Resultados_finais_emotions_ACCML_Evoimpy.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Resultados_finais_emotions_ACCML_Evoimpy.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1giSStSKiP6fg6JUfNT7w4G-v1I6-BH-i",
      "authorship_tag": "ABX9TyM+/Yz8SSPjZABqhGcGS8mx"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IG_dPTGoDaXa"
      },
      "source": [
        "#Importações\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PQwleV8MrFX_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "09c14bcf-2d36-475a-eb46-eebee2f0d514"
      },
      "source": [
        "import numpy as np\n",
        "import scipy as sp\n",
        "import future as ft\n",
        "import sklearn\n",
        "from scipy.io import arff\n",
        "import pandas as pd\n",
        "\n",
        "#instalações\n",
        "!pip install scikit-multilearn\n",
        "!pip install liac-arff # for loading ARFF files\n",
        "!pip install iterative-stratification \n",
        "!pip install missingpy\n",
        "\n",
        "\n",
        "#importações poś-intalações\n",
        "from skmultilearn.dataset import load_dataset\n",
        "import arff\n"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: scikit-multilearn in /usr/local/lib/python3.7/dist-packages (0.2.0)\n",
            "Requirement already satisfied: liac-arff in /usr/local/lib/python3.7/dist-packages (2.5.0)\n",
            "Requirement already satisfied: iterative-stratification in /usr/local/lib/python3.7/dist-packages (0.1.7)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from iterative-stratification) (1.21.5)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from iterative-stratification) (1.0.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from iterative-stratification) (1.4.1)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->iterative-stratification) (3.1.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->iterative-stratification) (1.1.0)\n",
            "Requirement already satisfied: missingpy in /usr/local/lib/python3.7/dist-packages (0.2.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AtaHZNhdUyuU"
      },
      "source": [
        "#Lendo datasets originais do Repositório Mulan\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def load_dataset(dataset_name):\n",
        "  from skmultilearn.dataset import load_dataset\n",
        "  X,y,feature_names, label_names = load_dataset(dataset_name, 'undivided')\n",
        "  return X.toarray(),y.toarray(),feature_names, label_names"
      ],
      "metadata": {
        "id": "XKbZefjIDRi_"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ypcrK6NDTzfN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "598e767d-f814-4614-d285-d516e291b273"
      },
      "source": [
        "##ordem dos dados (X,y,features e labels)\n",
        "data = load_dataset('emotions')"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "emotions:undivided - does not exists downloading\n",
            "Downloaded emotions-undivided\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = data[2]\n",
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BygJKirREz0i",
        "outputId": "ad1a0927-08d9-4d35-9765-0022d027014d"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('Mean_Acc1298_Mean_Mem40_Centroid', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Mean_Mem40_Rolloff', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Mean_Mem40_Flux', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Mean_Mem40_MFCC_0', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Mean_Mem40_MFCC_1', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Mean_Mem40_MFCC_2', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Mean_Mem40_MFCC_3', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Mean_Mem40_MFCC_4', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Mean_Mem40_MFCC_5', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Mean_Mem40_MFCC_6', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Mean_Mem40_MFCC_7', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Mean_Mem40_MFCC_8', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Mean_Mem40_MFCC_9', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Mean_Mem40_MFCC_10', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Mean_Mem40_MFCC_11', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Mean_Mem40_MFCC_12', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Std_Mem40_Centroid', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Std_Mem40_Rolloff', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Std_Mem40_Flux', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Std_Mem40_MFCC_0', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Std_Mem40_MFCC_1', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Std_Mem40_MFCC_2', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Std_Mem40_MFCC_3', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Std_Mem40_MFCC_4', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Std_Mem40_MFCC_5', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Std_Mem40_MFCC_6', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Std_Mem40_MFCC_7', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Std_Mem40_MFCC_8', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Std_Mem40_MFCC_9', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Std_Mem40_MFCC_10', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Std_Mem40_MFCC_11', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Std_Mem40_MFCC_12', 'NUMERIC'),\n",
              " ('Std_Acc1298_Mean_Mem40_Centroid', 'NUMERIC'),\n",
              " ('Std_Acc1298_Mean_Mem40_Rolloff', 'NUMERIC'),\n",
              " ('Std_Acc1298_Mean_Mem40_Flux', 'NUMERIC'),\n",
              " ('Std_Acc1298_Mean_Mem40_MFCC_0', 'NUMERIC'),\n",
              " ('Std_Acc1298_Mean_Mem40_MFCC_1', 'NUMERIC'),\n",
              " ('Std_Acc1298_Mean_Mem40_MFCC_2', 'NUMERIC'),\n",
              " ('Std_Acc1298_Mean_Mem40_MFCC_3', 'NUMERIC'),\n",
              " ('Std_Acc1298_Mean_Mem40_MFCC_4', 'NUMERIC'),\n",
              " ('Std_Acc1298_Mean_Mem40_MFCC_5', 'NUMERIC'),\n",
              " ('Std_Acc1298_Mean_Mem40_MFCC_6', 'NUMERIC'),\n",
              " ('Std_Acc1298_Mean_Mem40_MFCC_7', 'NUMERIC'),\n",
              " ('Std_Acc1298_Mean_Mem40_MFCC_8', 'NUMERIC'),\n",
              " ('Std_Acc1298_Mean_Mem40_MFCC_9', 'NUMERIC'),\n",
              " ('Std_Acc1298_Mean_Mem40_MFCC_10', 'NUMERIC'),\n",
              " ('Std_Acc1298_Mean_Mem40_MFCC_11', 'NUMERIC'),\n",
              " ('Std_Acc1298_Mean_Mem40_MFCC_12', 'NUMERIC'),\n",
              " ('Std_Acc1298_Std_Mem40_Centroid', 'NUMERIC'),\n",
              " ('Std_Acc1298_Std_Mem40_Rolloff', 'NUMERIC'),\n",
              " ('Std_Acc1298_Std_Mem40_Flux', 'NUMERIC'),\n",
              " ('Std_Acc1298_Std_Mem40_MFCC_0', 'NUMERIC'),\n",
              " ('Std_Acc1298_Std_Mem40_MFCC_1', 'NUMERIC'),\n",
              " ('Std_Acc1298_Std_Mem40_MFCC_2', 'NUMERIC'),\n",
              " ('Std_Acc1298_Std_Mem40_MFCC_3', 'NUMERIC'),\n",
              " ('Std_Acc1298_Std_Mem40_MFCC_4', 'NUMERIC'),\n",
              " ('Std_Acc1298_Std_Mem40_MFCC_5', 'NUMERIC'),\n",
              " ('Std_Acc1298_Std_Mem40_MFCC_6', 'NUMERIC'),\n",
              " ('Std_Acc1298_Std_Mem40_MFCC_7', 'NUMERIC'),\n",
              " ('Std_Acc1298_Std_Mem40_MFCC_8', 'NUMERIC'),\n",
              " ('Std_Acc1298_Std_Mem40_MFCC_9', 'NUMERIC'),\n",
              " ('Std_Acc1298_Std_Mem40_MFCC_10', 'NUMERIC'),\n",
              " ('Std_Acc1298_Std_Mem40_MFCC_11', 'NUMERIC'),\n",
              " ('Std_Acc1298_Std_Mem40_MFCC_12', 'NUMERIC'),\n",
              " ('BH_LowPeakAmp', 'NUMERIC'),\n",
              " ('BH_LowPeakBPM', 'NUMERIC'),\n",
              " ('BH_HighPeakAmp', 'NUMERIC'),\n",
              " ('BH_HighPeakBPM', 'NUMERIC'),\n",
              " ('BH_HighLowRatio', 'NUMERIC'),\n",
              " ('BHSUM1', 'NUMERIC'),\n",
              " ('BHSUM2', 'NUMERIC'),\n",
              " ('BHSUM3', 'NUMERIC')]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def counter(y):\n",
        "  from skmultilearn.model_selection.measures import get_combination_wise_output_matrix\n",
        "  from collections import Counter\n",
        "  distribuicao_labels = Counter(combination for row in get_combination_wise_output_matrix(y, order=1) for combination in row)\n",
        "  return distribuicao_labels\n"
      ],
      "metadata": {
        "id": "_Xh-CrlZMll8"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#counter(data[1])"
      ],
      "metadata": {
        "id": "zYONEm5gNNXK"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZgAyH1cdb2-w"
      },
      "source": [
        "\n",
        "\n",
        "\n",
        "## Aplicando Validação Cruzada k-fold \n",
        "Testes iniciais com 10-folds"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#X,y in numpy format\n",
        "def cross_validation(X,y,num_folds, randon_state=0):\n",
        "  from iterstrat.ml_stratifiers import MultilabelStratifiedKFold\n",
        "  mskf = MultilabelStratifiedKFold(n_splits=num_folds, shuffle=True, random_state=0)\n",
        "  for train_index, test_index in mskf.split(X, y):\n",
        "   #print(\"TRAIN:\", train_index, \"TEST:\", test_index)\n",
        "   X_train, X_test = X[train_index], X[test_index]\n",
        "   y_train, y_test = y[train_index], y[test_index]\n",
        "  return X_train, X_test, y_train, y_test"
      ],
      "metadata": {
        "id": "szroqYPqOZtj"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_train_and_test = cross_validation(data[0], data[1],10)"
      ],
      "metadata": {
        "id": "BnezxZn2PyGP"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cMcP_pKubIXk"
      },
      "source": [
        "X_train = data_train_and_test[0]\n",
        "X_test = data_train_and_test[1]\n",
        "y_train = data_train_and_test[2]\n",
        "y_test = data_train_and_test[3]"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8vvGRMiHF_SU"
      },
      "source": [
        "#Exibindo informações do dataset trabalhado"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#verificando a presença de valores ausentes\n",
        "def missing_values(df):\n",
        "  amount = (df.isna().sum()).sum()\n",
        "  return amount\n",
        "\n"
      ],
      "metadata": {
        "id": "ZB8ugceORFFn"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VVZdHXfNFoOb",
        "outputId": "9c955602-7c11-4d8d-cba4-b920b9312dfc"
      },
      "source": [
        "valores_ausentes = missing_values(pd.DataFrame(X_train))\n",
        "valores_ausentes"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#padronização Dataset de teste\n",
        "#X in numpy format\n",
        "def padronizacao(X):\n",
        "  from sklearn.preprocessing import StandardScaler\n",
        "  scl = StandardScaler()\n",
        "  X_scaler = scl.fit_transform(X)\n",
        "  return X_scaler\n",
        "\n"
      ],
      "metadata": {
        "id": "Je2sc5HTR13d"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_scaler = padronizacao(X_test)\n",
        "X_test_scaler"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kj0tolOdSpUA",
        "outputId": "13418797-59df-4be6-92f7-12a515abba29"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.52751657, -0.7163883 ,  1.21315006, ..., -0.04586578,\n",
              "        -0.22651953, -0.60392646],\n",
              "       [-0.15986713, -0.37935696, -0.83883174, ...,  1.14280483,\n",
              "         0.3797663 ,  0.38762244],\n",
              "       [ 2.04640878,  1.25442176,  1.24451415, ..., -1.00563697,\n",
              "        -0.19717983, -0.87608304],\n",
              "       ...,\n",
              "       [-0.96235614, -1.02193939, -0.6029958 , ..., -0.76221794,\n",
              "        -0.88382307, -0.78601863],\n",
              "       [ 1.4356929 ,  0.61769484,  1.20874808, ...,  0.29080106,\n",
              "         1.34725769,  0.49765303],\n",
              "       [-0.89368062, -1.08026503, -0.36782015, ..., -0.88947997,\n",
              "         0.12370162, -0.67435006]])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YmPwwJ5KEOmT"
      },
      "source": [
        "#Amputando dataset\n",
        "\n",
        "*   Mecanismo VA: MCAR\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N3D5JYQla4xA"
      },
      "source": [
        "Quantificando os percentuais de valores ausentes de acordo com cada database"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#retorna um lista com as posições com Valores ausentes e o dataset amputado\n",
        "def amputation(rate, X):\n",
        "  from numpy import random\n",
        "\n",
        "  amount_missing_values = int(X.size * rate)\n",
        "  print('Total dados: '+str(X.size)+'\\n Total VAs: '+str(amount_missing_values)+'\\n')\n",
        "\n",
        "  rows = X.shape[0]\n",
        "  cols = X.shape[1]\n",
        "  index_list = []\n",
        "  df = pd.DataFrame(X)\n",
        "\n",
        "  while(len(index_list)<amount_missing_values):\n",
        "    a = 0\n",
        "    b = 0\n",
        "    row = random.randint(0,rows)\n",
        "    col = random.randint(0,cols)\n",
        "    if(a != row and b != col):\n",
        "      a = row\n",
        "      b = col\n",
        "      df.iloc[row,col] = np.nan\n",
        "      index_list.append([row,col])\n",
        "  return df, index_list"
      ],
      "metadata": {
        "id": "vBh0rsycTlfV"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EGMtsgfm4SiY",
        "outputId": "878ba323-8485-47f7-ff3c-61c51f2efc60"
      },
      "source": [
        "a = amputation(0.25, X_train)\n"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total dados: 38160\n",
            " Total VAs: 9540\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "ZGhHxBeDaAsj",
        "outputId": "9ef5579e-a600-493d-b7e9-b1a45ebae989"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           0         1         2          3         4         5         6   \\\n",
              "0    0.034741  0.089665  0.091225 -73.302422  6.215179  0.615074  2.037160   \n",
              "1    0.081374  0.272747  0.085733 -62.584437  3.183163 -0.218145  0.163038   \n",
              "2    0.110545  0.273567       NaN        NaN       NaN  0.639047       NaN   \n",
              "3    0.042481       NaN       NaN        NaN  5.824409       NaN  1.754870   \n",
              "4    0.074550  0.140880  0.079789 -93.697749  5.543229  1.064262  0.899152   \n",
              "..        ...       ...       ...        ...       ...       ...       ...   \n",
              "525  0.027142  0.047551  0.072043 -79.881347       NaN  1.927310  1.696017   \n",
              "526  0.094829  0.204498  0.082824 -61.364436       NaN       NaN  1.440352   \n",
              "527  0.035169  0.065403  0.075227 -81.750533       NaN       NaN  0.818851   \n",
              "528  0.054276       NaN       NaN -71.009724  3.181340  1.547197  2.407780   \n",
              "529  0.073194  0.140733  0.080545 -74.517081  6.945590       NaN  1.952278   \n",
              "\n",
              "           7         8         9   ...        62        63        64     65  \\\n",
              "0    0.804065  1.301409  0.558576  ...  0.118630  0.094923  0.051035   68.0   \n",
              "1    0.620251       NaN  0.041426  ...  0.070075       NaN  0.295031   70.0   \n",
              "2         NaN  0.489412  0.627636  ...       NaN  0.085821  0.161574    NaN   \n",
              "3    1.495532  0.739909  0.809644  ...  0.129145  0.122330       NaN   66.0   \n",
              "4    0.890336  0.702328  0.490685  ...       NaN  0.189988  0.029308  100.0   \n",
              "..        ...       ...       ...  ...       ...       ...       ...    ...   \n",
              "525  0.397888  0.857559  0.302742  ...  0.129552  0.106260       NaN   87.0   \n",
              "526  0.856243  1.110282  0.394450  ...  0.128090  0.167160  0.035827   65.0   \n",
              "527  1.569606       NaN       NaN  ...  0.147194  0.098238  0.033681   88.0   \n",
              "528  0.618838  0.997950  0.825143  ...  0.080006       NaN  0.155650   84.0   \n",
              "529  0.341936  0.770582  0.144989  ...       NaN       NaN  0.056101   94.0   \n",
              "\n",
              "           66     67   68        69        70        71  \n",
              "0    0.014937  136.0  2.0  0.245457  0.105065  0.405399  \n",
              "1    0.276366  140.0  2.0       NaN  0.276366  0.710924  \n",
              "2    0.000000  183.0  3.0       NaN  0.045941  0.457372  \n",
              "3    0.206562  132.0  2.0  0.102839  0.241934  0.351009  \n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>530 rows × 72 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3b71901d-8fd4-4f24-91ed-c689e173eb7c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-convert {\n",
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              "\n",
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              "          document.querySelector('#df-3b71901d-8fd4-4f24-91ed-c689e173eb7c button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-3b71901d-8fd4-4f24-91ed-c689e173eb7c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V1a-5SqH8o-f",
        "outputId": "1b2f5308-1dc9-4fe0-c0e9-2c6df1db820b"
      },
      "source": [
        "total_nan = (a[0].isnull().sum()).sum()\n",
        "total_nan"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8428"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def grafic_bar_missing_values(df):\n",
        "  import missingno as msno\n",
        "  msno.bar(df)"
      ],
      "metadata": {
        "id": "y1cRz-CObLlw"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def grafic_matrix_missing_values(df):\n",
        "  import missingno as msno\n",
        "  msno.matrix(df)"
      ],
      "metadata": {
        "id": "XOGC9byecxSW"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "grafic_matrix_missing_values(pd.DataFrame(a[0]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "bmjD8H8Oc4ZN",
        "outputId": "437c0b4f-1aab-415c-ab7c-1feab9623652"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grafic_bar_missing_values(pd.DataFrame(a[0]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 803
        },
        "id": "e1RebPEvbqvd",
        "outputId": "9c6cdc49-e9aa-406b-f0b1-095fb385c5d0"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x1692 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def grafic_line_missing_values(df):\n",
        "  import matplotlib.pyplot as plt \n",
        "  plt.figure(figsize=(20,3))\n",
        "  plt.plot(df.isnull().sum(),marker='o')\n",
        "  plt.title(\"Distribuição de Missing Values por features\")\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "GQYYKC9idcl2"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "grafic_line_missing_values(pd.DataFrame(a[0]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "ykpnAYHndi7i",
        "outputId": "e0675cc0-83b6-478c-f0cd-a6c40bb40e15"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x216 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cOTTWvTOS74c"
      },
      "source": [
        "def describe(df):\n",
        "  return df.describe()"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "describe(pd.DataFrame(data[0]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "OnN8jqQgepik",
        "outputId": "d663ff4d-d97c-41cf-9334-a19e38d99e20"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               0           1           2           3           4           5   \\\n",
              "count  593.000000  593.000000  593.000000  593.000000  593.000000  593.000000   \n",
              "mean     0.069309    0.195399    0.085695  -72.998228    4.979178    0.602395   \n",
              "std      0.031293    0.117605    0.009260    7.580804    2.071792    1.017603   \n",
              "min      0.010201    0.038286    0.070932  -99.090802    0.051474   -2.277307   \n",
              "25%      0.044781    0.107742    0.079018  -77.078349    3.409922   -0.113441   \n",
              "50%      0.065251    0.169757    0.083637  -72.474302    4.642338    0.588380   \n",
              "75%      0.086290    0.257668    0.091323  -68.292848    6.439446    1.293202   \n",
              "max      0.195412    0.698277    0.159460  -56.297652   12.069754    3.910873   \n",
              "\n",
              "               6           7           8           9   ...          62  \\\n",
              "count  593.000000  593.000000  593.000000  593.000000  ...  593.000000   \n",
              "mean     1.694625    0.634156    0.833964    0.508060  ...    0.109876   \n",
              "std      0.606581    0.448746    0.368312    0.303277  ...    0.053819   \n",
              "min     -0.604609   -1.143864   -0.237114   -0.444623  ...    0.032472   \n",
              "25%      1.307242    0.373195    0.605057    0.309033  ...    0.075211   \n",
              "50%      1.731031    0.618838    0.819652    0.534665  ...    0.096668   \n",
              "75%      2.072488    0.925091    1.051206    0.708430  ...    0.125772   \n",
              "max      4.382370    2.252710    2.358098    1.484489  ...    0.471506   \n",
              "\n",
              "               63          64          65          66          67          68  \\\n",
              "count  593.000000  593.000000  593.000000  593.000000  593.000000  593.000000   \n",
              "mean     0.107114    0.230008   74.456998    0.247854  155.215852    2.099494   \n",
              "std      0.059361    0.295863   14.610203    0.366541   29.807954    0.316040   \n",
              "min      0.031945    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
              "25%      0.071286    0.047611   64.000000    0.007556  132.000000    2.000000   \n",
              "50%      0.088687    0.104043   71.000000    0.077408  150.000000    2.000000   \n",
              "75%      0.121432    0.278203   84.000000    0.325489  180.000000    2.000000   \n",
              "max      0.415202    1.598533  115.000000    1.762948  237.000000    3.000000   \n",
              "\n",
              "               69          70          71  \n",
              "count  593.000000  593.000000  593.000000  \n",
              "mean     0.412940    0.354901    0.959906  \n",
              "std      0.339559    0.362262    0.651827  \n",
              "min      0.000000    0.000000    0.000000  \n",
              "25%      0.179411    0.099424    0.513974  \n",
              "50%      0.309902    0.246193    0.804229  \n",
              "75%      0.529859    0.472593    1.242089  \n",
              "max      1.795128    1.797765    3.422899  \n",
              "\n",
              "[8 rows x 72 columns]"
            ],
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              "\n",
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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>...</th>\n",
              "      <th>62</th>\n",
              "      <th>63</th>\n",
              "      <th>64</th>\n",
              "      <th>65</th>\n",
              "      <th>66</th>\n",
              "      <th>67</th>\n",
              "      <th>68</th>\n",
              "      <th>69</th>\n",
              "      <th>70</th>\n",
              "      <th>71</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>593.000000</td>\n",
              "      <td>593.000000</td>\n",
              "      <td>593.000000</td>\n",
              "      <td>593.000000</td>\n",
              "      <td>593.000000</td>\n",
              "      <td>593.000000</td>\n",
              "      <td>593.000000</td>\n",
              "      <td>593.000000</td>\n",
              "      <td>593.000000</td>\n",
              "      <td>593.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>593.000000</td>\n",
              "      <td>593.000000</td>\n",
              "      <td>593.000000</td>\n",
              "      <td>593.000000</td>\n",
              "      <td>593.000000</td>\n",
              "      <td>593.000000</td>\n",
              "      <td>593.000000</td>\n",
              "      <td>593.000000</td>\n",
              "      <td>593.000000</td>\n",
              "      <td>593.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.069309</td>\n",
              "      <td>0.195399</td>\n",
              "      <td>0.085695</td>\n",
              "      <td>-72.998228</td>\n",
              "      <td>4.979178</td>\n",
              "      <td>0.602395</td>\n",
              "      <td>1.694625</td>\n",
              "      <td>0.634156</td>\n",
              "      <td>0.833964</td>\n",
              "      <td>0.508060</td>\n",
              "      <td>...</td>\n",
              "      <td>0.109876</td>\n",
              "      <td>0.107114</td>\n",
              "      <td>0.230008</td>\n",
              "      <td>74.456998</td>\n",
              "      <td>0.247854</td>\n",
              "      <td>155.215852</td>\n",
              "      <td>2.099494</td>\n",
              "      <td>0.412940</td>\n",
              "      <td>0.354901</td>\n",
              "      <td>0.959906</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.031293</td>\n",
              "      <td>0.117605</td>\n",
              "      <td>0.009260</td>\n",
              "      <td>7.580804</td>\n",
              "      <td>2.071792</td>\n",
              "      <td>1.017603</td>\n",
              "      <td>0.606581</td>\n",
              "      <td>0.448746</td>\n",
              "      <td>0.368312</td>\n",
              "      <td>0.303277</td>\n",
              "      <td>...</td>\n",
              "      <td>0.053819</td>\n",
              "      <td>0.059361</td>\n",
              "      <td>0.295863</td>\n",
              "      <td>14.610203</td>\n",
              "      <td>0.366541</td>\n",
              "      <td>29.807954</td>\n",
              "      <td>0.316040</td>\n",
              "      <td>0.339559</td>\n",
              "      <td>0.362262</td>\n",
              "      <td>0.651827</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.010201</td>\n",
              "      <td>0.038286</td>\n",
              "      <td>0.070932</td>\n",
              "      <td>-99.090802</td>\n",
              "      <td>0.051474</td>\n",
              "      <td>-2.277307</td>\n",
              "      <td>-0.604609</td>\n",
              "      <td>-1.143864</td>\n",
              "      <td>-0.237114</td>\n",
              "      <td>-0.444623</td>\n",
              "      <td>...</td>\n",
              "      <td>0.032472</td>\n",
              "      <td>0.031945</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.044781</td>\n",
              "      <td>0.107742</td>\n",
              "      <td>0.079018</td>\n",
              "      <td>-77.078349</td>\n",
              "      <td>3.409922</td>\n",
              "      <td>-0.113441</td>\n",
              "      <td>1.307242</td>\n",
              "      <td>0.373195</td>\n",
              "      <td>0.605057</td>\n",
              "      <td>0.309033</td>\n",
              "      <td>...</td>\n",
              "      <td>0.075211</td>\n",
              "      <td>0.071286</td>\n",
              "      <td>0.047611</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>0.007556</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.179411</td>\n",
              "      <td>0.099424</td>\n",
              "      <td>0.513974</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.065251</td>\n",
              "      <td>0.169757</td>\n",
              "      <td>0.083637</td>\n",
              "      <td>-72.474302</td>\n",
              "      <td>4.642338</td>\n",
              "      <td>0.588380</td>\n",
              "      <td>1.731031</td>\n",
              "      <td>0.618838</td>\n",
              "      <td>0.819652</td>\n",
              "      <td>0.534665</td>\n",
              "      <td>...</td>\n",
              "      <td>0.096668</td>\n",
              "      <td>0.088687</td>\n",
              "      <td>0.104043</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>0.077408</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.309902</td>\n",
              "      <td>0.246193</td>\n",
              "      <td>0.804229</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.086290</td>\n",
              "      <td>0.257668</td>\n",
              "      <td>0.091323</td>\n",
              "      <td>-68.292848</td>\n",
              "      <td>6.439446</td>\n",
              "      <td>1.293202</td>\n",
              "      <td>2.072488</td>\n",
              "      <td>0.925091</td>\n",
              "      <td>1.051206</td>\n",
              "      <td>0.708430</td>\n",
              "      <td>...</td>\n",
              "      <td>0.125772</td>\n",
              "      <td>0.121432</td>\n",
              "      <td>0.278203</td>\n",
              "      <td>84.000000</td>\n",
              "      <td>0.325489</td>\n",
              "      <td>180.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.529859</td>\n",
              "      <td>0.472593</td>\n",
              "      <td>1.242089</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.195412</td>\n",
              "      <td>0.698277</td>\n",
              "      <td>0.159460</td>\n",
              "      <td>-56.297652</td>\n",
              "      <td>12.069754</td>\n",
              "      <td>3.910873</td>\n",
              "      <td>4.382370</td>\n",
              "      <td>2.252710</td>\n",
              "      <td>2.358098</td>\n",
              "      <td>1.484489</td>\n",
              "      <td>...</td>\n",
              "      <td>0.471506</td>\n",
              "      <td>0.415202</td>\n",
              "      <td>1.598533</td>\n",
              "      <td>115.000000</td>\n",
              "      <td>1.762948</td>\n",
              "      <td>237.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.795128</td>\n",
              "      <td>1.797765</td>\n",
              "      <td>3.422899</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 72 columns</p>\n",
              "</div>\n",
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              "          document.querySelector('#df-f768f1e4-f91f-41ae-a0d0-78dec35c04db button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f768f1e4-f91f-41ae-a0d0-78dec35c04db');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BIcDtz3iR647"
      },
      "source": [
        "#Instalando o panda profile\n",
        "#!pip install pandas-profiling\n",
        "#from pandas_profiling import ProfileReport"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7wPYq4x_0KWO"
      },
      "source": [
        "#Imputação de dados\n",
        "\n",
        "\n",
        "*   k-Nearest Neighbors (kNN) Imputation\n",
        "*   mean\n",
        "*   most_frequent\n",
        "*   median\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WkyeF0EcTuKZ"
      },
      "source": [
        "# Criação da popoulação inicial"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AzeUjJXcrELk"
      },
      "source": [
        "imputando os indivíduos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vAOOBEGx7scY"
      },
      "source": [
        "#def knnImp(dataset_amputed):\n",
        "#  from missingpy import KNNImputer\n",
        "#  imputer_knn = KNNImputer(missing_values=\"NaN\", n_neighbors=5, weights=\"uniform\", \n",
        "#                 metric=\"masked_euclidean\", row_max_missing=0.5, \n",
        "#                 col_max_missing=0.8, copy=True)\n",
        "  \n",
        "  #X_imputed_KNNI = imputer_knn.fit_transform(dataset_amputed)\n",
        "  #return X_imputed_KNNI"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pd6sRrGrqZAg"
      },
      "source": [
        "from sklearn.impute import KNNImputer\n",
        "def knnImp(dataset_amputed,neighbors):\n",
        "  inputer_knn = KNNImputer(n_neighbors=neighbors)\n",
        "  X_imputed_knn = inputer_knn.fit_transform(dataset_amputed)\n",
        "  return X_imputed_knn"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C3SkM1xk7vIU"
      },
      "source": [
        "def rfImp(dataset_amputed):\n",
        "  from missingpy import MissForest\n",
        "  imputer_RFI = MissForest()\n",
        "  X_imputed_RFI = imputer_RFI.fit_transform(dataset_amputed)\n",
        "  return X_imputed_RFI"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yw0V-7kG7yOb"
      },
      "source": [
        "def meanImp(dataset_amputed):\n",
        "  from sklearn.impute import SimpleImputer\n",
        "  imputer_mean = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
        "  imputer_mean=imputer_mean.fit(dataset_amputed)\n",
        "  X_imputed_mean = imputer_mean.transform(dataset_amputed)\n",
        "  return X_imputed_mean"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KtK_OGWb6vgo"
      },
      "source": [
        "def mostFrequentImp(dataset_amputed):\n",
        "  from sklearn.impute import SimpleImputer\n",
        "  imputer_most_frequent = SimpleImputer(missing_values=np.nan, strategy='most_frequent')\n",
        "  imputer_most_frequent=imputer_most_frequent.fit(dataset_amputed)\n",
        "  X_imputed_most_frequent=imputer_most_frequent.transform(dataset_amputed)\n",
        "  return X_imputed_most_frequent"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TzfM_qWNrDpv"
      },
      "source": [
        "def medianImp(dataset_amputed):\n",
        "  from sklearn.impute import SimpleImputer\n",
        "  imputer_median = SimpleImputer(missing_values=np.nan, strategy='median')\n",
        "  imputer_median=imputer_median.fit(dataset_amputed)\n",
        "  X_imputed_median=imputer_median.transform(dataset_amputed)\n",
        "  return X_imputed_median"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def imputation(method, df):\n",
        "  #import gerais\n",
        "  import sklearn.neighbors._base\n",
        "  import sys\n",
        "  sys.modules['sklearn.neighbors.base'] = sklearn.neighbors._base\n",
        "  \n",
        "\n",
        "  if method == 'mean':\n",
        "    base_imputada = meanImp(df)\n",
        "  elif method == 'median':\n",
        "    base_imputada = medianImp(df)\n",
        "  elif method == 'most_frequent':\n",
        "    base_imputada = mostFrequentImp(df)\n",
        "  elif method == 'knn-3':\n",
        "    base_imputada = knnImp(df,3)\n",
        "  elif method == 'knn-5':\n",
        "    base_imputada = knnImp(df,5)\n",
        "  elif method == 'knn-10':\n",
        "    base_imputada = knnImp(df,10)\n",
        "  return base_imputada\n"
      ],
      "metadata": {
        "id": "N1XnhB0Vfz96"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rZbcIa_znjSZ"
      },
      "source": [
        "Padronização dos dados"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "imputation_meth  = ['knn-3','knn-5','knn-10', 'mean', 'most_frequent', 'median']\n",
        "inds = []\n",
        "df_amp = pd.DataFrame(a[0])\n",
        "for i  in imputation_meth:\n",
        "  inds.append(imputation(i,df_amp))"
      ],
      "metadata": {
        "id": "DQoEHMe-jHq-"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kS4lkrChphh4",
        "outputId": "a6f83e18-8fd4-4877-cac0-dcd789c74e9e"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([[0.034741  , 0.089665  , 0.091225  , ..., 0.245457  , 0.105065  ,\n",
              "         0.405399  ],\n",
              "        [0.081374  , 0.272747  , 0.085733  , ..., 0.474346  , 0.276366  ,\n",
              "         0.710924  ],\n",
              "        [0.110545  , 0.273567  , 0.094152  , ..., 0.378092  , 0.045941  ,\n",
              "         0.457372  ],\n",
              "        ...,\n",
              "        [0.035169  , 0.065403  , 0.075227  , ..., 0.184313  , 0.247136  ,\n",
              "         0.476993  ],\n",
              "        [0.054276  , 0.19704233, 0.084736  , ..., 0.547126  , 0.58776933,\n",
              "         0.732367  ],\n",
              "        [0.073194  , 0.140733  , 0.080545  , ..., 0.23395967, 0.19878833,\n",
              "         0.55921533]]),\n",
              " array([[0.034741 , 0.089665 , 0.091225 , ..., 0.245457 , 0.105065 ,\n",
              "         0.405399 ],\n",
              "        [0.081374 , 0.272747 , 0.085733 , ..., 0.4828466, 0.276366 ,\n",
              "         0.710924 ],\n",
              "        [0.110545 , 0.273567 , 0.0894968, ..., 0.2929662, 0.045941 ,\n",
              "         0.457372 ],\n",
              "        ...,\n",
              "        [0.035169 , 0.065403 , 0.075227 , ..., 0.184313 , 0.247136 ,\n",
              "         0.476993 ],\n",
              "        [0.054276 , 0.200158 , 0.088491 , ..., 0.547126 , 0.3905182,\n",
              "         0.7275364],\n",
              "        [0.073194 , 0.140733 , 0.080545 , ..., 0.3659356, 0.222819 ,\n",
              "         0.5001736]]),\n",
              " array([[0.034741 , 0.089665 , 0.091225 , ..., 0.245457 , 0.105065 ,\n",
              "         0.405399 ],\n",
              "        [0.081374 , 0.272747 , 0.085733 , ..., 0.3781286, 0.276366 ,\n",
              "         0.710924 ],\n",
              "        [0.110545 , 0.273567 , 0.0883457, ..., 0.2792487, 0.045941 ,\n",
              "         0.457372 ],\n",
              "        ...,\n",
              "        [0.035169 , 0.065403 , 0.075227 , ..., 0.184313 , 0.247136 ,\n",
              "         0.476993 ],\n",
              "        [0.054276 , 0.2385116, 0.0894163, ..., 0.547126 , 0.2384845,\n",
              "         0.7354476],\n",
              "        [0.073194 , 0.140733 , 0.080545 , ..., 0.3235382, 0.2392511,\n",
              "         0.5761374]]),\n",
              " array([[0.034741  , 0.089665  , 0.091225  , ..., 0.245457  , 0.105065  ,\n",
              "         0.405399  ],\n",
              "        [0.081374  , 0.272747  , 0.085733  , ..., 0.40987498, 0.276366  ,\n",
              "         0.710924  ],\n",
              "        [0.110545  , 0.273567  , 0.0855915 , ..., 0.40987498, 0.045941  ,\n",
              "         0.457372  ],\n",
              "        ...,\n",
              "        [0.035169  , 0.065403  , 0.075227  , ..., 0.184313  , 0.247136  ,\n",
              "         0.476993  ],\n",
              "        [0.054276  , 0.19718746, 0.0855915 , ..., 0.547126  , 0.35872702,\n",
              "         0.94730923],\n",
              "        [0.073194  , 0.140733  , 0.080545  , ..., 0.40987498, 0.35872702,\n",
              "         0.94730923]]),\n",
              " array([[0.034741, 0.089665, 0.091225, ..., 0.245457, 0.105065, 0.405399],\n",
              "        [0.081374, 0.272747, 0.085733, ..., 0.      , 0.276366, 0.710924],\n",
              "        [0.110545, 0.273567, 0.075452, ..., 0.      , 0.045941, 0.457372],\n",
              "        ...,\n",
              "        [0.035169, 0.065403, 0.075227, ..., 0.184313, 0.247136, 0.476993],\n",
              "        [0.054276, 0.038286, 0.075452, ..., 0.547126, 0.      , 0.      ],\n",
              "        [0.073194, 0.140733, 0.080545, ..., 0.      , 0.      , 0.      ]]),\n",
              " array([[0.034741 , 0.089665 , 0.091225 , ..., 0.245457 , 0.105065 ,\n",
              "         0.405399 ],\n",
              "        [0.081374 , 0.272747 , 0.085733 , ..., 0.298304 , 0.276366 ,\n",
              "         0.710924 ],\n",
              "        [0.110545 , 0.273567 , 0.083762 , ..., 0.298304 , 0.045941 ,\n",
              "         0.457372 ],\n",
              "        ...,\n",
              "        [0.035169 , 0.065403 , 0.075227 , ..., 0.184313 , 0.247136 ,\n",
              "         0.476993 ],\n",
              "        [0.054276 , 0.169757 , 0.083762 , ..., 0.547126 , 0.247136 ,\n",
              "         0.8039185],\n",
              "        [0.073194 , 0.140733 , 0.080545 , ..., 0.298304 , 0.247136 ,\n",
              "         0.8039185]])]"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CVNMKwXTymeK",
        "outputId": "e116ad18-323c-4a2b-f899-9de4a60bdd5e"
      },
      "source": [
        "len(inds)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wkb_zkp0yY2z"
      },
      "source": [
        "Normmalização da população inicial"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2mikxk_rycxi"
      },
      "source": [
        "populationInitial = inds\n",
        "#for i in range(len(individuos)):\n",
        "#  populationInitial.append(padronizacao(individuos[i]))"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I8iRKpnyzW0t",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9f89d644-5887-4917-9d42-7dc37b690bc8"
      },
      "source": [
        "populationInitial"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([[0.034741  , 0.089665  , 0.091225  , ..., 0.245457  , 0.105065  ,\n",
              "         0.405399  ],\n",
              "        [0.081374  , 0.272747  , 0.085733  , ..., 0.474346  , 0.276366  ,\n",
              "         0.710924  ],\n",
              "        [0.110545  , 0.273567  , 0.094152  , ..., 0.378092  , 0.045941  ,\n",
              "         0.457372  ],\n",
              "        ...,\n",
              "        [0.035169  , 0.065403  , 0.075227  , ..., 0.184313  , 0.247136  ,\n",
              "         0.476993  ],\n",
              "        [0.054276  , 0.19704233, 0.084736  , ..., 0.547126  , 0.58776933,\n",
              "         0.732367  ],\n",
              "        [0.073194  , 0.140733  , 0.080545  , ..., 0.23395967, 0.19878833,\n",
              "         0.55921533]]),\n",
              " array([[0.034741 , 0.089665 , 0.091225 , ..., 0.245457 , 0.105065 ,\n",
              "         0.405399 ],\n",
              "        [0.081374 , 0.272747 , 0.085733 , ..., 0.4828466, 0.276366 ,\n",
              "         0.710924 ],\n",
              "        [0.110545 , 0.273567 , 0.0894968, ..., 0.2929662, 0.045941 ,\n",
              "         0.457372 ],\n",
              "        ...,\n",
              "        [0.035169 , 0.065403 , 0.075227 , ..., 0.184313 , 0.247136 ,\n",
              "         0.476993 ],\n",
              "        [0.054276 , 0.200158 , 0.088491 , ..., 0.547126 , 0.3905182,\n",
              "         0.7275364],\n",
              "        [0.073194 , 0.140733 , 0.080545 , ..., 0.3659356, 0.222819 ,\n",
              "         0.5001736]]),\n",
              " array([[0.034741 , 0.089665 , 0.091225 , ..., 0.245457 , 0.105065 ,\n",
              "         0.405399 ],\n",
              "        [0.081374 , 0.272747 , 0.085733 , ..., 0.3781286, 0.276366 ,\n",
              "         0.710924 ],\n",
              "        [0.110545 , 0.273567 , 0.0883457, ..., 0.2792487, 0.045941 ,\n",
              "         0.457372 ],\n",
              "        ...,\n",
              "        [0.035169 , 0.065403 , 0.075227 , ..., 0.184313 , 0.247136 ,\n",
              "         0.476993 ],\n",
              "        [0.054276 , 0.2385116, 0.0894163, ..., 0.547126 , 0.2384845,\n",
              "         0.7354476],\n",
              "        [0.073194 , 0.140733 , 0.080545 , ..., 0.3235382, 0.2392511,\n",
              "         0.5761374]]),\n",
              " array([[0.034741  , 0.089665  , 0.091225  , ..., 0.245457  , 0.105065  ,\n",
              "         0.405399  ],\n",
              "        [0.081374  , 0.272747  , 0.085733  , ..., 0.40987498, 0.276366  ,\n",
              "         0.710924  ],\n",
              "        [0.110545  , 0.273567  , 0.0855915 , ..., 0.40987498, 0.045941  ,\n",
              "         0.457372  ],\n",
              "        ...,\n",
              "        [0.035169  , 0.065403  , 0.075227  , ..., 0.184313  , 0.247136  ,\n",
              "         0.476993  ],\n",
              "        [0.054276  , 0.19718746, 0.0855915 , ..., 0.547126  , 0.35872702,\n",
              "         0.94730923],\n",
              "        [0.073194  , 0.140733  , 0.080545  , ..., 0.40987498, 0.35872702,\n",
              "         0.94730923]]),\n",
              " array([[0.034741, 0.089665, 0.091225, ..., 0.245457, 0.105065, 0.405399],\n",
              "        [0.081374, 0.272747, 0.085733, ..., 0.      , 0.276366, 0.710924],\n",
              "        [0.110545, 0.273567, 0.075452, ..., 0.      , 0.045941, 0.457372],\n",
              "        ...,\n",
              "        [0.035169, 0.065403, 0.075227, ..., 0.184313, 0.247136, 0.476993],\n",
              "        [0.054276, 0.038286, 0.075452, ..., 0.547126, 0.      , 0.      ],\n",
              "        [0.073194, 0.140733, 0.080545, ..., 0.      , 0.      , 0.      ]]),\n",
              " array([[0.034741 , 0.089665 , 0.091225 , ..., 0.245457 , 0.105065 ,\n",
              "         0.405399 ],\n",
              "        [0.081374 , 0.272747 , 0.085733 , ..., 0.298304 , 0.276366 ,\n",
              "         0.710924 ],\n",
              "        [0.110545 , 0.273567 , 0.083762 , ..., 0.298304 , 0.045941 ,\n",
              "         0.457372 ],\n",
              "        ...,\n",
              "        [0.035169 , 0.065403 , 0.075227 , ..., 0.184313 , 0.247136 ,\n",
              "         0.476993 ],\n",
              "        [0.054276 , 0.169757 , 0.083762 , ..., 0.547126 , 0.247136 ,\n",
              "         0.8039185],\n",
              "        [0.073194 , 0.140733 , 0.080545 , ..., 0.298304 , 0.247136 ,\n",
              "         0.8039185]])]"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inds[0].shape, populationInitial[0].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L1QEylYqEcz5",
        "outputId": "53d30426-c8c1-471c-f83b-1100d6e17ae3"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((530, 72), (530, 72))"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape, y_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rd1TSLkHJCNb",
        "outputId": "090d1735-1064-4072-d830-f3551f86409e"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((530, 72), (530, 6))"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DoZPhSHmBA4e"
      },
      "source": [
        "# Função Fitness\n",
        "\n",
        "Os indivíduos da população são avaliados no aprendizado multirrótulo.\n",
        "Neste experimento, serão testados algorítmos que transformam o problema e algorítmos que adaptam o problema."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "accuracy_score = Exact Match\\\n",
        "a def acc_multilabel = acurácia para problemas multirrótulos"
      ],
      "metadata": {
        "id": "cC0iH-A59eNr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def acc_multilabel(y_true, y_pred):\n",
        "  class_accuracies = []\n",
        "  for class_ in np.unique(y_true):\n",
        "    class_acc = np.mean(y_pred[y_true == class_] == class_)\n",
        "    class_accuracies.append(class_acc)\n",
        "  return np.mean(class_accuracies)"
      ],
      "metadata": {
        "id": "ATDfA3GV9XDa"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gPVy0SV7A_f_"
      },
      "source": [
        "from skmultilearn.problem_transform import BinaryRelevance\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import hamming_loss\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "def evaluation_binaryRelevance(core_algorithm, individual, metric = 'F1'):\n",
        "  if core_algorithm == 'svm':\n",
        "    classifier_br = BinaryRelevance(classifier=SVC(C=50),require_dense=[False, True])#\n",
        "    classifier_br.fit(individual, y_train)\n",
        "    prediction_br = classifier_br.predict(X_test)\n",
        "\n",
        "    F1 = f1_score(y_test, prediction_br, average='micro')\n",
        "    ACC = accuracy_score(y_test, prediction_br)#EM\n",
        "    ACC_ML = acc_multilabel(y_test, prediction_br)#ACC MultiLabel\n",
        "    HL = hamming_loss(y_test, prediction_br)\n",
        "\n",
        "  elif core_algorithm == 'randomForest':\n",
        "    classifier = BinaryRelevance(classifier = RandomForestClassifier(),require_dense = [False, True])\n",
        "    classifier.fit(individual, y_train)\n",
        "    prediction_br_RFC=classifier.predict(X_test)\n",
        "\n",
        "    F1 = f1_score(y_test, prediction_br_RFC, average='micro')\n",
        "    ACC = accuracy_score(y_test, prediction_br_RFC)\n",
        "    ACC_ML = acc_multilabel(y_test, prediction_br_RFC)#ACC MultiLabel\n",
        "    HL = hamming_loss(y_test, prediction_br_RFC)\n",
        "  elif core_algorithm == '3nn':\n",
        "    classifier = BinaryRelevance(classifier = KNeighborsClassifier(n_neighbors=3))\n",
        "    classifier.fit(individual, y_train)\n",
        "    prediction_br_3knn=classifier.predict(X_test)\n",
        "\n",
        "    F1 = f1_score(y_test, prediction_br_3knn, average='micro')\n",
        "    ACC = accuracy_score(y_test, prediction_br_3knn)\n",
        "    ACC_ML = acc_multilabel(y_test, prediction_br_3knn)#ACC MultiLabel\n",
        "    HL = hamming_loss(y_test, prediction_br_3knn)\n",
        "  elif core_algorithm == '5nn':\n",
        "    classifier = BinaryRelevance(classifier = KNeighborsClassifier())\n",
        "    classifier.fit(individual, y_train)\n",
        "    prediction_br_5knn=classifier.predict(X_test)\n",
        "\n",
        "    F1 = f1_score(y_test, prediction_br_5knn, average='micro')\n",
        "    ACC = accuracy_score(y_test, prediction_br_5knn)\n",
        "    ACC_ML = acc_multilabel(y_test, prediction_br_5knn)#ACC MultiLabel\n",
        "    HL = hamming_loss(y_test, prediction_br_5knn)\n",
        "  elif core_algorithm == '10nn':\n",
        "    classifier = BinaryRelevance(classifier = KNeighborsClassifier(n_neighbors=10))\n",
        "    classifier.fit(individual, y_train)\n",
        "    prediction_br_10knn=classifier.predict(X_test)\n",
        "\n",
        "    F1 = f1_score(y_test, prediction_br_10knn, average='micro')\n",
        "    ACC = accuracy_score(y_test, prediction_br_10knn)\n",
        "    ACC_ML = acc_multilabel(y_test, prediction_br_10knn)#ACC MultiLabel\n",
        "    HL = hamming_loss(y_test, prediction_br_10knn) \n",
        "  elif core_algorithm == 'lr':\n",
        "    from sklearn.linear_model import LogisticRegression\n",
        "    classifier = BinaryRelevance(classifier = LogisticRegression())\n",
        "    classifier.fit(individual, y_train)\n",
        "    prediction_br_lr=classifier.predict(X_test)\n",
        "\n",
        "    F1 = f1_score(y_test, prediction_br_lr, average='micro')\n",
        "    ACC = accuracy_score(y_test, prediction_br_lr)\n",
        "    ACC_ML = acc_multilabel(y_test, prediction_br_lr)#ACC MultiLabel\n",
        "    HL = hamming_loss(y_test, prediction_br_lr)\n",
        "  if metric == 'ACC':\n",
        "    return  ACC\n",
        "  elif metric == 'ACC_ML':\n",
        "    return ACC_ML\n",
        "  elif metric == 'F1':\n",
        "    return F1\n",
        "  elif metric == 'HL':\n",
        "    return HL\n"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#svm = evaluation_binaryRelevance('svm', populationInitial[0])\n",
        "#knn3 = evaluation_binaryRelevance('3nn', populationInitial[0])\n",
        "#knn5 = evaluation_binaryRelevance('5nn', populationInitial[0])\n",
        "#knn10 = evaluation_binaryRelevance('10nn', populationInitial[0])\n",
        "#rf = evaluation_binaryRelevance('randomForest', populationInitial[0])\n"
      ],
      "metadata": {
        "id": "lDjUEDyVZde-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#print(svm, knn3, knn5,knn10, rf)"
      ],
      "metadata": {
        "id": "vtEsdbyTaR_m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CzRGlD9lW_we"
      },
      "source": [
        "Não executando Random Forest devido também ser um algorítmo que funciona como bagging"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X7VeJ7LYIyNU"
      },
      "source": [
        "def fitness(population,metric):\n",
        "  metrica = []\n",
        "  populacao = []\n",
        "  aval_lista_descrecente = []\n",
        "  aval_lista_crescente = []\n",
        "  indivuduos = {}\n",
        "  accuracy = {}\n",
        "  print('tam_pop_atual: ',len(population))\n",
        "  print('Avaliações com Binary Relevance\\n')\n",
        "  indexx = 0\n",
        "  for i in range(len(population)):\n",
        "    print('Individual - ',i)\n",
        "    metrica.append(evaluation_binaryRelevance('svm', population[indexx], metric=metric))\n",
        "    metrica.append(evaluation_binaryRelevance('randomForest', population[indexx],metric=metric))\n",
        "    metrica.append(evaluation_binaryRelevance('3nn', population[indexx],metric=metric))\n",
        "    metrica.append(evaluation_binaryRelevance('5nn', population[indexx],metric=metric))\n",
        "    metrica.append(evaluation_binaryRelevance('10nn', population[indexx],metric=metric))\n",
        "\n",
        "    aval_lista_descrecente = sorted(metrica, reverse=True)\n",
        "    aval_lista_crescente = sorted(metrica)\n",
        "\n",
        "    melhor = aval_lista_descrecente[0]\n",
        "    pior = aval_lista_crescente[0]\n",
        "\n",
        "    populacao.append([melhor, population[indexx]])\n",
        "\n",
        "    indivuduos[i] = melhor\n",
        "    indexx = indexx+1\n",
        "    print('Pior:', pior, ' melhor (escolhido):', melhor)\n",
        "    print('----------------------------------------------------')\n",
        "    melhor = ''\n",
        "    pior = ''\n",
        "    aval_lista_descrecente = []\n",
        "    aval_lista_crescente = []\n",
        "    metrica = []\n",
        "  return populacao\n",
        "\n"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ia_BbBmaYRGk",
        "outputId": "d9fd7115-274c-449d-c8b1-57f987a8005c"
      },
      "source": [
        "populacao_inicial_avaliada = fitness(populationInitial,'ACC_ML')"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tam_pop_atual:  6\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.6254748982360923  melhor (escolhido): 0.7774762550881953\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.6341248303934871  melhor (escolhido): 0.7632632293080055\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.621540027137042  melhor (escolhido): 0.7795793758480326\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.6265264586160109  melhor (escolhido): 0.7795793758480326\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.5683853459972863  melhor (escolhido): 0.7541723202170963\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.6079036635006785  melhor (escolhido): 0.7611601085481683\n",
            "----------------------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_jmojq6fKLe1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5513848b-7620-42be-e2b8-9f0abbdca8ea"
      },
      "source": [
        "populacao_inicial_avaliada"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[0.7774762550881953,\n",
              "  array([[0.034741  , 0.089665  , 0.091225  , ..., 0.245457  , 0.105065  ,\n",
              "          0.405399  ],\n",
              "         [0.081374  , 0.272747  , 0.085733  , ..., 0.474346  , 0.276366  ,\n",
              "          0.710924  ],\n",
              "         [0.110545  , 0.273567  , 0.094152  , ..., 0.378092  , 0.045941  ,\n",
              "          0.457372  ],\n",
              "         ...,\n",
              "         [0.035169  , 0.065403  , 0.075227  , ..., 0.184313  , 0.247136  ,\n",
              "          0.476993  ],\n",
              "         [0.054276  , 0.19704233, 0.084736  , ..., 0.547126  , 0.58776933,\n",
              "          0.732367  ],\n",
              "         [0.073194  , 0.140733  , 0.080545  , ..., 0.23395967, 0.19878833,\n",
              "          0.55921533]])],\n",
              " [0.7632632293080055,\n",
              "  array([[0.034741 , 0.089665 , 0.091225 , ..., 0.245457 , 0.105065 ,\n",
              "          0.405399 ],\n",
              "         [0.081374 , 0.272747 , 0.085733 , ..., 0.4828466, 0.276366 ,\n",
              "          0.710924 ],\n",
              "         [0.110545 , 0.273567 , 0.0894968, ..., 0.2929662, 0.045941 ,\n",
              "          0.457372 ],\n",
              "         ...,\n",
              "         [0.035169 , 0.065403 , 0.075227 , ..., 0.184313 , 0.247136 ,\n",
              "          0.476993 ],\n",
              "         [0.054276 , 0.200158 , 0.088491 , ..., 0.547126 , 0.3905182,\n",
              "          0.7275364],\n",
              "         [0.073194 , 0.140733 , 0.080545 , ..., 0.3659356, 0.222819 ,\n",
              "          0.5001736]])],\n",
              " [0.7795793758480326,\n",
              "  array([[0.034741 , 0.089665 , 0.091225 , ..., 0.245457 , 0.105065 ,\n",
              "          0.405399 ],\n",
              "         [0.081374 , 0.272747 , 0.085733 , ..., 0.3781286, 0.276366 ,\n",
              "          0.710924 ],\n",
              "         [0.110545 , 0.273567 , 0.0883457, ..., 0.2792487, 0.045941 ,\n",
              "          0.457372 ],\n",
              "         ...,\n",
              "         [0.035169 , 0.065403 , 0.075227 , ..., 0.184313 , 0.247136 ,\n",
              "          0.476993 ],\n",
              "         [0.054276 , 0.2385116, 0.0894163, ..., 0.547126 , 0.2384845,\n",
              "          0.7354476],\n",
              "         [0.073194 , 0.140733 , 0.080545 , ..., 0.3235382, 0.2392511,\n",
              "          0.5761374]])],\n",
              " [0.7795793758480326,\n",
              "  array([[0.034741  , 0.089665  , 0.091225  , ..., 0.245457  , 0.105065  ,\n",
              "          0.405399  ],\n",
              "         [0.081374  , 0.272747  , 0.085733  , ..., 0.40987498, 0.276366  ,\n",
              "          0.710924  ],\n",
              "         [0.110545  , 0.273567  , 0.0855915 , ..., 0.40987498, 0.045941  ,\n",
              "          0.457372  ],\n",
              "         ...,\n",
              "         [0.035169  , 0.065403  , 0.075227  , ..., 0.184313  , 0.247136  ,\n",
              "          0.476993  ],\n",
              "         [0.054276  , 0.19718746, 0.0855915 , ..., 0.547126  , 0.35872702,\n",
              "          0.94730923],\n",
              "         [0.073194  , 0.140733  , 0.080545  , ..., 0.40987498, 0.35872702,\n",
              "          0.94730923]])],\n",
              " [0.7541723202170963,\n",
              "  array([[0.034741, 0.089665, 0.091225, ..., 0.245457, 0.105065, 0.405399],\n",
              "         [0.081374, 0.272747, 0.085733, ..., 0.      , 0.276366, 0.710924],\n",
              "         [0.110545, 0.273567, 0.075452, ..., 0.      , 0.045941, 0.457372],\n",
              "         ...,\n",
              "         [0.035169, 0.065403, 0.075227, ..., 0.184313, 0.247136, 0.476993],\n",
              "         [0.054276, 0.038286, 0.075452, ..., 0.547126, 0.      , 0.      ],\n",
              "         [0.073194, 0.140733, 0.080545, ..., 0.      , 0.      , 0.      ]])],\n",
              " [0.7611601085481683,\n",
              "  array([[0.034741 , 0.089665 , 0.091225 , ..., 0.245457 , 0.105065 ,\n",
              "          0.405399 ],\n",
              "         [0.081374 , 0.272747 , 0.085733 , ..., 0.298304 , 0.276366 ,\n",
              "          0.710924 ],\n",
              "         [0.110545 , 0.273567 , 0.083762 , ..., 0.298304 , 0.045941 ,\n",
              "          0.457372 ],\n",
              "         ...,\n",
              "         [0.035169 , 0.065403 , 0.075227 , ..., 0.184313 , 0.247136 ,\n",
              "          0.476993 ],\n",
              "         [0.054276 , 0.169757 , 0.083762 , ..., 0.547126 , 0.247136 ,\n",
              "          0.8039185],\n",
              "         [0.073194 , 0.140733 , 0.080545 , ..., 0.298304 , 0.247136 ,\n",
              "          0.8039185]])]]"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mvg_7yoXVQ0D"
      },
      "source": [
        "# Elitismo\n",
        "O melhor individuo é passado para a proxima geracao - Elitismo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xzwPoeN1WXJX"
      },
      "source": [
        "def elitismo(populacao_avaliada):\n",
        "  #ordena a população passada de forma descrescente\n",
        "  populacao_ordenada  = sorted(populacao_avaliada, key=lambda x: x[0], reverse=True)\n",
        "  elitista = populacao_ordenada[0]\n",
        "  return elitista"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xltBmsu9Vab1"
      },
      "source": [
        "elitista = elitismo(populacao_inicial_avaliada)"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PKolrnNSNQC_",
        "outputId": "88a3c0e0-4dcc-4ed6-c56a-d5a1bceb648a"
      },
      "source": [
        "elitista"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.7795793758480326,\n",
              " array([[0.034741 , 0.089665 , 0.091225 , ..., 0.245457 , 0.105065 ,\n",
              "         0.405399 ],\n",
              "        [0.081374 , 0.272747 , 0.085733 , ..., 0.3781286, 0.276366 ,\n",
              "         0.710924 ],\n",
              "        [0.110545 , 0.273567 , 0.0883457, ..., 0.2792487, 0.045941 ,\n",
              "         0.457372 ],\n",
              "        ...,\n",
              "        [0.035169 , 0.065403 , 0.075227 , ..., 0.184313 , 0.247136 ,\n",
              "         0.476993 ],\n",
              "        [0.054276 , 0.2385116, 0.0894163, ..., 0.547126 , 0.2384845,\n",
              "         0.7354476],\n",
              "        [0.073194 , 0.140733 , 0.080545 , ..., 0.3235382, 0.2392511,\n",
              "         0.5761374]])]"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "14CjcZ3EV2yu"
      },
      "source": [
        "# Seleção"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZViuqxbjV-jh"
      },
      "source": [
        "O processo de seleçãp é dado por torneio, sorteando dois pais"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DGNcm10nVutM"
      },
      "source": [
        "import random\n",
        "def selecao(pop_atual):\n",
        "  sorteio1 = random.choice(range(len(pop_atual)))\n",
        "  #print(sorteio1)\n",
        "  sorteio2 = random.choice(range(len(pop_atual)))\n",
        "  #print(sorteio2)\n",
        "  pai1 = pop_atual[sorteio1]\n",
        "  pai2 = pop_atual[sorteio2]\n",
        "  #print(pai1[0])\n",
        "  #print(pai2[0])\n",
        "\n",
        "  if(pai1[0] >= pai2[0]):\n",
        "    return pai1\n",
        "  else:\n",
        "    return pai2"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "prBG_-G6mdlE"
      },
      "source": [
        "#Cruzamento"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ovbVfXg4ni-o"
      },
      "source": [
        "Cruzamento de n-points"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BQ8VOON4mcis"
      },
      "source": [
        "#indeces com a posição do valores ausentes\n",
        "index_vas = a[1]"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "index_vas"
      ],
      "metadata": {
        "id": "21NXvHUAb_Hh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7d0a023a-7074-4373-d868-14eed0b3e478"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              " ...]"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "olsDwQClo0rU"
      },
      "source": [
        "def crossover(list_index_vas,n_points, new_population, tam_population):\n",
        "  nova_populacao_com_crossover = []\n",
        "  #elitista = new_population[0]\n",
        "  #nova_populacao_com_crossover.append(elitista[1])\n",
        "\n",
        "  while(len(nova_populacao_com_crossover)<tam_population):\n",
        "    sorteio1 = random.choice(range(len(new_population)))\n",
        "    #escolha dos pais que terão materiais trocados\n",
        "    #print(sorteio1)\n",
        "    sorteio2 = random.choice(range(len(new_population)))\n",
        "    #print(sorteio2)\n",
        "    while(sorteio1 == sorteio2):\n",
        "      sorteio2 = random.choice(range(len(new_population)))\n",
        "    pai1 = new_population[sorteio1]\n",
        "    pai2 = new_population[sorteio2]\n",
        "\n",
        "    df_pai1 = pd.DataFrame(pai1[1])\n",
        "    df_pai2 = pd.DataFrame(pai2[1])\n",
        "  \n",
        "    genes_all_pai1 = []\n",
        "\n",
        "    genes_all_pai2 = []\n",
        "\n",
        "    #recuperando os genes de cada pai\n",
        "    for i in list_index_vas:\n",
        "      genes_all_pai1.append(df_pai1.iloc[i[0],i[1]])\n",
        "      genes_all_pai2.append(df_pai2.iloc[i[0],i[1]])\n",
        "    #dividindo os genes de acordo com o n_points\n",
        "    divide_genes_pai_1 = np.array_split(genes_all_pai1,n_points)\n",
        "    divide_genes_pai_2 = np.array_split(genes_all_pai2,n_points)\n",
        "\n",
        "    genes_0_pai1 = divide_genes_pai_1[0]\n",
        "    genes_1_pai1 = divide_genes_pai_1[1]\n",
        "\n",
        "    genes_0_pai2 = divide_genes_pai_2[0]\n",
        "    genes_1_pai2 = divide_genes_pai_2[1]\n",
        "\n",
        "    t1 = len(genes_1_pai1)\n",
        "    t2 = len(genes_1_pai2)\n",
        "\n",
        "    divide_list_index = np.array_split(list_index_vas,n_points)\n",
        "    primeira_parte = divide_list_index[0]\n",
        "    segunda_parte = divide_list_index[1]\n",
        "\n",
        "    genes_parte_2_pai1 = genes_1_pai1.tolist()\n",
        "    genes_parte_2_pai2 = genes_1_pai2.tolist()\n",
        "\n",
        "    #Trocando genes dos pais\n",
        "    index = 0\n",
        "    for i in segunda_parte:\n",
        "      df_pai1.iloc[i[0].astype(int), i[1].astype(int)] = genes_1_pai2[index]\n",
        "      df_pai2.iloc[i[0].astype(int), i[1].astype(int)] = genes_1_pai1[index]\n",
        "      index = index +1\n",
        "    filho1 = df_pai1\n",
        "    filho2 = df_pai2\n",
        "    nova_populacao_com_crossover.append(filho1.to_numpy())\n",
        "    nova_populacao_com_crossover.append(filho2.to_numpy())\n",
        "  #filho1, filho2,pai1[1],pai1[2]\n",
        "  print('Tamanho da nova população gerada por meio do cruzamento:',len(nova_populacao_com_crossover))\n",
        "  return nova_populacao_com_crossover"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SrTV9X19pUOO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "76cbb6b2-7d6e-4218-eedf-73ff50c5364a"
      },
      "source": [
        "nova_populacao_recombinada = crossover(index_vas,2,populacao_inicial_avaliada,10)\n",
        "nova_populacao_recombinada"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tamanho da nova população gerada por meio do cruzamento: 10\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "         0.732367  ],\n",
              "        [0.073194  , 0.140733  , 0.080545  , ..., 0.23395967, 0.19878833,\n",
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              "        [0.110545 , 0.273567 , 0.083762 , ..., 0.298304 , 0.045941 ,\n",
              "         0.457372 ],\n",
              "        ...,\n",
              "        [0.035169 , 0.065403 , 0.075227 , ..., 0.184313 , 0.247136 ,\n",
              "         0.476993 ],\n",
              "        [0.054276 , 0.169757 , 0.088491 , ..., 0.547126 , 0.247136 ,\n",
              "         0.8039185],\n",
              "        [0.073194 , 0.140733 , 0.080545 , ..., 0.298304 , 0.247136 ,\n",
              "         0.5001736]]),\n",
              " array([[0.034741  , 0.089665  , 0.091225  , ..., 0.245457  , 0.105065  ,\n",
              "         0.405399  ],\n",
              "        [0.081374  , 0.272747  , 0.085733  , ..., 0.474346  , 0.276366  ,\n",
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              "         0.476993  ],\n",
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              "        [0.073194  , 0.140733  , 0.080545  , ..., 0.23395967, 0.19878833,\n",
              "         0.55921533]]),\n",
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              "         0.457372 ],\n",
              "        ...,\n",
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              "         0.476993 ],\n",
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              "         0.7275364],\n",
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              "         0.8039185]])]"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T4EuGQVvnR2y"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0OD_YHb7WRYK"
      },
      "source": [
        "# Mutação"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TMvEp8kaWNpy"
      },
      "source": [
        "def mutacao(dX_amputed,ind, list_missing_values):\n",
        "  import random\n",
        "  #de numpy para pandas format\n",
        "  ind = pd.DataFrame(ind)\n",
        "  #serão selecionados x% de individuos da população e são trocadas todas as solucões\n",
        "  #candidatas já imputadas por uma nova solução da pool de soluções\n",
        "  #amount_inds_mutation = int(rate_mutation *  len(inds_pop))\n",
        "  for i in list_missing_values:\n",
        "    #criando um pool solutions de acordo com a o valor de x da lista de index\n",
        "    lista_do_indice_atual = dX_amputed.iloc[:,i[1]]\n",
        "    #removendo o indices NaN para criar a pool solutions só com soluções candatas não-nulas\n",
        "    pool_solutions = [x for x in lista_do_indice_atual if pd.isnull(x) == False]\n",
        "    #substituindo o gene do indivíduo por um dos genes da pool solutions\n",
        "    #print('index: ', i[0],',', i[1])\n",
        "    #print('antes: ',ind.iloc[i[0],i[1]])\n",
        "    ind.iloc[i[0],i[1]] = random.choice(pool_solutions)\n",
        "    #print('depois: ',ind.iloc[i[0],i[1]])\n",
        "  return ind.to_numpy()"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hcWIluclhe8l"
      },
      "source": [
        "#ind = mutacao(df_amp)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aZhxBzJ6yOjY"
      },
      "source": [
        "def mutations(dX_amputed,population, rate_mutation,list_missing_values):\n",
        "  populacao_cruzada = population.copy()\n",
        "  amount_inds_mutation = int(rate_mutation *  len(populacao_cruzada))\n",
        "  i = 0\n",
        "  while(i < amount_inds_mutation):\n",
        "    ind_escolhido_pra_mutacao = populacao_cruzada.pop(i)\n",
        "    ind_mutado = mutacao(dX_amputed, ind_escolhido_pra_mutacao,list_missing_values)\n",
        "    populacao_cruzada.append(ind_mutado)\n",
        "    i=i+1\n",
        "  return populacao_cruzada"
      ],
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C054OgBV14SY"
      },
      "source": [
        "# Algoritmo Genético"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZrkPY0FB0dFk"
      },
      "source": [
        "\n",
        "\n",
        "1.   elitisimo: 1\n",
        "2.   gerações: 6\n",
        "3.   geracoes_sizes{6,12,18,24,30}\n",
        "4.   cruzamento: n-points\n",
        "5.   cruzamento: torneio\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WXpNshUS0hoH"
      },
      "source": [
        "def nova_populacao(df_amp,individuo_elitista, pop_atual, tam_population,n_points, list_index_vas=index_vas):\n",
        "  formacao_nova_populacao = []\n",
        "  formacao_nova_populacao.append(individuo_elitista)\n",
        "  while(len(formacao_nova_populacao)<= tam_population):\n",
        "    formacao_nova_populacao.append(selecao(pop_atual))\n",
        "  #pega a população selecionada para fazer a recombinação\n",
        "  nova_populacao_combinada = crossover(list_index_vas,n_points, formacao_nova_populacao, tam_population)\n",
        "  #20% dos individuos sofrem mutações\n",
        "  #a nova populacao combinada é passada como parametro\n",
        "  populacao_cruzada_e_mutada = mutations(df_amp,nova_populacao_combinada, 0.20,list_index_vas)\n",
        "\n",
        "  return populacao_cruzada_e_mutada"
      ],
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Benckmarking Experimental"
      ],
      "metadata": {
        "id": "-B_1KVuBjpdT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#dados = ['a','b','c','d','e']\n",
        "#dados_ = ['1','1','1','1','1']\n",
        "def _geracoes_results(dados,name_file,dataset, missing_values_rate):\n",
        "  with open(name_file, 'a') as arquivo:\n",
        "    arquivo.write('\\n'+str(dataset)+'_mv_'+str(missing_values_rate)+' ')\n",
        "    for i in dados:\n",
        "      arquivo.write(' '+str(i)+' ')\n",
        "\n"
      ],
      "metadata": {
        "id": "d8cRkYtDKZS4"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#_geracoes_results(dados, 'a.txt','dataset',0.05)"
      ],
      "metadata": {
        "id": "-nopEa3NQrRa"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def results_exp(name_file,algorithm,dataset_name, vas, metric,dado):\n",
        "  with open(name_file, 'a') as arquivo:\n",
        "    arquivo.write('\\n'+str(algorithm)+'_'+dataset_name+'_'+str(vas)+'_'+metric+' '+dado)"
      ],
      "metadata": {
        "id": "RS_MuuDfcJTX"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def otimizacao(geracoes,populacao_inicial,elitista,metric,dataset_amputado, missing_values_rate,dataset_name):\n",
        "  melhores_por_geracao = []\n",
        "  for p in [8,10,12,14,16,18]:\n",
        "    melhor = elitista\n",
        "    melhores_por_geracao.append(melhor)\n",
        "    print('tamanho da população atual',len(populacao_inicial))\n",
        "    #Cria nova populacao\n",
        "    n_pop = nova_populacao(dataset_amputado, melhor, populacao_inicial, p,2)\n",
        "    #avalia populacao formada\n",
        "    populacao_atual_avaliada = fitness(n_pop,metric=metric)\n",
        "    #print(n_pop[0])\n",
        "    #passando o melhor elitista\n",
        "    #print(melhor)\n",
        "    populacao_inicial = []\n",
        "    melhor_atual  = elitismo(populacao_atual_avaliada)\n",
        "    if (melhor_atual[0]>melhor[0]):\n",
        "      melhores_por_geracao.append(melhor_atual)\n",
        "      #populacao_inicial.append(melhor_atual)\n",
        "      melhor = melhor_atual\n",
        "      print('Novo melhor:',melhor_atual[0])\n",
        "    else:\n",
        "      populacao_atual_avaliada.pop(len(populacao_atual_avaliada)-1)\n",
        "      melhores_por_geracao.append(melhor)\n",
        "      populacao_inicial.append(melhor)\n",
        "      print('melhor anterior foi escolhido: ',melhor[0])\n",
        "    populacao_inicial.extend(populacao_atual_avaliada)\n",
        "    print(len(populacao_inicial))\n",
        "    re = melhores_por_geracao\n",
        "    res = melhores_por_geracao[len(melhores_por_geracao)-1]\n",
        "    print('Melhor encontrado: ',melhores_por_geracao[len(melhores_por_geracao)-1])\n",
        "    r = []\n",
        "    for x in re:\n",
        "      r.append(str(x[0]))\n",
        "    print('tamanho da poppop',len(r) )\n",
        "    #file_name = dataset_name+'_'+str(missing_values_rate)+'.txt'\n",
        "    _geracoes_results(r,'geracoes_ag.txt',dataset_name,str(missing_values_rate))\n",
        "    return res[0]#,res[1]"
      ],
      "metadata": {
        "id": "3AoyMe61Ist2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fL4pHnpa3wiT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c99ca468-b6ce-416b-8695-43cafc6291fe"
      },
      "source": [
        "geracoes = [8,10,12,14,16,18] # 7-geracoes(com a populacao inicial)\n",
        "rates_missing_values = [0.05, 0.10, 0.15, 0.20, 0.25, 0.30]\n",
        "imputation_methods  = ['knn-3','knn-5','knn-10', 'mean', 'most_frequent', 'median']\n",
        "#lendo datasets via scikit-multilearning\n",
        "dataset_name = 'emotions'\n",
        "metric='ACC_ML'\n",
        "dataset = load_dataset(dataset_name)\n",
        "X = dataset[0]\n",
        "y = dataset[1]\n",
        "#Aplicando validação cruzada do tipo K-folds - (10-folds)\n",
        "data_validations = cross_validation(X,y,10)\n",
        "X_train = data_validations[0]\n",
        "X_test = data_validations[1]\n",
        "y_train = data_validations[2]\n",
        "y_test = data_validations[3]\n",
        "#verificando missing values \n",
        "#missing_values =missing_values(pd.DataFrame(X_train))\n",
        "#Amputação de dados\n",
        "accuracy = []\n",
        "for i in rates_missing_values:\n",
        "  dados_amputacao = amputation(i, X_train)\n",
        "  lista_indices_amputados = dados_amputacao[1]\n",
        "  dataset_amputado = dados_amputacao[0]\n",
        "  #imputação para formação da população incial\n",
        "  individuos = []\n",
        "  for j  in imputation_methods:\n",
        "    individuos.append(imputation(j,dataset_amputado))\n",
        "  #========================================================================\n",
        "  #Fazendo experimentos com a média dos avaliações para SVM e Random Forest\n",
        "  #para cada imputação simples realizada na população inicial é feito um experimento\n",
        "  #com RF e SVM o valor utilizado é a média do desempenho da classificação\n",
        "  #dessa forma é possivel considerar todas as formas de imputação para os \n",
        "  #classificados bases utilizados \n",
        "  #========================================================================\n",
        "  bases_completas = individuos\n",
        "  avalicoes_inds_rf = []\n",
        "  avalicoes_inds_svm = []\n",
        "  for l in range(len(bases_completas)):\n",
        "    avalicoes_inds_rf.append(evaluation_binaryRelevance('randomForest', bases_completas[l], metric=metric))\n",
        "    avalicoes_inds_svm.append(evaluation_binaryRelevance('svm', bases_completas[l],metric=metric))\n",
        "  #print(avalicoes_inds_rf) \n",
        "  #print(avalicoes_inds_svm)\n",
        "\n",
        "  print('media Random Forest: ',np.mean(np.array(avalicoes_inds_rf)) ) \n",
        "  print('media SVM: ',np.mean(np.array(avalicoes_inds_svm) )) \n",
        "\n",
        "  results_exp('/content/drive/MyDrive/Colab Notebooks/resultados_evoimp/resultados_ACCML.txt', 'SVM',dataset_name, i, metric,str(np.mean(np.array(avalicoes_inds_svm))))\n",
        "  results_exp('/content/drive/MyDrive/Colab Notebooks/resultados_evoimp/resultados_ACCML.txt', 'RF',dataset_name, i, metric,str(np.mean(np.array(avalicoes_inds_rf))))\n",
        "\n",
        "  #========================================================================\n",
        "  #Fazendo experimentos com o Algoritmo Genético\n",
        "  #========================================================================\n",
        "  populationInitial = individuos\n",
        "  #avaliando a pupolução inicial e ordenada (DESC) e avaliando com a acurácia\n",
        "  populacao_avaliada = fitness(populationInitial,metric)\n",
        "  #elitismo\n",
        "  elitista = elitismo(populacao_avaliada)\n",
        "  #iniciando o processo de otimização (criterio de parada é determinado pelas gerações)\n",
        "  populacao_inicial = populacao_avaliada\n",
        "  melhor = elitista\n",
        "  melhores_por_geracao = []\n",
        "  melhores_por_geracao.append(melhor[0])\n",
        "  for p in [8,10,12,14,16,18]:\n",
        "    print('p: ',p)\n",
        "    n_pop = nova_populacao(dataset_amputado, melhor, populacao_inicial, p,2)\n",
        "    populacao_atual_avaliada = fitness(n_pop,metric=metric)\n",
        "    populacao_inicial = []\n",
        "    melhor_atual  = elitismo(populacao_atual_avaliada)\n",
        "    if (melhor_atual[0]>melhor[0]):\n",
        "      melhores_por_geracao.append(melhor_atual[0])\n",
        "      #populacao_inicial.append(melhor_atual)\n",
        "      melhor = melhor_atual\n",
        "      print('Novo melhor:',melhor_atual[0])\n",
        "    else:\n",
        "      #melhor = melhor\n",
        "      populacao_atual_avaliada.pop(len(populacao_atual_avaliada)-1)\n",
        "      melhores_por_geracao.append(melhor[0])\n",
        "      populacao_inicial.append(melhor)\n",
        "      print('melhor anterior foi escolhido: ',melhor[0])\n",
        "    populacao_inicial.extend(populacao_atual_avaliada)\n",
        "  _geracoes_results(melhores_por_geracao,'/content/drive/MyDrive/Colab Notebooks/resultados_evoimp/geracoes_agACCML.txt',dataset_name,str(i))\n",
        "  print('melhores por geracao',len(melhores_por_geracao))\n",
        "  results_exp('/content/drive/MyDrive/Colab Notebooks/resultados_evoimp/resultados_ACCML.txt', 'AG',dataset_name, i, metric,str(melhores_por_geracao[len(melhores_por_geracao)-1]))\n",
        "  melhores_por_geracao = []\n",
        "    \n",
        "\n",
        "\n",
        "\n",
        "  #z = otimizacao(geracoes=geracoes, populacao_inicial=populacao_inicial, elitista=elitista,metric='ACC', dataset_amputado=dataset_amputado,missing_values_rate=i,dataset_name=dataset_name)\n",
        "  #\n",
        "  #z = ''"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "emotions:undivided - exists, not redownloading\n",
            "Total dados: 38160\n",
            " Total VAs: 1908\n",
            "\n",
            "media Random Forest:  0.7882236544549976\n",
            "media SVM:  0.6540366350067842\n",
            "tam_pop_atual:  6\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.6132632293080054  melhor (escolhido): 0.7932157394843962\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.61780868385346  melhor (escolhido): 0.7702510176390773\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.6151289009497964  melhor (escolhido): 0.7729308005427409\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.6068521031207599  melhor (escolhido): 0.7857530529172321\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.6023066485753052  melhor (escolhido): 0.7750339213025781\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.6023066485753052  melhor (escolhido): 0.7948439620081411\n",
            "----------------------------------------------------\n",
            "p:  8\n",
            "Tamanho da nova população gerada por meio do cruzamento: 8\n",
            "tam_pop_atual:  8\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.6068521031207599  melhor (escolhido): 0.7913500678426051\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.6068521031207599  melhor (escolhido): 0.7694369063772049\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.6068521031207599  melhor (escolhido): 0.7859905020352782\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.6068521031207599  melhor (escolhido): 0.7758480325644506\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.6068521031207599  melhor (escolhido): 0.7878561736770693\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.6068521031207599  melhor (escolhido): 0.7777137042062415\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.6023066485753052  melhor (escolhido): 0.7814450474898236\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.5932157394843962  melhor (escolhido): 0.7723541383989145\n",
            "----------------------------------------------------\n",
            "melhor anterior foi escolhido:  0.7948439620081411\n",
            "p:  10\n",
            "Tamanho da nova população gerada por meio do cruzamento: 10\n",
            "tam_pop_atual:  10\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.609158751696065  melhor (escolhido): 0.7758480325644506\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.6155698778833107  melhor (escolhido): 0.7806309362279511\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.6155698778833107  melhor (escolhido): 0.7622116689280869\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.609158751696065  melhor (escolhido): 0.7640773405698779\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.6155698778833107  melhor (escolhido): 0.7787652645861601\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.6023066485753052  melhor (escolhido): 0.7878561736770693\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.6155698778833107  melhor (escolhido): 0.7787652645861601\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.6023066485753052  melhor (escolhido): 0.7940298507462686\n",
            "----------------------------------------------------\n",
            "Individual -  8\n",
            "Pior: 0.6155698778833107  melhor (escolhido): 0.7667571234735414\n",
            "----------------------------------------------------\n",
            "Individual -  9\n",
            "Pior: 0.609158751696065  melhor (escolhido): 0.7859905020352782\n",
            "----------------------------------------------------\n",
            "melhor anterior foi escolhido:  0.7948439620081411\n",
            "p:  12\n",
            "Tamanho da nova população gerada por meio do cruzamento: 12\n",
            "tam_pop_atual:  12\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.6097354138398915  melhor (escolhido): 0.7568521031207598\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.6097354138398915  melhor (escolhido): 0.7576662143826323\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.6087177747625508  melhor (escolhido): 0.769674355495251\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.6087177747625508  melhor (escolhido): 0.7945047489823609\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.6097354138398915  melhor (escolhido): 0.769674355495251\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.6097354138398915  melhor (escolhido): 0.7520691994572593\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.6097354138398915  melhor (escolhido): 0.7659430122116689\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.6097354138398915  melhor (escolhido): 0.7531207598371777\n",
            "----------------------------------------------------\n",
            "Individual -  8\n",
            "Pior: 0.6097354138398915  melhor (escolhido): 0.7504409769335143\n",
            "----------------------------------------------------\n",
            "Individual -  9\n",
            "Pior: 0.6087177747625508  melhor (escolhido): 0.7715400271370421\n",
            "----------------------------------------------------\n",
            "Individual -  10\n",
            "Pior: 0.6097354138398915  melhor (escolhido): 0.7934531886024423\n",
            "----------------------------------------------------\n",
            "Individual -  11\n",
            "Pior: 0.6087177747625508  melhor (escolhido): 0.762449118046133\n",
            "----------------------------------------------------\n",
            "melhor anterior foi escolhido:  0.7948439620081411\n",
            "p:  14\n",
            "Tamanho da nova população gerada por meio do cruzamento: 14\n",
            "tam_pop_atual:  14\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.6052238805970149  melhor (escolhido): 0.7640773405698779\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.6052238805970149  melhor (escolhido): 0.7787652645861601\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.6052238805970149  melhor (escolhido): 0.7630257801899594\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.6052238805970149  melhor (escolhido): 0.7723541383989145\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.6275780189959295  melhor (escolhido): 0.7777137042062415\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.6052238805970149  melhor (escolhido): 0.7659430122116689\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.6275780189959295  melhor (escolhido): 0.7795793758480326\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.6052238805970149  melhor (escolhido): 0.7678086838534599\n",
            "----------------------------------------------------\n",
            "Individual -  8\n",
            "Pior: 0.6275780189959295  melhor (escolhido): 0.7686227951153324\n",
            "----------------------------------------------------\n",
            "Individual -  9\n",
            "Pior: 0.6052238805970149  melhor (escolhido): 0.7795793758480326\n",
            "----------------------------------------------------\n",
            "Individual -  10\n",
            "Pior: 0.6052238805970149  melhor (escolhido): 0.7742198100407056\n",
            "----------------------------------------------------\n",
            "Individual -  11\n",
            "Pior: 0.6052238805970149  melhor (escolhido): 0.7549864314789687\n",
            "----------------------------------------------------\n",
            "Individual -  12\n",
            "Pior: 0.6275780189959295  melhor (escolhido): 0.7897218453188602\n",
            "----------------------------------------------------\n",
            "Individual -  13\n",
            "Pior: 0.6275780189959295  melhor (escolhido): 0.769674355495251\n",
            "----------------------------------------------------\n",
            "melhor anterior foi escolhido:  0.7948439620081411\n",
            "p:  16\n",
            "Tamanho da nova população gerada por meio do cruzamento: 16\n",
            "tam_pop_atual:  16\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.6052238805970149  melhor (escolhido): 0.780393487109905\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.6052238805970149  melhor (escolhido): 0.7651289009497966\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.6052238805970149  melhor (escolhido): 0.7806309362279511\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.5798168249660787  melhor (escolhido): 0.7905359565807326\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.5798168249660787  melhor (escolhido): 0.7795793758480326\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.5798168249660787  melhor (escolhido): 0.7795793758480326\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.5798168249660787  melhor (escolhido): 0.7721166892808684\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.5798168249660787  melhor (escolhido): 0.7750339213025781\n",
            "----------------------------------------------------\n",
            "Individual -  8\n",
            "Pior: 0.5798168249660787  melhor (escolhido): 0.7777137042062415\n",
            "----------------------------------------------------\n",
            "Individual -  9\n",
            "Pior: 0.5798168249660787  melhor (escolhido): 0.7849389416553596\n",
            "----------------------------------------------------\n",
            "Individual -  10\n",
            "Pior: 0.5798168249660787  melhor (escolhido): 0.773168249660787\n",
            "----------------------------------------------------\n",
            "Individual -  11\n",
            "Pior: 0.6052238805970149  melhor (escolhido): 0.7713025780189959\n",
            "----------------------------------------------------\n",
            "Individual -  12\n",
            "Pior: 0.5798168249660787  melhor (escolhido): 0.8033582089552238\n",
            "----------------------------------------------------\n",
            "Individual -  13\n",
            "Pior: 0.5798168249660787  melhor (escolhido): 0.778527815468114\n",
            "----------------------------------------------------\n",
            "Individual -  14\n",
            "Pior: 0.5798168249660787  melhor (escolhido): 0.7651289009497966\n",
            "----------------------------------------------------\n",
            "Individual -  15\n",
            "Pior: 0.5798168249660787  melhor (escolhido): 0.773168249660787\n",
            "----------------------------------------------------\n",
            "Novo melhor: 0.8033582089552238\n",
            "p:  18\n",
            "Tamanho da nova população gerada por meio do cruzamento: 18\n",
            "tam_pop_atual:  18\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.6048846675712347  melhor (escolhido): 0.7787652645861601\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.6048846675712347  melhor (escolhido): 0.7777137042062415\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.6048846675712347  melhor (escolhido): 0.7859905020352782\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.6048846675712347  melhor (escolhido): 0.7806309362279511\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.6048846675712347  melhor (escolhido): 0.7779511533242877\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.5907734056987788  melhor (escolhido): 0.782259158751696\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.5907734056987788  melhor (escolhido): 0.7630257801899594\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.6048846675712347  melhor (escolhido): 0.7504409769335143\n",
            "----------------------------------------------------\n",
            "Individual -  8\n",
            "Pior: 0.5907734056987788  melhor (escolhido): 0.7686227951153324\n",
            "----------------------------------------------------\n",
            "Individual -  9\n",
            "Pior: 0.5907734056987788  melhor (escolhido): 0.7603459972862958\n",
            "----------------------------------------------------\n",
            "Individual -  10\n",
            "Pior: 0.6048846675712347  melhor (escolhido): 0.7758480325644506\n",
            "----------------------------------------------------\n",
            "Individual -  11\n",
            "Pior: 0.6048846675712347  melhor (escolhido): 0.7833107191316147\n",
            "----------------------------------------------------\n",
            "Individual -  12\n",
            "Pior: 0.5907734056987788  melhor (escolhido): 0.774796472184532\n",
            "----------------------------------------------------\n",
            "Individual -  13\n",
            "Pior: 0.6048846675712347  melhor (escolhido): 0.7632632293080055\n",
            "----------------------------------------------------\n",
            "Individual -  14\n",
            "Pior: 0.6048846675712347  melhor (escolhido): 0.7824966078697422\n",
            "----------------------------------------------------\n",
            "Individual -  15\n",
            "Pior: 0.6048846675712347  melhor (escolhido): 0.7841248303934871\n",
            "----------------------------------------------------\n",
            "Individual -  16\n",
            "Pior: 0.6048846675712347  melhor (escolhido): 0.776899592944369\n",
            "----------------------------------------------------\n",
            "Individual -  17\n",
            "Pior: 0.6048846675712347  melhor (escolhido): 0.7851763907734057\n",
            "----------------------------------------------------\n",
            "melhor anterior foi escolhido:  0.8033582089552238\n",
            "melhores por geracao 7\n",
            "Total dados: 38160\n",
            " Total VAs: 3816\n",
            "\n",
            "media Random Forest:  0.780958842152872\n",
            "media SVM:  0.6459011759384894\n",
            "tam_pop_atual:  6\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.6182496607869742  melhor (escolhido): 0.7878561736770693\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.590976933514247  melhor (escolhido): 0.7766621438263229\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.5746607869742199  melhor (escolhido): 0.7603459972862958\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.6031207598371777  melhor (escolhido): 0.7932157394843962\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.6147896879240162  melhor (escolhido): 0.7694369063772049\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.6120759837177747  melhor (escolhido): 0.7812075983717774\n",
            "----------------------------------------------------\n",
            "p:  8\n",
            "Tamanho da nova população gerada por meio do cruzamento: 8\n",
            "tam_pop_atual:  8\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.6076662143826324  melhor (escolhido): 0.7774762550881953\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.6076662143826324  melhor (escolhido): 0.7977611940298508\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.5939280868385346  melhor (escolhido): 0.7667571234735414\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.6076662143826324  melhor (escolhido): 0.782259158751696\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.6076662143826324  melhor (escolhido): 0.7830732700135685\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.5939280868385346  melhor (escolhido): 0.7686227951153324\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.6076662143826324  melhor (escolhido): 0.7886702849389416\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.5939280868385346  melhor (escolhido): 0.7878561736770693\n",
            "----------------------------------------------------\n",
            "Novo melhor: 0.7977611940298508\n",
            "p:  10\n",
            "Tamanho da nova população gerada por meio do cruzamento: 10\n",
            "tam_pop_atual:  10\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.5715400271370421  melhor (escolhido): 0.7758480325644506\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.6145183175033921  melhor (escolhido): 0.7849389416553596\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.5715400271370421  melhor (escolhido): 0.7934531886024423\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.5715400271370421  melhor (escolhido): 0.7686227951153324\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.5715400271370421  melhor (escolhido): 0.7704884667571235\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.5715400271370421  melhor (escolhido): 0.7833107191316147\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.5715400271370421  melhor (escolhido): 0.7870420624151968\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.5715400271370421  melhor (escolhido): 0.7766621438263229\n",
            "----------------------------------------------------\n",
            "Individual -  8\n",
            "Pior: 0.5715400271370421  melhor (escolhido): 0.7721166892808684\n",
            "----------------------------------------------------\n",
            "Individual -  9\n",
            "Pior: 0.5715400271370421  melhor (escolhido): 0.7795793758480326\n",
            "----------------------------------------------------\n",
            "melhor anterior foi escolhido:  0.7977611940298508\n",
            "p:  12\n",
            "Tamanho da nova população gerada por meio do cruzamento: 12\n",
            "tam_pop_atual:  12\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.5862279511533243  melhor (escolhido): 0.7833107191316147\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.5949796472184532  melhor (escolhido): 0.7704884667571235\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.5949796472184532  melhor (escolhido): 0.7686227951153324\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.5949796472184532  melhor (escolhido): 0.7678086838534599\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.5949796472184532  melhor (escolhido): 0.776899592944369\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.5949796472184532  melhor (escolhido): 0.7841248303934871\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.5949796472184532  melhor (escolhido): 0.7622116689280869\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.5949796472184532  melhor (escolhido): 0.780393487109905\n",
            "----------------------------------------------------\n",
            "Individual -  8\n",
            "Pior: 0.5949796472184532  melhor (escolhido): 0.7622116689280869\n",
            "----------------------------------------------------\n",
            "Individual -  9\n",
            "Pior: 0.5949796472184532  melhor (escolhido): 0.7787652645861601\n",
            "----------------------------------------------------\n",
            "Individual -  10\n",
            "Pior: 0.5949796472184532  melhor (escolhido): 0.776899592944369\n",
            "----------------------------------------------------\n",
            "Individual -  11\n",
            "Pior: 0.5949796472184532  melhor (escolhido): 0.7830732700135685\n",
            "----------------------------------------------------\n",
            "melhor anterior foi escolhido:  0.7977611940298508\n",
            "p:  14\n",
            "Tamanho da nova população gerada por meio do cruzamento: 14\n",
            "tam_pop_atual:  14\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.5885685210312076  melhor (escolhido): 0.7814450474898236\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.5885685210312076  melhor (escolhido): 0.769674355495251\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.5885685210312076  melhor (escolhido): 0.778527815468114\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.5885685210312076  melhor (escolhido): 0.7758480325644506\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.5885685210312076  melhor (escolhido): 0.7750339213025781\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.5885685210312076  melhor (escolhido): 0.7659430122116689\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.5885685210312076  melhor (escolhido): 0.7934531886024423\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.5885685210312076  melhor (escolhido): 0.7667571234735414\n",
            "----------------------------------------------------\n",
            "Individual -  8\n",
            "Pior: 0.5885685210312076  melhor (escolhido): 0.7795793758480326\n",
            "----------------------------------------------------\n",
            "Individual -  9\n",
            "Pior: 0.5885685210312076  melhor (escolhido): 0.778527815468114\n",
            "----------------------------------------------------\n",
            "Individual -  10\n",
            "Pior: 0.5885685210312076  melhor (escolhido): 0.7942672998643148\n",
            "----------------------------------------------------\n",
            "Individual -  11\n",
            "Pior: 0.5885685210312076  melhor (escolhido): 0.7958955223880597\n",
            "----------------------------------------------------\n",
            "Individual -  12\n",
            "Pior: 0.5885685210312076  melhor (escolhido): 0.7750339213025781\n",
            "----------------------------------------------------\n",
            "Individual -  13\n",
            "Pior: 0.5885685210312076  melhor (escolhido): 0.7777137042062415\n",
            "----------------------------------------------------\n",
            "melhor anterior foi escolhido:  0.7977611940298508\n",
            "p:  16\n",
            "Tamanho da nova população gerada por meio do cruzamento: 16\n",
            "tam_pop_atual:  16\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.590976933514247  melhor (escolhido): 0.7849389416553596\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.590976933514247  melhor (escolhido): 0.776899592944369\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.590976933514247  melhor (escolhido): 0.7814450474898236\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.590976933514247  melhor (escolhido): 0.7587177747625509\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.590976933514247  melhor (escolhido): 0.7742198100407056\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.590976933514247  melhor (escolhido): 0.7595318860244233\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.590976933514247  melhor (escolhido): 0.7841248303934871\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.590976933514247  melhor (escolhido): 0.7539348710990502\n",
            "----------------------------------------------------\n",
            "Individual -  8\n",
            "Pior: 0.590976933514247  melhor (escolhido): 0.7576662143826323\n",
            "----------------------------------------------------\n",
            "Individual -  9\n",
            "Pior: 0.590976933514247  melhor (escolhido): 0.7830732700135685\n",
            "----------------------------------------------------\n",
            "Individual -  10\n",
            "Pior: 0.590976933514247  melhor (escolhido): 0.7833107191316147\n",
            "----------------------------------------------------\n",
            "Individual -  11\n",
            "Pior: 0.590976933514247  melhor (escolhido): 0.7878561736770693\n",
            "----------------------------------------------------\n",
            "Individual -  12\n",
            "Pior: 0.590976933514247  melhor (escolhido): 0.7648914518317504\n",
            "----------------------------------------------------\n",
            "Individual -  13\n",
            "Pior: 0.590976933514247  melhor (escolhido): 0.7539348710990502\n",
            "----------------------------------------------------\n",
            "Individual -  14\n",
            "Pior: 0.590976933514247  melhor (escolhido): 0.7824966078697422\n",
            "----------------------------------------------------\n",
            "Individual -  15\n",
            "Pior: 0.590976933514247  melhor (escolhido): 0.7814450474898236\n",
            "----------------------------------------------------\n",
            "melhor anterior foi escolhido:  0.7977611940298508\n",
            "p:  18\n",
            "Tamanho da nova população gerada por meio do cruzamento: 18\n",
            "tam_pop_atual:  18\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.6014925373134329  melhor (escolhido): 0.7777137042062415\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.6014925373134329  melhor (escolhido): 0.778527815468114\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.6014925373134329  melhor (escolhido): 0.7742198100407056\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.6014925373134329  melhor (escolhido): 0.7675712347354138\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.6014925373134329  melhor (escolhido): 0.7648914518317504\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.6014925373134329  melhor (escolhido): 0.7721166892808684\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.6014925373134329  melhor (escolhido): 0.7830732700135685\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.6014925373134329  melhor (escolhido): 0.7851763907734057\n",
            "----------------------------------------------------\n",
            "Individual -  8\n",
            "Pior: 0.6014925373134329  melhor (escolhido): 0.7806309362279511\n",
            "----------------------------------------------------\n",
            "Individual -  9\n",
            "Pior: 0.6014925373134329  melhor (escolhido): 0.7678086838534599\n",
            "----------------------------------------------------\n",
            "Individual -  10\n",
            "Pior: 0.6014925373134329  melhor (escolhido): 0.7758480325644506\n",
            "----------------------------------------------------\n",
            "Individual -  11\n",
            "Pior: 0.6014925373134329  melhor (escolhido): 0.7622116689280869\n",
            "----------------------------------------------------\n",
            "Individual -  12\n",
            "Pior: 0.6014925373134329  melhor (escolhido): 0.7915875169606512\n",
            "----------------------------------------------------\n",
            "Individual -  13\n",
            "Pior: 0.6014925373134329  melhor (escolhido): 0.7950814111261872\n",
            "----------------------------------------------------\n",
            "Individual -  14\n",
            "Pior: 0.6014925373134329  melhor (escolhido): 0.7723541383989145\n",
            "----------------------------------------------------\n",
            "Individual -  15\n",
            "Pior: 0.6014925373134329  melhor (escolhido): 0.7814450474898236\n",
            "----------------------------------------------------\n",
            "Individual -  16\n",
            "Pior: 0.6014925373134329  melhor (escolhido): 0.7833107191316147\n",
            "----------------------------------------------------\n",
            "Individual -  17\n",
            "Pior: 0.6014925373134329  melhor (escolhido): 0.7568521031207598\n",
            "----------------------------------------------------\n",
            "melhor anterior foi escolhido:  0.7977611940298508\n",
            "melhores por geracao 7\n",
            "Total dados: 38160\n",
            " Total VAs: 5724\n",
            "\n",
            "media Random Forest:  0.7782960199004975\n",
            "media SVM:  0.6307892356399819\n",
            "tam_pop_atual:  6\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.5985753052917232  melhor (escolhido): 0.7932157394843962\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.6019335142469471  melhor (escolhido): 0.7758480325644506\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.5994911804613297  melhor (escolhido): 0.7932157394843962\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.6006784260515603  melhor (escolhido): 0.7841248303934871\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.5913500678426051  melhor (escolhido): 0.7678086838534599\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.5988127544097693  melhor (escolhido): 0.7921641791044776\n",
            "----------------------------------------------------\n",
            "p:  8\n",
            "Tamanho da nova população gerada por meio do cruzamento: 8\n",
            "tam_pop_atual:  8\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.6006784260515603  melhor (escolhido): 0.7902985074626865\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.6182496607869742  melhor (escolhido): 0.7924016282225237\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.6182496607869742  melhor (escolhido): 0.7924016282225237\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.6006784260515603  melhor (escolhido): 0.7894843962008141\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.6182496607869742  melhor (escolhido): 0.7812075983717774\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.5835481682496608  melhor (escolhido): 0.7694369063772049\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.6006784260515603  melhor (escolhido): 0.7913500678426051\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.5835481682496608  melhor (escolhido): 0.7833107191316147\n",
            "----------------------------------------------------\n",
            "melhor anterior foi escolhido:  0.7932157394843962\n",
            "p:  10\n",
            "Tamanho da nova população gerada por meio do cruzamento: 10\n",
            "tam_pop_atual:  10\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.5907734056987788  melhor (escolhido): 0.7851763907734057\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.6025440976933514  melhor (escolhido): 0.7868046132971506\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.5812075983717775  melhor (escolhido): 0.7798168249660787\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.6025440976933514  melhor (escolhido): 0.7812075983717774\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.5907734056987788  melhor (escolhido): 0.7851763907734057\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.5812075983717775  melhor (escolhido): 0.769674355495251\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.5907734056987788  melhor (escolhido): 0.7758480325644506\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.6025440976933514  melhor (escolhido): 0.7795793758480326\n",
            "----------------------------------------------------\n",
            "Individual -  8\n",
            "Pior: 0.5907734056987788  melhor (escolhido): 0.7750339213025781\n",
            "----------------------------------------------------\n",
            "Individual -  9\n",
            "Pior: 0.5812075983717775  melhor (escolhido): 0.7814450474898236\n",
            "----------------------------------------------------\n",
            "melhor anterior foi escolhido:  0.7932157394843962\n",
            "p:  12\n",
            "Tamanho da nova população gerada por meio do cruzamento: 12\n",
            "tam_pop_atual:  12\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.5814450474898236  melhor (escolhido): 0.778527815468114\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.5814450474898236  melhor (escolhido): 0.7640773405698779\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.5814450474898236  melhor (escolhido): 0.773168249660787\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.6025440976933514  melhor (escolhido): 0.7969470827679783\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.5814450474898236  melhor (escolhido): 0.7841248303934871\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.5814450474898236  melhor (escolhido): 0.782259158751696\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.5814450474898236  melhor (escolhido): 0.7868046132971506\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.5814450474898236  melhor (escolhido): 0.7758480325644506\n",
            "----------------------------------------------------\n",
            "Individual -  8\n",
            "Pior: 0.6025440976933514  melhor (escolhido): 0.7950814111261872\n",
            "----------------------------------------------------\n",
            "Individual -  9\n",
            "Pior: 0.5814450474898236  melhor (escolhido): 0.7868046132971506\n",
            "----------------------------------------------------\n",
            "Individual -  10\n",
            "Pior: 0.5814450474898236  melhor (escolhido): 0.7793419267299864\n",
            "----------------------------------------------------\n",
            "Individual -  11\n",
            "Pior: 0.5814450474898236  melhor (escolhido): 0.7766621438263229\n",
            "----------------------------------------------------\n",
            "Novo melhor: 0.7969470827679783\n",
            "p:  14\n",
            "Tamanho da nova população gerada por meio do cruzamento: 14\n",
            "tam_pop_atual:  14\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.6073270013568521  melhor (escolhido): 0.7694369063772049\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.560583446404342  melhor (escolhido): 0.7622116689280869\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.6073270013568521  melhor (escolhido): 0.7758480325644506\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.560583446404342  melhor (escolhido): 0.7694369063772049\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.560583446404342  melhor (escolhido): 0.776899592944369\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.6073270013568521  melhor (escolhido): 0.7648914518317504\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.560583446404342  melhor (escolhido): 0.7859905020352782\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.6073270013568521  melhor (escolhido): 0.783887381275441\n",
            "----------------------------------------------------\n",
            "Individual -  8\n",
            "Pior: 0.560583446404342  melhor (escolhido): 0.7905359565807326\n",
            "----------------------------------------------------\n",
            "Individual -  9\n",
            "Pior: 0.560583446404342  melhor (escolhido): 0.7659430122116689\n",
            "----------------------------------------------------\n",
            "Individual -  10\n",
            "Pior: 0.560583446404342  melhor (escolhido): 0.7686227951153324\n",
            "----------------------------------------------------\n",
            "Individual -  11\n",
            "Pior: 0.6073270013568521  melhor (escolhido): 0.782259158751696\n",
            "----------------------------------------------------\n",
            "Individual -  12\n",
            "Pior: 0.560583446404342  melhor (escolhido): 0.7787652645861601\n",
            "----------------------------------------------------\n",
            "Individual -  13\n",
            "Pior: 0.560583446404342  melhor (escolhido): 0.7787652645861601\n",
            "----------------------------------------------------\n",
            "melhor anterior foi escolhido:  0.7969470827679783\n",
            "p:  16\n",
            "Tamanho da nova população gerada por meio do cruzamento: 16\n",
            "tam_pop_atual:  16\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.5790027137042063  melhor (escolhido): 0.7568521031207598\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.5790027137042063  melhor (escolhido): 0.7576662143826323\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.5765264586160108  melhor (escolhido): 0.7806309362279511\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.5765264586160108  melhor (escolhido): 0.7686227951153324\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.5765264586160108  melhor (escolhido): 0.7723541383989145\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.5790027137042063  melhor (escolhido): 0.7787652645861601\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.5790027137042063  melhor (escolhido): 0.7824966078697422\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.5790027137042063  melhor (escolhido): 0.7798168249660787\n",
            "----------------------------------------------------\n",
            "Individual -  8\n",
            "Pior: 0.5765264586160108  melhor (escolhido): 0.7694369063772049\n",
            "----------------------------------------------------\n",
            "Individual -  9\n",
            "Pior: 0.5790027137042063  melhor (escolhido): 0.7713025780189959\n",
            "----------------------------------------------------\n",
            "Individual -  10\n",
            "Pior: 0.5790027137042063  melhor (escolhido): 0.7723541383989145\n",
            "----------------------------------------------------\n",
            "Individual -  11\n",
            "Pior: 0.5790027137042063  melhor (escolhido): 0.7723541383989145\n",
            "----------------------------------------------------\n",
            "Individual -  12\n",
            "Pior: 0.5790027137042063  melhor (escolhido): 0.7723541383989145\n",
            "----------------------------------------------------\n",
            "Individual -  13\n",
            "Pior: 0.5765264586160108  melhor (escolhido): 0.7833107191316147\n",
            "----------------------------------------------------\n",
            "Individual -  14\n",
            "Pior: 0.5790027137042063  melhor (escolhido): 0.769674355495251\n",
            "----------------------------------------------------\n",
            "Individual -  15\n",
            "Pior: 0.5765264586160108  melhor (escolhido): 0.7694369063772049\n",
            "----------------------------------------------------\n",
            "melhor anterior foi escolhido:  0.7969470827679783\n",
            "p:  18\n",
            "Tamanho da nova população gerada por meio do cruzamento: 18\n",
            "tam_pop_atual:  18\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.5813432835820895  melhor (escolhido): 0.7758480325644506\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.5813432835820895  melhor (escolhido): 0.7760854816824967\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.5813432835820895  melhor (escolhido): 0.7814450474898236\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.5813432835820895  melhor (escolhido): 0.782259158751696\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.5813432835820895  melhor (escolhido): 0.7640773405698779\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.5813432835820895  melhor (escolhido): 0.7833107191316147\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.6014925373134329  melhor (escolhido): 0.7704884667571235\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.5813432835820895  melhor (escolhido): 0.7686227951153324\n",
            "----------------------------------------------------\n",
            "Individual -  8\n",
            "Pior: 0.6014925373134329  melhor (escolhido): 0.7742198100407056\n",
            "----------------------------------------------------\n",
            "Individual -  9\n",
            "Pior: 0.5813432835820895  melhor (escolhido): 0.778527815468114\n",
            "----------------------------------------------------\n",
            "Individual -  10\n",
            "Pior: 0.5813432835820895  melhor (escolhido): 0.7704884667571235\n",
            "----------------------------------------------------\n",
            "Individual -  11\n",
            "Pior: 0.5813432835820895  melhor (escolhido): 0.7795793758480326\n",
            "----------------------------------------------------\n",
            "Individual -  12\n",
            "Pior: 0.5813432835820895  melhor (escolhido): 0.7830732700135685\n",
            "----------------------------------------------------\n",
            "Individual -  13\n",
            "Pior: 0.5813432835820895  melhor (escolhido): 0.7868046132971506\n",
            "----------------------------------------------------\n",
            "Individual -  14\n",
            "Pior: 0.5813432835820895  melhor (escolhido): 0.776899592944369\n",
            "----------------------------------------------------\n",
            "Individual -  15\n",
            "Pior: 0.5813432835820895  melhor (escolhido): 0.7878561736770693\n",
            "----------------------------------------------------\n",
            "Individual -  16\n",
            "Pior: 0.5813432835820895  melhor (escolhido): 0.7603459972862958\n",
            "----------------------------------------------------\n",
            "Individual -  17\n",
            "Pior: 0.6014925373134329  melhor (escolhido): 0.7841248303934871\n",
            "----------------------------------------------------\n",
            "melhor anterior foi escolhido:  0.7969470827679783\n",
            "melhores por geracao 7\n",
            "Total dados: 38160\n",
            " Total VAs: 7632\n",
            "\n",
            "media Random Forest:  0.769125961103573\n",
            "media SVM:  0.6298959746720941\n",
            "tam_pop_atual:  6\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.5977611940298507  melhor (escolhido): 0.7777137042062415\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.5967096336499322  melhor (escolhido): 0.7758480325644506\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.6175712347354139  melhor (escolhido): 0.7665196743554953\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.5746607869742199  melhor (escolhido): 0.7611601085481683\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.560583446404342  melhor (escolhido): 0.7504409769335143\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.5886702849389417  melhor (escolhido): 0.7932157394843962\n",
            "----------------------------------------------------\n",
            "p:  8\n",
            "Tamanho da nova população gerada por meio do cruzamento: 8\n",
            "tam_pop_atual:  8\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.5776119402985075  melhor (escolhido): 0.7851763907734057\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.5776119402985075  melhor (escolhido): 0.7824966078697422\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.576899592944369  melhor (escolhido): 0.7847014925373135\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.576899592944369  melhor (escolhido): 0.7817842605156038\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.576899592944369  melhor (escolhido): 0.8068521031207598\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.576899592944369  melhor (escolhido): 0.774796472184532\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.576899592944369  melhor (escolhido): 0.778527815468114\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.5776119402985075  melhor (escolhido): 0.7833107191316147\n",
            "----------------------------------------------------\n",
            "Novo melhor: 0.8068521031207598\n",
            "p:  10\n",
            "Tamanho da nova população gerada por meio do cruzamento: 10\n",
            "tam_pop_atual:  10\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.5776119402985075  melhor (escolhido): 0.7632632293080055\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.5776119402985075  melhor (escolhido): 0.7678086838534599\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.5734056987788331  melhor (escolhido): 0.7820217096336499\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.5776119402985075  melhor (escolhido): 0.7686227951153324\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.5776119402985075  melhor (escolhido): 0.7632632293080055\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.5734056987788331  melhor (escolhido): 0.7793419267299864\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.5776119402985075  melhor (escolhido): 0.776899592944369\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.5776119402985075  melhor (escolhido): 0.7787652645861601\n",
            "----------------------------------------------------\n",
            "Individual -  8\n",
            "Pior: 0.5734056987788331  melhor (escolhido): 0.7868046132971506\n",
            "----------------------------------------------------\n",
            "Individual -  9\n",
            "Pior: 0.5734056987788331  melhor (escolhido): 0.79297829036635\n",
            "----------------------------------------------------\n",
            "melhor anterior foi escolhido:  0.8068521031207598\n",
            "p:  12\n",
            "Tamanho da nova população gerada por meio do cruzamento: 12\n",
            "tam_pop_atual:  12\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.5843622795115333  melhor (escolhido): 0.7584803256445047\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.5843622795115333  melhor (escolhido): 0.7841248303934871\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.5843622795115333  melhor (escolhido): 0.7710651289009498\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.5843622795115333  melhor (escolhido): 0.7702510176390773\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.5833107191316147  melhor (escolhido): 0.7868046132971506\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.5843622795115333  melhor (escolhido): 0.7721166892808684\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.5833107191316147  melhor (escolhido): 0.7686227951153324\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.5843622795115333  melhor (escolhido): 0.7793419267299864\n",
            "----------------------------------------------------\n",
            "Individual -  8\n",
            "Pior: 0.5843622795115333  melhor (escolhido): 0.7739823609226595\n",
            "----------------------------------------------------\n",
            "Individual -  9\n",
            "Pior: 0.5843622795115333  melhor (escolhido): 0.7721166892808684\n",
            "----------------------------------------------------\n",
            "Individual -  10\n",
            "Pior: 0.5843622795115333  melhor (escolhido): 0.780393487109905\n",
            "----------------------------------------------------\n",
            "Individual -  11\n",
            "Pior: 0.5833107191316147  melhor (escolhido): 0.7630257801899594\n",
            "----------------------------------------------------\n",
            "melhor anterior foi escolhido:  0.8068521031207598\n",
            "p:  14\n",
            "Tamanho da nova população gerada por meio do cruzamento: 14\n",
            "tam_pop_atual:  14\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.5631614654002713  melhor (escolhido): 0.783887381275441\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.5631614654002713  melhor (escolhido): 0.7843622795115333\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.5631614654002713  melhor (escolhido): 0.7795793758480326\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.5794776119402986  melhor (escolhido): 0.7713025780189959\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.5631614654002713  melhor (escolhido): 0.7758480325644506\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.5631614654002713  melhor (escolhido): 0.778527815468114\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.5631614654002713  melhor (escolhido): 0.7833107191316147\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.5794776119402986  melhor (escolhido): 0.7675712347354138\n",
            "----------------------------------------------------\n",
            "Individual -  8\n",
            "Pior: 0.5631614654002713  melhor (escolhido): 0.7913500678426051\n",
            "----------------------------------------------------\n",
            "Individual -  9\n",
            "Pior: 0.5631614654002713  melhor (escolhido): 0.7750339213025781\n",
            "----------------------------------------------------\n",
            "Individual -  10\n",
            "Pior: 0.5631614654002713  melhor (escolhido): 0.7868046132971506\n",
            "----------------------------------------------------\n",
            "Individual -  11\n",
            "Pior: 0.5631614654002713  melhor (escolhido): 0.7841248303934871\n",
            "----------------------------------------------------\n",
            "Individual -  12\n",
            "Pior: 0.5794776119402986  melhor (escolhido): 0.7713025780189959\n",
            "----------------------------------------------------\n",
            "Individual -  13\n",
            "Pior: 0.5631614654002713  melhor (escolhido): 0.8068521031207598\n",
            "----------------------------------------------------\n",
            "melhor anterior foi escolhido:  0.8068521031207598\n",
            "p:  16\n",
            "Tamanho da nova população gerada por meio do cruzamento: 16\n",
            "tam_pop_atual:  16\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.5851763907734057  melhor (escolhido): 0.7630257801899594\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.5851763907734057  melhor (escolhido): 0.778527815468114\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.5851763907734057  melhor (escolhido): 0.7545115332428765\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.5851763907734057  melhor (escolhido): 0.7675712347354138\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.5851763907734057  melhor (escolhido): 0.7729308005427409\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.5851763907734057  melhor (escolhido): 0.7611601085481683\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.5851763907734057  melhor (escolhido): 0.778527815468114\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.5851763907734057  melhor (escolhido): 0.7702510176390773\n",
            "----------------------------------------------------\n",
            "Individual -  8\n",
            "Pior: 0.5851763907734057  melhor (escolhido): 0.7627883310719132\n",
            "----------------------------------------------------\n",
            "Individual -  9\n",
            "Pior: 0.5851763907734057  melhor (escolhido): 0.746472184531886\n",
            "----------------------------------------------------\n",
            "Individual -  10\n",
            "Pior: 0.5851763907734057  melhor (escolhido): 0.7603459972862958\n",
            "----------------------------------------------------\n",
            "Individual -  11\n",
            "Pior: 0.5816824966078697  melhor (escolhido): 0.783887381275441\n",
            "----------------------------------------------------\n",
            "Individual -  12\n",
            "Pior: 0.5851763907734057  melhor (escolhido): 0.7657055630936228\n",
            "----------------------------------------------------\n",
            "Individual -  13\n",
            "Pior: 0.5851763907734057  melhor (escolhido): 0.7718792401628223\n",
            "----------------------------------------------------\n",
            "Individual -  14\n",
            "Pior: 0.5851763907734057  melhor (escolhido): 0.7739823609226595\n",
            "----------------------------------------------------\n",
            "Individual -  15\n",
            "Pior: 0.5851763907734057  melhor (escolhido): 0.7657055630936228\n",
            "----------------------------------------------------\n",
            "melhor anterior foi escolhido:  0.8068521031207598\n",
            "p:  18\n",
            "Tamanho da nova população gerada por meio do cruzamento: 18\n",
            "tam_pop_atual:  18\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.576797829036635  melhor (escolhido): 0.7782903663500678\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.576797829036635  melhor (escolhido): 0.7857530529172321\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.5885685210312076  melhor (escolhido): 0.7648914518317504\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.5885685210312076  melhor (escolhido): 0.7795793758480326\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.576797829036635  melhor (escolhido): 0.782259158751696\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.576797829036635  melhor (escolhido): 0.782259158751696\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.576797829036635  melhor (escolhido): 0.7859905020352782\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.576797829036635  melhor (escolhido): 0.782259158751696\n",
            "----------------------------------------------------\n",
            "Individual -  8\n",
            "Pior: 0.576797829036635  melhor (escolhido): 0.778527815468114\n",
            "----------------------------------------------------\n",
            "Individual -  9\n",
            "Pior: 0.576797829036635  melhor (escolhido): 0.783887381275441\n",
            "----------------------------------------------------\n",
            "Individual -  10\n",
            "Pior: 0.576797829036635  melhor (escolhido): 0.7886702849389416\n",
            "----------------------------------------------------\n",
            "Individual -  11\n",
            "Pior: 0.576797829036635  melhor (escolhido): 0.7574287652645861\n",
            "----------------------------------------------------\n",
            "Individual -  12\n",
            "Pior: 0.5885685210312076  melhor (escolhido): 0.7721166892808684\n",
            "----------------------------------------------------\n",
            "Individual -  13\n",
            "Pior: 0.576797829036635  melhor (escolhido): 0.7648914518317504\n",
            "----------------------------------------------------\n",
            "Individual -  14\n",
            "Pior: 0.576797829036635  melhor (escolhido): 0.778527815468114\n",
            "----------------------------------------------------\n",
            "Individual -  15\n",
            "Pior: 0.576797829036635  melhor (escolhido): 0.7758480325644506\n",
            "----------------------------------------------------\n",
            "Individual -  16\n",
            "Pior: 0.576797829036635  melhor (escolhido): 0.8014925373134328\n",
            "----------------------------------------------------\n",
            "Individual -  17\n",
            "Pior: 0.576797829036635  melhor (escolhido): 0.7849389416553596\n",
            "----------------------------------------------------\n",
            "melhor anterior foi escolhido:  0.8068521031207598\n",
            "melhores por geracao 7\n",
            "Total dados: 38160\n",
            " Total VAs: 9540\n",
            "\n",
            "media Random Forest:  0.7664235639981909\n",
            "media SVM:  0.6200474898236092\n",
            "tam_pop_atual:  6\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.5739823609226594  melhor (escolhido): 0.7812075983717774\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.5785278154681139  melhor (escolhido): 0.7710651289009498\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.6081411126187246  melhor (escolhido): 0.7836499321573949\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.5882971506105834  melhor (escolhido): 0.7454206241519674\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.567774762550882  melhor (escolhido): 0.7429782903663501\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.58561736770692  melhor (escolhido): 0.7638398914518317\n",
            "----------------------------------------------------\n",
            "p:  8\n",
            "Tamanho da nova população gerada por meio do cruzamento: 8\n",
            "tam_pop_atual:  8\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.5872455902306649  melhor (escolhido): 0.7774762550881953\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.5872455902306649  melhor (escolhido): 0.7601085481682497\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.5688602442333786  melhor (escolhido): 0.7611601085481683\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.5766621438263229  melhor (escolhido): 0.7683853459972863\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.5872455902306649  melhor (escolhido): 0.7691994572591587\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.5766621438263229  melhor (escolhido): 0.7921641791044776\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.5766621438263229  melhor (escolhido): 0.7756105834464043\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.5688602442333786  melhor (escolhido): 0.7985753052917233\n",
            "----------------------------------------------------\n",
            "Novo melhor: 0.7985753052917233\n",
            "p:  10\n",
            "Tamanho da nova população gerada por meio do cruzamento: 10\n",
            "tam_pop_atual:  10\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.5353799185888738  melhor (escolhido): 0.7622116689280869\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.5353799185888738  melhor (escolhido): 0.7448439620081411\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.5353799185888738  melhor (escolhido): 0.7766621438263229\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.5353799185888738  melhor (escolhido): 0.7576662143826323\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.5353799185888738  melhor (escolhido): 0.7876187245590232\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.5907734056987788  melhor (escolhido): 0.7694369063772049\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.5359905020352782  melhor (escolhido): 0.7493894165535957\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.5353799185888738  melhor (escolhido): 0.7691994572591587\n",
            "----------------------------------------------------\n",
            "Individual -  8\n",
            "Pior: 0.5353799185888738  melhor (escolhido): 0.7691994572591587\n",
            "----------------------------------------------------\n",
            "Individual -  9\n",
            "Pior: 0.5359905020352782  melhor (escolhido): 0.7485753052917232\n",
            "----------------------------------------------------\n",
            "melhor anterior foi escolhido:  0.7985753052917233\n",
            "p:  12\n",
            "Tamanho da nova população gerada por meio do cruzamento: 12\n",
            "tam_pop_atual:  12\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.5353799185888738  melhor (escolhido): 0.7739823609226595\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.5353799185888738  melhor (escolhido): 0.7630257801899594\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.573066485753053  melhor (escolhido): 0.7630257801899594\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.5353799185888738  melhor (escolhido): 0.7828358208955224\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.573066485753053  melhor (escolhido): 0.7558005427408413\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.573066485753053  melhor (escolhido): 0.7713025780189959\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.5353799185888738  melhor (escolhido): 0.7723541383989145\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.5353799185888738  melhor (escolhido): 0.778527815468114\n",
            "----------------------------------------------------\n",
            "Individual -  8\n",
            "Pior: 0.5353799185888738  melhor (escolhido): 0.742740841248304\n",
            "----------------------------------------------------\n",
            "Individual -  9\n",
            "Pior: 0.573066485753053  melhor (escolhido): 0.7702510176390773\n",
            "----------------------------------------------------\n",
            "Individual -  10\n",
            "Pior: 0.573066485753053  melhor (escolhido): 0.7886702849389416\n",
            "----------------------------------------------------\n",
            "Individual -  11\n",
            "Pior: 0.573066485753053  melhor (escolhido): 0.7777137042062415\n",
            "----------------------------------------------------\n",
            "melhor anterior foi escolhido:  0.7985753052917233\n",
            "p:  14\n",
            "Tamanho da nova população gerada por meio do cruzamento: 14\n",
            "tam_pop_atual:  14\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.5586160108548168  melhor (escolhido): 0.7638398914518317\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.5682496607869743  melhor (escolhido): 0.7640773405698779\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.5586160108548168  melhor (escolhido): 0.7742198100407056\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.5586160108548168  melhor (escolhido): 0.7750339213025781\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.5586160108548168  melhor (escolhido): 0.7603459972862958\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.5586160108548168  melhor (escolhido): 0.7667571234735414\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.5586160108548168  melhor (escolhido): 0.7667571234735414\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.5682496607869743  melhor (escolhido): 0.7729308005427409\n",
            "----------------------------------------------------\n",
            "Individual -  8\n",
            "Pior: 0.5682496607869743  melhor (escolhido): 0.7766621438263229\n",
            "----------------------------------------------------\n",
            "Individual -  9\n",
            "Pior: 0.5682496607869743  melhor (escolhido): 0.7584803256445047\n",
            "----------------------------------------------------\n",
            "Individual -  10\n",
            "Pior: 0.5586160108548168  melhor (escolhido): 0.773168249660787\n",
            "----------------------------------------------------\n",
            "Individual -  11\n",
            "Pior: 0.5682496607869743  melhor (escolhido): 0.7475237449118046\n",
            "----------------------------------------------------\n",
            "Individual -  12\n",
            "Pior: 0.5682496607869743  melhor (escolhido): 0.7365671641791045\n",
            "----------------------------------------------------\n",
            "Individual -  13\n",
            "Pior: 0.5682496607869743  melhor (escolhido): 0.7756105834464043\n",
            "----------------------------------------------------\n",
            "melhor anterior foi escolhido:  0.7985753052917233\n",
            "p:  16\n",
            "Tamanho da nova população gerada por meio do cruzamento: 16\n",
            "tam_pop_atual:  16\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.5685210312075983  melhor (escolhido): 0.7750339213025781\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.5685210312075983  melhor (escolhido): 0.7750339213025781\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.5685210312075983  melhor (escolhido): 0.7729308005427409\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.5685210312075983  melhor (escolhido): 0.7750339213025781\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.5685210312075983  melhor (escolhido): 0.7694369063772049\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.5328358208955224  melhor (escolhido): 0.7884328358208955\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.5685210312075983  melhor (escolhido): 0.7694369063772049\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.5685210312075983  melhor (escolhido): 0.7648914518317504\n",
            "----------------------------------------------------\n",
            "Individual -  8\n",
            "Pior: 0.5685210312075983  melhor (escolhido): 0.7795793758480326\n",
            "----------------------------------------------------\n",
            "Individual -  9\n",
            "Pior: 0.5685210312075983  melhor (escolhido): 0.7721166892808684\n",
            "----------------------------------------------------\n",
            "Individual -  10\n",
            "Pior: 0.5685210312075983  melhor (escolhido): 0.780393487109905\n",
            "----------------------------------------------------\n",
            "Individual -  11\n",
            "Pior: 0.5328358208955224  melhor (escolhido): 0.7665196743554953\n",
            "----------------------------------------------------\n",
            "Individual -  12\n",
            "Pior: 0.5685210312075983  melhor (escolhido): 0.7721166892808684\n",
            "----------------------------------------------------\n",
            "Individual -  13\n",
            "Pior: 0.5328358208955224  melhor (escolhido): 0.7809701492537313\n",
            "----------------------------------------------------\n",
            "Individual -  14\n",
            "Pior: 0.5328358208955224  melhor (escolhido): 0.7636024423337856\n",
            "----------------------------------------------------\n",
            "Individual -  15\n",
            "Pior: 0.5328358208955224  melhor (escolhido): 0.7737449118046132\n",
            "----------------------------------------------------\n",
            "melhor anterior foi escolhido:  0.7985753052917233\n",
            "p:  18\n",
            "Tamanho da nova população gerada por meio do cruzamento: 18\n",
            "tam_pop_atual:  18\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.5640773405698779  melhor (escolhido): 0.7657055630936228\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.5640773405698779  melhor (escolhido): 0.7694369063772049\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.5328358208955224  melhor (escolhido): 0.7657055630936228\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.5328358208955224  melhor (escolhido): 0.7847014925373135\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.5640773405698779  melhor (escolhido): 0.7675712347354138\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.5640773405698779  melhor (escolhido): 0.7758480325644506\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.5328358208955224  melhor (escolhido): 0.7472862957937585\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.5640773405698779  melhor (escolhido): 0.7686227951153324\n",
            "----------------------------------------------------\n",
            "Individual -  8\n",
            "Pior: 0.5640773405698779  melhor (escolhido): 0.7868046132971506\n",
            "----------------------------------------------------\n",
            "Individual -  9\n",
            "Pior: 0.5640773405698779  melhor (escolhido): 0.7648914518317504\n",
            "----------------------------------------------------\n",
            "Individual -  10\n",
            "Pior: 0.5640773405698779  melhor (escolhido): 0.7702510176390773\n",
            "----------------------------------------------------\n",
            "Individual -  11\n",
            "Pior: 0.5640773405698779  melhor (escolhido): 0.7750339213025781\n",
            "----------------------------------------------------\n",
            "Individual -  12\n",
            "Pior: 0.5640773405698779  melhor (escolhido): 0.7750339213025781\n",
            "----------------------------------------------------\n",
            "Individual -  13\n",
            "Pior: 0.5328358208955224  melhor (escolhido): 0.7700135685210312\n",
            "----------------------------------------------------\n",
            "Individual -  14\n",
            "Pior: 0.5640773405698779  melhor (escolhido): 0.7648914518317504\n",
            "----------------------------------------------------\n",
            "Individual -  15\n",
            "Pior: 0.5640773405698779  melhor (escolhido): 0.7830732700135685\n",
            "----------------------------------------------------\n",
            "Individual -  16\n",
            "Pior: 0.5640773405698779  melhor (escolhido): 0.7665196743554953\n",
            "----------------------------------------------------\n",
            "Individual -  17\n",
            "Pior: 0.5640773405698779  melhor (escolhido): 0.7905359565807326\n",
            "----------------------------------------------------\n",
            "melhor anterior foi escolhido:  0.7985753052917233\n",
            "melhores por geracao 7\n",
            "Total dados: 38160\n",
            " Total VAs: 11448\n",
            "\n",
            "media Random Forest:  0.7590852555404793\n",
            "media SVM:  0.6017130257801899\n",
            "tam_pop_atual:  6\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.5812075983717775  melhor (escolhido): 0.7683853459972863\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.5777137042062415  melhor (escolhido): 0.7497286295793759\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.589145183175034  melhor (escolhido): 0.7536974219810041\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.558921302578019  melhor (escolhido): 0.7456580732700135\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.5485753052917232  melhor (escolhido): 0.7076662143826322\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.5701153324287653  melhor (escolhido): 0.7312075983717774\n",
            "----------------------------------------------------\n",
            "p:  8\n",
            "Tamanho da nova população gerada por meio do cruzamento: 8\n",
            "tam_pop_atual:  8\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.5532564450474898  melhor (escolhido): 0.7648914518317504\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.571302578018996  melhor (escolhido): 0.7603459972862958\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.5661804613297151  melhor (escolhido): 0.7539348710990502\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.5532564450474898  melhor (escolhido): 0.7675712347354138\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.5532564450474898  melhor (escolhido): 0.7475237449118046\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.571302578018996  melhor (escolhido): 0.7510176390773405\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.5532564450474898  melhor (escolhido): 0.7713025780189959\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.5532564450474898  melhor (escolhido): 0.7520691994572593\n",
            "----------------------------------------------------\n",
            "Novo melhor: 0.7713025780189959\n",
            "p:  10\n",
            "Tamanho da nova população gerada por meio do cruzamento: 10\n",
            "tam_pop_atual:  10\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.5495251017639078  melhor (escolhido): 0.7667571234735414\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.5495251017639078  melhor (escolhido): 0.7729308005427409\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.5495251017639078  melhor (escolhido): 0.7814450474898236\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.553697421981004  melhor (escolhido): 0.7657055630936228\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.5495251017639078  melhor (escolhido): 0.7729308005427409\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.5495251017639078  melhor (escolhido): 0.7429782903663501\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.5495251017639078  melhor (escolhido): 0.7611601085481683\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.5495251017639078  melhor (escolhido): 0.7510176390773405\n",
            "----------------------------------------------------\n",
            "Individual -  8\n",
            "Pior: 0.5495251017639078  melhor (escolhido): 0.7648914518317504\n",
            "----------------------------------------------------\n",
            "Individual -  9\n",
            "Pior: 0.5495251017639078  melhor (escolhido): 0.7793419267299864\n",
            "----------------------------------------------------\n",
            "Novo melhor: 0.7814450474898236\n",
            "p:  12\n",
            "Tamanho da nova população gerada por meio do cruzamento: 12\n",
            "tam_pop_atual:  12\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.5404341926729986  melhor (escolhido): 0.7657055630936228\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.5404341926729986  melhor (escolhido): 0.7619742198100408\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.5404341926729986  melhor (escolhido): 0.7638398914518317\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.5404341926729986  melhor (escolhido): 0.7665196743554953\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.5404341926729986  melhor (escolhido): 0.7584803256445047\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.5404341926729986  melhor (escolhido): 0.7531207598371777\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.5404341926729986  melhor (escolhido): 0.7528833107191316\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.5404341926729986  melhor (escolhido): 0.7549864314789687\n",
            "----------------------------------------------------\n",
            "Individual -  8\n",
            "Pior: 0.5404341926729986  melhor (escolhido): 0.7518317503392131\n",
            "----------------------------------------------------\n",
            "Individual -  9\n",
            "Pior: 0.5404341926729986  melhor (escolhido): 0.7691994572591587\n",
            "----------------------------------------------------\n",
            "Individual -  10\n",
            "Pior: 0.5404341926729986  melhor (escolhido): 0.7555630936227951\n",
            "----------------------------------------------------\n",
            "Individual -  11\n",
            "Pior: 0.5404341926729986  melhor (escolhido): 0.7261872455902307\n",
            "----------------------------------------------------\n",
            "melhor anterior foi escolhido:  0.7814450474898236\n",
            "p:  14\n",
            "Tamanho da nova população gerada por meio do cruzamento: 14\n",
            "tam_pop_atual:  14\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.5473880597014925  melhor (escolhido): 0.7638398914518317\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.5473880597014925  melhor (escolhido): 0.7613975576662144\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.5473880597014925  melhor (escolhido): 0.7766621438263229\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.5473880597014925  melhor (escolhido): 0.7657055630936228\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.5473880597014925  melhor (escolhido): 0.7510176390773405\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.5473880597014925  melhor (escolhido): 0.7654681139755767\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.5473880597014925  melhor (escolhido): 0.7502035278154682\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.5473880597014925  melhor (escolhido): 0.7510176390773405\n",
            "----------------------------------------------------\n",
            "Individual -  8\n",
            "Pior: 0.5473880597014925  melhor (escolhido): 0.7683853459972863\n",
            "----------------------------------------------------\n",
            "Individual -  9\n",
            "Pior: 0.5473880597014925  melhor (escolhido): 0.7611601085481683\n",
            "----------------------------------------------------\n",
            "Individual -  10\n",
            "Pior: 0.5473880597014925  melhor (escolhido): 0.7555630936227951\n",
            "----------------------------------------------------\n",
            "Individual -  11\n",
            "Pior: 0.5473880597014925  melhor (escolhido): 0.7638398914518317\n",
            "----------------------------------------------------\n",
            "Individual -  12\n",
            "Pior: 0.5473880597014925  melhor (escolhido): 0.7646540027137042\n",
            "----------------------------------------------------\n",
            "Individual -  13\n",
            "Pior: 0.5473880597014925  melhor (escolhido): 0.7301560379918589\n",
            "----------------------------------------------------\n",
            "melhor anterior foi escolhido:  0.7814450474898236\n",
            "p:  16\n",
            "Tamanho da nova população gerada por meio do cruzamento: 16\n",
            "tam_pop_atual:  16\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.5436906377204884  melhor (escolhido): 0.7611601085481683\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.5436906377204884  melhor (escolhido): 0.7574287652645861\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.5436906377204884  melhor (escolhido): 0.7737449118046132\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.5436906377204884  melhor (escolhido): 0.7713025780189959\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.5436906377204884  melhor (escolhido): 0.780393487109905\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.5436906377204884  melhor (escolhido): 0.7702510176390773\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.5436906377204884  melhor (escolhido): 0.7640773405698779\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.5436906377204884  melhor (escolhido): 0.7630257801899594\n",
            "----------------------------------------------------\n",
            "Individual -  8\n",
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            "Pior: 0.5436906377204884  melhor (escolhido): 0.7584803256445047\n",
            "----------------------------------------------------\n",
            "melhor anterior foi escolhido:  0.7814450474898236\n",
            "p:  18\n",
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            "Avaliações com Binary Relevance\n",
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            "Pior: 0.5404341926729986  melhor (escolhido): 0.7739823609226595\n",
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            "Individual -  17\n",
            "Pior: 0.5404341926729986  melhor (escolhido): 0.7777137042062415\n",
            "----------------------------------------------------\n",
            "Novo melhor: 0.7913500678426051\n",
            "melhores por geracao 7\n"
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "i-YsdGOjHDnd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "h4ph5kfF4Eyy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LgibKIEbAurT"
      },
      "source": [
        "# Graficos Resultantes "
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Gerações"
      ],
      "metadata": {
        "id": "FQ2HRWD3VVzA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_emotions_geracoes_accML = pd.DataFrame()\n",
        "with open('/content/drive/MyDrive/Colab Notebooks/resultados_evoimp/geracoes_agACCML.txt', 'r') as file:\n",
        "  for vals in file:\n",
        "    if vals != '':\n",
        "      instancias = vals.split()\n",
        "      if len(instancias) > 0:\n",
        "        col = instancias.pop(0)\n",
        "        df_emotions_geracoes_accML[str(col)] = instancias\n",
        "        print(len(instancias))\n",
        "\n",
        "    \n"
      ],
      "metadata": {
        "id": "rS8GQu3L_yYz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "665da71a-a7ad-4937-a07a-df1d00662348"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7\n",
            "7\n",
            "7\n",
            "7\n",
            "7\n",
            "7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_emotions_geracoes_accML"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "QCuxyTYBKIKB",
        "outputId": "9f920b59-ff6b-4922-face-202e744bc10e"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "     emotions_mv_0.05     emotions_mv_0.1    emotions_mv_0.15  \\\n",
              "0  0.7948439620081411  0.7932157394843962  0.7932157394843962   \n",
              "1  0.7948439620081411  0.7977611940298508  0.7932157394843962   \n",
              "2  0.7948439620081411  0.7977611940298508  0.7932157394843962   \n",
              "3  0.7948439620081411  0.7977611940298508  0.7969470827679783   \n",
              "4  0.7948439620081411  0.7977611940298508  0.7969470827679783   \n",
              "5  0.8033582089552238  0.7977611940298508  0.7969470827679783   \n",
              "6  0.8033582089552238  0.7977611940298508  0.7969470827679783   \n",
              "\n",
              "      emotions_mv_0.2    emotions_mv_0.25     emotions_mv_0.3  \n",
              "0  0.7932157394843962  0.7836499321573949  0.7683853459972863  \n",
              "1  0.8068521031207598  0.7985753052917233  0.7713025780189959  \n",
              "2  0.8068521031207598  0.7985753052917233  0.7814450474898236  \n",
              "3  0.8068521031207598  0.7985753052917233  0.7814450474898236  \n",
              "4  0.8068521031207598  0.7985753052917233  0.7814450474898236  \n",
              "5  0.8068521031207598  0.7985753052917233  0.7814450474898236  \n",
              "6  0.8068521031207598  0.7985753052917233  0.7913500678426051  "
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              "      <td>0.7932157394843962</td>\n",
              "      <td>0.7932157394843962</td>\n",
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              "      <td>0.7683853459972863</td>\n",
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              "      <th>1</th>\n",
              "      <td>0.7948439620081411</td>\n",
              "      <td>0.7977611940298508</td>\n",
              "      <td>0.7932157394843962</td>\n",
              "      <td>0.8068521031207598</td>\n",
              "      <td>0.7985753052917233</td>\n",
              "      <td>0.7713025780189959</td>\n",
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              "      <th>2</th>\n",
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              "      <td>0.7814450474898236</td>\n",
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              "      <th>3</th>\n",
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              "      <td>0.7977611940298508</td>\n",
              "      <td>0.7969470827679783</td>\n",
              "      <td>0.8068521031207598</td>\n",
              "      <td>0.7985753052917233</td>\n",
              "      <td>0.7814450474898236</td>\n",
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              "      <th>4</th>\n",
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              "      <td>0.7969470827679783</td>\n",
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              "      <td>0.7969470827679783</td>\n",
              "      <td>0.8068521031207598</td>\n",
              "      <td>0.7985753052917233</td>\n",
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              "          const element = document.querySelector('#df-4a863387-44eb-4de2-810d-857acce37239');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_emotions_geracoes_accML['5%'] = pd.to_numeric(df_emotions_geracoes_accML['emotions_mv_0.05'])\n",
        "df_emotions_geracoes_accML['10%'] = pd.to_numeric(df_emotions_geracoes_accML['emotions_mv_0.1'])\n",
        "df_emotions_geracoes_accML['15%'] = pd.to_numeric(df_emotions_geracoes_accML['emotions_mv_0.15'])\n",
        "df_emotions_geracoes_accML['20%'] = pd.to_numeric(df_emotions_geracoes_accML['emotions_mv_0.2'])\n",
        "df_emotions_geracoes_accML['25%'] = pd.to_numeric(df_emotions_geracoes_accML['emotions_mv_0.25'])\n",
        "df_emotions_geracoes_accML['30%'] = pd.to_numeric(df_emotions_geracoes_accML['emotions_mv_0.3'])"
      ],
      "metadata": {
        "id": "h1nNMMwHPQos"
      },
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9OysaJjwBFO3"
      },
      "source": [
        "df_emotions_geracoes_accML = df_emotions_geracoes_accML.iloc[0:,6:]"
      ],
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_emotions_geracoes_accML"
      ],
      "metadata": {
        "colab": {
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          "height": 269
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        "id": "zCr8EPHITRIz",
        "outputId": "fdbf6983-c416-4c1d-a90a-d04828349c68"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "         5%       10%       15%       20%       25%       30%\n",
              "0  0.794844  0.793216  0.793216  0.793216  0.783650  0.768385\n",
              "1  0.794844  0.797761  0.793216  0.806852  0.798575  0.771303\n",
              "2  0.794844  0.797761  0.793216  0.806852  0.798575  0.781445\n",
              "3  0.794844  0.797761  0.796947  0.806852  0.798575  0.781445\n",
              "4  0.794844  0.797761  0.796947  0.806852  0.798575  0.781445\n",
              "5  0.803358  0.797761  0.796947  0.806852  0.798575  0.781445\n",
              "6  0.803358  0.797761  0.796947  0.806852  0.798575  0.791350"
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              "      <td>0.793216</td>\n",
              "      <td>0.806852</td>\n",
              "      <td>0.798575</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.794844</td>\n",
              "      <td>0.797761</td>\n",
              "      <td>0.796947</td>\n",
              "      <td>0.806852</td>\n",
              "      <td>0.798575</td>\n",
              "      <td>0.781445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.794844</td>\n",
              "      <td>0.797761</td>\n",
              "      <td>0.796947</td>\n",
              "      <td>0.806852</td>\n",
              "      <td>0.798575</td>\n",
              "      <td>0.781445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.803358</td>\n",
              "      <td>0.797761</td>\n",
              "      <td>0.796947</td>\n",
              "      <td>0.806852</td>\n",
              "      <td>0.798575</td>\n",
              "      <td>0.781445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.803358</td>\n",
              "      <td>0.797761</td>\n",
              "      <td>0.796947</td>\n",
              "      <td>0.806852</td>\n",
              "      <td>0.798575</td>\n",
              "      <td>0.791350</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8d7bca9b-484e-402d-a5e0-4c36f896bf6a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8d7bca9b-484e-402d-a5e0-4c36f896bf6a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8d7bca9b-484e-402d-a5e0-4c36f896bf6a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_emotions_geracoes_accML['5%'].plot(style='.-',marker='o')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "218nCKC9eXi6",
        "outputId": "214f8ebb-1f92-42ee-a370-93981a0533e6"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f300c412610>"
            ]
          },
          "metadata": {},
          "execution_count": 97
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_emotions_geracoes_accML['10%'].plot(style='.-',marker='o')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "ap0XjLEtS75D",
        "outputId": "6a43ddf2-f59e-4204-8cd4-0cb8a7b64bd8"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f300c38c410>"
            ]
          },
          "metadata": {},
          "execution_count": 98
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_emotions_geracoes_accML['15%'].plot(style='.-',marker='o')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "59ipiQLVTBtd",
        "outputId": "5e0e7807-7839-4498-e413-546f9968641c"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f300e186e10>"
            ]
          },
          "metadata": {},
          "execution_count": 100
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_emotions_geracoes_accML['20%'].plot(style='.-',marker='o')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "yXXWpxsGTGE8",
        "outputId": "14d7abaf-586a-40d2-b4f0-b1cf74d88e94"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f300c28dcd0>"
            ]
          },
          "metadata": {},
          "execution_count": 101
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_emotions_geracoes_accML['25%'].plot(style='.-',marker='o')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "0fcQqS9sTY4c",
        "outputId": "b1f36298-50af-4c84-b347-463f6d44466e"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f300c1f5510>"
            ]
          },
          "metadata": {},
          "execution_count": 102
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_emotions_geracoes_accML['30%'].plot(style='.-',marker='o')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "Ur9fzlK3TcQL",
        "outputId": "b274ed1d-477a-4954-e990-6b0419bf741d"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f300c1daa90>"
            ]
          },
          "metadata": {},
          "execution_count": 103
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
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          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_emotions_geracoes_accML.plot(style='.-',marker='o')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "7Og5LoK7Th0h",
        "outputId": "886df516-d22c-4bd7-b635-5b84f93ab1db"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f300c1da0d0>"
            ]
          },
          "metadata": {},
          "execution_count": 104
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Dados comparativos"
      ],
      "metadata": {
        "id": "hfEBTGxcVgtz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "with open('/content/drive/MyDrive/Colab Notebooks/resultados_evoimp/resultados_ACCML.txt','r') as resultados_finais_accML:\n",
        "  print('Resultados comparativos para acurácia do tipo multilabel')\n",
        "  print( 'O algoritmo Multilabel utilizado em ambos os experimentos foi o BR')\n",
        "  for r in resultados_finais_accML:\n",
        "    print(r)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "__w6VcbNSoDS",
        "outputId": "98a2497c-b7a0-4eec-a4b0-3cf06a525fb7"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Resultados comparativos para acurácia do tipo multilabel\n",
            "O algoritmo Multilabel utilizado em ambos os experimentos foi o BR\n",
            "\n",
            "\n",
            "SVM_emotions_0.05_ACC_ML 0.6540366350067842\n",
            "\n",
            "RF_emotions_0.05_ACC_ML 0.7882236544549976\n",
            "\n",
            "AG_emotions_0.05_ACC_ML 0.8033582089552238\n",
            "\n",
            "SVM_emotions_0.1_ACC_ML 0.6459011759384894\n",
            "\n",
            "RF_emotions_0.1_ACC_ML 0.780958842152872\n",
            "\n",
            "AG_emotions_0.1_ACC_ML 0.7977611940298508\n",
            "\n",
            "SVM_emotions_0.15_ACC_ML 0.6307892356399819\n",
            "\n",
            "RF_emotions_0.15_ACC_ML 0.7782960199004975\n",
            "\n",
            "AG_emotions_0.15_ACC_ML 0.7969470827679783\n",
            "\n",
            "SVM_emotions_0.2_ACC_ML 0.6298959746720941\n",
            "\n",
            "RF_emotions_0.2_ACC_ML 0.769125961103573\n",
            "\n",
            "AG_emotions_0.2_ACC_ML 0.8068521031207598\n",
            "\n",
            "SVM_emotions_0.25_ACC_ML 0.6200474898236092\n",
            "\n",
            "RF_emotions_0.25_ACC_ML 0.7664235639981909\n",
            "\n",
            "AG_emotions_0.25_ACC_ML 0.7985753052917233\n",
            "\n",
            "SVM_emotions_0.3_ACC_ML 0.6017130257801899\n",
            "\n",
            "RF_emotions_0.3_ACC_ML 0.7590852555404793\n",
            "\n",
            "AG_emotions_0.3_ACC_ML 0.7913500678426051\n"
          ]
        }
      ]
    }
  ]
}