4_Imunologic/Imunologic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import ConfusionMatrixDisplay, classification_report, accuracy_score\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.tree import DecisionTreeClassifier, plot_tree, export_graphviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "# from tensorflow import keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Obtained through: [Zoo Dataset](https://archive.ics.uci.edu/dataset/111/zoo)\n",
    "- Information:\n",
    "A simple database containing 17 Boolean-valued attributes.  The \"type\" attribute appears to be the class attribute.  Here is a breakdown of which animals are in which type: (I find it unusual that there are 2 instances of \"frog\" and one of \"girl\"!)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Forsyth, R. (1990). Zoo [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5R59V."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imporing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aardvark</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>antelope</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bear</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>boar</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         0   1   2   3   4   5   6   7   8   9   10  11  12  13  14  15  16  \\\n",
       "0  aardvark   1   0   0   1   0   0   1   1   1   1   0   0   4   0   0   1   \n",
       "1  antelope   1   0   0   1   0   0   0   1   1   1   0   0   4   1   0   1   \n",
       "2      bass   0   0   1   0   0   1   1   1   1   0   0   1   0   1   0   0   \n",
       "3      bear   1   0   0   1   0   0   1   1   1   1   0   0   4   0   0   1   \n",
       "4      boar   1   0   0   1   0   0   1   1   1   1   0   0   4   1   0   1   \n",
       "\n",
       "   17  \n",
       "0   1  \n",
       "1   1  \n",
       "2   4  \n",
       "3   1  \n",
       "4   1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('zoo/zoo.data', header=None)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "header = {\n",
    "    \"0\" : \"animal_name\",\n",
    "    \"1\" : \"hair\",\n",
    "    \"2\" : \"feathers\",\n",
    "    \"3\" : \"eggs\",\n",
    "    \"4\" : \"milk\",\n",
    "    \"5\" : \"airborne\",\n",
    "    \"6\" : \"aquatic\",\n",
    "    \"7\" : \"predator\",\n",
    "    \"8\" : \"toothed\",\n",
    "    \"9\" : \"backbone\",\n",
    "    \"10\" : \"breathes\",\n",
    "    \"11\" : \"venomous\",\n",
    "    \"12\" : \"fins\",\n",
    "    \"13\" : \"legs\",\n",
    "    \"14\" : \"tail\",\n",
    "    \"15\" : \"domestic\",\n",
    "    \"16\" : \"catsize\",\n",
    "    \"17\" : \"type\"\n",
    "}\n",
    "heade = {}\n",
    "for x in range(len(df.columns)):\n",
    "    heade[x] = header[str(x)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>animal_name</th>\n",
       "      <th>hair</th>\n",
       "      <th>feathers</th>\n",
       "      <th>eggs</th>\n",
       "      <th>milk</th>\n",
       "      <th>airborne</th>\n",
       "      <th>aquatic</th>\n",
       "      <th>predator</th>\n",
       "      <th>toothed</th>\n",
       "      <th>backbone</th>\n",
       "      <th>breathes</th>\n",
       "      <th>venomous</th>\n",
       "      <th>fins</th>\n",
       "      <th>legs</th>\n",
       "      <th>tail</th>\n",
       "      <th>domestic</th>\n",
       "      <th>catsize</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aardvark</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>antelope</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bear</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>boar</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  animal_name  hair  feathers  eggs  milk  airborne  aquatic  predator  \\\n",
       "0    aardvark     1         0     0     1         0        0         1   \n",
       "1    antelope     1         0     0     1         0        0         0   \n",
       "2        bass     0         0     1     0         0        1         1   \n",
       "3        bear     1         0     0     1         0        0         1   \n",
       "4        boar     1         0     0     1         0        0         1   \n",
       "\n",
       "   toothed  backbone  breathes  venomous  fins  legs  tail  domestic  catsize  \\\n",
       "0        1         1         1         0     0     4     0         0        1   \n",
       "1        1         1         1         0     0     4     1         0        1   \n",
       "2        1         1         0         0     1     0     1         0        0   \n",
       "3        1         1         1         0     0     4     0         0        1   \n",
       "4        1         1         1         0     0     4     1         0        1   \n",
       "\n",
       "   type  \n",
       "0     1  \n",
       "1     1  \n",
       "2     4  \n",
       "3     1  \n",
       "4     1  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(columns=heade, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "animal_name    0\n",
       "hair           0\n",
       "feathers       0\n",
       "eggs           0\n",
       "milk           0\n",
       "airborne       0\n",
       "aquatic        0\n",
       "predator       0\n",
       "toothed        0\n",
       "backbone       0\n",
       "breathes       0\n",
       "venomous       0\n",
       "fins           0\n",
       "legs           0\n",
       "tail           0\n",
       "domestic       0\n",
       "catsize        0\n",
       "type           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df.info()\n",
    "# df.dtypes\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "1    41\n",
       "2    20\n",
       "4    13\n",
       "7    10\n",
       "6     8\n",
       "3     5\n",
       "5     4\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['type'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Binary Data type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>animal_name</th>\n",
       "      <th>hair</th>\n",
       "      <th>feathers</th>\n",
       "      <th>eggs</th>\n",
       "      <th>milk</th>\n",
       "      <th>airborne</th>\n",
       "      <th>aquatic</th>\n",
       "      <th>predator</th>\n",
       "      <th>toothed</th>\n",
       "      <th>backbone</th>\n",
       "      <th>breathes</th>\n",
       "      <th>venomous</th>\n",
       "      <th>fins</th>\n",
       "      <th>legs</th>\n",
       "      <th>tail</th>\n",
       "      <th>domestic</th>\n",
       "      <th>catsize</th>\n",
       "      <th>type</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   animal_name  hair  feathers  eggs  milk  airborne  aquatic  predator  \\\n",
       "0            1     1         0     0     1         0        0         1   \n",
       "1            1     1         0     0     1         0        0         0   \n",
       "2            1     0         0     1     0         0        1         1   \n",
       "3            1     1         0     0     1         0        0         1   \n",
       "4            1     1         0     0     1         0        0         1   \n",
       "\n",
       "   toothed  backbone  breathes  venomous  fins  legs  tail  domestic  catsize  \\\n",
       "0        1         1         1         0     0     1     0         0        1   \n",
       "1        1         1         1         0     0     1     1         0        1   \n",
       "2        1         1         0         0     1     0     1         0        0   \n",
       "3        1         1         1         0     0     1     0         0        1   \n",
       "4        1         1         1         0     0     1     1         0        1   \n",
       "\n",
       "   type  \n",
       "0     1  \n",
       "1     1  \n",
       "2     1  \n",
       "3     1  \n",
       "4     1  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# transfor he types of data to boolean\n",
    "binary_df = df.astype('bool').astype('int')\n",
    "binary_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split Data: train & test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = binary_df.drop(['animal_name', 'type'], axis=1).copy()\n",
    "y = df[['type']].copy()\n",
    "y = y[[\"type\"]]-1 # to start from 0\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16\n",
      "[0 1 2 3 4 5 6]\n"
     ]
    }
   ],
   "source": [
    "classes = y[\"type\"].unique()\n",
    "classes.sort()\n",
    "print(len(X.columns))\n",
    "print(classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Class names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names = {\n",
    "    1: \"Mammal\",\n",
    "    2: \"Bird\",\n",
    "    3: \"Reptile\",\n",
    "    4: \"Fish\",\n",
    "    5: \"Amphibian\",\n",
    "    6: \"Bug\",\n",
    "    7: \"Invertebrate\"\n",
    "}\n",
    "class_names = {\n",
    "    0: \"Mammal\",\n",
    "    1: \"Bird\",\n",
    "    2: \"Reptile\",\n",
    "    3: \"Fish\",\n",
    "    4: \"Amphibian\",\n",
    "    5: \"Bug\",\n",
    "    6: \"Invertebrate\"\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ML Aproach Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Scikit-Learn Clustering](https://scikit-learn.org/stable/modules/clustering.html#)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "number_of_classes = len(class_names)\n",
    "number_of_classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cluster Kmeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\JR-Ma\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\joblib\\externals\\loky\\backend\\context.py:136: UserWarning: Could not find the number of physical cores for the following reason:\n",
      "found 0 physical cores < 1\n",
      "Returning the number of logical cores instead. You can silence this warning by setting LOKY_MAX_CPU_COUNT to the number of cores you want to use.\n",
      "  warnings.warn(\n",
      "  File \"c:\\Users\\JR-Ma\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\joblib\\externals\\loky\\backend\\context.py\", line 282, in _count_physical_cores\n",
      "    raise ValueError(f\"found {cpu_count_physical} physical cores < 1\")\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(max_iter=500, n_clusters=7, n_init=8, random_state=42, verbose=False)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(max_iter=500, n_clusters=7, n_init=8, random_state=42, verbose=False)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KMeans(max_iter=500, n_clusters=7, n_init=8, random_state=42, verbose=False)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=7,\n",
    "                n_init=8,\n",
    "                max_iter=500,\n",
    "                verbose=False,\n",
    "                random_state=42,\n",
    "                algorithm=\"lloyd\")\n",
    "\n",
    "kmeans.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quantidade  (101,)\n",
      "Classes Definidas:  [0 1 2 3 4 5 6]\n",
      "Quantidade de animais em cada Classe:  [14 22 11 21 19  7  7]\n"
     ]
    }
   ],
   "source": [
    "values, counts = np.unique(kmeans.labels_, return_counts=True)\n",
    "\n",
    "print(\"Quantidade \", kmeans.labels_.shape)\n",
    "print(\"Classes Definidas: \", values)\n",
    "print(\"Quantidade de animais em cada Classe: \", counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>animal_name</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aardvark</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>antelope</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bass</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bear</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>boar</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  animal_name  type\n",
       "0    aardvark     1\n",
       "1    antelope     1\n",
       "2        bass     4\n",
       "3        bear     1\n",
       "4        boar     1"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"animal_name\", \"type\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>animal_name</th>\n",
       "      <th>type</th>\n",
       "      <th>pre_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aardvark</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>antelope</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bass</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bear</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>boar</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  animal_name  type  pre_type\n",
       "0    aardvark     1         1\n",
       "1    antelope     1         4\n",
       "2        bass     4         0\n",
       "3        bear     1         1\n",
       "4        boar     1         1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['pre_type'] = kmeans.predict(X) # Alocar as Classes no dataset\n",
    "df[[\"animal_name\", \"type\", \"pre_type\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pre_type\n",
       "1    22\n",
       "3    21\n",
       "4    19\n",
       "0    14\n",
       "2    11\n",
       "5     7\n",
       "6     7\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"pre_type\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "      <th>type</th>\n",
       "      <th>freq</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    pre_type  type  freq\n",
       "0          0     3     1\n",
       "1          0     4    13\n",
       "2          1     1    22\n",
       "3          2     6     8\n",
       "4          2     7     3\n",
       "5          3     2    20\n",
       "6          3     3     1\n",
       "7          4     1    19\n",
       "8          5     7     7\n",
       "9          6     3     3\n",
       "10         6     5     4"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pretype = df.groupby([\"pre_type\", \"type\"]).count()[[\"animal_name\"]].reset_index().copy()\n",
    "df_pretype.rename(columns={\"animal_name\": \"freq\"}, inplace=True)\n",
    "df_pretype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 5, 6, 7]\n",
      "[0, 1, 2, 3, 4, 5, 6]\n"
     ]
    }
   ],
   "source": [
    "# buscar o tipo predominante de cada classe gerada\n",
    "animal_types = df[\"type\"].unique()\n",
    "animal_types = list(animal_types)\n",
    "animal_types.sort()\n",
    "print(animal_types)\n",
    "\n",
    "animal_pre_types = df[\"pre_type\"].unique()\n",
    "animal_pre_types = list(animal_pre_types)\n",
    "animal_pre_types.sort()\n",
    "print(animal_pre_types)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def decode_pre_type(pre_type:int) -> int:\n",
    "    \"\"\"Define what are the cluster pre_type corresponded type\"\"\"\n",
    "    selecao = df_pretype[\"pre_type\"] == pre_type\n",
    "    id_type_max = df_pretype[selecao].idxmax()[\"freq\"]\n",
    "    return df_pretype.iloc[id_type_max][\"type\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'0': '4', '1': '1', '2': '6', '3': '2', '4': '1', '5': '7', '6': '5'}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decoded_pre_types = {}\n",
    "for pre_type in animal_pre_types:\n",
    "    decoded_pre_types[f\"{pre_type}\"] = f\"{decode_pre_type(pre_type)}\"\n",
    "decoded_pre_types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 4, 1: 1, 2: 6, 3: 2, 4: 1, 5: 7, 6: 5}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decoded_pre_types = {}\n",
    "for pre_type in animal_pre_types:\n",
    "    decoded_pre_types[pre_type] = decode_pre_type(pre_type)\n",
    "decoded_pre_types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     4\n",
       "1     4\n",
       "2     1\n",
       "3     6\n",
       "4     6\n",
       "5     2\n",
       "6     2\n",
       "7     1\n",
       "8     7\n",
       "9     5\n",
       "10    5\n",
       "Name: pre_type, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pretype[\"pre_type\"].map(decoded_pre_types)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"predict_type\"] = df[\"pre_type\"].map(decoded_pre_types)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>animal_name</th>\n",
       "      <th>hair</th>\n",
       "      <th>feathers</th>\n",
       "      <th>eggs</th>\n",
       "      <th>milk</th>\n",
       "      <th>airborne</th>\n",
       "      <th>aquatic</th>\n",
       "      <th>predator</th>\n",
       "      <th>toothed</th>\n",
       "      <th>backbone</th>\n",
       "      <th>breathes</th>\n",
       "      <th>venomous</th>\n",
       "      <th>fins</th>\n",
       "      <th>legs</th>\n",
       "      <th>tail</th>\n",
       "      <th>domestic</th>\n",
       "      <th>catsize</th>\n",
       "      <th>type</th>\n",
       "      <th>pre_type</th>\n",
       "      <th>predict_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>flea</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>gnat</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>honeybee</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>housefly</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>ladybird</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>moth</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>scorpion</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>slug</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>termite</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>wasp</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>worm</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   animal_name  hair  feathers  eggs  milk  airborne  aquatic  predator  \\\n",
       "24        flea     0         0     1     0         0        0         0   \n",
       "30        gnat     0         0     1     0         1        0         0   \n",
       "39    honeybee     1         0     1     0         1        0         0   \n",
       "40    housefly     1         0     1     0         1        0         0   \n",
       "42    ladybird     0         0     1     0         1        0         1   \n",
       "51        moth     1         0     1     0         1        0         0   \n",
       "72    scorpion     0         0     0     0         0        0         1   \n",
       "81        slug     0         0     1     0         0        0         0   \n",
       "88     termite     0         0     1     0         0        0         0   \n",
       "97        wasp     1         0     1     0         1        0         0   \n",
       "99        worm     0         0     1     0         0        0         0   \n",
       "\n",
       "    toothed  backbone  breathes  venomous  fins  legs  tail  domestic  \\\n",
       "24        0         0         1         0     0     6     0         0   \n",
       "30        0         0         1         0     0     6     0         0   \n",
       "39        0         0         1         1     0     6     0         1   \n",
       "40        0         0         1         0     0     6     0         0   \n",
       "42        0         0         1         0     0     6     0         0   \n",
       "51        0         0         1         0     0     6     0         0   \n",
       "72        0         0         1         1     0     8     1         0   \n",
       "81        0         0         1         0     0     0     0         0   \n",
       "88        0         0         1         0     0     6     0         0   \n",
       "97        0         0         1         1     0     6     0         0   \n",
       "99        0         0         1         0     0     0     0         0   \n",
       "\n",
       "    catsize  type  pre_type  predict_type  \n",
       "24        0     6         2             6  \n",
       "30        0     6         2             6  \n",
       "39        0     6         2             6  \n",
       "40        0     6         2             6  \n",
       "42        0     6         2             6  \n",
       "51        0     6         2             6  \n",
       "72        0     7         2             6  \n",
       "81        0     7         2             6  \n",
       "88        0     6         2             6  \n",
       "97        0     6         2             6  \n",
       "99        0     7         2             6  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selecao = df[\"pre_type\"] == 2\n",
    "df[selecao]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConfusionMatrixDisplay.from_predictions(df[\"type\"], df[\"predict_type\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       1.00      1.00      1.00        41\n",
      "           2       0.95      1.00      0.98        20\n",
      "           3       0.00      0.00      0.00         5\n",
      "           4       0.93      1.00      0.96        13\n",
      "           5       0.57      1.00      0.73         4\n",
      "           6       0.73      1.00      0.84         8\n",
      "           7       1.00      0.70      0.82        10\n",
      "\n",
      "    accuracy                           0.92       101\n",
      "   macro avg       0.74      0.81      0.76       101\n",
      "weighted avg       0.89      0.92      0.90       101\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(df[\"type\"], df[\"predict_type\"], zero_division=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spectral Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "technique that uses the eigenvalues of a similarity matrix to reduce dimensionality before clustering the data. It’s particularly useful when clusters are not well-separated and have complex shapes. Unlike K-Means, it doesn't assume spherical clusters, which makes it versatile in many cases."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Steps for Spectral Clustering:\n",
    "- Build a similarity graph based on your data points.\n",
    "- Compute the Laplacian matrix of the graph.\n",
    "- Perform eigen-decomposition to get the top eigenvalues and their corresponding eigenvectors.\n",
    "- Apply a clustering algorithm (like K-Means) on the resulting low-dimensional space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spectral Clustering Silhouette Score: 0.40075958351995833\n",
      "Adjusted Rand Index: 0.863585157478188\n",
      "Normalized Mutual Information: 0.864470270730845\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import SpectralClustering\n",
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "# Initialize the Spectral Clustering model\n",
    "spectral = SpectralClustering(n_clusters=number_of_classes, affinity='rbf', random_state=42)\n",
    "\n",
    "# Fit the model to the data\n",
    "spectral_labels = spectral.fit_predict(X)\n",
    "\n",
    "# Evaluate the clustering using Silhouette Score\n",
    "sil_score = silhouette_score(X, spectral_labels)\n",
    "print(f\"Spectral Clustering Silhouette Score: {sil_score}\")\n",
    "\n",
    "# Optionally, compare the cluster labels with the true class labels (if available)\n",
    "# Adjusted Rand Index or Normalized Mutual Information can be used\n",
    "from sklearn.metrics import adjusted_rand_score, normalized_mutual_info_score\n",
    "\n",
    "# Assuming you have true labels y\n",
    "ari = adjusted_rand_score(y.values.flatten(), spectral_labels)\n",
    "nmi = normalized_mutual_info_score(y.values.flatten(), spectral_labels)\n",
    "\n",
    "print(f\"Adjusted Rand Index: {ari}\")\n",
    "print(f\"Normalized Mutual Information: {nmi}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Agglomerative Hierarchical Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agglomerative clustering builds a hierarchy of clusters by merging or splitting clusters based on distance metrics like Euclidean, Manhattan, or Hamming (for binary data). You can visualize the hierarchy using a dendrogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Agglomerative Clustering Silhouette Score: 0.3536387953829943\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import AgglomerativeClustering\n",
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "# Initialize and fit the AgglomerativeClustering model\n",
    "agg_cluster = AgglomerativeClustering(n_clusters=number_of_classes, metric='euclidean', linkage='ward')\n",
    "agg_cluster.fit(X)\n",
    "\n",
    "# Assign cluster labels\n",
    "agg_labels = agg_cluster.labels_\n",
    "\n",
    "# Evaluate using silhouette score\n",
    "sil_score = silhouette_score(X, agg_labels)\n",
    "print(f\"Agglomerative Clustering Silhouette Score: {sil_score}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DBSCAN (Density-Based Spatial Clustering of Applications with Noise)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DBSCAN is a density-based clustering algorithm that groups together points that are closely packed, marking outliers as noise. It's useful if your data contains noise or clusters of varying densities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DBSCAN Silhouette Score: 0.064930772444219\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import DBSCAN\n",
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "# Initialize and fit the DBSCAN model\n",
    "dbscan = DBSCAN(eps=0.5, min_samples=5, metric='euclidean')\n",
    "dbscan.fit(X)\n",
    "\n",
    "# Assign cluster labels (-1 indicates outliers)\n",
    "dbscan_labels = dbscan.labels_\n",
    "\n",
    "# Evaluate using silhouette score (Note: DBSCAN may assign outliers as -1)\n",
    "sil_score = silhouette_score(X, dbscan_labels)\n",
    "print(f\"DBSCAN Silhouette Score: {sil_score}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation for Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For unsupervised clustering, evaluation metrics like silhouette score or Calinski-Harabasz Index are useful. If you have class labels and want to compare clusters with true labels, you can use metrics like adjusted Rand index (ARI) or normalized mutual information (NMI)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.metrics import adjusted_rand_score, normalized_mutual_info_score\n",
    "\n",
    "# # Assuming you have true labels y, you can compare the cluster assignments to the ground truth\n",
    "# ari = adjusted_rand_score(y, kmodes_labels)  # or kmeans_labels, agg_labels, etc.\n",
    "# nmi = normalized_mutual_info_score(y, kmodes_labels)\n",
    "\n",
    "# print(f\"Adjusted Rand Index: {ari}\")\n",
    "# print(f\"Normalized Mutual Information: {nmi}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ML Aproach Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Scikit-Learn Supervised Learning](https://scikit-learn.org/stable/supervised_learning.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Logistic Regression is a linear model suitable for binary classification. It predicts the probability of a class (0 or 1) based on the feature values.\n",
    "Pros: Simple, fast, interpretable.\n",
    "Cons: Assumes linearity between features and output. It may not perform well on complex non-linear data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 1.0\n",
      "Classification Report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00         9\n",
      "           1       1.00      1.00      1.00         5\n",
      "           3       1.00      1.00      1.00         3\n",
      "           5       1.00      1.00      1.00         3\n",
      "           6       1.00      1.00      1.00         1\n",
      "\n",
      "    accuracy                           1.00        21\n",
      "   macro avg       1.00      1.00      1.00        21\n",
      "weighted avg       1.00      1.00      1.00        21\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\JR-Ma\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\utils\\validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "\n",
    "# Initialize and train the logistic regression model\n",
    "model = LogisticRegression()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Predict on the test set\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Evaluate the model\n",
    "print(f\"Accuracy: {accuracy_score(y_test, y_pred)}\")\n",
    "print(f\"Classification Report: \\n{classification_report(y_test, y_pred)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Support Vector Machine (SVM)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SVM with a linear or non-linear kernel can work well for binary classification problems, especially when the classes are separable. SVM finds a hyperplane that separates the classes with the maximum margin. It can use different kernels (linear, polynomial, radial basis function) to capture complex relationships.\n",
    "Pros: Effective in high-dimensional spaces, handles non-linear boundaries with kernels.\n",
    "Cons: Can be slower to train, sensitive to tuning of hyperparameters.\n",
    "\n",
    "can also try other kernels ('rbf', 'poly') depending on the complexity of your data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM Accuracy: 1.0\n",
      "SVM Classification Report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00         9\n",
      "           1       1.00      1.00      1.00         5\n",
      "           3       1.00      1.00      1.00         3\n",
      "           5       1.00      1.00      1.00         3\n",
      "           6       1.00      1.00      1.00         1\n",
      "\n",
      "    accuracy                           1.00        21\n",
      "   macro avg       1.00      1.00      1.00        21\n",
      "weighted avg       1.00      1.00      1.00        21\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\JR-Ma\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\utils\\validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "\n",
    "# Initialize and train the Support Vector Classifier with a linear kernel\n",
    "svm_model = SVC(kernel='linear', random_state=42)\n",
    "svm_model.fit(X_train, y_train)\n",
    "\n",
    "# Predict on the test set\n",
    "y_pred_svm = svm_model.predict(X_test)\n",
    "\n",
    "# Evaluate the model\n",
    "print(f\"SVM Accuracy: {accuracy_score(y_test, y_pred_svm)}\")\n",
    "print(f\"SVM Classification Report: \\n{classification_report(y_test, y_pred_svm)}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Trees"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Decision trees work well with binary features as they are inherently based on decision rules (splits). Decision Trees split the data based on feature values to classify the data. This model is simple and interpretable but can overfit without proper tuning.\n",
    "Pros: Can handle non-linear relationships, works well with both numerical and categorical data.\n",
    "Cons: Prone to overfitting, which can be controlled using pruning or setting maximum depth."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [Decision Tree](https://scikit-learn.org/stable/modules/tree.html)\n",
    "- [DecisionTreeClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Accuracy: 0.9523809523809523\n",
      "Decision Tree Classification Report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00         9\n",
      "           1       1.00      1.00      1.00         5\n",
      "           3       1.00      1.00      1.00         3\n",
      "           5       1.00      0.67      0.80         3\n",
      "           6       0.50      1.00      0.67         1\n",
      "\n",
      "    accuracy                           0.95        21\n",
      "   macro avg       0.90      0.93      0.89        21\n",
      "weighted avg       0.98      0.95      0.96        21\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "\n",
    "# Initialize and train the Decision Tree classifier\n",
    "dt_model = DecisionTreeClassifier(random_state=42)\n",
    "dt_model.fit(X_train, y_train)\n",
    "\n",
    "# Predict on the test set\n",
    "y_pred_dt = dt_model.predict(X_test)\n",
    "\n",
    "# Evaluate the model\n",
    "print(f\"Decision Tree Accuracy: {accuracy_score(y_test, y_pred_dt)}\")\n",
    "print(f\"Decision Tree Classification Report: \\n{classification_report(y_test, y_pred_dt)}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Treinaremos com 80 elementos e testaremos com 21 elementos\n",
      "A acurácia foi 85.71%\n"
     ]
    }
   ],
   "source": [
    "print(\"Treinaremos com %d elementos e testaremos com %d elementos\" % (len(X_train), len(X_test)))\n",
    "\n",
    "modelo_tree = DecisionTreeClassifier(max_depth=3)\n",
    "modelo_tree.fit(X_train, y_train)\n",
    "previsoes = modelo_tree.predict(X_test)\n",
    "\n",
    "acuracia = accuracy_score(y_test, previsoes) * 100\n",
    "print(\"A acurácia foi %.2f%%\" % acuracia)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "dot_data = export_graphviz(modelo_tree, \n",
    "                           out_file=None,\n",
    "                           filled = True, \n",
    "                           rounded = True,\n",
    "                           feature_names = list(X.columns),\n",
    "                          class_names = [str(x) for x in range(1,8)])\n",
    "# necessário intalar no Path do Windows, sem permissão\n",
    "# grafico = graphviz.Source(dot_data)\n",
    "# grafico\n",
    "# Graphviz Online\n",
    "# https://dreampuf.github.io/GraphvizOnline/\n",
    "# for x in dot_data.split(\"\\n\"):\n",
    "#     print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.6666666666666666, 0.875, 'milk <= 0.5\\ngini = 0.766\\nsamples = 80\\nvalue = [32, 15, 5, 10, 4, 5, 9]'),\n",
       " Text(0.5, 0.625, 'feathers <= 0.5\\ngini = 0.795\\nsamples = 48\\nvalue = [0, 15, 5, 10, 4, 5, 9]'),\n",
       " Text(0.3333333333333333, 0.375, 'fins <= 0.5\\ngini = 0.773\\nsamples = 33\\nvalue = [0, 0, 5, 10, 4, 5, 9]'),\n",
       " Text(0.16666666666666666, 0.125, 'gini = 0.722\\nsamples = 23\\nvalue = [0, 0, 5, 0, 4, 5, 9]'),\n",
       " Text(0.5, 0.125, 'gini = 0.0\\nsamples = 10\\nvalue = [0, 0, 0, 10, 0, 0, 0]'),\n",
       " Text(0.6666666666666666, 0.375, 'gini = 0.0\\nsamples = 15\\nvalue = [0, 15, 0, 0, 0, 0, 0]'),\n",
       " Text(0.8333333333333334, 0.625, 'gini = 0.0\\nsamples = 32\\nvalue = [32, 0, 0, 0, 0, 0, 0]')]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x2000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(20, 20))\n",
    "plot_tree(  modelo_tree,\n",
    "            # out_file=None,\n",
    "            filled = True, \n",
    "            rounded = True,\n",
    "            feature_names = list(X.columns))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gradient Boosting (e.g., XGBoost, LightGBM)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gradient boosting algorithms build an ensemble of weak learners (like decision trees) in a sequential manner to minimize the classification error. Gradient Boosting builds an ensemble of weak learners (usually decision trees) in sequence to minimize the loss function. It's a powerful method for classification tasks.\n",
    "Pros: Very powerful, can capture complex relationships.\n",
    "Cons: More complex to tune, slower to train."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import xgboost as xgb\n",
    "# from sklearn.metrics import accuracy_score, classification_report\n",
    "\n",
    "# # Initialize and train the XGBoost classifier\n",
    "# xgb_model = xgb.XGBClassifier(use_label_encoder=False, eval_metric='logloss', random_state=42)\n",
    "# xgb_model.fit(X_train, y_train)\n",
    "\n",
    "# # Predict on the test set\n",
    "# y_pred_xgb = xgb_model.predict(X_test)\n",
    "\n",
    "# # Evaluate the model\n",
    "# print(f\"XGBoost Accuracy: {accuracy_score(y_test, y_pred_xgb)}\")\n",
    "# print(f\"XGBoost Classification Report: \\n{classification_report(y_test, y_pred_xgb)}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dimensionality reduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Scikit-Learn Dimensionality reduction](https://scikit-learn.org/stable/modules/decomposition.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Principal Component Analysis (PCA)\n",
    "- t-SNE (t-Distributed Stochastic Neighbor Embedding)\n",
    "- UMAP (Uniform Manifold Approximation and Projection)\n",
    "- Linear Discriminant Analysis (LDA)\n",
    "- Kernel PCA (Non-linear PCA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Principal Component Analysis (PCA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PCA is one of the most commonly used dimensionality reduction techniques. It transforms the data into a new coordinate system by finding the directions (principal components) that capture the most variance in the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When to Use PCA:\n",
    "- When you want to reduce dimensionality while preserving as much variance as possible.\n",
    "- For visualizing high-dimensional data in 2D or 3D.\n",
    "- When the relationship between features is linear."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Explained variance by each component: [0.34764486 0.21997868]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Initialize PCA, reduce data to 2 components for visualization\n",
    "pca = PCA(n_components=2)\n",
    "X_pca = pca.fit_transform(X)\n",
    "\n",
    "# Plot the data in 2D after PCA\n",
    "scatter = plt.scatter(X_pca[:, 0], X_pca[:, 1], c=y.values.flatten(), cmap='viridis')  # Convert 'y' to 1D array\n",
    "plt.title('PCA: Data Reduced to 2 Components')\n",
    "plt.xlabel('Principal Component 1')\n",
    "plt.ylabel('Principal Component 2')\n",
    "\n",
    "# Create a legend\n",
    "handles, _ = scatter.legend_elements()\n",
    "legend_labels = [class_names[i] for i in range(len(handles))]\n",
    "plt.legend(handles, legend_labels, title=\"Classes\", loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# Explained variance ratio\n",
    "print(f\"Explained variance by each component: {pca.explained_variance_ratio_}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## t-SNE (t-Distributed Stochastic Neighbor Embedding)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "t-SNE is a powerful non-linear dimensionality reduction technique, often used for visualization. It preserves the local structure of data (i.e., similar data points remain close to each other in the reduced space)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When to Use t-SNE:\n",
    "- When you want to visualize complex, non-linear relationships in data.\n",
    "- Best for reducing data to 2 or 3 dimensions for visualization purposes.\n",
    "- Suitable for small to medium datasets, as it’s computationally expensive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Initialize t-SNE, reduce data to 2 dimensions\n",
    "tsne = TSNE(n_components=2, perplexity=30, random_state=42)\n",
    "X_tsne = tsne.fit_transform(X)\n",
    "\n",
    "# Plot the data after t-SNE\n",
    "scatter = plt.scatter(X_tsne[:, 0], X_tsne[:, 1], c=y.values.flatten(), cmap='viridis')\n",
    "plt.title('t-SNE: Data Reduced to 2 Dimensions')\n",
    "plt.xlabel('t-SNE Component 1')\n",
    "plt.ylabel('t-SNE Component 2')\n",
    "\n",
    "# Create a legend\n",
    "handles, _ = scatter.legend_elements()\n",
    "legend_labels = [class_names[i] for i in range(len(handles))]\n",
    "plt.legend(handles, legend_labels, title=\"Classes\", loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Statistical Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Get basic statistics for numerical columns\n",
    "# print(df.describe())\n",
    "\n",
    "# # Check for missing values\n",
    "# print(df.isnull().sum())\n",
    "\n",
    "# # Visualize the distribution of a specific column\n",
    "# sns.histplot(df['type'], kde=True)\n",
    "# plt.title('Distribution of column_name')\n",
    "# plt.show()\n",
    "\n",
    "# # Pairplot for relationships between multiple features\n",
    "# sns.pairplot(df)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression Analysis with Statsmodels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ensures that the model will be unbiased, in other words, the mean of the residuals will be zero. By including a constant, one degree of freedom for error will be lost, but it's a small price to pay for the protection against bias."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The constant term in a regression model, often referred to as the intercept, serves several important purposes:\n",
    "\n",
    "1. **Baseline Value**: The constant term represents the expected value of the dependent variable when all the independent variables are equal to zero. It provides a baseline or starting point for the regression equation.\n",
    "\n",
    "2. **Adjustment for Mean**: Including a constant term ensures that the model can adjust for the mean of the dependent variable. Without it, the regression line would be forced to pass through the origin (0,0), which is often unrealistic and can lead to biased estimates of the coefficients.\n",
    "\n",
    "3. **Capturing Unobserved Factors**: The constant term can capture the effect of factors that are not included in the model. These could be variables that are difficult to measure or omitted due to lack of data.\n",
    "\n",
    "4. **Improving Model Fit**: Including a constant term generally improves the fit of the model by allowing it to better align with the data points. This can lead to more accurate predictions and a higher R-squared value.\n",
    "\n",
    "In the context of the provided OLS regression results, the constant term has a coefficient of 5.1828, which means that when all the predictor variables are zero, the expected value of the dependent variable \"type\" is 5.1828. This term is statistically significant with a p-value of 0.000, indicating that it plays an important role in the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   type   R-squared:                       0.982\n",
      "Model:                            OLS   Adj. R-squared:                  0.979\n",
      "Method:                 Least Squares   F-statistic:                     290.1\n",
      "Date:                Sun, 22 Sep 2024   Prob (F-statistic):           2.35e-66\n",
      "Time:                        16:09:18   Log-Likelihood:                -14.357\n",
      "No. Observations:                 101   AIC:                             62.71\n",
      "Df Residuals:                      84   BIC:                             107.2\n",
      "Df Model:                          16                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          5.1828      0.276     18.752      0.000       4.633       5.732\n",
      "hair          -0.3216      0.178     -1.805      0.075      -0.676       0.033\n",
      "feathers      -1.0237      0.277     -3.698      0.000      -1.574      -0.473\n",
      "eggs           0.3577      0.236      1.518      0.133      -0.111       0.826\n",
      "milk          -2.1982      0.308     -7.130      0.000      -2.811      -1.585\n",
      "airborne      -0.2765      0.124     -2.227      0.029      -0.523      -0.030\n",
      "aquatic        0.3415      0.107      3.205      0.002       0.130       0.553\n",
      "predator      -0.0634      0.074     -0.862      0.391      -0.210       0.083\n",
      "toothed        0.7374      0.270      2.733      0.008       0.201       1.274\n",
      "backbone      -3.2764      0.304    -10.775      0.000      -3.881      -2.672\n",
      "breathes      -0.1548      0.137     -1.131      0.261      -0.427       0.117\n",
      "venomous       0.0502      0.148      0.339      0.735      -0.244       0.344\n",
      "fins          -0.1287      0.170     -0.757      0.451      -0.467       0.209\n",
      "legs           0.2762      0.145      1.905      0.060      -0.012       0.565\n",
      "tail          -0.1920      0.115     -1.667      0.099      -0.421       0.037\n",
      "domestic      -0.0051      0.100     -0.050      0.960      -0.204       0.194\n",
      "catsize       -0.0727      0.085     -0.851      0.397      -0.243       0.097\n",
      "==============================================================================\n",
      "Omnibus:                       10.522   Durbin-Watson:                   2.230\n",
      "Prob(Omnibus):                  0.005   Jarque-Bera (JB):               27.926\n",
      "Skew:                           0.011   Prob(JB):                     8.63e-07\n",
      "Kurtosis:                       5.576   Cond. No.                         41.8\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "X = sm.add_constant(X)  # Adds a constant term to the model\n",
    "\n",
    "# Fit the model\n",
    "model = sm.OLS(y, X).fit()\n",
    "\n",
    "# Summary of the regression model\n",
    "print(model.summary())\n",
    "X.drop('const', axis=1, inplace=True)  # Drop the constant term"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The active selection is a detailed output from an Ordinary Least Squares (OLS) regression analysis, which is a method used to model the relationship between a dependent variable and one or more independent variables.\n",
    "In this case, the dependent variable is labeled \"type.\"\n",
    "\n",
    "The top section of the output provides an overview of the model's performance.\n",
    "The R-squared value is 0.982, indicating that approximately 98.2% of the variability in the dependent variable can be explained by the model.\n",
    "The adjusted R-squared value, which adjusts for the number of predictors in the model, is 0.979.\n",
    "The F-statistic is 290.1 with a p-value of 2.35e-66, suggesting that the model is statistically significant.\n",
    "\n",
    "The table in the middle lists the coefficients for each predictor variable along with their standard errors, t-statistics, p-values, and 95% confidence intervals. For example, the coefficient for \"hair\" is -0.3216, with a standard error of 0.178, a t-statistic of -1.805, and a p-value of 0.075.\n",
    "This suggests that \"hair\" is not statistically significant at the 5% level.\n",
    "In contrast, \"feathers\" has a coefficient of -1.0237, a standard error of 0.277, a t-statistic of -3.698, and a p-value of 0.000, indicating it is highly significant.\n",
    "\n",
    "The bottom section includes diagnostic statistics for the model.\n",
    "The Omnibus test and its p-value assess the normality of the residuals, with a p-value of 0.005 indicating potential non-normality.\n",
    "The Durbin-Watson statistic is 2.230, which tests for autocorrelation in the residuals.\n",
    "The Jarque-Bera test and its p-value also test for normality, with a p-value of 8.63e-07 suggesting non-normality.\n",
    "Skewness and kurtosis provide additional information about the distribution of the residuals.\n",
    "The condition number of 41.8 indicates potential multicollinearity issues, with higher values suggesting more severe multicollinearity.\n",
    "\n",
    "Overall, this output provides a comprehensive summary of the regression analysis, including the significance of each predictor and various diagnostic tests to assess the model's validity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Neural Net Aproach"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Research"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[TensorFlow - Tutorial: Basic Classification](https://www.tensorflow.org/tutorials/keras/classification?hl=pt-br)\n",
    "- this part of the topic was inspired on this tutorial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other Links\n",
    "[TensorFlow: Google CodeLabs](https://codelabs.developers.google.com/codelabs/tfjs-training-classfication/index.html#4)\n",
    "\n",
    "[DigitalOcean: Recognize Handwritten Digits](https://www.digitalocean.com/community/tutorials/how-to-build-a-neural-network-to-recognize-handwritten-digits-with-tensorflow#step-3-defining-the-neural-network-architecture)\n",
    "\n",
    "[Kaggle: MNIST Digits Classification](https://www.kaggle.com/code/smtkmr102/mnist-data-classification-using-ann)\n",
    "\n",
    "[Kaggle: Reference for Model structure and planning](https://www.kaggle.com/competitions/nfl-big-data-bowl-2020/discussion/119400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## model & training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.0521 - loss: 2.2251  \n",
      "Epoch 2/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.0436 - loss: 2.1726     \n",
      "Epoch 3/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1223 - loss: 2.1018 \n",
      "Epoch 4/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1365 - loss: 2.0339 \n",
      "Epoch 5/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.1419 - loss: 1.9738 \n",
      "Epoch 6/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.1659 - loss: 1.9043 \n",
      "Epoch 7/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1700 - loss: 1.8646 \n",
      "Epoch 8/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.3240 - loss: 1.8024 \n",
      "Epoch 9/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.3099 - loss: 1.7570 \n",
      "Epoch 10/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4393 - loss: 1.7062 \n",
      "Epoch 11/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4395 - loss: 1.6739 \n",
      "Epoch 12/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4611 - loss: 1.6243 \n",
      "Epoch 13/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4582 - loss: 1.5698 \n",
      "Epoch 14/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.4580 - loss: 1.5343 \n",
      "Epoch 15/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.4651 - loss: 1.5257 \n",
      "Epoch 16/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.4941 - loss: 1.4739 \n",
      "Epoch 17/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5482 - loss: 1.4138 \n",
      "Epoch 18/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5480 - loss: 1.3798 \n",
      "Epoch 19/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5426 - loss: 1.3820 \n",
      "Epoch 20/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5941 - loss: 1.3301 \n",
      "Epoch 21/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.5993 - loss: 1.2911 \n",
      "Epoch 22/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6035 - loss: 1.2486 \n",
      "Epoch 23/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5722 - loss: 1.2848 \n",
      "Epoch 24/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5626 - loss: 1.2619 \n",
      "Epoch 25/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5760 - loss: 1.2343 \n",
      "Epoch 26/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5978 - loss: 1.1795 \n",
      "Epoch 27/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6155 - loss: 1.1048 \n",
      "Epoch 28/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5685 - loss: 1.1562 \n",
      "Epoch 29/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6510 - loss: 1.1261 \n",
      "Epoch 30/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6374 - loss: 1.0991 \n",
      "Epoch 31/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6883 - loss: 1.0468 \n",
      "Epoch 32/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6737 - loss: 1.0434 \n",
      "Epoch 33/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6795 - loss: 1.0416 \n",
      "Epoch 34/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6645 - loss: 1.0225 \n",
      "Epoch 35/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7558 - loss: 0.9173 \n",
      "Epoch 36/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7514 - loss: 0.8978 \n",
      "Epoch 37/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7397 - loss: 0.9673 \n",
      "Epoch 38/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7743 - loss: 0.9089 \n",
      "Epoch 39/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8156 - loss: 0.8776 \n",
      "Epoch 40/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8245 - loss: 0.8834 \n",
      "Epoch 41/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8402 - loss: 0.8343 \n",
      "Epoch 42/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8556 - loss: 0.7869 \n",
      "Epoch 43/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8460 - loss: 0.8279 \n",
      "Epoch 44/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - accuracy: 0.8304 - loss: 0.8241  \n",
      "Epoch 45/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8366 - loss: 0.8053 \n",
      "Epoch 46/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8468 - loss: 0.7770 \n",
      "Epoch 47/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8302 - loss: 0.8015 \n",
      "Epoch 48/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8646 - loss: 0.7225 \n",
      "Epoch 49/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8666 - loss: 0.6758 \n",
      "Epoch 50/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8591 - loss: 0.6972 \n",
      "Epoch 51/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8835 - loss: 0.7072 \n",
      "Epoch 52/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9044 - loss: 0.6558 \n",
      "Epoch 53/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8866 - loss: 0.6655 \n",
      "Epoch 54/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9012 - loss: 0.6448 \n",
      "Epoch 55/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8856 - loss: 0.6498 \n",
      "Epoch 56/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8866 - loss: 0.6126 \n",
      "Epoch 57/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8960 - loss: 0.6169 \n",
      "Epoch 58/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8939 - loss: 0.5967 \n",
      "Epoch 59/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8960 - loss: 0.5924 \n",
      "Epoch 60/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9012 - loss: 0.5696 \n",
      "Epoch 61/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8919 - loss: 0.5557 \n",
      "Epoch 62/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9146 - loss: 0.5290 \n",
      "Epoch 63/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9031 - loss: 0.5462 \n",
      "Epoch 64/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9177 - loss: 0.5124 \n",
      "Epoch 65/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8979 - loss: 0.5068 \n",
      "Epoch 66/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9052 - loss: 0.5117 \n",
      "Epoch 67/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9196 - loss: 0.4933 \n",
      "Epoch 68/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9112 - loss: 0.4828 \n",
      "Epoch 69/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9383 - loss: 0.4503 \n",
      "Epoch 70/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8894 - loss: 0.5268 \n",
      "Epoch 71/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9442 - loss: 0.4492 \n",
      "Epoch 72/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9410 - loss: 0.4424 \n",
      "Epoch 73/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9317 - loss: 0.4462 \n",
      "Epoch 74/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9421 - loss: 0.4096 \n",
      "Epoch 75/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9264 - loss: 0.4304 \n",
      "Epoch 76/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9285 - loss: 0.4342 \n",
      "Epoch 77/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9285 - loss: 0.4150 \n",
      "Epoch 78/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9296 - loss: 0.3840 \n",
      "Epoch 79/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9285 - loss: 0.3749 \n",
      "Epoch 80/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9358 - loss: 0.3778 \n",
      "Epoch 81/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9327 - loss: 0.3701 \n",
      "Epoch 82/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9264 - loss: 0.3954 \n",
      "Epoch 83/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9389 - loss: 0.3858 \n",
      "Epoch 84/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9296 - loss: 0.3608 \n",
      "Epoch 85/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9317 - loss: 0.3556 \n",
      "Epoch 86/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9296 - loss: 0.3433 \n",
      "Epoch 87/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9389 - loss: 0.3487 \n",
      "Epoch 88/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9317 - loss: 0.3617 \n",
      "Epoch 89/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9358 - loss: 0.3376 \n",
      "Epoch 90/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9317 - loss: 0.3494 \n",
      "Epoch 91/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9389 - loss: 0.3195 \n",
      "Epoch 92/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9356 - loss: 0.3381 \n",
      "Epoch 93/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9262 - loss: 0.3548 \n",
      "Epoch 94/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9502 - loss: 0.3174 \n",
      "Epoch 95/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9512 - loss: 0.2925 \n",
      "Epoch 96/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9544 - loss: 0.3032 \n",
      "Epoch 97/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - accuracy: 0.9448 - loss: 0.2926  \n",
      "Epoch 98/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9448 - loss: 0.3328 \n",
      "Epoch 99/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9531 - loss: 0.2947 \n",
      "Epoch 100/100\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9542 - loss: 0.2987 \n"
     ]
    }
   ],
   "source": [
    "# Define the model\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Input(shape=(16,)),             # Input layer with 16 neurons\n",
    "    tf.keras.layers.Dense(16, activation='relu'),   # Hidden layer with 16 neurons and ReLU activation\n",
    "    tf.keras.layers.Dense(7, activation=\"softmax\")  # Output layer with 7 neurons and softmax activation\n",
    "])\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer='adam', loss='SparseCategoricalCrossentropy', metrics=['accuracy'])\n",
    "\n",
    "# Define your input and output data\n",
    "# Convert the data into NumPy arrays\n",
    "input_data = np.array(X)\n",
    "output_data = np.array(y)\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(input_data, output_data, epochs=100)  # You can adjust the number of epochs as needed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot training loss\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.title('Loss over Epochs')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "\n",
    "# Plot training accuracy\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
    "plt.title('Accuracy over Epochs')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 - 0s - 110ms/step - accuracy: 1.0000 - loss: 0.1489\n",
      "\n",
      "Test accuracy: 1.0\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(X_test,  y_test, verbose=2)\n",
    "\n",
    "print('\\nTest accuracy:', test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a probability model by adding a softmax layer to the existing model\n",
    "probability_model = tf.keras.Sequential([model, \n",
    "                                         tf.keras.layers.Softmax()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.12160849, 0.1207269 , 0.1239217 , 0.120831  , 0.1237556 ,\n",
       "       0.1279472 , 0.2612091 ], dtype=float32)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = probability_model.predict(X_test)\n",
    "predictions[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type    6\n",
       "Name: 46, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(predictions[0])\n",
    "y_test.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_characteristics(i:int, predictions_array:np.ndarray, true_label:pd.DataFrame, data:pd.DataFrame) -> None:\n",
    "    \"\"\"\n",
    "    Plot the characteristics of the animal and its predicted and true labels.\n",
    "\n",
    "    Args:\n",
    "        i: Index of the animal in the dataset\n",
    "        predictions_array: Array of predictions runned by the model indicating the probability of each class\n",
    "        true_label: DataFrame containing the True label of the animal\n",
    "        data: DataFrame containing the characteristics of the animals\n",
    "\n",
    "    Returns:\n",
    "        None\n",
    "    \"\"\"\n",
    "    true_label = true_label.iloc[i].values[0]\n",
    "    characteristics = data.iloc[i]\n",
    "    print(characteristics)\n",
    "    predicted_label = np.argmax(predictions_array) # returns the index of the maximum value in the array\n",
    "    if predicted_label == true_label:\n",
    "        color = 'blue'\n",
    "    else:\n",
    "        color = 'red'\n",
    "    print(f\"{class_names[predicted_label]} {100*np.max(predictions_array):2.0f}% ({class_names[true_label]})\")\n",
    "\n",
    "def plot_value_array(i:int, predictions_array:np.ndarray, true_label:pd.DataFrame) -> None:\n",
    "    \"\"\"\n",
    "    Plot the probability of each class for the animal.\n",
    "\n",
    "    Args:\n",
    "        i: Index of the animal in the dataset\n",
    "        predictions_array: Array of predictions runned by the model indicating the probability of each class\n",
    "        true_label: DataFrame containing the True label of the animal\n",
    "\n",
    "    Returns:\n",
    "        None\n",
    "    \"\"\"\n",
    "    true_label = true_label.iloc[i].values[0]\n",
    "    plt.grid(False)\n",
    "    plt.xticks(range(7))\n",
    "    plt.yticks([])\n",
    "    thisplot = plt.bar(range(7), predictions_array, color=\"#777777\")\n",
    "    plt.ylim([0, 1])\n",
    "    predicted_label = np.argmax(predictions_array)\n",
    "    plt.title(f\"{df['animal_name'].iloc[y_test.index[i]]} - {class_names[predicted_label]} {100*np.max(predictions_array):2.0f}% ({class_names[true_label]})\")\n",
    "\n",
    "    thisplot[predicted_label].set_color('red')\n",
    "    thisplot[true_label].set_color('blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gorilla\n",
      "hair        1\n",
      "feathers    0\n",
      "eggs        0\n",
      "milk        1\n",
      "airborne    0\n",
      "aquatic     0\n",
      "predator    0\n",
      "toothed     1\n",
      "backbone    1\n",
      "breathes    1\n",
      "venomous    0\n",
      "fins        0\n",
      "legs        1\n",
      "tail        0\n",
      "domestic    0\n",
      "catsize     1\n",
      "Name: 32, dtype: int32\n",
      "Mammal 30% (Mammal)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeQAAAEpCAYAAABC2FGTAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAe3UlEQVR4nO3deXTNd/7H8deVNBtCpIg0RNChqrakUms6dkWZGWvkNIpRHa1aMlPtz4itEp1SplVrj/aUoNqoYWyxt1XToplWp4qKpZZqCCGWkHx+f/S4x5UgKsn94Pk45x7y+X6+9/v+Ljev+/18v/fGYYwxAgAAblXC3QUAAAACGQAAKxDIAABYgEAGAMACBDIAABYgkAEAsACBDACABQhkAAAsQCADAGABAhk3VbVqVfXt29f586ZNm+RwOLRp0yZnW9++fVW1atVirw2u3nvvPTkcDh04cMDdpRSqv/zlL2rTpo27y7CWw+HQmDFjnD/PnDlTVapU0aVLl9xXFH4TAhl3tTFjxsjhcKhEiRI6fPhwnumZmZny9fWVw+HQCy+84IYK7bNlyxY9/fTTqly5snx8fBQUFKT27dvr888/z7f/1q1b1axZM/n5+SkoKEhDhgzRuXPnXPocOXJEHTt2lL+/v2rXrq3ly5fneZ7k5GRVqFBBZ86cKXCtaWlpmjt3rl599VVn24EDB+RwOORwODRhwoR85+vTp48cDodKlSpV4GXdK/r27avs7GzNmjXL3aXgNhHIuKkffvhBc+bMcXcZt+Tt7a2FCxfmaU9OTnZDNXbbs2ePSpQooUGDBmn69OmKi4vT8ePH1aJFC61evdqlb2pqqlq1aqXz589rypQpGjBggGbPnq3u3bu79IuNjdX+/fs1adIkNWzYUN27d3c5U7948aLi4uI0YcIElSlTpsC1Tps2TWFhYfr973+fZ5qPj0+++zwrK0vLli2Tj49PgZdzL/Hx8VFsbKymTJki/lTBXcYA18nNzTXnz5/Pd9rGjRuNJLNx40ZnW2xsrAkNDS2e4q4THx9vJJk//vGPpn79+nmmt2nTxvzpT38ykszgwYPdUGHxmTdvnpFk0tLSbnverKwsU7FiRdOuXTuX9g4dOphKlSqZM2fOONvmzJljJJk1a9YYY4w5f/68cTgcZvPmzcaYX4+fsLAwM3PmTOc848ePN/Xr1zc5OTkFrik7O9s8+OCDZtSoUS7taWlpzn0uyaSmprpMX7BggXnggQdM586dTcmSJQu8vLuVJBMfH+/Stn37diPJrF+/3j1F4TfhDPkutGnTJkVERMjHx0fVq1fXrFmznEO317py5YrGjx+v6tWry9vbW1WrVtWrr76a59pS1apV1alTJ61Zs0YRERHy9fV1Dnddfw25oN544w01adJEgYGB8vX1VXh4uD766KPfvM63Eh0drdTUVO3evdvZdvz4cW3YsEHR0dF5+mdnZ2v06NEKDw9XmTJlVLJkSTVv3lwbN2506Xd1ePSNN97Q9OnTVa1aNfn5+alt27Y6fPiwjDEaP368QkJC5Ovrqy5duujUqVMuz3F1+17db76+vnrsscec1+GTk5P12GOPycfHR+Hh4fr6669d5v/mm2/Ut29fVatWzTnE3K9fP508ebKQtp7k5+en8uXL6/Tp0862zMxMpaSkKCYmRv7+/s72Z555RqVKldKHH34o6dezX2OMAgICJP16TbNs2bI6f/68pF+HsxMTEzVt2jSVKFHwXzmfffaZ0tPT1bp163ynN27cWGFhYUpKSnJpX7Bggdq3b69y5crlmWfZsmXq2LGjgoOD5e3trerVq2v8+PHKyclx6ffkk0+qTp06+uabbxQVFSU/Pz/VqFHDeQxv3rxZkZGR8vX1Vc2aNbVu3TqX+a++Hvfs2aOYmBiVKVNG5cuX19///ncZY3T48GF16dJF/v7+CgoK0uTJk13mL+jxeSPh4eEqV66cli1bVqD+sAOBfJf5+uuv1b59e508eVJjx45V//79NW7cOH3yySd5+g4YMECjR49Ww4YN9eabbyoqKkoJCQnq1atXnr4//PCDevfurTZt2mjatGmqX7/+HdU5bdo0NWjQQOPGjdPEiRPl6emp7t2769///vcdPe+NtGjRQiEhIS6/nBcvXqxSpUqpY8eOefpnZmZq7ty5evLJJzVp0iSNGTNGv/zyi9q1a6fU1NQ8/RcsWKB33nlHL774okaMGKHNmzerR48eGjVqlFavXq2XX35ZAwcO1PLlyxUXF5dn/n379ik6OlqdO3dWQkKCMjIy1LlzZy1YsEDDhg1TTEyMxo4dqx9//FE9evRQbm6uc96UlBTt379fzz77rN566y316tVLixYt0lNPPXVHQ5KZmZlKT0/X7t279eqrr2rXrl1q1aqVc/q3336rK1euKCIiwmU+Ly8v1a9f3/nGISAgQNWrV9fEiROVlpamBQsWKDU1VY0aNZIk/e1vf1OHDh3UokWL26pv69atcjgcatCgwQ379O7dW4sWLXJuh/T0dK1duzbfN2HSrze+lSpVSsOHD9e0adMUHh6u0aNHa+TIkXn6ZmRkqFOnToqMjNTrr78ub29v9erVS4sXL1avXr301FNPKTExUVlZWerWrZvOnj2b5zl69uyp3NxcJSYmKjIyUhMmTNDUqVPVpk0bPfTQQ5o0aZJq1KihuLg4bdmyxTnf7R6f+WnYsOEN7wuApdx6fo7b1rlzZ+Pn52eOHDnibNu7d6/x9PQ01+7O1NRUI8kMGDDAZf64uDgjyWzYsMHZFhoaaiSZ1atX51leaGioiY2Ndf5c0CHr64e8s7OzTZ06dUzLli1vZ3Vv6eqQ9S+//GLi4uJMjRo1nNMef/xx8+yzzxpjTJ4h6ytXrphLly65PFdGRoapWLGi6devn7Pt6vBo+fLlzenTp53tr7zyipFk6tWrZy5fvuxs7927t/Hy8jIXL150tl3dvlu3bnW2rVmzxkgyvr6+5uDBg872WbNm5dm++V0+WLhwoZFktmzZ4my73SHrdu3aGUlGkvHy8jLPPfecuXDhgnP6kiVL8izjqu7du5ugoCDnz+vXrzcBAQHO5xs6dKgxxpjPP//c+Pr6mgMHDhSopmvFxMSYwMDAPO1X98k//vEPs2vXLiPJfPrpp8YYY6ZPn25KlSplsrKyTGxsbJ4h6/y25XPPPWf8/Pxc9llUVJSRZJKSkpxtu3fvNpJMiRIlzLZt25ztV/flvHnznG1Xj8uBAwc6265cuWJCQkKMw+EwiYmJzvaMjAzj6+vr8jor6PFpTP5D1sYYM3DgQOPr65unHfbiDPkukpOTo3Xr1qlr164KDg52tteoUUMdOnRw6bty5UpJ0vDhw13aR4wYIUl5zlTDwsLUrl27QqvV19fX+f+MjAydOXNGzZs3186dOwttGdeLjo7Wvn379NVXXzn/vdGZkoeHh7y8vCRJubm5OnXqlPNsML8au3fv7nIzUmRkpCQpJiZGnp6eLu3Z2dk6cuSIy/y1a9dW48aN88zfsmVLValSJU/7/v37nW3XbsuLFy8qPT1dTzzxhCTd0fZMTEzU2rVr9e677+qJJ55Qdna2rly54px+4cIFSb/eMHc9Hx8f5/Sr63Ho0CFt27ZNhw4d0ptvvqnc3FwNGTJEI0aMUGhoqGbMmKFatWqpZs2amjlz5i3rO3nypHMY/EYeffRR1a1b13lzV1JSkrp06SI/P798+1+7Lc+ePav09HQ1b95c58+fd7ncIUmlSpVyGU2qWbOmypYtq0ceecS5n6T899lVAwYMcP7fw8NDERERMsaof//+zvayZcuqZs2aLvPf7vGZn4CAAF24cMF56QD287x1F9jixIkTunDhgmrUqJFn2vVtBw8eVIkSJfK0BwUFqWzZsjp48KBLe1hYWKHWumLFCk2YMEGpqaku16yvv859vXPnzrl8pMbDw0Ply5cv0DIbNGigWrVqKSkpSWXLllVQUJBatmx5w/7vv/++Jk+erN27d+vy5cvO9vy2xbWhKckZzpUrV863PSMjo9DmP3XqlMaOHatFixbpxIkTLv1v5yNE17v2skRMTIwaNmyovn37Oq+TXg2v/D7PevHiRZdwk34NsGuDat68eTp+/LhGjhypdevW6a9//avmz58vh8Oh6Oho1axZM9+7p69lCjAkHx0drcmTJ2vYsGHaunWry0ekrvfdd99p1KhR2rBhgzIzM12mXb8tQ0JC8hyvZcqUKfA+l/Lf7z4+PnrwwQfztF9/T8DtHJ/5ubrtbvWagz04Q77HFfTFeP0v1zvx6aef6umnn5aPj4/eeecdrVy5UikpKYqOjr7lL9g33nhDlSpVcj4ef/zx21p2dHS0Fi9erKSkJPXs2fOGNxHNnz9fffv2VfXq1fXuu+9q9erVSklJUcuWLV2u317l4eGR7/PcqP369byT+Xv06KE5c+Zo0KBBSk5O1tq1a50fT8qv1t/Cy8tLTz/9tJKTk51nvpUqVZIkHTt2LE//Y8eOuYzSXC8zM1P/93//p8TERJUsWVILFy5Ut27d1LVrV3Xp0kXdunXTggULblpTYGBgviF3vd69eys9PV1//vOfFRgYqLZt2+bb7/Tp04qKitJ///tfjRs3TsuXL1dKSoomTZokKe+2vNN9fqO+BZn/do/P/GRkZMjPz69QX9soWpwh30UqVKggHx8f7du3L8+069tCQ0OVm5urvXv36pFHHnG2//zzzzp9+rRCQ0OLrM6PP/5YPj4+WrNmjctw57x582457zPPPKNmzZo5f77dXybR0dEaPXq0jh07pg8++OCG/T766CNVq1ZNycnJLm9a4uPjb2t5RS0jI0Pr16/X2LFjNXr0aGf73r17C31ZFy5ckDFGZ8+ela+vr+rUqSNPT09t375dPXr0cPbLzs5WamqqS9v1xo0bp7CwMPXp00eSdPToUZebs4KDg295c1KtWrW0YMECnTlz5qafXa5SpYqaNm2qTZs26fnnn3e5hHCtTZs26eTJk0pOTna5wSwtLe2mdbhDYRyfaWlpLq992I8z5LuIh4eHWrdurU8++URHjx51tu/bt0+rVq1y6fvUU09JkqZOnerSPmXKFEnK987jwqzT4XC4fJTkwIED+d4Jfr1q1aqpdevWzkfTpk1va9nVq1fX1KlTlZCQ4LzL90Y1Sq5nJf/5z3/0xRdf3Nbyilp+dUp59+vtuH7YW/r17PHjjz9W5cqVVaFCBUm/DqO2bt1a8+fPd7mD+IMPPtC5c+fyfDnIVXv27NHbb7+tadOmOcOkYsWKLtdov//+ewUFBd20zsaNG8sYox07dtxynSZMmKD4+Hi9+OKLN+yT37bMzs7WO++8c8vnL26FcXzu3LlTTZo0KfTaUHQ4Q77LjBkzRmvXrlXTpk31/PPPKycnR2+//bbq1KnjcsZRr149xcbGavbs2c6hui+//FLvv/++unbtestrd3eiY8eOmjJlitq3b6/o6GidOHFC06dPV40aNfTNN98U2XKveumll27Zp1OnTkpOTtYf/vAHdezYUWlpaZo5c6Zq166d52sh3cnf318tWrTQ66+/rsuXL+uhhx7S2rVr7+isrkOHDgoJCVFkZKQqVKigQ4cOad68eTp69KgWL17s0ve1115TkyZNFBUVpYEDB+qnn37S5MmT1bZtW7Vv3z7f5x82bJh69uzp8oaoW7du6tKli/P67vLly7VixYqb1tmsWTMFBgZq3bp1N70XQJKioqIUFRV10z5NmjRRQECAYmNjNWTIEDkcDn3wwQdWfpvVnR6fO3bs0KlTp9SlS5diqBaFhUC+y4SHh2vVqlWKi4vT3//+d1WuXFnjxo3T999/n+cu0blz56patWp67733tHTpUgUFBemVV14p8mHZli1b6t1331ViYqKGDh2qsLAwTZo0SQcOHCiWQC6Ivn376vjx45o1a5bWrFmj2rVra/78+VqyZInLH86wQVJSkl588UVNnz5dxhi1bdtWq1atuuk13Jvp16+fFi1apDfffFOnT59WQECAnnjiCSUlJal58+YufRs2bKh169bp5Zdf1rBhw1S6dGn1799fCQkJ+T73ypUrtWXLFu3Zs8elvVOnTnrttdf01ltvyRijhISEPJ8MuJ6Xl5f69OmjJUuWaOLEib9pXa8VGBioFStWaMSIERo1apQCAgIUExOjVq1aFeonDArDnR6fS5YsUZUqVW75RgZ2cRgb3x7itnXt2lXfffddkVxbBNxl//79qlWrllatWuXypSW4sUuXLqlq1aoaOXJkgUaLYA+uId+Frv38p/TrDT4rV67Uk08+6Z6CgCJSrVo19e/fX4mJie4u5a4xb948PfDAAxo0aJC7S8Ft4gz5LlSpUiXndxsfPHhQM2bM0KVLl/T111/r4Ycfdnd5AIDfgGvId6H27dtr4cKFOn78uLy9vdW4cWNNnDiRMAaAuxhnyAAAWIBryAAAWIBABgDAAgW6hpybm6ujR4+qdOnSfFE5AAC34epX0gYHB9/w+/WlAgby0aNH8/yFEwAAUHCHDx9WSEjIDacXKJBLly7tfDJ/f//CqQwAgPtAZmamKleu7MzSGylQIF8dpvb39yeQAQD4DW51yZebugAAsACBDACABQhkAAAsQCADAGABAhkAAAsQyAAAWIBABgDAAgQyAAAWIJABALAAgQwAgAUIZAAALEAgAwBgAQIZAAALEMgAAFiAQAYAwAIEMgAAFiCQAQCwAIEMAIAFCGQAACxAIAMAYAECGQAACxDIAABYgEAGAMACBDIAABYgkAEAsACBDACABQhkAAAsQCADAGABAhkAAAsQyAAAWIBABgDAAgQyAAAWIJABALAAgQwAgAUIZAAALEAgAwBgAQIZAAALEMgAAFiAQAYAwAIEMgAAFiCQAQCwAIEMAIAFCGQAACxAIAMAYAECGQAACxDIAABYgEAGAMACBDIAABYgkAEAsACBDACABQhkAAAsQCADAGABAhkAAAsQyAAAWIBABgDAAgQyAAAWIJABALAAgQwAgAUIZAAALEAgAwBgAQIZAAALEMgAAFiAQAYAwAIEMgAAFiCQAQCwAIEMAIAFCGQAACxAIAMAYAECGQAACxDIAABYgEAGAMACBDIAABYgkAEAsACBDACABQhkAAAsQCADAGABAhkAAAsQyAAAWIBABgDAAgQyAAAWIJABALAAgQwAgAUIZAAALEAgAwBgAQIZAAALEMgAAFiAQAYAwAIEMgAAFiCQAQCwAIEMAIAFCGQAACxAIAMAYAECGQAACxDIAABYgEAGAMACBDIAABYgkAEAsACBDACABQhkAAAsQCADAGABAhkAAAsQyAAAWIBABgDAAgQyAAAWIJABALAAgQwAgAUIZAAALEAgAwBgAQIZAAALEMgAAFiAQAYAwAIEMgAAFiCQAQCwAIEMAIAFCGQAACxAIAMAYAECGQAACxDIAABYgEAGAMACBDIAABYgkAEAsACBDACABQhkAAAsQCADAGABAhkAAAsQyAAAWIBABgDAAgQyAAAWIJABALAAgQwAgAUIZAAALEAgAwBgAQIZAAALEMgAAFiAQAYAwAIEMgAAFiCQAQCwAIEMAIAFCGQAACxAIAMAYAECGQAACxDIAABYgEAGAMACBDIAABYgkAEAsACBDACABQhkAAAsQCADAGABAhkAAAsQyAAAWIBABgDAAgQyAAAWIJABALAAgQwAgAUIZAAALEAgAwBgAQIZAAALeLpz4YcOSenp7qygcDz4oFSlirurAADczdwWyIcOSTVrShcvuquCwuPjI/3wA6EMAPjt3DZknZ5+b4Sx9Ot63Atn+gAA9+EaMgAAFiCQAQCwAIEMAIAFCGQAACxAIAMAYAECGQAACxDIAABYgEAGAMACBDIAABYgkAEAsACBDACABQhkAAAsQCADAGABAhkAAAsQyAAAWIBABgDAAgQyAAAWIJABALAAgQwAgAUIZAAALEAgAwBgAQIZAAALEMgAAFiAQAYAwAIEMgAAFiCQAQCwAIEMAIAFCGQAACxAIAMAYAECGQAACxDIAABYgEAGAMACBDIAABYgkAEAsACBDACABQhkAAAsQCADAGABAhkAAAsQyAAAWIBABgDAAgQyAAAW8HR3Abh/jBw50t0lFIrExMTbnudeWPffst73q3thf0vs8+JGILsBL1bcLzjW7y/s7zvDkDUAABYgkAEAsACBDACABQhkAAAsQCADAGABAhkAAAsQyAAAWIBABgDAAgQyAAAWIJABALAAgQwAgAUIZAAALEAgAwBgAQIZAAALEMgAAFiAQAYAwAIEMgAAFiCQAQCwAIEMAIAFCGQAACxAIAMAYAECGQAACxDIAABYgEAGAMACngXpZIyRJGVmZhbags+dK7SnssK5c1JBN8+lS5eKtphicrvHw/263tK9se7363pLHOsFdb+ud0Gf72qW3ojD3KqHpJ9++kmVK1cunMoAALgPHT58WCEhITecXqBAzs3N1dGjR1W6dGk5HI5CLbAoZWZmqnLlyjp8+LD8/f3dXU6xYb3vr/WW7t91Z71Z77uBMUZnz55VcHCwSpS48ZXiAg1ZlyhR4qapbjt/f/+7aucVFtb7/nO/rjvrfX+5G9e7TJkyt+zDTV0AAFiAQAYAwAL3dCB7e3srPj5e3t7e7i6lWLHe99d6S/fvurPerPe9pEA3dQEAgKJ1T58hAwBwtyCQAQCwAIEMAIAFCGQAACxwTwfy9OnTVbVqVfn4+CgyMlJffvmlu0sqUlu2bFHnzp0VHBwsh8OhTz75xN0lFYuEhAQ9/vjjKl26tCpUqKCuXbvqhx9+cHdZRW7GjBmqW7eu80sSGjdurFWrVrm7rGKXmJgoh8OhoUOHuruUIjdmzBg5HA6XR61atdxdVrE4cuSIYmJiFBgYKF9fXz322GPavn27u8sqVPdsIC9evFjDhw9XfHy8du7cqXr16qldu3Y6ceKEu0srMllZWapXr56mT5/u7lKK1ebNmzV48GBt27ZNKSkpunz5stq2bausrCx3l1akQkJClJiYqB07dmj79u1q2bKlunTpou+++87dpRWbr776SrNmzVLdunXdXUqxefTRR3Xs2DHn47PPPnN3SUUuIyNDTZs21QMPPKBVq1bpf//7nyZPnqyAgAB3l1a4zD2qUaNGZvDgwc6fc3JyTHBwsElISHBjVcVHklm6dKm7y3CLEydOGElm8+bN7i6l2AUEBJi5c+e6u4xicfbsWfPwww+blJQUExUVZV566SV3l1Tk4uPjTb169dxdRrF7+eWXTbNmzdxdRpG7J8+Qs7OztWPHDrVu3drZVqJECbVu3VpffPGFGytDcThz5owkqVy5cm6upPjk5ORo0aJFysrKUuPGjd1dTrEYPHiwOnbs6PI6vx/s3btXwcHBqlatmvr06aNDhw65u6Qi969//UsRERHq3r27KlSooAYNGmjOnDnuLqvQ3ZOBnJ6erpycHFWsWNGlvWLFijp+/LibqkJxyM3N1dChQ9W0aVPVqVPH3eUUuW+//ValSpWSt7e3Bg0apKVLl6p27druLqvILVq0SDt37lRCQoK7SylWkZGReu+997R69WrNmDFDaWlpat68uc6ePevu0orU/v37NWPGDD388MNas2aNnn/+eQ0ZMkTvv/++u0srVAX6a0/A3WLw4MHatWvXfXFdTZJq1qyp1NRUnTlzRh999JFiY2O1efPmezqUDx8+rJdeekkpKSny8fFxdznFqkOHDs7/161bV5GRkQoNDdWHH36o/v37u7GyopWbm6uIiAhNnDhRktSgQQPt2rVLM2fOVGxsrJurKzz35Bnygw8+KA8PD/38888u7T///LOCgoLcVBWK2gsvvKAVK1Zo48aNd/WfC70dXl5eqlGjhsLDw5WQkKB69epp2rRp7i6rSO3YsUMnTpxQw4YN5enpKU9PT23evFn//Oc/5enpqZycHHeXWGzKli2r3/3ud9q3b5+7SylSlSpVyvMm85FHHrnnhuvvyUD28vJSeHi41q9f72zLzc3V+vXr75vra/cTY4xeeOEFLV26VBs2bFBYWJi7S3Kb3NxcXbp0yd1lFKlWrVrp22+/VWpqqvMRERGhPn36KDU1VR4eHu4usdicO3dOP/74oypVquTuUopU06ZN83yUcc+ePQoNDXVTRUXjnh2yHj58uGJjYxUREaFGjRpp6tSpysrK0rPPPuvu0orMuXPnXN4pp6WlKTU1VeXKlVOVKlXcWFnRGjx4sJKSkrRs2TKVLl3aeZ9AmTJl5Ovr6+bqis4rr7yiDh06qEqVKjp79qySkpK0adMmrVmzxt2lFanSpUvnuT+gZMmSCgwMvOfvG4iLi1Pnzp0VGhqqo0ePKj4+Xh4eHurdu7e7SytSw4YNU5MmTTRx4kT16NFDX375pWbPnq3Zs2e7u7TC5e7bvIvSW2+9ZapUqWK8vLxMo0aNzLZt29xdUpHauHGjkZTnERsb6+7SilR+6yzJzJs3z92lFal+/fqZ0NBQ4+XlZcqXL29atWpl1q5d6+6y3OJ++dhTz549TaVKlYyXl5d56KGHTM+ePc2+ffvcXVaxWL58ualTp47x9vY2tWrVMrNnz3Z3SYWOP78IAIAF7slryAAA3G0IZAAALEAgAwBgAQIZAAALEMgAAFiAQAYAwAIEMgAAFiCQAQCwAIEMAIAFCGQAACxAIAMAYAECGQAAC/w/GtCGtwlNH80AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 1\n",
    "print(df[\"animal_name\"].iloc[y_test.index[i]])\n",
    "plot_characteristics(i, predictions[i], y_test, X_test)\n",
    "plt.figure(figsize=(6,3))\n",
    "plot_value_array(i, predictions[i],  y_test)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the first X test images, their predicted labels, and the true labels.\n",
    "# Color correct predictions in blue and incorrect predictions in red.\n",
    "num_rows = 3\n",
    "num_cols = 3\n",
    "num_images = num_rows*num_cols\n",
    "plt.figure(figsize=(10, 10))\n",
    "for i in range(num_images):\n",
    "  plt.subplot(num_rows, num_cols, i+1)\n",
    "  plot_value_array(i, predictions[i], y_test)\n",
    "plt.tight_layout(pad=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Artificial Imunologic Aproach"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Considering the set \"self\" as being that of animals of Class 1 (mammals) and as \"non-self\" the animals of the other classes,<br>\n",
    "use a Negative Binary Selection algorithm to generate a set of 1000 detectors,<br>\n",
    "using different layer limits and using the function 𝑎,<br>\n",
    "described below, as a surface layer function.<br>\n",
    "\n",
    "$$\n",
    "a(x, y) = \\frac{16 - \\text{HammingDistance}(x, y)}{16}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Research"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "“There is probably no system, living or manmade, that can protect itself as effectively, on <br>\n",
    "so many levels, and from so many different diseases and infections as the human immune <br>\n",
    "system.” <br>\n",
    "(N. Forbes, Imitation of Life: How Biology is Inspiring Computing, MIT Press, 2004; p. 98)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Immunological computation is a subfield of artificial intelligence that draws inspiration from the immune system to develop algorithms and models. The immune system is a complex biological system that is responsible for identifying and neutralizing foreign substances in the body. Immunological computation seeks to replicate the behavior of the immune system in software, with the goal of developing algorithms that can solve complex problems."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Artificial immune system (AIS) algorithms have a wide range of applications in various fields. Here are some examples:\n",
    "\n",
    "1. Anomaly detection: AIS algorithms can be used to detect anomalies in data, such as fraud detection in financial transactions or intrusion detection in computer networks.\n",
    "\n",
    "2. Optimization: AIS algorithms can be used to optimize complex systems, such as supply chain management or transportation logistics.\n",
    "\n",
    "3. Robotics: AIS algorithms can be used to control the behavior of robots, such as swarm robotics or autonomous navigation.\n",
    "\n",
    "4. Bioinformatics: AIS algorithms can be used to analyze biological data, such as DNA sequencing or protein structure prediction.\n",
    "\n",
    "5. Image processing: AIS algorithms can be used for image processing tasks, such as image segmentation or object recognition."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Artificial immune system (AIS) algorithms have several advantages over other machine learning algorithms:\n",
    "\n",
    "1. Robustness: AIS algorithms are inherently robust to noise and outliers in the data, which makes them well-suited for real-world applications where data can be noisy or incomplete.\n",
    "\n",
    "2. Adaptability: AIS algorithms are able to adapt to changing environments and can learn from new data in real-time, which makes them well-suited for dynamic systems.\n",
    "\n",
    "3. Diversity: AIS algorithms are able to maintain a diverse population of solutions, which helps to prevent premature convergence and can lead to better overall performance.\n",
    "\n",
    "4. Explainability: AIS algorithms are often more interpretable than other machine learning algorithms, which makes them well-suited for applications where understanding the reasoning behind a decision is important.\n",
    "\n",
    "5. Scalability: AIS algorithms are able to scale to large datasets and high-dimensional feature spaces, which makes them well-suited for big data applications."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While artificial immune system (AIS) algorithms have several advantages, they also have some limitations:\n",
    "\n",
    "1. Computational complexity: AIS algorithms can be computationally expensive, especially for large datasets or high-dimensional feature spaces.\n",
    "\n",
    "2. Parameter tuning: AIS algorithms have several parameters that need to be tuned, such as the population size, mutation rate, and selection method. Finding the optimal values for these parameters can be challenging and time-consuming.\n",
    "\n",
    "3. Lack of theoretical foundation: AIS algorithms are still a relatively new area of research, and there is a lack of theoretical foundation to guide their development and analysis.\n",
    "\n",
    "4. Limited performance on some tasks: While AIS algorithms can perform well on certain types of problems, they may not be the best choice for all tasks. For example, deep learning algorithms may be better suited for image recognition tasks.\n",
    "\n",
    "5. Limited interpretability: While AIS algorithms are often more interpretable than other machine learning algorithms, they can still be difficult to interpret in some cases, especially when dealing with complex data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are several examples of anomaly detection using artificial immune system (AIS) algorithms. Here are a few:\n",
    "\n",
    "1. Network intrusion detection: AIS algorithms can be used to detect network intrusions by analyzing network traffic and identifying patterns that are indicative of an attack.\n",
    "\n",
    "2. Fraud detection: AIS algorithms can be used to detect fraudulent transactions by analyzing patterns in transaction data and identifying transactions that are anomalous.\n",
    "\n",
    "3. Medical diagnosis: AIS algorithms can be used to diagnose medical conditions by analyzing patient data and identifying patterns that are indicative of a particular condition.\n",
    "\n",
    "4. Manufacturing quality control: AIS algorithms can be used to detect defects in manufacturing processes by analyzing sensor data and identifying patterns that are indicative of a defect."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are several examples of robotics applications using artificial immune system (AIS) algorithms. Here are a few:\n",
    "\n",
    "1. Swarm robotics: AIS algorithms can be used to control the behavior of swarms of robots, such as coordinating the movement of a group of drones or robots to perform a task.\n",
    "\n",
    "2. Autonomous navigation: AIS algorithms can be used to help robots navigate in complex environments, such as avoiding obstacles or finding the optimal path to a destination.\n",
    "\n",
    "3. Robot learning: AIS algorithms can be used to help robots learn from their environment and adapt to changing conditions, such as learning to recognize objects or perform tasks.\n",
    "\n",
    "4. Human-robot interaction: AIS algorithms can be used to help robots interact with humans in a more natural and intuitive way, such as recognizing speech or gestures."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O Algoritmo de Seleção Binária Negativa (Negative Selection Algorithm - NSA) é inspirado no sistema imunológico natural, em particular na capacidade do corpo de distinguir entre células \"self\" (próprias) e \"non-self\" (estranhas). Aplicando esse conceito, o objetivo do problema é gerar detectores que identifiquem padrões \"non-self\", ou seja, padrões que não pertencem ao conjunto de interesse (classe 1).\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>animal_name</th>\n",
       "      <th>hair</th>\n",
       "      <th>feathers</th>\n",
       "      <th>eggs</th>\n",
       "      <th>milk</th>\n",
       "      <th>airborne</th>\n",
       "      <th>aquatic</th>\n",
       "      <th>predator</th>\n",
       "      <th>toothed</th>\n",
       "      <th>backbone</th>\n",
       "      <th>breathes</th>\n",
       "      <th>venomous</th>\n",
       "      <th>fins</th>\n",
       "      <th>legs</th>\n",
       "      <th>tail</th>\n",
       "      <th>domestic</th>\n",
       "      <th>catsize</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   animal_name  hair  feathers  eggs  milk  airborne  aquatic  predator  \\\n",
       "0            1     1         0     0     1         0        0         1   \n",
       "\n",
       "   toothed  backbone  breathes  venomous  fins  legs  tail  domestic  catsize  \\\n",
       "0        1         1         1         0     0     1     0         0        1   \n",
       "\n",
       "   type  \n",
       "0     1  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "binary_df.head(1) # antes da normaização das classe, type - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hair</th>\n",
       "      <th>feathers</th>\n",
       "      <th>eggs</th>\n",
       "      <th>milk</th>\n",
       "      <th>airborne</th>\n",
       "      <th>aquatic</th>\n",
       "      <th>predator</th>\n",
       "      <th>toothed</th>\n",
       "      <th>backbone</th>\n",
       "      <th>breathes</th>\n",
       "      <th>venomous</th>\n",
       "      <th>fins</th>\n",
       "      <th>legs</th>\n",
       "      <th>tail</th>\n",
       "      <th>domestic</th>\n",
       "      <th>catsize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   hair  feathers  eggs  milk  airborne  aquatic  predator  toothed  backbone  \\\n",
       "0     1         0     0     1         0        0         1        1         1   \n",
       "\n",
       "   breathes  venomous  fins  legs  tail  domestic  catsize  \n",
       "0         1         0     0     1     0         0        1  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   type\n",
       "0     0"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "indexes_self = y[y['type'] == 0].index\n",
    "indexes_non_self = y[y['type'] != 0].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hair</th>\n",
       "      <th>feathers</th>\n",
       "      <th>eggs</th>\n",
       "      <th>milk</th>\n",
       "      <th>airborne</th>\n",
       "      <th>aquatic</th>\n",
       "      <th>predator</th>\n",
       "      <th>toothed</th>\n",
       "      <th>backbone</th>\n",
       "      <th>breathes</th>\n",
       "      <th>venomous</th>\n",
       "      <th>fins</th>\n",
       "      <th>legs</th>\n",
       "      <th>tail</th>\n",
       "      <th>domestic</th>\n",
       "      <th>catsize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    hair  feathers  eggs  milk  airborne  aquatic  predator  toothed  \\\n",
       "0      1         0     0     1         0        0         1        1   \n",
       "1      1         0     0     1         0        0         0        1   \n",
       "3      1         0     0     1         0        0         1        1   \n",
       "4      1         0     0     1         0        0         1        1   \n",
       "5      1         0     0     1         0        0         0        1   \n",
       "6      1         0     0     1         0        0         0        1   \n",
       "9      1         0     0     1         0        0         0        1   \n",
       "10     1         0     0     1         0        0         1        1   \n",
       "17     1         0     0     1         0        0         0        1   \n",
       "19     0         0     0     1         0        1         1        1   \n",
       "22     1         0     0     1         0        0         0        1   \n",
       "27     1         0     0     1         1        0         0        1   \n",
       "28     1         0     0     1         0        0         0        1   \n",
       "29     1         0     0     1         0        0         1        1   \n",
       "31     1         0     0     1         0        0         0        1   \n",
       "32     1         0     0     1         0        0         0        1   \n",
       "35     1         0     0     1         0        0         0        1   \n",
       "36     1         0     0     1         0        0         0        1   \n",
       "44     1         0     0     1         0        0         1        1   \n",
       "45     1         0     0     1         0        0         1        1   \n",
       "47     1         0     0     1         0        0         1        1   \n",
       "48     1         0     0     1         0        1         1        1   \n",
       "49     1         0     0     1         0        0         1        1   \n",
       "50     1         0     0     1         0        0         1        1   \n",
       "54     1         0     0     1         0        0         1        1   \n",
       "55     1         0     0     1         0        0         0        1   \n",
       "63     1         0     1     1         0        1         1        0   \n",
       "64     1         0     0     1         0        0         1        1   \n",
       "65     1         0     0     1         0        0         0        1   \n",
       "66     0         0     0     1         0        1         1        1   \n",
       "67     1         0     0     1         0        0         1        1   \n",
       "68     1         0     0     1         0        0         1        1   \n",
       "69     1         0     0     1         0        0         1        1   \n",
       "70     1         0     0     1         0        0         0        1   \n",
       "74     1         0     0     1         0        1         1        1   \n",
       "75     1         0     0     1         0        1         1        1   \n",
       "84     1         0     0     1         0        0         0        1   \n",
       "93     1         0     0     1         1        0         0        1   \n",
       "94     1         0     0     1         0        0         0        1   \n",
       "96     1         0     0     1         0        0         0        1   \n",
       "98     1         0     0     1         0        0         1        1   \n",
       "\n",
       "    backbone  breathes  venomous  fins  legs  tail  domestic  catsize  \n",
       "0          1         1         0     0     1     0         0        1  \n",
       "1          1         1         0     0     1     1         0        1  \n",
       "3          1         1         0     0     1     0         0        1  \n",
       "4          1         1         0     0     1     1         0        1  \n",
       "5          1         1         0     0     1     1         0        1  \n",
       "6          1         1         0     0     1     1         1        1  \n",
       "9          1         1         0     0     1     0         1        0  \n",
       "10         1         1         0     0     1     1         0        1  \n",
       "17         1         1         0     0     1     1         0        1  \n",
       "19         1         1         0     1     0     1         0        1  \n",
       "22         1         1         0     0     1     1         0        1  \n",
       "27         1         1         0     0     1     1         0        0  \n",
       "28         1         1         0     0     1     1         0        1  \n",
       "29         1         1         0     0     1     0         1        1  \n",
       "31         1         1         0     0     1     1         1        1  \n",
       "32         1         1         0     0     1     0         0        1  \n",
       "35         1         1         0     0     1     1         1        0  \n",
       "36         1         1         0     0     1     1         0        0  \n",
       "44         1         1         0     0     1     1         0        1  \n",
       "45         1         1         0     0     1     1         0        1  \n",
       "47         1         1         0     0     1     1         0        1  \n",
       "48         1         1         0     0     1     1         0        1  \n",
       "49         1         1         0     0     1     1         0        0  \n",
       "50         1         1         0     0     1     1         0        1  \n",
       "54         1         1         0     0     1     1         0        0  \n",
       "55         1         1         0     0     1     1         0        1  \n",
       "63         1         1         0     0     1     1         0        1  \n",
       "64         1         1         0     0     1     1         0        1  \n",
       "65         1         1         0     0     1     1         1        1  \n",
       "66         1         1         0     1     0     1         0        1  \n",
       "67         1         1         0     0     1     1         0        1  \n",
       "68         1         1         0     0     1     1         1        1  \n",
       "69         1         1         0     0     1     1         0        1  \n",
       "70         1         1         0     0     1     1         1        1  \n",
       "74         1         1         0     1     0     0         0        1  \n",
       "75         1         1         0     1     1     1         0        1  \n",
       "84         1         1         0     0     1     1         0        0  \n",
       "93         1         1         0     0     1     1         0        0  \n",
       "94         1         1         0     0     1     1         0        0  \n",
       "96         1         1         0     0     1     1         0        1  \n",
       "98         1         1         0     0     1     1         0        1  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.iloc[indexes_self]\n",
    "# X.iloc[indexes_non_self]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X.iloc[indexes_self])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 'Mammal',\n",
       " 1: 'Bird',\n",
       " 2: 'Reptile',\n",
       " 3: 'Fish',\n",
       " 4: 'Amphibian',\n",
       " 5: 'Bug',\n",
       " 6: 'Invertebrate'}"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classes - Agents to be implemented"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "from abc import ABC, abstractmethod\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "def hamming_distance(u:np.ndarray, v:np.ndarray) -> int:\n",
    "    \"\"\"\n",
    "    Calculate the Hamming distance between two vectors. The Hamming distance is the number of positions at which the corresponding elements are different.\n",
    "\n",
    "    Args:\n",
    "        u: First vector\n",
    "        v: Second vector\n",
    "\n",
    "    Returns:\n",
    "        distance(int): The Hamming distance between the two vectors\n",
    "    \"\"\"\n",
    "    if len(u) != len(v): # Corner case. If the vectors are of different lengths, raise an error.\n",
    "        raise ValueError(\"Vectors must be of the same length\")\n",
    "\n",
    "    distance = 0\n",
    "    for x, y in zip(u, v):\n",
    "        if x != y:\n",
    "            distance += 1\n",
    "\n",
    "    return distance\n",
    "\n",
    "# Example usage\n",
    "u = np.array([1, 0, 1, 1, 0, 1, 0, 1]) # str1 = \"karolin\"\n",
    "v = np.array([1, 0, 1, 1, 1, 0, 0, 1]) # str2 = \"kathrin\"\n",
    "print(hamming_distance(u, v))  # Output: 2\n",
    "\n",
    "# from scipy.spatial import distance\n",
    "# distance.hamming(u, v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Cells(ABC):\n",
    "    \"\"\"\n",
    "    Abstract class for the cells in the immune system. The cells have a name for identification and a data attribute that stores the characteristics of the cell.\n",
    "    \"\"\"\n",
    "    @abstractmethod\n",
    "    def __init__(self, name:str, data:np.ndarray) -> None:\n",
    "        \"\"\"\n",
    "        Initialize the cell with a name and data.\n",
    "\n",
    "        Attributes:\n",
    "            name(str): The name of the cell\n",
    "            data(np.ndarray): The characteristics of the cell\n",
    "        \"\"\"\n",
    "        self.name = name\n",
    "        self.data = data\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Antibody(Cells):\n",
    "    \"\"\"\n",
    "    Antibody Representation: The solution to the problem is represented as antibodies. These are the immune cells that respond to the antigens.\n",
    "    \"\"\"\n",
    "    def __init__(self, solution) -> None:\n",
    "        super().__init__()\n",
    "        self.solution = solution\n",
    "        self.affinity = 0\n",
    "        self.memory = []\n",
    "\n",
    "    def mutate(self):\n",
    "        # Implement mutation logic here\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Antigen(Cells):\n",
    "    \"\"\"\n",
    "    Antigen Representation: The problem to be solved is represented as antigens. These are the inputs that the immune system will respond to.\n",
    "    \"\"\"\n",
    "    def __init__(self) -> None:\n",
    "        super().__init__()\n",
    "        self.detected = []\n",
    "\n",
    "    def add_detected(self, cell:Cells) -> None:\n",
    "        self.detected.append(cell)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "class AIS:\n",
    "    \"\"\"Artificial immune system (AIS) algorithm.\"\"\"\n",
    "    def __init__(self, antigen_data, population_size=100) -> None:\n",
    "        self.antigen = Antigen(antigen_data)\n",
    "        self.population_size = population_size\n",
    "        self.population:list[Antibody] = self.initialize_population()\n",
    "\n",
    "    def initialize_population(self) -> list:\n",
    "        return [Antibody(self.random_solution()) for _ in range(self.population_size)]\n",
    "\n",
    "    def random_solution(self) -> np.ndarray:\n",
    "        # Generate a random solution\n",
    "        return random.choices(range(2), k=16)  # Example solution\n",
    "\n",
    "    def measure_affinity(self, antibody:Antibody) -> None:\n",
    "        \"\"\"\n",
    "        Affinity Measure: A function to measure the similarity or fitness between antigens and antibodies. This is often analogous to the concept of fitness in genetic algorithms.\n",
    "        \"\"\"\n",
    "        # Implement affinity measurement logic\n",
    "        # Example: inverse of the sum of differences\n",
    "        antibody.affinity = 1 / (1 + sum(abs(a - b) for a, b in zip(self.antigen.data, antibody.solution)))\n",
    "\n",
    "    def clonal_selection(self) -> list:\n",
    "        \"\"\"\n",
    "        Clonal Selection: A process inspired by the biological immune system where antibodies that better match the antigens are selected and cloned. These clones are then subjected to mutation to introduce diversity.\n",
    "        \"\"\"\n",
    "        # Implement clonal selection logic\n",
    "        sorted_population = sorted(self.population, key=lambda ab: ab.affinity, reverse=True)\n",
    "        return sorted_population[:self.population_size // 2]\n",
    "\n",
    "    def hypermutation(self, clones):\n",
    "        \"\"\"\n",
    "        Hypermutation: A process where the cloned antibodies undergo mutation at a higher rate. This helps in exploring the solution space more thoroughly.\n",
    "        \"\"\"\n",
    "        # Implement hypermutation logic\n",
    "        for clone in clones:\n",
    "            clone.mutate()\n",
    "\n",
    "    def negative_selection(self):\n",
    "        \"\"\"\n",
    "        Negative Selection: A process to eliminate antibodies that react to self-antigens (undesirable solutions). This helps in maintaining the quality of the solution pool.\n",
    "        \"\"\"\n",
    "        # Implement negative selection logic\n",
    "        pass\n",
    "\n",
    "    def run(self, generations:int=100):\n",
    "        # Main loop of the AIS algorithm\n",
    "        for generation in range(generations):\n",
    "            # Measure affinity for each antibody\n",
    "            for antibody in self.population:\n",
    "                self.measure_affinity(antibody)\n",
    "\n",
    "            # Clonal selection\n",
    "            clones = self.clonal_selection()\n",
    "\n",
    "            # Hypermutation\n",
    "            self.hypermutation(clones)\n",
    "\n",
    "            # Replace the worst half of the population with clones\n",
    "            self.population[self.population_size // 2:] = clones\n",
    "\n",
    "            # Negative selection (optional)\n",
    "            self.negative_selection()\n",
    "\n",
    "            # Optionally, store the best solution found so far\n",
    "            best_solution = max(self.population, key=lambda ab: ab.affinity)\n",
    "            print(f\"Generation {generation}: Best affinity = {best_solution.affinity}\")\n",
    "\n",
    "        return best_solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# if __name__ == \"__main__\":\n",
    "#     # Example antigen data\n",
    "#     antigen_data = [random.randint(0, 100) for _ in range(10)]\n",
    "\n",
    "#     # Initialize the AIS algorithm\n",
    "#     ais = AIS(antigen_data, population_size=100)\n",
    "\n",
    "#     # Run the AIS algorithm\n",
    "#     best_solution = ais.run(generations=100)\n",
    "\n",
    "#     # Print the best solution found\n",
    "#     print(\"Best solution found:\", best_solution.solution)\n",
    "#     print(\"Best solution affinity:\", best_solution.affinity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generated detectors: ['0101', '1111', '0111', '0111', '0011']\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "def random_string(length):\n",
    "    \"\"\"Generate a random binary string of given length.\"\"\"\n",
    "    return ''.join(random.choice('01') for _ in range(length))\n",
    "\n",
    "def affinity(r0, s):\n",
    "    \"\"\"Calculate the affinity between two strings (e.g., Hamming distance).\"\"\"\n",
    "    return sum(1 for a, b in zip(r0, s) if a == b) / len(r0)\n",
    "\n",
    "def NSA_censor(S, epsilon, N, L):\n",
    "    \"\"\"\n",
    "    Negative Selection Algorithm to generate a set of detectors.\n",
    "    \n",
    "    Parameters:\n",
    "    S (list of str): Set of self strings.\n",
    "    epsilon (float): Affinity threshold.\n",
    "    N (int): Number of detectors to generate.\n",
    "    L (int): Length of each detector string.\n",
    "    \n",
    "    Returns:\n",
    "    list of str: Set of detectors.\n",
    "    \"\"\"\n",
    "    R = []\n",
    "    t = 1\n",
    "    while t <= N:\n",
    "        r0 = random_string(L)\n",
    "        flag = 0\n",
    "        for s in S:\n",
    "            aff = affinity(r0, s)\n",
    "            if aff >= epsilon:\n",
    "                flag = 1\n",
    "                break\n",
    "        if flag == 0:\n",
    "            R.append(r0)\n",
    "            t += 1\n",
    "    return R\n",
    "\n",
    "# Example usage\n",
    "S = ['1100', '1010', '1001']  # Example self set\n",
    "epsilon = 0.75  # Affinity threshold\n",
    "N = 5  # Number of detectors to generate\n",
    "L = 4  # Length of each detector string\n",
    "\n",
    "detectors = NSA_censor(S, epsilon, N, L)\n",
    "print(\"Generated detectors:\", detectors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Detection flag: nonself detected. 0110 is detected by 0111\n"
     ]
    }
   ],
   "source": [
    "def NSA_monitor(S_star, R, epsilon):\n",
    "    \"\"\"\n",
    "    Monitor for non-self elements using the set of detectors.\n",
    "    \n",
    "    Parameters:\n",
    "    S_star (list of str): Set of non-self strings.\n",
    "    R (list of str): Set of detectors.\n",
    "    epsilon (float): Affinity threshold.\n",
    "    \n",
    "    Returns:\n",
    "    str: Detection flag (\"nonself detected\" or \"no nonself detected\").\n",
    "    \"\"\"\n",
    "    for s in S_star:\n",
    "        for r in R:\n",
    "            aff = affinity(r, s)\n",
    "            if aff >= epsilon:\n",
    "                return f\"nonself detected. {s} is detected by {r}\"\n",
    "    return \"no nonself detected\"\n",
    "\n",
    "# Example usage\n",
    "S_star = ['0110', '0011', '1111']  # Example non-self set\n",
    "R = ['1000', '0101', '0010', '0001', '1110']  # Example detectors\n",
    "R = detectors\n",
    "epsilon = 0.75  # Affinity threshold\n",
    "\n",
    "flag = NSA_monitor(S_star, R, epsilon)\n",
    "print(\"Detection flag:\", flag)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generated detectors: ['0001110000011010', '0101101000101010', '0111110000100101', '0100110101100011', '0101011110111001', '0001001110100100', '1110110010100100', '1000001110010001', '1101011110001101', '0011111110111100']\n",
      "Detection flag: no nonself detected\n"
     ]
    }
   ],
   "source": [
    "# Example usage - testing the NSA algorithm\n",
    "S = X.iloc[indexes_self].values.copy()  # Example self set\n",
    "S = S[:20]\n",
    "epsilon = 0.96  # Affinity threshold\n",
    "N = 10  # Number of detectors to generate\n",
    "L = len(S[0])  # Length of each detector string\n",
    "\n",
    "detectors = NSA_censor(S, epsilon, N, L) # Example detectors\n",
    "print(\"Generated detectors:\", detectors)\n",
    "S_star = X.iloc[indexes_non_self].values.copy()  # Example non-self set\n",
    "\n",
    "flag = NSA_monitor(S_star, detectors, epsilon)\n",
    "print(\"Detection flag:\", flag)"
   ]
  }
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