Python_para_ML_09.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Python para ML - 09.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOK3vdhYaDhm2A7eLVyEBfI",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ricardorocha86/Python-para-Machine-Learning/blob/master/Python_para_ML_09.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QvWwixKvVqYe",
        "colab_type": "text"
      },
      "source": [
        "![texto alternativo](https://raw.githubusercontent.com/ricardorocha86/Python-para-Machine-Learning/master/Imagens/capa%20-%20python%20para%20ml%2009.jpg)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "-b5mjxUoSzou"
      },
      "source": [
        "# **Python para Machine Learning** - Parte 9"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e5ci8BYxxrLn",
        "colab_type": "text"
      },
      "source": [
        "Nesse notebook vamos ver:\n",
        "- A Validação Holdout\n",
        "- A Validação Cruzada K-Fold\n",
        "- Exercícios\n",
        "- Links Úteis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wuN1mXjMTLuZ",
        "colab_type": "text"
      },
      "source": [
        "## **A Validação Holdout**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aCgrsXXFAhMm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "url = 'https://raw.githubusercontent.com/ricardorocha86/Dados-Redes-Sociais/master/Social_Network_Ads.csv'\n",
        "ads = pd.read_csv(url)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jtu4nnceuBhr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Fazendo a Divisão em Teste e Treinamento\n",
        "from sklearn.model_selection import train_test_split\n",
        "Xtreino, Xteste, Ytreino, Yteste = train_test_split(ads.loc[:, ['Age', 'EstimatedSalary']], \n",
        "                                                    ads['Purchased'], \n",
        "                                                    test_size = 0.25)\n",
        "\n",
        "#Padronizando os Dados\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "Xtreino = sc.fit_transform(Xtreino)\n",
        "Xteste = sc.transform(Xteste)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j5UL71JLEUHD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def Modelagem(k):\n",
        "\n",
        "    #Treinando o Modelo\n",
        "    from sklearn.neighbors import KNeighborsClassifier\n",
        "    knn = KNeighborsClassifier(n_neighbors = k)\n",
        "    knn.fit(Xtreino, Ytreino)\n",
        "\n",
        "    #Verificando o ACC\n",
        "    from sklearn.metrics import accuracy_score \n",
        "    preditos_teste = knn.predict(Xteste)\n",
        "    acc_teste = accuracy_score(preditos_teste, Yteste)\n",
        "    \n",
        "    return acc_teste\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sB-J-VlDEw-i",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ce42f363-751c-4ce7-effc-d14bc24799dc"
      },
      "source": [
        "Modelagem(15)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.89"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 479
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vEkuhGJRzKms",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "f45be25b-62ad-4773-b1fe-c97c8fc8fd0a"
      },
      "source": [
        "import numpy as np\n",
        "kas = np.arange(1, 100, 2)\n",
        "kas"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33,\n",
              "       35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63, 65, 67,\n",
              "       69, 71, 73, 75, 77, 79, 81, 83, 85, 87, 89, 91, 93, 95, 97, 99])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 480
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7nQZToRWC5e-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "accs = []\n",
        "\n",
        "for i in kas:\n",
        "    accs.append(Modelagem(i))\n",
        "\n",
        "accs\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KSsUhHzgDkG_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "24895a0d-a911-4315-fd1f-160714d30710"
      },
      "source": [
        "kas[accs.index(max(accs))]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 486
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kOPcxOnKDMNK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "a2cb2647-53bb-4e04-8a3c-f5bdf274179b"
      },
      "source": [
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#Qual é o valor de K que atinge o máximo da acurácia?\n",
        "kmax = kas[accs.index(max(accs))]\n",
        "\n",
        "\n",
        "plt.axvline(kmax, color = 'gray', linestyle = 'dashed')\n",
        "plt.axhline(max(accs), color = 'gray', linestyle = 'dashed')\n",
        "\n",
        "plt.plot(kas, accs, linewidth = 2)\n",
        "plt.ylim(min(accs), 1)\n",
        "frase = 'Acurácia máxima igual a {} usando K = {}.'.format(max(accs), kmax)\n",
        "plt.title(frase)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "nCanz0P5oy6I",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import accuracy_score     \n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def VariaTesteTreino():\n",
        "    #Fazendo a Divisão em Teste e Treinamento\n",
        "    Xtreino, Xteste, Ytreino, Yteste = train_test_split(ads.loc[:, ['Age', 'EstimatedSalary']], \n",
        "                                                        ads['Purchased'], \n",
        "                                                        test_size = 0.25)\n",
        "\n",
        "    #Padronizando os Dados\n",
        "    sc = StandardScaler()\n",
        "    Xtreino = sc.fit_transform(Xtreino)\n",
        "    Xteste = sc.transform(Xteste)\n",
        "\n",
        "    def Modelagem(k):\n",
        "        #Treinando o Modelo\n",
        "        knn = KNeighborsClassifier(n_neighbors = k)\n",
        "        knn.fit(Xtreino, Ytreino)\n",
        "\n",
        "        #Verificando o ACC\n",
        "        preditos_teste = knn.predict(Xteste)\n",
        "        acc_teste = accuracy_score(preditos_teste, Yteste)\n",
        "        return acc_teste\n",
        "\n",
        "    #Fazendo a variação dentre as possibilidades de K (de 1 a 99, de 2 em 2)\n",
        "\n",
        "    kas = np.arange(1,100,2)\n",
        "\n",
        "    accs = []\n",
        "    for i in kas:\n",
        "        accs.append(Modelagem(i))\n",
        " \n",
        "\n",
        "    #Qual é o valor de K que atinge o máximo da acurácia?\n",
        "    kmax = kas[accs.index(max(accs))]\n",
        "    return kmax"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hy9TrW7UH06X",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6e3ed071-99df-4940-f10e-259ae2790369"
      },
      "source": [
        "VariaTesteTreino()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "37"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 500
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R9G0APqmKAgv",
        "colab_type": "text"
      },
      "source": [
        "## **K-Fold**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "QR0M2zTbiHON",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "url = 'https://raw.githubusercontent.com/ricardorocha86/Dados-Redes-Sociais/master/Social_Network_Ads.csv'\n",
        "ads = pd.read_csv(url)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tQ452RQSiUWX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "c3ae136b-9701-427c-a1dd-420b5e71355b"
      },
      "source": [
        "ads.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>User ID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>EstimatedSalary</th>\n",
              "      <th>Purchased</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>15624510</td>\n",
              "      <td>Male</td>\n",
              "      <td>19.0</td>\n",
              "      <td>19000.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>15810944</td>\n",
              "      <td>Male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>20000.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>15668575</td>\n",
              "      <td>Female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>43000.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>15603246</td>\n",
              "      <td>Female</td>\n",
              "      <td>27.0</td>\n",
              "      <td>57000.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>15804002</td>\n",
              "      <td>Male</td>\n",
              "      <td>19.0</td>\n",
              "      <td>76000.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    User ID  Gender   Age  EstimatedSalary  Purchased\n",
              "0  15624510    Male  19.0          19000.0          0\n",
              "1  15810944    Male  35.0          20000.0          0\n",
              "2  15668575  Female  26.0          43000.0          0\n",
              "3  15603246  Female  27.0          57000.0          0\n",
              "4  15804002    Male  19.0          76000.0          0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 502
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "HeI0f5L1iHOv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "ca62c8ad-ddeb-46aa-84ac-230450a5c82c"
      },
      "source": [
        "ads.drop(['User ID', 'Gender'], axis = 1, inplace = True)\n",
        "ads.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Age</th>\n",
              "      <th>EstimatedSalary</th>\n",
              "      <th>Purchased</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19.0</td>\n",
              "      <td>19000.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>35.0</td>\n",
              "      <td>20000.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>26.0</td>\n",
              "      <td>43000.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>27.0</td>\n",
              "      <td>57000.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>19.0</td>\n",
              "      <td>76000.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Age  EstimatedSalary  Purchased\n",
              "0  19.0          19000.0          0\n",
              "1  35.0          20000.0          0\n",
              "2  26.0          43000.0          0\n",
              "3  27.0          57000.0          0\n",
              "4  19.0          76000.0          0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 503
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vxq5BN3kimGA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ads_vars = ads[['Age', 'EstimatedSalary']]\n",
        "ads_resp = ads['Purchased']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Xr7AUwiiw-t",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "f5adbaf6-774c-458a-c2c7-121fed0b7695"
      },
      "source": [
        "ads_vars"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.78179743, -1.49004624],\n",
              "       [-0.25358736, -1.46068138],\n",
              "       [-1.11320552, -0.78528968],\n",
              "       [-1.01769239, -0.37418169],\n",
              "       [-1.78179743,  0.18375059],\n",
              "       [-1.01769239, -0.34481683],\n",
              "       [-1.01769239,  0.41866944],\n",
              "       [-0.54012675,  2.35674998],\n",
              "       [-1.20871865, -1.07893824],\n",
              "       [-0.25358736, -0.13926283],\n",
              "       [-1.11320552,  0.30121002],\n",
              "       [-1.11320552, -0.52100597],\n",
              "       [-1.6862843 ,  0.47739916],\n",
              "       [-0.54012675, -1.51941109],\n",
              "       [-1.87731056,  0.35993973],\n",
              "       [-0.82666613,  0.30121002],\n",
              "       [ 0.89257019, -1.3138571 ],\n",
              "       [ 0.70154394, -1.28449224],\n",
              "       [ 0.79705706, -1.22576253],\n",
              "       [ 0.98808332, -1.19639767],\n",
              "       [ 0.70154394, -1.40195167],\n",
              "       [ 0.89257019, -0.60910054],\n",
              "       [ 0.98808332, -0.84401939],\n",
              "       [ 0.70154394, -1.40195167],\n",
              "       [ 0.79705706, -1.37258681],\n",
              "       [ 0.89257019, -1.46068138],\n",
              "       [ 1.08359645, -1.22576253],\n",
              "       [ 0.89257019, -1.16703281],\n",
              "       [-0.82666613, -0.78528968],\n",
              "       [-0.63563988, -1.51941109],\n",
              "       [-0.63563988,  0.12502088],\n",
              "       [-1.01769239,  1.97500684],\n",
              "       [-1.59077117, -1.5781408 ],\n",
              "       [-0.92217926, -0.75592482],\n",
              "       [-1.01769239,  0.59485858],\n",
              "       [-0.25358736, -1.25512738],\n",
              "       [-0.44461362, -1.22576253],\n",
              "       [-0.73115301, -0.60910054],\n",
              "       [-1.11320552,  0.06629116],\n",
              "       [-1.01769239, -1.13766796],\n",
              "       [-1.01769239, -1.54877595],\n",
              "       [-0.44461362, -0.55037082],\n",
              "       [-0.25358736,  1.123426  ],\n",
              "       [-0.73115301, -1.60750566],\n",
              "       [-0.92217926,  0.41866944],\n",
              "       [-1.39974491, -1.46068138],\n",
              "       [-1.20871865,  0.27184516],\n",
              "       [-1.01769239, -0.46227625],\n",
              "       [-0.73115301,  1.91627713],\n",
              "       [-0.63563988,  0.56549373],\n",
              "       [-1.30423178, -1.1083031 ],\n",
              "       [-1.87731056, -0.75592482],\n",
              "       [-0.82666613,  0.38930459],\n",
              "       [-0.25358736, -1.37258681],\n",
              "       [-1.01769239, -0.34481683],\n",
              "       [-1.30423178, -0.4329114 ],\n",
              "       [-1.39974491, -0.63846539],\n",
              "       [-0.92217926,  0.27184516],\n",
              "       [-1.49525804, -1.51941109],\n",
              "       [-0.54012675,  1.38770971],\n",
              "       [-1.01769239, -1.46068138],\n",
              "       [-1.20871865,  0.50676401],\n",
              "       [-1.39974491, -0.10989798],\n",
              "       [-0.54012675,  1.47580428],\n",
              "       [ 2.03872775,  0.38930459],\n",
              "       [-1.30423178, -0.34481683],\n",
              "       [-1.30423178, -1.49004624],\n",
              "       [-1.39974491,  0.35993973],\n",
              "       [-1.49525804, -0.19799255],\n",
              "       [-0.63563988, -0.05116826],\n",
              "       [-1.20871865,  0.30121002],\n",
              "       [-1.30423178, -1.25512738],\n",
              "       [-1.6862843 , -1.37258681],\n",
              "       [-0.44461362,  1.27025028],\n",
              "       [-0.54012675, -1.51941109],\n",
              "       [-0.34910049,  1.24088543],\n",
              "       [-1.87731056, -0.52100597],\n",
              "       [-1.49525804, -1.25512738],\n",
              "       [-0.92217926,  0.50676401],\n",
              "       [-1.11320552, -1.54877595],\n",
              "       [-0.73115301,  0.30121002],\n",
              "       [ 0.12846516, -0.81465453],\n",
              "       [-1.6862843 , -0.60910054],\n",
              "       [-0.25358736,  0.53612887],\n",
              "       [-0.73115301, -0.2273574 ],\n",
              "       [-0.63563988,  1.41707457],\n",
              "       [-1.30423178, -0.4329114 ],\n",
              "       [-0.92217926,  0.4480343 ],\n",
              "       [-1.11320552,  0.33057487],\n",
              "       [-0.25358736, -0.57973568],\n",
              "       [-1.49525804,  0.33057487],\n",
              "       [-0.73115301,  1.35834485],\n",
              "       [-1.11320552, -1.60750566],\n",
              "       [-0.82666613, -1.22576253],\n",
              "       [-0.82666613,  0.38930459],\n",
              "       [-0.25358736, -0.75592482],\n",
              "       [-0.25358736, -1.3138571 ],\n",
              "       [-0.92217926,  1.56389885],\n",
              "       [-0.25358736,  0.09565602],\n",
              "       [-0.92217926, -0.96147882],\n",
              "       [-1.01769239,  0.53612887],\n",
              "       [-0.92217926, -0.31545197],\n",
              "       [-0.54012675,  0.47739916],\n",
              "       [-0.44461362,  2.32738512],\n",
              "       [-1.78179743, -1.43131652],\n",
              "       [-1.59077117,  0.06629116],\n",
              "       [-1.11320552, -1.02020853],\n",
              "       [-1.01769239,  0.56549373],\n",
              "       [-1.11320552,  0.47739916],\n",
              "       [ 0.03295203,  0.30121002],\n",
              "       [ 0.12846516,  0.03692631],\n",
              "       [-0.0625611 ,  0.03692631],\n",
              "       [ 0.03295203, -0.25672226],\n",
              "       [-0.0625611 , -0.4329114 ],\n",
              "       [ 0.41500455,  0.30121002],\n",
              "       [ 0.22397829, -0.37418169],\n",
              "       [-0.25358736,  0.15438573],\n",
              "       [-0.15807423, -0.52100597],\n",
              "       [ 0.22397829, -0.31545197],\n",
              "       [ 0.31949142, -0.31545197],\n",
              "       [-0.15807423,  0.15438573],\n",
              "       [-0.0625611 ,  0.06629116],\n",
              "       [ 0.22397829,  0.15438573],\n",
              "       [-0.25358736, -0.49164111],\n",
              "       [ 0.31949142, -0.55037082],\n",
              "       [ 0.12846516, -0.25672226],\n",
              "       [ 0.41500455, -0.13926283],\n",
              "       [-1.11320552, -1.1083031 ],\n",
              "       [-0.73115301, -1.54877595],\n",
              "       [-1.11320552,  0.41866944],\n",
              "       [-0.63563988, -0.34481683],\n",
              "       [-0.44461362, -1.13766796],\n",
              "       [-0.73115301,  0.50676401],\n",
              "       [-1.59077117, -0.05116826],\n",
              "       [-0.92217926, -0.4329114 ],\n",
              "       [-1.39974491, -0.19799255],\n",
              "       [-1.6862843 ,  0.35993973],\n",
              "       [-0.73115301,  1.09406114],\n",
              "       [-0.92217926, -0.31545197],\n",
              "       [-1.78179743, -1.3138571 ],\n",
              "       [-1.78179743,  0.4480343 ],\n",
              "       [-1.87731056, -0.05116826],\n",
              "       [-0.25358736, -0.31545197],\n",
              "       [-0.73115301,  0.56549373],\n",
              "       [-0.34910049, -1.3138571 ],\n",
              "       [-1.30423178,  0.56549373],\n",
              "       [-1.01769239,  0.77104772],\n",
              "       [ 0.31949142, -1.16703281],\n",
              "       [-0.82666613, -0.25672226],\n",
              "       [-1.6862843 ,  0.12502088],\n",
              "       [-1.11320552, -1.60750566],\n",
              "       [ 0.31949142, -0.72655996],\n",
              "       [-0.63563988,  0.18375059],\n",
              "       [-0.15807423, -0.57973568],\n",
              "       [ 0.22397829, -0.66783025],\n",
              "       [-0.63563988, -1.60750566],\n",
              "       [ 0.79705706, -0.31545197],\n",
              "       [-0.82666613,  0.15438573],\n",
              "       [-1.11320552, -1.16703281],\n",
              "       [-0.54012675,  1.91627713],\n",
              "       [-0.54012675,  0.88850715],\n",
              "       [-1.20871865,  0.59485858],\n",
              "       [-0.0625611 , -1.07893824],\n",
              "       [-0.25358736, -0.93211396],\n",
              "       [-0.44461362, -0.02180341],\n",
              "       [-1.87731056,  0.47739916],\n",
              "       [-1.49525804, -0.4329114 ],\n",
              "       [-0.25358736,  0.03692631],\n",
              "       [-0.82666613,  2.29802026],\n",
              "       [-0.82666613, -0.66783025],\n",
              "       [-1.59077117,  0.53612887],\n",
              "       [-0.34910049,  1.32898   ],\n",
              "       [-1.11320552,  1.41707457],\n",
              "       [-0.34910049, -0.78528968],\n",
              "       [-0.34910049,  0.06629116],\n",
              "       [-1.39974491, -1.22576253],\n",
              "       [-0.25358736, -0.66783025],\n",
              "       [-1.20871865, -1.40195167],\n",
              "       [-1.30423178, -1.37258681],\n",
              "       [-0.63563988, -1.04957339],\n",
              "       [-1.11320552, -1.5781408 ],\n",
              "       [-0.63563988,  0.03692631],\n",
              "       [-0.54012675,  1.38770971],\n",
              "       [-0.44461362, -0.78528968],\n",
              "       [-0.44461362, -0.28608712],\n",
              "       [-0.63563988, -0.10989798],\n",
              "       [-1.6862843 ,  0.35993973],\n",
              "       [-0.44461362, -0.84401939],\n",
              "       [-0.25358736,  0.06629116],\n",
              "       [-0.92217926, -1.1083031 ],\n",
              "       [-1.30423178,  0.41866944],\n",
              "       [-1.78179743, -1.28449224],\n",
              "       [-0.82666613, -0.78528968],\n",
              "       [-1.78179743,  0.00756145],\n",
              "       [-0.92217926,  0.56549373],\n",
              "       [-0.34910049, -0.78528968],\n",
              "       [-0.73115301,  0.27184516],\n",
              "       [-1.6862843 , -0.99084367],\n",
              "       [-1.11320552,  0.30121002],\n",
              "       [-0.25358736, -1.40195167],\n",
              "       [-0.25358736, -0.9027491 ],\n",
              "       [ 1.08359645,  0.12502088],\n",
              "       [ 0.12846516,  1.88691227],\n",
              "       [ 0.31949142,  0.03692631],\n",
              "       [ 1.94321462,  0.917872  ],\n",
              "       [ 0.89257019, -0.66783025],\n",
              "       [ 1.65667523,  1.76945285],\n",
              "       [ 1.37013584,  1.29961514],\n",
              "       [ 0.22397829,  2.12183112],\n",
              "       [ 0.79705706, -1.40195167],\n",
              "       [ 0.98808332,  0.77104772],\n",
              "       [ 1.37013584,  2.35674998],\n",
              "       [ 2.03872775, -0.81465453],\n",
              "       [-0.25358736, -0.34481683],\n",
              "       [ 0.89257019, -0.78528968],\n",
              "       [ 2.13424088,  1.123426  ],\n",
              "       [ 1.08359645, -0.13926283],\n",
              "       [ 0.22397829,  0.2424803 ],\n",
              "       [ 0.79705706,  0.77104772],\n",
              "       [ 2.03872775,  2.15119598],\n",
              "       [ 0.31949142,  0.30121002],\n",
              "       [-0.25358736,  0.62422344],\n",
              "       [-0.0625611 ,  2.18056084],\n",
              "       [ 2.13424088,  0.94723686],\n",
              "       [-0.25358736, -0.28608712],\n",
              "       [-0.0625611 , -0.49164111],\n",
              "       [-0.15807423,  1.65199342],\n",
              "       [ 1.75218836,  1.85754742],\n",
              "       [ 0.22397829,  0.06629116],\n",
              "       [ 0.41500455,  0.30121002],\n",
              "       [-0.25358736,  2.26865541],\n",
              "       [ 0.12846516, -0.81465453],\n",
              "       [ 0.22397829,  1.09406114],\n",
              "       [ 1.08359645,  0.47739916],\n",
              "       [ 0.03295203,  1.24088543],\n",
              "       [ 0.79705706,  0.27184516],\n",
              "       [ 0.22397829, -0.37418169],\n",
              "       [-0.0625611 ,  0.30121002],\n",
              "       [ 0.79705706,  0.35993973],\n",
              "       [ 1.46564897,  2.15119598],\n",
              "       [ 0.41500455,  2.32738512],\n",
              "       [ 0.03295203, -0.31545197],\n",
              "       [ 1.17910958,  0.53612887],\n",
              "       [ 1.75218836,  1.00596657],\n",
              "       [ 0.31949142,  0.06629116],\n",
              "       [ 1.27462271,  2.23929055],\n",
              "       [-0.25358736, -0.57973568],\n",
              "       [ 1.84770149,  1.53453399],\n",
              "       [ 0.31949142, -0.52100597],\n",
              "       [-0.25358736,  0.80041258],\n",
              "       [ 0.60603081, -0.9027491 ],\n",
              "       [-0.0625611 , -0.52100597],\n",
              "       [ 0.98808332,  1.88691227],\n",
              "       [-0.0625611 ,  2.23929055],\n",
              "       [ 1.17910958, -0.75592482],\n",
              "       [ 1.37013584,  0.59485858],\n",
              "       [ 0.31949142,  0.06629116],\n",
              "       [ 0.22397829, -0.37418169],\n",
              "       [ 1.94321462,  0.74168287],\n",
              "       [ 0.70154394,  1.7988177 ],\n",
              "       [-0.25358736,  0.21311545],\n",
              "       [-0.15807423,  2.18056084],\n",
              "       [ 1.65667523,  1.62262856],\n",
              "       [-0.25358736,  0.06629116],\n",
              "       [ 0.98808332,  0.59485858],\n",
              "       [ 0.41500455,  1.123426  ],\n",
              "       [ 0.22397829,  0.15438573],\n",
              "       [-0.0625611 ,  0.12502088],\n",
              "       [ 0.89257019,  2.18056084],\n",
              "       [ 0.22397829, -0.25672226],\n",
              "       [ 0.51051768,  1.85754742],\n",
              "       [ 2.03872775,  0.18375059],\n",
              "       [ 2.13424088, -0.81465453],\n",
              "       [ 0.12846516,  1.06469629],\n",
              "       [ 1.84770149, -1.28449224],\n",
              "       [ 1.84770149,  0.12502088],\n",
              "       [ 0.03295203,  0.03692631],\n",
              "       [ 1.08359645,  0.53612887],\n",
              "       [ 1.37013584, -0.93211396],\n",
              "       [ 1.17910958, -0.99084367],\n",
              "       [ 2.03872775,  0.53612887],\n",
              "       [-0.25358736, -0.25672226],\n",
              "       [-0.0625611 ,  0.00756145],\n",
              "       [ 1.37013584, -1.43131652],\n",
              "       [ 0.98808332,  2.09246627],\n",
              "       [-0.0625611 ,  0.68295315],\n",
              "       [-0.0625611 , -0.2273574 ],\n",
              "       [ 0.98808332,  2.0043717 ],\n",
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              "       [-0.0625611 ,  0.2424803 ],\n",
              "       [ 0.12846516,  1.88691227],\n",
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              "       [ 0.41500455,  0.09565602],\n",
              "       [ 0.51051768,  1.24088543],\n",
              "       [ 0.70154394,  0.27184516],\n",
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              "       [ 1.94321462, -0.93211396],\n",
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              "       [-0.0625611 ,  1.97500684],\n",
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              "       [ 0.22397829, -0.28608712],\n",
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              "       [ 1.27462271,  1.88691227],\n",
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              "       [ 0.03295203, -0.57973568],\n",
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              "       [-0.0625611 ,  0.15438573],\n",
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              "       [ 1.5611621 ,  0.00756145],\n",
              "       [ 0.31949142,  0.06629116],\n",
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              "       [ 0.89257019, -0.57973568],\n",
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              "       [ 0.41500455,  1.00596657],\n",
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              "       [ 0.03295203,  0.03692631],\n",
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              "       [ 2.03872775,  1.76945285],\n",
              "       [-0.0625611 ,  0.30121002],\n",
              "       [ 0.79705706, -1.1083031 ],\n",
              "       [ 0.79705706,  0.12502088],\n",
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              "       [ 0.31949142,  0.50676401],\n",
              "       [ 1.94321462, -1.37258681],\n",
              "       [ 0.41500455, -0.16862769],\n",
              "       [ 0.98808332, -1.07893824],\n",
              "       [ 0.60603081,  2.03373655],\n",
              "       [ 1.08359645, -1.22576253],\n",
              "       [ 1.84770149, -1.07893824],\n",
              "       [ 1.75218836, -0.28608712],\n",
              "       [ 1.08359645, -0.9027491 ],\n",
              "       [ 0.12846516,  0.03692631],\n",
              "       [ 0.89257019, -1.04957339],\n",
              "       [ 0.98808332, -1.02020853],\n",
              "       [ 0.98808332, -1.07893824],\n",
              "       [ 0.89257019, -1.37258681],\n",
              "       [ 0.70154394, -0.72655996],\n",
              "       [ 2.13424088, -0.81465453],\n",
              "       [ 0.12846516, -0.31545197],\n",
              "       [ 0.79705706, -0.84401939],\n",
              "       [ 1.27462271, -1.37258681],\n",
              "       [ 1.17910958, -1.46068138],\n",
              "       [-0.15807423, -1.07893824],\n",
              "       [ 1.08359645, -0.99084367]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 508
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n9Hui665idHJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Padronizando os Dados\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "ads_vars = sc.fit_transform(ads_vars)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mXIm-mGvh-oH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import KFold"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6OsgdZujh29F",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "02da2d10-15b3-4033-ad20-340600a6f05e"
      },
      "source": [
        "modelo = KNeighborsClassifier(n_neighbors = 13)\n",
        "\n",
        "kf = KFold(n_splits = 400, shuffle = True)\n",
        "accs_vc = cross_val_score(modelo, ads_vars, ads_resp, cv = kf, scoring = 'accuracy')\n",
        "\n",
        "print(accs_vc)\n",
        "print('Acurácia: {:.2f}%'.format(100*accs_vc.mean()))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1. 0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 1. 1.\n",
            " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
            " 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
            " 1. 1. 1. 0. 1. 0. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 0. 1. 1. 1. 1. 1. 1. 1.\n",
            " 0. 1. 0. 1. 1. 1. 1. 0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1.\n",
            " 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 0. 1. 0. 1. 1. 1. 1. 1. 1. 1. 1.\n",
            " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
            " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 1. 1. 1. 1. 1. 1. 1. 1.\n",
            " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
            " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
            " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
            " 1. 1. 1. 0. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
            " 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 1. 0. 1. 1. 1.\n",
            " 1. 1. 1. 1. 0. 1. 1. 1. 1. 0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1.\n",
            " 0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 0. 1. 1.\n",
            " 1. 1. 1. 1. 1. 0. 1. 0. 1. 1. 0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
            " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
            "Acurácia: 91.25%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C8pXjWdrjY39",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def MeuKFold(k):\n",
        "    modelo = KNeighborsClassifier(n_neighbors = k)\n",
        "\n",
        "    kf = KFold(n_splits = 50, shuffle = True)\n",
        "    accs_vc = cross_val_score(modelo, ads_vars, ads_resp, cv = kf, scoring = 'accuracy')\n",
        "\n",
        "    return accs_vc.mean()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o2hFnevIjWzJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "kas = np.arange(1,100,2)\n",
        "\n",
        "accs = []\n",
        "for i in kas:\n",
        "    accs.append(MeuKFold(i))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IPtPMwgnn_Ad",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "accs"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i0sGr2OhoSj9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "490aaee1-7204-4741-d6b6-f6db09497754"
      },
      "source": [
        "kmax = kas[accs.index(max(accs))]\n",
        "\n",
        "plt.axvline(kmax, color = 'gray', linestyle = 'dashed')\n",
        "plt.axhline(max(accs), color = 'gray', linestyle = 'dashed')\n",
        "plt.plot(kas, accs, linewidth = 2)\n",
        "plt.ylim(min(accs), 1)\n",
        "frase = 'Acurácia máxima igual a {} usando K = {}.'.format(max(accs), kmax)\n",
        "plt.title(frase)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YEOdN1U0a0ElE2olIFHAdMMNzAhFJEJHSZT0MvOm+3gIMFpEIEYnEuRB7UtVNMMvMzCQzMzPQYRhjQliViV5Vi4B7gFk4SXqqqmaIyJMicqU72YVApoisA5oDT7nDPwY2AN/h1OOvVNVPfbsJtVtqaiqpqamBDsMYE8K8qqNX1c+Bz8sMe9Tj9cc4Sb3sfMXAHdWM0RhjTDVYFwjGGBPkLNEbY0yQs0RvjDFBzjo187NRo0YFOgRjTIizRO9ncXFxgQ7BGBPirOrGz1avXs3q1asDHYYxJoRZid7P0tPTAejZs2eAIzHGhCor0RtjTJCzRG+MMUHOEr0xxgQ5S/TGGBPk7GKsn40ePTrQIRhjQpwlej+LiYkJdAjGmBBnVTd+tmLFClasWBHoMIwxIcwSvZ9ZojfGBJolemOMCXJeJXoRGSYimSKSJSIPlTO+jYjMFZFVIrJARJI9xrUWkS9EZI2IfC8ibX0XvjHGmKpUmehFJByYCFwOdAfGiEj3MpP9FXhHVc8CngSe9hj3DvCsqnYD+gA7fBG4McYY73hTou8DZKlqtqoWAFOAkWWm6Q7Mc1/PLx3vHhAiVHU2gKoeVNXDPoncGGOMV7xpXtkS2OrxPgfoW2aalcDVwPPAKKChiMQDnYG9IjINaAfMAR5ynyUbEsaNGxfoEIwxIc5XF2MfBAaLyHJgMJALFOMcSAa6488D2gPjy84sIhNEJF1E0vPz830UUu0QGRlJZGRkoMMwxoQwbxJ9LtDK432yO+w4Vd2mqleram/gt+6wvTil/xVutU8R8B/gnLIrUNVJqpqiqimJiYmnuSm1U1paGmlpaYEOwxgTwrxJ9GlAJxFpJyJRwHXADM8JRCRBREqX9TDwpse8jUWkNHtfDHxf/bDrjoyMDDIyMgIdhjEmhFWZ6N2S+D3ALGANMFVVM0TkSRG50p3sQiBTRNYBzYGn3HmLcapt5orId4AAr/l8K4wxxlTIq75uVPVz4PMywx71eP0x8HEF884GzqpGjMYYY6rB7ow1xpggZ4neGGOCnHVT7Gfjx48PdAjGmBBnJXpjjAlyluj9bMmSJSxZsiTQYRhjQpglej9bt24d69atC3QYxpgQZoneGGOCnCV6Y4wJcpbojTEmyFnzSj+zniuNMYFmid7PrD96Y0ygWdWNMcYEOUv0frZw4UIWLlwY6DCMMSHMEr2fbdy4kY0bNwY6DGNMCLNEb4wxQc4SvTHGBDlL9MYYE+S8SvQiMkxEMkUkS0QeKmd8GxGZKyKrRGSBiCSXGd9IRHJE5EVfBV5XxMTEEBMTE+gwjDEhrMp29CISDkwELgVygDQRmaGqng/5/ivwjqq+LSIXA08DN3iM/wOwyHdh1x2jR48OdAjGmBDnTYm+D5ClqtmqWgBMAUaWmaY7MM99Pd9zvIici/PA8C+qH64xxphT5U2ibwls9Xif4w7ztBK42n09CmgoIvEiEgb8DXiwshWIyAQRSReR9Pz8fO8iryPmzJnDnDlzAh2GMSaE+epi7IPAYBFZDgwGcoFi4C7gc1XNqWxmVZ2kqimqmpKYmOijkGqHnJwccnIq3XxjjPErb/q6yQVaebxPdocdp6rbcEv0ItIAuEZV94pIf2CgiNwFNACiROSgqp50QdcYY4x/eJPo04BOItIOJ8FfB4z1nEBEEoDdqloCPAy8CaCq4zymGQ+kWJI3xpiaVWXVjaoWAfcAs4A1wFRVzRCRJ0XkSneyC4FMEVmHc+H1KT/Fa4wx5hR51U2xqn4OfF5m2KMerz8GPq5iGZOByaccYR3XqFGjQIdgjAlx1h+9n1199dVVT2SMMX5kXSAYY0yQs0TvZzNnzmTmzJmBDsMYE8Ks6sbP8vLyAh2CMSbEWYneGGOCnCV6Y4wJcpbojTEmyFkdvZ/Fx8cHOgRjTIizRO9nI0aMCHQIxpgQZ1U3xhgT5CzR+9mnn37Kp59+GugwjDEhzKpu/GzXrl2BDsEYE+KsRG+MMUHOEr0xxgQ5S/TGGBPkrI7ez5KSkgIdgjEmxFmi97Nhw4YFOgRjTIjzqupGRIaJSKaIZInISc98FZE2IjJXRFaJyAIRSXaH9xKRVBHJcMdd6+sNMMYYU7kqE72IhAMTgcuB7sAYEeleZrK/Au+o6lnAk8DT7vDDwI2q2gMYBvxDRBr7Kvi6YNq0aUybNi3QYRhjQpg3Jfo+QJaqZqtqATAFGFlmmu7APPf1/NLxqrpOVde7r7cBO4BEXwReV+zfv5/9+/cHOgxjTAjzJtG3BLZ6vM9xh3laCZQ+HHUU0FBETujNS0T6AFHAhrIrEJEJIpIuIun5+fnexm6MMcYLvmpe+SAwWESWA4OBXKC4dKSInAG8C9ysqiVlZ1bVSaqaoqopiYkhVeA3xhi/86bVTS7QyuN9sjvsOLda5moAEWkAXKOqe933jYD/Ar9V1aW+CNoYY4z3vEn0aUAnEWmHk+CvA8Z6TiAiCcBut7T+MPCmOzwKmI5zofZjXwZeVyQnJwc6BGNMiKsy0atqkYjcA8wCwoE3VTVDRJ4E0lV1BnAh8LSIKLAIuNudfTQwCIgXkfHusPGqusK3m1F7XXLJJYEOwRgT4kRVAx3DCVJSUjQ9PT3QYRhjTJ0iIstUNaW8cdbXjZ9NnTqVqVOnBjoMY0wIsy4Q/Ozw4cOBDsEYE+JqXaLftWsXkydPPmFYjx49OO+88ygsLOT9998/aZ5evXrRq1cvDh8+XG7pOSUlhZ49e7Jv3z6mT59+0vj+/fvTpUsXdu7cyWeffXbS+EGDBtG+fXvy8vKYOXPmSeOHDBlCq1at2Lp1K3Pnzj0+vETh880lNI6tx02qbNy4kUWLFp00//Dhw0lISCAzM5PU1NSTxo8aNYq4uDhWr15NedVao0ePJiYmhhUrVrBixcmXP8aNG0dkZCRpaWlkZGScNH78+PEALFmyhHXr1p0wLjIyknHjxgGwcOFCNm7ceML4mJgYRo8eDcCcOXPIyck5YXyjRo24+mrnFouZM2eSl5d3wvj4+Pjjz9X99NNPT3pQS1JS0vH+gqZNm3bSzWfJycnHr4NMnTr1pANru3btGDx4MADvv/8+hYWFJ4zv3Lkz559/PsBJ3zuou9+9UsOGDSMpKYns7Gz77oXYd8+TVd340bd7o0k/2pw5uxrzp8/XUNuuhxhjQoNdjPWT7fuOMPS5RRw4VoSgKMLdF3XgwaFdEJFAh2eMCTJ2MbaGqSq/m76aA8eKOKdZOPf0rk94mDBx/gZemJfl03XtO1LIlG+2sHh9frXPGHL3HuG1Rdnk7LHrCsYEk1pXRx8MPl21nblrd9AwOoKXbxtM80bRdOq6jfumLOe52euIigjjzsEdqrWOQ8eKmLxkE68u3MD+o0UApLRpwgNDu9C/Q3wVc59s6+7DXPtqKtv2HeWZWWsZ06c1d1/UkeaNoqsVpzEm8Kzqxsd2Hyrg0ucWsutQAU9ffSZj+rQ+Pu7fy3J48OOVqMLvh3fn1gvanfLyjxYW897Szby0YAO7DxUAcE7rxmzceYg9h52LPed3iOeBoV04t00Tr5a5be8Rrp2UytbdR2gRF832/UdRhXoRYdzQrw13XtiBhAb1TjlWY0zNqazqxhK9j93/0QqmL8+lf/t4Pri9Lx988AHA8dYDH36zhYenfQfAE1f24Mb+bbyqsz9WVMxHaVt5cV4WOw4cA6B368Y8OLQL53eI51BBMW99uZFJi7M54JbwL+qSyANDu9CzZVyFy92x/yjXTlrKxp2HODs5jndv60vevqP8ffY6/rfaaaUQExXOTee35cxyliNAn3ZNibcDgTEBZYm+hszP3MHNb6URHRnGzF8Oom1C7PFmU6XNyADeXrKJx2Y4Tc36tG3KA0M707d9+dUthcUlTPs2h3/OzSJ37xEAerRoxINDu3Bhl8STDhL7Dhfy+pfZvPnlRg4VOB2IDuuRxP2XdqZLUsMTpt158BjXTVpK1o6D9GjRiA9u60dcTOTx8atz9/Hc7HXMW7uj0u1OaBDFlAn96disQdUfkjHGLyzR14CDx4oY+txCtu07yiNXdGXCIKcOvrxEDzDlmy38eeZa9rrVLRd0TOBXQztzTmunuqW4RJmxMpd/zFnP5l3OxdHOzRtw/yWdGdYzqcqzgF0Hj/HKwg28k7qZY0UliMCIs1pw3yWdaJ/YgD2HChjz2lLW5h2gS/OGfDihH01jo8pd1rdb9vDB11s4dKzopHGbdh1mzfb9NGtYj4/u6E+7hFivPzNjjO+EfKIvKCohTCAivPqNjHbsP8qxopO61OelBRv48JstnJUcx7Sfn398XRUleoADRwt588tNvL44mwNuEh3StRmXdG/OG19uJGvHQQDaJcRy3yWdGH5WC8LDTq1p5o79R5k4P4sPv9lKQbHzOVx9TjJr8/azOnc/HRJjmTKhP4kNT6/q5UhBMbdMTiM1exdnxEUz9Y7+tGoac1rLMsacvpBO9IcLihj87ALaNI3hwwn9iDzNZL9s8x7+9kUmSzbsqnCaiDDh03svoNsZjY4PqyzRl9p7uIDXFmfz1lebOFxw/HktJDepzy+HdGJU75bVPkjl7j3Ci/PWMzU9h+ISZ5+3jY/hozv6V7tlzaFjRdz05jekb95DcpP6TL2jPy0a16/WMo0xpyakE/2a7fu5/PnFAPz6si7cfVHHU5p/de4+/vZFJvMznUccxkSFl1vFESbCLQPaMn7AiS1plixZAnD8VufK7Dx4jFcWbOC73H2MOLsFo1NaERXh21sdNu86xAvzssjdc4S/jj6blj5KyAeOFnL9G9+wcutenx1AjDHeC+lEvyRrJ2Nf/xqAqPAwPv/lQK8uGmbmHeDvs9cxM+PHlie3DGjH7QPbn3DB0vxo3+FCxr6+lIxtTpXQR3f0t2aZxtSQkL4zdpfb1hygoLiEh/69ipKSig9uqspfZ2Uy7PlFzMzIo15EGLcPbMfi31zEg5d1sSRfibiYSN67tS9dkxqyIf8QP3sllVkZedbHjzEB5lWiF5FhIpIpIlki8lA549uIyFwRWSUiC0Qk2WPcTSKy3v27yZfBe6P0pqIRZ7egWcN6pG/ew3tfb65w+udmr+PF+VmEi3BT/zYs+s1F/PYn3U+7nfjkyZPL7ZkuWDWJjeK92/rSuXkDNu48xB3vLmPkxK9YkLnDEr4xAVJloheRcGAicDnQHRgjIt3LTPZXnOfCngU8CTztztsUeAzoC/QBHhMR727X9JHSRN8uPoYnR/YE4C//W1tufy4vzF3PC/OyCA8TXhjTmydG9rR65tOQ0KAen957AU9c2YPEhvVYlbOP8W+l8bNXUkmt5GK2McY/vCnR9wGyVDVbVQuAKcDIMtN0B+a5r+d7jL8MmK2qu1V1DzAbGFb9sL1XmuibxEYxrGcSV5yZxKGCYn47ffUJJcxXF27gb7PXESbw3OizufzMM2oyzKBTL8K5m3bRry/it1d0o2lsFOmb9zDmtaWMfW0pyzbvqfGYZn//A/d+uJwlG3bW+LqNCSRvEn1LYKvH+xx3mKeVwNXu61FAQxGJ93JeRGSCiKSLSHp+fr63sXulNNGXtpR5/MoexNWPZOG6fKYvzwXgra828vT/1iICz/z0bEb2OilEc5rqR4Vz+6D2LPrNRTw4tDONoiNYsmEX17y8hJvf+obvcvbVSBwzV2/nzveW8enKbYx97Wv3YLO7RtZtTKD56mLsg8BgEVkODAZygeLKZ/mRqk5S1RRVTUlMTPRRSI5dh5x+YeJjnTr2Zg2j+d1PugHw5GffM3F+Fk98+j0AT111Jj89N7n8BZlqaVAvgnsu7sTi/7uYX1zckdiocOZn5jPixS+54910MvMO+G3dc9c4JfniEuWSbs09DjapNXqwMSZQvOmmOBdo5fE+2R12nKpuwy3Ri0gD4BpV3SsiucCFZeZdUI14T9meQ04XA55t3396bjIzVm5j8fqdPDsrE3A6GBvbt3W5y6iOHj16+HyZdVlc/Uh+NbQL4we049WFG3g7dROzMn7gi+9/YFiPJFrHn9pdtWe2jOPynmdUeMfwonX5/Py9byksViYMas/Dl3dl/9Ei3liczRtfbmR+Zj7zM/O5pFtzOjQ7ufsGQRjQMZ4LOrTbToMAABmuSURBVCbU6ANj9h0p5F/pW8k/eOyU5ktqFM2N/due8h3UJrhV2Y5eRCKAdcAQnASfBoxV1QyPaRKA3apaIiJPAcWq+qh7MXYZcI476bfAuapa4Tmzr9vRp/xxDjsPHmPpw0NIivvxwurW3YcZ+vdFHCks5rdXdOP2Qe19tk7jvR37j/LSgg188PUWCopP7lrCG52aNeBXl3bmsh5JhHkkuCUbdnLzW2kcKyph/PlteWxE9xOS9e5DBccPNkcLK193n7ZN+dXQzvSroPM5Xzl4rIjJX21k0qLs488ZOFWPDu/OLafRBbap26p9w5SIXAH8AwgH3lTVp0TkSSBdVWeIyE9xWtoosAi4W1WPufPeAjziLuopVX2rsnX5MtGXlCidfvc/ikuUzD8Oo15E+Anj12zfz+5DBQzomOCT9ZWn9IHAkZHW/r4yuXuPMHN1HgXl9CNUkWNFxfwrPeeEXj0fGNqZi7o0I33zHm584xuOFBYzpk9r/jSqZ4Ul8h37j/Lf77aXm+z3HSlkStqWCjuf85WjhcW8m7qZlxf++JyB/u3jGdg5AcG70vnuQ8d4bfFG6keG88X9g6zPoRATsnfG7j1cQK8nZ9OwXgTfPXGZT5Z5qrzp68acvmNFxUxN28oLHv30n92qMRt2HOTgsSKuOSeZZ3961gkl/VNVXudzF3dtxsheLQgr5+DRvFE057VtctrPGTin9DkDp1EAuffD5Xy6chsXdEzg3Vv7nFZ1k6ryXe4+khpF08yaF9cZlSX6oH6UoGfTShOc6kWEc0P/tvwspRXvLd3Myws2sHLrXsC5Se6ZaiZ5gIbRkfzykk7cdH6b453PzVu7o9J++nu3bswDl3ZhQMf4cpNtYXEJ/16W4/Q75J6R9GzZiAeGduHCzic/Z8Bbj43ozpfr8/kyayf/WpbD6JRWVc/kIXXDLv72RSbpm/fQJj6GOb8afNodAZraIyQSfUX9rJvgER0Zzm0D2zOmT2ve/3ozh44Vc8/FHX16UbJxTBS/vqwrNw9ox1tfbWTTrnIeoq6wNHsXy7fs5fo3vqZvu6Y8eFkXzmvbFCj/OQNdmjfk/ks7c1mP5tW+4JvQoB6PjejBfR+t4I+ffc+FnRO9KpWX1zvr5l2H+c/yXH52igcLU/sEdaIv7ecm3hJ9yIitF3H8oS/+ktCgHr++rGuF4w8dK+Lt1E28ujCbrzfu5mevpDKocyKX9WjOW19tOv6cgfYJsdx3aWeGn3lGtc86PI3s1YL/rMhlQWY+j36SwSs3nFvhtGV7Z20YHcGEge1pHBPJ7z/J4KUFG7j6nGRrxVPHBXWi32MlehMAsfUiuOvCjlzfrw1vLN7IG19uZNG6fBatc5JpcpP63HdJZ67q1cInD8MpS0R4atSZDH1uITMz8vjfd9tPutM7M+8Az83OZFbGD07MUeHcckE7brvA6Z21qLiE1xZvZOPOQ3y2apvdRFjHBXWi31ULEn2vXr0Ctm4TWI2iI7n/0s6MP78tkxZns2LLXoaffQY/O9f3zxkoq2Xj+jx0eVd+/0kGv/8kg/4d4mkcE0V2/kH+MWc9n67ahipER4ZxY/+23DGo/Qkd90WEh3HXhR14aNp3TJyfxYizWvj0rMPUrKBO9LWhjt4SvWkSG8X/Dau4qsdfxvVtw4yV20jbtIdHpn9HbFQE//42hxJ1ns0wtm9r7rqwQ4V1+Fefk8w/565n3Q8H+eL7PIb1tP6f6qqgvpxeGxL94cOHOXy4nIt2xvhZWJjw52vOIioijM+/y+Nfy3IIE2FMn1bM//WFPH5lj0ov1EZFhHHnhc71jhfmZVk303WYJXo/mzp1KlOnTg3Y+k1o65DYgN9e0Y2o8DCu7t2SuQ8M5umrz/L6EZKjU1qR2LAeGdv2syDTtx0Omppjid6YIHfT+W3J/OMwnru2F23iT+7PpzLRkeFMGOh0D/LPeeutVF9HhUSiL+250phQVZ32+eP6taZJTCTLt+w9oZ29qTtCItE3bWAlemNOV0xUBLe5pfoX5q0PcDTmdARtoj9SUMyRwmKiwsOIjQqvegZjTIVu6N+GRtERLM3eTdome2BLXRO0ib70gSNNY6NqtB/xslJSUkhJKbefIWPqjEbRkYwf4HR9/NwX6zhS4PVzhUwtELSJvrZciO3Zsyc9e/YMaAzG+MItA9rSoF4Eqdm7GPTsfN5esoljRZbw6wJL9H62b98+9u2zR9WZuq9xTBRv33IeZ7aMI//AMR6bkcFFzy5gyjdbKDzNh8aYmmGJ3s+mT5/O9OnTAxqDMb5ybpumzLhnAJNuOJeuSQ3Ztu8oD037jiF/W8h/luda88tayhK9MeaUiAhDeyTx+S8G8sKY3rRPiGXL7sPc99EK/jIz05J9LeRVoheRYSKSKSJZIvJQOeNbi8h8EVkuIqvcRw8iIpEi8raIfCcia0TkYV9vQEWsi2Jj/CssTBhxdgu+uH8Qfxp1JhFhwisLN/CPOdYEs7apMtGLSDgwEbgc6A6MEZHuZSb7HTBVVXsD1wEvucN/BtRT1TOBc4E7RKStb0Kv3B57upQxNSLC7SDt+et6Eybw/Nz1TJyfFeiwjAdvSvR9gCxVzVbVAmAKMLLMNAo0cl/HAds8hseKSARQHygA9lc7ai9Yid6YmvWTs87gudG9EIFnZ2Xy+uLsQIdkXN50U9wS2OrxPgfoW2aax4EvROReIBa4xB3+Mc5BYTsQA9yvqifdbSEiE4AJAK1btz6F8CtWW+ro+/fvH9D1G1OTrurdkoKiEn7z71X88b9riIpw+rs3geWri7FjgMmqmgxcAbwrImE4ZwPFQAugHfCAiLQvO7OqTlLVFFVNSUxM9ElApVU38QHu/qBLly506dIloDEYU5NGn9eKP1zl3Dvy6CcZfPjNlgBHZLxJ9LmA59OBk91hnm4FpgKoaioQDSQAY4GZqlqoqjuAr4AauU20tOqmSUxgE/3OnTvZuXNnQGMwpqbd0K8Njw53LuU9Mv07Pl6WE+CIQps3iT4N6CQi7UQkCudi64wy02wBhgCISDecRJ/vDr/YHR4L9APW+ib0ihUWl7DvSCEizk0egfTZZ5/x2WefBTQGYwLhlgva8dDlXVGF33y8khkrt1U9k/GLKhO9qhYB9wCzgDU4rWsyRORJEbnSnewB4HYRWQl8CIxXpzHtRKCBiGTgHDDeUtVV/tgQT3sO/1iat6fXGxM4dw7uwP2XdKZE4f6PVjBz9fZAhxSSvHpmrKp+DnxeZtijHq+/BwaUM99BnCaWNWrPoUIAmsRE1vSqjTFl/GJIRwqKi5k4fwP3fricV64PY0i35oEOK6QE5Z2xpT1X2gNHjAk8EeHBoV24fWA7CouVn7/3LYvW2WMJa1JQJvra0rTSGOMQER65ohs39W9DQXEJt7+TzpIN1kihpgRlot9Ti54sNWjQIAYNGhToMIwJOBHhsRE9GNOnFceKSrh1cro9xKSGBGWiL21a2TTALW4A2rdvT/v2J906YExICgsTnrrqTK45J5kjhcXc/FYay7fsCXRYQS8oE31tqrrJy8sjLy8v0GEYU2uEhQnP/PQsRpzdgoPHirjxzW9YnWvPbPCnoEz0u2rJXbEAM2fOZObMmYEOw5haJTxMeG702QzrkcSBo0Vc/8bXrNleI91ghaSgTPR7asldscaYikWGh/HPMb0Z0rUZew8Xcv3rX5O140CgwwpKQZnoa1PVjTGmYlERYUwcdw4DOyWw61ABY1/7mo07DwU6rKATlIm+NlXdGGMqFx0Zzms3ptC/fTw7Dhxj7GtL2br7cKDDCipBl+hV1apujKljoiPDeWN8Cue1bcL2fUf5yT8XM3F+FoeOFQU6tKAQdIl+/9EiikqU2KhwoiPDAx0OQ4YMYciQIYEOw5haLyYqgjfHn8fgzonsP1rEs7MyGfjMfF5fnM3RwuJAh1enBV2i312LbpYCaNWqFa1atap6QmMMDaMjefuWPnxwW1/Oad2Y3YcK+ON/1zDomfm8k7qJY0WW8E9HECZ6p5+bprWkn5utW7eydevWqic0xhx3fscE/v3z83lr/Hn0aNGIHQeO8egnGVz290V+qb8vKVE+W7WNkS9+yaiXvuKLjDycDniDg1e9V9Ylu92eK5vWkp4r586dC8D48eMDG4gxdYyIcFHXZlzYJZFZGT/w7Ky1bMg/xNjXlzL1jv6cEVe/2utQVeas2cHfvshkbd6PTTsnvLuMs5Lj+NWlnRncORGRut3duZXojTG1mogwrGcS0+8ewNnJcWzdfYSxr33Njv1HT3uZqsrCdflcNfErbn8nnbV5BzgjLpqnRvXk8RHdSWhQj1U5+xj/Vho/eyWV1A27fLhFNS/oSvTWtNKY4NQoOpJ3bunLmNeW8v32/Yx9/WumTOhHQoOTC3XFJcqMlbl8vCyHo4UlJ43ff6SQ9TsOApDQoB73XNSB6/q0Pt6A49rzWvNO6iZeWbiB9M17GPPaUi7u2oyXxp1TKxp5nCqvEr2IDAOeB8KB11X1z2XGtwbeBhq70zzkPqwEETkLeBVoBJQA56nq6R+Kq7D7oN0sZUywiouJ5L3b+nLdpFTW/XCQ61//mg9v70cT9/deUqLMzMjjudnryHITeUUax0Ty88EduLF/W+pHnZi860eFc8fgDozt25q3vtrEa4uzmbd2Bx9+s4WbB7Tz2/b5S5WJXkTCcR4JeCmQA6SJyAz3qVKlfofziMGXRaQ7ztOo2opIBPAecIOqrhSReKDQ51vhYffh2tNzpTHG95rGRvH+bf24dlIqa/MOcMObX/P+rf1I37ybv32xju/dPnNaNq7PPRd3pFOzBictQwS6JjUitl7lKbBhdCS/GNKJLkkNuePdZby6MJuxfVtTL6Juleq9KdH3AbJUNRtARKYAIwHPRK84JXaAOKD0KcBDgVWquhJAVf1e0VXbuj8YNmxYoEMwJugkNqzHB26yX527nwF/mcdB9+aq5o3qcc/Fnbg2pRVREb65DHlpt+Z0TWrI2rwD/HtZLmP7tvbJcmuKN59CS8CzfWCOO8zT48D1IpKDU5q/1x3eGVARmSUi34rIb8pbgYhMEJF0EUnPz6/eI8ZqWzv6pKQkkpKSAh2GMUEnKS6aD27vR8vG9Tl4rIiEBlH8fnh3Fv76Im7o18ZnSR6crpXvvqgjAC8tyKKw+OR6/9rMV5/EGGCyqiYDVwDvikgYzhnDBcA49/8oETnpNlFVnaSqKaqakpiYWK1AShN9fC0p0WdnZ5OdnR3oMIwJSi0b12f63efz/HW9WPjri7j1gnZ+u1h6xZln0D4hlpw9R/hkxbaqZ6hFvEn0uYDnrZ3J7jBPtwJTAVQ1FYgGEnBK/4tUdaeqHsYp7Z9T3aArU5rom9SSRL9o0SIWLVoU6DCMCVrNGkYzslfLKuvbqys8TLirtFQ/P4vikrpzQ5U3iT4N6CQi7UQkCrgOmFFmmi3AEAAR6YaT6POBWcCZIhLjXpgdzIl1+z51tLCYwwXFRIYLDf28040xoWdkrxa0alqf7J2H+Py77YEOx2tVJnpVLQLuwUnaa3Ba12SIyJMicqU72QPA7SKyEvgQGK+OPcBzOAeLFcC3qvpff2wIeDwrNjaqzt/JZoypfSLDw/j5YKdU/+K8LErqSKneq2Kv2yb+8zLDHvV4/T0woIJ538NpYul31j2xMcbfrjm3JS/MW0/mDweYveYHLutR+xtbBFUXCHZXrDHG3+pFhHPHoPaAU6qvC52fBVWir4393AwfPpzhw4cHOgxjjA9d16c1CQ2i+C53HwvWVa9JeE0IqkS/62DtaloJkJCQQEJCQqDDMMb4UHRkOLcPdEr1L8xdX+tL9UHVNGXP4dpXR5+ZmQlAly5dAhyJMcaXxvVrw8sLN/Dtlr10/O3/KNv8IyJceHxED67rE/i7aIOqRF/b7ooFSE1NJTU1NdBhGGN8rEG9CH51aWfCw4TiEqWozN/RwhKenZXJkYLAPxUrqEr0tbHqxhgTvG7s35axfVpTtuJGFX72aiort+7lg2+2cOsFge3xMqhK9LWx6sYYE9wiwsOILPMXFRHGve5dtJMWbQj4w82DKtFb80pjTG0xpFszup3RiB/2H+PjZTkBjSWoEn1t66LYGBO6RIR7L3ZK9S8v2BDQHi+Dpo6+qLiEvYcLEYHG9WvHg8EBRo0aFegQjDEBMqxHEh2bNSBrx0GmL89ldEqrqmfyg6Ap0e894jy4Kq5+JBHhtWez4uLiiIuLC3QYxpgAcPqx7wA4PV4WBahUX3syYjXV1mqb1atXs3r16kCHYYwJkBFntaBNfAybdh3mvwHq8TJoEn1tbVqZnp5Oenp6oMMwxgRIRHgYd13olOoD1eNl0CR6a1ppjKmtRvVOpkVcNOt3HGRWRl6Nrz9oEv0ZcdGM6dOKgZ2r9yhCY4zxtaiIMO50S/UvBKDHy6BpddO7dRN6t24S6DCMMaZco1Na8cK8LL7fvp/5mTu4uGvzGlt30JTojTGmNouO/LEf+5fmb6jRdXuV6EVkmIhkikiWiDxUzvjWIjJfRJaLyCoRuaKc8QdF5EFfBV5XjB49mtGjRwc6DGNMLTCmT2sa1IsgffMe1v9woMbWW2WiF5FwYCJwOdAdGCMi3ctM9jucZ8n2xnl4+Etlxj8H/K/64dY9MTExxMTEBDoMY0wtEFsvghFnnwHAR2lba2y93pTo+wBZqpqtqgXAFGBkmWkUaOS+jgO2lY4QkauAjUBG9cOte1asWMGKFSsCHYYxppa49jynf/ppy3MpKKqZG6i8SfQtAc9DT447zNPjwPUikoPzEPF7AUSkAfB/wBOVrUBEJohIuoik5+fX/sdynQpL9MYYT2cnx9GleUN2HypgzpofamSdvroYOwaYrKrJwBXAuyIShnMA+LuqHqxsZlWdpKopqpqSmGjNI40xwUtEuPY8p8+bmqq+8SbR5wKePfEku8M83QpMBVDVVCAaSAD6As+IyCbgPuAREbmnmjEbY0ydNqp3S6LCw1i0Pp9te4/4fX3eJPo0oJOItBORKJyLrTPKTLMFGAIgIt1wEn2+qg5U1baq2hb4B/AnVX3RZ9EbY0wd1CQ2iqE9mqMK/0r3f1/1VSZ6VS0C7gFmAWtwWtdkiMiTInKlO9kDwO0ishL4EBivtf2x6MYYE0Cl1TdT07f6vf8bqW35OCUlRYOpE7DCQqf75MjI2tNHvjEm8EpKlIHPzCd37xHevbUPAztV7/qkiCxT1ZTyxtmdsX4WGRlpSd4Yc5KwMDn+IBJ/X5S1RO9naWlppKWlBToMY0wt9NOUZETgi4wf2OM+U8MfLNH7WUZGBhkZIXmvmDGmCi0b12dQp0QKikuYvrxsY0bfsURvjDEB5Nmm3l/XTC3RG2NMAF3SrTlNY6PI/OEAK3P2+WUdluiNMSaAoiLCuLq306vMR2lb/LIOS/TGGBNgpdU3M1Zs49CxIp8vv9a1oxeRfGDzKc6WAOz0Qzi1nW13aLHtDi2nut1tVLXcxvi1LtGfDhFJr+hGgWBm2x1abLtDiy+326pujDEmyFmiN8aYIBcsiX5SoAMIENvu0GLbHVp8tt1BUUdvjDGmYsFSojfGGFMBS/TGGBPk6nSiF5FhIpIpIlki8lCg4/EXEWklIvNF5HsRyRCRX7rDm4rIbBFZ7/5vEuhY/UFEwkVkuYh85r5vJyJfu/v9I/fJZ0FFRBqLyMcislZE1ohI/1DY3yJyv/sdXy0iH4pIdLDubxF5U0R2iMhqj2Hl7mNx/NP9DFaJyDmnsq46m+hFJByYCFwOdAfGiEj3wEblN0XAA6raHegH3O1u60PAXFXtBMx13wejX+I83azUX3AeOt8R2IPzzOJg8zwwU1W7AmfjbH9Q728RaQn8AkhR1Z5AOM6jS4N1f08GhpUZVtE+vhzo5P5NAF4+lRXV2UQP9AGyVDVbVQuAKcDIAMfkF6q6XVW/dV8fwPnRt8TZ3rfdyd4GrgpMhP4jIsnAT4DX3fcCXAx87E4SdNstInHAIOANAFUtUNW9hMD+BiKA+iISAcQA2wnS/a2qi4DdZQZXtI9HAu+oYynQWETO8HZddTnRtwQ8H8uS4w4LaiLSFugNfA00V9Xt7qg8oHmAwvKnfwC/AUrc9/HAXvdZxhCc+70dkA+85VZZvS4isQT5/lbVXOCvwBacBL8PWEbw729PFe3jauW7upzoQ46INAD+Ddynqvs9x7kPYw+qtrIiMhzYoarLAh1LDYsAzgFeVtXewCHKVNME6f5uglNybQe0AGI5uWojZPhyH9flRJ8LtPJ4n+wOC0oiEomT5N9X1Wnu4B9KT9/c/zsCFZ+fDACuFJFNOFVzF+PUXTd2T+0hOPd7DpCjql+77z/GSfzBvr8vATaqar6qFgLTcL4Dwb6/PVW0j6uV7+pyok8DOrlX5KNwLtrMCHBMfuHWS78BrFHV5zxGzQBucl/fBHxS07H5k6o+rKrJqtoWZ//OU9VxwHzgp+5kwbjdecBWEeniDhoCfE+Q72+cKpt+IhLjfudLtzuo93cZFe3jGcCNbuubfsA+jyqeqqlqnf0DrgDWARuA3wY6Hj9u5wU4p3CrgBXu3xU49dVzgfXAHKBpoGP142dwIfCZ+7o98A2QBfwLqBfo+Pywvb2AdHef/wdoEgr7G3gCWAusBt4F6gXr/gY+xLkWUYhzFndrRfsYEJxWhhuA73BaJnm9LusCwRhjglxdrroxxhjjBUv0xhgT5CzRG2NMkLNEb4wxQc4SvTHGBDlL9MYYE+Qs0RtjTJD7f3F5oPya1shFAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tW_XnhDlpwsO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "6e4ad56e-cebf-45bb-e8da-53d6ad266cd9"
      },
      "source": [
        "def MeuKFold(k):\n",
        "    modelo = KNeighborsClassifier(n_neighbors = k)\n",
        "\n",
        "    kf = KFold(n_splits = 10, shuffle = True)\n",
        "    accs_vc = cross_val_score(modelo, ads_vars, ads_resp, cv = kf, scoring = 'accuracy')\n",
        "\n",
        "    return accs_vc.mean()\n",
        "\n",
        "kas = np.arange(1,80,2)\n",
        "\n",
        "accs = []\n",
        "for i in kas:\n",
        "    accs.append(MeuKFold(i))\n",
        "\n",
        "kmax = kas[accs.index(max(accs))]\n",
        "\n",
        "plt.axvline(kmax, color = 'gray', linestyle = 'dashed')\n",
        "plt.axhline(max(accs), color = 'gray', linestyle = 'dashed')\n",
        "plt.plot(kas, accs, linewidth = 2)\n",
        "plt.ylim(min(accs), 1)\n",
        "frase = 'Acurácia máxima igual a {:.4f} usando K = {}.'.format(max(accs), kmax)\n",
        "plt.title(frase)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6e3qEfhp-cld",
        "colab_type": "text"
      },
      "source": [
        "## **Exercício**\n",
        "\n",
        "Verifique o EQM no exemplo da aula anterior utilizando o método da Validação Cruzada K-Fold"
      ]
    }
  ]
}