{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustering - pam\n",
    "## Libraries and Datasets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: daltoolbox\n",
      "\n",
      "Registered S3 method overwritten by 'quantmod':\n",
      "  method            from\n",
      "  as.zoo.data.frame zoo \n",
      "\n",
      "\n",
      "Attaching package: ‘daltoolbox’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:base’:\n",
      "\n",
      "    transform\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# DAL ToolBox\n",
    "# version 1.01.727\n",
    "\n",
    "source(\"https://raw.githubusercontent.com/cefet-rj-dal/daltoolbox/main/jupyter.R\")\n",
    "\n",
    "#loading DAL\n",
    "load_library(\"daltoolbox\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load dataset\n",
    "data(iris)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "General entropy of dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## General function to test clustering methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# setup clustering\n",
    "model <- cluster_pam(k=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "clu\n",
       " 1  2  3 \n",
       "50 62 38 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# build model\n",
    "model <- fit(model, iris[,1:4])\n",
    "clu <- cluster(model, iris[,1:4])\n",
    "table(clu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$clusters_entropy</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A tibble: 3 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>x</th><th scope=col>ce</th><th scope=col>qtd</th><th scope=col>ceg</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1</td><td>0.0000000</td><td>50</td><td>0.00000000</td></tr>\n",
       "\t<tr><td>2</td><td>0.7706291</td><td>62</td><td>0.31852668</td></tr>\n",
       "\t<tr><td>3</td><td>0.2974722</td><td>38</td><td>0.07535964</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$clustering_entropy</dt>\n",
       "\t\t<dd>0.393886318396649</dd>\n",
       "\t<dt>$data_entropy</dt>\n",
       "\t\t<dd>1.58496250072116</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$clusters\\_entropy] A tibble: 3 × 4\n",
       "\\begin{tabular}{llll}\n",
       " x & ce & qtd & ceg\\\\\n",
       " <fct> & <dbl> & <int> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 1 & 0.0000000 & 50 & 0.00000000\\\\\n",
       "\t 2 & 0.7706291 & 62 & 0.31852668\\\\\n",
       "\t 3 & 0.2974722 & 38 & 0.07535964\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$clustering\\_entropy] 0.393886318396649\n",
       "\\item[\\$data\\_entropy] 1.58496250072116\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$clusters_entropy\n",
       ":   \n",
       "A tibble: 3 × 4\n",
       "\n",
       "| x &lt;fct&gt; | ce &lt;dbl&gt; | qtd &lt;int&gt; | ceg &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| 1 | 0.0000000 | 50 | 0.00000000 |\n",
       "| 2 | 0.7706291 | 62 | 0.31852668 |\n",
       "| 3 | 0.2974722 | 38 | 0.07535964 |\n",
       "\n",
       "\n",
       "$clustering_entropy\n",
       ":   0.393886318396649\n",
       "$data_entropy\n",
       ":   1.58496250072116\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "$clusters_entropy\n",
       "\u001b[90m# A tibble: 3 × 4\u001b[39m\n",
       "  x        ce   qtd    ceg\n",
       "  \u001b[3m\u001b[90m<fct>\u001b[39m\u001b[23m \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m \u001b[3m\u001b[90m<int>\u001b[39m\u001b[23m  \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m\n",
       "\u001b[90m1\u001b[39m 1     0        50 0     \n",
       "\u001b[90m2\u001b[39m 2     0.771    62 0.319 \n",
       "\u001b[90m3\u001b[39m 3     0.297    38 0.075\u001b[4m4\u001b[24m\n",
       "\n",
       "$clustering_entropy\n",
       "[1] 0.3938863\n",
       "\n",
       "$data_entropy\n",
       "[1] 1.584963\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# evaluate model using external metric\n",
    "eval <- evaluate(model, clu, iris$Species)\n",
    "eval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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