birds_30_Evoimpy.ipynb

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    "colab": {
      "name": "birds-30-Evoimpy.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOIcyhkqHEHh71BtXFGj3Ll"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "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": "609c3da8-daca-4725-f449-bf5934ae758b"
      },
      "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": 1,
      "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 21.1 MB/s eta 0:00:01\r\u001b[K     |███████▍                        | 20 kB 26.9 MB/s eta 0:00:01\r\u001b[K     |███████████                     | 30 kB 13.1 MB/s eta 0:00:01\r\u001b[K     |██████████████▊                 | 40 kB 9.9 MB/s eta 0:00:01\r\u001b[K     |██████████████████▍             | 51 kB 4.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████████          | 61 kB 4.6 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▊      | 71 kB 4.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▍  | 81 kB 4.8 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 89 kB 3.3 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=c8258d9be0083c7d7fc9729e1fb4a56d7e9f716a8eb054095f31127bf6a11183\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": "170767c7-2bcb-468f-be8b-979783bd3b36"
      },
      "source": [
        "dataset_name = 'birds'\n",
        "from skmultilearn.dataset import load_dataset\n",
        "X,y,feature_names, label_names = load_dataset(dataset_name, 'undivided')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "birds:undivided - does not exists downloading\n",
            "Downloaded birds-undivided\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ypcrK6NDTzfN",
        "colab": {
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        },
        "outputId": "88b7d035-e19d-4b5d-8ed2-ba060c8ad08d"
      },
      "source": [
        "X.toarray()\n",
        "y.toarray()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       ...,\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0]])"
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        "outputId": "b1e7df72-571d-4e67-dba6-e06592c84c51"
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      "source": [
        "feature_names"
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      "execution_count": 4,
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          "metadata": {},
          "execution_count": 4
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      "source": [
        "label_names"
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      "execution_count": 5,
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              " ('Warbling Vireo', ['0', '1']),\n",
              " (\"MacGillivray\\\\'s Warbler\", ['0', '1']),\n",
              " (\"Stellar\\\\'s Jay\", ['0', '1']),\n",
              " ('Common Nighthawk', ['0', '1'])]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "CqPz6APyVzKd",
        "outputId": "b3b25e69-52d6-40c0-9cfc-73bbade7a1a1"
      },
      "source": [
        "df_features = pd.DataFrame(X.toarray())\n",
        "df_labels = pd.DataFrame(y.toarray())\n",
        "df_labels"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>645 rows × 19 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     0   1   2   3   4   5   6   7   8   9   10  11  12  13  14  15  16  17  18\n",
              "0     0   0   0   0   0   0   0   0   0   0   0   1   1   0   0   0   0   0   0\n",
              "1     0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
              "2     0   0   0   0   0   0   0   0   0   0   1   0   0   0   0   0   0   0   0\n",
              "3     0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
              "4     0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
              "..   ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..\n",
              "640   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
              "641   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
              "642   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
              "643   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
              "644   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
              "\n",
              "[645 rows x 19 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e2Ca8-6uaffD"
      },
      "source": [
        "from skmultilearn.model_selection.measures import get_combination_wise_output_matrix"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VWEyRS_kahut",
        "outputId": "58a1806c-82b1-4572-fe4f-c5f31cc0a6a3"
      },
      "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": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Counter({(0,): 14,\n",
              "         (1,): 81,\n",
              "         (2,): 46,\n",
              "         (3,): 9,\n",
              "         (4,): 20,\n",
              "         (5,): 14,\n",
              "         (6,): 47,\n",
              "         (7,): 40,\n",
              "         (8,): 61,\n",
              "         (9,): 53,\n",
              "         (10,): 103,\n",
              "         (11,): 28,\n",
              "         (12,): 33,\n",
              "         (13,): 9,\n",
              "         (14,): 37,\n",
              "         (15,): 17,\n",
              "         (16,): 6,\n",
              "         (17,): 10,\n",
              "         (18,): 26})"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "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": "0e0973fc-340b-4db5-aaa8-6ee846e8130e"
      },
      "source": [
        "!pip install iterative-stratification\n",
        "from iterstrat.ml_stratifiers import MultilabelStratifiedKFold"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting iterative-stratification\n",
            "  Downloading iterative_stratification-0.1.7-py3-none-any.whl (8.5 kB)\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: scipy in /usr/local/lib/python3.7/dist-packages (from iterative-stratification) (1.4.1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->iterative-stratification) (1.1.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->iterative-stratification) (3.0.0)\n",
            "Installing collected packages: iterative-stratification\n",
            "Successfully installed iterative-stratification-0.1.7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cMcP_pKubIXk",
        "outputId": "13685be0-137f-465e-d06d-30f842f04a00"
      },
      "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": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TRAIN: [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
            "  18  19  20  21  22  23  24  25  26  28  30  31  32  33  34  35  36  37\n",
            "  38  39  40  41  42  43  44  45  46  47  48  49  50  51  54  55  56  58\n",
            "  59  60  61  62  63  64  65  66  68  69  70  71  72  74  75  77  78  79\n",
            "  81  82  83  84  85  86  87  89  90  91  92  93  95  96  97  98  99 100\n",
            " 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118\n",
            " 119 120 121 122 123 124 125 126 127 128 129 130 132 133 134 135 136 137\n",
            " 138 139 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156\n",
            " 157 158 159 160 161 163 164 165 166 167 168 169 170 172 173 174 176 177\n",
            " 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195\n",
            " 196 197 198 199 200 201 202 204 205 206 208 209 210 211 212 213 214 215\n",
            " 216 217 218 219 220 222 223 224 226 227 228 229 230 231 232 233 234 235\n",
            " 236 238 240 241 242 243 244 245 247 248 249 250 251 252 253 254 255 256\n",
            " 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274\n",
            " 276 278 279 280 281 282 283 284 286 287 289 290 291 292 293 294 295 296\n",
            " 297 300 301 302 304 305 306 307 308 309 310 311 312 313 314 315 316 317\n",
            " 320 321 322 323 324 326 327 328 329 331 332 333 334 335 336 337 338 339\n",
            " 340 341 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358\n",
            " 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376\n",
            " 377 378 379 380 381 383 384 385 387 388 390 391 392 393 394 395 396 397\n",
            " 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 414 415 416\n",
            " 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434\n",
            " 435 436 437 438 440 441 442 443 444 445 446 447 448 449 450 451 452 454\n",
            " 455 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473\n",
            " 475 477 478 479 481 482 483 484 485 486 487 488 489 490 491 492 494 495\n",
            " 496 497 498 499 502 503 504 506 507 508 510 511 512 514 515 517 518 519\n",
            " 520 521 522 523 524 526 527 528 529 530 531 532 534 535 536 537 538 539\n",
            " 540 541 542 543 544 545 546 547 548 549 550 551 552 554 555 556 557 558\n",
            " 559 560 561 563 564 565 566 567 568 569 570 571 572 573 575 576 577 578\n",
            " 579 580 581 583 584 585 586 588 589 590 592 593 594 595 596 597 598 599\n",
            " 600 601 602 603 604 605 606 607 609 610 611 613 614 615 616 617 618 619\n",
            " 620 621 622 623 624 625 626 627 628 629 630 631 632 634 635 636 637 638\n",
            " 640 641 642 643 644] TEST: [ 27  29  52  53  57  67  73  76  80  88  94 131 140 162 171 175 203 207\n",
            " 221 225 237 239 246 275 277 285 288 298 299 303 318 319 325 330 342 382\n",
            " 386 389 413 439 453 456 474 476 480 493 500 501 505 509 513 516 525 533\n",
            " 553 562 574 582 587 591 608 612 633 639]\n",
            "TRAIN: [  0   1   2   3   4   5   7   9  10  11  12  13  15  16  17  18  19  20\n",
            "  21  22  23  24  25  26  27  29  30  31  32  33  34  35  36  37  38  39\n",
            "  40  41  43  44  45  46  47  49  50  51  52  53  54  55  56  57  58  59\n",
            "  60  62  63  64  65  67  68  69  70  71  72  73  74  75  76  77  78  79\n",
            "  80  81  83  84  85  87  88  89  90  91  93  94  95  96  97  98  99 100\n",
            " 101 102 103 104 106 107 109 110 111 112 114 115 118 119 120 121 122 123\n",
            " 124 125 126 127 128 130 131 132 133 134 135 136 137 138 139 140 141 142\n",
            " 143 144 146 147 148 150 151 152 153 154 155 156 157 158 159 160 161 162\n",
            " 163 165 166 167 168 169 171 172 173 174 175 176 177 178 180 181 182 183\n",
            " 184 185 186 187 188 189 190 191 192 193 194 196 197 198 199 200 201 203\n",
            " 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221\n",
            " 222 223 224 225 226 227 229 230 231 232 233 234 236 237 238 239 240 241\n",
            " 242 243 244 245 246 247 248 249 250 251 252 254 255 256 257 258 259 260\n",
            " 261 263 264 265 266 267 268 270 271 272 273 274 275 276 277 278 280 281\n",
            " 283 284 285 286 287 288 289 290 291 292 293 294 296 297 298 299 300 301\n",
            " 302 303 304 305 306 307 309 310 311 312 313 314 315 316 317 318 319 320\n",
            " 321 322 323 324 325 326 327 329 330 331 332 333 334 336 337 338 339 340\n",
            " 341 342 343 344 345 346 347 348 349 350 352 354 355 356 357 358 359 360\n",
            " 361 362 363 364 365 366 367 368 369 370 371 372 374 375 376 377 378 379\n",
            " 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397\n",
            " 398 399 401 402 403 404 405 406 407 408 409 410 413 414 415 416 417 418\n",
            " 419 420 421 422 423 424 426 427 429 430 431 432 433 434 436 437 439 440\n",
            " 441 442 443 444 445 448 449 450 451 452 453 454 455 456 457 458 459 460\n",
            " 461 463 464 466 467 468 469 470 471 472 473 474 475 476 478 479 480 481\n",
            " 482 483 484 485 486 487 488 489 490 491 493 494 496 497 498 499 500 501\n",
            " 502 503 504 505 506 507 509 510 511 512 513 514 515 516 517 518 519 520\n",
            " 521 522 524 525 526 527 528 529 530 532 533 534 535 536 538 539 540 541\n",
            " 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 559 560 561\n",
            " 562 563 564 565 566 567 568 570 571 572 573 574 575 576 577 579 580 581\n",
            " 582 583 584 585 586 587 588 589 590 591 592 593 594 596 597 598 599 600\n",
            " 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618\n",
            " 619 620 621 623 624 625 627 628 629 630 631 632 633 634 635 636 637 639\n",
            " 640 641 642 643 644] TEST: [  6   8  14  28  42  48  61  66  82  86  92 105 108 113 116 117 129 145\n",
            " 149 164 170 179 195 202 228 235 253 262 269 279 282 295 308 328 335 351\n",
            " 353 373 400 411 412 425 428 435 438 446 447 462 465 477 492 495 508 523\n",
            " 531 537 557 558 569 578 595 622 626 638]\n",
            "TRAIN: [  0   1   2   3   4   5   6   7   8  10  11  12  13  14  15  17  18  19\n",
            "  21  22  23  24  25  27  28  29  30  33  36  37  38  39  40  41  42  43\n",
            "  44  45  46  47  48  49  51  52  53  54  55  56  57  58  59  60  61  63\n",
            "  64  65  66  67  68  70  71  72  73  74  75  76  77  78  79  80  81  82\n",
            "  83  84  85  86  88  89  90  91  92  94  95  96  97  98  99 100 101 102\n",
            " 103 104 105 106 108 109 110 112 113 114 115 116 117 118 120 121 122 123\n",
            " 124 126 127 128 129 130 131 132 134 135 136 137 138 140 141 142 143 144\n",
            " 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162\n",
            " 163 164 165 169 170 171 172 173 174 175 177 178 179 180 182 183 184 185\n",
            " 186 187 188 189 190 191 192 193 194 195 196 198 199 200 201 202 203 204\n",
            " 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 223\n",
            " 224 225 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 244\n",
            " 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262\n",
            " 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280\n",
            " 281 282 283 284 285 286 288 290 291 292 293 294 295 296 297 298 299 300\n",
            " 301 302 303 304 305 306 307 308 309 310 311 313 314 315 316 317 318 319\n",
            " 320 322 323 325 327 328 329 330 332 333 334 335 336 337 338 339 340 341\n",
            " 342 343 344 345 346 347 348 349 350 351 353 354 355 356 357 358 359 360\n",
            " 361 362 363 364 366 367 368 369 370 371 372 373 374 375 376 377 379 380\n",
            " 381 382 383 384 385 386 388 389 390 391 392 394 395 396 397 398 400 401\n",
            " 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 420 421\n",
            " 422 423 424 425 426 427 428 429 431 432 433 435 436 438 439 440 442 443\n",
            " 444 445 446 447 449 450 451 452 453 454 455 456 457 458 459 460 461 462\n",
            " 463 464 465 466 468 469 470 471 472 473 474 475 476 477 478 479 480 481\n",
            " 482 483 484 485 486 487 488 489 490 491 492 493 495 496 497 498 499 500\n",
            " 501 502 503 505 507 508 509 511 512 513 516 517 518 519 520 521 522 523\n",
            " 524 525 526 527 528 529 530 531 532 533 535 536 537 538 539 541 542 543\n",
            " 544 545 546 547 548 549 550 551 552 553 554 555 557 558 559 560 561 562\n",
            " 563 565 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582\n",
            " 583 584 585 586 587 588 589 590 591 592 593 594 595 596 599 600 601 602\n",
            " 603 604 605 606 607 608 609 611 612 613 614 615 616 617 618 619 620 621\n",
            " 622 623 624 625 626 628 629 630 631 632 633 634 635 636 637 638 639 640\n",
            " 641 642 643 644] TEST: [  9  16  20  26  31  32  34  35  50  62  69  87  93 107 111 119 125 133\n",
            " 139 166 167 168 176 181 197 222 226 242 243 287 289 312 321 324 326 331\n",
            " 352 365 378 387 393 399 402 419 430 434 437 441 448 467 494 504 506 510\n",
            " 514 515 534 540 556 564 566 597 598 610 627]\n",
            "TRAIN: [  1   2   3   4   5   6   8   9  11  13  14  15  16  17  18  19  20  21\n",
            "  22  23  24  25  26  27  28  29  30  31  32  33  34  35  38  39  40  41\n",
            "  42  43  44  45  46  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  80\n",
            "  81  82  83  84  85  86  87  88  89  90  92  93  94  95  96  97  98  99\n",
            " 100 101 102 103 104 105 107 108 109 110 111 112 113 114 115 116 117 118\n",
            " 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 136 137\n",
            " 138 139 140 141 142 144 145 146 148 149 150 151 153 154 155 157 158 160\n",
            " 162 163 164 165 166 167 168 169 170 171 172 173 175 176 177 178 179 180\n",
            " 181 182 183 185 186 187 188 189 191 192 194 195 196 197 198 200 201 202\n",
            " 203 204 205 206 207 208 209 210 212 213 214 215 216 217 218 219 220 221\n",
            " 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239\n",
            " 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 256 257 258\n",
            " 259 260 261 262 263 264 265 267 269 270 271 272 273 274 275 276 277 278\n",
            " 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296\n",
            " 298 299 300 301 302 303 304 305 306 307 308 309 310 312 313 314 315 316\n",
            " 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334\n",
            " 335 337 338 339 340 341 342 344 345 348 349 350 351 352 353 355 356 357\n",
            " 358 359 360 361 362 363 364 365 366 368 369 370 371 372 373 374 375 376\n",
            " 377 378 379 382 383 385 386 387 389 390 391 392 393 394 395 396 397 398\n",
            " 399 400 401 402 403 404 405 406 408 410 411 412 413 414 415 416 417 418\n",
            " 419 420 421 422 423 424 425 426 427 428 429 430 431 433 434 435 437 438\n",
            " 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456\n",
            " 457 458 459 461 462 463 464 465 466 467 468 469 472 474 475 476 477 478\n",
            " 479 480 481 482 483 484 485 486 487 489 490 491 492 493 494 495 496 497\n",
            " 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515\n",
            " 516 517 518 519 520 521 522 523 524 525 527 528 529 530 531 532 533 534\n",
            " 535 537 538 540 541 542 543 544 545 546 548 549 550 551 552 553 554 555\n",
            " 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573\n",
            " 574 577 578 579 580 581 582 584 585 586 587 588 589 590 591 593 594 595\n",
            " 597 598 600 602 603 604 605 606 608 609 610 611 612 613 614 615 617 619\n",
            " 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638\n",
            " 639 640 641 642 643] TEST: [  0   7  10  12  36  37  47  74  79  91 106 135 143 147 152 156 159 161\n",
            " 174 184 190 193 199 211 255 266 268 297 311 336 343 346 347 354 367 380\n",
            " 381 384 388 407 409 432 436 460 470 471 473 488 526 536 539 547 575 576\n",
            " 583 592 596 599 601 607 616 618 620 644]\n",
            "TRAIN: [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
            "  18  19  20  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  48  49  50  52  53  54  56  57\n",
            "  58  61  62  63  64  65  66  67  68  69  70  71  72  73  74  75  76  77\n",
            "  78  79  80  81  82  83  84  86  87  88  89  90  91  92  93  94  95  96\n",
            "  97  98  99 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116\n",
            " 117 119 120 121 123 125 126 127 128 129 130 131 132 133 135 136 137 138\n",
            " 139 140 141 142 143 144 145 147 148 149 151 152 153 154 155 156 157 158\n",
            " 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176\n",
            " 178 179 180 181 183 184 185 186 187 188 189 190 192 193 194 195 196 197\n",
            " 198 199 200 201 202 203 204 205 206 207 209 210 211 212 213 214 215 216\n",
            " 217 218 219 220 221 222 223 224 225 226 227 228 229 230 232 233 234 235\n",
            " 236 237 238 239 240 241 242 243 244 246 247 248 249 250 251 252 253 255\n",
            " 256 257 258 259 260 261 262 264 266 268 269 270 271 272 274 275 276 277\n",
            " 278 279 280 281 282 283 284 285 286 287 288 289 291 292 295 296 297 298\n",
            " 299 300 301 303 304 306 308 310 311 312 313 315 316 317 318 319 320 321\n",
            " 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339\n",
            " 340 341 342 343 344 345 346 347 349 350 351 352 353 354 355 356 358 359\n",
            " 360 362 363 364 365 366 367 368 369 370 371 373 375 376 377 378 379 380\n",
            " 381 382 383 384 385 386 387 388 389 390 391 392 393 395 396 397 399 400\n",
            " 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 419\n",
            " 420 423 424 425 426 427 428 429 430 431 432 434 435 436 437 438 439 440\n",
            " 441 442 443 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459\n",
            " 460 462 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479\n",
            " 480 481 482 483 485 487 488 490 491 492 493 494 495 496 497 498 499 500\n",
            " 501 502 503 504 505 506 507 508 509 510 512 513 514 515 516 517 518 519\n",
            " 521 522 523 524 525 526 527 529 531 532 533 534 535 536 537 539 540 541\n",
            " 543 544 545 546 547 548 549 550 552 553 555 556 557 558 559 560 561 562\n",
            " 563 564 565 566 567 568 569 570 571 572 573 574 575 576 578 580 581 582\n",
            " 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600\n",
            " 601 602 603 604 605 606 607 608 609 610 612 613 615 616 617 618 619 620\n",
            " 621 622 623 624 625 626 627 628 629 631 632 633 634 635 636 637 638 639\n",
            " 640 641 642 643 644] TEST: [ 21  22  51  55  59  60  85 100 101 118 122 124 134 146 150 177 182 191\n",
            " 208 231 245 254 263 265 267 273 290 293 294 302 305 307 309 314 348 357\n",
            " 361 372 374 394 398 418 421 422 433 444 461 463 484 486 489 511 520 528\n",
            " 530 538 542 551 554 577 579 611 614 630]\n",
            "TRAIN: [  0   1   2   5   6   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  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54  55  56\n",
            "  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71  72  73  74\n",
            "  75  76  77  79  80  82  83  85  86  87  88  89  90  91  92  93  94  95\n",
            "  96  97  98  99 100 101 102 103 104 105 106 107 108 110 111 112 113 114\n",
            " 115 116 117 118 119 120 121 122 124 125 126 128 129 130 131 133 134 135\n",
            " 136 137 138 139 140 142 143 144 145 146 147 148 149 150 151 152 154 156\n",
            " 157 158 159 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175\n",
            " 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193\n",
            " 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212\n",
            " 213 214 215 216 217 219 220 221 222 223 224 225 226 227 228 231 232 233\n",
            " 234 235 236 237 238 239 240 241 242 243 245 246 247 248 249 251 252 253\n",
            " 254 255 256 257 259 261 262 263 265 266 267 268 269 270 271 272 273 274\n",
            " 275 276 277 278 279 280 282 284 285 286 287 288 289 290 293 294 295 297\n",
            " 298 299 301 302 303 304 305 307 308 309 310 311 312 313 314 315 316 317\n",
            " 318 319 320 321 322 323 324 325 326 328 329 330 331 333 334 335 336 337\n",
            " 338 339 341 342 343 344 346 347 348 349 351 352 353 354 355 356 357 358\n",
            " 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 376 377\n",
            " 378 379 380 381 382 384 385 386 387 388 389 390 391 392 393 394 395 398\n",
            " 399 400 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 418\n",
            " 419 420 421 422 423 424 425 426 428 429 430 432 433 434 435 436 437 438\n",
            " 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456\n",
            " 457 458 460 461 462 463 465 466 467 468 469 470 471 472 473 474 476 477\n",
            " 478 480 481 482 483 484 485 486 487 488 489 492 493 494 495 496 497 498\n",
            " 499 500 501 502 504 505 506 507 508 509 510 511 512 513 514 515 516 518\n",
            " 519 520 521 522 523 524 525 526 528 529 530 531 533 534 535 536 537 538\n",
            " 539 540 541 542 544 545 546 547 548 550 551 553 554 555 556 557 558 559\n",
            " 560 561 562 563 564 565 566 567 568 569 570 571 573 574 575 576 577 578\n",
            " 579 580 581 582 583 585 586 587 590 591 592 593 594 595 596 597 598 599\n",
            " 600 601 602 603 604 605 606 607 608 609 610 611 612 614 615 616 617 618\n",
            " 619 620 621 622 624 625 626 627 629 630 631 632 633 634 635 636 638 639\n",
            " 640 641 642 643 644] TEST: [  3   4  39  78  81  84 109 123 127 132 141 153 155 160 194 218 229 230\n",
            " 244 250 258 260 264 281 283 291 292 296 300 306 327 332 340 345 350 375\n",
            " 383 396 397 401 417 427 431 459 464 475 479 490 491 503 517 527 532 543\n",
            " 549 552 572 584 588 589 613 623 628 637]\n",
            "TRAIN: [  0   1   2   3   4   5   6   7   8   9  10  11  12  14  16  17  18  19\n",
            "  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37\n",
            "  39  41  42  43  45  46  47  48  49  50  51  52  53  55  56  57  59  60\n",
            "  61  62  65  66  67  68  69  70  71  72  73  74  75  76  77  78  79  80\n",
            "  81  82  83  84  85  86  87  88  89  90  91  92  93  94  97  99 100 101\n",
            " 102 103 104 105 106 107 108 109 111 113 114 115 116 117 118 119 120 122\n",
            " 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140\n",
            " 141 142 143 145 146 147 148 149 150 152 153 154 155 156 157 158 159 160\n",
            " 161 162 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179\n",
            " 180 181 182 183 184 186 188 189 190 191 192 193 194 195 196 197 198 199\n",
            " 200 202 203 204 205 207 208 210 211 212 213 215 218 219 220 221 222 225\n",
            " 226 227 228 229 230 231 233 234 235 237 238 239 240 242 243 244 245 246\n",
            " 248 250 251 252 253 254 255 256 258 259 260 261 262 263 264 265 266 267\n",
            " 268 269 270 271 272 273 274 275 276 277 278 279 281 282 283 284 285 287\n",
            " 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305\n",
            " 306 307 308 309 311 312 313 314 315 317 318 319 320 321 324 325 326 327\n",
            " 328 329 330 331 332 333 334 335 336 337 338 340 341 342 343 344 345 346\n",
            " 347 348 349 350 351 352 353 354 355 356 357 359 360 361 362 363 364 365\n",
            " 366 367 368 369 370 371 372 373 374 375 377 378 379 380 381 382 383 384\n",
            " 386 387 388 389 390 391 392 393 394 396 397 398 399 400 401 402 403 406\n",
            " 407 408 409 410 411 412 413 417 418 419 421 422 424 425 426 427 428 429\n",
            " 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447\n",
            " 448 450 451 452 453 455 456 457 458 459 460 461 462 463 464 465 467 468\n",
            " 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 486 487\n",
            " 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505\n",
            " 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523\n",
            " 524 525 526 527 528 530 531 532 533 534 535 536 537 538 539 540 541 542\n",
            " 543 544 545 546 547 549 550 551 552 553 554 555 556 557 558 561 562 563\n",
            " 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581\n",
            " 582 583 584 586 587 588 589 590 591 592 594 595 596 597 598 599 600 601\n",
            " 602 603 604 605 607 608 609 610 611 612 613 614 615 616 617 618 620 621\n",
            " 622 623 624 625 626 627 628 629 630 631 632 633 634 636 637 638 639 640\n",
            " 641 642 643 644] TEST: [ 13  15  38  40  44  54  58  63  64  95  96  98 110 112 121 144 151 163\n",
            " 185 187 201 206 209 214 216 217 223 224 232 236 241 247 249 257 280 286\n",
            " 310 316 322 323 339 358 376 385 395 404 405 414 415 416 420 423 449 454\n",
            " 466 485 529 548 559 560 585 593 606 619 635]\n",
            "TRAIN: [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
            "  18  19  20  21  22  23  25  26  27  28  29  30  31  32  33  34  35  36\n",
            "  37  38  39  40  42  43  44  46  47  48  50  51  52  53  54  55  57  58\n",
            "  59  60  61  62  63  64  66  67  69  70  71  73  74  76  78  79  80  81\n",
            "  82  84  85  86  87  88  89  90  91  92  93  94  95  96  97  98 100 101\n",
            " 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119\n",
            " 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 137 138\n",
            " 139 140 141 143 144 145 146 147 149 150 151 152 153 154 155 156 159 160\n",
            " 161 162 163 164 166 167 168 169 170 171 173 174 175 176 177 178 179 181\n",
            " 182 183 184 185 186 187 188 189 190 191 193 194 195 196 197 199 200 201\n",
            " 202 203 204 206 207 208 209 210 211 212 214 215 216 217 218 220 221 222\n",
            " 223 224 225 226 228 229 230 231 232 234 235 236 237 238 239 241 242 243\n",
            " 244 245 246 247 248 249 250 252 253 254 255 257 258 260 261 262 263 264\n",
            " 265 266 267 268 269 270 271 272 273 275 277 278 279 280 281 282 283 284\n",
            " 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302\n",
            " 303 304 305 306 307 308 309 310 311 312 314 315 316 317 318 319 320 321\n",
            " 322 323 324 325 326 327 328 330 331 332 333 334 335 336 337 338 339 340\n",
            " 341 342 343 345 346 347 348 349 350 351 352 353 354 356 357 358 359 360\n",
            " 361 362 364 365 366 367 368 372 373 374 375 376 377 378 380 381 382 383\n",
            " 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401\n",
            " 402 403 404 405 407 408 409 411 412 413 414 415 416 417 418 419 420 421\n",
            " 422 423 425 427 428 430 431 432 433 434 435 436 437 438 439 440 441 442\n",
            " 444 445 446 447 448 449 450 451 452 453 454 455 456 457 459 460 461 462\n",
            " 463 464 465 466 467 469 470 471 473 474 475 476 477 479 480 482 483 484\n",
            " 485 486 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503\n",
            " 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521\n",
            " 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539\n",
            " 540 541 542 543 544 546 547 548 549 550 551 552 553 554 556 557 558 559\n",
            " 560 561 562 563 564 566 567 569 570 571 572 573 574 575 576 577 578 579\n",
            " 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598\n",
            " 599 600 601 602 604 605 606 607 608 609 610 611 612 613 614 615 616 617\n",
            " 618 619 620 622 623 625 626 627 628 629 630 632 633 634 635 637 638 639\n",
            " 640 641 643 644] TEST: [ 24  41  45  49  56  65  68  72  75  77  83  99 136 142 148 157 158 165\n",
            " 172 180 192 198 205 213 219 227 233 240 251 256 259 274 276 313 329 344\n",
            " 355 363 369 370 371 379 406 410 424 426 429 443 458 468 472 478 481 487\n",
            " 545 555 565 568 580 603 621 624 631 636 642]\n",
            "TRAIN: [  0   3   4   6   7   8   9  10  12  13  14  15  16  17  20  21  22  23\n",
            "  24  25  26  27  28  29  31  32  33  34  35  36  37  38  39  40  41  42\n",
            "  44  45  47  48  49  50  51  52  53  54  55  56  57  58  59  60  61  62\n",
            "  63  64  65  66  67  68  69  71  72  73  74  75  76  77  78  79  80  81\n",
            "  82  83  84  85  86  87  88  91  92  93  94  95  96  98  99 100 101 103\n",
            " 105 106 107 108 109 110 111 112 113 114 116 117 118 119 121 122 123 124\n",
            " 125 126 127 128 129 131 132 133 134 135 136 137 139 140 141 142 143 144\n",
            " 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162\n",
            " 163 164 165 166 167 168 170 171 172 174 175 176 177 178 179 180 181 182\n",
            " 184 185 187 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204\n",
            " 205 206 207 208 209 211 212 213 214 215 216 217 218 219 220 221 222 223\n",
            " 224 225 226 227 228 229 230 231 232 233 234 235 236 237 239 240 241 242\n",
            " 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260\n",
            " 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 279\n",
            " 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297\n",
            " 298 299 300 302 303 305 306 307 308 309 310 311 312 313 314 316 317 318\n",
            " 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336\n",
            " 337 338 339 340 342 343 344 345 346 347 348 350 351 352 353 354 355 357\n",
            " 358 359 360 361 363 364 365 366 367 368 369 370 371 372 373 374 375 376\n",
            " 377 378 379 380 381 382 383 384 385 386 387 388 389 390 393 394 395 396\n",
            " 397 398 399 400 401 402 404 405 406 407 408 409 410 411 412 413 414 415\n",
            " 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433\n",
            " 434 435 436 437 438 439 441 443 444 445 446 447 448 449 451 452 453 454\n",
            " 455 456 458 459 460 461 462 463 464 465 466 467 468 470 471 472 473 474\n",
            " 475 476 477 478 479 480 481 482 484 485 486 487 488 489 490 491 492 493\n",
            " 494 495 496 497 498 499 500 501 503 504 505 506 507 508 509 510 511 513\n",
            " 514 515 516 517 520 521 522 523 524 525 526 527 528 529 530 531 532 533\n",
            " 534 536 537 538 539 540 542 543 544 545 547 548 549 551 552 553 554 555\n",
            " 556 557 558 559 560 561 562 563 564 565 566 568 569 570 572 574 575 576\n",
            " 577 578 579 580 582 583 584 585 586 587 588 589 591 592 593 594 595 596\n",
            " 597 598 599 601 602 603 604 606 607 608 610 611 612 613 614 615 616 617\n",
            " 618 619 620 621 622 623 624 626 627 628 630 631 633 635 636 637 638 639\n",
            " 640 642 643 644] TEST: [  1   2   5  11  18  19  30  43  46  70  89  90  97 102 104 115 120 130\n",
            " 138 169 173 183 186 188 189 210 238 278 301 304 315 341 349 356 362 391\n",
            " 392 403 440 442 450 457 469 483 502 512 518 519 535 541 546 550 567 571\n",
            " 573 581 590 600 605 609 625 629 632 634 641]\n",
            "TRAIN: [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  18\n",
            "  19  20  21  22  24  26  27  28  29  30  31  32  34  35  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  72  73  74  75  76\n",
            "  77  78  79  80  81  82  83  84  85  86  87  88  89  90  91  92  93  94\n",
            "  95  96  97  98  99 100 101 102 104 105 106 107 108 109 110 111 112 113\n",
            " 115 116 117 118 119 120 121 122 123 124 125 127 129 130 131 132 133 134\n",
            " 135 136 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153\n",
            " 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172\n",
            " 173 174 175 176 177 179 180 181 182 183 184 185 186 187 188 189 190 191\n",
            " 192 193 194 195 197 198 199 201 202 203 205 206 207 208 209 210 211 213\n",
            " 214 216 217 218 219 221 222 223 224 225 226 227 228 229 230 231 232 233\n",
            " 235 236 237 238 239 240 241 242 243 244 245 246 247 249 250 251 253 254\n",
            " 255 256 257 258 259 260 262 263 264 265 266 267 268 269 273 274 275 276\n",
            " 277 278 279 280 281 282 283 285 286 287 288 289 290 291 292 293 294 295\n",
            " 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313\n",
            " 314 315 316 318 319 321 322 323 324 325 326 327 328 329 330 331 332 335\n",
            " 336 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355\n",
            " 356 357 358 361 362 363 365 367 369 370 371 372 373 374 375 376 378 379\n",
            " 380 381 382 383 384 385 386 387 388 389 391 392 393 394 395 396 397 398\n",
            " 399 400 401 402 403 404 405 406 407 409 410 411 412 413 414 415 416 417\n",
            " 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435\n",
            " 436 437 438 439 440 441 442 443 444 446 447 448 449 450 453 454 456 457\n",
            " 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475\n",
            " 476 477 478 479 480 481 483 484 485 486 487 488 489 490 491 492 493 494\n",
            " 495 500 501 502 503 504 505 506 508 509 510 511 512 513 514 515 516 517\n",
            " 518 519 520 523 525 526 527 528 529 530 531 532 533 534 535 536 537 538\n",
            " 539 540 541 542 543 545 546 547 548 549 550 551 552 553 554 555 556 557\n",
            " 558 559 560 562 564 565 566 567 568 569 571 572 573 574 575 576 577 578\n",
            " 579 580 581 582 583 584 585 587 588 589 590 591 592 593 595 596 597 598\n",
            " 599 600 601 603 605 606 607 608 609 610 611 612 613 614 616 618 619 620\n",
            " 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638\n",
            " 639 641 642 644] TEST: [ 17  23  25  33  71 103 114 126 128 137 154 178 196 200 204 212 215 220\n",
            " 234 248 252 261 270 271 272 284 317 320 333 334 337 338 359 360 364 366\n",
            " 368 377 390 408 445 451 452 455 482 496 497 498 499 507 521 522 524 544\n",
            " 561 563 570 586 594 602 604 615 617 640 643]\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": "f579703c-1812-44a4-e13d-f415734f8604"
      },
      "source": [
        "f =np.array(feature_names)\n",
        "features = pd.DataFrame(f)\n",
        "features_x = features = features.iloc[:,0]\n",
        "features_x\n"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0            audio-ssd1\n",
              "1            audio-ssd2\n",
              "2            audio-ssd3\n",
              "3            audio-ssd4\n",
              "4            audio-ssd5\n",
              "             ...       \n",
              "255     std_rect_height\n",
              "256    mean_rect_volume\n",
              "257     std_rect_volume\n",
              "258         hasSegments\n",
              "259            location\n",
              "Name: 0, Length: 260, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eJ-Njs7TISoW",
        "outputId": "fb582cce-fa7d-410c-da74-65ae27c0dcf6"
      },
      "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": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.65210000e-02, 3.99260000e-02, 8.96320000e-02, ...,\n",
              "        1.76180218e+03, 1.00000000e+00, 2.00000000e+00],\n",
              "       [6.60000000e-03, 3.59840000e-02, 8.99560000e-02, ...,\n",
              "        0.00000000e+00, 0.00000000e+00, 2.00000000e+00],\n",
              "       [6.89400000e-03, 1.77220000e-02, 4.80620000e-02, ...,\n",
              "        1.13137085e+02, 1.00000000e+00, 2.00000000e+00],\n",
              "       ...,\n",
              "       [3.74320000e-02, 1.04400000e-02, 2.10090000e-02, ...,\n",
              "        0.00000000e+00, 0.00000000e+00, 6.00000000e+00],\n",
              "       [2.00058000e-01, 5.47870000e-02, 1.37048000e-01, ...,\n",
              "        0.00000000e+00, 0.00000000e+00, 6.00000000e+00],\n",
              "       [8.69700000e-03, 1.20310000e-02, 2.12120000e-02, ...,\n",
              "        0.00000000e+00, 0.00000000e+00, 6.00000000e+00]])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "OqtLuuoLJVjV",
        "outputId": "f54207e4-dc8d-4c23-f165-d19eaa1aa167"
      },
      "source": [
        "df = pd.DataFrame(dX_train)\n",
        "dfy = pd.DataFrame(dy_train)\n",
        "df_origin = df\n",
        "df"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>1</th>\n",
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              "      <td>0.179655</td>\n",
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              "      <td>0.163678</td>\n",
              "      <td>0.161606</td>\n",
              "      <td>0.159523</td>\n",
              "      <td>0.171042</td>\n",
              "      <td>0.217206</td>\n",
              "      <td>0.254929</td>\n",
              "      <td>0.307129</td>\n",
              "      <td>0.293592</td>\n",
              "      <td>0.242930</td>\n",
              "      <td>0.151817</td>\n",
              "      <td>0.105817</td>\n",
              "      <td>0.062566</td>\n",
              "      <td>0.001245</td>\n",
              "      <td>0.000118</td>\n",
              "      <td>0.000972</td>\n",
              "      <td>0.003037</td>\n",
              "      <td>0.004070</td>\n",
              "      <td>0.004311</td>\n",
              "      <td>0.004704</td>\n",
              "      <td>0.003967</td>\n",
              "      <td>0.003847</td>\n",
              "      <td>0.002940</td>\n",
              "      <td>0.002346</td>\n",
              "      <td>0.002030</td>\n",
              "      <td>0.001950</td>\n",
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              "      <td>0.002420</td>\n",
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              "      <td>0.001428</td>\n",
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              "      <td>0.0</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>0.0</td>\n",
              "      <td>2.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.006894</td>\n",
              "      <td>0.017722</td>\n",
              "      <td>0.048062</td>\n",
              "      <td>0.065802</td>\n",
              "      <td>0.103443</td>\n",
              "      <td>0.091397</td>\n",
              "      <td>0.084931</td>\n",
              "      <td>0.088666</td>\n",
              "      <td>0.075676</td>\n",
              "      <td>0.074408</td>\n",
              "      <td>0.074683</td>\n",
              "      <td>0.083202</td>\n",
              "      <td>0.088820</td>\n",
              "      <td>0.125175</td>\n",
              "      <td>0.165580</td>\n",
              "      <td>0.212101</td>\n",
              "      <td>0.217109</td>\n",
              "      <td>0.153888</td>\n",
              "      <td>0.099709</td>\n",
              "      <td>0.074910</td>\n",
              "      <td>0.045928</td>\n",
              "      <td>0.001095</td>\n",
              "      <td>0.000125</td>\n",
              "      <td>0.000368</td>\n",
              "      <td>0.001433</td>\n",
              "      <td>0.002134</td>\n",
              "      <td>0.002286</td>\n",
              "      <td>0.001869</td>\n",
              "      <td>0.001417</td>\n",
              "      <td>0.001420</td>\n",
              "      <td>0.001047</td>\n",
              "      <td>0.000855</td>\n",
              "      <td>0.000764</td>\n",
              "      <td>0.000865</td>\n",
              "      <td>0.000837</td>\n",
              "      <td>0.001438</td>\n",
              "      <td>0.002761</td>\n",
              "      <td>0.005301</td>\n",
              "      <td>0.005453</td>\n",
              "      <td>0.000806</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>2.828427</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>1.414214</td>\n",
              "      <td>674.000000</td>\n",
              "      <td>113.137085</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.031046</td>\n",
              "      <td>0.127675</td>\n",
              "      <td>0.221428</td>\n",
              "      <td>0.272707</td>\n",
              "      <td>0.358743</td>\n",
              "      <td>0.349389</td>\n",
              "      <td>0.316029</td>\n",
              "      <td>0.330656</td>\n",
              "      <td>0.310752</td>\n",
              "      <td>0.306288</td>\n",
              "      <td>0.300054</td>\n",
              "      <td>0.304569</td>\n",
              "      <td>0.295422</td>\n",
              "      <td>0.367728</td>\n",
              "      <td>0.398225</td>\n",
              "      <td>0.457381</td>\n",
              "      <td>0.429034</td>\n",
              "      <td>0.330248</td>\n",
              "      <td>0.213530</td>\n",
              "      <td>0.131256</td>\n",
              "      <td>0.075369</td>\n",
              "      <td>0.001508</td>\n",
              "      <td>0.000894</td>\n",
              "      <td>0.005276</td>\n",
              "      <td>0.008511</td>\n",
              "      <td>0.010244</td>\n",
              "      <td>0.010371</td>\n",
              "      <td>0.009083</td>\n",
              "      <td>0.008407</td>\n",
              "      <td>0.006876</td>\n",
              "      <td>0.006512</td>\n",
              "      <td>0.005826</td>\n",
              "      <td>0.004873</td>\n",
              "      <td>0.004481</td>\n",
              "      <td>0.004114</td>\n",
              "      <td>0.004384</td>\n",
              "      <td>0.004423</td>\n",
              "      <td>0.004448</td>\n",
              "      <td>0.002896</td>\n",
              "      <td>0.002198</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</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>0.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.064721</td>\n",
              "      <td>0.226644</td>\n",
              "      <td>0.304482</td>\n",
              "      <td>0.274662</td>\n",
              "      <td>0.346980</td>\n",
              "      <td>0.334063</td>\n",
              "      <td>0.307223</td>\n",
              "      <td>0.324666</td>\n",
              "      <td>0.297070</td>\n",
              "      <td>0.292258</td>\n",
              "      <td>0.287987</td>\n",
              "      <td>0.289535</td>\n",
              "      <td>0.286109</td>\n",
              "      <td>0.354479</td>\n",
              "      <td>0.389812</td>\n",
              "      <td>0.444778</td>\n",
              "      <td>0.407931</td>\n",
              "      <td>0.312861</td>\n",
              "      <td>0.197836</td>\n",
              "      <td>0.122053</td>\n",
              "      <td>0.069657</td>\n",
              "      <td>0.001287</td>\n",
              "      <td>0.002601</td>\n",
              "      <td>0.015984</td>\n",
              "      <td>0.014081</td>\n",
              "      <td>0.008860</td>\n",
              "      <td>0.009464</td>\n",
              "      <td>0.008694</td>\n",
              "      <td>0.007648</td>\n",
              "      <td>0.007977</td>\n",
              "      <td>0.006727</td>\n",
              "      <td>0.005021</td>\n",
              "      <td>0.004376</td>\n",
              "      <td>0.004248</td>\n",
              "      <td>0.004151</td>\n",
              "      <td>0.004636</td>\n",
              "      <td>0.004249</td>\n",
              "      <td>0.004037</td>\n",
              "      <td>0.002778</td>\n",
              "      <td>0.001979</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</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>0.0</td>\n",
              "      <td>2.0</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",
              "      <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>575</th>\n",
              "      <td>0.179147</td>\n",
              "      <td>0.006886</td>\n",
              "      <td>0.015871</td>\n",
              "      <td>0.023817</td>\n",
              "      <td>0.038627</td>\n",
              "      <td>0.037004</td>\n",
              "      <td>0.036252</td>\n",
              "      <td>0.039497</td>\n",
              "      <td>0.034407</td>\n",
              "      <td>0.034986</td>\n",
              "      <td>0.041187</td>\n",
              "      <td>0.050917</td>\n",
              "      <td>0.061310</td>\n",
              "      <td>0.096372</td>\n",
              "      <td>0.148460</td>\n",
              "      <td>0.262028</td>\n",
              "      <td>0.246051</td>\n",
              "      <td>0.176690</td>\n",
              "      <td>0.105253</td>\n",
              "      <td>0.066403</td>\n",
              "      <td>0.039532</td>\n",
              "      <td>0.001074</td>\n",
              "      <td>0.029440</td>\n",
              "      <td>0.000091</td>\n",
              "      <td>0.000332</td>\n",
              "      <td>0.000542</td>\n",
              "      <td>0.000716</td>\n",
              "      <td>0.000558</td>\n",
              "      <td>0.000510</td>\n",
              "      <td>0.000476</td>\n",
              "      <td>0.000378</td>\n",
              "      <td>0.000344</td>\n",
              "      <td>0.000345</td>\n",
              "      <td>0.000424</td>\n",
              "      <td>0.000697</td>\n",
              "      <td>0.002066</td>\n",
              "      <td>0.006039</td>\n",
              "      <td>0.021735</td>\n",
              "      <td>0.011000</td>\n",
              "      <td>0.005959</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.125</td>\n",
              "      <td>8.0</td>\n",
              "      <td>36.125000</td>\n",
              "      <td>64.922069</td>\n",
              "      <td>27.125000</td>\n",
              "      <td>40.704729</td>\n",
              "      <td>1131.375000</td>\n",
              "      <td>3191.526574</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>576</th>\n",
              "      <td>0.190918</td>\n",
              "      <td>0.008894</td>\n",
              "      <td>0.018812</td>\n",
              "      <td>0.034375</td>\n",
              "      <td>0.050993</td>\n",
              "      <td>0.043364</td>\n",
              "      <td>0.040725</td>\n",
              "      <td>0.042285</td>\n",
              "      <td>0.035883</td>\n",
              "      <td>0.036860</td>\n",
              "      <td>0.041500</td>\n",
              "      <td>0.048485</td>\n",
              "      <td>0.055813</td>\n",
              "      <td>0.075812</td>\n",
              "      <td>0.099272</td>\n",
              "      <td>0.132566</td>\n",
              "      <td>0.151214</td>\n",
              "      <td>0.120017</td>\n",
              "      <td>0.084974</td>\n",
              "      <td>0.062687</td>\n",
              "      <td>0.038255</td>\n",
              "      <td>0.001069</td>\n",
              "      <td>0.038495</td>\n",
              "      <td>0.000131</td>\n",
              "      <td>0.000402</td>\n",
              "      <td>0.000869</td>\n",
              "      <td>0.000915</td>\n",
              "      <td>0.000731</td>\n",
              "      <td>0.000662</td>\n",
              "      <td>0.000549</td>\n",
              "      <td>0.000428</td>\n",
              "      <td>0.000336</td>\n",
              "      <td>0.000375</td>\n",
              "      <td>0.000348</td>\n",
              "      <td>0.000423</td>\n",
              "      <td>0.000502</td>\n",
              "      <td>0.000607</td>\n",
              "      <td>0.000767</td>\n",
              "      <td>0.000758</td>\n",
              "      <td>0.000479</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</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>0.0</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>577</th>\n",
              "      <td>0.037432</td>\n",
              "      <td>0.010440</td>\n",
              "      <td>0.021009</td>\n",
              "      <td>0.025018</td>\n",
              "      <td>0.089126</td>\n",
              "      <td>0.037404</td>\n",
              "      <td>0.037024</td>\n",
              "      <td>0.046730</td>\n",
              "      <td>0.033445</td>\n",
              "      <td>0.036546</td>\n",
              "      <td>0.040642</td>\n",
              "      <td>0.043644</td>\n",
              "      <td>0.049023</td>\n",
              "      <td>0.073586</td>\n",
              "      <td>0.095925</td>\n",
              "      <td>0.134497</td>\n",
              "      <td>0.152782</td>\n",
              "      <td>0.120145</td>\n",
              "      <td>0.080947</td>\n",
              "      <td>0.062313</td>\n",
              "      <td>0.039486</td>\n",
              "      <td>0.001069</td>\n",
              "      <td>0.002389</td>\n",
              "      <td>0.000170</td>\n",
              "      <td>0.000431</td>\n",
              "      <td>0.000546</td>\n",
              "      <td>0.002381</td>\n",
              "      <td>0.000590</td>\n",
              "      <td>0.000576</td>\n",
              "      <td>0.000650</td>\n",
              "      <td>0.000363</td>\n",
              "      <td>0.000351</td>\n",
              "      <td>0.000352</td>\n",
              "      <td>0.000350</td>\n",
              "      <td>0.000327</td>\n",
              "      <td>0.000493</td>\n",
              "      <td>0.000583</td>\n",
              "      <td>0.000852</td>\n",
              "      <td>0.000745</td>\n",
              "      <td>0.000489</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</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>0.0</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>578</th>\n",
              "      <td>0.200058</td>\n",
              "      <td>0.054787</td>\n",
              "      <td>0.137048</td>\n",
              "      <td>0.162441</td>\n",
              "      <td>0.192939</td>\n",
              "      <td>0.177832</td>\n",
              "      <td>0.178606</td>\n",
              "      <td>0.174532</td>\n",
              "      <td>0.136221</td>\n",
              "      <td>0.142075</td>\n",
              "      <td>0.166657</td>\n",
              "      <td>0.176568</td>\n",
              "      <td>0.154023</td>\n",
              "      <td>0.171847</td>\n",
              "      <td>0.173904</td>\n",
              "      <td>0.210485</td>\n",
              "      <td>0.205475</td>\n",
              "      <td>0.155457</td>\n",
              "      <td>0.100882</td>\n",
              "      <td>0.072642</td>\n",
              "      <td>0.045765</td>\n",
              "      <td>0.001107</td>\n",
              "      <td>0.033289</td>\n",
              "      <td>0.001789</td>\n",
              "      <td>0.004978</td>\n",
              "      <td>0.005558</td>\n",
              "      <td>0.004902</td>\n",
              "      <td>0.004208</td>\n",
              "      <td>0.004364</td>\n",
              "      <td>0.003474</td>\n",
              "      <td>0.002551</td>\n",
              "      <td>0.002294</td>\n",
              "      <td>0.002291</td>\n",
              "      <td>0.002045</td>\n",
              "      <td>0.001747</td>\n",
              "      <td>0.001522</td>\n",
              "      <td>0.001527</td>\n",
              "      <td>0.001834</td>\n",
              "      <td>0.001194</td>\n",
              "      <td>0.000668</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</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>0.0</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>579</th>\n",
              "      <td>0.008697</td>\n",
              "      <td>0.012031</td>\n",
              "      <td>0.021212</td>\n",
              "      <td>0.028663</td>\n",
              "      <td>0.044081</td>\n",
              "      <td>0.041791</td>\n",
              "      <td>0.044002</td>\n",
              "      <td>0.046693</td>\n",
              "      <td>0.040220</td>\n",
              "      <td>0.041678</td>\n",
              "      <td>0.045603</td>\n",
              "      <td>0.047064</td>\n",
              "      <td>0.053430</td>\n",
              "      <td>0.081807</td>\n",
              "      <td>0.105669</td>\n",
              "      <td>0.146873</td>\n",
              "      <td>0.159271</td>\n",
              "      <td>0.131206</td>\n",
              "      <td>0.088501</td>\n",
              "      <td>0.064915</td>\n",
              "      <td>0.041199</td>\n",
              "      <td>0.001072</td>\n",
              "      <td>0.000256</td>\n",
              "      <td>0.000224</td>\n",
              "      <td>0.000520</td>\n",
              "      <td>0.000614</td>\n",
              "      <td>0.000777</td>\n",
              "      <td>0.000800</td>\n",
              "      <td>0.000687</td>\n",
              "      <td>0.000542</td>\n",
              "      <td>0.000434</td>\n",
              "      <td>0.000414</td>\n",
              "      <td>0.000361</td>\n",
              "      <td>0.000355</td>\n",
              "      <td>0.000395</td>\n",
              "      <td>0.000497</td>\n",
              "      <td>0.000619</td>\n",
              "      <td>0.000977</td>\n",
              "      <td>0.000927</td>\n",
              "      <td>0.000555</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</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>0.0</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>580 rows × 260 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          0         1         2         3    ...          256          257  258  259\n",
              "0    0.016521  0.039926  0.089632  0.134119  ...   788.923077  1761.802180  1.0  2.0\n",
              "1    0.006600  0.035984  0.089956  0.123214  ...     0.000000     0.000000  0.0  2.0\n",
              "2    0.006894  0.017722  0.048062  0.065802  ...   674.000000   113.137085  1.0  2.0\n",
              "3    0.031046  0.127675  0.221428  0.272707  ...     0.000000     0.000000  0.0  2.0\n",
              "4    0.064721  0.226644  0.304482  0.274662  ...     0.000000     0.000000  0.0  2.0\n",
              "..        ...       ...       ...       ...  ...          ...          ...  ...  ...\n",
              "575  0.179147  0.006886  0.015871  0.023817  ...  1131.375000  3191.526574  1.0  6.0\n",
              "576  0.190918  0.008894  0.018812  0.034375  ...     0.000000     0.000000  0.0  6.0\n",
              "577  0.037432  0.010440  0.021009  0.025018  ...     0.000000     0.000000  0.0  6.0\n",
              "578  0.200058  0.054787  0.137048  0.162441  ...     0.000000     0.000000  0.0  6.0\n",
              "579  0.008697  0.012031  0.021212  0.028663  ...     0.000000     0.000000  0.0  6.0\n",
              "\n",
              "[580 rows x 260 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VVZdHXfNFoOb",
        "outputId": "d3a99e65-8425-4867-dde1-a54173fb2e31"
      },
      "source": [
        "va = (df.isna().sum()).sum()\n",
        "va"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "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": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R3gRnSNF6BnZ",
        "outputId": "cc9fe10a-ec8d-40d8-ce7f-0344ad716721"
      },
      "source": [
        "X_test"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.49383281, -0.26081303,  0.22294006, ..., -0.43629527,\n",
              "        -0.98473193, -1.59020465],\n",
              "       [ 5.27822809, -0.22628217, -0.22710335, ...,  0.28708429,\n",
              "         1.0155048 , -0.39869684],\n",
              "       [-0.0346086 ,  0.15686974,  0.28806941, ...,  0.06726874,\n",
              "         1.0155048 , -0.39869684],\n",
              "       ...,\n",
              "       [ 0.17647579, -0.03651472, -0.06815397, ..., -0.19144568,\n",
              "         1.0155048 ,  1.68644182],\n",
              "       [-0.12009145, -0.43589483, -0.72717503, ..., -0.43629527,\n",
              "        -0.98473193,  0.19705706],\n",
              "       [-0.1313602 , -0.40416376, -0.6411166 , ..., -0.43629527,\n",
              "        -0.98473193,  0.19705706]])"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "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": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0sh7m7mI2SFM",
        "outputId": "edb72fe1-0cf3-4e1c-83e0-0ac49d7f37d0"
      },
      "source": [
        "dX_train.size"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "150800"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "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": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-BXupnbMhj82",
        "outputId": "13472e7c-51a5-4bea-9b7e-51b65b2cf64a"
      },
      "source": [
        "taxa_de_missing_values = 0.30\n",
        "missing_values = amount_missing_values(taxa_de_missing_values, dX_train.size)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total de dados:  150800 \n",
            "Total de Valores Ausentes:  45240\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EGMtsgfm4SiY",
        "outputId": "ff7b2796-1424-413d-8a31-b5f1d2357a23"
      },
      "source": [
        "type(missing_values)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "int"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "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": 22,
      "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": 23,
      "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": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2HGLJ44z0-QO"
      },
      "source": [
        "list_positions_missing_values = ampute(rows,cols)"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oWNk210nu74B",
        "outputId": "8c5daef2-5b7b-4d1d-e37e-080a08555b40"
      },
      "source": [
        "len(indexList), len(list_positions_missing_values)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(45240, 45240)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bu-xX7GH9yQI",
        "outputId": "29283b0e-a6f4-441a-daa9-9cb85bf652fd"
      },
      "source": [
        "list_positions_missing_values"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[280, 216],\n",
              " [511, 252],\n",
              " [367, 151],\n",
              " [552, 214],\n",
              " [344, 39],\n",
              " [373, 187],\n",
              " [107, 61],\n",
              " [475, 164],\n",
              " [124, 68],\n",
              " [438, 7],\n",
              " [145, 64],\n",
              " [290, 5],\n",
              " [246, 128],\n",
              " [578, 73],\n",
              " [248, 67],\n",
              " [17, 123],\n",
              " [483, 226],\n",
              " [226, 199],\n",
              " [463, 135],\n",
              " [406, 144],\n",
              " [184, 88],\n",
              " [168, 235],\n",
              " [190, 176],\n",
              " [380, 152],\n",
              " [191, 57],\n",
              " [456, 226],\n",
              " [230, 173],\n",
              " [504, 167],\n",
              " [408, 32],\n",
              " [409, 33],\n",
              " [250, 10],\n",
              " [471, 92],\n",
              " [28, 237],\n",
              " [234, 109],\n",
              " [291, 221],\n",
              " [132, 206],\n",
              " [433, 96],\n",
              " [114, 157],\n",
              " [114, 248],\n",
              " [143, 134],\n",
              " [560, 103],\n",
              " [37, 129],\n",
              " [20, 27],\n",
              " [228, 115],\n",
              " [510, 207],\n",
              " [453, 216],\n",
              " [430, 60],\n",
              " [438, 100],\n",
              " [494, 224],\n",
              " [46, 77],\n",
              " [362, 35],\n",
              " [363, 21],\n",
              " [30, 72],\n",
              " [575, 253],\n",
              " [529, 11],\n",
              " [463, 190],\n",
              " [64, 92],\n",
              " [218, 240],\n",
              " [125, 12],\n",
              " [336, 226],\n",
              " [335, 230],\n",
              " [66, 196],\n",
              " [86, 51],\n",
              " [274, 241],\n",
              " [193, 3],\n",
              " [199, 224],\n",
              " [436, 83],\n",
              " [181, 208],\n",
              " [403, 233],\n",
              " [426, 49],\n",
              " [158, 129],\n",
              " [39, 99],\n",
              " [252, 50],\n",
              " [142, 63],\n",
              " [144, 108],\n",
              " [178, 15],\n",
              " [311, 130],\n",
              " [277, 226],\n",
              " [282, 209],\n",
              " [545, 257],\n",
              " [190, 79],\n",
              " [358, 48],\n",
              " [409, 153],\n",
              " [46, 133],\n",
              " [317, 101],\n",
              " [303, 233],\n",
              " [458, 259],\n",
              " [151, 168],\n",
              " [428, 209],\n",
              " [251, 239],\n",
              " [121, 119],\n",
              " [560, 158],\n",
              " [446, 12],\n",
              " [83, 84],\n",
              " [165, 186],\n",
              " [280, 217],\n",
              " [314, 178],\n",
              " [303, 118],\n",
              " [230, 114],\n",
              " [118, 17],\n",
              " [119, 48],\n",
              " [287, 113],\n",
              " [319, 66],\n",
              " [119, 6],\n",
              " [175, 144],\n",
              " [489, 150],\n",
              " [348, 217],\n",
              " [359, 98],\n",
              " [240, 221],\n",
              " [58, 45],\n",
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              " [451, 87],\n",
              " [335, 29],\n",
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              " [389, 174],\n",
              " [360, 130],\n",
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              " [394, 25],\n",
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              " [365, 13],\n",
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              " [479, 121],\n",
              " [230, 31],\n",
              " [222, 215],\n",
              " [447, 193],\n",
              " [542, 179],\n",
              " [551, 35],\n",
              " [250, 196],\n",
              " [418, 62],\n",
              " [516, 2],\n",
              " [9, 197],\n",
              " [253, 31],\n",
              " [491, 122],\n",
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            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "WhTjlES08e00",
        "outputId": "c525bf62-8c25-4726-a5f7-accef6c8c645"
      },
      "source": [
        "df"
      ],
      "execution_count": 28,
      "outputs": [
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              "      <td>0.000414</td>\n",
              "      <td>0.000361</td>\n",
              "      <td>0.000355</td>\n",
              "      <td>0.000395</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000619</td>\n",
              "      <td>0.000977</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>580 rows × 260 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          0         1         2         3    ...         256          257  258  259\n",
              "0    0.016521  0.039926  0.089632  0.134119  ...  788.923077  1761.802180  1.0  2.0\n",
              "1    0.006600  0.035984  0.089956  0.123214  ...         NaN          NaN  0.0  NaN\n",
              "2    0.006894  0.017722  0.048062       NaN  ...         NaN   113.137085  1.0  2.0\n",
              "3    0.031046       NaN       NaN  0.272707  ...    0.000000     0.000000  0.0  2.0\n",
              "4    0.064721  0.226644  0.304482  0.274662  ...    0.000000     0.000000  0.0  NaN\n",
              "..        ...       ...       ...       ...  ...         ...          ...  ...  ...\n",
              "575  0.179147  0.006886       NaN  0.023817  ...         NaN  3191.526574  NaN  NaN\n",
              "576  0.190918  0.008894       NaN  0.034375  ...         NaN     0.000000  0.0  NaN\n",
              "577  0.037432       NaN  0.021009  0.025018  ...    0.000000     0.000000  0.0  6.0\n",
              "578  0.200058  0.054787  0.137048       NaN  ...    0.000000     0.000000  0.0  NaN\n",
              "579  0.008697  0.012031  0.021212  0.028663  ...         NaN     0.000000  0.0  6.0\n",
              "\n",
              "[580 rows x 260 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V1a-5SqH8o-f",
        "outputId": "ff05169d-f247-4aaf-9a7e-9d67b1f93d4d"
      },
      "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": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(39068, 6172)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "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": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WwFZdRyx7Cop"
      },
      "source": [
        "#df com missiing values\n",
        "df\n",
        "df_amp = df.copy()"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 494
        },
        "id": "tXZXkvNE8SD-",
        "outputId": "f0f20c24-c56e-4807-f46a-2eb4e47bc3c6"
      },
      "source": [
        "#visualizando missing values\n",
        "import missingno as msno\n",
        "msno.matrix(df)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f45fc7caed0>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "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": "8d827405-33e0-43b5-ba8c-861554c90857"
      },
      "source": [
        "msno.bar(df)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f45f24d5b50>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x8460 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GnhXBOpE9l7D",
        "outputId": "36889761-f210-453f-8615-ea7f70803a72"
      },
      "source": [
        "missing_values = (df.isnull().sum()).sum()\n",
        "missing_values"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "39068"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lEUwlKyGRMNE"
      },
      "source": [
        "missing_values = df.isnull().sum()"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 227
        },
        "id": "Yovav_L--cfg",
        "outputId": "dc576266-7728-4360-b7d8-63f444b2c8f6"
      },
      "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": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "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": 350
        },
        "outputId": "daa51abe-f681-4587-c241-3fec46831102"
      },
      "source": [
        "df.describe()\n"
      ],
      "execution_count": 37,
      "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>...</th>\n",
              "      <th>220</th>\n",
              "      <th>221</th>\n",
              "      <th>222</th>\n",
              "      <th>223</th>\n",
              "      <th>224</th>\n",
              "      <th>225</th>\n",
              "      <th>226</th>\n",
              "      <th>227</th>\n",
              "      <th>228</th>\n",
              "      <th>229</th>\n",
              "      <th>230</th>\n",
              "      <th>231</th>\n",
              "      <th>232</th>\n",
              "      <th>233</th>\n",
              "      <th>234</th>\n",
              "      <th>235</th>\n",
              "      <th>236</th>\n",
              "      <th>237</th>\n",
              "      <th>238</th>\n",
              "      <th>239</th>\n",
              "      <th>240</th>\n",
              "      <th>241</th>\n",
              "      <th>242</th>\n",
              "      <th>243</th>\n",
              "      <th>244</th>\n",
              "      <th>245</th>\n",
              "      <th>246</th>\n",
              "      <th>247</th>\n",
              "      <th>248</th>\n",
              "      <th>249</th>\n",
              "      <th>250</th>\n",
              "      <th>251</th>\n",
              "      <th>252</th>\n",
              "      <th>253</th>\n",
              "      <th>254</th>\n",
              "      <th>255</th>\n",
              "      <th>256</th>\n",
              "      <th>257</th>\n",
              "      <th>258</th>\n",
              "      <th>259</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>580.000000</td>\n",
              "      <td>426.000000</td>\n",
              "      <td>441.000000</td>\n",
              "      <td>425.000000</td>\n",
              "      <td>430.000000</td>\n",
              "      <td>433.000000</td>\n",
              "      <td>445.000000</td>\n",
              "      <td>431.000000</td>\n",
              "      <td>428.000000</td>\n",
              "      <td>416.000000</td>\n",
              "      <td>422.000000</td>\n",
              "      <td>425.000000</td>\n",
              "      <td>434.000000</td>\n",
              "      <td>427.000000</td>\n",
              "      <td>426.000000</td>\n",
              "      <td>413.000000</td>\n",
              "      <td>423.000000</td>\n",
              "      <td>432.000000</td>\n",
              "      <td>432.000000</td>\n",
              "      <td>434.000000</td>\n",
              "      <td>438.000000</td>\n",
              "      <td>438.000000</td>\n",
              "      <td>436.000000</td>\n",
              "      <td>439.000000</td>\n",
              "      <td>433.000000</td>\n",
              "      <td>418.000000</td>\n",
              "      <td>427.000000</td>\n",
              "      <td>447.000000</td>\n",
              "      <td>432.000000</td>\n",
              "      <td>448.000000</td>\n",
              "      <td>422.000000</td>\n",
              "      <td>416.000000</td>\n",
              "      <td>450.000000</td>\n",
              "      <td>436.000000</td>\n",
              "      <td>425.000000</td>\n",
              "      <td>421.000000</td>\n",
              "      <td>416.000000</td>\n",
              "      <td>431.000000</td>\n",
              "      <td>430.000000</td>\n",
              "      <td>443.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>441.000000</td>\n",
              "      <td>402.000000</td>\n",
              "      <td>433.000000</td>\n",
              "      <td>425.000000</td>\n",
              "      <td>421.000000</td>\n",
              "      <td>440.000000</td>\n",
              "      <td>408.000000</td>\n",
              "      <td>444.000000</td>\n",
              "      <td>425.000000</td>\n",
              "      <td>421.000000</td>\n",
              "      <td>423.000000</td>\n",
              "      <td>423.000000</td>\n",
              "      <td>424.000000</td>\n",
              "      <td>430.000000</td>\n",
              "      <td>412.000000</td>\n",
              "      <td>450.000000</td>\n",
              "      <td>423.000000</td>\n",
              "      <td>438.000000</td>\n",
              "      <td>431.000000</td>\n",
              "      <td>427.000000</td>\n",
              "      <td>437.000000</td>\n",
              "      <td>427.000000</td>\n",
              "      <td>412.000000</td>\n",
              "      <td>421.000000</td>\n",
              "      <td>433.000000</td>\n",
              "      <td>443.000000</td>\n",
              "      <td>450.000000</td>\n",
              "      <td>438.000000</td>\n",
              "      <td>454.000000</td>\n",
              "      <td>420.000000</td>\n",
              "      <td>427.000000</td>\n",
              "      <td>427.000000</td>\n",
              "      <td>413.000000</td>\n",
              "      <td>418.000000</td>\n",
              "      <td>423.000000</td>\n",
              "      <td>434.000000</td>\n",
              "      <td>438.000000</td>\n",
              "      <td>441.000000</td>\n",
              "      <td>429.000000</td>\n",
              "      <td>422.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.067947</td>\n",
              "      <td>0.064403</td>\n",
              "      <td>0.100877</td>\n",
              "      <td>0.112453</td>\n",
              "      <td>0.140364</td>\n",
              "      <td>0.132609</td>\n",
              "      <td>0.128588</td>\n",
              "      <td>0.127110</td>\n",
              "      <td>0.110335</td>\n",
              "      <td>0.106430</td>\n",
              "      <td>0.110319</td>\n",
              "      <td>0.129554</td>\n",
              "      <td>0.127976</td>\n",
              "      <td>0.168917</td>\n",
              "      <td>0.187270</td>\n",
              "      <td>0.232954</td>\n",
              "      <td>0.257130</td>\n",
              "      <td>0.206753</td>\n",
              "      <td>0.138181</td>\n",
              "      <td>0.101070</td>\n",
              "      <td>0.061299</td>\n",
              "      <td>0.002777</td>\n",
              "      <td>0.008080</td>\n",
              "      <td>0.003982</td>\n",
              "      <td>0.004769</td>\n",
              "      <td>0.004644</td>\n",
              "      <td>0.004131</td>\n",
              "      <td>0.003868</td>\n",
              "      <td>0.003473</td>\n",
              "      <td>0.002860</td>\n",
              "      <td>0.002295</td>\n",
              "      <td>0.001999</td>\n",
              "      <td>0.001607</td>\n",
              "      <td>0.001876</td>\n",
              "      <td>0.002098</td>\n",
              "      <td>0.003613</td>\n",
              "      <td>0.004910</td>\n",
              "      <td>0.010918</td>\n",
              "      <td>0.014000</td>\n",
              "      <td>0.008489</td>\n",
              "      <td>...</td>\n",
              "      <td>0.003264</td>\n",
              "      <td>0.001320</td>\n",
              "      <td>0.010960</td>\n",
              "      <td>0.003082</td>\n",
              "      <td>0.003112</td>\n",
              "      <td>0.006137</td>\n",
              "      <td>0.006133</td>\n",
              "      <td>0.004767</td>\n",
              "      <td>0.001647</td>\n",
              "      <td>0.001635</td>\n",
              "      <td>0.004252</td>\n",
              "      <td>0.002841</td>\n",
              "      <td>0.007892</td>\n",
              "      <td>0.008479</td>\n",
              "      <td>0.005415</td>\n",
              "      <td>0.010109</td>\n",
              "      <td>0.003273</td>\n",
              "      <td>0.001142</td>\n",
              "      <td>0.002671</td>\n",
              "      <td>0.002976</td>\n",
              "      <td>0.001881</td>\n",
              "      <td>0.003603</td>\n",
              "      <td>0.000936</td>\n",
              "      <td>0.003932</td>\n",
              "      <td>0.003818</td>\n",
              "      <td>0.000346</td>\n",
              "      <td>0.010378</td>\n",
              "      <td>0.000664</td>\n",
              "      <td>0.007356</td>\n",
              "      <td>0.005942</td>\n",
              "      <td>0.007598</td>\n",
              "      <td>3.419204</td>\n",
              "      <td>20.925726</td>\n",
              "      <td>34.964005</td>\n",
              "      <td>17.597620</td>\n",
              "      <td>22.306526</td>\n",
              "      <td>1004.562122</td>\n",
              "      <td>3702.812749</td>\n",
              "      <td>0.466200</td>\n",
              "      <td>5.094787</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.103288</td>\n",
              "      <td>0.114783</td>\n",
              "      <td>0.117210</td>\n",
              "      <td>0.122238</td>\n",
              "      <td>0.125277</td>\n",
              "      <td>0.121809</td>\n",
              "      <td>0.116343</td>\n",
              "      <td>0.113534</td>\n",
              "      <td>0.105342</td>\n",
              "      <td>0.096965</td>\n",
              "      <td>0.102303</td>\n",
              "      <td>0.120771</td>\n",
              "      <td>0.111087</td>\n",
              "      <td>0.130606</td>\n",
              "      <td>0.124296</td>\n",
              "      <td>0.134220</td>\n",
              "      <td>0.137991</td>\n",
              "      <td>0.119626</td>\n",
              "      <td>0.085656</td>\n",
              "      <td>0.064868</td>\n",
              "      <td>0.038107</td>\n",
              "      <td>0.004705</td>\n",
              "      <td>0.020148</td>\n",
              "      <td>0.012116</td>\n",
              "      <td>0.009284</td>\n",
              "      <td>0.006763</td>\n",
              "      <td>0.005220</td>\n",
              "      <td>0.005036</td>\n",
              "      <td>0.004790</td>\n",
              "      <td>0.004028</td>\n",
              "      <td>0.003365</td>\n",
              "      <td>0.003206</td>\n",
              "      <td>0.002425</td>\n",
              "      <td>0.002905</td>\n",
              "      <td>0.003310</td>\n",
              "      <td>0.009233</td>\n",
              "      <td>0.013675</td>\n",
              "      <td>0.049148</td>\n",
              "      <td>0.050352</td>\n",
              "      <td>0.024293</td>\n",
              "      <td>...</td>\n",
              "      <td>0.023922</td>\n",
              "      <td>0.010030</td>\n",
              "      <td>0.052386</td>\n",
              "      <td>0.028153</td>\n",
              "      <td>0.022568</td>\n",
              "      <td>0.058712</td>\n",
              "      <td>0.041918</td>\n",
              "      <td>0.034010</td>\n",
              "      <td>0.014761</td>\n",
              "      <td>0.013659</td>\n",
              "      <td>0.035616</td>\n",
              "      <td>0.029093</td>\n",
              "      <td>0.061842</td>\n",
              "      <td>0.047874</td>\n",
              "      <td>0.052891</td>\n",
              "      <td>0.084903</td>\n",
              "      <td>0.022945</td>\n",
              "      <td>0.023891</td>\n",
              "      <td>0.019764</td>\n",
              "      <td>0.018796</td>\n",
              "      <td>0.016001</td>\n",
              "      <td>0.024144</td>\n",
              "      <td>0.013191</td>\n",
              "      <td>0.032865</td>\n",
              "      <td>0.049489</td>\n",
              "      <td>0.005236</td>\n",
              "      <td>0.068558</td>\n",
              "      <td>0.009008</td>\n",
              "      <td>0.052761</td>\n",
              "      <td>0.040890</td>\n",
              "      <td>0.044466</td>\n",
              "      <td>5.491342</td>\n",
              "      <td>28.980047</td>\n",
              "      <td>71.193787</td>\n",
              "      <td>23.940620</td>\n",
              "      <td>44.151783</td>\n",
              "      <td>2070.256480</td>\n",
              "      <td>10992.502861</td>\n",
              "      <td>0.499439</td>\n",
              "      <td>3.322781</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.001333</td>\n",
              "      <td>0.002663</td>\n",
              "      <td>0.004912</td>\n",
              "      <td>0.007605</td>\n",
              "      <td>0.015742</td>\n",
              "      <td>0.017490</td>\n",
              "      <td>0.018854</td>\n",
              "      <td>0.022800</td>\n",
              "      <td>0.019511</td>\n",
              "      <td>0.021898</td>\n",
              "      <td>0.025766</td>\n",
              "      <td>0.029972</td>\n",
              "      <td>0.034489</td>\n",
              "      <td>0.056896</td>\n",
              "      <td>0.078951</td>\n",
              "      <td>0.114577</td>\n",
              "      <td>0.127964</td>\n",
              "      <td>0.104308</td>\n",
              "      <td>0.070256</td>\n",
              "      <td>0.052076</td>\n",
              "      <td>0.032272</td>\n",
              "      <td>0.001066</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>0.000021</td>\n",
              "      <td>0.000056</td>\n",
              "      <td>0.000087</td>\n",
              "      <td>0.000187</td>\n",
              "      <td>0.000205</td>\n",
              "      <td>0.000208</td>\n",
              "      <td>0.000202</td>\n",
              "      <td>0.000162</td>\n",
              "      <td>0.000167</td>\n",
              "      <td>0.000178</td>\n",
              "      <td>0.000186</td>\n",
              "      <td>0.000214</td>\n",
              "      <td>0.000307</td>\n",
              "      <td>0.000457</td>\n",
              "      <td>0.000600</td>\n",
              "      <td>0.000593</td>\n",
              "      <td>0.000361</td>\n",
              "      <td>...</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",
              "      <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",
              "      <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",
              "      <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",
              "      <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>0.008568</td>\n",
              "      <td>0.010692</td>\n",
              "      <td>0.021547</td>\n",
              "      <td>0.029348</td>\n",
              "      <td>0.048915</td>\n",
              "      <td>0.044409</td>\n",
              "      <td>0.043804</td>\n",
              "      <td>0.045297</td>\n",
              "      <td>0.038744</td>\n",
              "      <td>0.040392</td>\n",
              "      <td>0.041581</td>\n",
              "      <td>0.048128</td>\n",
              "      <td>0.053949</td>\n",
              "      <td>0.075276</td>\n",
              "      <td>0.099211</td>\n",
              "      <td>0.132784</td>\n",
              "      <td>0.153166</td>\n",
              "      <td>0.122404</td>\n",
              "      <td>0.081734</td>\n",
              "      <td>0.060630</td>\n",
              "      <td>0.038749</td>\n",
              "      <td>0.001079</td>\n",
              "      <td>0.000208</td>\n",
              "      <td>0.000195</td>\n",
              "      <td>0.000476</td>\n",
              "      <td>0.000754</td>\n",
              "      <td>0.000914</td>\n",
              "      <td>0.000806</td>\n",
              "      <td>0.000698</td>\n",
              "      <td>0.000587</td>\n",
              "      <td>0.000461</td>\n",
              "      <td>0.000377</td>\n",
              "      <td>0.000358</td>\n",
              "      <td>0.000347</td>\n",
              "      <td>0.000377</td>\n",
              "      <td>0.000514</td>\n",
              "      <td>0.000671</td>\n",
              "      <td>0.000948</td>\n",
              "      <td>0.000913</td>\n",
              "      <td>0.000590</td>\n",
              "      <td>...</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",
              "      <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",
              "      <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",
              "      <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",
              "      <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>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.024445</td>\n",
              "      <td>0.020527</td>\n",
              "      <td>0.049167</td>\n",
              "      <td>0.060551</td>\n",
              "      <td>0.090934</td>\n",
              "      <td>0.085646</td>\n",
              "      <td>0.084995</td>\n",
              "      <td>0.088062</td>\n",
              "      <td>0.067624</td>\n",
              "      <td>0.066744</td>\n",
              "      <td>0.066744</td>\n",
              "      <td>0.077779</td>\n",
              "      <td>0.085711</td>\n",
              "      <td>0.119262</td>\n",
              "      <td>0.143763</td>\n",
              "      <td>0.187002</td>\n",
              "      <td>0.207671</td>\n",
              "      <td>0.162674</td>\n",
              "      <td>0.101289</td>\n",
              "      <td>0.072549</td>\n",
              "      <td>0.045997</td>\n",
              "      <td>0.001128</td>\n",
              "      <td>0.001162</td>\n",
              "      <td>0.000636</td>\n",
              "      <td>0.001433</td>\n",
              "      <td>0.001919</td>\n",
              "      <td>0.002344</td>\n",
              "      <td>0.002149</td>\n",
              "      <td>0.001802</td>\n",
              "      <td>0.001442</td>\n",
              "      <td>0.001033</td>\n",
              "      <td>0.000857</td>\n",
              "      <td>0.000751</td>\n",
              "      <td>0.000818</td>\n",
              "      <td>0.000805</td>\n",
              "      <td>0.001412</td>\n",
              "      <td>0.001667</td>\n",
              "      <td>0.002078</td>\n",
              "      <td>0.001934</td>\n",
              "      <td>0.001565</td>\n",
              "      <td>...</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",
              "      <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",
              "      <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",
              "      <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",
              "      <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>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.090967</td>\n",
              "      <td>0.061377</td>\n",
              "      <td>0.138106</td>\n",
              "      <td>0.142725</td>\n",
              "      <td>0.190515</td>\n",
              "      <td>0.180290</td>\n",
              "      <td>0.174058</td>\n",
              "      <td>0.173888</td>\n",
              "      <td>0.151037</td>\n",
              "      <td>0.139941</td>\n",
              "      <td>0.155552</td>\n",
              "      <td>0.162524</td>\n",
              "      <td>0.163824</td>\n",
              "      <td>0.215078</td>\n",
              "      <td>0.222108</td>\n",
              "      <td>0.277862</td>\n",
              "      <td>0.311222</td>\n",
              "      <td>0.248098</td>\n",
              "      <td>0.158450</td>\n",
              "      <td>0.115519</td>\n",
              "      <td>0.068006</td>\n",
              "      <td>0.001785</td>\n",
              "      <td>0.006101</td>\n",
              "      <td>0.001888</td>\n",
              "      <td>0.004978</td>\n",
              "      <td>0.006052</td>\n",
              "      <td>0.005202</td>\n",
              "      <td>0.004545</td>\n",
              "      <td>0.004139</td>\n",
              "      <td>0.003384</td>\n",
              "      <td>0.002736</td>\n",
              "      <td>0.002155</td>\n",
              "      <td>0.001887</td>\n",
              "      <td>0.002028</td>\n",
              "      <td>0.002231</td>\n",
              "      <td>0.002906</td>\n",
              "      <td>0.004009</td>\n",
              "      <td>0.005522</td>\n",
              "      <td>0.006034</td>\n",
              "      <td>0.005312</td>\n",
              "      <td>...</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",
              "      <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",
              "      <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",
              "      <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>5.500000</td>\n",
              "      <td>35.700000</td>\n",
              "      <td>44.329246</td>\n",
              "      <td>31.188889</td>\n",
              "      <td>26.487542</td>\n",
              "      <td>1137.666667</td>\n",
              "      <td>2121.708627</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.774630</td>\n",
              "      <td>1.186912</td>\n",
              "      <td>0.553816</td>\n",
              "      <td>0.582580</td>\n",
              "      <td>0.629374</td>\n",
              "      <td>0.632144</td>\n",
              "      <td>0.602372</td>\n",
              "      <td>0.623863</td>\n",
              "      <td>0.571296</td>\n",
              "      <td>0.559225</td>\n",
              "      <td>0.547230</td>\n",
              "      <td>0.573955</td>\n",
              "      <td>0.560946</td>\n",
              "      <td>0.635504</td>\n",
              "      <td>0.695642</td>\n",
              "      <td>0.764285</td>\n",
              "      <td>0.752770</td>\n",
              "      <td>0.661413</td>\n",
              "      <td>0.500618</td>\n",
              "      <td>0.395318</td>\n",
              "      <td>0.290303</td>\n",
              "      <td>0.051685</td>\n",
              "      <td>0.212005</td>\n",
              "      <td>0.129201</td>\n",
              "      <td>0.085371</td>\n",
              "      <td>0.063333</td>\n",
              "      <td>0.053241</td>\n",
              "      <td>0.042378</td>\n",
              "      <td>0.038797</td>\n",
              "      <td>0.037668</td>\n",
              "      <td>0.032571</td>\n",
              "      <td>0.031652</td>\n",
              "      <td>0.030512</td>\n",
              "      <td>0.030676</td>\n",
              "      <td>0.030080</td>\n",
              "      <td>0.124198</td>\n",
              "      <td>0.155700</td>\n",
              "      <td>0.823855</td>\n",
              "      <td>0.762962</td>\n",
              "      <td>0.272247</td>\n",
              "      <td>...</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.307692</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.272727</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.090909</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>188.000000</td>\n",
              "      <td>510.453580</td>\n",
              "      <td>187.500000</td>\n",
              "      <td>251.984335</td>\n",
              "      <td>18638.333333</td>\n",
              "      <td>103512.089769</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>11.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 260 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "              0           1    ...         258         259\n",
              "count  580.000000  426.000000  ...  429.000000  422.000000\n",
              "mean     0.067947    0.064403  ...    0.466200    5.094787\n",
              "std      0.103288    0.114783  ...    0.499439    3.322781\n",
              "min      0.001333    0.002663  ...    0.000000    0.000000\n",
              "25%      0.008568    0.010692  ...    0.000000    2.000000\n",
              "50%      0.024445    0.020527  ...    0.000000    5.000000\n",
              "75%      0.090967    0.061377  ...    1.000000    8.000000\n",
              "max      0.774630    1.186912  ...    1.000000   11.000000\n",
              "\n",
              "[8 rows x 260 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
        },
        "id": "J0MTDI2WeTG_",
        "outputId": "e53d9919-052b-4d12-dc98-19b54ecab752"
      },
      "source": [
        "df.head(2)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>221</th>\n",
              "      <th>222</th>\n",
              "      <th>223</th>\n",
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              "      <th>225</th>\n",
              "      <th>226</th>\n",
              "      <th>227</th>\n",
              "      <th>228</th>\n",
              "      <th>229</th>\n",
              "      <th>230</th>\n",
              "      <th>231</th>\n",
              "      <th>232</th>\n",
              "      <th>233</th>\n",
              "      <th>234</th>\n",
              "      <th>235</th>\n",
              "      <th>236</th>\n",
              "      <th>237</th>\n",
              "      <th>238</th>\n",
              "      <th>239</th>\n",
              "      <th>240</th>\n",
              "      <th>241</th>\n",
              "      <th>242</th>\n",
              "      <th>243</th>\n",
              "      <th>244</th>\n",
              "      <th>245</th>\n",
              "      <th>246</th>\n",
              "      <th>247</th>\n",
              "      <th>248</th>\n",
              "      <th>249</th>\n",
              "      <th>250</th>\n",
              "      <th>251</th>\n",
              "      <th>252</th>\n",
              "      <th>253</th>\n",
              "      <th>254</th>\n",
              "      <th>255</th>\n",
              "      <th>256</th>\n",
              "      <th>257</th>\n",
              "      <th>258</th>\n",
              "      <th>259</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>0.016521</td>\n",
              "      <td>0.039926</td>\n",
              "      <td>0.089632</td>\n",
              "      <td>0.134119</td>\n",
              "      <td>0.170470</td>\n",
              "      <td>0.176872</td>\n",
              "      <td>0.171546</td>\n",
              "      <td>0.182392</td>\n",
              "      <td>0.162482</td>\n",
              "      <td>0.159083</td>\n",
              "      <td>0.164531</td>\n",
              "      <td>0.163366</td>\n",
              "      <td>0.171633</td>\n",
              "      <td>0.219787</td>\n",
              "      <td>0.270805</td>\n",
              "      <td>0.339206</td>\n",
              "      <td>0.327098</td>\n",
              "      <td>0.264581</td>\n",
              "      <td>0.173363</td>\n",
              "      <td>0.131426</td>\n",
              "      <td>0.068158</td>\n",
              "      <td>0.001216</td>\n",
              "      <td>0.000493</td>\n",
              "      <td>0.001114</td>\n",
              "      <td>0.003419</td>\n",
              "      <td>0.004479</td>\n",
              "      <td>0.004231</td>\n",
              "      <td>0.004013</td>\n",
              "      <td>0.003757</td>\n",
              "      <td>0.003314</td>\n",
              "      <td>0.003246</td>\n",
              "      <td>0.002308</td>\n",
              "      <td>0.002129</td>\n",
              "      <td>0.001777</td>\n",
              "      <td>0.002338</td>\n",
              "      <td>0.002030</td>\n",
              "      <td>0.003741</td>\n",
              "      <td>0.005809</td>\n",
              "      <td>0.006852</td>\n",
              "      <td>0.004594</td>\n",
              "      <td>...</td>\n",
              "      <td>0.230769</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>16.384615</td>\n",
              "      <td>20.617394</td>\n",
              "      <td>46.769231</td>\n",
              "      <td>71.863118</td>\n",
              "      <td>788.923077</td>\n",
              "      <td>1761.80218</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.006600</td>\n",
              "      <td>0.035984</td>\n",
              "      <td>0.089956</td>\n",
              "      <td>0.123214</td>\n",
              "      <td>0.172273</td>\n",
              "      <td>0.177068</td>\n",
              "      <td>0.165507</td>\n",
              "      <td>0.179655</td>\n",
              "      <td>0.161744</td>\n",
              "      <td>0.163678</td>\n",
              "      <td>0.161606</td>\n",
              "      <td>0.159523</td>\n",
              "      <td>0.171042</td>\n",
              "      <td>0.217206</td>\n",
              "      <td>0.254929</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.242930</td>\n",
              "      <td>0.151817</td>\n",
              "      <td>0.105817</td>\n",
              "      <td>0.062566</td>\n",
              "      <td>0.001245</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000972</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.004070</td>\n",
              "      <td>0.004311</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.003967</td>\n",
              "      <td>0.003847</td>\n",
              "      <td>0.002940</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.001950</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.002207</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.001875</td>\n",
              "      <td>0.001428</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 260 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        0         1         2         3    ...         256         257  258  259\n",
              "0  0.016521  0.039926  0.089632  0.134119  ...  788.923077  1761.80218  1.0  2.0\n",
              "1  0.006600  0.035984  0.089956  0.123214  ...         NaN         NaN  0.0  NaN\n",
              "\n",
              "[2 rows x 260 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BIcDtz3iR647"
      },
      "source": [
        "#Instalando o panda profile\n",
        "#!pip install pandas-profiling\n",
        "#from pandas_profiling import ProfileReport"
      ],
      "execution_count": 39,
      "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": "5ddbdb28-d4f4-43f5-ac83-c5b027751d46"
      },
      "source": [
        "#installing\n",
        "!pip install missingpy"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting missingpy\n",
            "  Downloading missingpy-0.2.0-py3-none-any.whl (49 kB)\n",
            "\u001b[?25l\r\u001b[K     |██████▊                         | 10 kB 21.5 MB/s eta 0:00:01\r\u001b[K     |█████████████▍                  | 20 kB 9.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████            | 30 kB 8.2 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▊     | 40 kB 7.6 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 49 kB 2.1 MB/s \n",
            "\u001b[?25hInstalling collected packages: missingpy\n",
            "Successfully installed missingpy-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": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dl6nOtFdgfAo"
      },
      "source": [
        "#!pip install sklearn\n",
        "#import sklearn\n",
        "#from sklearn import neighbors"
      ],
      "execution_count": 42,
      "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": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WhGruSVm7qL3"
      },
      "source": [
        "from sklearn.impute import SimpleImputer"
      ],
      "execution_count": 44,
      "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": 45,
      "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": 46,
      "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": 47,
      "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": 48,
      "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": 49,
      "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": 50,
      "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": 51,
      "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": 52,
      "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": 53,
      "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": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CVNMKwXTymeK",
        "outputId": "62fc2dd8-79a3-4de8-a8d4-b69cc0c7e744"
      },
      "source": [
        "len(individuos)"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "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": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I8iRKpnyzW0t",
        "outputId": "f79816fc-0d1b-46a8-8376-8180ba863352"
      },
      "source": [
        "populationInitial"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([[-4.98315417e-01, -2.49118584e-01, -1.10149931e-01, ...,\n",
              "         -2.02730444e-01,  1.24419398e+00, -1.09320578e+00],\n",
              "        [-5.94449894e-01, -2.89238307e-01, -1.06976216e-01, ...,\n",
              "         -5.22461068e-16, -1.08663229e+00,  2.19618768e-15],\n",
              "        [-5.91601035e-01, -4.75099900e-01, -5.17345334e-01, ...,\n",
              "         -3.74926625e-01,  1.24419398e+00, -1.09320578e+00],\n",
              "        ...,\n",
              "        [-2.95687854e-01,  1.12993002e-15, -7.82340697e-01, ...,\n",
              "         -3.86743320e-01, -1.08663229e+00,  3.19758507e-01],\n",
              "        [ 1.28015787e+00, -9.78706848e-02,  3.54309469e-01, ...,\n",
              "         -3.86743320e-01, -1.08663229e+00,  2.19618768e-15],\n",
              "        [-5.74129967e-01, -5.33020080e-01, -7.80352227e-01, ...,\n",
              "         -3.86743320e-01, -1.08663229e+00,  3.19758507e-01]]),\n",
              " array([[-0.49831542, -0.12808597,  0.01098666, ..., -0.10857575,\n",
              "          1.37840488, -1.08396425],\n",
              "        [-0.59444989, -0.16744766,  0.01408869, ..., -0.29013113,\n",
              "         -0.72547625, -0.02435875],\n",
              "        [-0.59160103, -0.34979751, -0.38701156, ..., -0.27847224,\n",
              "          1.37840488, -1.08396425],\n",
              "        ...,\n",
              "        [-0.29568785, -0.32178401, -0.64602156, ..., -0.29013113,\n",
              "         -0.72547625,  0.32884309],\n",
              "        [ 1.28015787,  0.0203042 ,  0.46495547, ..., -0.29013113,\n",
              "         -0.72547625, -0.02435875],\n",
              "        [-0.57412997, -0.40662333, -0.64407801, ..., -0.29013113,\n",
              "         -0.72547625,  0.32884309]]),\n",
              " array([[-0.49831542, -0.11277901,  0.07883843, ..., -0.10857575,\n",
              "          1.37840488, -1.16853135],\n",
              "        [-0.59444989, -0.15194366,  0.08183886, ..., -0.29013113,\n",
              "         -0.72547625,  0.23032971],\n",
              "        [-0.59160103, -0.33338074, -0.30612402, ..., -0.27847224,\n",
              "          1.37840488, -1.16853135],\n",
              "        ...,\n",
              "        [-0.29568785, -0.36074235, -0.55665058, ..., -0.29013113,\n",
              "         -0.72547625,  0.23032971],\n",
              "        [ 1.28015787,  0.03486837,  0.51793824, ..., -0.29013113,\n",
              "         -0.72547625,  0.23032971],\n",
              "        [-0.57412997, -0.38992211, -0.55477068, ..., -0.29013113,\n",
              "         -0.72547625,  0.23032971]]),\n",
              " array([[-0.49831542, -0.28052764, -0.09920063, ..., -0.14832196,\n",
              "          1.24049507, -0.9740829 ],\n",
              "        [-0.59444989, -0.31483356, -0.09620596, ..., -0.32273203,\n",
              "         -0.89026645, -0.75261364],\n",
              "        [-0.59160103, -0.47376173, -0.4834239 , ..., -0.311532  ,\n",
              "          1.24049507, -0.9740829 ],\n",
              "        ...,\n",
              "        [-0.29568785, -0.51708073, -0.73346941, ..., -0.32273203,\n",
              "         -0.89026645,  0.35473266],\n",
              "        [ 1.28015787, -0.15119725,  0.33905605, ..., -0.32273203,\n",
              "         -0.89026645,  0.90840581],\n",
              "        [-0.57412997, -0.52328863, -0.73159313, ..., -0.32273203,\n",
              "         -0.89026645,  0.35473266]]),\n",
              " array([[-0.49831542, -0.27819013, -0.09098964, ..., -0.14697543,\n",
              "          1.27475258, -1.04426014],\n",
              "        [-0.59444989, -0.3167186 , -0.08790017, ..., -0.31940429,\n",
              "         -0.89217736, -0.41683865],\n",
              "        [-0.59160103, -0.49520845, -0.48737693, ..., -0.31496597,\n",
              "          1.27475258, -1.04426014],\n",
              "        ...,\n",
              "        [-0.29568785, -0.23918078, -0.74533857, ..., -0.32649406,\n",
              "         -0.89217736,  0.35000984],\n",
              "        [ 1.28015787, -0.1329411 ,  0.36114169, ..., -0.32649406,\n",
              "         -0.89217736,  0.3151531 ],\n",
              "        [-0.57412997, -0.55083137, -0.74340288, ..., -0.32649406,\n",
              "         -0.89217736,  0.35000984]])]"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "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": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oCZgVleqW-wI"
      },
      "source": [
        ""
      ],
      "execution_count": 58,
      "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('10nn', 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": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ia_BbBmaYRGk",
        "outputId": "4451d736-4a85-4dc8-d5d5-e297ca4f43a1"
      },
      "source": [
        "populacao_inicial_avaliada = fitness(populationInitial)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tam_pop_atual:  5\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.46153846153846156  melhor: 0.5384615384615384  escolhido:  0.5384615384615384\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.4  melhor: 0.5076923076923077  escolhido:  0.5076923076923077\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.4461538461538462  melhor: 0.5076923076923077  escolhido:  0.5076923076923077\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.46153846153846156  melhor: 0.5076923076923077  escolhido:  0.5076923076923077\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.46153846153846156  melhor: 0.5076923076923077  escolhido:  0.5076923076923077\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": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_jmojq6fKLe1",
        "outputId": "f0b1a1df-4d27-404d-eed6-56fd6a69e770"
      },
      "source": [
        "populacao_inicial_avaliada"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[0.5384615384615384,\n",
              "  array([[-4.98315417e-01, -2.49118584e-01, -1.10149931e-01, ...,\n",
              "          -2.02730444e-01,  1.24419398e+00, -1.09320578e+00],\n",
              "         [-5.94449894e-01, -2.89238307e-01, -1.06976216e-01, ...,\n",
              "          -5.22461068e-16, -1.08663229e+00,  2.19618768e-15],\n",
              "         [-5.91601035e-01, -4.75099900e-01, -5.17345334e-01, ...,\n",
              "          -3.74926625e-01,  1.24419398e+00, -1.09320578e+00],\n",
              "         ...,\n",
              "         [-2.95687854e-01,  1.12993002e-15, -7.82340697e-01, ...,\n",
              "          -3.86743320e-01, -1.08663229e+00,  3.19758507e-01],\n",
              "         [ 1.28015787e+00, -9.78706848e-02,  3.54309469e-01, ...,\n",
              "          -3.86743320e-01, -1.08663229e+00,  2.19618768e-15],\n",
              "         [-5.74129967e-01, -5.33020080e-01, -7.80352227e-01, ...,\n",
              "          -3.86743320e-01, -1.08663229e+00,  3.19758507e-01]])],\n",
              " [0.5076923076923077,\n",
              "  array([[-0.49831542, -0.12808597,  0.01098666, ..., -0.10857575,\n",
              "           1.37840488, -1.08396425],\n",
              "         [-0.59444989, -0.16744766,  0.01408869, ..., -0.29013113,\n",
              "          -0.72547625, -0.02435875],\n",
              "         [-0.59160103, -0.34979751, -0.38701156, ..., -0.27847224,\n",
              "           1.37840488, -1.08396425],\n",
              "         ...,\n",
              "         [-0.29568785, -0.32178401, -0.64602156, ..., -0.29013113,\n",
              "          -0.72547625,  0.32884309],\n",
              "         [ 1.28015787,  0.0203042 ,  0.46495547, ..., -0.29013113,\n",
              "          -0.72547625, -0.02435875],\n",
              "         [-0.57412997, -0.40662333, -0.64407801, ..., -0.29013113,\n",
              "          -0.72547625,  0.32884309]])],\n",
              " [0.5076923076923077,\n",
              "  array([[-0.49831542, -0.11277901,  0.07883843, ..., -0.10857575,\n",
              "           1.37840488, -1.16853135],\n",
              "         [-0.59444989, -0.15194366,  0.08183886, ..., -0.29013113,\n",
              "          -0.72547625,  0.23032971],\n",
              "         [-0.59160103, -0.33338074, -0.30612402, ..., -0.27847224,\n",
              "           1.37840488, -1.16853135],\n",
              "         ...,\n",
              "         [-0.29568785, -0.36074235, -0.55665058, ..., -0.29013113,\n",
              "          -0.72547625,  0.23032971],\n",
              "         [ 1.28015787,  0.03486837,  0.51793824, ..., -0.29013113,\n",
              "          -0.72547625,  0.23032971],\n",
              "         [-0.57412997, -0.38992211, -0.55477068, ..., -0.29013113,\n",
              "          -0.72547625,  0.23032971]])],\n",
              " [0.5076923076923077,\n",
              "  array([[-0.49831542, -0.28052764, -0.09920063, ..., -0.14832196,\n",
              "           1.24049507, -0.9740829 ],\n",
              "         [-0.59444989, -0.31483356, -0.09620596, ..., -0.32273203,\n",
              "          -0.89026645, -0.75261364],\n",
              "         [-0.59160103, -0.47376173, -0.4834239 , ..., -0.311532  ,\n",
              "           1.24049507, -0.9740829 ],\n",
              "         ...,\n",
              "         [-0.29568785, -0.51708073, -0.73346941, ..., -0.32273203,\n",
              "          -0.89026645,  0.35473266],\n",
              "         [ 1.28015787, -0.15119725,  0.33905605, ..., -0.32273203,\n",
              "          -0.89026645,  0.90840581],\n",
              "         [-0.57412997, -0.52328863, -0.73159313, ..., -0.32273203,\n",
              "          -0.89026645,  0.35473266]])],\n",
              " [0.5076923076923077,\n",
              "  array([[-0.49831542, -0.27819013, -0.09098964, ..., -0.14697543,\n",
              "           1.27475258, -1.04426014],\n",
              "         [-0.59444989, -0.3167186 , -0.08790017, ..., -0.31940429,\n",
              "          -0.89217736, -0.41683865],\n",
              "         [-0.59160103, -0.49520845, -0.48737693, ..., -0.31496597,\n",
              "           1.27475258, -1.04426014],\n",
              "         ...,\n",
              "         [-0.29568785, -0.23918078, -0.74533857, ..., -0.32649406,\n",
              "          -0.89217736,  0.35000984],\n",
              "         [ 1.28015787, -0.1329411 ,  0.36114169, ..., -0.32649406,\n",
              "          -0.89217736,  0.3151531 ],\n",
              "         [-0.57412997, -0.55083137, -0.74340288, ..., -0.32649406,\n",
              "          -0.89217736,  0.35000984]])]]"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "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": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xltBmsu9Vab1"
      },
      "source": [
        "elitista = elitismo(populacao_inicial_avaliada)"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PKolrnNSNQC_",
        "outputId": "257f77d2-c1d6-4800-e8e6-ee1910802ae1"
      },
      "source": [
        "elitista"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.5384615384615384,\n",
              " array([[-4.98315417e-01, -2.49118584e-01, -1.10149931e-01, ...,\n",
              "         -2.02730444e-01,  1.24419398e+00, -1.09320578e+00],\n",
              "        [-5.94449894e-01, -2.89238307e-01, -1.06976216e-01, ...,\n",
              "         -5.22461068e-16, -1.08663229e+00,  2.19618768e-15],\n",
              "        [-5.91601035e-01, -4.75099900e-01, -5.17345334e-01, ...,\n",
              "         -3.74926625e-01,  1.24419398e+00, -1.09320578e+00],\n",
              "        ...,\n",
              "        [-2.95687854e-01,  1.12993002e-15, -7.82340697e-01, ...,\n",
              "         -3.86743320e-01, -1.08663229e+00,  3.19758507e-01],\n",
              "        [ 1.28015787e+00, -9.78706848e-02,  3.54309469e-01, ...,\n",
              "         -3.86743320e-01, -1.08663229e+00,  2.19618768e-15],\n",
              "        [-5.74129967e-01, -5.33020080e-01, -7.80352227e-01, ...,\n",
              "         -3.86743320e-01, -1.08663229e+00,  3.19758507e-01]])]"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "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": 66,
      "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": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kcba-uAIsF91",
        "outputId": "3fb94388-8666-4402-aa3e-11fa13bba6bd"
      },
      "source": [
        "import numpy\n",
        "ss = index_vas\n",
        "final_list = numpy.array_split(ss,2);\n",
        "final_list"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([[280, 216],\n",
              "        [511, 252],\n",
              "        [367, 151],\n",
              "        ...,\n",
              "        [111,  94],\n",
              "        [472, 227],\n",
              "        [221,  20]]), array([[209, 253],\n",
              "        [144, 249],\n",
              "        [163, 195],\n",
              "        ...,\n",
              "        [247,  78],\n",
              "        [ 95, 217],\n",
              "        [521, 181]])]"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "spXPuxaWsdaY",
        "outputId": "508ef760-9efb-42b8-ef80-09725387ae9a"
      },
      "source": [
        "len(ss), len(final_list[0]),len(final_list[1]) "
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(45240, 22620, 22620)"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "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": 70,
      "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": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SrTV9X19pUOO"
      },
      "source": [
        "#nova_populacao_recombinada = crossover(ss,2,atual,4)"
      ],
      "execution_count": 72,
      "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": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hcWIluclhe8l"
      },
      "source": [
        "#ind = mutacao(df_amp)"
      ],
      "execution_count": 74,
      "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": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ESP4G7Fj9-V7",
        "outputId": "7be1f594-af2e-4d4c-dfb8-bfddf1bf813c"
      },
      "source": [
        "populationInitial[0]"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-4.98315417e-01, -2.49118584e-01, -1.10149931e-01, ...,\n",
              "        -2.02730444e-01,  1.24419398e+00, -1.09320578e+00],\n",
              "       [-5.94449894e-01, -2.89238307e-01, -1.06976216e-01, ...,\n",
              "        -5.22461068e-16, -1.08663229e+00,  2.19618768e-15],\n",
              "       [-5.91601035e-01, -4.75099900e-01, -5.17345334e-01, ...,\n",
              "        -3.74926625e-01,  1.24419398e+00, -1.09320578e+00],\n",
              "       ...,\n",
              "       [-2.95687854e-01,  1.12993002e-15, -7.82340697e-01, ...,\n",
              "        -3.86743320e-01, -1.08663229e+00,  3.19758507e-01],\n",
              "       [ 1.28015787e+00, -9.78706848e-02,  3.54309469e-01, ...,\n",
              "        -3.86743320e-01, -1.08663229e+00,  2.19618768e-15],\n",
              "       [-5.74129967e-01, -5.33020080e-01, -7.80352227e-01, ...,\n",
              "        -3.86743320e-01, -1.08663229e+00,  3.19758507e-01]])"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8ZQv6VWu6NaI"
      },
      "source": [
        "a = populationInitial.pop(0)"
      ],
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "opUvT2THJtIP",
        "outputId": "df5b79ef-5ae8-44e2-8b99-e977233897b9"
      },
      "source": [
        "a"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-4.98315417e-01, -2.49118584e-01, -1.10149931e-01, ...,\n",
              "        -2.02730444e-01,  1.24419398e+00, -1.09320578e+00],\n",
              "       [-5.94449894e-01, -2.89238307e-01, -1.06976216e-01, ...,\n",
              "        -5.22461068e-16, -1.08663229e+00,  2.19618768e-15],\n",
              "       [-5.91601035e-01, -4.75099900e-01, -5.17345334e-01, ...,\n",
              "        -3.74926625e-01,  1.24419398e+00, -1.09320578e+00],\n",
              "       ...,\n",
              "       [-2.95687854e-01,  1.12993002e-15, -7.82340697e-01, ...,\n",
              "        -3.86743320e-01, -1.08663229e+00,  3.19758507e-01],\n",
              "       [ 1.28015787e+00, -9.78706848e-02,  3.54309469e-01, ...,\n",
              "        -3.86743320e-01, -1.08663229e+00,  2.19618768e-15],\n",
              "       [-5.74129967e-01, -5.33020080e-01, -7.80352227e-01, ...,\n",
              "        -3.86743320e-01, -1.08663229e+00,  3.19758507e-01]])"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C054OgBV14SY"
      },
      "source": [
        "# Algoritmo Genético"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZrkPY0FB0dFk"
      },
      "source": [
        "\n",
        "\n",
        "1.   elitisimo: 1\n",
        "2.   gerações: 6\n",
        "3.   geracoes_sizes{6,12,18,24,30}\n",
        "4.   cruzamento: n-points\n",
        "5.   cruzamento: torneio\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WXpNshUS0hoH"
      },
      "source": [
        "def nova_populacao(df_amp,individuo_elitista, pop_atual, tam_population,n_points, list_index_vas=index_vas):\n",
        "  formacao_nova_populacao = []\n",
        "  formacao_nova_populacao.append(individuo_elitista)\n",
        "  while(len(formacao_nova_populacao)<= tam_population):\n",
        "    formacao_nova_populacao.append(selecao(pop_atual))\n",
        "  #pega a população selecionada para fazer a recombinação\n",
        "  nova_populacao_combinada = crossover(list_index_vas,n_points, formacao_nova_populacao, tam_population)\n",
        "  #20% dos individuos sofrem mutações\n",
        "  #a nova populacao combinada é passada como parametro\n",
        "  populacao_cruzada_e_mutada = mutations(df_amp,nova_populacao_combinada, 0.20)\n",
        "\n",
        "  return populacao_cruzada_e_mutada"
      ],
      "execution_count": 79,
      "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": 80,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LgibKIEbAurT"
      },
      "source": [
        "# Running"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9OysaJjwBFO3",
        "outputId": "cf0c5098-d875-40b5-ff1b-dcb752644fa0"
      },
      "source": [
        "gen_sizes = [10,20,40,20,10,5]\n",
        "result,p = main(gen_sizes)\n",
        "result"
      ],
      "execution_count": 81,
      "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: 0.46153846153846156  melhor: 0.5076923076923077  escolhido:  0.5076923076923077\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.46153846153846156  melhor: 0.5384615384615384  escolhido:  0.5384615384615384\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.46153846153846156  melhor: 0.5076923076923077  escolhido:  0.5076923076923077\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.4307692307692308  melhor: 0.5076923076923077  escolhido:  0.5076923076923077\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.4307692307692308  melhor: 0.5384615384615384  escolhido:  0.5384615384615384\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.4461538461538462  melhor: 0.46153846153846156  escolhido:  0.46153846153846156\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.46153846153846156  melhor: 0.5384615384615384  escolhido:  0.5384615384615384\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.46153846153846156  melhor: 0.5076923076923077  escolhido:  0.5076923076923077\n",
            "----------------------------------------------------\n",
            "Individual -  8\n",
            "Pior: 0.4461538461538462  melhor: 0.46153846153846156  escolhido:  0.46153846153846156\n",
            "----------------------------------------------------\n",
            "Individual -  9\n",
            "Pior: 0.4461538461538462  melhor: 0.49230769230769234  escolhido:  0.49230769230769234\n",
            "----------------------------------------------------\n",
            "melhor anterior foi escolhido:  0.5384615384615384\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: 0.4461538461538462  melhor: 0.46153846153846156  escolhido:  0.46153846153846156\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.4307692307692308  melhor: 0.46153846153846156  escolhido:  0.46153846153846156\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.4461538461538462  melhor: 0.46153846153846156  escolhido:  0.46153846153846156\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.4461538461538462  melhor: 0.4461538461538462  escolhido:  0.4461538461538462\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.2153846153846154  melhor: 0.47692307692307695  escolhido:  0.47692307692307695\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.4461538461538462  melhor: 0.4461538461538462  escolhido:  0.4461538461538462\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.4461538461538462  melhor: 0.4461538461538462  escolhido:  0.4461538461538462\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.4307692307692308  melhor: 0.4461538461538462  escolhido:  0.4461538461538462\n",
            "----------------------------------------------------\n",
            "Individual -  8\n",
            "Pior: 0.4461538461538462  melhor: 0.4461538461538462  escolhido:  0.4461538461538462\n",
            "----------------------------------------------------\n",
            "Individual -  9\n",
            "Pior: 0.2153846153846154  melhor: 0.4461538461538462  escolhido:  0.4461538461538462\n",
            "----------------------------------------------------\n",
            "Individual -  10\n",
            "Pior: 0.4461538461538462  melhor: 0.4461538461538462  escolhido:  0.4461538461538462\n",
            "----------------------------------------------------\n",
            "Individual -  11\n",
            "Pior: 0.4307692307692308  melhor: 0.4461538461538462  escolhido:  0.4461538461538462\n",
            "----------------------------------------------------\n",
            "Individual -  12\n",
            "Pior: 0.4461538461538462  melhor: 0.46153846153846156  escolhido:  0.46153846153846156\n",
            "----------------------------------------------------\n",
            "Individual -  13\n",
            "Pior: 0.4461538461538462  melhor: 0.46153846153846156  escolhido:  0.46153846153846156\n",
            "----------------------------------------------------\n",
            "Individual -  14\n",
            "Pior: 0.2153846153846154  melhor: 0.4461538461538462  escolhido:  0.4461538461538462\n",
            "----------------------------------------------------\n",
            "Individual -  15\n",
            "Pior: 0.4461538461538462  melhor: 0.46153846153846156  escolhido:  0.46153846153846156\n",
            "----------------------------------------------------\n",
            "Individual -  16\n",
            "Pior: 0.2153846153846154  melhor: 0.4461538461538462  escolhido:  0.4461538461538462\n",
            "----------------------------------------------------\n",
            "Individual -  17\n",
            "Pior: 0.4461538461538462  melhor: 0.47692307692307695  escolhido:  0.47692307692307695\n",
            "----------------------------------------------------\n",
            "Individual -  18\n",
            "Pior: 0.4461538461538462  melhor: 0.46153846153846156  escolhido:  0.46153846153846156\n",
            "----------------------------------------------------\n",
            "Individual -  19\n",
            "Pior: 0.4461538461538462  melhor: 0.4461538461538462  escolhido:  0.4461538461538462\n",
            "----------------------------------------------------\n",
            "melhor anterior foi escolhido:  0.5384615384615384\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: 0.4461538461538462  melhor: 0.4461538461538462  escolhido:  0.4461538461538462\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.4461538461538462  melhor: 0.46153846153846156  escolhido:  0.46153846153846156\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.4461538461538462  melhor: 0.46153846153846156  escolhido:  0.46153846153846156\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.4461538461538462  melhor: 0.4461538461538462  escolhido:  0.4461538461538462\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.4461538461538462  melhor: 0.4461538461538462  escolhido:  0.4461538461538462\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.4461538461538462  melhor: 0.4461538461538462  escolhido:  0.4461538461538462\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.4461538461538462  melhor: 0.47692307692307695  escolhido:  0.47692307692307695\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.4461538461538462  melhor: 0.4461538461538462  escolhido:  0.4461538461538462\n",
            "----------------------------------------------------\n",
            "Individual -  8\n",
            "Pior: 0.4461538461538462  melhor: 0.46153846153846156  escolhido:  0.46153846153846156\n",
            "----------------------------------------------------\n",
            "Individual -  9\n",
            "Pior: 0.4461538461538462  melhor: 0.4461538461538462  escolhido:  0.4461538461538462\n",
            "----------------------------------------------------\n",
            "Individual -  10\n",
            "Pior: 0.4461538461538462  melhor: 0.46153846153846156  escolhido:  0.46153846153846156\n",
            "----------------------------------------------------\n",
            "Individual -  11\n",
            "Pior: 0.4461538461538462  melhor: 0.46153846153846156  escolhido:  0.46153846153846156\n",
            "----------------------------------------------------\n",
            "Individual -  12\n",
            "Pior: 0.4307692307692308  melhor: 0.4461538461538462  escolhido:  0.4461538461538462\n",
            "----------------------------------------------------\n",
            "Individual -  13\n",
            "Pior: 0.4153846153846154  melhor: 0.4461538461538462  escolhido:  0.4461538461538462\n",
            "----------------------------------------------------\n",
            "Individual -  14\n",
            "Pior: 0.4461538461538462  melhor: 0.46153846153846156  escolhido:  0.46153846153846156\n",
            "----------------------------------------------------\n",
            "Individual -  15\n",
            "Pior: 0.4461538461538462  melhor: 0.4461538461538462  escolhido:  0.4461538461538462\n",
            "----------------------------------------------------\n",
            "Individual -  16\n",
            "Pior: 0.4307692307692308  melhor: 0.4461538461538462  escolhido:  0.4461538461538462\n",
            "----------------------------------------------------\n",
            "Individual -  17\n",
            "Pior: 0.4461538461538462  melhor: 0.4461538461538462  escolhido:  0.4461538461538462\n",
            "----------------------------------------------------\n",
            "Individual -  18\n",
            "Pior: 0.4461538461538462  melhor: 0.46153846153846156  escolhido:  0.46153846153846156\n",
            "----------------------------------------------------\n",
            "Individual -  19\n",
            "Pior: 0.4461538461538462  melhor: 0.4461538461538462  escolhido:  0.4461538461538462\n",
            "----------------------------------------------------\n",
            "Individual -  20\n",
            "Pior: 0.4307692307692308  melhor: 0.4461538461538462  escolhido:  0.4461538461538462\n",
            "----------------------------------------------------\n",
            "Individual -  21\n",
            "Pior: 0.4461538461538462  melhor: 0.46153846153846156  escolhido:  0.46153846153846156\n",
            "----------------------------------------------------\n",
            "Individual -  22\n",
            "Pior: 0.4461538461538462  melhor: 0.46153846153846156  escolhido:  0.46153846153846156\n",
            "----------------------------------------------------\n",
            "Individual -  23\n",
            "Pior: 0.4461538461538462  melhor: 0.47692307692307695  escolhido:  0.47692307692307695\n",
            "----------------------------------------------------\n",
            "Individual -  24\n",
            "Pior: 0.4461538461538462  melhor: 0.47692307692307695  escolhido:  0.47692307692307695\n",
            "----------------------------------------------------\n",
            "Individual -  25\n",
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            "----------------------------------------------------\n",
            "Individual -  26\n",
            "Pior: 0.4461538461538462  melhor: 0.46153846153846156  escolhido:  0.46153846153846156\n",
            "----------------------------------------------------\n",
            "Individual -  27\n",
            "Pior: 0.4461538461538462  melhor: 0.4461538461538462  escolhido:  0.4461538461538462\n",
            "----------------------------------------------------\n",
            "Individual -  28\n",
            "Pior: 0.4461538461538462  melhor: 0.46153846153846156  escolhido:  0.46153846153846156\n",
            "----------------------------------------------------\n",
            "Individual -  29\n",
            "Pior: 0.4461538461538462  melhor: 0.46153846153846156  escolhido:  0.46153846153846156\n",
            "----------------------------------------------------\n",
            "Individual -  30\n",
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            "----------------------------------------------------\n",
            "Individual -  31\n",
            "Pior: 0.4461538461538462  melhor: 0.46153846153846156  escolhido:  0.46153846153846156\n",
            "----------------------------------------------------\n",
            "Individual -  32\n",
            "Pior: 0.4461538461538462  melhor: 0.46153846153846156  escolhido:  0.46153846153846156\n",
            "----------------------------------------------------\n",
            "Individual -  33\n",
            "Pior: 0.4461538461538462  melhor: 0.46153846153846156  escolhido:  0.46153846153846156\n",
            "----------------------------------------------------\n",
            "Individual -  34\n",
            "Pior: 0.4461538461538462  melhor: 0.46153846153846156  escolhido:  0.46153846153846156\n",
            "----------------------------------------------------\n",
            "Individual -  35\n",
            "Pior: 0.4307692307692308  melhor: 0.4461538461538462  escolhido:  0.4461538461538462\n",
            "----------------------------------------------------\n",
            "Individual -  36\n",
            "Pior: 0.4461538461538462  melhor: 0.46153846153846156  escolhido:  0.46153846153846156\n",
            "----------------------------------------------------\n",
            "Individual -  37\n",
            "Pior: 0.4461538461538462  melhor: 0.4461538461538462  escolhido:  0.4461538461538462\n",
            "----------------------------------------------------\n",
            "Individual -  38\n",
            "Pior: 0.4461538461538462  melhor: 0.46153846153846156  escolhido:  0.46153846153846156\n",
            "----------------------------------------------------\n",
            "Individual -  39\n",
            "Pior: 0.4461538461538462  melhor: 0.46153846153846156  escolhido:  0.46153846153846156\n",
            "----------------------------------------------------\n",
            "melhor anterior foi escolhido:  0.5384615384615384\n",
            "41\n",
            "tamanho da população atual 41\n",
            "pop_cruzada 20\n",
            "tam_pop_atual:  20\n",
            "Avaliações com Binary Relevance\n",
            "\n",
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            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.4461538461538462  melhor: 0.47692307692307695  escolhido:  0.47692307692307695\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.4461538461538462  melhor: 0.46153846153846156  escolhido:  0.46153846153846156\n",
            "----------------------------------------------------\n",
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            "Pior: 0.4307692307692308  melhor: 0.4461538461538462  escolhido:  0.4461538461538462\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.4461538461538462  melhor: 0.4461538461538462  escolhido:  0.4461538461538462\n",
            "----------------------------------------------------\n",
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            "Pior: 0.4307692307692308  melhor: 0.4461538461538462  escolhido:  0.4461538461538462\n",
            "----------------------------------------------------\n",
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            "Pior: 0.4461538461538462  melhor: 0.4461538461538462  escolhido:  0.4461538461538462\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.4461538461538462  melhor: 0.46153846153846156  escolhido:  0.46153846153846156\n",
            "----------------------------------------------------\n",
            "Individual -  8\n",
            "Pior: 0.4461538461538462  melhor: 0.46153846153846156  escolhido:  0.46153846153846156\n",
            "----------------------------------------------------\n",
            "Individual -  9\n",
            "Pior: 0.4461538461538462  melhor: 0.4461538461538462  escolhido:  0.4461538461538462\n",
            "----------------------------------------------------\n",
            "Individual -  10\n",
            "Pior: 0.4461538461538462  melhor: 0.46153846153846156  escolhido:  0.46153846153846156\n",
            "----------------------------------------------------\n",
            "Individual -  11\n",
            "Pior: 0.4461538461538462  melhor: 0.4461538461538462  escolhido:  0.4461538461538462\n",
            "----------------------------------------------------\n",
            "Individual -  12\n",
            "Pior: 0.4461538461538462  melhor: 0.47692307692307695  escolhido:  0.47692307692307695\n",
            "----------------------------------------------------\n",
            "Individual -  13\n",
            "Pior: 0.4461538461538462  melhor: 0.46153846153846156  escolhido:  0.46153846153846156\n",
            "----------------------------------------------------\n",
            "Individual -  14\n",
            "Pior: 0.4461538461538462  melhor: 0.47692307692307695  escolhido:  0.47692307692307695\n",
            "----------------------------------------------------\n",
            "Individual -  15\n",
            "Pior: 0.4461538461538462  melhor: 0.4461538461538462  escolhido:  0.4461538461538462\n",
            "----------------------------------------------------\n",
            "Individual -  16\n",
            "Pior: 0.4461538461538462  melhor: 0.46153846153846156  escolhido:  0.46153846153846156\n",
            "----------------------------------------------------\n",
            "Individual -  17\n",
            "Pior: 0.4461538461538462  melhor: 0.4461538461538462  escolhido:  0.4461538461538462\n",
            "----------------------------------------------------\n",
            "Individual -  18\n",
            "Pior: 0.4461538461538462  melhor: 0.47692307692307695  escolhido:  0.47692307692307695\n",
            "----------------------------------------------------\n",
            "Individual -  19\n",
            "Pior: 0.4461538461538462  melhor: 0.4461538461538462  escolhido:  0.4461538461538462\n",
            "----------------------------------------------------\n",
            "melhor anterior foi escolhido:  0.5384615384615384\n",
            "21\n",
            "tamanho da população atual 21\n",
            "pop_cruzada 10\n",
            "tam_pop_atual:  10\n",
            "Avaliações com Binary Relevance\n",
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            "----------------------------------------------------\n",
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            "Pior: 0.4307692307692308  melhor: 0.46153846153846156  escolhido:  0.46153846153846156\n",
            "----------------------------------------------------\n",
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            "----------------------------------------------------\n",
            "Individual -  3\n",
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            "----------------------------------------------------\n",
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            "----------------------------------------------------\n",
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            "Pior: 0.4307692307692308  melhor: 0.4461538461538462  escolhido:  0.4461538461538462\n",
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            "----------------------------------------------------\n",
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            "Pior: 0.4461538461538462  melhor: 0.46153846153846156  escolhido:  0.46153846153846156\n",
            "----------------------------------------------------\n",
            "Individual -  8\n",
            "Pior: 0.4461538461538462  melhor: 0.46153846153846156  escolhido:  0.46153846153846156\n",
            "----------------------------------------------------\n",
            "Individual -  9\n",
            "Pior: 0.4307692307692308  melhor: 0.4461538461538462  escolhido:  0.4461538461538462\n",
            "----------------------------------------------------\n",
            "melhor anterior foi escolhido:  0.5384615384615384\n",
            "11\n",
            "tamanho da população atual 11\n",
            "pop_cruzada 6\n",
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            "Avaliações com Binary Relevance\n",
            "\n",
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            "Pior: 0.4461538461538462  melhor: 0.4461538461538462  escolhido:  0.4461538461538462\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.4461538461538462  melhor: 0.46153846153846156  escolhido:  0.46153846153846156\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.4461538461538462  melhor: 0.4461538461538462  escolhido:  0.4461538461538462\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.4461538461538462  melhor: 0.46153846153846156  escolhido:  0.46153846153846156\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.4461538461538462  melhor: 0.4461538461538462  escolhido:  0.4461538461538462\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.4461538461538462  melhor: 0.47692307692307695  escolhido:  0.47692307692307695\n",
            "----------------------------------------------------\n",
            "melhor anterior foi escolhido:  0.5384615384615384\n",
            "7\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "           1.24419398, -1.09320578],\n",
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              "          -1.08663229,  0.31975851]])]]"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DHiPqqv754Jn",
        "outputId": "6e4be4fc-601b-4d7c-f28c-20f0ad2262f0"
      },
      "source": [
        "p"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2WjQSIcIeXWr"
      },
      "source": [
        "dados = []\n",
        "for i in result:\n",
        "  dados.append(i[0])"
      ],
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dados"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VI2HZYbm18IA",
        "outputId": "f4452c2e-3580-4214-9475-a439c521652d"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.5384615384615384,\n",
              " 0.5384615384615384,\n",
              " 0.5384615384615384,\n",
              " 0.5384615384615384,\n",
              " 0.5384615384615384,\n",
              " 0.5384615384615384,\n",
              " 0.5384615384615384]"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SK_JnhgtmyTr"
      },
      "source": [
        "geracoes = pd.DataFrame({'geracoes':dados})"
      ],
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
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        "id": "5hBXWVyZnZ3_",
        "outputId": "474f354c-ee67-4ad5-e96c-61f100271a60"
      },
      "source": [
        "geracoes"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "   geracoes\n",
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              "1  0.538462\n",
              "2  0.538462\n",
              "3  0.538462\n",
              "4  0.538462\n",
              "5  0.538462\n",
              "6  0.538462"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "t65Beac9oHgQ",
        "outputId": "f9e917a9-f582-46b8-962f-b61657d4ed26"
      },
      "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": 87,
      "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": "bdab3d18-7d40-4339-bef8-632cdd385212"
      },
      "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": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVM:            0.46153846153846156\n",
            "Random Forest:  0.46153846153846156\n"
          ]
        }
      ]
    }
  ]
}