P412_2', '77C091_0', '77C152_0', '77C162_0', '77C272_0', '77C302_0', '77C312_0', '77C392_0', '77C402_0', '77C442_0', '77C502_0', '77C522_0', '77C742_0', '77P071_1', '77P412_2', '78C091_0', '78C152_0', '78C162_0', '78C272_0', '78C302_0', '78C312_0', '78C392_0', '78C402_0', '78C442_0', '78C502_0', '78C522_0', '78C742_0', '78P071_1', '78P412_2', '79C091_0', '79C152_0', '79C162_0', '79C272_0', '79C302_0', '79C312_0', '79C392_0', '79C402_0', '79C442_0', '79C502_0', '79C522_0', '79C742_0', '79P071_1', '79P412_2', '7C022_0', '7C041_0', '7C072_0', '7C082_0', '7C091_0', '7C101_0', '7C131_0', '7C152_0', '7C162_0', '7C172_0', '7C182_0', '7C202_0', '7C212_0', '7C222_0', '7C272_0', '7C282_0', '7C292_0', '7C302_0', '7C312_0', '7C322_0', '7C342_0', '7C372_0', '7C382_0', '7C392_0', '7C402_0', '7C412_0', '7C422_0', '7C432_0', '7C442_0', '7C502_0', '7C522_0', '7C562_0', '7C582_0', '7C602_0', '7C612_0', '7C622_0', '7C652_0', '7C672_0', '7C682_0', '7C712_0', '7C732_0', '7C742_0', '7P042_2', '7P052_2', '7P071_1', '7P092_2', '7P111_1', '7P132_2', '7P141_1', '7P162_2', '7P201_1', '7P212_2', '7P332_2', '7P352_2', '7P412_2', '7P422_2', '7P432_2', '7P542_2', '7P642_2', '7P672_2', '7P712_2', '80C091_0', '80C152_0', '80C162_0', '80C272_0', '80C302_0', '80C312_0', '80C392_0', '80C402_0', '80C442_0', '80C502_0', '80C522_0', '80C742_0', '80P071_1', '80P412_2', '81C091_0', '81C152_0', '81C162_0', '81C272_0', '81C302_0', '81C312_0', '81C392_0', '81C402_0', '81C442_0', '81C502_0', '81C522_0', '81C742_0', '81P071_1', '81P412_2', '82C091_0', '82C152_0', '82C162_0', '82C272_0', '82C302_0', '82C312_0', '82C392_0', '82C402_0', '82C442_0', '82C502_0', '82C522_0', '82C742_0', '82P071_1', '82P412_2', '83C091_0', '83C152_0', '83C162_0', '83C272_0', '83C302_0', '83C312_0', '83C392_0', '83C402_0', '83C442_0', '83C502_0', '83C522_0', '83C742_0', '83P071_1', '83P412_2', '84C091_0', '84C152_0', '84C162_0', '84C272_0', '84C302_0', '84C312_0', '84C392_0', '84C402_0', '84C442_0', '84C502_0', '84C522_0', '84C742_0', '84P071_1', '84P412_2', '85C091_0', '85C152_0', '85C162_0', '85C272_0', '85C302_0', '85C312_0', '85C392_0', '85C402_0', '85C442_0', '85C502_0', '85C522_0', '85C742_0', '85P071_1', '86C091_0', '86C152_0', '86C162_0', '86C272_0', '86C302_0', '86C312_0', '86C392_0', '86C402_0', '86C442_0', '86C502_0', '86C742_0', '86P071_1', '87C091_0', '87C152_0', '87C162_0', '87C272_0', '87C302_0', '87C312_0', '87C392_0', '87C402_0', '87C442_0', '87C502_0', '87C742_0', '87P071_1', '88C091_0', '88C152_0', '88C162_0', '88C272_0', '88C302_0', '88C312_0', '88C392_0', '88C402_0', '88C442_0', '88C502_0', '88C742_0', '88P071_1', '89C091_0', '89C152_0', '89C162_0', '89C272_0', '89C302_0', '89C312_0', '89C392_0', '89C402_0', '89C442_0', '89C502_0', '89C742_0', '89P071_1', '8C041_0', '8C072_0', '8C082_0', '8C091_0', '8C101_0', '8C131_0', '8C152_0', '8C162_0', '8C172_0', '8C182_0', '8C202_0', '8C212_0', '8C222_0', '8C272_0', '8C282_0', '8C292_0', '8C302_0', '8C312_0', '8C322_0', '8C342_0', '8C372_0', '8C382_0', '8C392_0', '8C402_0', '8C412_0', '8C422_0', '8C432_0', '8C442_0', '8C502_0', '8C522_0', '8C562_0', '8C582_0', '8C602_0', '8C612_0', '8C622_0', '8C652_0', '8C672_0', '8C682_0', '8C712_0', '8C732_0', '8C742_0', '8P042_2', '8P052_2', '8P071_1', '8P092_2', '8P111_1', '8P132_2', '8P141_1', '8P162_2', '8P201_1', '8P212_2', '8P332_2', '8P412_2', '8P422_2', '8P432_2', '8P542_2', '8P642_2', '8P672_2', '8P712_2', '90C091_0', '90C152_0', '90C162_0', '90C272_0', '90C302_0', '90C312_0', '90C392_0', '90C402_0', '90C442_0', '90C502_0', '90C742_0', '90P071_1', '91C091_0', '91C152_0', '91C162_0', '91C272_0', '91C302_0', '91C312_0', '91C392_0', '91C402_0', '91C442_0', '91C502_0', '91C742_0', '92C091_0', '92C152_0', '92C162_0', '92C272_0', '92C302_0', '92C312_0', '92C392_0', '92C402_0', '92C442_0', '92C502_0', '92C742_0', '93C091_0', '93C152_0', '93C162_0', '93C272_0', '93C302_0', '93C312_0', '93C392_0', '93C402_0', '93C502_0', '94C091_0', '94C152_0', '94C162_0', '94C272_0', '94C302_0', '94C312_0', '94C392_0', '94C402_0', '94C502_0', '95C091_0', '95C152_0', '95C162_0', '95C272_0', '95C302_0', '95C312_0', '95C392_0', '95C402_0', '95C502_0', '96C091_0', '96C152_0', '96C162_0', '96C272_0', '96C302_0', '96C312_0', '96C392_0', '96C402_0', '96C502_0', '97C091_0', '97C152_0', '97C162_0', '97C272_0', '97C302_0', '97C312_0', '97C392_0', '97C402_0', '97C502_0', '98C091_0', '98C152_0', '98C162_0', '98C272_0', '98C302_0', '98C312_0', '98C392_0', '98C402_0', '98C502_0', '99C091_0', '99C152_0', '99C162_0', '99C272_0', '99C302_0', '99C312_0', '99C392_0', '99C402_0', '99C502_0', '9C041_0', '9C072_0', '9C082_0', '9C091_0', '9C101_0', '9C131_0', '9C152_0', '9C162_0', '9C172_0', '9C182_0', '9C202_0', '9C212_0', '9C222_0', '9C272_0', '9C282_0', '9C292_0', '9C302_0', '9C312_0', '9C322_0', '9C342_0', '9C372_0', '9C382_0', '9C392_0', '9C402_0', '9C412_0', '9C422_0', '9C432_0', '9C442_0', '9C502_0', '9C522_0', '9C562_0', '9C582_0', '9C602_0', '9C612_0', '9C622_0', '9C652_0', '9C672_0', '9C682_0', '9C712_0', '9C732_0', '9C742_0', '9P052_2', '9P071_1', '9P111_1', '9P132_2', '9P141_1', '9P162_2', '9P201_1', '9P212_2', '9P332_2', '9P412_2', '9P422_2', '9P542_2', '9P642_2', '9P672_2', '9P712_2']\n",
    "X = df_parameters.copy()\n",
    "X = X.drop([\"Label\", \"Label CONvsACUREM\", \"Label CONREMvsACU\"], axis=1)\n",
    "y = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]\n",
    "y_con_vs_acurem = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
    "y_conrem_vs_acu = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
    "\n",
    "def naivebayes(X,y, X_train, y_train, X_test):\n",
    "    model = MultinomialNB()\n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    scores = cross_val_score(model, X, y, cv=5)\n",
    "    print(\"---------- Naive Bayes ----------\")\n",
    "    return model, y_pred, scores\n",
    "\n",
    "def supportvectormachine(X,y, X_train, y_train, X_test):\n",
    "    model = SVC(kernel='linear', C=1)\n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    scores = cross_val_score(model, X, y, cv=5)\n",
    "    print(\"---------- Support Vector Machine ----------\",\"\\n\")\n",
    "    return model, y_pred, scores\n",
    "\n",
    "def classifierresults(X, y, y_test, y_pred, scores, model):\n",
    "    print(\"Confusion Matrix:\",\"\\n\",confusion_matrix(y_test,y_pred))\n",
    "    print(classification_report(y_test,y_pred))\n",
    "    print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))\n",
    "    seed = 7\n",
    "    test_size = 0.33\n",
    "    #kfold = KFold(n_splits=25, random_state=seed)\n",
    "    #results = cross_val_score(model, X, y, cv=kfold)\n",
    "    #print(\"K-Fold Cross Validation: Accuracy: %.3f%% (+/- %.3f%%)\" % (results.mean()*100.0, results.std()*100.0))\n",
    "    #loocv = LeaveOneOut()\n",
    "    #results = cross_val_score(model, X, y, cv=loocv)\n",
    "    #print(\"Leave-One-Out Cross Validation: Accuracy: %.3f%% (+/- %.3f%%)\" % (results.mean()*100.0, results.std()*100.0))\n",
    "    kfold = ShuffleSplit(n_splits=10, test_size=test_size, random_state=seed)\n",
    "    results = cross_val_score(model, X, y, cv=kfold)\n",
    "    print(\"Repeated Random Test-Train Splits: Accuracy: %.3f%% (+/- %.3f%%)\" % (results.mean()*100.0, results.std()*100.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Word Graph Analysis without word windows (complete, raw input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "task = \"Taaloefening 1\"\n",
    "task2 = \"Taaloefening 2\"\n",
    "filenames = []\n",
    "for participant,row in df.iterrows():\n",
    "    if isinstance(df[task][participant], float) is not True:\n",
    "        filename = \"1_\" + participant + str(row[0])\n",
    "        #text_file = open(filename, \"w\")\n",
    "        #text_file.write(df[task][participant])\n",
    "        #text_file.close()\n",
    "        filenames.append(filename)\n",
    "    if isinstance(df[task2][participant], float) is not True:\n",
    "        filename = \"2_\" + participant + str(row[0])\n",
    "        #text_file = open(filename, \"w\")\n",
    "        #text_file.write(df[task2][participant])\n",
    "        #text_file.close()\n",
    "        filenames.append(filename)\n",
    "filenames = list(set(filenames))\n",
    "filenames.sort()\n",
    "filenames1 = filenames[:84]\n",
    "filenames2 = filenames[84:]\n",
    "\n",
    "y=[]\n",
    "y_con_vs_acurem = []\n",
    "y_conrem_vs_acu = []\n",
    "y1=[]\n",
    "y_con_vs_acurem1 = []\n",
    "y_conrem_vs_acu1 = []\n",
    "y2=[]\n",
    "y_con_vs_acurem2 = []\n",
    "y_conrem_vs_acu2 = []\n",
    "        \n",
    "for file in filenames:\n",
    "    # controls\n",
    "    if file.endswith('0'):\n",
    "        y.append(0)\n",
    "        y_con_vs_acurem.append(0)\n",
    "        y_conrem_vs_acu.append(0)\n",
    "        # task 1\n",
    "        if file.startswith('1'):\n",
    "            y1.append(0)\n",
    "            y_con_vs_acurem1.append(0)\n",
    "            y_conrem_vs_acu1.append(0)\n",
    "        # task 2\n",
    "        elif file.startswith('2'):\n",
    "            y2.append(0)\n",
    "            y_con_vs_acurem2.append(0)\n",
    "            y_conrem_vs_acu2.append(0)\n",
    "    # acute patients        \n",
    "    elif file.endswith('1'):\n",
    "        y.append(1)\n",
    "        y_con_vs_acurem.append(1)\n",
    "        y_conrem_vs_acu.append(1)\n",
    "        \n",
    "        # task 1\n",
    "        if file.startswith('1'):\n",
    "            y1.append(1)\n",
    "            y_con_vs_acurem1.append(1)\n",
    "            y_conrem_vs_acu1.append(1)\n",
    "        # task 2\n",
    "        elif file.startswith('2'):\n",
    "            y2.append(1)\n",
    "            y_con_vs_acurem2.append(1)\n",
    "            y_conrem_vs_acu2.append(1)\n",
    "            \n",
    "    # remission patients\n",
    "    elif file.endswith('2'):\n",
    "        y.append(2)\n",
    "        y_con_vs_acurem.append(1)\n",
    "        y_conrem_vs_acu.append(0)\n",
    "        \n",
    "        # task 1\n",
    "        if file.startswith('1'):\n",
    "            y1.append(2)\n",
    "            y_con_vs_acurem1.append(1)\n",
    "            y_conrem_vs_acu1.append(0)\n",
    "        # task 2\n",
    "        elif file.startswith('2'):\n",
    "            y2.append(2)\n",
    "            y_con_vs_acurem2.append(1)\n",
    "            y_conrem_vs_acu2.append(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The following lines read in the tables \"params_table\" as created by the SpeechGraph Software, extracts the features and stores them in a table"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "parameterslist = []\n",
    "with open(\"params_table_task1.txt\",\"r\") as parameter_file:\n",
    "    parameters = parameter_file.read()\n",
    "    parameters = parameters.split()\n",
    "    for parameter in parameters:\n",
    "        parameter = parameter.split(',')\n",
    "        parameterslist.append(parameter)\n",
    "\n",
    "columns = ['File', 'WC', 'Nodes', 'Edges', 'RE', 'PE', 'L1', 'L2', 'L3', 'LCC', 'LSC', 'ATD', 'Density', 'Diameter', 'ASP', 'CC']\n",
    "df_parameters_task1 = pd.DataFrame(parameterslist[1:], columns=columns)\n",
    "df_parameters_task1.drop(\"File\", inplace=True, axis=1)\n",
    "df_parameters_task1[\"Label\"] = y1\n",
    "df_parameters_task1[\"Label CONvsACUREM\"] = y_con_vs_acurem1\n",
    "df_parameters_task1[\"Label CONREMvsACU\"] = y_conrem_vs_acu1\n",
    "df_parameters_task1.to_excel(\"df_parameters_task1.xlsx\")\n",
    "\n",
    "parameterslist = []\n",
    "with open(\"params_table_task2.txt\",\"r\") as parameter_file:\n",
    "    parameters = parameter_file.read()\n",
    "    parameters = parameters.split()\n",
    "    for parameter in parameters:\n",
    "        parameter = parameter.split(',')\n",
    "        parameterslist.append(parameter)\n",
    "\n",
    "columns = ['File', 'WC', 'Nodes', 'Edges', 'RE', 'PE', 'L1', 'L2', 'L3', 'LCC', 'LSC', 'ATD', 'Density', 'Diameter', 'ASP', 'CC']\n",
    "df_parameters_task2 = pd.DataFrame(parameterslist[1:], columns=columns)\n",
    "df_parameters_task2.drop(\"File\", inplace=True, axis=1)\n",
    "df_parameters_task2[\"Label\"] = y2\n",
    "df_parameters_task2[\"Label CONvsACUREM\"] = y_con_vs_acurem2\n",
    "df_parameters_task2[\"Label CONREMvsACU\"] = y_conrem_vs_acu2\n",
    "df_parameters_task2.to_excel(\"df_parameters_task2.xlsx\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naive Bayes and Support Vector Machines raw input (no windows used) (classification and report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X1 = df_parameters_task1.copy()\n",
    "X1 = X1.drop([\"Label\", \"Label CONvsACUREM\",\"Label CONREMvsACU\" ], axis =1)\n",
    "X2 = df_parameters_task2.copy()\n",
    "X2 = X2.drop([\"Label\", \"Label CONvsACUREM\",\"Label CONREMvsACU\" ], axis = 1)\n",
    "X_both = X1.append(X2)\n",
    "y_both = y1+y2\n",
    "y_con_vs_acurem_both = y_con_vs_acurem1 + y_con_vs_acurem2\n",
    "y_conrem_vs_acu_both = y_conrem_vs_acu1 + y_conrem_vs_acu2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_both, y_both, test_size = 0.20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------- Naive Bayes ----------\n",
      "Confusion Matrix: \n",
      " [[10  4  5]\n",
      " [ 2  2  4]\n",
      " [ 2  3  1]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.71      0.53      0.61        19\n",
      "           1       0.22      0.25      0.24         8\n",
      "           2       0.10      0.17      0.12         6\n",
      "\n",
      "    accuracy                           0.39        33\n",
      "   macro avg       0.35      0.31      0.32        33\n",
      "weighted avg       0.48      0.39      0.43        33\n",
      "\n",
      "Accuracy: 0.51 (+/- 0.19)\n",
      "Repeated Random Test-Train Splits: Accuracy: 47.778% (+/- 4.596%)\n",
      "---------- Support Vector Machine ---------- \n",
      "\n",
      "Confusion Matrix: \n",
      " [[18  0  1]\n",
      " [ 7  0  1]\n",
      " [ 6  0  0]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.58      0.95      0.72        19\n",
      "           1       0.00      0.00      0.00         8\n",
      "           2       0.00      0.00      0.00         6\n",
      "\n",
      "    accuracy                           0.55        33\n",
      "   macro avg       0.19      0.32      0.24        33\n",
      "weighted avg       0.33      0.55      0.41        33\n",
      "\n",
      "Accuracy: 0.64 (+/- 0.08)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lisabecker/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Repeated Random Test-Train Splits: Accuracy: 59.444% (+/- 5.459%)\n"
     ]
    }
   ],
   "source": [
    "model, y_pred, scores = naivebayes(X_both,y_both,X_train, y_train, X_test)\n",
    "classifierresults(X_both, y_both, y_test, y_pred, scores, model)\n",
    "model, y_pred, scores = supportvectormachine(X_both,y_both,X_train, y_train, X_test)\n",
    "classifierresults(X_both, y_both, y_test, y_pred, scores, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------- Naive Bayes ----------\n",
      "Confusion Matrix: \n",
      " [[ 4 16]\n",
      " [ 2 11]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.67      0.20      0.31        20\n",
      "           1       0.41      0.85      0.55        13\n",
      "\n",
      "    accuracy                           0.45        33\n",
      "   macro avg       0.54      0.52      0.43        33\n",
      "weighted avg       0.56      0.45      0.40        33\n",
      "\n",
      "Accuracy: 0.63 (+/- 0.17)\n",
      "Repeated Random Test-Train Splits: Accuracy: 60.926% (+/- 5.135%)\n",
      "---------- Support Vector Machine ---------- \n",
      "\n",
      "Confusion Matrix: \n",
      " [[13  7]\n",
      " [ 7  6]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.65      0.65      0.65        20\n",
      "           1       0.46      0.46      0.46        13\n",
      "\n",
      "    accuracy                           0.58        33\n",
      "   macro avg       0.56      0.56      0.56        33\n",
      "weighted avg       0.58      0.58      0.58        33\n",
      "\n",
      "Accuracy: 0.68 (+/- 0.09)\n",
      "Repeated Random Test-Train Splits: Accuracy: 61.481% (+/- 6.186%)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_both, y_con_vs_acurem_both, test_size = 0.20)\n",
    "model, y_pred, scores = naivebayes(X_both,y_con_vs_acurem_both,X_train, y_train, X_test)\n",
    "classifierresults(X_both, y_con_vs_acurem_both, y_test, y_pred, scores, model)\n",
    "model, y_pred, scores = supportvectormachine(X_both,y_con_vs_acurem_both,X_train, y_train, X_test)\n",
    "classifierresults(X_both, y_con_vs_acurem_both, y_test, y_pred, scores, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------- Naive Bayes ----------\n",
      "Confusion Matrix: \n",
      " [[22  5]\n",
      " [ 2  4]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.81      0.86        27\n",
      "           1       0.44      0.67      0.53         6\n",
      "\n",
      "    accuracy                           0.79        33\n",
      "   macro avg       0.68      0.74      0.70        33\n",
      "weighted avg       0.83      0.79      0.80        33\n",
      "\n",
      "Accuracy: 0.61 (+/- 0.25)\n",
      "Repeated Random Test-Train Splits: Accuracy: 56.111% (+/- 4.540%)\n",
      "---------- Support Vector Machine ---------- \n",
      "\n",
      "Confusion Matrix: \n",
      " [[27  0]\n",
      " [ 6  0]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      1.00      0.90        27\n",
      "           1       0.00      0.00      0.00         6\n",
      "\n",
      "    accuracy                           0.82        33\n",
      "   macro avg       0.41      0.50      0.45        33\n",
      "weighted avg       0.67      0.82      0.74        33\n",
      "\n",
      "Accuracy: 0.79 (+/- 0.01)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lisabecker/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Repeated Random Test-Train Splits: Accuracy: 78.333% (+/- 3.976%)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_both, y_conrem_vs_acu_both, test_size = 0.20)\n",
    "model, y_pred, scores = naivebayes(X_both,y_conrem_vs_acu_both,X_train, y_train,X_test)\n",
    "classifierresults(X_both, y_conrem_vs_acu_both, y_test, y_pred, scores, model)\n",
    "model, y_pred, scores = supportvectormachine(X_both,y_conrem_vs_acu_both,X_train, y_train, X_test)\n",
    "classifierresults(X_both, y_conrem_vs_acu_both, y_test, y_pred, scores, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------- Naive Bayes ----------\n",
      "Confusion Matrix: \n",
      " [[4 4 1]\n",
      " [2 3 2]\n",
      " [0 0 1]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.67      0.44      0.53         9\n",
      "           1       0.43      0.43      0.43         7\n",
      "           2       0.25      1.00      0.40         1\n",
      "\n",
      "    accuracy                           0.47        17\n",
      "   macro avg       0.45      0.62      0.45        17\n",
      "weighted avg       0.54      0.47      0.48        17\n",
      "\n",
      "Accuracy: 0.44 (+/- 0.12)\n",
      "Repeated Random Test-Train Splits: Accuracy: 52.500% (+/- 9.320%)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lisabecker/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_split.py:657: Warning: The least populated class in y has only 4 members, which is too few. The minimum number of members in any class cannot be less than n_splits=5.\n",
      "  % (min_groups, self.n_splits)), Warning)\n",
      "/Users/lisabecker/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_split.py:657: Warning: The least populated class in y has only 4 members, which is too few. The minimum number of members in any class cannot be less than n_splits=5.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------- Support Vector Machine ---------- \n",
      "\n",
      "Confusion Matrix: \n",
      " [[8 1 0]\n",
      " [7 0 0]\n",
      " [1 0 0]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.50      0.89      0.64         9\n",
      "           1       0.00      0.00      0.00         7\n",
      "           2       0.00      0.00      0.00         1\n",
      "\n",
      "    accuracy                           0.47        17\n",
      "   macro avg       0.17      0.30      0.21        17\n",
      "weighted avg       0.26      0.47      0.34        17\n",
      "\n",
      "Accuracy: 0.59 (+/- 0.09)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lisabecker/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Repeated Random Test-Train Splits: Accuracy: 52.857% (+/- 3.113%)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X1, y1, test_size = 0.20)\n",
    "model, y_pred, scores = naivebayes(X1,y1,X_train, y_train, X_test)\n",
    "classifierresults(X1, y1, y_test, y_pred, scores, model)\n",
    "model, y_pred, scores = supportvectormachine(X1,y1,X_train, y_train, X_test)\n",
    "classifierresults(X1, y1, y_test, y_pred, scores, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------- Naive Bayes ----------\n",
      "Confusion Matrix: \n",
      " [[7 3]\n",
      " [1 6]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.70      0.78        10\n",
      "           1       0.67      0.86      0.75         7\n",
      "\n",
      "    accuracy                           0.76        17\n",
      "   macro avg       0.77      0.78      0.76        17\n",
      "weighted avg       0.79      0.76      0.77        17\n",
      "\n",
      "Accuracy: 0.57 (+/- 0.19)\n",
      "Repeated Random Test-Train Splits: Accuracy: 64.286% (+/- 9.035%)\n",
      "---------- Support Vector Machine ---------- \n",
      "\n",
      "Confusion Matrix: \n",
      " [[9 1]\n",
      " [4 3]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.69      0.90      0.78        10\n",
      "           1       0.75      0.43      0.55         7\n",
      "\n",
      "    accuracy                           0.71        17\n",
      "   macro avg       0.72      0.66      0.66        17\n",
      "weighted avg       0.72      0.71      0.68        17\n",
      "\n",
      "Accuracy: 0.59 (+/- 0.16)\n",
      "Repeated Random Test-Train Splits: Accuracy: 58.571% (+/- 7.178%)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X1, y_con_vs_acurem1, test_size = 0.20)\n",
    "model, y_pred, scores = naivebayes(X1,y_con_vs_acurem1,X_train, y_train, X_test)\n",
    "classifierresults(X1, y_con_vs_acurem1, y_test, y_pred, scores, model)\n",
    "model, y_pred, scores = supportvectormachine(X1,y_con_vs_acurem1,X_train, y_train, X_test)\n",
    "classifierresults(X1, y_con_vs_acurem1, y_test, y_pred, scores, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------- Naive Bayes ----------\n",
      "Confusion Matrix: \n",
      " [[5 7]\n",
      " [0 5]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.42      0.59        12\n",
      "           1       0.42      1.00      0.59         5\n",
      "\n",
      "    accuracy                           0.59        17\n",
      "   macro avg       0.71      0.71      0.59        17\n",
      "weighted avg       0.83      0.59      0.59        17\n",
      "\n",
      "Accuracy: 0.52 (+/- 0.21)\n",
      "Repeated Random Test-Train Splits: Accuracy: 59.643% (+/- 6.971%)\n",
      "---------- Support Vector Machine ---------- \n",
      "\n",
      "Confusion Matrix: \n",
      " [[12  0]\n",
      " [ 5  0]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.71      1.00      0.83        12\n",
      "           1       0.00      0.00      0.00         5\n",
      "\n",
      "    accuracy                           0.71        17\n",
      "   macro avg       0.35      0.50      0.41        17\n",
      "weighted avg       0.50      0.71      0.58        17\n",
      "\n",
      "Accuracy: 0.65 (+/- 0.05)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lisabecker/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Repeated Random Test-Train Splits: Accuracy: 61.786% (+/- 3.589%)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X1, y_conrem_vs_acu1, test_size = 0.20)\n",
    "model, y_pred, scores = naivebayes(X1,y_conrem_vs_acu1,X_train, y_train, X_test)\n",
    "classifierresults(X1, y_conrem_vs_acu1, y_test, y_pred, scores, model)\n",
    "model, y_pred, scores = supportvectormachine(X1,y_conrem_vs_acu1,X_train, y_train, X_test)\n",
    "classifierresults(X1, y_conrem_vs_acu1, y_test, y_pred, scores, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------- Naive Bayes ----------\n",
      "Confusion Matrix: \n",
      " [[5 0 5]\n",
      " [0 0 1]\n",
      " [0 0 5]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.50      0.67        10\n",
      "           1       0.00      0.00      0.00         1\n",
      "           2       0.45      1.00      0.62         5\n",
      "\n",
      "    accuracy                           0.62        16\n",
      "   macro avg       0.48      0.50      0.43        16\n",
      "weighted avg       0.77      0.62      0.61        16\n",
      "\n",
      "Accuracy: 0.62 (+/- 0.11)\n",
      "Repeated Random Test-Train Splits: Accuracy: 60.370% (+/- 10.351%)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lisabecker/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------- Support Vector Machine ---------- \n",
      "\n",
      "Confusion Matrix: \n",
      " [[8 0 2]\n",
      " [1 0 0]\n",
      " [3 0 2]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.67      0.80      0.73        10\n",
      "           1       0.00      0.00      0.00         1\n",
      "           2       0.50      0.40      0.44         5\n",
      "\n",
      "    accuracy                           0.62        16\n",
      "   macro avg       0.39      0.40      0.39        16\n",
      "weighted avg       0.57      0.62      0.59        16\n",
      "\n",
      "Accuracy: 0.67 (+/- 0.10)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lisabecker/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Repeated Random Test-Train Splits: Accuracy: 61.111% (+/- 8.646%)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X2, y2, test_size = 0.20)\n",
    "model, y_pred, scores = naivebayes(X2,y2,X_train, y_train, X_test)\n",
    "classifierresults(X2, y2, y_test, y_pred, scores, model)\n",
    "model, y_pred, scores = supportvectormachine(X2,y2,X_train, y_train, X_test)\n",
    "classifierresults(X2, y2, y_test, y_pred, scores, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------- Naive Bayes ----------\n",
      "Confusion Matrix: \n",
      " [[8 2]\n",
      " [2 4]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.80      0.80        10\n",
      "           1       0.67      0.67      0.67         6\n",
      "\n",
      "    accuracy                           0.75        16\n",
      "   macro avg       0.73      0.73      0.73        16\n",
      "weighted avg       0.75      0.75      0.75        16\n",
      "\n",
      "Accuracy: 0.66 (+/- 0.14)\n",
      "Repeated Random Test-Train Splits: Accuracy: 65.556% (+/- 6.839%)\n",
      "---------- Support Vector Machine ---------- \n",
      "\n",
      "Confusion Matrix: \n",
      " [[9 1]\n",
      " [4 2]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.69      0.90      0.78        10\n",
      "           1       0.67      0.33      0.44         6\n",
      "\n",
      "    accuracy                           0.69        16\n",
      "   macro avg       0.68      0.62      0.61        16\n",
      "weighted avg       0.68      0.69      0.66        16\n",
      "\n",
      "Accuracy: 0.66 (+/- 0.26)\n",
      "Repeated Random Test-Train Splits: Accuracy: 72.593% (+/- 4.743%)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X2, y_con_vs_acurem2, test_size = 0.20)\n",
    "model, y_pred, scores = naivebayes(X2,y_con_vs_acurem2,X_train, y_train, X_test)\n",
    "classifierresults(X2, y_con_vs_acurem2, y_test, y_pred, scores, model)\n",
    "model, y_pred, scores = supportvectormachine(X2,y_con_vs_acurem2,X_train, y_train, X_test)\n",
    "classifierresults(X2, y_con_vs_acurem2, y_test, y_pred, scores, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------- Naive Bayes ----------\n",
      "Confusion Matrix: \n",
      " [[15  0]\n",
      " [ 1  0]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      1.00      0.97        15\n",
      "           1       0.00      0.00      0.00         1\n",
      "\n",
      "    accuracy                           0.94        16\n",
      "   macro avg       0.47      0.50      0.48        16\n",
      "weighted avg       0.88      0.94      0.91        16\n",
      "\n",
      "Accuracy: 0.81 (+/- 0.18)\n",
      "Repeated Random Test-Train Splits: Accuracy: 84.815% (+/- 4.521%)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lisabecker/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------- Support Vector Machine ---------- \n",
      "\n",
      "Confusion Matrix: \n",
      " [[15  0]\n",
      " [ 1  0]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      1.00      0.97        15\n",
      "           1       0.00      0.00      0.00         1\n",
      "\n",
      "    accuracy                           0.94        16\n",
      "   macro avg       0.47      0.50      0.48        16\n",
      "weighted avg       0.88      0.94      0.91        16\n",
      "\n",
      "Accuracy: 0.91 (+/- 0.11)\n",
      "Repeated Random Test-Train Splits: Accuracy: 88.889% (+/- 8.765%)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lisabecker/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X2, y_conrem_vs_acu2, test_size = 0.20)\n",
    "model, y_pred, scores = naivebayes(X2,y_conrem_vs_acu2,X_train, y_train, X_test)\n",
    "classifierresults(X2, y_conrem_vs_acu2, y_test, y_pred, scores, model)\n",
    "model, y_pred, scores = supportvectormachine(X2,y_conrem_vs_acu2,X_train, y_train, X_test)\n",
    "classifierresults(X2, y_conrem_vs_acu2, y_test, y_pred, scores, model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Word Graph Analysis with 30-word-windows\n",
    "Creates the dataframes with 30-word-windows"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "def listToString(s):  \n",
    "    \n",
    "    # initialize an empty string \n",
    "    str1 = \" \" \n",
    "    \n",
    "    # return string   \n",
    "    return (str1.join(s)) \n",
    "movingtext = []\n",
    "movinglemmas = []\n",
    "label = []\n",
    "ids = []\n",
    "task = []\n",
    "participants = []\n",
    "text2 = []\n",
    "lemmas2 = []\n",
    "for participant, row in df.iterrows():\n",
    "    lemmas = row[4].split()\n",
    "    text = row[3].split()\n",
    "    \n",
    "    for index, word in enumerate(lemmas):\n",
    "        if len(lemmas[index:index+30]) > 29:\n",
    "            movinglemmas = lemmas[index:index+30]\n",
    "            #print(movinglemmas)\n",
    "            movingtext = text[index:index+30]\n",
    "            label.append(row[0])\n",
    "            ids.append(index)\n",
    "            participants.append(participant)\n",
    "            if isinstance(df.loc[participant, \"Taaloefening 2\"], float) is not True:\n",
    "                task.append(2)\n",
    "            if isinstance(df.loc[participant, \"Taaloefening 1\"], float) is not True:\n",
    "                task.append(1)   \n",
    "            text2.append(listToString(text[index:index+30]))           \n",
    "            lemmas2.append(listToString(lemmas[index:index+30]))\n",
    "parameterslist = list(zip(participants, ids, label, text2, lemmas2, task))\n",
    "columns = ['Participant', 'ID', \"Label\",'Text', 'Lemmas', 'Task']\n",
    "df_movingwindows = pd.DataFrame(parameterslist, columns=columns)\n",
    "df_movingwindows.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Participant</th>\n",
       "      <th>ID</th>\n",
       "      <th>Label</th>\n",
       "      <th>Text</th>\n",
       "      <th>Lemmas</th>\n",
       "      <th>Task</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P642</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Terwijl de kok zijn deeg aan het voorbereiden ...</td>\n",
       "      <td>terwijl de kok zijn deeg aan_het voorbereiden ...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P642</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>de kok zijn deeg aan het voorbereiden is staat...</td>\n",
       "      <td>de kok zijn deeg aan_het voorbereiden zijn sta...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P642</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>kok zijn deeg aan het voorbereiden is staat er...</td>\n",
       "      <td>kok zijn deeg aan_het voorbereiden zijn staan ...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P642</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>zijn deeg aan het voorbereiden is staat er iem...</td>\n",
       "      <td>zijn deeg aan_het voorbereiden zijn staan er i...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P642</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>deeg aan het voorbereiden is staat er iemand t...</td>\n",
       "      <td>deeg aan_het voorbereiden zijn staan er iemand...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Participant  ID  Label                                               Text  \\\n",
       "0        P642   0      2  Terwijl de kok zijn deeg aan het voorbereiden ...   \n",
       "1        P642   1      2  de kok zijn deeg aan het voorbereiden is staat...   \n",
       "2        P642   2      2  kok zijn deeg aan het voorbereiden is staat er...   \n",
       "3        P642   3      2  zijn deeg aan het voorbereiden is staat er iem...   \n",
       "4        P642   4      2  deeg aan het voorbereiden is staat er iemand t...   \n",
       "\n",
       "                                              Lemmas  Task  \n",
       "0  terwijl de kok zijn deeg aan_het voorbereiden ...     2  \n",
       "1  de kok zijn deeg aan_het voorbereiden zijn sta...     2  \n",
       "2  kok zijn deeg aan_het voorbereiden zijn staan ...     2  \n",
       "3  zijn deeg aan_het voorbereiden zijn staan er i...     2  \n",
       "4  deeg aan_het voorbereiden zijn staan er iemand...     2  "
      ]
     },
     "execution_count": 368,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_movingwindows.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "filenames1_mw=[]\n",
    "for index, row in df_movingwindows.iterrows():\n",
    "    if df_movingwindows.loc[index][\"Task\"] == 1:\n",
    "        if isinstance(df_movingwindows.loc[index, \"Text\"], float) is not True:\n",
    "            filename = str(row[1]) + str(row[0]) + \"_\" + str(row[2])\n",
    "            #text_file = open(filename, \"w\")\n",
    "            #text_file.write(df_movingwindows.loc[index][\"Text\"])\n",
    "            #text_file.close()\n",
    "            filenames1_mw.append(filename)\n",
    "            \n",
    "filenames2_mw=[]\n",
    "for index, row in df_movingwindows.iterrows():\n",
    "    if df_movingwindows.loc[index][\"Task\"] == 2:\n",
    "        if isinstance(df_movingwindows.loc[index, \"Text\"], float) is not True:\n",
    "            filename = str(row[1]) + str(row[0]) + \"_\" + str(row[2])\n",
    "            #text_file = open(filename, \"w\")\n",
    "            #text_file.write(df_movingwindows.loc[index][\"Text\"])\n",
    "            #text_file.close()\n",
    "            filenames2_mw.append(filename)\n",
    "filenames1_mw.sort()\n",
    "filenames2_mw.sort()\n",
    "\n",
    "y1_mw = []\n",
    "y_con_vs_acurem1_mw = []\n",
    "y_conrem_vs_acu1_mw = []\n",
    "\n",
    "for file in filenames1_mw:\n",
    "    if file.endswith('0'):\n",
    "        y1_mw.append(0)\n",
    "        y_con_vs_acurem1_mw.append(0)\n",
    "        y_conrem_vs_acu1_mw.append(0)\n",
    "    elif file.endswith('1'):\n",
    "        y1_mw.append(1)\n",
    "        y_con_vs_acurem1_mw.append(1)\n",
    "        y_conrem_vs_acu1_mw.append(1)\n",
    "    elif file.endswith('2'):\n",
    "        y1_mw.append(2)\n",
    "        y_con_vs_acurem1_mw.append(1)\n",
    "        y_conrem_vs_acu1_mw.append(0)\n",
    "        \n",
    "        \n",
    "        \n",
    "y2_mw = []\n",
    "y_con_vs_acurem2_mw = []\n",
    "y_conrem_vs_acu2_mw = []\n",
    "\n",
    "for file in filenames2_mw:\n",
    "    if file.endswith('0'):\n",
    "        y2_mw.append(0)\n",
    "        y_con_vs_acurem2_mw.append(0)\n",
    "        y_conrem_vs_acu2_mw.append(0)\n",
    "    elif file.endswith('1'):\n",
    "        y2_mw.append(1)\n",
    "        y_con_vs_acurem2_mw.append(1)\n",
    "        y_conrem_vs_acu2_mw.append(1)\n",
    "    elif file.endswith('2'):\n",
    "        y2_mw.append(2)\n",
    "        y_con_vs_acurem2_mw.append(1)\n",
    "        y_conrem_vs_acu2_mw.append(0)\n",
    "        \n",
    "y_both_mw = y1_mw + y2_mw\n",
    "y_con_vs_acurem_both_mw = y_con_vs_acurem1_mw + y_con_vs_acurem2_mw\n",
    "y_conrem_vs_acu_both_mw = y_conrem_vs_acu1_mw + y_conrem_vs_acu2_mw\n",
    "df_parameters_windows_both = df_parameters_windows_task1.append(df_parameters_windows_task2)\n",
    "\n",
    "X1_mw = df_parameters_windows_task1.copy()\n",
    "X1_mw = np.nan_to_num(X1_mw)\n",
    "y1_mw = np.nan_to_num(y1_mw)\n",
    "X2_mw = df_parameters_windows_task2.copy()\n",
    "X2_mw = np.nan_to_num(X2_mw)\n",
    "y2_mw = np.nan_to_num(y2_mw)\n",
    "X_both_mw = df_parameters_windows_both.copy()\n",
    "X_both_mw = np.nan_to_num(X_both_mw)\n",
    "y_both_mw = np.nan_to_num(y_both_mw)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naive Bayes and Support Vector Machines on 30-word-windows (classification and report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------- Naive Bayes ----------\n",
      "Confusion Matrix: \n",
      " [[710   0  61]\n",
      " [265   0  13]\n",
      " [  5   0   5]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.72      0.92      0.81       771\n",
      "           1       0.00      0.00      0.00       278\n",
      "           2       0.06      0.50      0.11        10\n",
      "\n",
      "    accuracy                           0.68      1059\n",
      "   macro avg       0.26      0.47      0.31      1059\n",
      "weighted avg       0.53      0.68      0.59      1059\n",
      "\n",
      "Accuracy: 0.68 (+/- 0.08)\n",
      "Repeated Random Test-Train Splits: Accuracy: 68.169% (+/- 0.963%)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lisabecker/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "/Users/lisabecker/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_split.py:657: Warning: The least populated class in y has only 4 members, which is too few. The minimum number of members in any class cannot be less than n_splits=5.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------- Support Vector Machine ---------- \n",
      "\n",
      "Confusion Matrix: \n",
      " [[771   0   0]\n",
      " [278   0   0]\n",
      " [ 10   0   0]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.73      1.00      0.84       771\n",
      "           1       0.00      0.00      0.00       278\n",
      "           2       0.00      0.00      0.00        10\n",
      "\n",
      "    accuracy                           0.73      1059\n",
      "   macro avg       0.24      0.33      0.28      1059\n",
      "weighted avg       0.53      0.73      0.61      1059\n",
      "\n",
      "Accuracy: 0.59 (+/- 0.09)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lisabecker/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Repeated Random Test-Train Splits: Accuracy: 73.793% (+/- 0.777%)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X1_mw, y1_mw, test_size = 0.20)\n",
    "model, y_pred, scores = naivebayes(X1_mw,y1_mw,X_train, y_train, X_test)\n",
    "classifierresults(X1_mw, y1_mw, y_test, y_pred, scores, model)\n",
    "model, y_pred, scores = supportvectormachine(X1,y1,X_train, y_train, X_test)\n",
    "classifierresults(X1_mw, y1_mw, y_test, y_pred, scores, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------- Naive Bayes ----------\n",
      "Confusion Matrix: \n",
      " [[448 354]\n",
      " [127 130]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.56      0.65       802\n",
      "           1       0.27      0.51      0.35       257\n",
      "\n",
      "    accuracy                           0.55      1059\n",
      "   macro avg       0.52      0.53      0.50      1059\n",
      "weighted avg       0.66      0.55      0.58      1059\n",
      "\n",
      "Accuracy: 0.56 (+/- 0.49)\n",
      "Repeated Random Test-Train Splits: Accuracy: 55.915% (+/- 1.753%)\n",
      "---------- Support Vector Machine ---------- \n",
      "\n",
      "Confusion Matrix: \n",
      " [[785  17]\n",
      " [248   9]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.76      0.98      0.86       802\n",
      "           1       0.35      0.04      0.06       257\n",
      "\n",
      "    accuracy                           0.75      1059\n",
      "   macro avg       0.55      0.51      0.46      1059\n",
      "weighted avg       0.66      0.75      0.66      1059\n",
      "\n",
      "Accuracy: 0.72 (+/- 0.05)\n",
      "Repeated Random Test-Train Splits: Accuracy: 72.740% (+/- 1.021%)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X1_mw, y_con_vs_acurem1_mw, test_size = 0.20)\n",
    "model, y_pred, scores = naivebayes(X1_mw,y_con_vs_acurem1_mw,X_train, y_train, X_test)\n",
    "classifierresults(X1_mw, y_con_vs_acurem1_mw, y_test, y_pred, scores, model)\n",
    "model, y_pred, scores = supportvectormachine(X1_mw,y_con_vs_acurem1_mw,X_train, y_train, X_test)\n",
    "classifierresults(X1_mw, y_con_vs_acurem1_mw, y_test, y_pred, scores, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------- Naive Bayes ----------\n",
      "Confusion Matrix: \n",
      " [[440 352]\n",
      " [130 137]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.77      0.56      0.65       792\n",
      "           1       0.28      0.51      0.36       267\n",
      "\n",
      "    accuracy                           0.54      1059\n",
      "   macro avg       0.53      0.53      0.50      1059\n",
      "weighted avg       0.65      0.54      0.57      1059\n",
      "\n",
      "Accuracy: 0.56 (+/- 0.51)\n",
      "Repeated Random Test-Train Splits: Accuracy: 56.104% (+/- 1.642%)\n",
      "---------- Support Vector Machine ---------- \n",
      "\n",
      "Confusion Matrix: \n",
      " [[773  19]\n",
      " [259   8]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.75      0.98      0.85       792\n",
      "           1       0.30      0.03      0.05       267\n",
      "\n",
      "    accuracy                           0.74      1059\n",
      "   macro avg       0.52      0.50      0.45      1059\n",
      "weighted avg       0.63      0.74      0.65      1059\n",
      "\n",
      "Accuracy: 0.73 (+/- 0.05)\n",
      "Repeated Random Test-Train Splits: Accuracy: 73.930% (+/- 0.883%)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X1_mw, y_conrem_vs_acu1_mw, test_size = 0.20)\n",
    "model, y_pred, scores = naivebayes(X1_mw,y_conrem_vs_acu1_mw,X_train, y_train, X_test)\n",
    "classifierresults(X1_mw, y_conrem_vs_acu1_mw, y_test, y_pred, scores, model)\n",
    "model, y_pred, scores = supportvectormachine(X1_mw,y_conrem_vs_acu1_mw,X_train, y_train, X_test)\n",
    "classifierresults(X1_mw, y_conrem_vs_acu1_mw, y_test, y_pred, scores, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------- Naive Bayes ----------\n",
      "Confusion Matrix: \n",
      " [[311 191  16]\n",
      " [ 13  11   0]\n",
      " [ 55  38   7]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.60      0.69       518\n",
      "           1       0.05      0.46      0.08        24\n",
      "           2       0.30      0.07      0.11       100\n",
      "\n",
      "    accuracy                           0.51       642\n",
      "   macro avg       0.39      0.38      0.30       642\n",
      "weighted avg       0.71      0.51      0.58       642\n",
      "\n",
      "Accuracy: 0.49 (+/- 0.72)\n",
      "Repeated Random Test-Train Splits: Accuracy: 51.840% (+/- 2.442%)\n",
      "---------- Support Vector Machine ---------- \n",
      "\n",
      "Confusion Matrix: \n",
      " [[516   0   2]\n",
      " [ 24   0   0]\n",
      " [100   0   0]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      1.00      0.89       518\n",
      "           1       0.00      0.00      0.00        24\n",
      "           2       0.00      0.00      0.00       100\n",
      "\n",
      "    accuracy                           0.80       642\n",
      "   macro avg       0.27      0.33      0.30       642\n",
      "weighted avg       0.65      0.80      0.72       642\n",
      "\n",
      "Accuracy: 0.78 (+/- 0.07)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lisabecker/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Repeated Random Test-Train Splits: Accuracy: 79.151% (+/- 0.964%)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X2_mw, y2_mw, test_size = 0.20)\n",
    "model, y_pred, scores = naivebayes(X2_mw,y2_mw,X_train, y_train, X_test)\n",
    "classifierresults(X2_mw, y2_mw, y_test, y_pred, scores, model)\n",
    "model, y_pred, scores = supportvectormachine(X2_mw,y2_mw,X_train, y_train, X_test)\n",
    "classifierresults(X2_mw, y2_mw, y_test, y_pred, scores, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------- Naive Bayes ----------\n",
      "Confusion Matrix: \n",
      " [[307 210]\n",
      " [ 74  51]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.59      0.68       517\n",
      "           1       0.20      0.41      0.26       125\n",
      "\n",
      "    accuracy                           0.56       642\n",
      "   macro avg       0.50      0.50      0.47       642\n",
      "weighted avg       0.69      0.56      0.60       642\n",
      "\n",
      "Accuracy: 0.55 (+/- 0.58)\n",
      "Repeated Random Test-Train Splits: Accuracy: 57.632% (+/- 2.951%)\n",
      "---------- Support Vector Machine ---------- \n",
      "\n",
      "Confusion Matrix: \n",
      " [[506  11]\n",
      " [121   4]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.98      0.88       517\n",
      "           1       0.27      0.03      0.06       125\n",
      "\n",
      "    accuracy                           0.79       642\n",
      "   macro avg       0.54      0.51      0.47       642\n",
      "weighted avg       0.70      0.79      0.72       642\n",
      "\n",
      "Accuracy: 0.78 (+/- 0.06)\n",
      "Repeated Random Test-Train Splits: Accuracy: 79.217% (+/- 0.907%)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X2_mw, y_con_vs_acurem2_mw, test_size = 0.20)\n",
    "model, y_pred, scores = naivebayes(X2_mw,y_con_vs_acurem2_mw,X_train, y_train, X_test)\n",
    "classifierresults(X2_mw, y_con_vs_acurem2_mw, y_test, y_pred, scores, model)\n",
    "model, y_pred, scores = supportvectormachine(X2_mw,y_con_vs_acurem2_mw,X_train, y_train, X_test)\n",
    "classifierresults(X2_mw, y_con_vs_acurem2_mw, y_test, y_pred, scores, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------- Naive Bayes ----------\n",
      "Confusion Matrix: \n",
      " [[406 214]\n",
      " [ 14   8]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.65      0.78       620\n",
      "           1       0.04      0.36      0.07        22\n",
      "\n",
      "    accuracy                           0.64       642\n",
      "   macro avg       0.50      0.51      0.42       642\n",
      "weighted avg       0.93      0.64      0.76       642\n",
      "\n",
      "Accuracy: 0.59 (+/- 0.88)\n",
      "Repeated Random Test-Train Splits: Accuracy: 64.453% (+/- 2.990%)\n",
      "---------- Support Vector Machine ---------- \n",
      "\n",
      "Confusion Matrix: \n",
      " [[620   0]\n",
      " [ 22   0]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.98       620\n",
      "           1       0.00      0.00      0.00        22\n",
      "\n",
      "    accuracy                           0.97       642\n",
      "   macro avg       0.48      0.50      0.49       642\n",
      "weighted avg       0.93      0.97      0.95       642\n",
      "\n",
      "Accuracy: 0.95 (+/- 0.01)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lisabecker/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Repeated Random Test-Train Splits: Accuracy: 95.642% (+/- 0.490%)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X2_mw, y_conrem_vs_acu2_mw, test_size = 0.20)\n",
    "model, y_pred, scores = naivebayes(X2_mw,y_conrem_vs_acu2_mw,X_train, y_train, X_test)\n",
    "classifierresults(X2_mw, y_conrem_vs_acu2_mw, y_test, y_pred, scores, model)\n",
    "model, y_pred, scores = supportvectormachine(X2_mw,y_conrem_vs_acu2_mw,X_train, y_train, X_test)\n",
    "classifierresults(X2_mw, y_conrem_vs_acu2_mw, y_test, y_pred, scores, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------- Naive Bayes ----------\n",
      "Confusion Matrix: \n",
      " [[1290    0    4]\n",
      " [ 303    0    4]\n",
      " [  97    0    3]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.76      1.00      0.86      1294\n",
      "           1       0.00      0.00      0.00       307\n",
      "           2       0.27      0.03      0.05       100\n",
      "\n",
      "    accuracy                           0.76      1701\n",
      "   macro avg       0.35      0.34      0.31      1701\n",
      "weighted avg       0.60      0.76      0.66      1701\n",
      "\n",
      "Accuracy: 0.76 (+/- 0.01)\n",
      "Repeated Random Test-Train Splits: Accuracy: 76.192% (+/- 0.527%)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lisabecker/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_both_mw, y_both_mw, test_size = 0.20)\n",
    "model, y_pred, scores = naivebayes(X_both_mw,y_both_mw,X_train, y_train, X_test)\n",
    "classifierresults(X_both_mw, y_both_mw, y_test, y_pred, scores, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# never compiled completely (both language tasks on support vector machine, C vs PA vs PR)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_both_mw, y_both_mw, test_size = 0.20)\n",
    "model, y_pred, scores = supportvectormachine(X_both_mw,y_both_mw,X_train, y_train, X_test)\n",
    "classifierresults(X_both_mw, y_both_mw, y_test, y_pred, scores, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------- Naive Bayes ----------\n",
      "Confusion Matrix: \n",
      " [[1288    8]\n",
      " [ 401    4]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.76      0.99      0.86      1296\n",
      "           1       0.33      0.01      0.02       405\n",
      "\n",
      "    accuracy                           0.76      1701\n",
      "   macro avg       0.55      0.50      0.44      1701\n",
      "weighted avg       0.66      0.76      0.66      1701\n",
      "\n",
      "Accuracy: 0.76 (+/- 0.01)\n",
      "Repeated Random Test-Train Splits: Accuracy: 76.152% (+/- 0.476%)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_both_mw, y_con_vs_acurem_both_mw, test_size = 0.20)\n",
    "model, y_pred, scores = naivebayes(X_both_mw,y_con_vs_acurem_both_mw,X_train, y_train, X_test)\n",
    "classifierresults(X_both_mw, y_con_vs_acurem_both_mw, y_test, y_pred, scores, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# never compiled completely (both language tasks on support vector machine, C vs PA+PR)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_both_mw, y_con_vs_acurem_both_mw, test_size = 0.20)\n",
    "model, y_pred, scores = supportvectormachine(X_both_mw,y_con_vs_acurem_both_mw,X_train, y_train, X_test)\n",
    "classifierresults(X_both_mw, y_con_vs_acurem_both_mw, y_test, y_pred, scores, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------- Naive Bayes ----------\n",
      "Confusion Matrix: \n",
      " [[1409    0]\n",
      " [ 292    0]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      1.00      0.91      1409\n",
      "           1       0.00      0.00      0.00       292\n",
      "\n",
      "    accuracy                           0.83      1701\n",
      "   macro avg       0.41      0.50      0.45      1701\n",
      "weighted avg       0.69      0.83      0.75      1701\n",
      "\n",
      "Accuracy: 0.83 (+/- 0.00)\n",
      "Repeated Random Test-Train Splits: Accuracy: 82.558% (+/- 0.512%)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lisabecker/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_both_mw, y_conrem_vs_acu_both_mw, test_size = 0.20)\n",
    "model, y_pred, scores = naivebayes(X_both_mw,y_conrem_vs_acu_both_mw,X_train, y_train, X_test)\n",
    "classifierresults(X_both_mw, y_conrem_vs_acu_both_mw, y_test, y_pred, scores, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# never compiled fully (both language tasks on support vector machine, C+PR vs PA)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_both_mw, y_conrem_vs_acu_both_mw, test_size = 0.20)\n",
    "model, y_pred, scores = supportvectormachine(X_both_mw,y_conrem_vs_acu_both_mw,X_train, y_train, X_test)\n",
    "classifierresults(X_both_mw, y_conrem_vs_acu_both_mw, y_test, y_pred, scores, model)"
   ]
  }
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