Support vector machines (R).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Support vector machines\n",
    "\n",
    "**Data** [Gender-annoted dataset of European parliament talks](https://www.kaggle.com/ellarabi/europarl-annotated-for-speaker-gender-and-age)\n",
    "\n",
    "**Overreaching question** Can we develop a model which correctly predicts speakers' based on what they are saying?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data management\n",
    "\n",
    "We connect the variable of interest into the textual data each speaker has said.\n",
    "That data is stored as XML, so we need to do a bit of work before we can easily use it.\n",
    "Also, transform the textual data to a feature matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata_all <- readLines('./data/europarl-annotated-for-speaker-gender-and-age/europarl.de-en/europarl.de-en.dat')\n",
    "texts_all <- readLines('./data/europarl-annotated-for-speaker-gender-and-age/europarl.de-en/europarl.de-en.en.aligned.tok')\n",
    "\n",
    "## this time processign these takes already some time, so let's choose a random set of 1000 texts\n",
    "set.seed(1)\n",
    "\n",
    "all_ids <- 1:length( metadata_all )\n",
    "selected_ids <- sample( all_ids, 1000 )\n",
    "\n",
    "\n",
    "metadata <- metadata_all[ selected_ids ]\n",
    "texts <- texts_all[ selected_ids ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: XML\n",
      "\n"
     ]
    }
   ],
   "source": [
    "require(XML)\n",
    "\n",
    "clean <- function( entry ){\n",
    "    xml <- xmlTreeParse( entry )\n",
    "    return( xmlGetAttr( xml$doc$children$LINE  , \"GENDER\" ) )\n",
    "}\n",
    "\n",
    "gender <- sapply( metadata, FUN = clean )\n",
    "names( gender ) <- NA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Package version: 3.1.0\n",
      "Unicode version: 13.0\n",
      "ICU version: 69.1\n",
      "\n",
      "Parallel computing: 12 of 12 threads used.\n",
      "\n",
      "See https://quanteda.io for tutorials and examples.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(quanteda)\n",
    "\n",
    "corp <- corpus( texts )\n",
    "\n",
    "token <- tokens( corp )\n",
    "\n",
    "document_terms <- dfm( token )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>1000</li><li>4059</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 1000\n",
       "\\item 4059\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 1000\n",
       "2. 4059\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 1000 4059"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data <- convert( document_terms, \"data.frame\" )\n",
    "data$label_for_ml <- as.vector( gender ) ## adding the label we seek to learn to data\n",
    "data$label_for_ml <- as.factor( data$label_for_ml )\n",
    "data <- data[, -c(1)] ## this column is added when converting to data frame, but it is useless => remove from analysis\n",
    "\n",
    "dim( data )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Separate the train-test split\n",
    "\n",
    "This is used later in the analysis to ensure we do not [overfit](https://en.wikipedia.org/wiki/Overfitting) the data when we train the machine learning classifier.\n",
    "We choose to use 20% of data for testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: lattice\n",
      "\n",
      "Loading required package: ggplot2\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library( caret )\n",
    "\n",
    "trainIndex <- createDataPartition(data$label_for_ml, p = .8, list = FALSE)\n",
    "\n",
    "dataTrain <- data[ trainIndex,]\n",
    "dataTest  <- data[-trainIndex,]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run and evaluate machine learning tasks\n",
    "\n",
    "We now train the model using the **training** data and measure how well accuracy we achieved by examining **test data**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support Vector Machines with Linear Kernel \n",
      "\n",
      " 801 samples\n",
      "4058 predictors\n",
      "   2 classes: 'FEMALE', 'MALE' \n",
      "\n",
      "No pre-processing\n",
      "Resampling: Bootstrapped (25 reps) \n",
      "Summary of sample sizes: 801, 801, 801, 801, 801, 801, ... \n",
      "Resampling results:\n",
      "\n",
      "  Accuracy   Kappa      \n",
      "  0.5418977  0.002907022\n",
      "\n",
      "Tuning parameter 'C' was held constant at a value of 1\n"
     ]
    }
   ],
   "source": [
    "model <- train( label_for_ml ~., data = dataTrain, method = \"svmLinear\")\n",
    "## this prints a lot of warnings\n",
    "print( model )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".dl-inline {width: auto; margin:0; padding: 0}\n",
       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
       "</style><dl class=dl-inline><dt>Accuracy</dt><dd>0.477386934673367</dd><dt>Kappa</dt><dd>-0.13167104111986</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[Accuracy] 0.477386934673367\n",
       "\\item[Kappa] -0.13167104111986\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "Accuracy\n",
       ":   0.477386934673367Kappa\n",
       ":   -0.13167104111986\n",
       "\n"
      ],
      "text/plain": [
       "  Accuracy      Kappa \n",
       " 0.4773869 -0.1316710 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_pred <- predict( model, newdata = dataTest )\n",
    "postResample( test_pred, as.factor(dataTest$label_for_ml) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "           Length Class      Mode     \n",
       "importance 2      data.frame list     \n",
       "model      1      -none-     character\n",
       "calledFrom 1      -none-     character"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'špidla' in 'mbcsToSbcs': dot substituted for <c5>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'špidla' in 'mbcsToSbcs': dot substituted for <a1>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'őry' in 'mbcsToSbcs': dot substituted for <c5>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'őry' in 'mbcsToSbcs': dot substituted for <91>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'băsescu' in 'mbcsToSbcs': dot substituted for <c4>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'băsescu' in 'mbcsToSbcs': dot substituted for <83>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'győri' in 'mbcsToSbcs': dot substituted for <c5>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'győri' in 'mbcsToSbcs': dot substituted for <91>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'šefčovič' in 'mbcsToSbcs': dot substituted for <c5>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'šefčovič' in 'mbcsToSbcs': dot substituted for <a1>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'šefčovič' in 'mbcsToSbcs': dot substituted for <c4>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'šefčovič' in 'mbcsToSbcs': dot substituted for <8d>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'šefčovič' in 'mbcsToSbcs': dot substituted for <c4>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'šefčovič' in 'mbcsToSbcs': dot substituted for <8d>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'špidla' in 'mbcsToSbcs': dot substituted for <c5>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'špidla' in 'mbcsToSbcs': dot substituted for <a1>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'őry' in 'mbcsToSbcs': dot substituted for <c5>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'őry' in 'mbcsToSbcs': dot substituted for <91>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'băsescu' in 'mbcsToSbcs': dot substituted for <c4>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'băsescu' in 'mbcsToSbcs': dot substituted for <83>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'győri' in 'mbcsToSbcs': dot substituted for <c5>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'győri' in 'mbcsToSbcs': dot substituted for <91>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'šefčovič' in 'mbcsToSbcs': dot substituted for <c5>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'šefčovič' in 'mbcsToSbcs': dot substituted for <a1>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'šefčovič' in 'mbcsToSbcs': dot substituted for <c4>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'šefčovič' in 'mbcsToSbcs': dot substituted for <8d>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'šefčovič' in 'mbcsToSbcs': dot substituted for <c4>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'šefčovič' in 'mbcsToSbcs': dot substituted for <8d>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'špidla' in 'mbcsToSbcs': dot substituted for <c5>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'špidla' in 'mbcsToSbcs': dot substituted for <a1>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'őry' in 'mbcsToSbcs': dot substituted for <c5>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'őry' in 'mbcsToSbcs': dot substituted for <91>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'băsescu' in 'mbcsToSbcs': dot substituted for <c4>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'băsescu' in 'mbcsToSbcs': dot substituted for <83>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'győri' in 'mbcsToSbcs': dot substituted for <c5>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'győri' in 'mbcsToSbcs': dot substituted for <91>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'šefčovič' in 'mbcsToSbcs': dot substituted for <c5>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'šefčovič' in 'mbcsToSbcs': dot substituted for <a1>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'šefčovič' in 'mbcsToSbcs': dot substituted for <c4>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'šefčovič' in 'mbcsToSbcs': dot substituted for <8d>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'šefčovič' in 'mbcsToSbcs': dot substituted for <c4>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'šefčovič' in 'mbcsToSbcs': dot substituted for <8d>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'špidla' in 'mbcsToSbcs': dot substituted for <c5>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'špidla' in 'mbcsToSbcs': dot substituted for <a1>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'őry' in 'mbcsToSbcs': dot substituted for <c5>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'őry' in 'mbcsToSbcs': dot substituted for <91>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'băsescu' in 'mbcsToSbcs': dot substituted for <c4>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'băsescu' in 'mbcsToSbcs': dot substituted for <83>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'győri' in 'mbcsToSbcs': dot substituted for <c5>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'győri' in 'mbcsToSbcs': dot substituted for <91>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'šefčovič' in 'mbcsToSbcs': dot substituted for <c5>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'šefčovič' in 'mbcsToSbcs': dot substituted for <a1>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'šefčovič' in 'mbcsToSbcs': dot substituted for <c4>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'šefčovič' in 'mbcsToSbcs': dot substituted for <8d>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'šefčovič' in 'mbcsToSbcs': dot substituted for <c4>”\n",
      "Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'šefčovič' in 'mbcsToSbcs': dot substituted for <8d>”\n",
      "Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'špidla' in 'mbcsToSbcs': dot substituted for <c5>”\n",
      "Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'špidla' in 'mbcsToSbcs': dot substituted for <a1>”\n",
      "Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'őry' in 'mbcsToSbcs': dot substituted for <c5>”\n",
      "Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'őry' in 'mbcsToSbcs': dot substituted for <91>”\n",
      "Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'băsescu' in 'mbcsToSbcs': dot substituted for <c4>”\n",
      "Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'băsescu' in 'mbcsToSbcs': dot substituted for <83>”\n",
      "Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'győri' in 'mbcsToSbcs': dot substituted for <c5>”\n",
      "Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'győri' in 'mbcsToSbcs': dot substituted for <91>”\n",
      "Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'šefčovič' in 'mbcsToSbcs': dot substituted for <c5>”\n",
      "Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'šefčovič' in 'mbcsToSbcs': dot substituted for <a1>”\n",
      "Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'šefčovič' in 'mbcsToSbcs': dot substituted for <c4>”\n",
      "Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'šefčovič' in 'mbcsToSbcs': dot substituted for <8d>”\n",
      "Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'šefčovič' in 'mbcsToSbcs': dot substituted for <c4>”\n",
      "Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :\n",
      "“conversion failure on 'šefčovič' in 'mbcsToSbcs': dot substituted for <8d>”\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAYAAAD958/bAAAEGWlDQ1BrQ0dDb2xvclNwYWNl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\nYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## TODO: Broken :(\n",
    "importance <- varImp( model, data = dataTest )\n",
    "\n",
    "summary( importance )\n",
    "plot( importance )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tasks\n",
    "\n",
    "* Run the code as is and interprent the accuracy. What does that mean?\n",
    "* Examine different metrics for [classification accuracy](https://topepo.github.io/caret/measuring-performance.html).\n",
    "* Fix issues in the text pre-processing: account for stop words, frequent terms ans stem content in the document-term-matrix: does it have any implications on accuracy?\n",
    "* Predictors includes each feature (as a key) and how good the variable was for said problem (as a value). Extract from this the best predictors.\n",
    "* Count the number of different labels in the dataset of 10,000 comments. What can you observe?\n",
    "* Modify the code to use [Naive Bayes](https://topepo.github.io/caret/train-models-by-tag.html#bayesian-model) model and SVM model. Which one seems to work better?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Advanced magics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* There are many different ways to build a models using various supervised machine learning methods.\n",
    "One can use different parameters of methods. This is known as *tuning* the model and can improve models' performance in terms of accuracy.\n",
    "* [Grid search](https://topepo.github.io/caret/model-training-and-tuning.html#basic-parameter-tuning) is an approach to examine different parameters and examine what paremeters lead to best models.\n",
    "* You can also work on data preprocessing to [scale them](https://topepo.github.io/caret/pre-processing.html#centering-and-scaling) or try to more acressively to clean or remove data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "## defining parameters for different models\n",
    "grid <- expand.grid( \"C\" = c(1, 10, 100, 1000) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n",
      "Warning message in .local(x, ...):\n",
      "“Variable(s) `' constant. Cannot scale data.”\n"
     ]
    }
   ],
   "source": [
    "model <- train( label_for_ml ~., data = dataTrain, method = \"svmLinear\", tuneGrid = grid )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We have used a binary variable (male/female), however support vector machines can be used to multi-category classification or linear variables through regression models - see [different models](https://topepo.github.io/caret/available-models.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* If doing category classification, the algorithm is senstive to inbalances between classification, i.e. if there are more cases belonging to Category 1 than in Category 2. We might need to do [magic](https://topepo.github.io/caret/subsampling-for-class-imbalances.html) to control for these."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loaded ROSE 0.0-4\n",
      "\n",
      "\n"
     ]
    },
    {
     "ename": "ERROR",
     "evalue": "Error in str2lang(x): <text>:1:29: unexpected ','\n1: label_for_ml ~ mr+president+,\n                                ^\n",
     "output_type": "error",
     "traceback": [
      "Error in str2lang(x): <text>:1:29: unexpected ','\n1: label_for_ml ~ mr+president+,\n                                ^\nTraceback:\n",
      "1. ROSE(label_for_ml ~ ., data = dataTrain)",
      "2. omnibus.balancing(formula, data, subset, na.action, N, p, method = \"rose\", \n .     seed, hmult.majo, hmult.mino)",
      "3. adj.formula(formula, data)",
      "4. as.formula(paste(vars[1], \"~\", paste(vars[-1], collapse = \"+\")))",
      "5. formula(object, env = baseenv())",
      "6. formula.character(object, env = baseenv())",
      "7. str2lang(x)"
     ]
    }
   ],
   "source": [
    "library(ROSE)\n",
    "\n",
    "fixedDataTrain <- ROSE( label_for_ml ~ ., data = dataTrain) ## note: clean out , before running this code\n",
    "\n",
    "model <- train( label_for_ml ~., data = fixedDataTrain, method = \"svmLinear\" )\n",
    "print( model )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tasks\n",
    "\n",
    "* Try different grid seaarcg parameters, see if your accuracy metric improve.\n",
    "* Does balancing improve accuracy with our data?\n",
    "* Use age variable to develop a regression model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "4.1.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}