Fake_News_code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Project 2 : Multinomial Naive Bayes Classifier for Fake News Recognition\n",
    "\n",
    "#### Group Members: \n",
    "- Jelin Raphael Akkara - 2072064\n",
    "- Amirhosseign Rostami - 2084299"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "library(tidyverse)  #Contains dplyr\n",
    "library(SnowballC)  #Used for stemming\n",
    "library(udpipe)     #Used for lemmatization\n",
    "library(tidytext)   #Used for tokenization\n",
    "library(caTools)    #Used for sample.split\n",
    "library(gridExtra)  #Used for arranging plots in grids\n",
    "library(viridis)    #Used for colormap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "#Used for lemmatization\n",
    "#ud_model <- udpipe_download_model(language = 'english-ewt')\n",
    "#ud_model <- udpipe_load_model('english-ewt-ud-2.5-191206.udpipe')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning/Preprocessing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "#First Dataset\n",
    "df_train_valid <- read.csv('archive/train.csv')  \n",
    "df_test <- read.csv('archive/test.csv')\n",
    "\n",
    "reorder_labels <- c(4, 1, 0, 2, 3, 5)\n",
    "\n",
    "labels <- factor(0:5, levels = reorder_labels)\n",
    "names <- c('Barely_True', 'False', 'Half_True', 'Mostly_True', 'Not_Known', 'True')\n",
    "\n",
    "labels_descrip <- tibble(Labels = labels, Name = factor(names, levels = names[reorder_labels + 1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.height=8, repr.plot.width=10)\n",
    "\n",
    "#Get counts per label\n",
    "label_counts <- df_train_valid %>%\n",
    "                    group_by(Labels) %>%\n",
    "                    summarise(counts = n()) %>%\n",
    "                    mutate(Labels = factor(Labels, levels = reorder_labels)) %>%\n",
    "                    left_join(labels_descrip, by='Labels')\n",
    "\n",
    "ggplot(label_counts, aes(x = Name, y = counts)) +\n",
    "            theme_bw(base_size = 15) +\n",
    "            geom_bar(stat = 'identity', fill='darkseagreen', width=0.7) +\n",
    "            ggtitle('Documents in each Label (Train + Valid Set)') +\n",
    "            xlab('\\nLabel') +\n",
    "            ylab('Counts\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Split into Training, Validation Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "set.seed(100)\n",
    "split_perc <- 0.8\n",
    "\n",
    "split_bool <- sample.split(df_train_valid$Text, SplitRatio = split_perc)\n",
    "df_train <- subset(df_train_valid, split_bool == TRUE)\n",
    "df_valid <- subset(df_train_valid, split_bool == FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "prepare_tibble <- function(df, dataset_num, remove_tag=FALSE, factor_labels=FALSE){\n",
    "    \n",
    "    tb <- df %>% as_tibble #Converts to tibble\n",
    "\n",
    "    if (dataset_num == 1){\n",
    "        tb <- tb %>%\n",
    "                add_column(document_id = 1:nrow(df))        %>%     #Adds column labelling document id\n",
    "                relocate(document_id)                               #Repositions document id column\n",
    "    }\n",
    "\n",
    "    if (remove_tag){\n",
    "        tb <- tb %>% select(-c(Text_Tag))\n",
    "        }\n",
    "    \n",
    "    if (factor_labels){\n",
    "        tb <- tb %>% mutate(Labels = as.factor(Labels))\n",
    "    }\n",
    "    \n",
    "    return(tb)            \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] 8192    3\n",
      "[1] 2048    3\n",
      "[1] 1267    2\n"
     ]
    }
   ],
   "source": [
    "tb_train <- prepare_tibble(df_train, dataset_num=1, factor_labels = TRUE, remove_tag = TRUE)\n",
    "tb_valid <- prepare_tibble(df_valid, dataset_num=1, factor_labels = TRUE, remove_tag = TRUE)\n",
    "tb_test <- prepare_tibble(df_test, dataset_num=1, remove_tag = TRUE)\n",
    "\n",
    "print(dim(tb_train))\n",
    "print(dim(tb_valid))\n",
    "print(dim(tb_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 3 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>document_id</th><th scope=col>Labels</th><th scope=col>Text</th></tr>\n",
       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1</td><td>1</td><td>Says the Annies List political group supports third-trimester abortions on demand.                                                           </td></tr>\n",
       "\t<tr><td>2</td><td>2</td><td>When did the decline of coal start? It started when natural gas took off that started to begin in (President George W.) Bushs administration.</td></tr>\n",
       "\t<tr><td>3</td><td>3</td><td>Hillary Clinton agrees with John McCain \"by voting to give George Bush the benefit of the doubt on Iran.\"                                    </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 3 × 3\n",
       "\\begin{tabular}{lll}\n",
       " document\\_id & Labels & Text\\\\\n",
       " <int> & <fct> & <chr>\\\\\n",
       "\\hline\n",
       "\t 1 & 1 & Says the Annies List political group supports third-trimester abortions on demand.                                                           \\\\\n",
       "\t 2 & 2 & When did the decline of coal start? It started when natural gas took off that started to begin in (President George W.) Bushs administration.\\\\\n",
       "\t 3 & 3 & Hillary Clinton agrees with John McCain \"by voting to give George Bush the benefit of the doubt on Iran.\"                                    \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 3 × 3\n",
       "\n",
       "| document_id &lt;int&gt; | Labels &lt;fct&gt; | Text &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| 1 | 1 | Says the Annies List political group supports third-trimester abortions on demand.                                                            |\n",
       "| 2 | 2 | When did the decline of coal start? It started when natural gas took off that started to begin in (President George W.) Bushs administration. |\n",
       "| 3 | 3 | Hillary Clinton agrees with John McCain \"by voting to give George Bush the benefit of the doubt on Iran.\"                                     |\n",
       "\n"
      ],
      "text/plain": [
       "  document_id Labels\n",
       "1 1           1     \n",
       "2 2           2     \n",
       "3 3           3     \n",
       "  Text                                                                                                                                         \n",
       "1 Says the Annies List political group supports third-trimester abortions on demand.                                                           \n",
       "2 When did the decline of coal start? It started when natural gas took off that started to begin in (President George W.) Bushs administration.\n",
       "3 Hillary Clinton agrees with John McCain \"by voting to give George Bush the benefit of the doubt on Iran.\"                                    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(tb_train, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cleaning using tidyverse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "clean_tibble <- function(tb, stem=TRUE, lem=FALSE){\n",
    "\n",
    "    if (lem){\n",
    "        tb <- tb %>%\n",
    "                rowwise() %>%      #Required for lemmatization\n",
    "                mutate(Text = paste(udpipe(Text, object=ud_model)$lemma, collapse=' '))     #Lemmatization\n",
    "    }\n",
    "\n",
    "    tb_clean <- tb %>% \n",
    "                    mutate(Text = tolower(Text))                                            %>%     #Lowering Case \n",
    "                    mutate(Text =  str_replace_all(Text, \"[[:punct:]]\", \"\"))                %>%     #Removes Punctuations\n",
    "                    mutate(Text = str_remove_all(Text, '\\\\d+'))                             %>%     #Removes Numbers\n",
    "                    mutate(Text = str_squish(Text))                                                 #Removes Extra Whitespaces\n",
    "\n",
    "    tb_tokenize <- tb_clean %>% \n",
    "                    unnest_tokens(output='word', Text) %>%      #Tokenize\n",
    "                    anti_join(stop_words)                       #Remove Stop Words\n",
    "    \n",
    "    if (stem){\n",
    "        tb_tokenize <- tb_tokenize %>%\n",
    "                        mutate(word = wordStem(word))               #Stemming\n",
    "    }\n",
    "                    \n",
    "\n",
    "    return(tb_tokenize)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[22mJoining with `by = join_by(word)`\n"
     ]
    }
   ],
   "source": [
    "tb_train_clean <- clean_tibble(tb_train, stem=TRUE, lem=FALSE)\n",
    "vocab <- unique(tb_train_clean$word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 3 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>document_id</th><th scope=col>Labels</th><th scope=col>word</th></tr>\n",
       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1</td><td>1</td><td>anni </td></tr>\n",
       "\t<tr><td>1</td><td>1</td><td>list </td></tr>\n",
       "\t<tr><td>1</td><td>1</td><td>polit</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 3 × 3\n",
       "\\begin{tabular}{lll}\n",
       " document\\_id & Labels & word\\\\\n",
       " <int> & <fct> & <chr>\\\\\n",
       "\\hline\n",
       "\t 1 & 1 & anni \\\\\n",
       "\t 1 & 1 & list \\\\\n",
       "\t 1 & 1 & polit\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 3 × 3\n",
       "\n",
       "| document_id &lt;int&gt; | Labels &lt;fct&gt; | word &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| 1 | 1 | anni  |\n",
       "| 1 | 1 | list  |\n",
       "| 1 | 1 | polit |\n",
       "\n"
      ],
      "text/plain": [
       "  document_id Labels word \n",
       "1 1           1      anni \n",
       "2 1           1      list \n",
       "3 1           1      polit"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(tb_train_clean, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "7417"
      ],
      "text/latex": [
       "7417"
      ],
      "text/markdown": [
       "7417"
      ],
      "text/plain": [
       "[1] 7417"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(vocab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Selection with Mutual Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "implement_feature_selection <- function(tb, tb_clean, vocab, top_k=100, laplace_factor=1, keep_ties=FALSE){\n",
    "\n",
    "    label_counts <- table(tb$Labels)\n",
    "    label_vals <- as.data.frame(label_counts)[, 1]\n",
    "\n",
    "    tb_ref <- tibble(Labels = rep(as.factor(label_vals), each=length(vocab)), \n",
    "                    word = rep(vocab, times=length(label_vals)))\n",
    "\n",
    "    # N_a1 : Get the number of docs in each label irrespective of the words\n",
    "    tb_N_a1 <- tb %>%\n",
    "                group_by(Labels) %>%\n",
    "                summarise(N_a1 = n())\n",
    "\n",
    "    #N_11 : Number of docs containing specific combination of (Label, Word)\n",
    "    tb_N_11 <- tb_clean %>%\n",
    "                    group_by(across(all_of(c('Labels', 'word')))) %>%\n",
    "                    summarise(N_11 = length(unique(document_id))) \n",
    "\n",
    "    #Joins N_11, N_a1 to tb_ref to create tb_mi that tracks all values for combinations of (Label, Word)\n",
    "    tb_mi <- tb_ref %>%\n",
    "                left_join(tb_N_11, by=c('Labels', 'word')) %>%\n",
    "                replace_na(list(N_11 = 0)) %>%          #Replace possible NA with zero\n",
    "\n",
    "                left_join(tb_N_a1, by='Labels') %>%     #Add N_a1\n",
    "                mutate(N_01 = N_a1 - N_11)              #Add N_01\n",
    "\n",
    "    #N_1a : Number of documents containing a word irrespective of the class\n",
    "    tb_N_1a <- tb_clean %>%\n",
    "                group_by(word) %>%\n",
    "                summarise(N_1a = length(unique(document_id)))\n",
    "\n",
    "    #N : Total Number of documents\n",
    "    tb_N <- tibble(N = nrow(tb)) %>%\n",
    "                slice(rep(1:n(), times=nrow(tb_mi)))\n",
    "\n",
    "\n",
    "    tb_mi <- tb_mi %>%\n",
    "                left_join(tb_N_1a, by='word') %>%       #Add N_1a column\n",
    "                replace_na(list(N_1a = 0)) %>%          #Replace possible NA with zero\n",
    "\n",
    "                mutate(N_10 = N_1a - N_11) %>%          #Add N_10 column\n",
    "                add_column(N = tb_N$N) %>%              #Add N column\n",
    "                mutate(N_0a = N - N_1a) %>%             #Add N_0a column\n",
    "                mutate(N_00 = N_0a - N_01) %>%          #Add N_00 column\n",
    "                mutate(N_a0 = N_00 + N_10) %>%          #Add N_a0 column\n",
    "\n",
    "                #Laplace smoothing over all columns\n",
    "                mutate(across(where(is.integer), ~ .x + laplace_factor)) %>%\n",
    "\n",
    "                #Add Mutual Information Score\n",
    "                mutate(mi_score = ( (N_11/N) * log2((N*N_11)/(N_1a * N_a1)) ) + \n",
    "                                    ( (N_01/N) * log2((N*N_01) / (N_0a * N_a1))) +\n",
    "                                    ( (N_10/N) * log2((N*N_10) / (N_1a * N_a0))) +\n",
    "                                    ( (N_00/N) * log2((N*N_00) / (N_0a * N_a0)) )) %>%\n",
    "                \n",
    "                select(Labels, word, mi_score) %>%\n",
    "\n",
    "                #Selects top_k words for each label (with the option of breaking ties)\n",
    "                group_by(Labels) %>%\n",
    "                slice_max(n=top_k, order_by=mi_score, with_ties = keep_ties) %>% #Breaks ties wrt position\n",
    "                select(Labels, word)\n",
    "\n",
    "    return(tb_mi)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[22m`summarise()` has grouped output by 'Labels'. You can override using the\n",
      "`.groups` argument.\n"
     ]
    },
    {
     "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>3000</li><li>2</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 3000\n",
       "\\item 2\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 3000\n",
       "2. 2\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 3000    2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A grouped_df: 3 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Labels</th><th scope=col>word</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>0</td><td>obamacar</td></tr>\n",
       "\t<tr><td>0</td><td>uranium </td></tr>\n",
       "\t<tr><td>0</td><td>benghazi</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A grouped\\_df: 3 × 2\n",
       "\\begin{tabular}{ll}\n",
       " Labels & word\\\\\n",
       " <fct> & <chr>\\\\\n",
       "\\hline\n",
       "\t 0 & obamacar\\\\\n",
       "\t 0 & uranium \\\\\n",
       "\t 0 & benghazi\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A grouped_df: 3 × 2\n",
       "\n",
       "| Labels &lt;fct&gt; | word &lt;chr&gt; |\n",
       "|---|---|\n",
       "| 0 | obamacar |\n",
       "| 0 | uranium  |\n",
       "| 0 | benghazi |\n",
       "\n"
      ],
      "text/plain": [
       "  Labels word    \n",
       "1 0      obamacar\n",
       "2 0      uranium \n",
       "3 0      benghazi"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tb_features <- implement_feature_selection(tb_train, tb_train_clean, vocab, \n",
    "                                            top_k = 500, \n",
    "                                            laplace_factor = 1,\n",
    "                                            keep_ties = FALSE)\n",
    "\n",
    "dim(tb_features)\n",
    "head(tb_features, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "#Get most occuring words in a Label\n",
    "tb_features %>% \n",
    "    left_join(labels_descrip, by='Labels') %>%\n",
    "    filter(Labels == 0) %>%\n",
    "    select(Labels, Name, word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.height = 8, repr.plot.width = 10)\n",
    "\n",
    "#Gets and plots feature counts in each label\n",
    "features_per_label <- tb_features %>% \n",
    "                        group_by(Labels) %>% \n",
    "                        summarise(counts = n()) %>%\n",
    "                        left_join(labels_descrip, by='Labels')\n",
    "\n",
    "ggplot(features_per_label, aes(x = Name, y = counts)) +\n",
    "    theme_bw(base_size = 15) +\n",
    "    geom_bar(stat = 'identity', fill='darkseagreen', width=0.7) +\n",
    "    ggtitle('Feature Counts in each Label: Not Keeping Ties, Top 500') +\n",
    "    xlab('\\nLabel') +\n",
    "    ylab('Counts\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "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>13792</li><li>3</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 13792\n",
       "\\item 3\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 13792\n",
       "2. 3\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 13792     3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Reduced cleaned training set after feature selection\n",
    "tb_train_clean_reduced <- tb_train_clean %>%\n",
    "                            inner_join(tb_features, by=c('Labels', 'word'))\n",
    "\n",
    "dim(tb_train_clean_reduced)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building and Training Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "build_mnb_model <- function(tb, tb_clean, laplace_factor=1){\n",
    "\n",
    "    vocab <- unique(tb_clean$word)\n",
    "\n",
    "    #Get priors : Number of docs in label / Total number of docs    \n",
    "    label_counts <- table(tb$Labels)\n",
    "    label_vals <- as.data.frame(label_counts)[, 1]\n",
    "    label_priors <- tibble(Labels = as.factor(label_vals), \n",
    "                            prior = as.double(label_counts / sum(label_counts)) )\n",
    "\n",
    "    #Get reference tb with all labels, words\n",
    "    tb_ref <- tibble(Labels = rep(as.factor(label_vals), each=length(vocab)), \n",
    "                        word = rep(vocab, times=length(label_vals)))\n",
    "\n",
    "    #Get tb with word occurrences in labels\n",
    "    tb_counts <- tb_clean %>%\n",
    "                    group_by(Labels) %>%\n",
    "                    count(Labels, word)\n",
    "    \n",
    "    #Update tb_ref with counts by left joining the tb_counts\n",
    "    tb_cond_probs <- tb_ref %>%\n",
    "                    left_join(tb_counts, by=c('Labels', 'word')) %>%\n",
    "                    replace_na(list(n = 0)) %>%\n",
    "                    \n",
    "                    #Get conditional probabilites for vocab in each label\n",
    "                    mutate(log_cond_prob = log((n + laplace_factor) / (sum(n) + (laplace_factor * n_distinct(word))))) %>%\n",
    "\n",
    "                    select(Labels, word, log_cond_prob)\n",
    "\n",
    "    return (list(prior = label_priors, cond = tb_cond_probs, vocab = vocab))\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "#Passsing uncleaned tibble, returns prior, vocabulary and conditional probability of all words for each label\n",
    "#We use the uncleaned tibble to get priors (cleaned tibble will not return correct priors)\n",
    "#mnb_model <- build_mnb_model(tb_train, tb_train_clean_reduced, vocab, laplace_factor = 1)\n",
    "mnb_model <- build_mnb_model(tb_train, tb_train_clean_reduced, laplace_factor = 1)\n",
    "\n",
    "tb_priors <- mnb_model$prior\n",
    "tb_cond <- mnb_model$cond\n",
    "vocab <- mnb_model$vocab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] 6 2\n",
      "[1] 17370     3\n",
      "[1] 2895\n"
     ]
    }
   ],
   "source": [
    "print(dim(tb_priors))\n",
    "print(dim(tb_cond))\n",
    "print(length(vocab))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Validating / Testing Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "predict_labels <- function(tb, tb_priors, tb_cond, remove_perc=0.75, keep_ties=FALSE){\n",
    "\n",
    "    #Clean, tokenize tibble\n",
    "    tb_clean <- tb %>% clean_tibble\n",
    "\n",
    "    #Get predictions for words in vocabulary\n",
    "    label_predictions <- tb_clean %>%\n",
    "                            select(document_id, word) %>%   #Only these two columns are needed for making predictions\n",
    "\n",
    "                            #Assign conditional probabilities to present words\n",
    "                            #NA values mean out of vocabulary words\n",
    "                            left_join(tb_cond, by='word', multiple='all') %>%   \n",
    "\n",
    "                            #Now, we don't care for the words.  We group by (doc, label) and sum the log_cond_prob for each label in a doc\n",
    "                            #Also, it adds a new column that gets the number of words present in (doc, label). This helps in getting the \n",
    "                            #percentage of new words in each document\n",
    "                            group_by(across(all_of(c('document_id', 'Labels')))) %>%\n",
    "                            summarise(log_likelihood = sum(log_cond_prob), counts=n())\n",
    "\n",
    "    #Using the counts column, we can get the percentage of out of vocab words in each document\n",
    "    #If the number of such words is high (> remove_perc), then we ignore the document\n",
    "    #This is done so that there is a certain degree of reliability for the predictions.\n",
    "    #It should not happend that the label was predicted using a single word that is not really relevant to the document.\n",
    "    delete_docs <- label_predictions %>% \n",
    "\n",
    "                    #Groups by id and gets the na_ratio for each doc\n",
    "                    group_by(document_id) %>% \n",
    "                    summarise(na_ratio = sum(counts[is.na(Labels)] / sum(counts) )) %>%\n",
    "                    #summarise(sum_na = sum(counts[is.na(Labels)]), sum_total = sum(counts)) %>%\n",
    "                    #mutate(na_ratio = sum_na / sum_total) %>%\n",
    "\n",
    "                    #Filters to find docs with high na_ratio\n",
    "                    filter(na_ratio >= remove_perc) %>%\n",
    "                    select(document_id)\n",
    "\n",
    "    #Removes the unreliable documents, and predicts labels for each document wrt log probability\n",
    "    label_predictions_reliable <- label_predictions %>%\n",
    "\n",
    "                                    #Removing unreliable documents\n",
    "                                    filter(!document_id %in% delete_docs$document_id) %>%\n",
    "\n",
    "                                    #Gets corresponding priors of labels\n",
    "                                    left_join(tb_priors, by='Labels') %>%\n",
    "\n",
    "                                    #Gets log probability of each label in each doc\n",
    "                                    mutate(log_prob = log_likelihood + log(prior)) %>%\n",
    "                                    \n",
    "                                    #Select the label having highest log_prob for each doc\n",
    "                                    slice_max(n=1, order_by=log_prob, with_ties=keep_ties) %>%\n",
    "\n",
    "                                    #Select only the document_id and labels\n",
    "                                    select(document_id, Labels)\n",
    "    \n",
    "    return (label_predictions_reliable)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[22mJoining with `by = join_by(word)`\n",
      "\u001b[1m\u001b[22m`summarise()` has grouped output by 'document_id'. You can override using the\n",
      "`.groups` argument.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] 1968    2\n"
     ]
    }
   ],
   "source": [
    "tb_valid_preds <- predict_labels(tb_valid, tb_priors, tb_cond, \n",
    "                                    remove_perc=0.75, \n",
    "                                    keep_ties=TRUE )\n",
    "                                    \n",
    "print(dim(tb_valid_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 3 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>document_id</th><th scope=col>Labels</th><th scope=col>Text</th></tr>\n",
       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1</td><td>0</td><td>Jim Dunnam has not lived in the district he represents for years now.                                                                   </td></tr>\n",
       "\t<tr><td>2</td><td>2</td><td>Since 2000, nearly 12 million Americans have slipped out of the middle class and into poverty.                                          </td></tr>\n",
       "\t<tr><td>3</td><td>2</td><td>Rick Perry has never lost an election and remains the only person to have won the Texas governorship three times in landslide elections.</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 3 × 3\n",
       "\\begin{tabular}{lll}\n",
       " document\\_id & Labels & Text\\\\\n",
       " <int> & <fct> & <chr>\\\\\n",
       "\\hline\n",
       "\t 1 & 0 & Jim Dunnam has not lived in the district he represents for years now.                                                                   \\\\\n",
       "\t 2 & 2 & Since 2000, nearly 12 million Americans have slipped out of the middle class and into poverty.                                          \\\\\n",
       "\t 3 & 2 & Rick Perry has never lost an election and remains the only person to have won the Texas governorship three times in landslide elections.\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 3 × 3\n",
       "\n",
       "| document_id &lt;int&gt; | Labels &lt;fct&gt; | Text &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| 1 | 0 | Jim Dunnam has not lived in the district he represents for years now.                                                                    |\n",
       "| 2 | 2 | Since 2000, nearly 12 million Americans have slipped out of the middle class and into poverty.                                           |\n",
       "| 3 | 2 | Rick Perry has never lost an election and remains the only person to have won the Texas governorship three times in landslide elections. |\n",
       "\n"
      ],
      "text/plain": [
       "  document_id Labels\n",
       "1 1           0     \n",
       "2 2           2     \n",
       "3 3           2     \n",
       "  Text                                                                                                                                    \n",
       "1 Jim Dunnam has not lived in the district he represents for years now.                                                                   \n",
       "2 Since 2000, nearly 12 million Americans have slipped out of the middle class and into poverty.                                          \n",
       "3 Rick Perry has never lost an election and remains the only person to have won the Texas governorship three times in landslide elections."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(tb_valid, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get ratios for valid and removed documents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "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>80</li><li>3</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 80\n",
       "\\item 3\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 80\n",
       "2. 3\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 80  3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tb_valid_removed <- tb_valid %>% anti_join(tb_valid_preds, by='document_id')\n",
    "dim(tb_valid_removed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 2 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Type</th><th scope=col>Counts</th><th scope=col>Percs</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Valid Documents  </td><td>1968</td><td>0.960938</td></tr>\n",
       "\t<tr><td>Removed Documents</td><td>  80</td><td>0.039062</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 2 × 3\n",
       "\\begin{tabular}{lll}\n",
       " Type & Counts & Percs\\\\\n",
       " <chr> & <int> & <dbl>\\\\\n",
       "\\hline\n",
       "\t Valid Documents   & 1968 & 0.960938\\\\\n",
       "\t Removed Documents &   80 & 0.039062\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 2 × 3\n",
       "\n",
       "| Type &lt;chr&gt; | Counts &lt;int&gt; | Percs &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| Valid Documents   | 1968 | 0.960938 |\n",
       "| Removed Documents |   80 | 0.039062 |\n",
       "\n"
      ],
      "text/plain": [
       "  Type              Counts Percs   \n",
       "1 Valid Documents   1968   0.960938\n",
       "2 Removed Documents   80   0.039062"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "preds_present_ratio <- round(dim(tb_valid_preds)[1] / dim(tb_valid)[1], 6)\n",
    "preds_removed_ratio <- round(dim(tb_valid_removed)[1] / dim(tb_valid)[1], 6)\n",
    "\n",
    "tb_premoved <- tibble(Type = c('Valid Documents', 'Removed Documents'),\n",
    "                        Counts = c(dim(tb_valid_preds)[1], dim(tb_valid_removed)[1]),\n",
    "                        Percs = c(preds_present_ratio, preds_removed_ratio))\n",
    "tb_premoved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Plot with title \"Valid vs Invalid Documents: Remove Percentage =  0.25\""
      ]
     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=10, repr.plot.height=8)\n",
    "\n",
    "barplot(tb_premoved$Percs, names=tb_premoved$Type, ylim=c(0, 1),\n",
    "        main=paste('Valid vs Invalid Documents: Remove Percentage = ', 0.25), \n",
    "        ylab='Ratio',\n",
    "        col='darkseagreen')\n",
    "\n",
    "text(c(0.675, 1.9), 0.5, tb_premoved$Counts, cex=1.25)\n",
    "text(c(0.675, 1.9), 0.6, tb_premoved$Percs, cex=1.25)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get corresponding true labels for validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 3 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>document_id</th><th scope=col>True_Label</th><th scope=col>Pred_Label</th></tr>\n",
       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1</td><td>0</td><td>5</td></tr>\n",
       "\t<tr><td>2</td><td>2</td><td>3</td></tr>\n",
       "\t<tr><td>3</td><td>2</td><td>3</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 3 × 3\n",
       "\\begin{tabular}{lll}\n",
       " document\\_id & True\\_Label & Pred\\_Label\\\\\n",
       " <int> & <fct> & <fct>\\\\\n",
       "\\hline\n",
       "\t 1 & 0 & 5\\\\\n",
       "\t 2 & 2 & 3\\\\\n",
       "\t 3 & 2 & 3\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 3 × 3\n",
       "\n",
       "| document_id &lt;int&gt; | True_Label &lt;fct&gt; | Pred_Label &lt;fct&gt; |\n",
       "|---|---|---|\n",
       "| 1 | 0 | 5 |\n",
       "| 2 | 2 | 3 |\n",
       "| 3 | 2 | 3 |\n",
       "\n"
      ],
      "text/plain": [
       "  document_id True_Label Pred_Label\n",
       "1 1           0          5         \n",
       "2 2           2          3         \n",
       "3 3           2          3         "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>1968</li><li>3</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 1968\n",
       "\\item 3\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 1968\n",
       "2. 3\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 1968    3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tb_valid_compare <- tb_valid %>%\n",
    "\n",
    "                        #Removes Text column, gets distinct rows\n",
    "                        select(document_id, Labels) %>%\n",
    "\n",
    "                        #Joins prediction tibble for comparison\n",
    "                        right_join(tb_valid_preds, by='document_id', multiple='all') %>%\n",
    "                        select(document_id, True_Label = Labels.x, Pred_Label = Labels.y)\n",
    "\n",
    "head(tb_valid_compare, 3)\n",
    "dim(tb_valid_compare)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Testing Case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[22mJoining with `by = join_by(word)`\n",
      "\u001b[1m\u001b[22m`summarise()` has grouped output by 'document_id'. You can override using the\n",
      "`.groups` argument.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] 5150    2\n"
     ]
    }
   ],
   "source": [
    "tb_test_preds <- predict_labels(tb_test, tb_priors, tb_cond, remove_perc = 0.75, keep_ties=FALSE )\n",
    "print(dim(tb_test_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "label_counts <- tb_test_preds %>%\n",
    "                    group_by(Labels) %>%\n",
    "                    summarise(counts = n()) %>%\n",
    "                    mutate(Labels = factor(Labels, levels = reorder_labels)) %>%\n",
    "                    left_join(labels_descrip, by='Labels')\n",
    "\n",
    "ggplot(label_counts, aes(x = Name, y = counts)) +\n",
    "            theme_bw(base_size = 15) +\n",
    "            geom_bar(stat = 'identity', fill='darkseagreen', width=0.7) +\n",
    "            ggtitle('Documents in each Label (Test Set)') +\n",
    "            xlab('\\nLabel') +\n",
    "            ylab('Counts\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Obtaining Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "# Function for getting tibble of metrics\n",
    "get_metrics <- function(tb_compare, labels_descrip, beta_val=1){\n",
    "    \n",
    "    #Get confusion matrix\n",
    "    confusion_mat <- table(tb_compare$True_Label, tb_compare$Pred_Label)\n",
    "\n",
    "    #Get tp, tn, fp, fn\n",
    "    mat_sum_rep <- rep(sum(confusion_mat), nrow(confusion_mat))\n",
    "\n",
    "    tp_vals <- diag(confusion_mat)  %>% as.integer                              #Diagonal Elements give True Positive Values\n",
    "    fp_vals <- (colSums(confusion_mat) - diag(confusion_mat)) %>% as.integer    #Col Sum - Diag gives False Positive Values\n",
    "    fn_vals <- (rowSums(confusion_mat) - diag(confusion_mat)) %>% as.integer    #Row Sum - Diag gives False Negative Values\n",
    "    tn_vals <- (mat_sum_rep - (tp_vals + fp_vals + fn_vals)) %>% as.integer     #Total Sum - (Rest of metrics) gives True Negative Values\n",
    "\n",
    "    #Get total accuracy - check for correct predictions (both tp and tn) / total rows\n",
    "    total_accuracy <- tb_compare %>% \n",
    "                        mutate(diff = ifelse((as.integer(True_Label) - as.integer(Pred_Label)) == 0, 1, 0)) %>%\n",
    "                        summarise(total_acc = sum(diff) / nrow(tb_compare)) %>%\n",
    "                        as.double\n",
    "\n",
    "    #Put obtained metrics in a tibble, and obtain label-wise accuracy, precision, recall and f_beta score\n",
    "    tb_metrics <- labels_descrip %>%\n",
    "                        add_column(tp = tp_vals, fp = fp_vals, \n",
    "                            fn = fn_vals, tn = tn_vals) %>%\n",
    "\n",
    "                            #Add total accuracy column\n",
    "                            add_column(total_acc = rep(total_accuracy, nrow(labels_descrip))) %>%\n",
    "\n",
    "                            #Add accuracy, precision, recall, f_beta values\n",
    "                            mutate(acc = (tp + tn)/(tp + fp + tn + fn)) %>%\n",
    "                            mutate(precision = tp / (tp + fp) ) %>%\n",
    "                            mutate(recall = tp / (tp + fn) ) %>%\n",
    "                            mutate(f_beta = ( (1 + beta_val^2) * (precision * recall) / ( ( beta_val^2 * precision ) + recall ))) %>%\n",
    "                            replace_na(list(f_beta = 0))\n",
    "\n",
    "    return (tb_metrics)\n",
    "    \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 11</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Labels</th><th scope=col>Name</th><th scope=col>tp</th><th scope=col>fp</th><th scope=col>fn</th><th scope=col>tn</th><th scope=col>total_acc</th><th scope=col>acc</th><th scope=col>precision</th><th scope=col>recall</th><th scope=col>f_beta</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>0</td><td>Barely_True</td><td> 58</td><td>206</td><td>282</td><td>1422</td><td>0.2027439</td><td>0.7520325</td><td>0.2196970</td><td>0.17058824</td><td>0.2077364</td></tr>\n",
       "\t<tr><td>1</td><td>False      </td><td> 42</td><td>183</td><td>341</td><td>1402</td><td>0.2027439</td><td>0.7337398</td><td>0.1866667</td><td>0.10966057</td><td>0.1636789</td></tr>\n",
       "\t<tr><td>2</td><td>Half_True  </td><td>118</td><td>405</td><td>270</td><td>1175</td><td>0.2027439</td><td>0.6570122</td><td>0.2256214</td><td>0.30412371</td><td>0.2379032</td></tr>\n",
       "\t<tr><td>3</td><td>Mostly_True</td><td>110</td><td>486</td><td>248</td><td>1124</td><td>0.2027439</td><td>0.6270325</td><td>0.1845638</td><td>0.30726257</td><td>0.2005835</td></tr>\n",
       "\t<tr><td>4</td><td>Not_Known  </td><td> 11</td><td> 32</td><td>161</td><td>1764</td><td>0.2027439</td><td>0.9019309</td><td>0.2558140</td><td>0.06395349</td><td>0.1598837</td></tr>\n",
       "\t<tr><td>5</td><td>True       </td><td> 60</td><td>257</td><td>267</td><td>1384</td><td>0.2027439</td><td>0.7337398</td><td>0.1892744</td><td>0.18348624</td><td>0.1880878</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 11\n",
       "\\begin{tabular}{lllllllllll}\n",
       " Labels & Name & tp & fp & fn & tn & total\\_acc & acc & precision & recall & f\\_beta\\\\\n",
       " <fct> & <fct> & <int> & <int> & <int> & <int> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 0 & Barely\\_True &  58 & 206 & 282 & 1422 & 0.2027439 & 0.7520325 & 0.2196970 & 0.17058824 & 0.2077364\\\\\n",
       "\t 1 & False       &  42 & 183 & 341 & 1402 & 0.2027439 & 0.7337398 & 0.1866667 & 0.10966057 & 0.1636789\\\\\n",
       "\t 2 & Half\\_True   & 118 & 405 & 270 & 1175 & 0.2027439 & 0.6570122 & 0.2256214 & 0.30412371 & 0.2379032\\\\\n",
       "\t 3 & Mostly\\_True & 110 & 486 & 248 & 1124 & 0.2027439 & 0.6270325 & 0.1845638 & 0.30726257 & 0.2005835\\\\\n",
       "\t 4 & Not\\_Known   &  11 &  32 & 161 & 1764 & 0.2027439 & 0.9019309 & 0.2558140 & 0.06395349 & 0.1598837\\\\\n",
       "\t 5 & True        &  60 & 257 & 267 & 1384 & 0.2027439 & 0.7337398 & 0.1892744 & 0.18348624 & 0.1880878\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 11\n",
       "\n",
       "| Labels &lt;fct&gt; | Name &lt;fct&gt; | tp &lt;int&gt; | fp &lt;int&gt; | fn &lt;int&gt; | tn &lt;int&gt; | total_acc &lt;dbl&gt; | acc &lt;dbl&gt; | precision &lt;dbl&gt; | recall &lt;dbl&gt; | f_beta &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 0 | Barely_True |  58 | 206 | 282 | 1422 | 0.2027439 | 0.7520325 | 0.2196970 | 0.17058824 | 0.2077364 |\n",
       "| 1 | False       |  42 | 183 | 341 | 1402 | 0.2027439 | 0.7337398 | 0.1866667 | 0.10966057 | 0.1636789 |\n",
       "| 2 | Half_True   | 118 | 405 | 270 | 1175 | 0.2027439 | 0.6570122 | 0.2256214 | 0.30412371 | 0.2379032 |\n",
       "| 3 | Mostly_True | 110 | 486 | 248 | 1124 | 0.2027439 | 0.6270325 | 0.1845638 | 0.30726257 | 0.2005835 |\n",
       "| 4 | Not_Known   |  11 |  32 | 161 | 1764 | 0.2027439 | 0.9019309 | 0.2558140 | 0.06395349 | 0.1598837 |\n",
       "| 5 | True        |  60 | 257 | 267 | 1384 | 0.2027439 | 0.7337398 | 0.1892744 | 0.18348624 | 0.1880878 |\n",
       "\n"
      ],
      "text/plain": [
       "  Labels Name        tp  fp  fn  tn   total_acc acc       precision recall    \n",
       "1 0      Barely_True  58 206 282 1422 0.2027439 0.7520325 0.2196970 0.17058824\n",
       "2 1      False        42 183 341 1402 0.2027439 0.7337398 0.1866667 0.10966057\n",
       "3 2      Half_True   118 405 270 1175 0.2027439 0.6570122 0.2256214 0.30412371\n",
       "4 3      Mostly_True 110 486 248 1124 0.2027439 0.6270325 0.1845638 0.30726257\n",
       "5 4      Not_Known    11  32 161 1764 0.2027439 0.9019309 0.2558140 0.06395349\n",
       "6 5      True         60 257 267 1384 0.2027439 0.7337398 0.1892744 0.18348624\n",
       "  f_beta   \n",
       "1 0.2077364\n",
       "2 0.1636789\n",
       "3 0.2379032\n",
       "4 0.2005835\n",
       "5 0.1598837\n",
       "6 0.1880878"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tb_metrics <- get_metrics(tb_valid_compare, labels_descrip, beta_val=0.5)\n",
    "\n",
    "tb_metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Barplot: Accuracy, Precision, Recall, F_Beta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "plot_bar <- function(tb, tb_metric_name, plot_metric_name, beta_val=1){\n",
    "\n",
    "    main <- ggplot(tb, aes(x=Name, y=!!sym(tb_metric_name))) + \n",
    "                geom_bar(stat = 'identity', fill='darkseagreen', width=0.5) + \n",
    "                theme_bw(base_size = 15) +\n",
    "\n",
    "                theme(plot.title = element_text(hjust = 0.5, face='bold', size=17)) +\n",
    "\n",
    "                labs(x='\\nLabel',\n",
    "                        y=paste(plot_metric_name, '\\n'),\n",
    "                        title=paste(plot_metric_name, ' per Label'))\n",
    "\n",
    "    if (tb_metric_name %in% c('total_acc', 'acc', 'precision', 'recall', 'f_beta')){\n",
    "        main <- main + ylim(0, 1)\n",
    "    }\n",
    "    \n",
    "    return (main)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 1200
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width = 20, repr.plot.height = 8)\n",
    "\n",
    "beta_val = 1\n",
    "\n",
    "accuracy_plot <- plot_bar(tb_metrics, 'acc', 'Accuracy')\n",
    "precision_plot <- plot_bar(tb_metrics, 'precision', 'Precision')\n",
    "recall_plot <- plot_bar(tb_metrics, 'recall', 'Recall')\n",
    "fbeta_plot <- plot_bar(tb_metrics, 'f_beta', paste('F -', beta_val, ' score'))\n",
    "\n",
    "grid.arrange(precision_plot, recall_plot,\n",
    "                accuracy_plot, fbeta_plot,\n",
    "                nrow=2, ncol=2,\n",
    "                padding = unit(5, 'cm'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 1200
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tp_plot <- plot_bar(tb_metrics, 'tp', paste('True Positives'))\n",
    "fn_plot <- plot_bar(tb_metrics, 'fn', paste('False Negatives'))\n",
    "fp_plot <- plot_bar(tb_metrics, 'fp', paste('False Positives'))\n",
    "tn_plot <- plot_bar(tb_metrics, 'tn', paste('True Negatives'))\n",
    "\n",
    "grid.arrange(tp_plot, fn_plot,\n",
    "                fp_plot, tn_plot,\n",
    "                nrow=2, ncol=2,\n",
    "                padding = unit(5, 'cm'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Heatmap : Full Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "plot_heatmap <- function(tb_compare, labels_descrip, normalize, dataset_num, plot_title='Heatmap', legend_title='Counts'){\n",
    "    \n",
    "        tb_ref <- tibble(True_Label = rep(labels_descrip$Labels, times = nrow(labels_descrip)), \n",
    "                        Pred_Label= rep(labels_descrip$Labels, each = nrow(labels_descrip)),\n",
    "                        True_Name = rep(labels_descrip$Name, times = nrow(labels_descrip)),\n",
    "                        Pred_Name= rep(labels_descrip$Name, each = nrow(labels_descrip)))\n",
    "\n",
    "        tb_counts <- tb_compare %>% \n",
    "                        group_by(across(all_of(c('True_Label', 'Pred_Label')))) %>%\n",
    "                        summarise(n = n())\n",
    "\n",
    "        tb_grid <- tb_ref %>% \n",
    "                        left_join(tb_counts, by=c('True_Label', 'Pred_Label')) %>%\n",
    "                        replace_na(list(n = 0)) %>%\n",
    "                        mutate(n_norm = round(n / sum(n), 3))\n",
    "\n",
    "        norm_str <- ifelse(normalize, 'n_norm', 'n')\n",
    "        main <- ggplot(tb_grid, aes(Pred_Name, True_Name, fill=!!sym(norm_str))) +\n",
    "                        geom_tile() +\n",
    "                        geom_text(aes(label=!!sym(norm_str)), size = 5, color = 'white') +\n",
    "                        theme_minimal() +\n",
    "\n",
    "                        #scale_fill_gradient(low='indianred3', high='royalblue1') +\n",
    "                        #scale_fill_gradient(low='lightsalmon3', high='royalblue1') +\n",
    "                        scale_fill_viridis(option='E') +\n",
    "                        #scale_fill_distiller(palette = 'RdBu', direction = 1) +\n",
    "\n",
    "                        theme(plot.title = element_text(hjust = 0, face='bold', size=17),\n",
    "                                legend.title = element_text(face='bold', margin = margin(b = 10, unit='pt')),\n",
    "                                axis.title = element_text(face='bold', size=14),\n",
    "                                axis.title.x = element_text(margin = margin(t = 20, unit = \"pt\")),\n",
    "                                axis.title.y = element_text(margin = margin(r = 20, unit = \"pt\")),\n",
    "                                axis.text = element_text(face=\"bold\", size=12) ) +\n",
    "\n",
    "                        labs(title = plot_title,\n",
    "                                x = '\\nPredicted Label',\n",
    "                                y = 'Actual Label\\n',\n",
    "                                fill = legend_title)\n",
    "        \n",
    "        if (dataset_num == 1){\n",
    "                main <- main + scale_x_discrete(limits = c('True', 'Mostly_True', 'Half_True', 'Barely_True', 'False', 'Not_Known'))\n",
    "        }\n",
    "        else if (dataset_num == 2){\n",
    "                main <- main + scale_x_discrete(limits = c('Unreliable', 'Reliable'))\n",
    "        }\n",
    "\n",
    "        return(main)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[22m`summarise()` has grouped output by 'True_Label'. You can override using the\n",
      "`.groups` argument.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=10, repr.plot.height=8)\n",
    "\n",
    "plot_heatmap(tb_valid_compare, labels_descrip, \n",
    "                dataset_num = 1,\n",
    "                normalize = FALSE,\n",
    "                plot_title = 'Heatmap: Validation Set',\n",
    "                legend_title = 'Counts')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[22m`summarise()` has grouped output by 'True_Label'. You can override using the\n",
      "`.groups` argument.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=10, repr.plot.height=8)\n",
    "\n",
    "plot_heatmap(tb_valid_compare, labels_descrip, \n",
    "                dataset_num = 1,\n",
    "                normalize = TRUE,\n",
    "                plot_title = 'Heatmap: Validation Set',\n",
    "                legend_title = 'Normalized \\n Counts')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running all for both Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "#For Both Datasets\n",
    "train_validate_model <- function(df_train_valid, labels_descrip, \n",
    "                                    dataset_num = 2,\n",
    "                                    split_perc = 0.8,               #Split percentage of train and valid sets\n",
    "                                    stem = TRUE,                    #Implement Stemming\n",
    "                                    lem = FALSE,                    #Implement Lemmatization\n",
    "                                    feature_selection = TRUE,       #If feature selection should be implmented\n",
    "                                    top_k = 1000,                   #Top k features to be selected\n",
    "                                    laplace_factor = 1,             #Laplace smoothing factor\n",
    "                                    keep_ties = FALSE,              #Keep ties for feature selection, prediction\n",
    "                                    remove_perc = 0.5,              #Remove document with high number of new words\n",
    "                                    fbeta_val = 1){                 #F_Beta value to be used for metric\n",
    "\n",
    "    #Splitting the Data\n",
    "    set.seed(100)\n",
    "\n",
    "    #Shuffles and splits the data by percentage\n",
    "    split_bool <- sample.split(df_train_valid$Text, SplitRatio = split_perc)    \n",
    "    df_train <- subset(df_train_valid, split_bool == TRUE)\n",
    "    df_valid <- subset(df_train_valid, split_bool == FALSE)\n",
    "\n",
    "    #print('Finished Splitting...')\n",
    "\n",
    "    tb_train <- prepare_tibble(df_train, dataset_num=dataset_num, factor_labels = TRUE, remove_tag = ifelse(dataset_num == 1, TRUE, FALSE))\n",
    "    tb_valid <- prepare_tibble(df_valid, dataset_num=dataset_num, factor_labels = TRUE, remove_tag = ifelse(dataset_num == 1, TRUE, FALSE))\n",
    "\n",
    "    #print('Finished Preparing Tibble...')\n",
    "\n",
    "    #Cleaning Data and getting vocabulary\n",
    "    tb_train_clean <- clean_tibble(tb_train, stem=stem, lem=lem)\n",
    "    vocab <- unique(tb_train_clean$word)\n",
    "\n",
    "    #print('Finished Tokenizing...')\n",
    "\n",
    "    # Feature Selection\n",
    "    if (feature_selection){\n",
    "        \n",
    "        tb_features <- implement_feature_selection(tb_train, tb_train_clean, vocab, \n",
    "                                                    top_k = top_k, \n",
    "                                                    laplace_factor = laplace_factor,\n",
    "                                                    keep_ties = keep_ties)\n",
    "\n",
    "\n",
    "        tb_train_clean_reduced <- tb_train_clean %>% inner_join(tb_features, by=c('Labels', 'word'))\n",
    "\n",
    "        #print('Finished Feature Selection...')\n",
    "\n",
    "        # Building and Training Model\n",
    "        mnb_model <- build_mnb_model(tb_train, tb_train_clean_reduced, laplace_factor = laplace_factor)\n",
    "    }\n",
    "    #No feature selection\n",
    "    else {  \n",
    "        # Building and Training Model\n",
    "        mnb_model <- build_mnb_model(tb_train, tb_train_clean, laplace_factor = laplace_factor)\n",
    "    }\n",
    "    \n",
    "    tb_priors <- mnb_model$prior\n",
    "    tb_cond <- mnb_model$cond\n",
    "    vocab <- mnb_model$vocab\n",
    "\n",
    "    #print('Finished Training Model...')\n",
    "\n",
    "    #Validating Model\n",
    "    tb_valid_preds <- predict_labels(tb_valid, tb_priors, tb_cond, \n",
    "                                        remove_perc = remove_perc, \n",
    "                                        keep_ties = keep_ties )\n",
    "    \n",
    "    #print('Finished Predicting...')\n",
    "\n",
    "    tb_valid_compare <- tb_valid %>%\n",
    "\n",
    "                            #Removes Text column, gets distinct rows\n",
    "                            select(document_id, Labels) %>%\n",
    "\n",
    "                            #Joins prediction tibble for comparison\n",
    "                            right_join(tb_valid_preds, by='document_id', multiple='all') %>%\n",
    "                            select(document_id, True_Label = Labels.x, Pred_Label = Labels.y)\n",
    "\n",
    "    \n",
    "    #Get metrics\n",
    "    tb_metrics <- get_metrics(tb_valid_compare, labels_descrip, beta_val=fbeta_val)   \n",
    "\n",
    "    #Add param values to tb_metrics\n",
    "    tb_param_metrics <- tibble(stem = rep(stem, nrow(tb_metrics)),\n",
    "                                lem = rep(lem, nrow(tb_metrics)),\n",
    "                                feat = rep(feature_selection, nrow(tb_metrics)), \n",
    "                                top_k = rep(top_k, nrow(tb_metrics)), \n",
    "                                keep_ties = rep(keep_ties, nrow(tb_metrics)),\n",
    "                                remove_perc = rep(remove_perc, nrow(tb_metrics))) %>%\n",
    "\n",
    "                                        #Add tb_metrics\n",
    "                                        add_column(tb_metrics) \n",
    "\n",
    "    #Add prior values to tb_param_metrics\n",
    "    tb_param_metrics <- tb_param_metrics %>% left_join(tb_priors, by='Labels')\n",
    "\n",
    "    #print('Finished Getting Metrics...')\n",
    "\n",
    "    return (list(metrics = tb_param_metrics, priors = tb_priors, conds = tb_cond, vocab = vocab))\n",
    "\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For First Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "#First Dataset: Loading the data\n",
    "df_train_valid <- read.csv('archive/train.csv')  \n",
    "df_test <- read.csv('archive/test.csv')\n",
    "\n",
    "#Assign names and reorder labels by assigning factor levels\n",
    "reorder_labels <- c(4, 1, 0, 2, 3, 5)\n",
    "\n",
    "labels <- factor(0:5, levels = reorder_labels)\n",
    "names <- c('Barely_True', 'False', 'Half_True', 'Mostly_True', 'Not_Known', 'True')\n",
    "\n",
    "labels_descrip <- tibble(Labels = labels, Name = factor(names, levels = names[reorder_labels + 1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plots the doc counts per label\n",
    "label_counts <- df_train_valid %>%\n",
    "                    group_by(Labels) %>%\n",
    "                    summarise(counts = n()) %>%\n",
    "                    mutate(Labels = factor(Labels, levels = reorder_labels)) %>%\n",
    "                    left_join(labels_descrip, by='Labels')\n",
    "\n",
    "ggplot(label_counts, aes(x = Name, y = counts)) +\n",
    "            theme_bw(base_size = 15) +\n",
    "            geom_bar(stat = 'identity', fill='darkseagreen', width=0.7) +\n",
    "            ggtitle('Documents in each Label (Train + Valid Set)') +\n",
    "            xlab('\\nLabel') +\n",
    "            ylab('Counts\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "#Implements a grid search over list of parameters\n",
    "run_count <- 0\n",
    "metric_list <- list()\n",
    "\n",
    "\n",
    "#Parameter values\n",
    "feat_values <- c(TRUE, FALSE)\n",
    "#stem_values <- c(TRUE, FALSE)\n",
    "#ties_values <- c(TRUE, FALSE)\n",
    "#remperc_values <- c(0.25, 0.5, 0.75)\n",
    "\n",
    "top_k_values <- c(10, 100, 500, 750, 1000, 1700, 2500, 3750, 5000, 6250, 7000, 7500, 8750, 9500, 10000)\n",
    "\n",
    "param_list <- tibble(feat = rep(feat_values, each=length(top_k_values)), \n",
    "                        #perc = rep(remperc_values, each=length(top_k_values)),\n",
    "                        #stem = rep(stem_values, each=length(top_k_values)),\n",
    "                        #ties = rep(ties_values, each=length(top_k_values)),\n",
    "                        top_k = rep(top_k_values, times=length(feat_values)))\n",
    "\n",
    "#Grid Search\n",
    "for (i in 1:dim(param_list)[1]){\n",
    "\n",
    "    run_count <- run_count + 1\n",
    "    message(paste('\\nStarted run : ', run_count))\n",
    "\n",
    "    suppressMessages({\n",
    "\n",
    "        model_results <- train_validate_model(df_train_valid, labels_descrip,\n",
    "                                                dataset_num = 1,\n",
    "                                                split_perc = 0.8,\n",
    "                                                stem = TRUE,\n",
    "                                                lem = FALSE,\n",
    "                                                feature_selection = param_list[i, ]$feat,\n",
    "                                                top_k = param_list[i, ]$top_k,                                                \n",
    "                                                laplace_factor = 1,\n",
    "                                                keep_ties = FALSE,\n",
    "                                                remove_perc = 0.75,\n",
    "                                                fbeta_val = 1)\n",
    "\n",
    "    })\n",
    "\n",
    "    metric_list <- c(list(model_results$metrics), metric_list)\n",
    "    if (run_count == 1){ tb_joined <- model_results$metrics }\n",
    "    else { tb_joined <- tb_joined %>% add_row(model_results$metrics)}\n",
    "\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[22m`summarise()` has grouped output by 'remove_perc'. You can override using the\n",
      "`.groups` argument.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A grouped_df: 3 × 9</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>remove_perc</th><th scope=col>top_k</th><th scope=col>micro_acc</th><th scope=col>macro_weigh_acc</th><th scope=col>macro_weigh_precision</th><th scope=col>macro_weigh_recall</th><th scope=col>macro_weigh_fbeta</th><th scope=col>macro_acc</th><th scope=col>macro_fbeta</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.25</td><td> 10</td><td>0.2352941</td><td>0.1241112</td><td>       NaN</td><td>0.04288364</td><td>0.03524701</td><td>0.7450980</td><td>0.2075264</td></tr>\n",
       "\t<tr><td>0.25</td><td>100</td><td>0.1864686</td><td>0.1178379</td><td>0.03259344</td><td>0.03172340</td><td>0.03050952</td><td>0.7288229</td><td>0.1766200</td></tr>\n",
       "\t<tr><td>0.25</td><td>500</td><td>0.2064808</td><td>0.1188873</td><td>0.03728377</td><td>0.03529609</td><td>0.03290667</td><td>0.7354936</td><td>0.1874906</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A grouped\\_df: 3 × 9\n",
       "\\begin{tabular}{lllllllll}\n",
       " remove\\_perc & top\\_k & micro\\_acc & macro\\_weigh\\_acc & macro\\_weigh\\_precision & macro\\_weigh\\_recall & macro\\_weigh\\_fbeta & macro\\_acc & macro\\_fbeta\\\\\n",
       " <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 0.25 &  10 & 0.2352941 & 0.1241112 &        NaN & 0.04288364 & 0.03524701 & 0.7450980 & 0.2075264\\\\\n",
       "\t 0.25 & 100 & 0.1864686 & 0.1178379 & 0.03259344 & 0.03172340 & 0.03050952 & 0.7288229 & 0.1766200\\\\\n",
       "\t 0.25 & 500 & 0.2064808 & 0.1188873 & 0.03728377 & 0.03529609 & 0.03290667 & 0.7354936 & 0.1874906\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A grouped_df: 3 × 9\n",
       "\n",
       "| remove_perc &lt;dbl&gt; | top_k &lt;dbl&gt; | micro_acc &lt;dbl&gt; | macro_weigh_acc &lt;dbl&gt; | macro_weigh_precision &lt;dbl&gt; | macro_weigh_recall &lt;dbl&gt; | macro_weigh_fbeta &lt;dbl&gt; | macro_acc &lt;dbl&gt; | macro_fbeta &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| 0.25 |  10 | 0.2352941 | 0.1241112 |        NaN | 0.04288364 | 0.03524701 | 0.7450980 | 0.2075264 |\n",
       "| 0.25 | 100 | 0.1864686 | 0.1178379 | 0.03259344 | 0.03172340 | 0.03050952 | 0.7288229 | 0.1766200 |\n",
       "| 0.25 | 500 | 0.2064808 | 0.1188873 | 0.03728377 | 0.03529609 | 0.03290667 | 0.7354936 | 0.1874906 |\n",
       "\n"
      ],
      "text/plain": [
       "  remove_perc top_k micro_acc macro_weigh_acc macro_weigh_precision\n",
       "1 0.25         10   0.2352941 0.1241112              NaN           \n",
       "2 0.25        100   0.1864686 0.1178379       0.03259344           \n",
       "3 0.25        500   0.2064808 0.1188873       0.03728377           \n",
       "  macro_weigh_recall macro_weigh_fbeta macro_acc macro_fbeta\n",
       "1 0.04288364         0.03524701        0.7450980 0.2075264  \n",
       "2 0.03172340         0.03050952        0.7288229 0.1766200  \n",
       "3 0.03529609         0.03290667        0.7354936 0.1874906  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Group by changing parameters, and get micro, macro metric values\n",
    "tb_reduced <- tb_joined %>% \n",
    "                group_by(across(all_of(c('feat', 'top_k')))) %>%\n",
    "                summarise(micro_acc = mean(total_acc), \n",
    "                            macro_weigh_acc = sum(acc * prior) / n(),\n",
    "                            macro_weigh_precision = sum(precision * prior) / n(),\n",
    "                            macro_weigh_recall = sum(recall * prior) / n(),\n",
    "                            macro_weigh_fbeta = sum(f_beta * prior) / n(),\n",
    "                            macro_acc = mean(acc),\n",
    "                            macro_fbeta = mean(f_beta))\n",
    "\n",
    "head(tb_reduced, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "options(repr.plot.height=8, repr.plot.width=13)\n",
    "\n",
    "#Line Plot for comparison between parameter evolutions( Eg.: Number of Features vs Mico Accuracy)\n",
    "ggplot(tb_reduced, aes(x=top_k, y=macro_fbeta, col=feat, fill=feat)) +\n",
    "        geom_line(size = 1) +\n",
    "        geom_point(size = 2.5, color='black') +\n",
    "        theme_bw(base_size = 15) +\n",
    "        \n",
    "        theme(plot.title = element_text(hjust = 0.5, face='bold', size=19),\n",
    "                plot.subtitle = element_text(hjust = 0.5, size=15),\n",
    "                legend.title = element_text(face='bold'),\n",
    "                axis.title = element_text(face='bold')) +\n",
    "\n",
    "        labs(x = '\\nNumber of Features', \n",
    "                y = 'Marco - F-1 Score\\n',\n",
    "                title = '\\nMarco - F-1 Score vs Number of Features\\n',\n",
    "                #subtitle = 'Comparison',\n",
    "                fill = 'Feature Selection',\n",
    "                col = 'Feature Selection'\n",
    "                )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 1200
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width = 20, repr.plot.height = 8)\n",
    "\n",
    "fbeta_val <- 1\n",
    "\n",
    "#Plot Bar plots of metric values for each label\n",
    "for (tb in metric_list){\n",
    "\n",
    "    accuracy_plot <- plot_bar(tb, 'acc', 'Accuracy')\n",
    "    precision_plot <- plot_bar(tb, 'precision', 'Precision')\n",
    "    recall_plot <- plot_bar(tb, 'recall', 'Recall')\n",
    "    fbeta_plot <- plot_bar(tb, 'f_beta', paste('F -', fbeta_val, ' score'))\n",
    "\n",
    "    grid.arrange(precision_plot, recall_plot,\n",
    "                    accuracy_plot, fbeta_plot,\n",
    "                    nrow=2, ncol=2,\n",
    "                    padding = unit(5, 'cm'))\n",
    "\n",
    "}\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For Second Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "#Second Dataset: Loading the Data\n",
    "df_train_valid <- read.csv('E:/new_dataset/train.csv')\n",
    "df_test <- read.csv('E:/new_dataset/test.csv')\n",
    "\n",
    "#Get the train_valid and test sets\n",
    "df_train_valid <- df_train_valid %>% select(document_id = id, Labels = label, Text = text)\n",
    "df_test <- df_test %>% select(document_id = id, Text = text)\n",
    "\n",
    "#Reorder the labels by assigning corresponding factor levels\n",
    "reorder_labels <- c(0, 1)\n",
    "labels <- factor(c(0, 1), levels = reorder_labels)\n",
    "names <- c('Reliable', 'Unreliable')\n",
    "\n",
    "labels_descrip <- tibble(Labels = labels, Name = factor(names, levels = names[reorder_labels + 1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plots the doc counts per label\n",
    "label_counts <- df_train_valid %>%\n",
    "                    group_by(Labels) %>%\n",
    "                    summarise(counts = n()) %>%\n",
    "                    mutate(Labels = factor(Labels, levels = reorder_labels)) %>%\n",
    "                    left_join(labels_descrip, by='Labels')\n",
    "\n",
    "ggplot(label_counts, aes(x = Name, y = counts)) +\n",
    "            theme_bw(base_size = 15) +\n",
    "            geom_bar(stat = 'identity', fill='darkseagreen', width=0.5) +\n",
    "            ggtitle('Documents in each Label (Train + Valid Set)') +\n",
    "            xlab('\\nLabel') +\n",
    "            ylab('Counts\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "#Single Run \n",
    "suppressMessages({\n",
    "\n",
    "    model_results <- train_validate_model(df_train_valid, labels_descrip,\n",
    "                                            dataset_num = 2,\n",
    "                                            split_perc = 0.8,\n",
    "                                            stem = TRUE,\n",
    "                                            lem = FALSE,\n",
    "                                            feature_selection = TRUE,\n",
    "                                            top_k = 20000,\n",
    "                                            laplace_factor = 1,\n",
    "                                            keep_ties = FALSE,\n",
    "                                            remove_perc = 0.75,\n",
    "                                            fbeta_val = 1)\n",
    "\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 2 × 18</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>stem</th><th scope=col>lem</th><th scope=col>feat</th><th scope=col>top_k</th><th scope=col>keep_ties</th><th scope=col>remove_perc</th><th scope=col>Labels</th><th scope=col>Name</th><th scope=col>tp</th><th scope=col>fp</th><th scope=col>fn</th><th scope=col>tn</th><th scope=col>total_acc</th><th scope=col>acc</th><th scope=col>precision</th><th scope=col>recall</th><th scope=col>f_beta</th><th scope=col>prior</th></tr>\n",
       "\t<tr><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>TRUE</td><td>FALSE</td><td>TRUE</td><td>20000</td><td>FALSE</td><td>0.75</td><td>0</td><td>Reliable  </td><td>2095</td><td>1399</td><td>   1</td><td> 631</td><td>0.6606883</td><td>0.6606883</td><td>0.5995993</td><td>0.9995229</td><td>0.7495528</td><td>0.4981971</td></tr>\n",
       "\t<tr><td>TRUE</td><td>FALSE</td><td>TRUE</td><td>20000</td><td>FALSE</td><td>0.75</td><td>1</td><td>Unreliable</td><td> 631</td><td>   1</td><td>1399</td><td>2095</td><td>0.6606883</td><td>0.6606883</td><td>0.9984177</td><td>0.3108374</td><td>0.4740796</td><td>0.5018029</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 2 × 18\n",
       "\\begin{tabular}{llllllllllllllllll}\n",
       " stem & lem & feat & top\\_k & keep\\_ties & remove\\_perc & Labels & Name & tp & fp & fn & tn & total\\_acc & acc & precision & recall & f\\_beta & prior\\\\\n",
       " <lgl> & <lgl> & <lgl> & <dbl> & <lgl> & <dbl> & <fct> & <fct> & <int> & <int> & <int> & <int> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t TRUE & FALSE & TRUE & 20000 & FALSE & 0.75 & 0 & Reliable   & 2095 & 1399 &    1 &  631 & 0.6606883 & 0.6606883 & 0.5995993 & 0.9995229 & 0.7495528 & 0.4981971\\\\\n",
       "\t TRUE & FALSE & TRUE & 20000 & FALSE & 0.75 & 1 & Unreliable &  631 &    1 & 1399 & 2095 & 0.6606883 & 0.6606883 & 0.9984177 & 0.3108374 & 0.4740796 & 0.5018029\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 2 × 18\n",
       "\n",
       "| stem &lt;lgl&gt; | lem &lt;lgl&gt; | feat &lt;lgl&gt; | top_k &lt;dbl&gt; | keep_ties &lt;lgl&gt; | remove_perc &lt;dbl&gt; | Labels &lt;fct&gt; | Name &lt;fct&gt; | tp &lt;int&gt; | fp &lt;int&gt; | fn &lt;int&gt; | tn &lt;int&gt; | total_acc &lt;dbl&gt; | acc &lt;dbl&gt; | precision &lt;dbl&gt; | recall &lt;dbl&gt; | f_beta &lt;dbl&gt; | prior &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| TRUE | FALSE | TRUE | 20000 | FALSE | 0.75 | 0 | Reliable   | 2095 | 1399 |    1 |  631 | 0.6606883 | 0.6606883 | 0.5995993 | 0.9995229 | 0.7495528 | 0.4981971 |\n",
       "| TRUE | FALSE | TRUE | 20000 | FALSE | 0.75 | 1 | Unreliable |  631 |    1 | 1399 | 2095 | 0.6606883 | 0.6606883 | 0.9984177 | 0.3108374 | 0.4740796 | 0.5018029 |\n",
       "\n"
      ],
      "text/plain": [
       "  stem lem   feat top_k keep_ties remove_perc Labels Name       tp   fp   fn  \n",
       "1 TRUE FALSE TRUE 20000 FALSE     0.75        0      Reliable   2095 1399    1\n",
       "2 TRUE FALSE TRUE 20000 FALSE     0.75        1      Unreliable  631    1 1399\n",
       "  tn   total_acc acc       precision recall    f_beta    prior    \n",
       "1  631 0.6606883 0.6606883 0.5995993 0.9995229 0.7495528 0.4981971\n",
       "2 2095 0.6606883 0.6606883 0.9984177 0.3108374 0.4740796 0.5018029"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_results$metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "#Implement a grid search over parameter values\n",
    "run_count <- 0\n",
    "metric_list <- list()\n",
    "\n",
    "#Parameter values\n",
    "feat_values <- c(TRUE)\n",
    "stem_values <- c(TRUE, FALSE)\n",
    "ties_values <- c(TRUE, FALSE)\n",
    "remperc_values <- c(0.25, 0.5, 0.75)\n",
    "top_k_values <- c(10, 100, 500, 750, 1000, 1700, 2500, 3750, 5000, 6250, 7000, 7500, 8750, 9500, 10000)\n",
    "\n",
    "param_list <- tibble(feat = rep(feat_values, each=length(top_k_values)), \n",
    "                        #perc = rep(remperc_values, each=length(top_k_values)),\n",
    "                        #stem = rep(stem_values, each=length(top_k_values)),\n",
    "                        #ties = rep(ties_values, each=length(top_k_values)),\n",
    "                        top_k = rep(top_k_values, times=length(feat_values)))\n",
    "\n",
    "#Grid Search over parameter values\n",
    "for (i in 1:dim(param_list)[1]){\n",
    "\n",
    "    run_count <- run_count + 1\n",
    "    message(paste('\\nStarted run : ', run_count))\n",
    "\n",
    "    suppressMessages({\n",
    "\n",
    "        model_results <- train_validate_model(df_train_valid, labels_descrip,\n",
    "                                                dataset_num = 2,\n",
    "                                                split_perc = 0.8,\n",
    "                                                stem = TRUE,\n",
    "                                                lem = FALSE,\n",
    "                                                feature_selection = param_list[i, ]$feat,\n",
    "                                                top_k = param_list[i, ]$top_k,\n",
    "                                                laplace_factor = 1,\n",
    "                                                keep_ties = FALSE,\n",
    "                                                remove_perc = 0.75,\n",
    "                                                fbeta_val = 1)\n",
    "\n",
    "    })\n",
    "\n",
    "    metric_list <- c(list(model_results$metrics), metric_list)\n",
    "    if (run_count == 1){ tb_joined <- model_results$metrics }\n",
    "    else { tb_joined <- tb_joined %>% add_row(model_results$metrics)}\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[22m`summarise()` has grouped output by 'feat'. You can override using the\n",
      "`.groups` argument.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A grouped_df: 2 × 9</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>feat</th><th scope=col>top_k</th><th scope=col>micro_acc</th><th scope=col>macro_weigh_acc</th><th scope=col>macro_weigh_precision</th><th scope=col>macro_weigh_recall</th><th scope=col>macro_weigh_fbeta</th><th scope=col>macro_acc</th><th scope=col>macro_fbeta</th></tr>\n",
       "\t<tr><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>FALSE</td><td>20000</td><td>0.6860184</td><td>0.3430092</td><td>0.4041221</td><td>0.3401315</td><td>0.3239367</td><td>0.6860184</td><td>0.6482887</td></tr>\n",
       "\t<tr><td> TRUE</td><td>20000</td><td>0.6606883</td><td>0.3303442</td><td>0.3998638</td><td>0.3269693</td><td>0.3056598</td><td>0.6606883</td><td>0.6118162</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A grouped\\_df: 2 × 9\n",
       "\\begin{tabular}{lllllllll}\n",
       " feat & top\\_k & micro\\_acc & macro\\_weigh\\_acc & macro\\_weigh\\_precision & macro\\_weigh\\_recall & macro\\_weigh\\_fbeta & macro\\_acc & macro\\_fbeta\\\\\n",
       " <lgl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t FALSE & 20000 & 0.6860184 & 0.3430092 & 0.4041221 & 0.3401315 & 0.3239367 & 0.6860184 & 0.6482887\\\\\n",
       "\t  TRUE & 20000 & 0.6606883 & 0.3303442 & 0.3998638 & 0.3269693 & 0.3056598 & 0.6606883 & 0.6118162\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A grouped_df: 2 × 9\n",
       "\n",
       "| feat &lt;lgl&gt; | top_k &lt;dbl&gt; | micro_acc &lt;dbl&gt; | macro_weigh_acc &lt;dbl&gt; | macro_weigh_precision &lt;dbl&gt; | macro_weigh_recall &lt;dbl&gt; | macro_weigh_fbeta &lt;dbl&gt; | macro_acc &lt;dbl&gt; | macro_fbeta &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| FALSE | 20000 | 0.6860184 | 0.3430092 | 0.4041221 | 0.3401315 | 0.3239367 | 0.6860184 | 0.6482887 |\n",
       "|  TRUE | 20000 | 0.6606883 | 0.3303442 | 0.3998638 | 0.3269693 | 0.3056598 | 0.6606883 | 0.6118162 |\n",
       "\n"
      ],
      "text/plain": [
       "  feat  top_k micro_acc macro_weigh_acc macro_weigh_precision\n",
       "1 FALSE 20000 0.6860184 0.3430092       0.4041221            \n",
       "2  TRUE 20000 0.6606883 0.3303442       0.3998638            \n",
       "  macro_weigh_recall macro_weigh_fbeta macro_acc macro_fbeta\n",
       "1 0.3401315          0.3239367         0.6860184 0.6482887  \n",
       "2 0.3269693          0.3056598         0.6606883 0.6118162  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Group by changing parameters, and get micro, macro metric values\n",
    "tb_reduced <- tb_joined %>% \n",
    "                group_by(across(all_of(c('feat', 'top_k')))) %>%\n",
    "                summarise(micro_acc = mean(total_acc), \n",
    "                            macro_weigh_acc = sum(acc * prior) / n(),\n",
    "                            macro_weigh_precision = sum(precision * prior) / n(),\n",
    "                            macro_weigh_recall = sum(recall * prior) / n(),\n",
    "                            macro_weigh_fbeta = sum(f_beta * prior) / n(),\n",
    "                            macro_acc = mean(acc),\n",
    "                            macro_fbeta = mean(f_beta))\n",
    "\n",
    "head(tb_reduced, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "options(repr.plot.height=8, repr.plot.width=13)\n",
    "\n",
    "#Line Plot for comparison between parameter evolutions( Eg.: Number of Features vs Mico Accuracy)\n",
    "ggplot(tb_reduced, aes(x=top_k, y=macro_fbeta, col=feat, fill=feat)) +\n",
    "        geom_line(size = 1) +\n",
    "        geom_point(size = 2.5, color='black') +\n",
    "        theme_bw(base_size = 15) +\n",
    "        \n",
    "        theme(plot.title = element_text(hjust = 0.5, face='bold', size=19),\n",
    "                plot.subtitle = element_text(hjust = 0.5, size=15),\n",
    "                legend.title = element_text(face='bold'),\n",
    "                axis.title = element_text(face='bold')) +\n",
    "\n",
    "        labs(x = '\\nNumber of Features', \n",
    "                y = 'Accuracy\\n',\n",
    "                title = '\\nAccuracy vs Number of Features\\n',\n",
    "                #subtitle = 'Comparison',\n",
    "                fill = 'Feature Selection',\n",
    "                col = 'Feature Selection'\n",
    "                )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 1200
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 1200
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width = 20, repr.plot.height = 8)\n",
    "\n",
    "fbeta_val <- 1\n",
    "\n",
    "#Plot Bar plots of metric values for each label\n",
    "for (tb in metric_list){\n",
    "\n",
    "    accuracy_plot <- plot_bar(tb, 'acc', 'Accuracy')\n",
    "    precision_plot <- plot_bar(tb, 'precision', 'Precision')\n",
    "    recall_plot <- plot_bar(tb, 'recall', 'Recall')\n",
    "    fbeta_plot <- plot_bar(tb, 'f_beta', paste('F -', fbeta_val, ' score'))\n",
    "\n",
    "    grid.arrange(precision_plot, recall_plot,\n",
    "                    accuracy_plot, fbeta_plot,\n",
    "                    nrow=2, ncol=2,\n",
    "                    padding = unit(5, 'cm'))\n",
    "\n",
    "}"
   ]
  }
 ],
 "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": "4.2.2"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}