emotions_10_Evoimpy.ipynb

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    "colab": {
      "name": "emotions-10-Evoimpy.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyP9pNiF8jVuvdNs7LzKF0Mq",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/fabrycio30/machine-learning/blob/main/emotions_10_Evoimpy.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sd90ZgLjFMKQ"
      },
      "source": [
        "#Evoimpy Algorítmo - Tratamento de Valores Ausentes\n",
        "\n",
        "A **Missing data** é um problema recorrente nas análises dados do mundo real. Quando não tratados de maneira adequada, os resultados finais podem ser seriamente comprometidos. Visando contornar essa questão, os **métodos de imputação** buscam valores plausíveis para preencher esses dados faltantes. Dependendo do desempenho do método, por exemplo, o classificador também tem ganho de performance quando tais analises são aplicadas em tarefas de classificação. O algorítmo em questão foca no cenário da **Classificação Multilabel (CM)**. A CM é um problema de aprendizado supervisionado onde uma instância pode estar associada a múltiplos rótulos,  diferente da classificação tradicional/simples que associa um exemplo a um único, aumentando assim a complexidade. São exemplos de aplicações com CM:  anotações semânticas de vídeos e imagens, categorização de música e diagnósticos médicos. \n",
        "O evoimpy é um algorítmo de **Imputação Multipla** de dados baseado em **algoritmos genéticos**. Aqui, os indivíduos (dataset completo, neste caso) são avaliado mediante o desempenho de Classificadores Multilabel. \n",
        "Neste notebook é apresentado  a implementação do algorítmo, em Python, utilizando datasets multilabel do repositorio Mulan. Este repositório disponibiliza vários datasets para o cenário pretendido.\n",
        "No experimento é destribuído percentuais de missing values e verificado o desempenho do Evoimpy.\n",
        "Como mecanismo de ausência de dados é utilzado *Missing Completely At Random* nos experimentos."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "datasets multilabel: yeast, scene, emotions, birds\n",
        "amputacoes: 10,20,30"
      ],
      "metadata": {
        "id": "H7pGn8WjyCsq"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IG_dPTGoDaXa"
      },
      "source": [
        "#Importações\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PQwleV8MrFX_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7e981458-8922-4560-e933-9cdd2ca344ba"
      },
      "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",
        "\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": [
            "Collecting scikit-multilearn\n",
            "  Downloading scikit_multilearn-0.2.0-py3-none-any.whl (89 kB)\n",
            "\u001b[?25l\r\u001b[K     |███▊                            | 10 kB 25.9 MB/s eta 0:00:01\r\u001b[K     |███████▍                        | 20 kB 27.8 MB/s eta 0:00:01\r\u001b[K     |███████████                     | 30 kB 12.4 MB/s eta 0:00:01\r\u001b[K     |██████████████▊                 | 40 kB 9.0 MB/s eta 0:00:01\r\u001b[K     |██████████████████▍             | 51 kB 4.9 MB/s eta 0:00:01\r\u001b[K     |██████████████████████          | 61 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▊      | 71 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▍  | 81 kB 6.0 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 89 kB 4.0 MB/s \n",
            "\u001b[?25hInstalling collected packages: scikit-multilearn\n",
            "Successfully installed scikit-multilearn-0.2.0\n",
            "Collecting liac-arff\n",
            "  Downloading liac-arff-2.5.0.tar.gz (13 kB)\n",
            "Building wheels for collected packages: liac-arff\n",
            "  Building wheel for liac-arff (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for liac-arff: filename=liac_arff-2.5.0-py3-none-any.whl size=11731 sha256=032cd74005f90d44bec9eb8d4746f22eb71ce8b6062e239ed24f6500f81e3c71\n",
            "  Stored in directory: /root/.cache/pip/wheels/1f/0f/15/332ca86cbebf25ddf98518caaf887945fbe1712b97a0f2493b\n",
            "Successfully built liac-arff\n",
            "Installing collected packages: liac-arff\n",
            "Successfully installed liac-arff-2.5.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AtaHZNhdUyuU"
      },
      "source": [
        "#Lendo datasets originais do Repositório Mulan\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tNKdQOG3buA-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "51285fd4-7a1a-4a22-ee3d-54c21b7c83f3"
      },
      "source": [
        "dataset_name = 'emotions'\n",
        "from skmultilearn.dataset import load_dataset\n",
        "X,y,feature_names, label_names = load_dataset(dataset_name, 'undivided')"
      ],
      "execution_count": 717,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "emotions:undivided - exists, not redownloading\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ypcrK6NDTzfN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "16d328f1-e4bc-4405-9ed2-d43763240320"
      },
      "source": [
        "X.toarray()\n",
        "y.toarray()"
      ],
      "execution_count": 718,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 1, 1, 0, 0, 0],\n",
              "       [1, 0, 0, 0, 0, 1],\n",
              "       [0, 1, 0, 0, 0, 1],\n",
              "       ...,\n",
              "       [0, 0, 1, 1, 1, 0],\n",
              "       [0, 1, 1, 0, 0, 0],\n",
              "       [0, 1, 0, 0, 0, 0]])"
            ]
          },
          "metadata": {},
          "execution_count": 718
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lQiS-aqkY3sb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "13a2b370-1aec-4024-979e-b8e0eb88481f"
      },
      "source": [
        "feature_names"
      ],
      "execution_count": 719,
      "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": 719
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yGJHw9DYY8ZH",
        "outputId": "2ff2ba57-8eff-44c5-b7a5-122bb91571d4"
      },
      "source": [
        "label_names"
      ],
      "execution_count": 720,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('amazed-suprised', ['0', '1']),\n",
              " ('happy-pleased', ['0', '1']),\n",
              " ('relaxing-calm', ['0', '1']),\n",
              " ('quiet-still', ['0', '1']),\n",
              " ('sad-lonely', ['0', '1']),\n",
              " ('angry-aggresive', ['0', '1'])]"
            ]
          },
          "metadata": {},
          "execution_count": 720
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "CqPz6APyVzKd",
        "outputId": "dd09c112-c911-4e68-c5d4-23a5bc6e7620"
      },
      "source": [
        "df_features = pd.DataFrame(X.toarray())\n",
        "df_labels = pd.DataFrame(y.toarray())\n",
        "df_labels"
      ],
      "execution_count": 721,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "     0  1  2  3  4  5\n",
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          },
          "metadata": {},
          "execution_count": 721
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e2Ca8-6uaffD"
      },
      "source": [
        "from skmultilearn.model_selection.measures import get_combination_wise_output_matrix"
      ],
      "execution_count": 722,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VWEyRS_kahut",
        "outputId": "48b75731-f830-4e67-c45d-fc80ae8a9bcf"
      },
      "source": [
        "from collections import Counter\n",
        "Counter(combination for row in get_combination_wise_output_matrix(y.A, order=1) for combination in row)"
      ],
      "execution_count": 723,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Counter({(0,): 173, (1,): 166, (2,): 264, (3,): 148, (4,): 168, (5,): 189})"
            ]
          },
          "metadata": {},
          "execution_count": 723
        }
      ]
    },
    {
      "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",
      "metadata": {
        "id": "Jx6kUTbXcKut",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "120f71b1-db5e-4e61-f228-e54d6cca2fea"
      },
      "source": [
        "!pip install iterative-stratification\n",
        "from iterstrat.ml_stratifiers import MultilabelStratifiedKFold"
      ],
      "execution_count": 724,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: iterative-stratification in /usr/local/lib/python3.7/dist-packages (0.1.7)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from iterative-stratification) (1.4.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from iterative-stratification) (1.19.5)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from iterative-stratification) (1.0.1)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->iterative-stratification) (3.0.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->iterative-stratification) (1.1.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cMcP_pKubIXk",
        "outputId": "c399413c-0b1c-4364-f8af-38fea9ddd791"
      },
      "source": [
        "\n",
        "mskf = MultilabelStratifiedKFold(n_splits=10, shuffle=True, random_state=0)\n",
        "\n",
        "for train_index, test_index in mskf.split(X.toarray(), y.toarray()):\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]"
      ],
      "execution_count": 725,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TRAIN: [  0   1   2   3   4   5   6   7   8   9  10  13  14  15  16  17  18  19\n",
            "  20  21  22  23  24  25  26  27  28  29  30  31  32  34  36  37  38  39\n",
            "  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54  55  56  57\n",
            "  58  59  60  61  62  63  64  65  66  67  68  69  70  71  72  73  74  75\n",
            "  76  77  78  79  80  81  82  84  85  86  87  88  89  90  91  92  93  96\n",
            "  97  98  99 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115\n",
            " 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 134 137\n",
            " 138 139 140 141 142 143 144 145 146 147 148 149 151 152 154 155 156 158\n",
            " 159 160 161 162 163 164 166 167 169 170 171 172 173 174 175 176 177 179\n",
            " 180 182 183 184 186 187 188 189 190 191 192 193 194 195 196 197 198 199\n",
            " 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217\n",
            " 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 235 236\n",
            " 237 238 239 241 242 243 244 246 247 248 249 250 251 252 253 254 255 256\n",
            " 257 259 260 261 262 263 264 266 267 268 269 271 272 273 274 275 276 277\n",
            " 278 279 280 281 282 285 286 287 288 289 290 292 293 294 295 296 297 298\n",
            " 300 301 302 303 304 305 306 307 309 310 311 312 313 314 315 316 317 319\n",
            " 320 321 322 323 324 326 327 328 332 333 334 335 336 337 338 339 340 341\n",
            " 342 343 344 345 346 347 348 349 350 351 352 353 354 355 357 358 359 360\n",
            " 361 362 363 364 365 366 367 368 369 370 371 372 374 375 377 379 380 382\n",
            " 383 384 385 386 387 389 391 392 393 394 395 396 397 398 399 400 401 402\n",
            " 404 405 406 409 410 411 412 413 414 416 417 418 419 420 421 422 423 424\n",
            " 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442\n",
            " 443 444 445 447 448 451 452 453 454 455 456 457 458 459 460 461 462 463\n",
            " 465 466 467 468 469 471 472 473 474 475 476 477 478 479 480 481 482 483\n",
            " 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501\n",
            " 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519\n",
            " 520 522 523 525 526 527 528 529 530 531 532 533 534 536 537 539 540 541\n",
            " 542 543 544 545 547 548 549 550 552 553 554 555 556 557 558 559 560 561\n",
            " 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579\n",
            " 580 581 582 583 585 586 588 589 592] TEST: [ 11  12  33  35  83  94  95 100 132 133 135 136 150 153 157 165 168 178\n",
            " 181 185 234 240 245 258 265 270 283 284 291 299 308 318 325 329 330 331\n",
            " 356 373 376 378 381 388 390 403 407 408 415 446 449 450 464 470 521 524\n",
            " 535 538 546 551 584 587 590 591]\n",
            "TRAIN: [  0   2   3   4   5   6   7   8   9  10  11  12  13  14  15  17  18  19\n",
            "  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37\n",
            "  38  39  40  41  44  45  46  47  48  50  51  53  54  55  56  57  58  59\n",
            "  60  61  62  63  64  65  66  67  68  69  70  71  73  74  75  76  77  79\n",
            "  80  81  82  83  84  85  86  87  88  89  90  91  92  93  94  95  96  97\n",
            "  99 100 102 104 106 107 108 110 111 112 113 114 115 116 117 118 119 120\n",
            " 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138\n",
            " 139 140 142 143 144 145 146 147 148 150 151 152 153 154 155 156 157 158\n",
            " 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176\n",
            " 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194\n",
            " 195 196 197 198 199 200 201 202 203 206 207 208 209 210 211 213 214 215\n",
            " 216 217 218 219 220 221 222 223 224 226 228 229 231 232 233 234 235 236\n",
            " 237 239 240 241 242 244 245 246 247 248 249 250 251 252 255 257 258 259\n",
            " 260 261 262 263 264 265 267 268 269 270 271 272 273 274 276 277 278 280\n",
            " 281 282 283 284 285 286 287 288 289 291 292 293 294 295 296 297 298 299\n",
            " 302 303 305 307 308 309 310 311 312 313 314 317 318 319 320 321 322 323\n",
            " 324 325 326 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342\n",
            " 343 344 345 346 347 348 349 350 351 352 353 354 356 357 358 359 360 362\n",
            " 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380\n",
            " 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398\n",
            " 399 400 401 402 403 404 405 406 407 408 411 412 413 414 415 416 417 418\n",
            " 419 420 421 422 424 425 426 427 429 430 432 433 434 435 436 437 439 440\n",
            " 441 442 443 444 446 447 448 449 450 451 452 453 454 455 456 457 458 459\n",
            " 460 461 462 463 464 465 467 469 470 471 472 473 474 475 476 477 478 479\n",
            " 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497\n",
            " 498 499 500 501 503 504 505 506 507 508 509 512 513 515 516 517 518 519\n",
            " 520 521 522 523 524 525 526 527 528 529 531 533 534 535 537 538 540 542\n",
            " 543 544 545 546 547 548 549 551 552 553 554 555 556 557 558 559 560 562\n",
            " 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 580 581\n",
            " 582 583 584 586 587 588 589 590 591 592] TEST: [  1  16  42  43  49  52  72  78  98 101 103 105 109 141 149 204 205 212\n",
            " 225 227 230 238 243 253 254 256 266 275 279 290 300 301 304 306 315 316\n",
            " 327 355 361 409 410 423 428 431 438 445 466 468 502 510 511 514 530 532\n",
            " 536 539 541 550 561 579 585]\n",
            "TRAIN: [  0   1   2   3   5   6   7   9  10  11  12  13  14  15  16  18  19  20\n",
            "  21  23  24  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40\n",
            "  41  42  43  44  45  46  47  48  49  50  51  52  53  54  55  56  58  59\n",
            "  61  62  63  64  65  66  67  68  69  70  71  72  73  74  75  77  78  79\n",
            "  80  81  82  83  84  85  86  87  88  89  90  91  92  93  94  95  96  97\n",
            "  98  99 100 101 102 103 104 105 106 108 109 110 111 112 113 114 115 116\n",
            " 117 118 120 121 122 124 125 126 127 128 129 130 132 133 134 135 136 137\n",
            " 138 141 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158\n",
            " 159 160 161 162 163 164 165 166 167 168 169 170 171 173 174 175 176 177\n",
            " 178 179 180 181 182 183 184 185 186 187 188 189 190 191 193 194 195 196\n",
            " 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214\n",
            " 215 216 217 218 219 220 221 223 224 225 226 227 228 229 230 231 232 233\n",
            " 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251\n",
            " 253 254 255 256 258 260 261 262 264 265 266 267 268 269 270 271 272 273\n",
            " 274 275 276 277 278 279 281 282 283 284 285 286 287 288 289 290 291 292\n",
            " 293 294 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311\n",
            " 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329\n",
            " 330 331 332 333 334 335 336 337 339 340 341 342 343 344 345 347 349 350\n",
            " 352 353 354 355 356 357 358 359 360 361 362 363 365 367 368 369 371 372\n",
            " 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390\n",
            " 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409\n",
            " 410 411 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428\n",
            " 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 449 450\n",
            " 451 453 455 456 457 458 459 461 462 463 464 465 466 467 468 469 470 471\n",
            " 472 474 475 476 477 478 480 481 482 484 485 486 487 488 489 490 491 492\n",
            " 493 494 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511\n",
            " 512 513 514 515 516 517 518 520 521 522 523 524 526 528 529 530 532 533\n",
            " 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551\n",
            " 553 555 556 557 558 559 560 561 563 564 566 568 569 570 571 572 573 574\n",
            " 575 576 578 579 580 581 584 585 586 587 588 589 590 591 592] TEST: [  4   8  17  22  25  57  60  76 107 119 123 131 139 140 142 172 192 222\n",
            " 252 257 259 263 280 295 338 346 348 351 364 366 370 391 412 429 430 447\n",
            " 448 452 454 460 473 479 483 495 519 525 527 531 552 554 562 565 567 577\n",
            " 582 583]\n",
            "TRAIN: [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  16  17  18  20\n",
            "  21  22  23  24  25  26  28  29  30  31  33  34  35  36  37  38  39  40\n",
            "  42  43  44  45  46  47  48  49  50  52  53  54  55  56  57  58  59  60\n",
            "  61  62  63  66  67  69  70  71  72  74  75  76  77  78  79  80  81  82\n",
            "  83  84  85  86  87  88  89  91  93  94  95  96  97  98  99 100 101 102\n",
            " 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120\n",
            " 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138\n",
            " 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156\n",
            " 157 158 159 161 162 163 165 166 167 168 169 170 171 172 173 174 175 176\n",
            " 177 178 179 181 183 184 185 186 188 189 190 191 192 193 196 197 199 200\n",
            " 201 202 204 205 206 207 208 209 210 211 212 213 217 218 219 220 221 222\n",
            " 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242\n",
            " 243 244 245 246 247 248 249 250 251 252 253 254 256 257 258 259 260 261\n",
            " 263 264 265 266 267 268 270 271 272 273 274 275 276 277 278 279 280 281\n",
            " 282 283 284 285 286 287 288 290 291 292 293 294 295 296 297 298 299 300\n",
            " 301 302 303 304 305 306 307 308 309 310 311 312 313 315 316 317 318 319\n",
            " 320 321 322 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339\n",
            " 340 341 342 343 345 346 347 348 349 350 351 353 354 355 356 357 358 359\n",
            " 360 361 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378\n",
            " 379 380 381 382 383 384 385 386 387 388 389 390 391 393 394 395 396 397\n",
            " 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415\n",
            " 416 417 418 419 422 423 425 426 428 429 430 431 432 433 434 435 436 437\n",
            " 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455\n",
            " 456 457 458 459 460 462 463 464 465 466 467 468 470 471 472 473 474 475\n",
            " 476 477 478 479 480 481 482 483 484 485 487 488 489 490 491 492 493 495\n",
            " 496 497 498 499 500 501 502 503 504 506 507 509 510 511 512 513 514 515\n",
            " 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533\n",
            " 534 535 536 537 538 539 541 542 543 544 546 547 548 549 550 551 552 553\n",
            " 554 555 557 558 559 561 562 563 564 565 566 567 568 569 570 571 572 573\n",
            " 574 575 577 578 579 580 581 582 583 584 585 586 587 588 590 591] TEST: [ 14  15  19  27  32  41  51  64  65  68  73  90  92 160 164 180 182 187\n",
            " 194 195 198 203 214 215 216 223 224 255 262 269 289 314 323 324 344 352\n",
            " 362 392 420 421 424 427 461 469 486 494 505 508 540 545 556 560 576 589\n",
            " 592]\n",
            "TRAIN: [  1   2   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18  19\n",
            "  20  22  23  24  25  26  27  28  30  31  32  33  34  35  36  37  39  41\n",
            "  42  43  44  45  46  47  48  49  51  52  54  55  56  57  58  59  60  61\n",
            "  62  63  64  65  66  68  69  70  71  72  73  74  75  76  77  78  79  80\n",
            "  81  83  84  85  86  87  88  90  91  92  93  94  95  96  97  98  99 100\n",
            " 101 102 103 104 105 107 108 109 110 112 113 114 115 116 117 118 119 120\n",
            " 121 123 124 125 127 128 129 130 131 132 133 134 135 136 137 138 139 140\n",
            " 141 142 143 144 146 147 148 149 150 151 152 153 155 156 157 158 160 162\n",
            " 163 164 165 167 168 169 170 171 172 173 174 175 177 178 179 180 181 182\n",
            " 183 184 185 186 187 188 189 190 192 194 195 196 197 198 200 203 204 205\n",
            " 206 207 208 209 210 211 212 213 214 215 216 217 218 220 221 222 223 224\n",
            " 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 243\n",
            " 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261\n",
            " 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279\n",
            " 280 281 282 283 284 285 286 289 290 291 292 294 295 296 297 299 300 301\n",
            " 302 303 304 305 306 308 309 310 311 313 314 315 316 317 318 319 320 321\n",
            " 322 323 324 325 326 327 329 330 331 332 333 334 335 336 337 338 340 341\n",
            " 342 343 344 346 347 348 349 350 351 352 353 354 355 356 357 358 360 361\n",
            " 362 363 364 365 366 368 369 370 371 372 373 374 375 376 377 378 379 380\n",
            " 381 382 383 384 386 387 388 390 391 392 393 394 395 396 398 401 402 403\n",
            " 404 405 407 408 409 410 412 413 414 415 417 418 419 420 421 422 423 424\n",
            " 425 427 428 429 430 431 432 434 436 437 438 439 440 442 443 444 445 446\n",
            " 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464\n",
            " 465 466 467 468 469 470 471 472 473 474 475 476 478 479 480 481 482 483\n",
            " 484 485 486 487 488 489 490 491 492 493 494 495 497 498 499 500 501 502\n",
            " 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520\n",
            " 521 522 523 524 525 526 527 528 530 531 532 533 534 535 536 537 538 539\n",
            " 540 541 542 544 545 546 547 548 550 551 552 553 554 555 556 557 558 559\n",
            " 560 561 562 563 564 565 566 567 568 569 570 571 574 576 577 578 579 580\n",
            " 581 582 583 584 585 586 587 589 590 591 592] TEST: [  0   3  21  29  38  40  50  53  67  82  89 106 111 122 126 145 154 159\n",
            " 161 166 176 191 193 199 201 202 219 242 287 288 293 298 307 312 328 339\n",
            " 345 359 367 385 389 397 399 400 406 411 416 426 433 435 441 477 496 529\n",
            " 543 549 572 573 575 588]\n",
            "TRAIN: [  0   1   2   3   4   5   6   8   9  10  11  12  13  14  15  16  17  18\n",
            "  19  20  21  22  23  24  25  26  27  29  30  32  33  34  35  36  38  40\n",
            "  41  42  43  45  46  47  48  49  50  51  52  53  54  55  56  57  58  60\n",
            "  62  63  64  65  66  67  68  69  72  73  74  75  76  77  78  79  81  82\n",
            "  83  85  86  87  88  89  90  91  92  93  94  95  96  97  98  99 100 101\n",
            " 102 103 104 105 106 107 109 110 111 112 114 115 116 117 118 119 120 122\n",
            " 123 124 125 126 127 128 130 131 132 133 134 135 136 137 138 139 140 141\n",
            " 142 143 144 145 146 147 148 149 150 151 153 154 157 159 160 161 163 164\n",
            " 165 166 167 168 169 171 172 173 174 175 176 177 178 179 180 181 182 184\n",
            " 185 186 187 190 191 192 193 194 195 196 197 198 199 201 202 203 204 205\n",
            " 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223\n",
            " 224 225 226 227 228 229 230 231 232 233 234 235 237 238 239 240 241 242\n",
            " 243 244 245 247 248 249 250 251 252 253 254 255 256 257 258 259 261 262\n",
            " 263 264 265 266 267 269 270 271 272 273 274 275 278 279 280 281 282 283\n",
            " 284 285 286 287 288 289 290 291 292 293 295 297 298 299 300 301 302 303\n",
            " 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 320 321 322\n",
            " 323 324 325 326 327 328 329 330 331 332 334 335 337 338 339 340 341 342\n",
            " 343 344 345 346 347 348 349 350 351 352 353 354 355 356 358 359 361 362\n",
            " 363 364 365 366 367 368 369 370 371 373 374 375 376 377 378 379 380 381\n",
            " 382 383 384 385 388 389 390 391 392 393 394 396 397 398 399 400 402 403\n",
            " 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421\n",
            " 422 423 424 425 426 427 428 429 430 431 432 433 435 436 437 438 439 440\n",
            " 441 443 444 445 446 447 448 449 450 452 453 454 455 456 457 458 459 460\n",
            " 461 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 479 480\n",
            " 481 482 483 484 486 487 489 490 491 492 493 494 495 496 498 499 500 501\n",
            " 502 503 504 505 506 507 508 509 510 511 512 513 514 515 518 519 520 521\n",
            " 522 523 524 525 526 527 528 529 530 531 532 533 535 536 537 538 539 540\n",
            " 541 542 543 544 545 546 547 549 550 551 552 553 554 555 556 557 558 559\n",
            " 560 561 562 563 564 565 567 568 569 570 571 572 573 574 575 576 577 579\n",
            " 581 582 583 584 585 586 587 588 589 590 591 592] TEST: [  7  28  31  37  39  44  59  61  70  71  80  84 108 113 121 129 152 155\n",
            " 156 158 162 170 183 188 189 200 236 246 260 268 276 277 294 296 319 333\n",
            " 336 357 360 372 386 387 395 401 434 442 451 462 478 485 488 497 516 517\n",
            " 534 548 566 578 580]\n",
            "TRAIN: [  0   1   2   3   4   5   6   7   8   9  11  12  13  14  15  16  17  18\n",
            "  19  21  22  24  25  27  28  29  31  32  33  34  35  36  37  38  39  40\n",
            "  41  42  43  44  45  46  47  48  49  50  51  52  53  54  55  56  57  58\n",
            "  59  60  61  62  63  64  65  66  67  68  69  70  71  72  73  74  75  76\n",
            "  77  78  79  80  81  82  83  84  85  86  87  89  90  91  92  93  94  95\n",
            "  96  97  98  99 100 101 102 103 104 105 106 107 108 109 111 112 113 115\n",
            " 116 117 118 119 120 121 122 123 124 126 127 128 129 130 131 132 133 134\n",
            " 135 136 137 139 140 141 142 143 144 145 146 147 148 149 150 152 153 154\n",
            " 155 156 157 158 159 160 161 162 164 165 166 167 168 169 170 171 172 173\n",
            " 175 176 178 179 180 181 182 183 185 187 188 189 190 191 192 193 194 195\n",
            " 196 198 199 200 201 202 203 204 205 207 208 210 211 212 213 214 215 216\n",
            " 217 218 219 221 222 223 224 225 226 227 230 231 232 233 234 235 236 237\n",
            " 238 239 240 241 242 243 244 245 246 247 248 249 251 252 253 254 255 256\n",
            " 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 273 274 275\n",
            " 276 277 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294\n",
            " 295 296 297 298 299 300 301 302 303 304 305 306 307 308 311 312 313 314\n",
            " 315 316 317 318 319 320 321 323 324 325 326 327 328 329 330 331 332 333\n",
            " 334 335 336 337 338 339 340 341 342 344 345 346 348 349 350 351 352 353\n",
            " 354 355 356 357 359 360 361 362 364 365 366 367 368 369 370 371 372 373\n",
            " 375 376 377 378 379 381 382 384 385 386 387 388 389 390 391 392 393 394\n",
            " 395 397 398 399 400 401 403 406 407 408 409 410 411 412 413 414 415 416\n",
            " 417 418 419 420 421 423 424 425 426 427 428 429 430 431 432 433 434 435\n",
            " 436 438 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455\n",
            " 456 459 460 461 462 464 465 466 468 469 470 471 472 473 474 475 477 478\n",
            " 479 480 481 482 483 484 485 486 488 489 493 494 495 496 497 498 499 500\n",
            " 501 502 504 505 506 508 509 510 511 514 515 516 517 518 519 520 521 523\n",
            " 524 525 527 528 529 530 531 532 533 534 535 536 538 539 540 541 542 543\n",
            " 544 545 546 547 548 549 550 551 552 554 555 556 557 558 560 561 562 563\n",
            " 565 566 567 568 569 570 572 573 574 575 576 577 578 579 580 581 582 583\n",
            " 584 585 586 587 588 589 590 591 592] TEST: [ 10  20  23  26  30  88 110 114 125 138 151 163 174 177 184 186 197 206\n",
            " 209 220 228 229 250 272 278 309 310 322 343 347 358 363 374 380 383 396\n",
            " 402 404 405 422 437 439 457 458 463 467 476 487 490 491 492 503 507 512\n",
            " 513 522 526 537 553 559 564 571]\n",
            "TRAIN: [  0   1   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  19\n",
            "  20  21  22  23  24  25  26  27  28  29  30  31  32  33  35  36  37  38\n",
            "  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54  57  58\n",
            "  59  60  61  62  64  65  66  67  68  70  71  72  73  74  76  77  78  80\n",
            "  81  82  83  84  87  88  89  90  91  92  94  95  96  98  99 100 101 102\n",
            " 103 104 105 106 107 108 109 110 111 112 113 114 116 117 119 120 121 122\n",
            " 123 124 125 126 127 129 131 132 133 135 136 138 139 140 141 142 143 144\n",
            " 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162\n",
            " 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180\n",
            " 181 182 183 184 185 186 187 188 189 191 192 193 194 195 197 198 199 200\n",
            " 201 202 203 204 205 206 208 209 210 212 213 214 215 216 217 218 219 220\n",
            " 221 222 223 224 225 227 228 229 230 231 232 234 235 236 237 238 240 242\n",
            " 243 245 246 247 248 249 250 252 253 254 255 256 257 258 259 260 261 262\n",
            " 263 265 266 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282\n",
            " 283 284 285 287 288 289 290 291 293 294 295 296 297 298 299 300 301 302\n",
            " 303 304 306 307 308 309 310 312 314 315 316 317 318 319 320 321 322 323\n",
            " 324 325 327 328 329 330 331 332 333 334 336 337 338 339 340 341 342 343\n",
            " 344 345 346 347 348 351 352 353 354 355 356 357 358 359 360 361 362 363\n",
            " 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381\n",
            " 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399\n",
            " 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 418\n",
            " 419 420 421 422 423 424 426 427 428 429 430 431 433 434 435 436 437 438\n",
            " 439 440 441 442 444 445 446 447 448 449 450 451 452 453 454 456 457 458\n",
            " 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 476 477 478\n",
            " 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496\n",
            " 497 499 500 502 503 504 505 507 508 509 510 511 512 513 514 515 516 517\n",
            " 518 519 521 522 524 525 526 527 528 529 530 531 532 533 534 535 536 537\n",
            " 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 556\n",
            " 559 560 561 562 563 564 565 566 567 571 572 573 574 575 576 577 578 579\n",
            " 580 581 582 583 584 585 586 587 588 589 590 591 592] TEST: [  2  18  34  55  56  63  69  75  79  85  86  93  97 115 118 128 130 134\n",
            " 137 190 196 207 211 226 233 239 241 244 251 264 267 286 292 305 311 313\n",
            " 326 335 349 350 417 425 432 443 455 459 475 498 501 506 520 523 555 557\n",
            " 558 568 569 570]\n",
            "TRAIN: [  0   1   2   3   4   5   6   7   8  10  11  12  14  15  16  17  18  19\n",
            "  20  21  22  23  25  26  27  28  29  30  31  32  33  34  35  37  38  39\n",
            "  40  41  42  43  44  48  49  50  51  52  53  54  55  56  57  58  59  60\n",
            "  61  62  63  64  65  66  67  68  69  70  71  72  73  75  76  77  78  79\n",
            "  80  81  82  83  84  85  86  87  88  89  90  92  93  94  95  96  97  98\n",
            " 100 101 102 103 105 106 107 108 109 110 111 112 113 114 115 117 118 119\n",
            " 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137\n",
            " 138 139 140 141 142 143 145 146 147 149 150 151 152 153 154 155 156 157\n",
            " 158 159 160 161 162 163 164 165 166 168 169 170 172 173 174 176 177 178\n",
            " 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196\n",
            " 197 198 199 200 201 202 203 204 205 206 207 208 209 211 212 213 214 215\n",
            " 216 217 219 220 222 223 224 225 226 227 228 229 230 231 233 234 236 237\n",
            " 238 239 240 241 242 243 244 245 246 247 250 251 252 253 254 255 256 257\n",
            " 258 259 260 261 262 263 264 265 266 267 268 269 270 272 274 275 276 277\n",
            " 278 279 280 281 282 283 284 286 287 288 289 290 291 292 293 294 295 296\n",
            " 298 299 300 301 304 305 306 307 308 309 310 311 312 313 314 315 316 318\n",
            " 319 320 322 323 324 325 326 327 328 329 330 331 332 333 335 336 337 338\n",
            " 339 340 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358\n",
            " 359 360 361 362 363 364 366 367 368 369 370 371 372 373 374 375 376 377\n",
            " 378 380 381 383 385 386 387 388 389 390 391 392 393 394 395 396 397 398\n",
            " 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416\n",
            " 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434\n",
            " 435 436 437 438 439 440 441 442 443 445 446 447 448 449 450 451 452 453\n",
            " 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 472\n",
            " 473 475 476 477 478 479 480 481 483 485 486 487 488 490 491 492 493 494\n",
            " 495 496 497 498 499 501 502 503 504 505 506 507 508 510 511 512 513 514\n",
            " 516 517 519 520 521 522 523 524 525 526 527 529 530 531 532 534 535 536\n",
            " 537 538 539 540 541 543 545 546 548 549 550 551 552 553 554 555 556 557\n",
            " 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 575 576\n",
            " 577 578 579 580 582 583 584 585 587 588 589 590 591 592] TEST: [  9  13  24  36  45  46  47  74  91  99 104 116 144 148 167 171 175 210\n",
            " 218 221 232 235 248 249 271 273 285 297 302 303 317 321 334 341 342 365\n",
            " 379 382 384 444 471 474 482 484 489 500 509 515 518 528 533 542 544 547\n",
            " 574 581 586]\n",
            "TRAIN: [  0   1   2   3   4   7   8   9  10  11  12  13  14  15  16  17  18  19\n",
            "  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37\n",
            "  38  39  40  41  42  43  44  45  46  47  49  50  51  52  53  55  56  57\n",
            "  59  60  61  63  64  65  67  68  69  70  71  72  73  74  75  76  78  79\n",
            "  80  82  83  84  85  86  88  89  90  91  92  93  94  95  97  98  99 100\n",
            " 101 103 104 105 106 107 108 109 110 111 113 114 115 116 118 119 121 122\n",
            " 123 125 126 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142\n",
            " 144 145 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163\n",
            " 164 165 166 167 168 170 171 172 174 175 176 177 178 180 181 182 183 184\n",
            " 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202\n",
            " 203 204 205 206 207 209 210 211 212 214 215 216 218 219 220 221 222 223\n",
            " 224 225 226 227 228 229 230 232 233 234 235 236 238 239 240 241 242 243\n",
            " 244 245 246 248 249 250 251 252 253 254 255 256 257 258 259 260 262 263\n",
            " 264 265 266 267 268 269 270 271 272 273 275 276 277 278 279 280 283 284\n",
            " 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302\n",
            " 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 321\n",
            " 322 323 324 325 326 327 328 329 330 331 333 334 335 336 338 339 341 342\n",
            " 343 344 345 346 347 348 349 350 351 352 355 356 357 358 359 360 361 362\n",
            " 363 364 365 366 367 370 372 373 374 376 378 379 380 381 382 383 384 385\n",
            " 386 387 388 389 390 391 392 395 396 397 399 400 401 402 403 404 405 406\n",
            " 407 408 409 410 411 412 415 416 417 420 421 422 423 424 425 426 427 428\n",
            " 429 430 431 432 433 434 435 437 438 439 441 442 443 444 445 446 447 448\n",
            " 449 450 451 452 454 455 457 458 459 460 461 462 463 464 466 467 468 469\n",
            " 470 471 473 474 475 476 477 478 479 482 483 484 485 486 487 488 489 490\n",
            " 491 492 494 495 496 497 498 500 501 502 503 505 506 507 508 509 510 511\n",
            " 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529\n",
            " 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547\n",
            " 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 564 565 566\n",
            " 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584\n",
            " 585 586 587 588 589 590 591 592] TEST: [  5   6  48  54  58  62  66  77  81  87  96 102 112 117 120 124 127 143\n",
            " 146 147 169 173 179 208 213 217 231 237 247 261 274 281 282 320 332 337\n",
            " 340 353 354 368 369 371 375 377 393 394 398 413 414 418 419 436 440 453\n",
            " 456 465 472 480 481 493 499 504 563]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8vvGRMiHF_SU"
      },
      "source": [
        "#Exibindo informações do dataset trabalhado"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QsESXBp1C1O3",
        "outputId": "3b7fa4f0-50b2-4e7d-943a-c11cebbd960e"
      },
      "source": [
        "f =np.array(feature_names)\n",
        "features = pd.DataFrame(f)\n",
        "features_x = features = features.iloc[:,0]\n",
        "features_x\n"
      ],
      "execution_count": 726,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     Mean_Acc1298_Mean_Mem40_Centroid\n",
              "1      Mean_Acc1298_Mean_Mem40_Rolloff\n",
              "2         Mean_Acc1298_Mean_Mem40_Flux\n",
              "3       Mean_Acc1298_Mean_Mem40_MFCC_0\n",
              "4       Mean_Acc1298_Mean_Mem40_MFCC_1\n",
              "                    ...               \n",
              "67                      BH_HighPeakBPM\n",
              "68                     BH_HighLowRatio\n",
              "69                              BHSUM1\n",
              "70                              BHSUM2\n",
              "71                              BHSUM3\n",
              "Name: 0, Length: 72, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 726
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eJ-Njs7TISoW",
        "outputId": "fad44ba9-9123-4c6f-ca19-f528c131c639"
      },
      "source": [
        "dX_train = X_train.toarray()\n",
        "dy_train = y_train.toarray() \n",
        "dX_train\n",
        "\n",
        "\n",
        "#print(pd.DataFrame(X.toarray()).astype(float))"
      ],
      "execution_count": 727,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.034741, 0.089665, 0.091225, ..., 0.245457, 0.105065, 0.405399],\n",
              "       [0.081374, 0.272747, 0.085733, ..., 0.343547, 0.276366, 0.710924],\n",
              "       [0.110545, 0.273567, 0.08441 , ..., 0.188693, 0.045941, 0.457372],\n",
              "       ...,\n",
              "       [0.035169, 0.065403, 0.075227, ..., 0.184313, 0.247136, 0.476993],\n",
              "       [0.054276, 0.238158, 0.095935, ..., 0.547126, 0.183494, 1.25582 ],\n",
              "       [0.073194, 0.140733, 0.080545, ..., 0.087328, 0.236815, 0.451701]])"
            ]
          },
          "metadata": {},
          "execution_count": 727
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "OqtLuuoLJVjV",
        "outputId": "c5536f01-cc4b-4f69-f4cd-99e19f7b0e3f"
      },
      "source": [
        "df = pd.DataFrame(dX_train)\n",
        "dfy = pd.DataFrame(dy_train)\n",
        "df_origin = df\n",
        "df"
      ],
      "execution_count": 728,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>40</th>\n",
              "      <th>41</th>\n",
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              "      <th>43</th>\n",
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              "      <th>47</th>\n",
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              "      <th>49</th>\n",
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              "      <th>51</th>\n",
              "      <th>52</th>\n",
              "      <th>53</th>\n",
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              "      <th>55</th>\n",
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              "      <th>57</th>\n",
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              "      <th>59</th>\n",
              "      <th>60</th>\n",
              "      <th>61</th>\n",
              "      <th>62</th>\n",
              "      <th>63</th>\n",
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              "      <th>65</th>\n",
              "      <th>66</th>\n",
              "      <th>67</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.034741</td>\n",
              "      <td>0.089665</td>\n",
              "      <td>0.091225</td>\n",
              "      <td>-73.302422</td>\n",
              "      <td>6.215179</td>\n",
              "      <td>0.615074</td>\n",
              "      <td>2.037160</td>\n",
              "      <td>0.804065</td>\n",
              "      <td>1.301409</td>\n",
              "      <td>0.558576</td>\n",
              "      <td>0.672063</td>\n",
              "      <td>0.783788</td>\n",
              "      <td>0.766640</td>\n",
              "      <td>0.458712</td>\n",
              "      <td>0.530384</td>\n",
              "      <td>0.812429</td>\n",
              "      <td>0.028851</td>\n",
              "      <td>0.129039</td>\n",
              "      <td>0.039614</td>\n",
              "      <td>5.762173</td>\n",
              "      <td>1.636819</td>\n",
              "      <td>1.170034</td>\n",
              "      <td>1.051511</td>\n",
              "      <td>0.764163</td>\n",
              "      <td>0.642705</td>\n",
              "      <td>0.617868</td>\n",
              "      <td>0.510265</td>\n",
              "      <td>0.566213</td>\n",
              "      <td>0.509149</td>\n",
              "      <td>0.477275</td>\n",
              "      <td>0.505073</td>\n",
              "      <td>0.463535</td>\n",
              "      <td>0.013519</td>\n",
              "      <td>0.050591</td>\n",
              "      <td>0.009025</td>\n",
              "      <td>8.156257</td>\n",
              "      <td>1.077167</td>\n",
              "      <td>0.624711</td>\n",
              "      <td>0.810244</td>\n",
              "      <td>0.399568</td>\n",
              "      <td>0.279947</td>\n",
              "      <td>0.314215</td>\n",
              "      <td>0.231439</td>\n",
              "      <td>0.345401</td>\n",
              "      <td>0.285389</td>\n",
              "      <td>0.210613</td>\n",
              "      <td>0.321896</td>\n",
              "      <td>0.290551</td>\n",
              "      <td>0.022774</td>\n",
              "      <td>0.095801</td>\n",
              "      <td>0.015057</td>\n",
              "      <td>4.748694</td>\n",
              "      <td>0.536378</td>\n",
              "      <td>0.296306</td>\n",
              "      <td>0.273210</td>\n",
              "      <td>0.175800</td>\n",
              "      <td>0.105508</td>\n",
              "      <td>0.168246</td>\n",
              "      <td>0.115849</td>\n",
              "      <td>0.136020</td>\n",
              "      <td>0.110514</td>\n",
              "      <td>0.100517</td>\n",
              "      <td>0.118630</td>\n",
              "      <td>0.094923</td>\n",
              "      <td>0.051035</td>\n",
              "      <td>68.0</td>\n",
              "      <td>0.014937</td>\n",
              "      <td>136.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.245457</td>\n",
              "      <td>0.105065</td>\n",
              "      <td>0.405399</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.081374</td>\n",
              "      <td>0.272747</td>\n",
              "      <td>0.085733</td>\n",
              "      <td>-62.584437</td>\n",
              "      <td>3.183163</td>\n",
              "      <td>-0.218145</td>\n",
              "      <td>0.163038</td>\n",
              "      <td>0.620251</td>\n",
              "      <td>0.458514</td>\n",
              "      <td>0.041426</td>\n",
              "      <td>0.308287</td>\n",
              "      <td>0.538152</td>\n",
              "      <td>0.594871</td>\n",
              "      <td>0.734332</td>\n",
              "      <td>0.415489</td>\n",
              "      <td>0.761508</td>\n",
              "      <td>0.066288</td>\n",
              "      <td>0.262370</td>\n",
              "      <td>0.034438</td>\n",
              "      <td>3.480874</td>\n",
              "      <td>1.596532</td>\n",
              "      <td>0.943803</td>\n",
              "      <td>0.804444</td>\n",
              "      <td>0.511229</td>\n",
              "      <td>0.498670</td>\n",
              "      <td>0.523039</td>\n",
              "      <td>0.480916</td>\n",
              "      <td>0.488657</td>\n",
              "      <td>0.483166</td>\n",
              "      <td>0.445187</td>\n",
              "      <td>0.415994</td>\n",
              "      <td>0.405593</td>\n",
              "      <td>0.013621</td>\n",
              "      <td>0.073041</td>\n",
              "      <td>0.010094</td>\n",
              "      <td>1.243981</td>\n",
              "      <td>0.829790</td>\n",
              "      <td>0.252972</td>\n",
              "      <td>0.347831</td>\n",
              "      <td>0.205087</td>\n",
              "      <td>0.168601</td>\n",
              "      <td>0.178009</td>\n",
              "      <td>0.144080</td>\n",
              "      <td>0.178703</td>\n",
              "      <td>0.146937</td>\n",
              "      <td>0.125580</td>\n",
              "      <td>0.128202</td>\n",
              "      <td>0.107007</td>\n",
              "      <td>0.020028</td>\n",
              "      <td>0.066940</td>\n",
              "      <td>0.029483</td>\n",
              "      <td>3.963534</td>\n",
              "      <td>0.382360</td>\n",
              "      <td>0.168389</td>\n",
              "      <td>0.117525</td>\n",
              "      <td>0.098341</td>\n",
              "      <td>0.087046</td>\n",
              "      <td>0.057991</td>\n",
              "      <td>0.059393</td>\n",
              "      <td>0.059457</td>\n",
              "      <td>0.053439</td>\n",
              "      <td>0.067684</td>\n",
              "      <td>0.070075</td>\n",
              "      <td>0.041565</td>\n",
              "      <td>0.295031</td>\n",
              "      <td>70.0</td>\n",
              "      <td>0.276366</td>\n",
              "      <td>140.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.343547</td>\n",
              "      <td>0.276366</td>\n",
              "      <td>0.710924</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.110545</td>\n",
              "      <td>0.273567</td>\n",
              "      <td>0.084410</td>\n",
              "      <td>-65.235325</td>\n",
              "      <td>2.794964</td>\n",
              "      <td>0.639047</td>\n",
              "      <td>1.281297</td>\n",
              "      <td>0.757896</td>\n",
              "      <td>0.489412</td>\n",
              "      <td>0.627636</td>\n",
              "      <td>0.469322</td>\n",
              "      <td>0.644336</td>\n",
              "      <td>0.441556</td>\n",
              "      <td>0.335964</td>\n",
              "      <td>0.290713</td>\n",
              "      <td>0.158538</td>\n",
              "      <td>0.082743</td>\n",
              "      <td>0.215373</td>\n",
              "      <td>0.035970</td>\n",
              "      <td>4.834742</td>\n",
              "      <td>1.213443</td>\n",
              "      <td>0.864034</td>\n",
              "      <td>0.909222</td>\n",
              "      <td>0.780572</td>\n",
              "      <td>0.550833</td>\n",
              "      <td>0.639740</td>\n",
              "      <td>0.573309</td>\n",
              "      <td>0.526312</td>\n",
              "      <td>0.562622</td>\n",
              "      <td>0.538407</td>\n",
              "      <td>0.492292</td>\n",
              "      <td>0.455562</td>\n",
              "      <td>0.029112</td>\n",
              "      <td>0.070433</td>\n",
              "      <td>0.008525</td>\n",
              "      <td>2.759906</td>\n",
              "      <td>0.592634</td>\n",
              "      <td>0.761852</td>\n",
              "      <td>0.568740</td>\n",
              "      <td>0.589827</td>\n",
              "      <td>0.281181</td>\n",
              "      <td>0.437752</td>\n",
              "      <td>0.479889</td>\n",
              "      <td>0.227320</td>\n",
              "      <td>0.296224</td>\n",
              "      <td>0.273855</td>\n",
              "      <td>0.191804</td>\n",
              "      <td>0.198025</td>\n",
              "      <td>0.038119</td>\n",
              "      <td>0.065427</td>\n",
              "      <td>0.029622</td>\n",
              "      <td>3.371796</td>\n",
              "      <td>0.430373</td>\n",
              "      <td>0.172862</td>\n",
              "      <td>0.177523</td>\n",
              "      <td>0.184333</td>\n",
              "      <td>0.095718</td>\n",
              "      <td>0.139323</td>\n",
              "      <td>0.109279</td>\n",
              "      <td>0.090650</td>\n",
              "      <td>0.117886</td>\n",
              "      <td>0.100852</td>\n",
              "      <td>0.079917</td>\n",
              "      <td>0.085821</td>\n",
              "      <td>0.161574</td>\n",
              "      <td>61.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>183.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.188693</td>\n",
              "      <td>0.045941</td>\n",
              "      <td>0.457372</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.042481</td>\n",
              "      <td>0.199281</td>\n",
              "      <td>0.093447</td>\n",
              "      <td>-80.305152</td>\n",
              "      <td>5.824409</td>\n",
              "      <td>0.648848</td>\n",
              "      <td>1.754870</td>\n",
              "      <td>1.495532</td>\n",
              "      <td>0.739909</td>\n",
              "      <td>0.809644</td>\n",
              "      <td>0.460945</td>\n",
              "      <td>0.409566</td>\n",
              "      <td>0.680122</td>\n",
              "      <td>0.590405</td>\n",
              "      <td>0.481380</td>\n",
              "      <td>0.621956</td>\n",
              "      <td>0.049939</td>\n",
              "      <td>0.281616</td>\n",
              "      <td>0.044727</td>\n",
              "      <td>6.719538</td>\n",
              "      <td>1.377811</td>\n",
              "      <td>1.265771</td>\n",
              "      <td>0.986178</td>\n",
              "      <td>0.710955</td>\n",
              "      <td>0.706904</td>\n",
              "      <td>0.710147</td>\n",
              "      <td>0.688825</td>\n",
              "      <td>0.699573</td>\n",
              "      <td>0.577976</td>\n",
              "      <td>0.533882</td>\n",
              "      <td>0.501818</td>\n",
              "      <td>0.495368</td>\n",
              "      <td>0.020749</td>\n",
              "      <td>0.106318</td>\n",
              "      <td>0.009108</td>\n",
              "      <td>3.992357</td>\n",
              "      <td>0.656429</td>\n",
              "      <td>0.927692</td>\n",
              "      <td>0.569916</td>\n",
              "      <td>0.378919</td>\n",
              "      <td>0.530714</td>\n",
              "      <td>0.317807</td>\n",
              "      <td>0.308447</td>\n",
              "      <td>0.324934</td>\n",
              "      <td>0.263444</td>\n",
              "      <td>0.359477</td>\n",
              "      <td>0.274257</td>\n",
              "      <td>0.233287</td>\n",
              "      <td>0.032678</td>\n",
              "      <td>0.119480</td>\n",
              "      <td>0.028707</td>\n",
              "      <td>4.125111</td>\n",
              "      <td>0.461304</td>\n",
              "      <td>0.280751</td>\n",
              "      <td>0.246108</td>\n",
              "      <td>0.142805</td>\n",
              "      <td>0.183657</td>\n",
              "      <td>0.124399</td>\n",
              "      <td>0.155513</td>\n",
              "      <td>0.167114</td>\n",
              "      <td>0.113774</td>\n",
              "      <td>0.112815</td>\n",
              "      <td>0.129145</td>\n",
              "      <td>0.122330</td>\n",
              "      <td>0.043012</td>\n",
              "      <td>66.0</td>\n",
              "      <td>0.206562</td>\n",
              "      <td>132.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.102839</td>\n",
              "      <td>0.241934</td>\n",
              "      <td>0.351009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.074550</td>\n",
              "      <td>0.140880</td>\n",
              "      <td>0.079789</td>\n",
              "      <td>-93.697749</td>\n",
              "      <td>5.543229</td>\n",
              "      <td>1.064262</td>\n",
              "      <td>0.899152</td>\n",
              "      <td>0.890336</td>\n",
              "      <td>0.702328</td>\n",
              "      <td>0.490685</td>\n",
              "      <td>0.796904</td>\n",
              "      <td>0.745373</td>\n",
              "      <td>0.911234</td>\n",
              "      <td>0.594429</td>\n",
              "      <td>0.454186</td>\n",
              "      <td>0.384836</td>\n",
              "      <td>0.035751</td>\n",
              "      <td>0.085592</td>\n",
              "      <td>0.029413</td>\n",
              "      <td>4.755293</td>\n",
              "      <td>1.116290</td>\n",
              "      <td>0.926772</td>\n",
              "      <td>0.634988</td>\n",
              "      <td>0.639660</td>\n",
              "      <td>0.552653</td>\n",
              "      <td>0.527708</td>\n",
              "      <td>0.584705</td>\n",
              "      <td>0.696173</td>\n",
              "      <td>0.648611</td>\n",
              "      <td>0.689096</td>\n",
              "      <td>0.643595</td>\n",
              "      <td>0.578063</td>\n",
              "      <td>0.047014</td>\n",
              "      <td>0.136984</td>\n",
              "      <td>0.010356</td>\n",
              "      <td>7.713140</td>\n",
              "      <td>1.592642</td>\n",
              "      <td>1.027190</td>\n",
              "      <td>0.591399</td>\n",
              "      <td>0.565654</td>\n",
              "      <td>0.524420</td>\n",
              "      <td>0.554501</td>\n",
              "      <td>0.606200</td>\n",
              "      <td>0.616760</td>\n",
              "      <td>0.596926</td>\n",
              "      <td>0.524291</td>\n",
              "      <td>0.637971</td>\n",
              "      <td>0.637960</td>\n",
              "      <td>0.036151</td>\n",
              "      <td>0.087741</td>\n",
              "      <td>0.030180</td>\n",
              "      <td>5.085385</td>\n",
              "      <td>0.551937</td>\n",
              "      <td>0.257562</td>\n",
              "      <td>0.159950</td>\n",
              "      <td>0.175855</td>\n",
              "      <td>0.150907</td>\n",
              "      <td>0.142092</td>\n",
              "      <td>0.222804</td>\n",
              "      <td>0.329188</td>\n",
              "      <td>0.251668</td>\n",
              "      <td>0.265049</td>\n",
              "      <td>0.284196</td>\n",
              "      <td>0.189988</td>\n",
              "      <td>0.029308</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.144039</td>\n",
              "      <td>200.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.195196</td>\n",
              "      <td>0.310801</td>\n",
              "      <td>0.683817</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>525</th>\n",
              "      <td>0.027142</td>\n",
              "      <td>0.047551</td>\n",
              "      <td>0.072043</td>\n",
              "      <td>-79.881347</td>\n",
              "      <td>8.119313</td>\n",
              "      <td>1.927310</td>\n",
              "      <td>1.696017</td>\n",
              "      <td>0.397888</td>\n",
              "      <td>0.857559</td>\n",
              "      <td>0.302742</td>\n",
              "      <td>0.672649</td>\n",
              "      <td>0.748163</td>\n",
              "      <td>0.412952</td>\n",
              "      <td>0.852625</td>\n",
              "      <td>0.597127</td>\n",
              "      <td>0.456126</td>\n",
              "      <td>0.013099</td>\n",
              "      <td>0.029163</td>\n",
              "      <td>0.030049</td>\n",
              "      <td>3.076905</td>\n",
              "      <td>0.862406</td>\n",
              "      <td>0.640101</td>\n",
              "      <td>0.615202</td>\n",
              "      <td>0.557800</td>\n",
              "      <td>0.514684</td>\n",
              "      <td>0.577850</td>\n",
              "      <td>0.600797</td>\n",
              "      <td>0.544352</td>\n",
              "      <td>0.540142</td>\n",
              "      <td>0.553717</td>\n",
              "      <td>0.493653</td>\n",
              "      <td>0.451593</td>\n",
              "      <td>0.012848</td>\n",
              "      <td>0.044107</td>\n",
              "      <td>0.008805</td>\n",
              "      <td>2.560837</td>\n",
              "      <td>1.008119</td>\n",
              "      <td>0.523135</td>\n",
              "      <td>0.802130</td>\n",
              "      <td>0.694118</td>\n",
              "      <td>0.412457</td>\n",
              "      <td>0.461475</td>\n",
              "      <td>0.500786</td>\n",
              "      <td>0.492573</td>\n",
              "      <td>0.454346</td>\n",
              "      <td>0.437815</td>\n",
              "      <td>0.345676</td>\n",
              "      <td>0.366906</td>\n",
              "      <td>0.028604</td>\n",
              "      <td>0.069883</td>\n",
              "      <td>0.030413</td>\n",
              "      <td>5.042254</td>\n",
              "      <td>0.455528</td>\n",
              "      <td>0.177015</td>\n",
              "      <td>0.165939</td>\n",
              "      <td>0.124718</td>\n",
              "      <td>0.118118</td>\n",
              "      <td>0.194543</td>\n",
              "      <td>0.146845</td>\n",
              "      <td>0.139167</td>\n",
              "      <td>0.161785</td>\n",
              "      <td>0.159234</td>\n",
              "      <td>0.129552</td>\n",
              "      <td>0.106260</td>\n",
              "      <td>0.089991</td>\n",
              "      <td>87.0</td>\n",
              "      <td>0.564842</td>\n",
              "      <td>174.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.261742</td>\n",
              "      <td>0.002657</td>\n",
              "      <td>1.149211</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>526</th>\n",
              "      <td>0.094829</td>\n",
              "      <td>0.204498</td>\n",
              "      <td>0.082824</td>\n",
              "      <td>-61.364436</td>\n",
              "      <td>2.966229</td>\n",
              "      <td>0.627740</td>\n",
              "      <td>1.440352</td>\n",
              "      <td>0.856243</td>\n",
              "      <td>1.110282</td>\n",
              "      <td>0.394450</td>\n",
              "      <td>0.726323</td>\n",
              "      <td>0.885339</td>\n",
              "      <td>0.517613</td>\n",
              "      <td>-0.186371</td>\n",
              "      <td>0.531083</td>\n",
              "      <td>0.312116</td>\n",
              "      <td>0.052306</td>\n",
              "      <td>0.131783</td>\n",
              "      <td>0.029793</td>\n",
              "      <td>3.512784</td>\n",
              "      <td>0.918634</td>\n",
              "      <td>1.043679</td>\n",
              "      <td>0.778114</td>\n",
              "      <td>0.613929</td>\n",
              "      <td>0.598554</td>\n",
              "      <td>0.566087</td>\n",
              "      <td>0.568431</td>\n",
              "      <td>0.541677</td>\n",
              "      <td>0.552420</td>\n",
              "      <td>0.546411</td>\n",
              "      <td>0.580214</td>\n",
              "      <td>0.608745</td>\n",
              "      <td>0.021612</td>\n",
              "      <td>0.047508</td>\n",
              "      <td>0.007370</td>\n",
              "      <td>1.419983</td>\n",
              "      <td>0.529261</td>\n",
              "      <td>0.388876</td>\n",
              "      <td>0.434100</td>\n",
              "      <td>0.318737</td>\n",
              "      <td>0.265518</td>\n",
              "      <td>0.210297</td>\n",
              "      <td>0.169365</td>\n",
              "      <td>0.189288</td>\n",
              "      <td>0.247443</td>\n",
              "      <td>0.228670</td>\n",
              "      <td>0.249573</td>\n",
              "      <td>0.221631</td>\n",
              "      <td>0.023890</td>\n",
              "      <td>0.047186</td>\n",
              "      <td>0.030264</td>\n",
              "      <td>4.043908</td>\n",
              "      <td>0.362876</td>\n",
              "      <td>0.210280</td>\n",
              "      <td>0.213201</td>\n",
              "      <td>0.114466</td>\n",
              "      <td>0.158569</td>\n",
              "      <td>0.100762</td>\n",
              "      <td>0.115185</td>\n",
              "      <td>0.092511</td>\n",
              "      <td>0.093992</td>\n",
              "      <td>0.108324</td>\n",
              "      <td>0.128090</td>\n",
              "      <td>0.167160</td>\n",
              "      <td>0.035827</td>\n",
              "      <td>65.0</td>\n",
              "      <td>0.003942</td>\n",
              "      <td>130.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.282122</td>\n",
              "      <td>0.052218</td>\n",
              "      <td>0.335371</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>527</th>\n",
              "      <td>0.035169</td>\n",
              "      <td>0.065403</td>\n",
              "      <td>0.075227</td>\n",
              "      <td>-81.750533</td>\n",
              "      <td>10.311701</td>\n",
              "      <td>0.092224</td>\n",
              "      <td>0.818851</td>\n",
              "      <td>1.569606</td>\n",
              "      <td>1.831909</td>\n",
              "      <td>0.057216</td>\n",
              "      <td>0.419342</td>\n",
              "      <td>0.482342</td>\n",
              "      <td>0.745151</td>\n",
              "      <td>0.403275</td>\n",
              "      <td>0.838195</td>\n",
              "      <td>0.711755</td>\n",
              "      <td>0.013232</td>\n",
              "      <td>0.035040</td>\n",
              "      <td>0.029861</td>\n",
              "      <td>4.111372</td>\n",
              "      <td>0.911620</td>\n",
              "      <td>1.057301</td>\n",
              "      <td>0.496378</td>\n",
              "      <td>0.666827</td>\n",
              "      <td>0.556064</td>\n",
              "      <td>0.465213</td>\n",
              "      <td>0.499597</td>\n",
              "      <td>0.422874</td>\n",
              "      <td>0.516194</td>\n",
              "      <td>0.375284</td>\n",
              "      <td>0.473949</td>\n",
              "      <td>0.386111</td>\n",
              "      <td>0.016030</td>\n",
              "      <td>0.035553</td>\n",
              "      <td>0.009759</td>\n",
              "      <td>5.776610</td>\n",
              "      <td>1.123743</td>\n",
              "      <td>0.830796</td>\n",
              "      <td>0.339320</td>\n",
              "      <td>1.003937</td>\n",
              "      <td>0.441073</td>\n",
              "      <td>0.563689</td>\n",
              "      <td>0.474611</td>\n",
              "      <td>0.358808</td>\n",
              "      <td>0.543528</td>\n",
              "      <td>0.340304</td>\n",
              "      <td>0.396751</td>\n",
              "      <td>0.496262</td>\n",
              "      <td>0.027062</td>\n",
              "      <td>0.053435</td>\n",
              "      <td>0.030620</td>\n",
              "      <td>4.801035</td>\n",
              "      <td>0.632917</td>\n",
              "      <td>0.351023</td>\n",
              "      <td>0.133192</td>\n",
              "      <td>0.348863</td>\n",
              "      <td>0.161952</td>\n",
              "      <td>0.129868</td>\n",
              "      <td>0.137076</td>\n",
              "      <td>0.118185</td>\n",
              "      <td>0.120515</td>\n",
              "      <td>0.083030</td>\n",
              "      <td>0.147194</td>\n",
              "      <td>0.098238</td>\n",
              "      <td>0.033681</td>\n",
              "      <td>88.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>176.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.184313</td>\n",
              "      <td>0.247136</td>\n",
              "      <td>0.476993</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>528</th>\n",
              "      <td>0.054276</td>\n",
              "      <td>0.238158</td>\n",
              "      <td>0.095935</td>\n",
              "      <td>-71.009724</td>\n",
              "      <td>3.181340</td>\n",
              "      <td>1.547197</td>\n",
              "      <td>2.407780</td>\n",
              "      <td>0.618838</td>\n",
              "      <td>0.997950</td>\n",
              "      <td>0.825143</td>\n",
              "      <td>0.758816</td>\n",
              "      <td>0.727540</td>\n",
              "      <td>0.500074</td>\n",
              "      <td>0.402084</td>\n",
              "      <td>0.539242</td>\n",
              "      <td>0.326410</td>\n",
              "      <td>0.052974</td>\n",
              "      <td>0.283645</td>\n",
              "      <td>0.047596</td>\n",
              "      <td>6.714642</td>\n",
              "      <td>1.600006</td>\n",
              "      <td>1.309167</td>\n",
              "      <td>0.878546</td>\n",
              "      <td>0.712208</td>\n",
              "      <td>0.595806</td>\n",
              "      <td>0.516834</td>\n",
              "      <td>0.578153</td>\n",
              "      <td>0.519924</td>\n",
              "      <td>0.483610</td>\n",
              "      <td>0.478800</td>\n",
              "      <td>0.470226</td>\n",
              "      <td>0.415839</td>\n",
              "      <td>0.016953</td>\n",
              "      <td>0.091381</td>\n",
              "      <td>0.008863</td>\n",
              "      <td>3.245796</td>\n",
              "      <td>0.754218</td>\n",
              "      <td>0.787304</td>\n",
              "      <td>0.442779</td>\n",
              "      <td>0.310832</td>\n",
              "      <td>0.323179</td>\n",
              "      <td>0.247374</td>\n",
              "      <td>0.257608</td>\n",
              "      <td>0.222008</td>\n",
              "      <td>0.232946</td>\n",
              "      <td>0.233224</td>\n",
              "      <td>0.232351</td>\n",
              "      <td>0.163934</td>\n",
              "      <td>0.026424</td>\n",
              "      <td>0.088106</td>\n",
              "      <td>0.028770</td>\n",
              "      <td>4.094957</td>\n",
              "      <td>0.584713</td>\n",
              "      <td>0.312613</td>\n",
              "      <td>0.214629</td>\n",
              "      <td>0.169042</td>\n",
              "      <td>0.115469</td>\n",
              "      <td>0.111847</td>\n",
              "      <td>0.112750</td>\n",
              "      <td>0.090504</td>\n",
              "      <td>0.091698</td>\n",
              "      <td>0.090272</td>\n",
              "      <td>0.080006</td>\n",
              "      <td>0.058181</td>\n",
              "      <td>0.155650</td>\n",
              "      <td>84.0</td>\n",
              "      <td>0.276695</td>\n",
              "      <td>168.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.547126</td>\n",
              "      <td>0.183494</td>\n",
              "      <td>1.255820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>529</th>\n",
              "      <td>0.073194</td>\n",
              "      <td>0.140733</td>\n",
              "      <td>0.080545</td>\n",
              "      <td>-74.517081</td>\n",
              "      <td>6.945590</td>\n",
              "      <td>-1.047953</td>\n",
              "      <td>1.952278</td>\n",
              "      <td>0.341936</td>\n",
              "      <td>0.770582</td>\n",
              "      <td>0.144989</td>\n",
              "      <td>0.405832</td>\n",
              "      <td>0.186847</td>\n",
              "      <td>0.496161</td>\n",
              "      <td>0.389096</td>\n",
              "      <td>0.324653</td>\n",
              "      <td>0.193508</td>\n",
              "      <td>0.020042</td>\n",
              "      <td>0.043408</td>\n",
              "      <td>0.028217</td>\n",
              "      <td>3.012740</td>\n",
              "      <td>0.804194</td>\n",
              "      <td>0.524353</td>\n",
              "      <td>0.508190</td>\n",
              "      <td>0.485743</td>\n",
              "      <td>0.518681</td>\n",
              "      <td>0.481357</td>\n",
              "      <td>0.482828</td>\n",
              "      <td>0.514857</td>\n",
              "      <td>0.508133</td>\n",
              "      <td>0.516179</td>\n",
              "      <td>0.474873</td>\n",
              "      <td>0.447746</td>\n",
              "      <td>0.015537</td>\n",
              "      <td>0.034044</td>\n",
              "      <td>0.007524</td>\n",
              "      <td>5.018377</td>\n",
              "      <td>0.852230</td>\n",
              "      <td>0.410106</td>\n",
              "      <td>0.275035</td>\n",
              "      <td>0.300508</td>\n",
              "      <td>0.248464</td>\n",
              "      <td>0.362048</td>\n",
              "      <td>0.491118</td>\n",
              "      <td>0.446384</td>\n",
              "      <td>0.482079</td>\n",
              "      <td>0.438563</td>\n",
              "      <td>0.356211</td>\n",
              "      <td>0.254899</td>\n",
              "      <td>0.024653</td>\n",
              "      <td>0.052302</td>\n",
              "      <td>0.030290</td>\n",
              "      <td>4.244648</td>\n",
              "      <td>0.350933</td>\n",
              "      <td>0.149544</td>\n",
              "      <td>0.122719</td>\n",
              "      <td>0.063955</td>\n",
              "      <td>0.078072</td>\n",
              "      <td>0.068832</td>\n",
              "      <td>0.081025</td>\n",
              "      <td>0.139319</td>\n",
              "      <td>0.103590</td>\n",
              "      <td>0.114416</td>\n",
              "      <td>0.073605</td>\n",
              "      <td>0.081219</td>\n",
              "      <td>0.056101</td>\n",
              "      <td>94.0</td>\n",
              "      <td>0.007080</td>\n",
              "      <td>188.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.087328</td>\n",
              "      <td>0.236815</td>\n",
              "      <td>0.451701</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>530 rows × 72 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           0         1         2          3   ...   68        69        70        71\n",
              "0    0.034741  0.089665  0.091225 -73.302422  ...  2.0  0.245457  0.105065  0.405399\n",
              "1    0.081374  0.272747  0.085733 -62.584437  ...  2.0  0.343547  0.276366  0.710924\n",
              "2    0.110545  0.273567  0.084410 -65.235325  ...  3.0  0.188693  0.045941  0.457372\n",
              "3    0.042481  0.199281  0.093447 -80.305152  ...  2.0  0.102839  0.241934  0.351009\n",
              "4    0.074550  0.140880  0.079789 -93.697749  ...  2.0  0.195196  0.310801  0.683817\n",
              "..        ...       ...       ...        ...  ...  ...       ...       ...       ...\n",
              "525  0.027142  0.047551  0.072043 -79.881347  ...  2.0  0.261742  0.002657  1.149211\n",
              "526  0.094829  0.204498  0.082824 -61.364436  ...  2.0  0.282122  0.052218  0.335371\n",
              "527  0.035169  0.065403  0.075227 -81.750533  ...  2.0  0.184313  0.247136  0.476993\n",
              "528  0.054276  0.238158  0.095935 -71.009724  ...  2.0  0.547126  0.183494  1.255820\n",
              "529  0.073194  0.140733  0.080545 -74.517081  ...  2.0  0.087328  0.236815  0.451701\n",
              "\n",
              "[530 rows x 72 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 728
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VVZdHXfNFoOb",
        "outputId": "c0ef302b-b705-45b9-bb92-31c0082c0361"
      },
      "source": [
        "va = (df.isna().sum()).sum()\n",
        "va"
      ],
      "execution_count": 729,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 729
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fmWrh5uT4NnE"
      },
      "source": [
        "padronização Dataset de teste"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hYkv2Gka4MQz"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scl = StandardScaler()\n",
        "X_test = scl.fit_transform(X_test.toarray())"
      ],
      "execution_count": 730,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R3gRnSNF6BnZ",
        "outputId": "e094cada-b878-4de8-eeff-ffd80798601c"
      },
      "source": [
        "X_test"
      ],
      "execution_count": 731,
      "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": 731
        }
      ]
    },
    {
      "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",
      "metadata": {
        "id": "W3GkQmP_X6du"
      },
      "source": [
        "#rate - taxa de Missing values - em float (ex.: 0.5 == 50%)\n",
        "#data_size = quantidade de dados na matriz (col*rows)\n",
        "def amount_ampute(rate, data_size):\n",
        "  amount_missing_values = data_size * rate\n",
        "  return int(amount_missing_values)\n"
      ],
      "execution_count": 732,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0sh7m7mI2SFM",
        "outputId": "6e59e724-0ca7-45d5-ab08-e3c81b07ad84"
      },
      "source": [
        "dX_train.size"
      ],
      "execution_count": 733,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "38160"
            ]
          },
          "metadata": {},
          "execution_count": 733
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8IjkyMzc2oi_"
      },
      "source": [
        "#calcula o percentual de valores ausentes a serem aplicados no dataset\n",
        "def amount_missing_values(rate,df_x_size):\n",
        "  amount_mv = amount_ampute(rate, df_x_size)\n",
        "  print('Total de dados: ',df_x_size,'\\nTotal de Valores Ausentes: ',amount_mv)\n",
        "  return amount_mv\n",
        "\n"
      ],
      "execution_count": 734,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-BXupnbMhj82",
        "outputId": "4c142c36-d1f4-4633-c054-587bf4b92bd7"
      },
      "source": [
        "taxa_de_missing_values = 0.10\n",
        "missing_values = amount_missing_values(taxa_de_missing_values, dX_train.size)"
      ],
      "execution_count": 735,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total de dados:  38160 \n",
            "Total de Valores Ausentes:  3816\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EGMtsgfm4SiY",
        "outputId": "3d80c195-1ec4-4f83-b475-43b4bb4d325d"
      },
      "source": [
        "type(missing_values)"
      ],
      "execution_count": 736,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "int"
            ]
          },
          "metadata": {},
          "execution_count": 736
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y8lsfafuauVt"
      },
      "source": [
        "Adicionado amputações de acordo com a quantidade estabelecida no passo anterior"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pri2lrdLxO9p"
      },
      "source": [
        "from numpy import random\n",
        "def selectIndex(x,y):\n",
        "  return random.randint(0,x), random.randint(0,y)"
      ],
      "execution_count": 737,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4OSY8ThSxH7O"
      },
      "source": [
        "#rows e cols são os tamanhos dos ranges percorridos para x e y\n",
        "rows = X_train.shape[0]\n",
        "cols = X_train.shape[1]\n",
        "indexList = []"
      ],
      "execution_count": 738,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D2SHWoB0zSlf"
      },
      "source": [
        "def ampute(i,j): \n",
        "  while(len(indexList)<missing_values):\n",
        "    a = 0\n",
        "    b = 0\n",
        "    r,c= selectIndex(i,j)\n",
        "    if(a != r and b != c):\n",
        "      a = r\n",
        "      b = c\n",
        "      df.iloc[r,c] = np.nan\n",
        "      indexList.append([r,c])\n",
        "  return indexList\n"
      ],
      "execution_count": 739,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2HGLJ44z0-QO"
      },
      "source": [
        "list_positions_missing_values = ampute(rows,cols)"
      ],
      "execution_count": 740,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oWNk210nu74B",
        "outputId": "0cae8d92-3007-4f2d-cb4f-73922524494e"
      },
      "source": [
        "len(indexList), len(list_positions_missing_values)"
      ],
      "execution_count": 741,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3816, 3816)"
            ]
          },
          "metadata": {},
          "execution_count": 741
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bu-xX7GH9yQI",
        "outputId": "b13ad512-095f-41cc-aba5-58d15824d078"
      },
      "source": [
        "list_positions_missing_values"
      ],
      "execution_count": 742,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[495, 42],\n",
              " [331, 5],\n",
              " [516, 4],\n",
              " [293, 53],\n",
              " [150, 7],\n",
              " [168, 60],\n",
              " [220, 29],\n",
              " [397, 28],\n",
              " [388, 59],\n",
              " [438, 47],\n",
              " [313, 14],\n",
              " [173, 17],\n",
              " [321, 10],\n",
              " [108, 16],\n",
              " [290, 44],\n",
              " [92, 54],\n",
              " [100, 47],\n",
              " [229, 57],\n",
              " [365, 40],\n",
              " [65, 39],\n",
              " [31, 62],\n",
              " [90, 64],\n",
              " [504, 30],\n",
              " [442, 69],\n",
              " [297, 29],\n",
              " [495, 41],\n",
              " [259, 14],\n",
              " [139, 37],\n",
              " [172, 37],\n",
              " [345, 24],\n",
              " [490, 54],\n",
              " [418, 52],\n",
              " [524, 66],\n",
              " [490, 19],\n",
              " [348, 55],\n",
              " [341, 12],\n",
              " [520, 55],\n",
              " [253, 37],\n",
              " [153, 1],\n",
              " [124, 18],\n",
              " [313, 8],\n",
              " [96, 15],\n",
              " [361, 49],\n",
              " [426, 13],\n",
              " [247, 1],\n",
              " [173, 8],\n",
              " [110, 35],\n",
              " [41, 62],\n",
              " [32, 34],\n",
              " [257, 69],\n",
              " [175, 32],\n",
              " [507, 4],\n",
              " [237, 23],\n",
              " [370, 25],\n",
              " [357, 57],\n",
              " [201, 4],\n",
              " [378, 21],\n",
              " [117, 14],\n",
              " [245, 37],\n",
              " [483, 55],\n",
              " [197, 2],\n",
              " [94, 18],\n",
              " [323, 51],\n",
              " [303, 31],\n",
              " [352, 20],\n",
              " [474, 62],\n",
              " [395, 54],\n",
              " [342, 22],\n",
              " [91, 36],\n",
              " [213, 43],\n",
              " [165, 55],\n",
              " [180, 44],\n",
              " [211, 6],\n",
              " [180, 60],\n",
              " [200, 34],\n",
              " [277, 45],\n",
              " [24, 58],\n",
              " [391, 35],\n",
              " [314, 4],\n",
              " [89, 22],\n",
              " [83, 6],\n",
              " [61, 32],\n",
              " [479, 63],\n",
              " [394, 64],\n",
              " [188, 57],\n",
              " [331, 27],\n",
              " [372, 65],\n",
              " [323, 26],\n",
              " [383, 53],\n",
              " [64, 28],\n",
              " [483, 18],\n",
              " [529, 63],\n",
              " [234, 22],\n",
              " [465, 64],\n",
              " [195, 11],\n",
              " [69, 60],\n",
              " [231, 54],\n",
              " [27, 62],\n",
              " [369, 35],\n",
              " [121, 6],\n",
              " [163, 1],\n",
              " [400, 13],\n",
              " [195, 11],\n",
              " [392, 43],\n",
              " [236, 5],\n",
              " [278, 56],\n",
              " [391, 7],\n",
              " [419, 60],\n",
              " [192, 59],\n",
              " [169, 26],\n",
              " [293, 55],\n",
              " [95, 24],\n",
              " [10, 38],\n",
              " [468, 4],\n",
              " [414, 62],\n",
              " [510, 3],\n",
              " [31, 69],\n",
              " [183, 18],\n",
              " [473, 71],\n",
              " [175, 7],\n",
              " [510, 5],\n",
              " [23, 70],\n",
              " [323, 1],\n",
              " [352, 64],\n",
              " [269, 27],\n",
              " [438, 70],\n",
              " [331, 23],\n",
              " [240, 67],\n",
              " [208, 44],\n",
              " [359, 67],\n",
              " [513, 42],\n",
              " [178, 60],\n",
              " [116, 10],\n",
              " [141, 53],\n",
              " [145, 62],\n",
              " [70, 38],\n",
              " [273, 21],\n",
              " [344, 11],\n",
              " [54, 21],\n",
              " [150, 27],\n",
              " [119, 48],\n",
              " [255, 66],\n",
              " [291, 5],\n",
              " [279, 43],\n",
              " [514, 64],\n",
              " [341, 56],\n",
              " [491, 53],\n",
              " [521, 70],\n",
              " [230, 54],\n",
              " [428, 39],\n",
              " [99, 35],\n",
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              " [378, 49],\n",
              " [373, 57],\n",
              " ...]"
            ]
          },
          "metadata": {},
          "execution_count": 742
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "WhTjlES08e00",
        "outputId": "5de49f42-a835-492c-e1e9-8a31cb68b07d"
      },
      "source": [
        "df"
      ],
      "execution_count": 743,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>0.812429</td>\n",
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              "      <td>0.039614</td>\n",
              "      <td>5.762173</td>\n",
              "      <td>1.636819</td>\n",
              "      <td>1.170034</td>\n",
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              "      <td>3.183163</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.163038</td>\n",
              "      <td>0.620251</td>\n",
              "      <td>0.458514</td>\n",
              "      <td>0.041426</td>\n",
              "      <td>0.308287</td>\n",
              "      <td>0.538152</td>\n",
              "      <td>0.594871</td>\n",
              "      <td>0.734332</td>\n",
              "      <td>0.415489</td>\n",
              "      <td>0.761508</td>\n",
              "      <td>0.066288</td>\n",
              "      <td>0.262370</td>\n",
              "      <td>0.034438</td>\n",
              "      <td>3.480874</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.943803</td>\n",
              "      <td>0.804444</td>\n",
              "      <td>0.511229</td>\n",
              "      <td>0.498670</td>\n",
              "      <td>0.523039</td>\n",
              "      <td>0.480916</td>\n",
              "      <td>0.488657</td>\n",
              "      <td>0.483166</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.415994</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.013621</td>\n",
              "      <td>0.073041</td>\n",
              "      <td>0.010094</td>\n",
              "      <td>1.243981</td>\n",
              "      <td>0.829790</td>\n",
              "      <td>0.252972</td>\n",
              "      <td>0.347831</td>\n",
              "      <td>0.205087</td>\n",
              "      <td>0.168601</td>\n",
              "      <td>0.178009</td>\n",
              "      <td>0.144080</td>\n",
              "      <td>0.178703</td>\n",
              "      <td>0.146937</td>\n",
              "      <td>0.125580</td>\n",
              "      <td>0.128202</td>\n",
              "      <td>0.107007</td>\n",
              "      <td>0.020028</td>\n",
              "      <td>0.066940</td>\n",
              "      <td>0.029483</td>\n",
              "      <td>3.963534</td>\n",
              "      <td>0.382360</td>\n",
              "      <td>0.168389</td>\n",
              "      <td>0.117525</td>\n",
              "      <td>0.098341</td>\n",
              "      <td>0.087046</td>\n",
              "      <td>0.057991</td>\n",
              "      <td>0.059393</td>\n",
              "      <td>0.059457</td>\n",
              "      <td>0.053439</td>\n",
              "      <td>0.067684</td>\n",
              "      <td>0.070075</td>\n",
              "      <td>0.041565</td>\n",
              "      <td>0.295031</td>\n",
              "      <td>70.0</td>\n",
              "      <td>0.276366</td>\n",
              "      <td>140.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.343547</td>\n",
              "      <td>0.276366</td>\n",
              "      <td>0.710924</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.110545</td>\n",
              "      <td>0.273567</td>\n",
              "      <td>0.084410</td>\n",
              "      <td>-65.235325</td>\n",
              "      <td>2.794964</td>\n",
              "      <td>0.639047</td>\n",
              "      <td>1.281297</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.489412</td>\n",
              "      <td>0.627636</td>\n",
              "      <td>0.469322</td>\n",
              "      <td>0.644336</td>\n",
              "      <td>0.441556</td>\n",
              "      <td>0.335964</td>\n",
              "      <td>0.290713</td>\n",
              "      <td>0.158538</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.215373</td>\n",
              "      <td>0.035970</td>\n",
              "      <td>4.834742</td>\n",
              "      <td>1.213443</td>\n",
              "      <td>0.864034</td>\n",
              "      <td>0.909222</td>\n",
              "      <td>0.780572</td>\n",
              "      <td>0.550833</td>\n",
              "      <td>0.639740</td>\n",
              "      <td>0.573309</td>\n",
              "      <td>0.526312</td>\n",
              "      <td>0.562622</td>\n",
              "      <td>0.538407</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.455562</td>\n",
              "      <td>0.029112</td>\n",
              "      <td>0.070433</td>\n",
              "      <td>0.008525</td>\n",
              "      <td>2.759906</td>\n",
              "      <td>0.592634</td>\n",
              "      <td>0.761852</td>\n",
              "      <td>0.568740</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.281181</td>\n",
              "      <td>0.437752</td>\n",
              "      <td>0.479889</td>\n",
              "      <td>0.227320</td>\n",
              "      <td>0.296224</td>\n",
              "      <td>0.273855</td>\n",
              "      <td>0.191804</td>\n",
              "      <td>0.198025</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.065427</td>\n",
              "      <td>0.029622</td>\n",
              "      <td>3.371796</td>\n",
              "      <td>0.430373</td>\n",
              "      <td>0.172862</td>\n",
              "      <td>0.177523</td>\n",
              "      <td>0.184333</td>\n",
              "      <td>0.095718</td>\n",
              "      <td>0.139323</td>\n",
              "      <td>0.109279</td>\n",
              "      <td>0.090650</td>\n",
              "      <td>0.117886</td>\n",
              "      <td>0.100852</td>\n",
              "      <td>0.079917</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.161574</td>\n",
              "      <td>61.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>183.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.188693</td>\n",
              "      <td>0.045941</td>\n",
              "      <td>0.457372</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.042481</td>\n",
              "      <td>0.199281</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-80.305152</td>\n",
              "      <td>5.824409</td>\n",
              "      <td>0.648848</td>\n",
              "      <td>1.754870</td>\n",
              "      <td>1.495532</td>\n",
              "      <td>0.739909</td>\n",
              "      <td>0.809644</td>\n",
              "      <td>0.460945</td>\n",
              "      <td>0.409566</td>\n",
              "      <td>0.680122</td>\n",
              "      <td>0.590405</td>\n",
              "      <td>0.481380</td>\n",
              "      <td>0.621956</td>\n",
              "      <td>0.049939</td>\n",
              "      <td>0.281616</td>\n",
              "      <td>0.044727</td>\n",
              "      <td>6.719538</td>\n",
              "      <td>1.377811</td>\n",
              "      <td>1.265771</td>\n",
              "      <td>0.986178</td>\n",
              "      <td>0.710955</td>\n",
              "      <td>0.706904</td>\n",
              "      <td>0.710147</td>\n",
              "      <td>0.688825</td>\n",
              "      <td>0.699573</td>\n",
              "      <td>0.577976</td>\n",
              "      <td>0.533882</td>\n",
              "      <td>0.501818</td>\n",
              "      <td>0.495368</td>\n",
              "      <td>0.020749</td>\n",
              "      <td>0.106318</td>\n",
              "      <td>0.009108</td>\n",
              "      <td>3.992357</td>\n",
              "      <td>0.656429</td>\n",
              "      <td>0.927692</td>\n",
              "      <td>0.569916</td>\n",
              "      <td>0.378919</td>\n",
              "      <td>0.530714</td>\n",
              "      <td>0.317807</td>\n",
              "      <td>0.308447</td>\n",
              "      <td>0.324934</td>\n",
              "      <td>0.263444</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.274257</td>\n",
              "      <td>0.233287</td>\n",
              "      <td>0.032678</td>\n",
              "      <td>0.119480</td>\n",
              "      <td>0.028707</td>\n",
              "      <td>4.125111</td>\n",
              "      <td>0.461304</td>\n",
              "      <td>0.280751</td>\n",
              "      <td>0.246108</td>\n",
              "      <td>0.142805</td>\n",
              "      <td>0.183657</td>\n",
              "      <td>0.124399</td>\n",
              "      <td>0.155513</td>\n",
              "      <td>0.167114</td>\n",
              "      <td>0.113774</td>\n",
              "      <td>0.112815</td>\n",
              "      <td>0.129145</td>\n",
              "      <td>0.122330</td>\n",
              "      <td>0.043012</td>\n",
              "      <td>66.0</td>\n",
              "      <td>0.206562</td>\n",
              "      <td>132.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.102839</td>\n",
              "      <td>0.241934</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.074550</td>\n",
              "      <td>0.140880</td>\n",
              "      <td>0.079789</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.543229</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.899152</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.702328</td>\n",
              "      <td>0.490685</td>\n",
              "      <td>0.796904</td>\n",
              "      <td>0.745373</td>\n",
              "      <td>0.911234</td>\n",
              "      <td>0.594429</td>\n",
              "      <td>0.454186</td>\n",
              "      <td>0.384836</td>\n",
              "      <td>0.035751</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.029413</td>\n",
              "      <td>4.755293</td>\n",
              "      <td>1.116290</td>\n",
              "      <td>0.926772</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.639660</td>\n",
              "      <td>0.552653</td>\n",
              "      <td>0.527708</td>\n",
              "      <td>0.584705</td>\n",
              "      <td>0.696173</td>\n",
              "      <td>0.648611</td>\n",
              "      <td>0.689096</td>\n",
              "      <td>0.643595</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.136984</td>\n",
              "      <td>0.010356</td>\n",
              "      <td>7.713140</td>\n",
              "      <td>1.592642</td>\n",
              "      <td>1.027190</td>\n",
              "      <td>0.591399</td>\n",
              "      <td>0.565654</td>\n",
              "      <td>0.524420</td>\n",
              "      <td>0.554501</td>\n",
              "      <td>0.606200</td>\n",
              "      <td>0.616760</td>\n",
              "      <td>0.596926</td>\n",
              "      <td>0.524291</td>\n",
              "      <td>0.637971</td>\n",
              "      <td>0.637960</td>\n",
              "      <td>0.036151</td>\n",
              "      <td>0.087741</td>\n",
              "      <td>0.030180</td>\n",
              "      <td>5.085385</td>\n",
              "      <td>0.551937</td>\n",
              "      <td>0.257562</td>\n",
              "      <td>0.159950</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.150907</td>\n",
              "      <td>0.142092</td>\n",
              "      <td>0.222804</td>\n",
              "      <td>0.329188</td>\n",
              "      <td>0.251668</td>\n",
              "      <td>0.265049</td>\n",
              "      <td>0.284196</td>\n",
              "      <td>0.189988</td>\n",
              "      <td>0.029308</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.144039</td>\n",
              "      <td>200.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.310801</td>\n",
              "      <td>0.683817</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>525</th>\n",
              "      <td>0.027142</td>\n",
              "      <td>0.047551</td>\n",
              "      <td>0.072043</td>\n",
              "      <td>-79.881347</td>\n",
              "      <td>8.119313</td>\n",
              "      <td>1.927310</td>\n",
              "      <td>1.696017</td>\n",
              "      <td>0.397888</td>\n",
              "      <td>0.857559</td>\n",
              "      <td>0.302742</td>\n",
              "      <td>0.672649</td>\n",
              "      <td>0.748163</td>\n",
              "      <td>0.412952</td>\n",
              "      <td>0.852625</td>\n",
              "      <td>0.597127</td>\n",
              "      <td>0.456126</td>\n",
              "      <td>0.013099</td>\n",
              "      <td>0.029163</td>\n",
              "      <td>0.030049</td>\n",
              "      <td>3.076905</td>\n",
              "      <td>0.862406</td>\n",
              "      <td>0.640101</td>\n",
              "      <td>0.615202</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.514684</td>\n",
              "      <td>0.577850</td>\n",
              "      <td>0.600797</td>\n",
              "      <td>0.544352</td>\n",
              "      <td>0.540142</td>\n",
              "      <td>0.553717</td>\n",
              "      <td>0.493653</td>\n",
              "      <td>0.451593</td>\n",
              "      <td>0.012848</td>\n",
              "      <td>0.044107</td>\n",
              "      <td>0.008805</td>\n",
              "      <td>2.560837</td>\n",
              "      <td>1.008119</td>\n",
              "      <td>0.523135</td>\n",
              "      <td>0.802130</td>\n",
              "      <td>0.694118</td>\n",
              "      <td>0.412457</td>\n",
              "      <td>0.461475</td>\n",
              "      <td>0.500786</td>\n",
              "      <td>0.492573</td>\n",
              "      <td>0.454346</td>\n",
              "      <td>0.437815</td>\n",
              "      <td>0.345676</td>\n",
              "      <td>0.366906</td>\n",
              "      <td>0.028604</td>\n",
              "      <td>0.069883</td>\n",
              "      <td>0.030413</td>\n",
              "      <td>5.042254</td>\n",
              "      <td>0.455528</td>\n",
              "      <td>0.177015</td>\n",
              "      <td>0.165939</td>\n",
              "      <td>0.124718</td>\n",
              "      <td>0.118118</td>\n",
              "      <td>0.194543</td>\n",
              "      <td>0.146845</td>\n",
              "      <td>0.139167</td>\n",
              "      <td>0.161785</td>\n",
              "      <td>0.159234</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.106260</td>\n",
              "      <td>0.089991</td>\n",
              "      <td>87.0</td>\n",
              "      <td>0.564842</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.261742</td>\n",
              "      <td>0.002657</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>526</th>\n",
              "      <td>0.094829</td>\n",
              "      <td>0.204498</td>\n",
              "      <td>0.082824</td>\n",
              "      <td>-61.364436</td>\n",
              "      <td>2.966229</td>\n",
              "      <td>0.627740</td>\n",
              "      <td>1.440352</td>\n",
              "      <td>0.856243</td>\n",
              "      <td>1.110282</td>\n",
              "      <td>0.394450</td>\n",
              "      <td>0.726323</td>\n",
              "      <td>0.885339</td>\n",
              "      <td>0.517613</td>\n",
              "      <td>-0.186371</td>\n",
              "      <td>0.531083</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.052306</td>\n",
              "      <td>0.131783</td>\n",
              "      <td>0.029793</td>\n",
              "      <td>3.512784</td>\n",
              "      <td>0.918634</td>\n",
              "      <td>1.043679</td>\n",
              "      <td>0.778114</td>\n",
              "      <td>0.613929</td>\n",
              "      <td>0.598554</td>\n",
              "      <td>0.566087</td>\n",
              "      <td>0.568431</td>\n",
              "      <td>0.541677</td>\n",
              "      <td>0.552420</td>\n",
              "      <td>0.546411</td>\n",
              "      <td>0.580214</td>\n",
              "      <td>0.608745</td>\n",
              "      <td>0.021612</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.007370</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.529261</td>\n",
              "      <td>0.388876</td>\n",
              "      <td>0.434100</td>\n",
              "      <td>0.318737</td>\n",
              "      <td>0.265518</td>\n",
              "      <td>0.210297</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.247443</td>\n",
              "      <td>0.228670</td>\n",
              "      <td>0.249573</td>\n",
              "      <td>0.221631</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.047186</td>\n",
              "      <td>0.030264</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.362876</td>\n",
              "      <td>0.210280</td>\n",
              "      <td>0.213201</td>\n",
              "      <td>0.114466</td>\n",
              "      <td>0.158569</td>\n",
              "      <td>0.100762</td>\n",
              "      <td>0.115185</td>\n",
              "      <td>0.092511</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.108324</td>\n",
              "      <td>0.128090</td>\n",
              "      <td>0.167160</td>\n",
              "      <td>0.035827</td>\n",
              "      <td>65.0</td>\n",
              "      <td>0.003942</td>\n",
              "      <td>130.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.282122</td>\n",
              "      <td>0.052218</td>\n",
              "      <td>0.335371</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>527</th>\n",
              "      <td>0.035169</td>\n",
              "      <td>0.065403</td>\n",
              "      <td>0.075227</td>\n",
              "      <td>-81.750533</td>\n",
              "      <td>10.311701</td>\n",
              "      <td>0.092224</td>\n",
              "      <td>0.818851</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.831909</td>\n",
              "      <td>0.057216</td>\n",
              "      <td>0.419342</td>\n",
              "      <td>0.482342</td>\n",
              "      <td>0.745151</td>\n",
              "      <td>0.403275</td>\n",
              "      <td>0.838195</td>\n",
              "      <td>0.711755</td>\n",
              "      <td>0.013232</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.029861</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.911620</td>\n",
              "      <td>1.057301</td>\n",
              "      <td>0.496378</td>\n",
              "      <td>0.666827</td>\n",
              "      <td>0.556064</td>\n",
              "      <td>0.465213</td>\n",
              "      <td>0.499597</td>\n",
              "      <td>0.422874</td>\n",
              "      <td>0.516194</td>\n",
              "      <td>0.375284</td>\n",
              "      <td>0.473949</td>\n",
              "      <td>0.386111</td>\n",
              "      <td>0.016030</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.009759</td>\n",
              "      <td>5.776610</td>\n",
              "      <td>1.123743</td>\n",
              "      <td>0.830796</td>\n",
              "      <td>0.339320</td>\n",
              "      <td>1.003937</td>\n",
              "      <td>0.441073</td>\n",
              "      <td>0.563689</td>\n",
              "      <td>0.474611</td>\n",
              "      <td>0.358808</td>\n",
              "      <td>0.543528</td>\n",
              "      <td>0.340304</td>\n",
              "      <td>0.396751</td>\n",
              "      <td>0.496262</td>\n",
              "      <td>0.027062</td>\n",
              "      <td>0.053435</td>\n",
              "      <td>0.030620</td>\n",
              "      <td>4.801035</td>\n",
              "      <td>0.632917</td>\n",
              "      <td>0.351023</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.348863</td>\n",
              "      <td>0.161952</td>\n",
              "      <td>0.129868</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.118185</td>\n",
              "      <td>0.120515</td>\n",
              "      <td>0.083030</td>\n",
              "      <td>0.147194</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.033681</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>176.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.184313</td>\n",
              "      <td>0.247136</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>528</th>\n",
              "      <td>0.054276</td>\n",
              "      <td>0.238158</td>\n",
              "      <td>0.095935</td>\n",
              "      <td>-71.009724</td>\n",
              "      <td>3.181340</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.618838</td>\n",
              "      <td>0.997950</td>\n",
              "      <td>0.825143</td>\n",
              "      <td>0.758816</td>\n",
              "      <td>0.727540</td>\n",
              "      <td>0.500074</td>\n",
              "      <td>0.402084</td>\n",
              "      <td>0.539242</td>\n",
              "      <td>0.326410</td>\n",
              "      <td>0.052974</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.047596</td>\n",
              "      <td>6.714642</td>\n",
              "      <td>1.600006</td>\n",
              "      <td>1.309167</td>\n",
              "      <td>0.878546</td>\n",
              "      <td>0.712208</td>\n",
              "      <td>0.595806</td>\n",
              "      <td>0.516834</td>\n",
              "      <td>0.578153</td>\n",
              "      <td>0.519924</td>\n",
              "      <td>0.483610</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.470226</td>\n",
              "      <td>0.415839</td>\n",
              "      <td>0.016953</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.008863</td>\n",
              "      <td>3.245796</td>\n",
              "      <td>0.754218</td>\n",
              "      <td>0.787304</td>\n",
              "      <td>0.442779</td>\n",
              "      <td>0.310832</td>\n",
              "      <td>0.323179</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.257608</td>\n",
              "      <td>0.222008</td>\n",
              "      <td>0.232946</td>\n",
              "      <td>0.233224</td>\n",
              "      <td>0.232351</td>\n",
              "      <td>0.163934</td>\n",
              "      <td>0.026424</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.028770</td>\n",
              "      <td>4.094957</td>\n",
              "      <td>0.584713</td>\n",
              "      <td>0.312613</td>\n",
              "      <td>0.214629</td>\n",
              "      <td>0.169042</td>\n",
              "      <td>0.115469</td>\n",
              "      <td>0.111847</td>\n",
              "      <td>0.112750</td>\n",
              "      <td>0.090504</td>\n",
              "      <td>0.091698</td>\n",
              "      <td>0.090272</td>\n",
              "      <td>0.080006</td>\n",
              "      <td>0.058181</td>\n",
              "      <td>NaN</td>\n",
              "      <td>84.0</td>\n",
              "      <td>0.276695</td>\n",
              "      <td>168.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.547126</td>\n",
              "      <td>0.183494</td>\n",
              "      <td>1.255820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>529</th>\n",
              "      <td>0.073194</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.080545</td>\n",
              "      <td>-74.517081</td>\n",
              "      <td>6.945590</td>\n",
              "      <td>-1.047953</td>\n",
              "      <td>1.952278</td>\n",
              "      <td>0.341936</td>\n",
              "      <td>0.770582</td>\n",
              "      <td>0.144989</td>\n",
              "      <td>0.405832</td>\n",
              "      <td>0.186847</td>\n",
              "      <td>0.496161</td>\n",
              "      <td>0.389096</td>\n",
              "      <td>0.324653</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.020042</td>\n",
              "      <td>0.043408</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.012740</td>\n",
              "      <td>0.804194</td>\n",
              "      <td>0.524353</td>\n",
              "      <td>0.508190</td>\n",
              "      <td>0.485743</td>\n",
              "      <td>0.518681</td>\n",
              "      <td>0.481357</td>\n",
              "      <td>0.482828</td>\n",
              "      <td>0.514857</td>\n",
              "      <td>0.508133</td>\n",
              "      <td>0.516179</td>\n",
              "      <td>0.474873</td>\n",
              "      <td>0.447746</td>\n",
              "      <td>0.015537</td>\n",
              "      <td>0.034044</td>\n",
              "      <td>0.007524</td>\n",
              "      <td>5.018377</td>\n",
              "      <td>0.852230</td>\n",
              "      <td>0.410106</td>\n",
              "      <td>0.275035</td>\n",
              "      <td>0.300508</td>\n",
              "      <td>0.248464</td>\n",
              "      <td>0.362048</td>\n",
              "      <td>0.491118</td>\n",
              "      <td>0.446384</td>\n",
              "      <td>0.482079</td>\n",
              "      <td>0.438563</td>\n",
              "      <td>0.356211</td>\n",
              "      <td>0.254899</td>\n",
              "      <td>0.024653</td>\n",
              "      <td>0.052302</td>\n",
              "      <td>0.030290</td>\n",
              "      <td>4.244648</td>\n",
              "      <td>0.350933</td>\n",
              "      <td>0.149544</td>\n",
              "      <td>0.122719</td>\n",
              "      <td>0.063955</td>\n",
              "      <td>0.078072</td>\n",
              "      <td>0.068832</td>\n",
              "      <td>0.081025</td>\n",
              "      <td>0.139319</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.114416</td>\n",
              "      <td>0.073605</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>94.0</td>\n",
              "      <td>0.007080</td>\n",
              "      <td>188.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.087328</td>\n",
              "      <td>0.236815</td>\n",
              "      <td>0.451701</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>530 rows × 72 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           0         1         2          3   ...   68        69        70        71\n",
              "0    0.034741  0.089665  0.091225 -73.302422  ...  2.0  0.245457  0.105065  0.405399\n",
              "1    0.081374       NaN  0.085733 -62.584437  ...  2.0  0.343547  0.276366  0.710924\n",
              "2    0.110545  0.273567  0.084410 -65.235325  ...  3.0  0.188693  0.045941  0.457372\n",
              "3    0.042481  0.199281       NaN -80.305152  ...  NaN  0.102839  0.241934       NaN\n",
              "4    0.074550  0.140880  0.079789        NaN  ...  2.0       NaN  0.310801  0.683817\n",
              "..        ...       ...       ...        ...  ...  ...       ...       ...       ...\n",
              "525  0.027142  0.047551  0.072043 -79.881347  ...  2.0  0.261742  0.002657       NaN\n",
              "526  0.094829  0.204498  0.082824 -61.364436  ...  2.0  0.282122  0.052218  0.335371\n",
              "527  0.035169  0.065403  0.075227 -81.750533  ...  2.0  0.184313  0.247136       NaN\n",
              "528  0.054276  0.238158  0.095935 -71.009724  ...  2.0  0.547126  0.183494  1.255820\n",
              "529  0.073194       NaN  0.080545 -74.517081  ...  2.0  0.087328  0.236815  0.451701\n",
              "\n",
              "[530 rows x 72 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 743
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V1a-5SqH8o-f",
        "outputId": "5c8b7b68-b66d-44ca-88ed-968d88336f15"
      },
      "source": [
        "total_de_indices = (df.isnull().sum()).sum()\n",
        "a = len(list_positions_missing_values) - total_de_indices\n",
        "total_de_indices,a"
      ],
      "execution_count": 744,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3631, 185)"
            ]
          },
          "metadata": {},
          "execution_count": 744
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BzarULf3g7JE"
      },
      "source": [
        "#indices dos missing values\n",
        "#indexList[0]\n",
        "#for i in indexList:\n",
        "#  df.iloc[indexList[0][0],indexList[0][1]] = np.nan"
      ],
      "execution_count": 745,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WwFZdRyx7Cop"
      },
      "source": [
        "#df com missiing values\n",
        "df\n",
        "df_amp = df.copy()"
      ],
      "execution_count": 746,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "id": "tXZXkvNE8SD-",
        "outputId": "234ec0cd-ed28-4553-9b98-a6cbad6e4d24"
      },
      "source": [
        "#visualizando missing values\n",
        "import missingno as msno\n",
        "msno.matrix(df)"
      ],
      "execution_count": 747,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8c82ccced0>"
            ]
          },
          "metadata": {},
          "execution_count": 747
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 1800x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "CrlNN_CS89IZ",
        "outputId": "5a0c7063-b3a3-4187-ee85-aec6b3c47b05"
      },
      "source": [
        "msno.bar(df)"
      ],
      "execution_count": 748,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8c831b6a90>"
            ]
          },
          "metadata": {},
          "execution_count": 748
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x1692 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GnhXBOpE9l7D",
        "outputId": "a72bcec1-ed42-4f12-fabf-43ec9554676e"
      },
      "source": [
        "missing_values = (df.isnull().sum()).sum()\n",
        "missing_values"
      ],
      "execution_count": 749,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3631"
            ]
          },
          "metadata": {},
          "execution_count": 749
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lEUwlKyGRMNE"
      },
      "source": [
        "missing_values = df.isnull().sum()"
      ],
      "execution_count": 750,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 227
        },
        "id": "Yovav_L--cfg",
        "outputId": "fddb1437-22a2-4d82-8eb4-509698ddcac0"
      },
      "source": [
        "import matplotlib.pyplot as plt \n",
        "plt.figure(figsize=(20,3))\n",
        "plt.plot(missing_values,marker='o')\n",
        "\n",
        "\n",
        "plt.title(\"Distribuição de Missing Values por features\")\n",
        "plt.show()"
      ],
      "execution_count": 751,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cOTTWvTOS74c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "outputId": "c4cc9e4a-4655-4dbf-dbaf-954fec94df7e"
      },
      "source": [
        "df.describe()\n"
      ],
      "execution_count": 752,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "      <th>13</th>\n",
              "      <th>14</th>\n",
              "      <th>15</th>\n",
              "      <th>16</th>\n",
              "      <th>17</th>\n",
              "      <th>18</th>\n",
              "      <th>19</th>\n",
              "      <th>20</th>\n",
              "      <th>21</th>\n",
              "      <th>22</th>\n",
              "      <th>23</th>\n",
              "      <th>24</th>\n",
              "      <th>25</th>\n",
              "      <th>26</th>\n",
              "      <th>27</th>\n",
              "      <th>28</th>\n",
              "      <th>29</th>\n",
              "      <th>30</th>\n",
              "      <th>31</th>\n",
              "      <th>32</th>\n",
              "      <th>33</th>\n",
              "      <th>34</th>\n",
              "      <th>35</th>\n",
              "      <th>36</th>\n",
              "      <th>37</th>\n",
              "      <th>38</th>\n",
              "      <th>39</th>\n",
              "      <th>40</th>\n",
              "      <th>41</th>\n",
              "      <th>42</th>\n",
              "      <th>43</th>\n",
              "      <th>44</th>\n",
              "      <th>45</th>\n",
              "      <th>46</th>\n",
              "      <th>47</th>\n",
              "      <th>48</th>\n",
              "      <th>49</th>\n",
              "      <th>50</th>\n",
              "      <th>51</th>\n",
              "      <th>52</th>\n",
              "      <th>53</th>\n",
              "      <th>54</th>\n",
              "      <th>55</th>\n",
              "      <th>56</th>\n",
              "      <th>57</th>\n",
              "      <th>58</th>\n",
              "      <th>59</th>\n",
              "      <th>60</th>\n",
              "      <th>61</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>530.000000</td>\n",
              "      <td>481.000000</td>\n",
              "      <td>476.000000</td>\n",
              "      <td>473.000000</td>\n",
              "      <td>485.000000</td>\n",
              "      <td>466.000000</td>\n",
              "      <td>485.000000</td>\n",
              "      <td>475.000000</td>\n",
              "      <td>475.000000</td>\n",
              "      <td>478.000000</td>\n",
              "      <td>484.000000</td>\n",
              "      <td>486.000000</td>\n",
              "      <td>477.000000</td>\n",
              "      <td>482.000000</td>\n",
              "      <td>473.000000</td>\n",
              "      <td>481.000000</td>\n",
              "      <td>460.000000</td>\n",
              "      <td>474.000000</td>\n",
              "      <td>472.000000</td>\n",
              "      <td>475.000000</td>\n",
              "      <td>480.000000</td>\n",
              "      <td>481.000000</td>\n",
              "      <td>471.000000</td>\n",
              "      <td>472.000000</td>\n",
              "      <td>480.000000</td>\n",
              "      <td>467.000000</td>\n",
              "      <td>467.000000</td>\n",
              "      <td>481.000000</td>\n",
              "      <td>480.000000</td>\n",
              "      <td>497.000000</td>\n",
              "      <td>480.000000</td>\n",
              "      <td>479.000000</td>\n",
              "      <td>486.000000</td>\n",
              "      <td>472.000000</td>\n",
              "      <td>489.000000</td>\n",
              "      <td>470.000000</td>\n",
              "      <td>476.000000</td>\n",
              "      <td>483.000000</td>\n",
              "      <td>494.000000</td>\n",
              "      <td>480.000000</td>\n",
              "      <td>492.000000</td>\n",
              "      <td>476.000000</td>\n",
              "      <td>477.000000</td>\n",
              "      <td>468.000000</td>\n",
              "      <td>473.000000</td>\n",
              "      <td>490.000000</td>\n",
              "      <td>490.000000</td>\n",
              "      <td>479.000000</td>\n",
              "      <td>478.000000</td>\n",
              "      <td>483.000000</td>\n",
              "      <td>485.000000</td>\n",
              "      <td>476.000000</td>\n",
              "      <td>481.000000</td>\n",
              "      <td>493.000000</td>\n",
              "      <td>481.000000</td>\n",
              "      <td>474.000000</td>\n",
              "      <td>472.000000</td>\n",
              "      <td>476.000000</td>\n",
              "      <td>482.000000</td>\n",
              "      <td>480.000000</td>\n",
              "      <td>473.000000</td>\n",
              "      <td>480.000000</td>\n",
              "      <td>477.000000</td>\n",
              "      <td>481.000000</td>\n",
              "      <td>482.000000</td>\n",
              "      <td>483.000000</td>\n",
              "      <td>475.000000</td>\n",
              "      <td>484.000000</td>\n",
              "      <td>484.000000</td>\n",
              "      <td>476.000000</td>\n",
              "      <td>482.000000</td>\n",
              "      <td>473.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.069331</td>\n",
              "      <td>0.193865</td>\n",
              "      <td>0.085717</td>\n",
              "      <td>-72.653884</td>\n",
              "      <td>5.000805</td>\n",
              "      <td>0.596071</td>\n",
              "      <td>1.697905</td>\n",
              "      <td>0.628329</td>\n",
              "      <td>0.846181</td>\n",
              "      <td>0.506336</td>\n",
              "      <td>0.544054</td>\n",
              "      <td>0.474829</td>\n",
              "      <td>0.508855</td>\n",
              "      <td>0.479319</td>\n",
              "      <td>0.439522</td>\n",
              "      <td>0.445825</td>\n",
              "      <td>0.046178</td>\n",
              "      <td>0.155419</td>\n",
              "      <td>0.035681</td>\n",
              "      <td>4.578438</td>\n",
              "      <td>1.297795</td>\n",
              "      <td>0.941551</td>\n",
              "      <td>0.781965</td>\n",
              "      <td>0.654558</td>\n",
              "      <td>0.577359</td>\n",
              "      <td>0.553890</td>\n",
              "      <td>0.537962</td>\n",
              "      <td>0.519024</td>\n",
              "      <td>0.511752</td>\n",
              "      <td>0.493453</td>\n",
              "      <td>0.484711</td>\n",
              "      <td>0.468328</td>\n",
              "      <td>0.023898</td>\n",
              "      <td>0.062852</td>\n",
              "      <td>0.008115</td>\n",
              "      <td>3.625864</td>\n",
              "      <td>0.847085</td>\n",
              "      <td>0.615585</td>\n",
              "      <td>0.499472</td>\n",
              "      <td>0.389753</td>\n",
              "      <td>0.330383</td>\n",
              "      <td>0.314040</td>\n",
              "      <td>0.307161</td>\n",
              "      <td>0.305318</td>\n",
              "      <td>0.295662</td>\n",
              "      <td>0.287831</td>\n",
              "      <td>0.278814</td>\n",
              "      <td>0.268700</td>\n",
              "      <td>0.029013</td>\n",
              "      <td>0.066547</td>\n",
              "      <td>0.029499</td>\n",
              "      <td>4.150146</td>\n",
              "      <td>0.410170</td>\n",
              "      <td>0.239701</td>\n",
              "      <td>0.197832</td>\n",
              "      <td>0.151461</td>\n",
              "      <td>0.122944</td>\n",
              "      <td>0.116743</td>\n",
              "      <td>0.114431</td>\n",
              "      <td>0.112279</td>\n",
              "      <td>0.114497</td>\n",
              "      <td>0.109289</td>\n",
              "      <td>0.108883</td>\n",
              "      <td>0.106659</td>\n",
              "      <td>0.229826</td>\n",
              "      <td>73.807453</td>\n",
              "      <td>0.258382</td>\n",
              "      <td>154.966942</td>\n",
              "      <td>2.107438</td>\n",
              "      <td>0.406626</td>\n",
              "      <td>0.352028</td>\n",
              "      <td>0.968881</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.031252</td>\n",
              "      <td>0.115518</td>\n",
              "      <td>0.009420</td>\n",
              "      <td>7.418393</td>\n",
              "      <td>2.048481</td>\n",
              "      <td>1.012779</td>\n",
              "      <td>0.616570</td>\n",
              "      <td>0.462013</td>\n",
              "      <td>0.375820</td>\n",
              "      <td>0.300185</td>\n",
              "      <td>0.290726</td>\n",
              "      <td>0.282954</td>\n",
              "      <td>0.245754</td>\n",
              "      <td>0.218022</td>\n",
              "      <td>0.210174</td>\n",
              "      <td>0.188109</td>\n",
              "      <td>0.030557</td>\n",
              "      <td>0.106500</td>\n",
              "      <td>0.008719</td>\n",
              "      <td>1.589816</td>\n",
              "      <td>0.405926</td>\n",
              "      <td>0.270914</td>\n",
              "      <td>0.211219</td>\n",
              "      <td>0.153972</td>\n",
              "      <td>0.108366</td>\n",
              "      <td>0.092612</td>\n",
              "      <td>0.088742</td>\n",
              "      <td>0.083983</td>\n",
              "      <td>0.080130</td>\n",
              "      <td>0.072606</td>\n",
              "      <td>0.074132</td>\n",
              "      <td>0.074978</td>\n",
              "      <td>0.010776</td>\n",
              "      <td>0.030723</td>\n",
              "      <td>0.001527</td>\n",
              "      <td>1.810652</td>\n",
              "      <td>0.365725</td>\n",
              "      <td>0.245873</td>\n",
              "      <td>0.205063</td>\n",
              "      <td>0.154151</td>\n",
              "      <td>0.136965</td>\n",
              "      <td>0.138934</td>\n",
              "      <td>0.142263</td>\n",
              "      <td>0.153423</td>\n",
              "      <td>0.151229</td>\n",
              "      <td>0.162767</td>\n",
              "      <td>0.153566</td>\n",
              "      <td>0.160584</td>\n",
              "      <td>0.007609</td>\n",
              "      <td>0.024365</td>\n",
              "      <td>0.001496</td>\n",
              "      <td>0.518027</td>\n",
              "      <td>0.141394</td>\n",
              "      <td>0.087365</td>\n",
              "      <td>0.079649</td>\n",
              "      <td>0.061740</td>\n",
              "      <td>0.042196</td>\n",
              "      <td>0.037627</td>\n",
              "      <td>0.038061</td>\n",
              "      <td>0.041954</td>\n",
              "      <td>0.048677</td>\n",
              "      <td>0.048990</td>\n",
              "      <td>0.051732</td>\n",
              "      <td>0.058813</td>\n",
              "      <td>0.297139</td>\n",
              "      <td>14.491169</td>\n",
              "      <td>0.379711</td>\n",
              "      <td>29.939465</td>\n",
              "      <td>0.329418</td>\n",
              "      <td>0.338848</td>\n",
              "      <td>0.359564</td>\n",
              "      <td>0.673608</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>-0.092158</td>\n",
              "      <td>-0.444623</td>\n",
              "      <td>-0.895966</td>\n",
              "      <td>-0.451919</td>\n",
              "      <td>-0.294770</td>\n",
              "      <td>-0.258025</td>\n",
              "      <td>-0.275685</td>\n",
              "      <td>-0.079200</td>\n",
              "      <td>0.006918</td>\n",
              "      <td>0.017434</td>\n",
              "      <td>0.025297</td>\n",
              "      <td>1.665689</td>\n",
              "      <td>0.544490</td>\n",
              "      <td>0.426085</td>\n",
              "      <td>0.436355</td>\n",
              "      <td>0.375337</td>\n",
              "      <td>0.376085</td>\n",
              "      <td>0.329479</td>\n",
              "      <td>0.322254</td>\n",
              "      <td>0.327975</td>\n",
              "      <td>0.296855</td>\n",
              "      <td>0.297832</td>\n",
              "      <td>0.281625</td>\n",
              "      <td>0.273264</td>\n",
              "      <td>0.010684</td>\n",
              "      <td>0.018333</td>\n",
              "      <td>0.005696</td>\n",
              "      <td>1.103874</td>\n",
              "      <td>0.181559</td>\n",
              "      <td>0.107836</td>\n",
              "      <td>0.113009</td>\n",
              "      <td>0.057821</td>\n",
              "      <td>0.054569</td>\n",
              "      <td>0.050367</td>\n",
              "      <td>0.071779</td>\n",
              "      <td>0.064871</td>\n",
              "      <td>0.057608</td>\n",
              "      <td>0.048837</td>\n",
              "      <td>0.041973</td>\n",
              "      <td>0.033739</td>\n",
              "      <td>0.008712</td>\n",
              "      <td>0.015678</td>\n",
              "      <td>0.009090</td>\n",
              "      <td>1.908520</td>\n",
              "      <td>0.105014</td>\n",
              "      <td>0.053278</td>\n",
              "      <td>0.054015</td>\n",
              "      <td>0.034480</td>\n",
              "      <td>0.039299</td>\n",
              "      <td>0.040493</td>\n",
              "      <td>0.040339</td>\n",
              "      <td>0.032399</td>\n",
              "      <td>0.035843</td>\n",
              "      <td>0.038015</td>\n",
              "      <td>0.034151</td>\n",
              "      <td>0.035386</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>0.045049</td>\n",
              "      <td>0.107742</td>\n",
              "      <td>0.079229</td>\n",
              "      <td>-76.692269</td>\n",
              "      <td>3.392900</td>\n",
              "      <td>-0.096875</td>\n",
              "      <td>1.307242</td>\n",
              "      <td>0.361463</td>\n",
              "      <td>0.614016</td>\n",
              "      <td>0.310775</td>\n",
              "      <td>0.385353</td>\n",
              "      <td>0.300665</td>\n",
              "      <td>0.344916</td>\n",
              "      <td>0.357212</td>\n",
              "      <td>0.310364</td>\n",
              "      <td>0.317884</td>\n",
              "      <td>0.023070</td>\n",
              "      <td>0.064333</td>\n",
              "      <td>0.030048</td>\n",
              "      <td>3.480422</td>\n",
              "      <td>1.004512</td>\n",
              "      <td>0.724864</td>\n",
              "      <td>0.598569</td>\n",
              "      <td>0.534423</td>\n",
              "      <td>0.492443</td>\n",
              "      <td>0.484824</td>\n",
              "      <td>0.473830</td>\n",
              "      <td>0.455864</td>\n",
              "      <td>0.456184</td>\n",
              "      <td>0.441344</td>\n",
              "      <td>0.432277</td>\n",
              "      <td>0.420100</td>\n",
              "      <td>0.017650</td>\n",
              "      <td>0.042928</td>\n",
              "      <td>0.007283</td>\n",
              "      <td>2.374443</td>\n",
              "      <td>0.595501</td>\n",
              "      <td>0.423766</td>\n",
              "      <td>0.361630</td>\n",
              "      <td>0.285040</td>\n",
              "      <td>0.237651</td>\n",
              "      <td>0.222512</td>\n",
              "      <td>0.207068</td>\n",
              "      <td>0.201483</td>\n",
              "      <td>0.194795</td>\n",
              "      <td>0.185535</td>\n",
              "      <td>0.177662</td>\n",
              "      <td>0.173605</td>\n",
              "      <td>0.024826</td>\n",
              "      <td>0.051118</td>\n",
              "      <td>0.029032</td>\n",
              "      <td>3.757319</td>\n",
              "      <td>0.308454</td>\n",
              "      <td>0.172876</td>\n",
              "      <td>0.137808</td>\n",
              "      <td>0.104046</td>\n",
              "      <td>0.091674</td>\n",
              "      <td>0.089728</td>\n",
              "      <td>0.087831</td>\n",
              "      <td>0.082796</td>\n",
              "      <td>0.081774</td>\n",
              "      <td>0.077304</td>\n",
              "      <td>0.074954</td>\n",
              "      <td>0.071286</td>\n",
              "      <td>0.045029</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>0.007876</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.178948</td>\n",
              "      <td>0.100452</td>\n",
              "      <td>0.511409</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.065049</td>\n",
              "      <td>0.170118</td>\n",
              "      <td>0.083768</td>\n",
              "      <td>-72.291300</td>\n",
              "      <td>4.760137</td>\n",
              "      <td>0.588138</td>\n",
              "      <td>1.717564</td>\n",
              "      <td>0.618551</td>\n",
              "      <td>0.832635</td>\n",
              "      <td>0.532877</td>\n",
              "      <td>0.529148</td>\n",
              "      <td>0.466581</td>\n",
              "      <td>0.505536</td>\n",
              "      <td>0.488817</td>\n",
              "      <td>0.436872</td>\n",
              "      <td>0.456240</td>\n",
              "      <td>0.037650</td>\n",
              "      <td>0.125104</td>\n",
              "      <td>0.033861</td>\n",
              "      <td>4.422491</td>\n",
              "      <td>1.247477</td>\n",
              "      <td>0.929244</td>\n",
              "      <td>0.751568</td>\n",
              "      <td>0.629288</td>\n",
              "      <td>0.557805</td>\n",
              "      <td>0.544671</td>\n",
              "      <td>0.527033</td>\n",
              "      <td>0.513161</td>\n",
              "      <td>0.502568</td>\n",
              "      <td>0.485628</td>\n",
              "      <td>0.479063</td>\n",
              "      <td>0.462220</td>\n",
              "      <td>0.021460</td>\n",
              "      <td>0.054067</td>\n",
              "      <td>0.007818</td>\n",
              "      <td>3.242829</td>\n",
              "      <td>0.773714</td>\n",
              "      <td>0.577433</td>\n",
              "      <td>0.471390</td>\n",
              "      <td>0.373008</td>\n",
              "      <td>0.309626</td>\n",
              "      <td>0.288631</td>\n",
              "      <td>0.285753</td>\n",
              "      <td>0.281884</td>\n",
              "      <td>0.270912</td>\n",
              "      <td>0.249316</td>\n",
              "      <td>0.244247</td>\n",
              "      <td>0.232030</td>\n",
              "      <td>0.027286</td>\n",
              "      <td>0.062218</td>\n",
              "      <td>0.029800</td>\n",
              "      <td>4.069430</td>\n",
              "      <td>0.393556</td>\n",
              "      <td>0.233287</td>\n",
              "      <td>0.183771</td>\n",
              "      <td>0.140719</td>\n",
              "      <td>0.118517</td>\n",
              "      <td>0.113303</td>\n",
              "      <td>0.108785</td>\n",
              "      <td>0.105883</td>\n",
              "      <td>0.104325</td>\n",
              "      <td>0.100936</td>\n",
              "      <td>0.097060</td>\n",
              "      <td>0.089506</td>\n",
              "      <td>0.103814</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>0.080307</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.298304</td>\n",
              "      <td>0.241028</td>\n",
              "      <td>0.804229</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.086249</td>\n",
              "      <td>0.254369</td>\n",
              "      <td>0.091170</td>\n",
              "      <td>-68.058668</td>\n",
              "      <td>6.439446</td>\n",
              "      <td>1.289226</td>\n",
              "      <td>2.075306</td>\n",
              "      <td>0.913865</td>\n",
              "      <td>1.069396</td>\n",
              "      <td>0.701731</td>\n",
              "      <td>0.708811</td>\n",
              "      <td>0.649448</td>\n",
              "      <td>0.684912</td>\n",
              "      <td>0.605738</td>\n",
              "      <td>0.580951</td>\n",
              "      <td>0.572569</td>\n",
              "      <td>0.061584</td>\n",
              "      <td>0.240948</td>\n",
              "      <td>0.039051</td>\n",
              "      <td>5.512189</td>\n",
              "      <td>1.531684</td>\n",
              "      <td>1.124299</td>\n",
              "      <td>0.944250</td>\n",
              "      <td>0.763086</td>\n",
              "      <td>0.641158</td>\n",
              "      <td>0.613768</td>\n",
              "      <td>0.594712</td>\n",
              "      <td>0.571129</td>\n",
              "      <td>0.554329</td>\n",
              "      <td>0.538407</td>\n",
              "      <td>0.523782</td>\n",
              "      <td>0.502507</td>\n",
              "      <td>0.026974</td>\n",
              "      <td>0.072600</td>\n",
              "      <td>0.008638</td>\n",
              "      <td>4.439496</td>\n",
              "      <td>1.058685</td>\n",
              "      <td>0.765344</td>\n",
              "      <td>0.595613</td>\n",
              "      <td>0.479050</td>\n",
              "      <td>0.400827</td>\n",
              "      <td>0.372562</td>\n",
              "      <td>0.374248</td>\n",
              "      <td>0.361296</td>\n",
              "      <td>0.367167</td>\n",
              "      <td>0.338817</td>\n",
              "      <td>0.337416</td>\n",
              "      <td>0.324180</td>\n",
              "      <td>0.030868</td>\n",
              "      <td>0.077600</td>\n",
              "      <td>0.030255</td>\n",
              "      <td>4.459316</td>\n",
              "      <td>0.499161</td>\n",
              "      <td>0.292623</td>\n",
              "      <td>0.249224</td>\n",
              "      <td>0.186337</td>\n",
              "      <td>0.147371</td>\n",
              "      <td>0.143101</td>\n",
              "      <td>0.137226</td>\n",
              "      <td>0.133503</td>\n",
              "      <td>0.133610</td>\n",
              "      <td>0.127975</td>\n",
              "      <td>0.126250</td>\n",
              "      <td>0.121031</td>\n",
              "      <td>0.276100</td>\n",
              "      <td>84.000000</td>\n",
              "      <td>0.332635</td>\n",
              "      <td>180.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.507573</td>\n",
              "      <td>0.472322</td>\n",
              "      <td>1.261903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.195412</td>\n",
              "      <td>0.666776</td>\n",
              "      <td>0.159460</td>\n",
              "      <td>-56.297652</td>\n",
              "      <td>11.853471</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>1.722420</td>\n",
              "      <td>1.846221</td>\n",
              "      <td>1.064865</td>\n",
              "      <td>1.146151</td>\n",
              "      <td>1.107886</td>\n",
              "      <td>0.957591</td>\n",
              "      <td>0.168399</td>\n",
              "      <td>0.471795</td>\n",
              "      <td>0.116190</td>\n",
              "      <td>15.262381</td>\n",
              "      <td>3.664476</td>\n",
              "      <td>2.045210</td>\n",
              "      <td>1.373682</td>\n",
              "      <td>1.321038</td>\n",
              "      <td>1.016693</td>\n",
              "      <td>0.825064</td>\n",
              "      <td>0.829619</td>\n",
              "      <td>1.098111</td>\n",
              "      <td>0.842697</td>\n",
              "      <td>0.852488</td>\n",
              "      <td>0.772496</td>\n",
              "      <td>1.001184</td>\n",
              "      <td>0.117437</td>\n",
              "      <td>0.234718</td>\n",
              "      <td>0.022730</td>\n",
              "      <td>13.447154</td>\n",
              "      <td>2.925425</td>\n",
              "      <td>1.703221</td>\n",
              "      <td>1.281424</td>\n",
              "      <td>1.025277</td>\n",
              "      <td>1.038158</td>\n",
              "      <td>1.155625</td>\n",
              "      <td>1.334833</td>\n",
              "      <td>1.075426</td>\n",
              "      <td>1.169477</td>\n",
              "      <td>1.281840</td>\n",
              "      <td>1.160050</td>\n",
              "      <td>1.809666</td>\n",
              "      <td>0.063173</td>\n",
              "      <td>0.186134</td>\n",
              "      <td>0.031105</td>\n",
              "      <td>6.536928</td>\n",
              "      <td>1.046212</td>\n",
              "      <td>0.618704</td>\n",
              "      <td>0.543927</td>\n",
              "      <td>0.415798</td>\n",
              "      <td>0.271085</td>\n",
              "      <td>0.241432</td>\n",
              "      <td>0.304417</td>\n",
              "      <td>0.329188</td>\n",
              "      <td>0.296556</td>\n",
              "      <td>0.344459</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",
              "</div>"
            ],
            "text/plain": [
              "               0           1           2   ...          69          70          71\n",
              "count  530.000000  481.000000  476.000000  ...  476.000000  482.000000  473.000000\n",
              "mean     0.069331    0.193865    0.085717  ...    0.406626    0.352028    0.968881\n",
              "std      0.031252    0.115518    0.009420  ...    0.338848    0.359564    0.673608\n",
              "min      0.010201    0.038286    0.070932  ...    0.000000    0.000000    0.000000\n",
              "25%      0.045049    0.107742    0.079229  ...    0.178948    0.100452    0.511409\n",
              "50%      0.065049    0.170118    0.083768  ...    0.298304    0.241028    0.804229\n",
              "75%      0.086249    0.254369    0.091170  ...    0.507573    0.472322    1.261903\n",
              "max      0.195412    0.666776    0.159460  ...    1.795128    1.797765    3.422899\n",
              "\n",
              "[8 rows x 72 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 752
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 132
        },
        "id": "J0MTDI2WeTG_",
        "outputId": "f71e774d-f75e-44f5-d7b3-4c15df52efdd"
      },
      "source": [
        "df.head(2)"
      ],
      "execution_count": 753,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>36</th>\n",
              "      <th>37</th>\n",
              "      <th>38</th>\n",
              "      <th>39</th>\n",
              "      <th>40</th>\n",
              "      <th>41</th>\n",
              "      <th>42</th>\n",
              "      <th>43</th>\n",
              "      <th>44</th>\n",
              "      <th>45</th>\n",
              "      <th>46</th>\n",
              "      <th>47</th>\n",
              "      <th>48</th>\n",
              "      <th>49</th>\n",
              "      <th>50</th>\n",
              "      <th>51</th>\n",
              "      <th>52</th>\n",
              "      <th>53</th>\n",
              "      <th>54</th>\n",
              "      <th>55</th>\n",
              "      <th>56</th>\n",
              "      <th>57</th>\n",
              "      <th>58</th>\n",
              "      <th>59</th>\n",
              "      <th>60</th>\n",
              "      <th>61</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>0</th>\n",
              "      <td>0.034741</td>\n",
              "      <td>0.089665</td>\n",
              "      <td>0.091225</td>\n",
              "      <td>-73.302422</td>\n",
              "      <td>6.215179</td>\n",
              "      <td>0.615074</td>\n",
              "      <td>2.037160</td>\n",
              "      <td>0.804065</td>\n",
              "      <td>1.301409</td>\n",
              "      <td>0.558576</td>\n",
              "      <td>0.672063</td>\n",
              "      <td>0.783788</td>\n",
              "      <td>0.766640</td>\n",
              "      <td>0.458712</td>\n",
              "      <td>0.530384</td>\n",
              "      <td>0.812429</td>\n",
              "      <td>0.028851</td>\n",
              "      <td>0.129039</td>\n",
              "      <td>0.039614</td>\n",
              "      <td>5.762173</td>\n",
              "      <td>1.636819</td>\n",
              "      <td>1.170034</td>\n",
              "      <td>1.051511</td>\n",
              "      <td>0.764163</td>\n",
              "      <td>0.642705</td>\n",
              "      <td>0.617868</td>\n",
              "      <td>0.510265</td>\n",
              "      <td>0.566213</td>\n",
              "      <td>0.509149</td>\n",
              "      <td>0.477275</td>\n",
              "      <td>0.505073</td>\n",
              "      <td>0.463535</td>\n",
              "      <td>0.013519</td>\n",
              "      <td>0.050591</td>\n",
              "      <td>0.009025</td>\n",
              "      <td>8.156257</td>\n",
              "      <td>1.077167</td>\n",
              "      <td>0.624711</td>\n",
              "      <td>0.810244</td>\n",
              "      <td>0.399568</td>\n",
              "      <td>0.279947</td>\n",
              "      <td>0.314215</td>\n",
              "      <td>0.231439</td>\n",
              "      <td>0.345401</td>\n",
              "      <td>0.285389</td>\n",
              "      <td>0.210613</td>\n",
              "      <td>0.321896</td>\n",
              "      <td>0.290551</td>\n",
              "      <td>0.022774</td>\n",
              "      <td>0.095801</td>\n",
              "      <td>0.015057</td>\n",
              "      <td>4.748694</td>\n",
              "      <td>0.536378</td>\n",
              "      <td>0.296306</td>\n",
              "      <td>0.273210</td>\n",
              "      <td>0.175800</td>\n",
              "      <td>0.105508</td>\n",
              "      <td>0.168246</td>\n",
              "      <td>0.115849</td>\n",
              "      <td>0.136020</td>\n",
              "      <td>0.110514</td>\n",
              "      <td>0.100517</td>\n",
              "      <td>0.118630</td>\n",
              "      <td>0.094923</td>\n",
              "      <td>0.051035</td>\n",
              "      <td>68.0</td>\n",
              "      <td>0.014937</td>\n",
              "      <td>136.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.245457</td>\n",
              "      <td>0.105065</td>\n",
              "      <td>0.405399</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.081374</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.085733</td>\n",
              "      <td>-62.584437</td>\n",
              "      <td>3.183163</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.163038</td>\n",
              "      <td>0.620251</td>\n",
              "      <td>0.458514</td>\n",
              "      <td>0.041426</td>\n",
              "      <td>0.308287</td>\n",
              "      <td>0.538152</td>\n",
              "      <td>0.594871</td>\n",
              "      <td>0.734332</td>\n",
              "      <td>0.415489</td>\n",
              "      <td>0.761508</td>\n",
              "      <td>0.066288</td>\n",
              "      <td>0.262370</td>\n",
              "      <td>0.034438</td>\n",
              "      <td>3.480874</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.943803</td>\n",
              "      <td>0.804444</td>\n",
              "      <td>0.511229</td>\n",
              "      <td>0.498670</td>\n",
              "      <td>0.523039</td>\n",
              "      <td>0.480916</td>\n",
              "      <td>0.488657</td>\n",
              "      <td>0.483166</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.415994</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.013621</td>\n",
              "      <td>0.073041</td>\n",
              "      <td>0.010094</td>\n",
              "      <td>1.243981</td>\n",
              "      <td>0.829790</td>\n",
              "      <td>0.252972</td>\n",
              "      <td>0.347831</td>\n",
              "      <td>0.205087</td>\n",
              "      <td>0.168601</td>\n",
              "      <td>0.178009</td>\n",
              "      <td>0.144080</td>\n",
              "      <td>0.178703</td>\n",
              "      <td>0.146937</td>\n",
              "      <td>0.125580</td>\n",
              "      <td>0.128202</td>\n",
              "      <td>0.107007</td>\n",
              "      <td>0.020028</td>\n",
              "      <td>0.066940</td>\n",
              "      <td>0.029483</td>\n",
              "      <td>3.963534</td>\n",
              "      <td>0.382360</td>\n",
              "      <td>0.168389</td>\n",
              "      <td>0.117525</td>\n",
              "      <td>0.098341</td>\n",
              "      <td>0.087046</td>\n",
              "      <td>0.057991</td>\n",
              "      <td>0.059393</td>\n",
              "      <td>0.059457</td>\n",
              "      <td>0.053439</td>\n",
              "      <td>0.067684</td>\n",
              "      <td>0.070075</td>\n",
              "      <td>0.041565</td>\n",
              "      <td>0.295031</td>\n",
              "      <td>70.0</td>\n",
              "      <td>0.276366</td>\n",
              "      <td>140.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.343547</td>\n",
              "      <td>0.276366</td>\n",
              "      <td>0.710924</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         0         1         2          3   ...   68        69        70        71\n",
              "0  0.034741  0.089665  0.091225 -73.302422  ...  2.0  0.245457  0.105065  0.405399\n",
              "1  0.081374       NaN  0.085733 -62.584437  ...  2.0  0.343547  0.276366  0.710924\n",
              "\n",
              "[2 rows x 72 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 753
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BIcDtz3iR647"
      },
      "source": [
        "#Instalando o panda profile\n",
        "#!pip install pandas-profiling\n",
        "#from pandas_profiling import ProfileReport"
      ],
      "execution_count": 754,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7wPYq4x_0KWO"
      },
      "source": [
        "#Imputação de dados\n",
        "\n",
        "\n",
        "*   k-Nearest Neighbors (kNN) Imputation\n",
        "*   Random Forest Imputation (MissForest)\n",
        "*   mean\n",
        "*   most_frequent\n",
        "*   median\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e77vfwp_AqrO"
      },
      "source": [
        "Library Missingpy\n",
        "\n",
        "**missingpy** is a library for missing data imputation in Python. It has an API consistent with scikit-learn, so users already comfortable with that interface will find themselves in familiar terrain. Currently, the library supports k-Nearest Neighbors based imputation and Random Forest based imputation (MissForest) but we plan to add other imputation tools in the future so please stay tuned!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sOEHuwuWAPtU",
        "outputId": "e01408d8-24f1-4111-ca8d-4bc69995ce50"
      },
      "source": [
        "#installing\n",
        "!pip install missingpy"
      ],
      "execution_count": 755,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: missingpy in /usr/local/lib/python3.7/dist-packages (0.2.0)\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": "NAfA-01Bfkwb"
      },
      "source": [
        "#!pip install -U scikit-learn"
      ],
      "execution_count": 756,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dl6nOtFdgfAo"
      },
      "source": [
        "#!pip install sklearn\n",
        "#import sklearn\n",
        "#from sklearn import neighbors"
      ],
      "execution_count": 757,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0M1xobuxh4Us"
      },
      "source": [
        "import sklearn.neighbors._base\n",
        "import sys\n",
        "\n",
        "sys.modules['sklearn.neighbors.base'] = sklearn.neighbors._base"
      ],
      "execution_count": 758,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WhGruSVm7qL3"
      },
      "source": [
        "from sklearn.impute import SimpleImputer"
      ],
      "execution_count": 759,
      "outputs": []
    },
    {
      "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": 760,
      "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": 761,
      "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": 762,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yw0V-7kG7yOb"
      },
      "source": [
        "def meanImp(dataset_amputed):\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": 763,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KtK_OGWb6vgo"
      },
      "source": [
        "def mostFrequentImp(dataset_amputed):\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": 764,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TzfM_qWNrDpv"
      },
      "source": [
        "def medianImp(dataset_amputed):\n",
        "  imputer_median = SimpleImputer(missing_values=np.nan, strategy='median')\n",
        "  imputer_median=imputer_median.fit(df_median)\n",
        "  X_imputed_median=imputer_median.transform(df_median)\n",
        "  return X_imputed_median"
      ],
      "execution_count": 765,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rZbcIa_znjSZ"
      },
      "source": [
        "Padronização dos dados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FqsVEpn3ni5r"
      },
      "source": [
        "df_Mean = df\n",
        "df_median = df\n",
        "df_Most_frequent = df\n",
        "df_kNN = df\n",
        "df_RF = df"
      ],
      "execution_count": 766,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bJkmEVTDdXvb"
      },
      "source": [
        "ind1 =  meanImp(df_Mean)\n",
        "ind2 =  medianImp(df_median)\n",
        "ind3 =  mostFrequentImp(df_Most_frequent)\n"
      ],
      "execution_count": 767,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tG5Bd0jkrt9x"
      },
      "source": [
        "ind4 = knnImp(df_kNN, 3)\n",
        "#ind5 = knnImp(df_kNN, 5)\n",
        "ind5 = knnImp(df_kNN, 10)"
      ],
      "execution_count": 768,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EakGXqITmMcn"
      },
      "source": [
        "#dataset_amputed - base já com valores ausentes\n",
        "#imputation_type - list de métodos de iputação simples ['kNN', 'RF', 'mean', 'most_frequent', 'median']\n",
        "#imputations = ['kNN', 'RF', 'mean', 'most_frequent', 'median']\n",
        "#dfs = ['df_kNN', 'df_RF', 'df_Mean', 'df_Most_frequent', 'df_median']\n",
        "\n",
        "#cópias do database aputudo para cada método no formação da pop. inicial\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "individuos = [ind1, ind2, ind3, ind4, ind5]\n"
      ],
      "execution_count": 769,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CVNMKwXTymeK",
        "outputId": "859cb179-b2b6-408a-ae28-a17926531e07"
      },
      "source": [
        "len(individuos)"
      ],
      "execution_count": 770,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {},
          "execution_count": 770
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wkb_zkp0yY2z"
      },
      "source": [
        "Normmalização da população inicial"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2mikxk_rycxi"
      },
      "source": [
        "populationInitial = []\n",
        "for i in range(len(individuos)):\n",
        "  scaler = StandardScaler()\n",
        "  populationInitial.append(scaler.fit_transform(individuos[i]))"
      ],
      "execution_count": 771,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I8iRKpnyzW0t",
        "outputId": "19a1b92e-7f13-405d-fb17-59e4640ede47"
      },
      "source": [
        "populationInitial"
      ],
      "execution_count": 772,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([[-1.10787768e+00, -9.47838957e-01,  6.17580802e-01, ...,\n",
              "         -5.02419222e-01, -7.20976194e-01, -8.86420288e-01],\n",
              "        [ 3.85709262e-01, -1.26237551e-15,  1.76744768e-03, ...,\n",
              "         -1.96638132e-01, -2.20885188e-01, -4.05795461e-01],\n",
              "        [ 1.32001383e+00,  7.25002450e-01, -1.46579433e-01, ...,\n",
              "         -6.79372609e-01, -8.93581028e-01, -8.04660975e-01],\n",
              "        ...,\n",
              "        [-1.09416947e+00, -1.16853519e+00, -1.17626152e+00, ...,\n",
              "         -6.93026612e-01, -3.06218374e-01,  1.22255306e-15],\n",
              "        [-4.82200136e-01,  4.02908936e-01,  1.14570915e+00, ...,\n",
              "          4.37989338e-01, -4.92012922e-01,  4.51386588e-01],\n",
              "        [ 1.23715799e-01, -1.26237551e-15, -5.79958641e-01, ...,\n",
              "         -9.95363028e-01, -3.36349192e-01, -8.13582089e-01]]),\n",
              " array([[-1.10787768, -0.92605739,  0.6384509 , ..., -0.46559176,\n",
              "         -0.68865648, -0.85581282],\n",
              "        [ 0.38570926, -0.19565516,  0.02397844, ..., -0.16139355,\n",
              "         -0.19071418, -0.376728  ],\n",
              "        [ 1.32001383,  0.74351902, -0.12404543, ..., -0.64162914,\n",
              "         -0.86051969, -0.77431548],\n",
              "        ...,\n",
              "        [-1.09416947, -1.14632287, -1.15148547, ..., -0.65521246,\n",
              "         -0.27568072, -0.23041916],\n",
              "        [-0.48220014,  0.42205416,  1.1654293 , ...,  0.46994875,\n",
              "         -0.46067697,  0.47770748],\n",
              "        [ 0.1237158 , -0.19565516, -0.55648099, ..., -0.95598383,\n",
              "         -0.30568207, -0.78320801]]),\n",
              " array([[-1.10787768, -0.75594815,  0.69399129, ..., -0.34852435,\n",
              "         -0.6022527 , -0.65332758],\n",
              "        [ 0.38570926, -1.18838645,  0.11242263, ..., -0.06301327,\n",
              "         -0.12258963, -0.2187188 ],\n",
              "        [ 1.32001383,  0.79188788, -0.02767484, ..., -0.51374762,\n",
              "         -0.76780689, -0.57939608],\n",
              "        ...,\n",
              "        [-1.09416947, -0.96015255, -1.00009745, ..., -0.52649651,\n",
              "         -0.20443709, -1.23000695],\n",
              "        [-0.48220014,  0.49386325,  1.192751  , ...,  0.52954521,\n",
              "         -0.38264222,  0.55639481],\n",
              "        [ 0.1237158 , -1.18838645, -0.43695431, ..., -0.80879126,\n",
              "         -0.23333711, -0.58746307]]),\n",
              " array([[-1.10787768, -0.91428336,  0.63120682, ..., -0.509783  ,\n",
              "         -0.73015236, -0.88602745],\n",
              "        [ 0.38570926,  1.46026298,  0.02573858, ..., -0.20929214,\n",
              "         -0.23942605, -0.4138776 ],\n",
              "        [ 1.32001383,  0.68610397, -0.12011621, ..., -0.68367496,\n",
              "         -0.899525  , -0.80570982],\n",
              "        ...,\n",
              "        [-1.09416947, -1.1254208 , -1.13250057, ..., -0.69709274,\n",
              "         -0.32316129, -0.68626388],\n",
              "        [-0.48220014,  0.37796097,  1.15046309, ...,  0.41435582,\n",
              "         -0.50547665,  0.42818954],\n",
              "        [ 0.1237158 , -0.96700245, -0.54621504, ..., -0.99419852,\n",
              "         -0.35272788, -0.81447363]]),\n",
              " array([[-1.10787768, -0.93895278,  0.62208393, ..., -0.50467831,\n",
              "         -0.72326652, -0.88406022],\n",
              "        [ 0.38570926,  0.42029938,  0.01105592, ..., -0.20380035,\n",
              "         -0.23360153, -0.41004402],\n",
              "        [ 1.32001383,  0.69295023, -0.13613819, ..., -0.67879429,\n",
              "         -0.89227285, -0.8034251 ],\n",
              "        ...,\n",
              "        [-1.09416947, -1.15424805, -1.15781885, ..., -0.69222935,\n",
              "         -0.31715567, -0.61286198],\n",
              "        [-0.48220014,  0.37873911,  1.14610831, ...,  0.420651  ,\n",
              "         -0.49907673,  0.43535173],\n",
              "        [ 0.1237158 , -0.78092258, -0.56614971, ..., -0.98971787,\n",
              "         -0.34665831, -0.81222355]])]"
            ]
          },
          "metadata": {},
          "execution_count": 772
        }
      ]
    },
    {
      "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": "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):\n",
        "  if core_algorithm == 'svm':\n",
        "    classifier_br = BinaryRelevance(\n",
        "    classifier=SVC(C=50.0))#\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)\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",
        "    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",
        "    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",
        "    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",
        "    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",
        "    HL = hamming_loss(y_test, prediction_br_lr)\n",
        "  return  ACC"
      ],
      "execution_count": 773,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oCZgVleqW-wI"
      },
      "source": [
        ""
      ],
      "execution_count": 773,
      "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(populationInitial):\n",
        "  acc = []\n",
        "  populacao = []\n",
        "  aval_lista_descrecente = []\n",
        "  aval_lista_crescente = []\n",
        "  indivuduos = {}\n",
        "  accuracy = {}\n",
        "  #population_atual = []\n",
        "  #population_atual.append(populationAtual)\n",
        "  print('tam_pop_atual: ',len(populationInitial))\n",
        "\n",
        "  print('Avaliações com Binary Relevance\\n')\n",
        "  indexx = 0\n",
        "  for i in range(len(populationInitial)):\n",
        "    print('Individual - ',i)\n",
        "    \n",
        "    #F_svm,A_svm,H_svm = evaluation_binaryRelevance('svm', populationAtual[indexx])\n",
        "    #F_RF,A_RF,H_RF = evaluation_binaryRelevance('randomForest', populationAtual[indexx])\n",
        "    #F_3nn,A_3nn,H_3nn = evaluation_binaryRelevance('3nn', populationAtual[indexx])\n",
        "    #F_5nn,A_5nn,H_5nn = evaluation_binaryRelevance('5nn', populationAtual[indexx])\n",
        "    #F_10nn,A_10nn,H_10nn = evaluation_binaryRelevance('10nn', populationAtual[indexx])\n",
        "    acc.append(evaluation_binaryRelevance('svm', populationInitial[indexx]))\n",
        "    acc.append(evaluation_binaryRelevance('randomForest', populationInitial[indexx]))\n",
        "    acc.append(evaluation_binaryRelevance('3nn', populationInitial[indexx]))\n",
        "    acc.append(evaluation_binaryRelevance('5nn', populationInitial[indexx]))\n",
        "    acc.append(evaluation_binaryRelevance('10nn', populationInitial[indexx]))\n",
        "    #acc.append(evaluation_binaryRelevance('lr', populationInitial[indexx]))\n",
        "    \n",
        "    #print(populationInitialScale[i])\n",
        "    aval_lista_descrecente = sorted(acc, reverse=True)\n",
        "    #print(aval_lista_descrecente)\n",
        "    aval_lista_crescente = sorted(acc)\n",
        "    #print(aval_lista_crescente)\n",
        "\n",
        "    melhor = aval_lista_descrecente[0]\n",
        "    pior = aval_lista_crescente[0]\n",
        "\n",
        "    populacao.append([melhor, populationInitial[indexx]])\n",
        "\n",
        "    indivuduos[i] = melhor\n",
        "    indexx = indexx+1\n",
        "    print('Pior:', pior, ' melhor:', melhor, ' escolhido: ', melhor)\n",
        "    print('----------------------------------------------------')\n",
        "    melhor = ''\n",
        "    pior = ''\n",
        "    aval_lista_descrecente = []\n",
        "    aval_lista_crescente = []\n",
        "    acc = []\n",
        "  return populacao\n",
        "\n"
      ],
      "execution_count": 774,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ia_BbBmaYRGk",
        "outputId": "027529b4-1227-4f41-d0a9-bf89be6efdb0"
      },
      "source": [
        "populacao_inicial_avaliada = fitness(populationInitial)"
      ],
      "execution_count": 775,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tam_pop_atual:  5\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.20634920634920634  melhor: 0.3492063492063492  escolhido:  0.3492063492063492\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.20634920634920634  melhor: 0.38095238095238093  escolhido:  0.38095238095238093\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.23809523809523808  melhor: 0.36507936507936506  escolhido:  0.36507936507936506\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.2222222222222222  melhor: 0.3492063492063492  escolhido:  0.3492063492063492\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.2222222222222222  melhor: 0.38095238095238093  escolhido:  0.38095238095238093\n",
            "----------------------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mqh_QMw0sbt8"
      },
      "source": [
        "def fitness_population(populationInitial):\n",
        "  acc = []\n",
        "  populacao = []\n",
        "  populacao.append(populationInitial[0])\n",
        "  aval_lista_descrecente = []\n",
        "  aval_lista_crescente = []\n",
        "  indivuduos = {}\n",
        "  accuracy = {}\n",
        "  #population_atual = []\n",
        "  #population_atual.append(populationAtual)\n",
        "  print(len(populationInitial))\n",
        "\n",
        "  print('Avaliações com Binary Relevance\\n')\n",
        "  indexx = 0\n",
        "  for i in range(len(populationInitial)):\n",
        "    print('Individual - ',i)\n",
        "    ind_atual = populationInitial[indexx]\n",
        "    acc.append(evaluation_binaryRelevance('svm', ind_atual[1]))\n",
        "    acc.append(evaluation_binaryRelevance('randomForest', ind_atual[1]))\n",
        "    acc.append(evaluation_binaryRelevance('3nn', ind_atual[1]))\n",
        "    acc.append(evaluation_binaryRelevance('5nn', ind_atual[1]))\n",
        "    acc.append(evaluation_binaryRelevance('10nn', ind_atual[1]))\n",
        "    indexx = indexx+1\n",
        "    #print(populationInitialScale[i])\n",
        "    aval_lista_descrecente = sorted(acc, reverse=True)\n",
        "    print(aval_lista_descrecente)\n",
        "    aval_lista_crescente = sorted(acc)\n",
        "    print(aval_lista_crescente)\n",
        "\n",
        "    melhor = aval_lista_descrecente[0]\n",
        "    pior = aval_lista_crescente[0]\n",
        "\n",
        "    populacao.append([melhor, ind_atual[1]])\n",
        "    indivuduos[i] = melhor\n",
        "    \n",
        "    print('Pior:', pior, ' melhor:', melhor, ' escolhido: ', melhor)\n",
        "    print('----------------------------------------------------')\n",
        "    melhor = ''\n",
        "    pior = ''\n",
        "    aval_lista_descrecente = []\n",
        "    aval_lista_crescente = []\n",
        "    acc = []\n",
        "  return populacao"
      ],
      "execution_count": 776,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_jmojq6fKLe1",
        "outputId": "72541bee-7f97-45e8-8388-ff49f17791c1"
      },
      "source": [
        "populacao_inicial_avaliada"
      ],
      "execution_count": 777,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[0.3492063492063492,\n",
              "  array([[-1.10787768e+00, -9.47838957e-01,  6.17580802e-01, ...,\n",
              "          -5.02419222e-01, -7.20976194e-01, -8.86420288e-01],\n",
              "         [ 3.85709262e-01, -1.26237551e-15,  1.76744768e-03, ...,\n",
              "          -1.96638132e-01, -2.20885188e-01, -4.05795461e-01],\n",
              "         [ 1.32001383e+00,  7.25002450e-01, -1.46579433e-01, ...,\n",
              "          -6.79372609e-01, -8.93581028e-01, -8.04660975e-01],\n",
              "         ...,\n",
              "         [-1.09416947e+00, -1.16853519e+00, -1.17626152e+00, ...,\n",
              "          -6.93026612e-01, -3.06218374e-01,  1.22255306e-15],\n",
              "         [-4.82200136e-01,  4.02908936e-01,  1.14570915e+00, ...,\n",
              "           4.37989338e-01, -4.92012922e-01,  4.51386588e-01],\n",
              "         [ 1.23715799e-01, -1.26237551e-15, -5.79958641e-01, ...,\n",
              "          -9.95363028e-01, -3.36349192e-01, -8.13582089e-01]])],\n",
              " [0.38095238095238093,\n",
              "  array([[-1.10787768, -0.92605739,  0.6384509 , ..., -0.46559176,\n",
              "          -0.68865648, -0.85581282],\n",
              "         [ 0.38570926, -0.19565516,  0.02397844, ..., -0.16139355,\n",
              "          -0.19071418, -0.376728  ],\n",
              "         [ 1.32001383,  0.74351902, -0.12404543, ..., -0.64162914,\n",
              "          -0.86051969, -0.77431548],\n",
              "         ...,\n",
              "         [-1.09416947, -1.14632287, -1.15148547, ..., -0.65521246,\n",
              "          -0.27568072, -0.23041916],\n",
              "         [-0.48220014,  0.42205416,  1.1654293 , ...,  0.46994875,\n",
              "          -0.46067697,  0.47770748],\n",
              "         [ 0.1237158 , -0.19565516, -0.55648099, ..., -0.95598383,\n",
              "          -0.30568207, -0.78320801]])],\n",
              " [0.36507936507936506,\n",
              "  array([[-1.10787768, -0.75594815,  0.69399129, ..., -0.34852435,\n",
              "          -0.6022527 , -0.65332758],\n",
              "         [ 0.38570926, -1.18838645,  0.11242263, ..., -0.06301327,\n",
              "          -0.12258963, -0.2187188 ],\n",
              "         [ 1.32001383,  0.79188788, -0.02767484, ..., -0.51374762,\n",
              "          -0.76780689, -0.57939608],\n",
              "         ...,\n",
              "         [-1.09416947, -0.96015255, -1.00009745, ..., -0.52649651,\n",
              "          -0.20443709, -1.23000695],\n",
              "         [-0.48220014,  0.49386325,  1.192751  , ...,  0.52954521,\n",
              "          -0.38264222,  0.55639481],\n",
              "         [ 0.1237158 , -1.18838645, -0.43695431, ..., -0.80879126,\n",
              "          -0.23333711, -0.58746307]])],\n",
              " [0.3492063492063492,\n",
              "  array([[-1.10787768, -0.91428336,  0.63120682, ..., -0.509783  ,\n",
              "          -0.73015236, -0.88602745],\n",
              "         [ 0.38570926,  1.46026298,  0.02573858, ..., -0.20929214,\n",
              "          -0.23942605, -0.4138776 ],\n",
              "         [ 1.32001383,  0.68610397, -0.12011621, ..., -0.68367496,\n",
              "          -0.899525  , -0.80570982],\n",
              "         ...,\n",
              "         [-1.09416947, -1.1254208 , -1.13250057, ..., -0.69709274,\n",
              "          -0.32316129, -0.68626388],\n",
              "         [-0.48220014,  0.37796097,  1.15046309, ...,  0.41435582,\n",
              "          -0.50547665,  0.42818954],\n",
              "         [ 0.1237158 , -0.96700245, -0.54621504, ..., -0.99419852,\n",
              "          -0.35272788, -0.81447363]])],\n",
              " [0.38095238095238093,\n",
              "  array([[-1.10787768, -0.93895278,  0.62208393, ..., -0.50467831,\n",
              "          -0.72326652, -0.88406022],\n",
              "         [ 0.38570926,  0.42029938,  0.01105592, ..., -0.20380035,\n",
              "          -0.23360153, -0.41004402],\n",
              "         [ 1.32001383,  0.69295023, -0.13613819, ..., -0.67879429,\n",
              "          -0.89227285, -0.8034251 ],\n",
              "         ...,\n",
              "         [-1.09416947, -1.15424805, -1.15781885, ..., -0.69222935,\n",
              "          -0.31715567, -0.61286198],\n",
              "         [-0.48220014,  0.37873911,  1.14610831, ...,  0.420651  ,\n",
              "          -0.49907673,  0.43535173],\n",
              "         [ 0.1237158 , -0.78092258, -0.56614971, ..., -0.98971787,\n",
              "          -0.34665831, -0.81222355]])]]"
            ]
          },
          "metadata": {},
          "execution_count": 777
        }
      ]
    },
    {
      "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": 778,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xltBmsu9Vab1"
      },
      "source": [
        "elitista = elitismo(populacao_inicial_avaliada)"
      ],
      "execution_count": 779,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PKolrnNSNQC_",
        "outputId": "3bb6c5df-edba-4f1f-8210-6650f4e783e3"
      },
      "source": [
        "elitista"
      ],
      "execution_count": 780,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.38095238095238093,\n",
              " array([[-1.10787768, -0.92605739,  0.6384509 , ..., -0.46559176,\n",
              "         -0.68865648, -0.85581282],\n",
              "        [ 0.38570926, -0.19565516,  0.02397844, ..., -0.16139355,\n",
              "         -0.19071418, -0.376728  ],\n",
              "        [ 1.32001383,  0.74351902, -0.12404543, ..., -0.64162914,\n",
              "         -0.86051969, -0.77431548],\n",
              "        ...,\n",
              "        [-1.09416947, -1.14632287, -1.15148547, ..., -0.65521246,\n",
              "         -0.27568072, -0.23041916],\n",
              "        [-0.48220014,  0.42205416,  1.1654293 , ...,  0.46994875,\n",
              "         -0.46067697,  0.47770748],\n",
              "        [ 0.1237158 , -0.19565516, -0.55648099, ..., -0.95598383,\n",
              "         -0.30568207, -0.78320801]])]"
            ]
          },
          "metadata": {},
          "execution_count": 780
        }
      ]
    },
    {
      "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": 781,
      "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 = indexList"
      ],
      "execution_count": 782,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kcba-uAIsF91",
        "outputId": "aedeb124-9121-4f24-b710-6aa9e2032814"
      },
      "source": [
        "import numpy\n",
        "ss = index_vas\n",
        "final_list = numpy.array_split(ss,2);\n",
        "final_list"
      ],
      "execution_count": 783,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([[495,  42],\n",
              "        [331,   5],\n",
              "        [516,   4],\n",
              "        ...,\n",
              "        [493,  49],\n",
              "        [476,  15],\n",
              "        [ 52,  46]]), array([[155,  26],\n",
              "        [395,  52],\n",
              "        [ 55,   3],\n",
              "        ...,\n",
              "        [451,  30],\n",
              "        [183,  14],\n",
              "        [161,  23]])]"
            ]
          },
          "metadata": {},
          "execution_count": 783
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "spXPuxaWsdaY",
        "outputId": "7c9b7e99-6e25-482d-dd53-3d4a92e56c68"
      },
      "source": [
        "len(ss), len(final_list[0]),len(final_list[1]) "
      ],
      "execution_count": 784,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3816, 1908, 1908)"
            ]
          },
          "metadata": {},
          "execution_count": 784
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-SPfceaux8QS"
      },
      "source": [
        "#indices dos missing values\n",
        "#indexList[0]\n",
        "#for i in indexList:\n",
        "#  df.iloc[indexList[0][0],indexList[0][1]] = np.nan"
      ],
      "execution_count": 785,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "olsDwQClo0rU"
      },
      "source": [
        "import numpy\n",
        "\n",
        "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",
        "    #print(pai1[0])\n",
        "    #print(pai2[0])\n",
        "    #troca de material genético\n",
        "    #mudando pra dataframe\n",
        "\n",
        "    df_pai1 = pd.DataFrame(pai1[1])\n",
        "    df_pai2 = pd.DataFrame(pai2[1])\n",
        "\n",
        "    \n",
        "    genes_all_pai1 = []\n",
        "\n",
        "    genes_all_pai2 = []\n",
        "\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 = numpy.array_split(genes_all_pai1,n_points)\n",
        "    divide_genes_pai_2 = numpy.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 = numpy.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",
        "\n",
        "      #print('pai1: ',df_pai1.iloc[i[0].astype(int), i[1].astype(int)])\n",
        "      #print('pai2: ',df_pai2.iloc[i[0].astype(int), i[1].astype(int)])\n",
        "      #print('------------------------------------------------------')\n",
        "\n",
        "\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",
        "\n",
        "      #print('filho1: ',df_pai1.iloc[i[0].astype(int), i[1].astype(int)])\n",
        "      #print('filho2: ',df_pai2.iloc[i[0].astype(int), i[1].astype(int)])\n",
        "      #print('------------------------------------------------------')\n",
        "      #print('Gene pai1: ',genes_1_pai1[index] )\n",
        "      #print('Gene pai2: ',genes_1_pai2[index] )\n",
        "      #print('--------------------------------------------------------')\n",
        "      #print('--------------------------------------------------------')\n",
        "      index = index +1\n",
        "      \n",
        "      \n",
        "\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",
        "\n",
        "    \n",
        "\n",
        "  \n",
        "\n",
        "  #filho1, filho2,pai1[1],pai1[2]\n",
        "  print('pop_cruzada',len(nova_populacao_com_crossover))\n",
        "  return nova_populacao_com_crossover"
      ],
      "execution_count": 786,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SrTV9X19pUOO"
      },
      "source": [
        "#nova_populacao_recombinada = crossover(ss,2,atual,4)"
      ],
      "execution_count": 787,
      "outputs": []
    },
    {
      "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=index_vas):\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",
        "    \n",
        "    \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()\n"
      ],
      "execution_count": 788,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hcWIluclhe8l"
      },
      "source": [
        "#ind = mutacao(df_amp)"
      ],
      "execution_count": 789,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aZhxBzJ6yOjY"
      },
      "source": [
        "def mutations(dX_amputed,population, rate_mutation):\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)\n",
        "    populacao_cruzada.append(ind_mutado)\n",
        "    i=i+1\n",
        "  return populacao_cruzada"
      ],
      "execution_count": 790,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ESP4G7Fj9-V7",
        "outputId": "83772476-6cf1-43fe-e108-801708a07d7e"
      },
      "source": [
        "populationInitial[0]"
      ],
      "execution_count": 791,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.10787768e+00, -9.47838957e-01,  6.17580802e-01, ...,\n",
              "        -5.02419222e-01, -7.20976194e-01, -8.86420288e-01],\n",
              "       [ 3.85709262e-01, -1.26237551e-15,  1.76744768e-03, ...,\n",
              "        -1.96638132e-01, -2.20885188e-01, -4.05795461e-01],\n",
              "       [ 1.32001383e+00,  7.25002450e-01, -1.46579433e-01, ...,\n",
              "        -6.79372609e-01, -8.93581028e-01, -8.04660975e-01],\n",
              "       ...,\n",
              "       [-1.09416947e+00, -1.16853519e+00, -1.17626152e+00, ...,\n",
              "        -6.93026612e-01, -3.06218374e-01,  1.22255306e-15],\n",
              "       [-4.82200136e-01,  4.02908936e-01,  1.14570915e+00, ...,\n",
              "         4.37989338e-01, -4.92012922e-01,  4.51386588e-01],\n",
              "       [ 1.23715799e-01, -1.26237551e-15, -5.79958641e-01, ...,\n",
              "        -9.95363028e-01, -3.36349192e-01, -8.13582089e-01]])"
            ]
          },
          "metadata": {},
          "execution_count": 791
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8ZQv6VWu6NaI"
      },
      "source": [
        "a = populationInitial.pop(0)"
      ],
      "execution_count": 792,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "opUvT2THJtIP",
        "outputId": "f3c4d896-9093-4a5b-b0f0-5a9e48e777fe"
      },
      "source": [
        "a"
      ],
      "execution_count": 793,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.10787768e+00, -9.47838957e-01,  6.17580802e-01, ...,\n",
              "        -5.02419222e-01, -7.20976194e-01, -8.86420288e-01],\n",
              "       [ 3.85709262e-01, -1.26237551e-15,  1.76744768e-03, ...,\n",
              "        -1.96638132e-01, -2.20885188e-01, -4.05795461e-01],\n",
              "       [ 1.32001383e+00,  7.25002450e-01, -1.46579433e-01, ...,\n",
              "        -6.79372609e-01, -8.93581028e-01, -8.04660975e-01],\n",
              "       ...,\n",
              "       [-1.09416947e+00, -1.16853519e+00, -1.17626152e+00, ...,\n",
              "        -6.93026612e-01, -3.06218374e-01,  1.22255306e-15],\n",
              "       [-4.82200136e-01,  4.02908936e-01,  1.14570915e+00, ...,\n",
              "         4.37989338e-01, -4.92012922e-01,  4.51386588e-01],\n",
              "       [ 1.23715799e-01, -1.26237551e-15, -5.79958641e-01, ...,\n",
              "        -9.95363028e-01, -3.36349192e-01, -8.13582089e-01]])"
            ]
          },
          "metadata": {},
          "execution_count": 793
        }
      ]
    },
    {
      "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, 0.2)\n",
        "\n",
        "  return populacao_cruzada_e_mutada"
      ],
      "execution_count": 794,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fL4pHnpa3wiT"
      },
      "source": [
        "def main(gen_sizes):\n",
        "  melhor = elitista\n",
        "  otimizacao = []\n",
        "  otimizacao.append(melhor)\n",
        "  #populacao inicial\n",
        "  populacao_inicial = populacao_inicial_avaliada\n",
        "  for i in gen_sizes:\n",
        "    print('tamanho da população atual',len(populacao_inicial))\n",
        "    #Cria nova populacao\n",
        "    n_pop = nova_populacao(df_amp, melhor, populacao_inicial, i,2)\n",
        "    #avalia populacao formada\n",
        "    \n",
        "    populacao_atual_avaliada = fitness(n_pop)\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",
        "      otimizacao.append(melhor_atual)\n",
        "      populacao_inicial.append(melhor_atual)\n",
        "      melhor = melhor_atual\n",
        "      print('Novo melhor:',melhor_atual[0])\n",
        "    else:\n",
        "      otimizacao.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",
        "    ##populancao anterior recebe a nova população avliada\n",
        "    \n",
        "\n",
        "\n",
        "  return otimizacao, len(n_pop)\n"
      ],
      "execution_count": 795,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LgibKIEbAurT"
      },
      "source": [
        "# Running"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9OysaJjwBFO3",
        "outputId": "8e71b725-4326-4144-f05b-2cd6c5b2e225"
      },
      "source": [
        "gen_sizes = [10,20,40,20,10,5]\n",
        "result,p = main(gen_sizes)\n",
        "result"
      ],
      "execution_count": 796,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tamanho da população atual 5\n",
            "pop_cruzada 10\n",
            "tam_pop_atual:  10\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.23809523809523808  melhor: 0.3492063492063492  escolhido:  0.3492063492063492\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.23809523809523808  melhor: 0.36507936507936506  escolhido:  0.36507936507936506\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.2222222222222222  melhor: 0.38095238095238093  escolhido:  0.38095238095238093\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.2222222222222222  melhor: 0.4126984126984127  escolhido:  0.4126984126984127\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.2222222222222222  melhor: 0.3968253968253968  escolhido:  0.3968253968253968\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.2222222222222222  melhor: 0.36507936507936506  escolhido:  0.36507936507936506\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.23809523809523808  melhor: 0.3492063492063492  escolhido:  0.3492063492063492\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.2222222222222222  melhor: 0.3968253968253968  escolhido:  0.3968253968253968\n",
            "----------------------------------------------------\n",
            "Individual -  8\n",
            "Pior: 0.23809523809523808  melhor: 0.3968253968253968  escolhido:  0.3968253968253968\n",
            "----------------------------------------------------\n",
            "Individual -  9\n",
            "Pior: 0.23809523809523808  melhor: 0.38095238095238093  escolhido:  0.38095238095238093\n",
            "----------------------------------------------------\n",
            "Novo melhor: 0.4126984126984127\n",
            "11\n",
            "tamanho da população atual 11\n",
            "pop_cruzada 20\n",
            "tam_pop_atual:  20\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.25396825396825395  melhor: 0.4126984126984127  escolhido:  0.4126984126984127\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.25396825396825395  melhor: 0.38095238095238093  escolhido:  0.38095238095238093\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.2222222222222222  melhor: 0.3492063492063492  escolhido:  0.3492063492063492\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.2222222222222222  melhor: 0.36507936507936506  escolhido:  0.36507936507936506\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.2222222222222222  melhor: 0.38095238095238093  escolhido:  0.38095238095238093\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.2222222222222222  melhor: 0.38095238095238093  escolhido:  0.38095238095238093\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.25396825396825395  melhor: 0.36507936507936506  escolhido:  0.36507936507936506\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.25396825396825395  melhor: 0.36507936507936506  escolhido:  0.36507936507936506\n",
            "----------------------------------------------------\n",
            "Individual -  8\n",
            "Pior: 0.2222222222222222  melhor: 0.3333333333333333  escolhido:  0.3333333333333333\n",
            "----------------------------------------------------\n",
            "Individual -  9\n",
            "Pior: 0.2222222222222222  melhor: 0.38095238095238093  escolhido:  0.38095238095238093\n",
            "----------------------------------------------------\n",
            "Individual -  10\n",
            "Pior: 0.2222222222222222  melhor: 0.38095238095238093  escolhido:  0.38095238095238093\n",
            "----------------------------------------------------\n",
            "Individual -  11\n",
            "Pior: 0.25396825396825395  melhor: 0.4126984126984127  escolhido:  0.4126984126984127\n",
            "----------------------------------------------------\n",
            "Individual -  12\n",
            "Pior: 0.25396825396825395  melhor: 0.38095238095238093  escolhido:  0.38095238095238093\n",
            "----------------------------------------------------\n",
            "Individual -  13\n",
            "Pior: 0.2222222222222222  melhor: 0.38095238095238093  escolhido:  0.38095238095238093\n",
            "----------------------------------------------------\n",
            "Individual -  14\n",
            "Pior: 0.23809523809523808  melhor: 0.38095238095238093  escolhido:  0.38095238095238093\n",
            "----------------------------------------------------\n",
            "Individual -  15\n",
            "Pior: 0.25396825396825395  melhor: 0.38095238095238093  escolhido:  0.38095238095238093\n",
            "----------------------------------------------------\n",
            "Individual -  16\n",
            "Pior: 0.25396825396825395  melhor: 0.3968253968253968  escolhido:  0.3968253968253968\n",
            "----------------------------------------------------\n",
            "Individual -  17\n",
            "Pior: 0.25396825396825395  melhor: 0.3333333333333333  escolhido:  0.3333333333333333\n",
            "----------------------------------------------------\n",
            "Individual -  18\n",
            "Pior: 0.25396825396825395  melhor: 0.3333333333333333  escolhido:  0.3333333333333333\n",
            "----------------------------------------------------\n",
            "Individual -  19\n",
            "Pior: 0.23809523809523808  melhor: 0.3968253968253968  escolhido:  0.3968253968253968\n",
            "----------------------------------------------------\n",
            "melhor anterior foi escolhido:  0.4126984126984127\n",
            "21\n",
            "tamanho da população atual 21\n",
            "pop_cruzada 40\n",
            "tam_pop_atual:  40\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.25396825396825395  melhor: 0.3492063492063492  escolhido:  0.3492063492063492\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.25396825396825395  melhor: 0.38095238095238093  escolhido:  0.38095238095238093\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.25396825396825395  melhor: 0.36507936507936506  escolhido:  0.36507936507936506\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.2698412698412698  melhor: 0.36507936507936506  escolhido:  0.36507936507936506\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.25396825396825395  melhor: 0.38095238095238093  escolhido:  0.38095238095238093\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.25396825396825395  melhor: 0.38095238095238093  escolhido:  0.38095238095238093\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.25396825396825395  melhor: 0.3492063492063492  escolhido:  0.3492063492063492\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.25396825396825395  melhor: 0.31746031746031744  escolhido:  0.31746031746031744\n",
            "----------------------------------------------------\n",
            "Individual -  8\n",
            "Pior: 0.25396825396825395  melhor: 0.38095238095238093  escolhido:  0.38095238095238093\n",
            "----------------------------------------------------\n",
            "Individual -  9\n",
            "Pior: 0.25396825396825395  melhor: 0.3492063492063492  escolhido:  0.3492063492063492\n",
            "----------------------------------------------------\n",
            "Individual -  10\n",
            "Pior: 0.2698412698412698  melhor: 0.36507936507936506  escolhido:  0.36507936507936506\n",
            "----------------------------------------------------\n",
            "Individual -  11\n",
            "Pior: 0.2698412698412698  melhor: 0.36507936507936506  escolhido:  0.36507936507936506\n",
            "----------------------------------------------------\n",
            "Individual -  12\n",
            "Pior: 0.2698412698412698  melhor: 0.3333333333333333  escolhido:  0.3333333333333333\n",
            "----------------------------------------------------\n",
            "Individual -  13\n",
            "Pior: 0.2698412698412698  melhor: 0.38095238095238093  escolhido:  0.38095238095238093\n",
            "----------------------------------------------------\n",
            "Individual -  14\n",
            "Pior: 0.25396825396825395  melhor: 0.36507936507936506  escolhido:  0.36507936507936506\n",
            "----------------------------------------------------\n",
            "Individual -  15\n",
            "Pior: 0.25396825396825395  melhor: 0.3492063492063492  escolhido:  0.3492063492063492\n",
            "----------------------------------------------------\n",
            "Individual -  16\n",
            "Pior: 0.25396825396825395  melhor: 0.3333333333333333  escolhido:  0.3333333333333333\n",
            "----------------------------------------------------\n",
            "Individual -  17\n",
            "Pior: 0.25396825396825395  melhor: 0.3492063492063492  escolhido:  0.3492063492063492\n",
            "----------------------------------------------------\n",
            "Individual -  18\n",
            "Pior: 0.25396825396825395  melhor: 0.36507936507936506  escolhido:  0.36507936507936506\n",
            "----------------------------------------------------\n",
            "Individual -  19\n",
            "Pior: 0.25396825396825395  melhor: 0.3492063492063492  escolhido:  0.3492063492063492\n",
            "----------------------------------------------------\n",
            "Individual -  20\n",
            "Pior: 0.2698412698412698  melhor: 0.38095238095238093  escolhido:  0.38095238095238093\n",
            "----------------------------------------------------\n",
            "Individual -  21\n",
            "Pior: 0.25396825396825395  melhor: 0.3492063492063492  escolhido:  0.3492063492063492\n",
            "----------------------------------------------------\n",
            "Individual -  22\n",
            "Pior: 0.25396825396825395  melhor: 0.38095238095238093  escolhido:  0.38095238095238093\n",
            "----------------------------------------------------\n",
            "Individual -  23\n",
            "Pior: 0.25396825396825395  melhor: 0.4126984126984127  escolhido:  0.4126984126984127\n",
            "----------------------------------------------------\n",
            "Individual -  24\n",
            "Pior: 0.2698412698412698  melhor: 0.38095238095238093  escolhido:  0.38095238095238093\n",
            "----------------------------------------------------\n",
            "Individual -  25\n",
            "Pior: 0.25396825396825395  melhor: 0.38095238095238093  escolhido:  0.38095238095238093\n",
            "----------------------------------------------------\n",
            "Individual -  26\n",
            "Pior: 0.25396825396825395  melhor: 0.36507936507936506  escolhido:  0.36507936507936506\n",
            "----------------------------------------------------\n",
            "Individual -  27\n",
            "Pior: 0.25396825396825395  melhor: 0.3492063492063492  escolhido:  0.3492063492063492\n",
            "----------------------------------------------------\n",
            "Individual -  28\n",
            "Pior: 0.25396825396825395  melhor: 0.36507936507936506  escolhido:  0.36507936507936506\n",
            "----------------------------------------------------\n",
            "Individual -  29\n",
            "Pior: 0.25396825396825395  melhor: 0.36507936507936506  escolhido:  0.36507936507936506\n",
            "----------------------------------------------------\n",
            "Individual -  30\n",
            "Pior: 0.25396825396825395  melhor: 0.3492063492063492  escolhido:  0.3492063492063492\n",
            "----------------------------------------------------\n",
            "Individual -  31\n",
            "Pior: 0.25396825396825395  melhor: 0.3492063492063492  escolhido:  0.3492063492063492\n",
            "----------------------------------------------------\n",
            "Individual -  32\n",
            "Pior: 0.25396825396825395  melhor: 0.3968253968253968  escolhido:  0.3968253968253968\n",
            "----------------------------------------------------\n",
            "Individual -  33\n",
            "Pior: 0.2698412698412698  melhor: 0.3968253968253968  escolhido:  0.3968253968253968\n",
            "----------------------------------------------------\n",
            "Individual -  34\n",
            "Pior: 0.25396825396825395  melhor: 0.36507936507936506  escolhido:  0.36507936507936506\n",
            "----------------------------------------------------\n",
            "Individual -  35\n",
            "Pior: 0.2698412698412698  melhor: 0.3492063492063492  escolhido:  0.3492063492063492\n",
            "----------------------------------------------------\n",
            "Individual -  36\n",
            "Pior: 0.2698412698412698  melhor: 0.3333333333333333  escolhido:  0.3333333333333333\n",
            "----------------------------------------------------\n",
            "Individual -  37\n",
            "Pior: 0.25396825396825395  melhor: 0.3492063492063492  escolhido:  0.3492063492063492\n",
            "----------------------------------------------------\n",
            "Individual -  38\n",
            "Pior: 0.2698412698412698  melhor: 0.3492063492063492  escolhido:  0.3492063492063492\n",
            "----------------------------------------------------\n",
            "Individual -  39\n",
            "Pior: 0.25396825396825395  melhor: 0.36507936507936506  escolhido:  0.36507936507936506\n",
            "----------------------------------------------------\n",
            "melhor anterior foi escolhido:  0.4126984126984127\n",
            "41\n",
            "tamanho da população atual 41\n",
            "pop_cruzada 20\n",
            "tam_pop_atual:  20\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.25396825396825395  melhor: 0.38095238095238093  escolhido:  0.38095238095238093\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.25396825396825395  melhor: 0.38095238095238093  escolhido:  0.38095238095238093\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.25396825396825395  melhor: 0.3492063492063492  escolhido:  0.3492063492063492\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.2698412698412698  melhor: 0.3492063492063492  escolhido:  0.3492063492063492\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.25396825396825395  melhor: 0.4126984126984127  escolhido:  0.4126984126984127\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.25396825396825395  melhor: 0.36507936507936506  escolhido:  0.36507936507936506\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.25396825396825395  melhor: 0.3492063492063492  escolhido:  0.3492063492063492\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.2698412698412698  melhor: 0.36507936507936506  escolhido:  0.36507936507936506\n",
            "----------------------------------------------------\n",
            "Individual -  8\n",
            "Pior: 0.25396825396825395  melhor: 0.3333333333333333  escolhido:  0.3333333333333333\n",
            "----------------------------------------------------\n",
            "Individual -  9\n",
            "Pior: 0.2698412698412698  melhor: 0.36507936507936506  escolhido:  0.36507936507936506\n",
            "----------------------------------------------------\n",
            "Individual -  10\n",
            "Pior: 0.2698412698412698  melhor: 0.38095238095238093  escolhido:  0.38095238095238093\n",
            "----------------------------------------------------\n",
            "Individual -  11\n",
            "Pior: 0.2698412698412698  melhor: 0.36507936507936506  escolhido:  0.36507936507936506\n",
            "----------------------------------------------------\n",
            "Individual -  12\n",
            "Pior: 0.2698412698412698  melhor: 0.3492063492063492  escolhido:  0.3492063492063492\n",
            "----------------------------------------------------\n",
            "Individual -  13\n",
            "Pior: 0.25396825396825395  melhor: 0.38095238095238093  escolhido:  0.38095238095238093\n",
            "----------------------------------------------------\n",
            "Individual -  14\n",
            "Pior: 0.25396825396825395  melhor: 0.3492063492063492  escolhido:  0.3492063492063492\n",
            "----------------------------------------------------\n",
            "Individual -  15\n",
            "Pior: 0.25396825396825395  melhor: 0.38095238095238093  escolhido:  0.38095238095238093\n",
            "----------------------------------------------------\n",
            "Individual -  16\n",
            "Pior: 0.25396825396825395  melhor: 0.3968253968253968  escolhido:  0.3968253968253968\n",
            "----------------------------------------------------\n",
            "Individual -  17\n",
            "Pior: 0.25396825396825395  melhor: 0.3333333333333333  escolhido:  0.3333333333333333\n",
            "----------------------------------------------------\n",
            "Individual -  18\n",
            "Pior: 0.25396825396825395  melhor: 0.36507936507936506  escolhido:  0.36507936507936506\n",
            "----------------------------------------------------\n",
            "Individual -  19\n",
            "Pior: 0.25396825396825395  melhor: 0.36507936507936506  escolhido:  0.36507936507936506\n",
            "----------------------------------------------------\n",
            "melhor anterior foi escolhido:  0.4126984126984127\n",
            "21\n",
            "tamanho da população atual 21\n",
            "pop_cruzada 10\n",
            "tam_pop_atual:  10\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.25396825396825395  melhor: 0.38095238095238093  escolhido:  0.38095238095238093\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.25396825396825395  melhor: 0.38095238095238093  escolhido:  0.38095238095238093\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.23809523809523808  melhor: 0.38095238095238093  escolhido:  0.38095238095238093\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.25396825396825395  melhor: 0.36507936507936506  escolhido:  0.36507936507936506\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.23809523809523808  melhor: 0.36507936507936506  escolhido:  0.36507936507936506\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.25396825396825395  melhor: 0.36507936507936506  escolhido:  0.36507936507936506\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.23809523809523808  melhor: 0.3968253968253968  escolhido:  0.3968253968253968\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.23809523809523808  melhor: 0.3968253968253968  escolhido:  0.3968253968253968\n",
            "----------------------------------------------------\n",
            "Individual -  8\n",
            "Pior: 0.25396825396825395  melhor: 0.36507936507936506  escolhido:  0.36507936507936506\n",
            "----------------------------------------------------\n",
            "Individual -  9\n",
            "Pior: 0.23809523809523808  melhor: 0.3492063492063492  escolhido:  0.3492063492063492\n",
            "----------------------------------------------------\n",
            "melhor anterior foi escolhido:  0.4126984126984127\n",
            "11\n",
            "tamanho da população atual 11\n",
            "pop_cruzada 6\n",
            "tam_pop_atual:  6\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.2698412698412698  melhor: 0.36507936507936506  escolhido:  0.36507936507936506\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.2698412698412698  melhor: 0.3968253968253968  escolhido:  0.3968253968253968\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.2698412698412698  melhor: 0.36507936507936506  escolhido:  0.36507936507936506\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.2698412698412698  melhor: 0.31746031746031744  escolhido:  0.31746031746031744\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.2698412698412698  melhor: 0.36507936507936506  escolhido:  0.36507936507936506\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.2698412698412698  melhor: 0.38095238095238093  escolhido:  0.38095238095238093\n",
            "----------------------------------------------------\n",
            "melhor anterior foi escolhido:  0.4126984126984127\n",
            "7\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "          -0.89227285, -0.8034251 ],\n",
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              "          -0.89227285, -0.8034251 ],\n",
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              "         [-1.09416947, -1.15424805, -1.15781885, ..., -0.69222935,\n",
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              "          -0.89227285, -0.8034251 ],\n",
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              "          -0.34665831, -0.81222355]])]]"
            ]
          },
          "metadata": {},
          "execution_count": 796
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DHiPqqv754Jn",
        "outputId": "c7a9bec9-fa04-4300-fdeb-a175759e1552"
      },
      "source": [
        "p"
      ],
      "execution_count": 797,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6"
            ]
          },
          "metadata": {},
          "execution_count": 797
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2WjQSIcIeXWr"
      },
      "source": [
        "dados = []\n",
        "for i in result:\n",
        "  dados.append(i[0])"
      ],
      "execution_count": 798,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dados"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VI2HZYbm18IA",
        "outputId": "727c8900-f287-46e6-a18d-24588ace214f"
      },
      "execution_count": 799,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.38095238095238093,\n",
              " 0.4126984126984127,\n",
              " 0.4126984126984127,\n",
              " 0.4126984126984127,\n",
              " 0.4126984126984127,\n",
              " 0.4126984126984127,\n",
              " 0.4126984126984127]"
            ]
          },
          "metadata": {},
          "execution_count": 799
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SK_JnhgtmyTr"
      },
      "source": [
        "geracoes = pd.DataFrame({'geracoes':dados})"
      ],
      "execution_count": 800,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "5hBXWVyZnZ3_",
        "outputId": "ea815403-2e96-4c2a-98b5-b0b5cc792c2d"
      },
      "source": [
        "geracoes"
      ],
      "execution_count": 801,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>geracoes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.380952</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.412698</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.412698</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.412698</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.412698</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.412698</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.412698</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   geracoes\n",
              "0  0.380952\n",
              "1  0.412698\n",
              "2  0.412698\n",
              "3  0.412698\n",
              "4  0.412698\n",
              "5  0.412698\n",
              "6  0.412698"
            ]
          },
          "metadata": {},
          "execution_count": 801
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "t65Beac9oHgQ",
        "outputId": "4f85028e-5c5f-4e90-faa8-82d77855afff"
      },
      "source": [
        "import matplotlib.pyplot as plt \n",
        "plt.figure()#figsize=(20,3)\n",
        "plt.plot(geracoes,marker='o')\n",
        "title = 'Desempenho para o dataset '+ dataset_name +' com '+ str(taxa_de_missing_values)+' de VAs'\n",
        "plt.title(title)\n",
        "plt.show()"
      ],
      "execution_count": 802,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G8GdTEgY22AI"
      },
      "source": [
        "###Teste isolado para avaliar o desempenho do dataset original e sem missing values e com outros classificadores"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pLqYreLnA_aj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d9d84cf9-9e64-4256-f244-eb7b95282fc8"
      },
      "source": [
        "sc = StandardScaler()\n",
        "sc = sc.fit_transform(X_train.toarray())\n",
        "Arf= evaluation_binaryRelevance('randomForest', sc)\n",
        "Asvm = evaluation_binaryRelevance('svm', sc)\n",
        "print('SVM:           ',Asvm) \n",
        "print('Random Forest: ',Arf) "
      ],
      "execution_count": 803,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVM:            0.25396825396825395\n",
            "Random Forest:  0.36507936507936506\n"
          ]
        }
      ]
    }
  ]
}