ML-aula-7-Agrupamento.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "Clf9EWTOVfrS"
   },
   "outputs": [],
   "source": [
    "# Exemplo de Agrupamento (Clustering) de Machine Learning utilizando o dataset:\n",
    "# iris\n",
    "# e com o algoritmo de K-Means\n",
    "\n",
    "import pandas as pd\n",
    "import io"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 73,
     "resources": {
      "http://localhost:8080/nbextensions/google.colab/files.js": {
       "data": "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",
       "headers": [
        [
         "content-type",
         "application/javascript"
        ]
       ],
       "ok": true,
       "status": 200,
       "status_text": ""
      }
     }
    },
    "id": "tvVd-HWrV90Z",
    "outputId": "7108b94d-1557-47c3-8efa-106e472b44cc"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "     <input type=\"file\" id=\"files-2b27c16c-8433-4235-8cbe-59f9954158fa\" name=\"files[]\" multiple disabled\n",
       "        style=\"border:none\" />\n",
       "     <output id=\"result-2b27c16c-8433-4235-8cbe-59f9954158fa\">\n",
       "      Upload widget is only available when the cell has been executed in the\n",
       "      current browser session. Please rerun this cell to enable.\n",
       "      </output>\n",
       "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving iris.csv to iris.csv\n"
     ]
    }
   ],
   "source": [
    "# carregando dados para o Google Colab\n",
    "from google.colab import files\n",
    "uploaded = files.upload()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "yOJP704EXcv_"
   },
   "outputs": [],
   "source": [
    "# carregando o arquivo para o dataframe iris\n",
    "iris = pd.read_csv(io.BytesIO(uploaded['iris.csv']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 203
    },
    "id": "eaG7eOpoWBc8",
    "outputId": "0b6c070e-72f1-4390-dc7f-fcf103190e01"
   },
   "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>comprimento-sepala</th>\n",
       "      <th>largura-sepala</th>\n",
       "      <th>comprimento-petala</th>\n",
       "      <th>largura-petala</th>\n",
       "      <th>classificacao</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   comprimento-sepala  largura-sepala  ...  largura-petala  classificacao\n",
       "0                 5.1             3.5  ...             0.2    Iris-setosa\n",
       "1                 4.9             3.0  ...             0.2    Iris-setosa\n",
       "2                 4.7             3.2  ...             0.2    Iris-setosa\n",
       "3                 4.6             3.1  ...             0.2    Iris-setosa\n",
       "4                 5.0             3.6  ...             0.2    Iris-setosa\n",
       "\n",
       "[5 rows x 5 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mostrando os dados no dataframe iris\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5q1F2yYAYwtb"
   },
   "source": [
    "Para saber sobre o que é sépala e pétala consulte:\n",
    "https://www.sobiologia.com.br/conteudos/Morfofisiologia_vegetal/morfovegetal8.php"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "c_DcZWoWWo-e",
    "outputId": "d10e516e-6604-4809-f18d-d696c2e45125"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "comprimento-sepala    float64\n",
       "largura-sepala        float64\n",
       "comprimento-petala    float64\n",
       "largura-petala        float64\n",
       "classificacao          object\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# verificando os tipos do dados\n",
    "iris.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "hZiZ8DU_ZHLH"
   },
   "outputs": [],
   "source": [
    "# dividindo os dados entre variáveis dependentes ( X ) e independente ( y )\n",
    "X = iris.iloc[:, 0:4]\n",
    "y = iris.iloc[:, 4]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "Ze9sW3ndZ2xu"
   },
   "outputs": [],
   "source": [
    "# importando bibliotecas para aplicar a classificação de Machine Learning\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "e9zSV9DUZWWw"
   },
   "outputs": [],
   "source": [
    "# aplicando o kmeans no conjunto de variáveis dependentes\n",
    "# desta forma omitimos as classes de cada instância de flor\n",
    "# definindo 3 como o número de clusters - k\n",
    "\n",
    "kmeans = KMeans(n_clusters=3, random_state=0).fit(X)\n",
    "X_clustered = kmeans.fit_predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "QYwoB4eKaFvn"
   },
   "outputs": [],
   "source": [
    "# para verificar o agrupamento podemos criar uma tabela de resultados contendo a coluna das classes esperadas\n",
    "# e a coluna dos clusters criados:\n",
    "resultado = iris[['classificacao']].copy()\n",
    "resultado['numero-cluster'] = X_clustered\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 417
    },
    "id": "4r20Ya0nbUTv",
    "outputId": "6fc5a3ab-8e9a-4cfe-92d4-0c2b05f18324"
   },
   "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>classificacao</th>\n",
       "      <th>numero-cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      classificacao  numero-cluster\n",
       "0       Iris-setosa               1\n",
       "1       Iris-setosa               1\n",
       "2       Iris-setosa               1\n",
       "3       Iris-setosa               1\n",
       "4       Iris-setosa               1\n",
       "..              ...             ...\n",
       "145  Iris-virginica               0\n",
       "146  Iris-virginica               2\n",
       "147  Iris-virginica               0\n",
       "148  Iris-virginica               0\n",
       "149  Iris-virginica               2\n",
       "\n",
       "[150 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visualizando o resultado:\n",
    "resultado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "re69C_ZvidFY"
   },
   "outputs": [],
   "source": [
    "# vamos verificar os acertos\n",
    "quantidade = iris.groupby(\"classificacao\")[\"classificacao\"].count()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "u9EcgSCHi1Xf",
    "outputId": "a4338ff4-48ca-4da8-87f5-72be5585b862"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "classificacao\n",
       "Iris-setosa        50\n",
       "Iris-versicolor    50\n",
       "Iris-virginica     50\n",
       "Name: classificacao, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visualizando a quantidade\n",
    "quantidade.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "DTkSvE8Li-QW"
   },
   "outputs": [],
   "source": [
    "# vamos verificar os acertos\n",
    "quantidadeNova = resultado.groupby(\"numero-cluster\")[\"numero-cluster\"].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vxA2Xhz_jKYR",
    "outputId": "0ed52aa9-583a-4041-efbb-5a960bf7c0a0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numero-cluster\n",
       "0    38\n",
       "1    50\n",
       "2    62\n",
       "Name: numero-cluster, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visualizando a quantidade\n",
    "quantidadeNova.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "JHwJQ2g7ihqA"
   },
   "outputs": [],
   "source": [
    "# aplicação do Método Cotovelo, onde o KMeans é executado várias vezes com diferentes quantidades de clusters\n",
    "# para indicar qual é o número ótimo de clusters\n",
    "# utilizando a soma dos quadrados intra-clusters\n",
    "def calcular_cluster(data):\n",
    "    wcss = []\n",
    "    for n in range(2, 21):\n",
    "        kmeans = KMeans(n_clusters=n)\n",
    "        kmeans.fit(X=data)\n",
    "        wcss.append(kmeans.inertia_)\n",
    "\n",
    "    return wcss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "RXWAISuTKgeQ"
   },
   "outputs": [],
   "source": [
    "# excluindo a classificacao dos dados, mantendo somente os valores\n",
    "cotovelo = iris.drop('classificacao', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Euekp7zGKBte",
    "outputId": "95423206-fa3d-4a43-f44b-517f3ec458a5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[152.36870647733906,\n",
       " 78.94084142614602,\n",
       " 57.31787321428571,\n",
       " 46.53558205128205,\n",
       " 38.964787851037855,\n",
       " 34.28464983108679,\n",
       " 29.963345571095573,\n",
       " 28.212540935672514,\n",
       " 26.580944911126025,\n",
       " 24.27805459899578,\n",
       " 22.79127242348295,\n",
       " 21.576753808103927,\n",
       " 20.424332689369454,\n",
       " 19.27000865800866,\n",
       " 17.71079936729937,\n",
       " 17.0872905982906,\n",
       " 16.425385939224178,\n",
       " 15.609051438757323,\n",
       " 14.83964643689644]"
      ]
     },
     "execution_count": 19,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculando o cluster, para isto executando a função\n",
    "calcular_cluster(cotovelo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "k1_NIRutMUz3"
   },
   "outputs": [],
   "source": [
    "# salvando o resultado para poder visualizar como gráfico\n",
    "saida = calcular_cluster(cotovelo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "Qb9vwbS1Lo6I"
   },
   "outputs": [],
   "source": [
    "# importar biblioteca gráfica para plotar\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "id": "b6OpdvoHMKCO",
    "outputId": "1e7a0ba2-5999-457a-f57b-0fa7314d59fa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5577326850>]"
      ]
     },
     "execution_count": 22,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizando o gráfico com a saida\n",
    "plt.plot(saida)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Agrupamento.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}