emo_10_f1_gen_small_Evoimpy.ipynb

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      "name": "emo_10_f1_gen-small-Evoimpy.ipynb",
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      "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": "fa0b5e42-5ea0-4d7c-f459-01e0093ad6aa"
      },
      "source": [
        "dataset_name = 'emotions'\n",
        "from skmultilearn.dataset import load_dataset\n",
        "X,y,feature_names, label_names = load_dataset(dataset_name, 'undivided')"
      ],
      "execution_count": 684,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "emotions:undivided - exists, not redownloading\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ypcrK6NDTzfN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0880df49-b7ad-4f5e-ed72-aa9181694f17"
      },
      "source": [
        "X.toarray()\n",
        "y.toarray()"
      ],
      "execution_count": 685,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 1, 1, 0, 0, 0],\n",
              "       [1, 0, 0, 0, 0, 1],\n",
              "       [0, 1, 0, 0, 0, 1],\n",
              "       ...,\n",
              "       [0, 0, 1, 1, 1, 0],\n",
              "       [0, 1, 1, 0, 0, 0],\n",
              "       [0, 1, 0, 0, 0, 0]])"
            ]
          },
          "metadata": {},
          "execution_count": 685
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lQiS-aqkY3sb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bdae8b5f-b175-49f2-ec7b-8ceb5a3106a1"
      },
      "source": [
        "feature_names"
      ],
      "execution_count": 686,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('Mean_Acc1298_Mean_Mem40_Centroid', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Mean_Mem40_Rolloff', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Mean_Mem40_Flux', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Mean_Mem40_MFCC_0', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Mean_Mem40_MFCC_1', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Mean_Mem40_MFCC_2', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Mean_Mem40_MFCC_3', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Mean_Mem40_MFCC_4', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Mean_Mem40_MFCC_5', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Mean_Mem40_MFCC_6', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Mean_Mem40_MFCC_7', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Mean_Mem40_MFCC_8', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Mean_Mem40_MFCC_9', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Mean_Mem40_MFCC_10', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Mean_Mem40_MFCC_11', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Mean_Mem40_MFCC_12', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Std_Mem40_Centroid', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Std_Mem40_Rolloff', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Std_Mem40_Flux', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Std_Mem40_MFCC_0', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Std_Mem40_MFCC_1', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Std_Mem40_MFCC_2', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Std_Mem40_MFCC_3', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Std_Mem40_MFCC_4', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Std_Mem40_MFCC_5', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Std_Mem40_MFCC_6', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Std_Mem40_MFCC_7', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Std_Mem40_MFCC_8', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Std_Mem40_MFCC_9', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Std_Mem40_MFCC_10', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Std_Mem40_MFCC_11', 'NUMERIC'),\n",
              " ('Mean_Acc1298_Std_Mem40_MFCC_12', 'NUMERIC'),\n",
              " ('Std_Acc1298_Mean_Mem40_Centroid', 'NUMERIC'),\n",
              " ('Std_Acc1298_Mean_Mem40_Rolloff', 'NUMERIC'),\n",
              " ('Std_Acc1298_Mean_Mem40_Flux', 'NUMERIC'),\n",
              " ('Std_Acc1298_Mean_Mem40_MFCC_0', 'NUMERIC'),\n",
              " ('Std_Acc1298_Mean_Mem40_MFCC_1', 'NUMERIC'),\n",
              " ('Std_Acc1298_Mean_Mem40_MFCC_2', 'NUMERIC'),\n",
              " ('Std_Acc1298_Mean_Mem40_MFCC_3', 'NUMERIC'),\n",
              " ('Std_Acc1298_Mean_Mem40_MFCC_4', 'NUMERIC'),\n",
              " ('Std_Acc1298_Mean_Mem40_MFCC_5', 'NUMERIC'),\n",
              " ('Std_Acc1298_Mean_Mem40_MFCC_6', 'NUMERIC'),\n",
              " ('Std_Acc1298_Mean_Mem40_MFCC_7', 'NUMERIC'),\n",
              " ('Std_Acc1298_Mean_Mem40_MFCC_8', 'NUMERIC'),\n",
              " ('Std_Acc1298_Mean_Mem40_MFCC_9', 'NUMERIC'),\n",
              " ('Std_Acc1298_Mean_Mem40_MFCC_10', 'NUMERIC'),\n",
              " ('Std_Acc1298_Mean_Mem40_MFCC_11', 'NUMERIC'),\n",
              " ('Std_Acc1298_Mean_Mem40_MFCC_12', 'NUMERIC'),\n",
              " ('Std_Acc1298_Std_Mem40_Centroid', 'NUMERIC'),\n",
              " ('Std_Acc1298_Std_Mem40_Rolloff', 'NUMERIC'),\n",
              " ('Std_Acc1298_Std_Mem40_Flux', 'NUMERIC'),\n",
              " ('Std_Acc1298_Std_Mem40_MFCC_0', 'NUMERIC'),\n",
              " ('Std_Acc1298_Std_Mem40_MFCC_1', 'NUMERIC'),\n",
              " ('Std_Acc1298_Std_Mem40_MFCC_2', 'NUMERIC'),\n",
              " ('Std_Acc1298_Std_Mem40_MFCC_3', 'NUMERIC'),\n",
              " ('Std_Acc1298_Std_Mem40_MFCC_4', 'NUMERIC'),\n",
              " ('Std_Acc1298_Std_Mem40_MFCC_5', 'NUMERIC'),\n",
              " ('Std_Acc1298_Std_Mem40_MFCC_6', 'NUMERIC'),\n",
              " ('Std_Acc1298_Std_Mem40_MFCC_7', 'NUMERIC'),\n",
              " ('Std_Acc1298_Std_Mem40_MFCC_8', 'NUMERIC'),\n",
              " ('Std_Acc1298_Std_Mem40_MFCC_9', 'NUMERIC'),\n",
              " ('Std_Acc1298_Std_Mem40_MFCC_10', 'NUMERIC'),\n",
              " ('Std_Acc1298_Std_Mem40_MFCC_11', 'NUMERIC'),\n",
              " ('Std_Acc1298_Std_Mem40_MFCC_12', 'NUMERIC'),\n",
              " ('BH_LowPeakAmp', 'NUMERIC'),\n",
              " ('BH_LowPeakBPM', 'NUMERIC'),\n",
              " ('BH_HighPeakAmp', 'NUMERIC'),\n",
              " ('BH_HighPeakBPM', 'NUMERIC'),\n",
              " ('BH_HighLowRatio', 'NUMERIC'),\n",
              " ('BHSUM1', 'NUMERIC'),\n",
              " ('BHSUM2', 'NUMERIC'),\n",
              " ('BHSUM3', 'NUMERIC')]"
            ]
          },
          "metadata": {},
          "execution_count": 686
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yGJHw9DYY8ZH",
        "outputId": "29dfcb8e-aa84-4385-ecd6-f13ff7fa84d9"
      },
      "source": [
        "label_names"
      ],
      "execution_count": 687,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('amazed-suprised', ['0', '1']),\n",
              " ('happy-pleased', ['0', '1']),\n",
              " ('relaxing-calm', ['0', '1']),\n",
              " ('quiet-still', ['0', '1']),\n",
              " ('sad-lonely', ['0', '1']),\n",
              " ('angry-aggresive', ['0', '1'])]"
            ]
          },
          "metadata": {},
          "execution_count": 687
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "CqPz6APyVzKd",
        "outputId": "5ae3eb34-6adc-4dec-efbc-99c2f2d13dad"
      },
      "source": [
        "df_features = pd.DataFrame(X.toarray())\n",
        "df_labels = pd.DataFrame(y.toarray())\n",
        "df_labels"
      ],
      "execution_count": 688,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "     0  1  2  3  4  5\n",
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          },
          "metadata": {},
          "execution_count": 688
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e2Ca8-6uaffD"
      },
      "source": [
        "from skmultilearn.model_selection.measures import get_combination_wise_output_matrix"
      ],
      "execution_count": 689,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VWEyRS_kahut",
        "outputId": "3915a8db-f722-4a24-c78f-47c14dab424b"
      },
      "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": 690,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Counter({(0,): 173, (1,): 166, (2,): 264, (3,): 148, (4,): 168, (5,): 189})"
            ]
          },
          "metadata": {},
          "execution_count": 690
        }
      ]
    },
    {
      "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": "67ff2388-3c5e-4d14-dd5d-9ec873ff0f64"
      },
      "source": [
        "!pip install iterative-stratification\n",
        "from iterstrat.ml_stratifiers import MultilabelStratifiedKFold"
      ],
      "execution_count": 691,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: iterative-stratification in /usr/local/lib/python3.7/dist-packages (0.1.7)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from iterative-stratification) (1.0.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from iterative-stratification) (1.19.5)\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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cMcP_pKubIXk",
        "outputId": "0260038e-aae7-4b5c-ef43-34f8bc6bf3cb"
      },
      "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": 692,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TRAIN: [  0   1   2   3   4   5   6   7   8   9  10  13  14  15  16  17  18  19\n",
            "  20  21  22  23  24  25  26  27  28  29  30  31  32  34  36  37  38  39\n",
            "  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54  55  56  57\n",
            "  58  59  60  61  62  63  64  65  66  67  68  69  70  71  72  73  74  75\n",
            "  76  77  78  79  80  81  82  84  85  86  87  88  89  90  91  92  93  96\n",
            "  97  98  99 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115\n",
            " 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 134 137\n",
            " 138 139 140 141 142 143 144 145 146 147 148 149 151 152 154 155 156 158\n",
            " 159 160 161 162 163 164 166 167 169 170 171 172 173 174 175 176 177 179\n",
            " 180 182 183 184 186 187 188 189 190 191 192 193 194 195 196 197 198 199\n",
            " 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217\n",
            " 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 235 236\n",
            " 237 238 239 241 242 243 244 246 247 248 249 250 251 252 253 254 255 256\n",
            " 257 259 260 261 262 263 264 266 267 268 269 271 272 273 274 275 276 277\n",
            " 278 279 280 281 282 285 286 287 288 289 290 292 293 294 295 296 297 298\n",
            " 300 301 302 303 304 305 306 307 309 310 311 312 313 314 315 316 317 319\n",
            " 320 321 322 323 324 326 327 328 332 333 334 335 336 337 338 339 340 341\n",
            " 342 343 344 345 346 347 348 349 350 351 352 353 354 355 357 358 359 360\n",
            " 361 362 363 364 365 366 367 368 369 370 371 372 374 375 377 379 380 382\n",
            " 383 384 385 386 387 389 391 392 393 394 395 396 397 398 399 400 401 402\n",
            " 404 405 406 409 410 411 412 413 414 416 417 418 419 420 421 422 423 424\n",
            " 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442\n",
            " 443 444 445 447 448 451 452 453 454 455 456 457 458 459 460 461 462 463\n",
            " 465 466 467 468 469 471 472 473 474 475 476 477 478 479 480 481 482 483\n",
            " 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501\n",
            " 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519\n",
            " 520 522 523 525 526 527 528 529 530 531 532 533 534 536 537 539 540 541\n",
            " 542 543 544 545 547 548 549 550 552 553 554 555 556 557 558 559 560 561\n",
            " 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579\n",
            " 580 581 582 583 585 586 588 589 592] TEST: [ 11  12  33  35  83  94  95 100 132 133 135 136 150 153 157 165 168 178\n",
            " 181 185 234 240 245 258 265 270 283 284 291 299 308 318 325 329 330 331\n",
            " 356 373 376 378 381 388 390 403 407 408 415 446 449 450 464 470 521 524\n",
            " 535 538 546 551 584 587 590 591]\n",
            "TRAIN: [  0   2   3   4   5   6   7   8   9  10  11  12  13  14  15  17  18  19\n",
            "  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37\n",
            "  38  39  40  41  44  45  46  47  48  50  51  53  54  55  56  57  58  59\n",
            "  60  61  62  63  64  65  66  67  68  69  70  71  73  74  75  76  77  79\n",
            "  80  81  82  83  84  85  86  87  88  89  90  91  92  93  94  95  96  97\n",
            "  99 100 102 104 106 107 108 110 111 112 113 114 115 116 117 118 119 120\n",
            " 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138\n",
            " 139 140 142 143 144 145 146 147 148 150 151 152 153 154 155 156 157 158\n",
            " 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176\n",
            " 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194\n",
            " 195 196 197 198 199 200 201 202 203 206 207 208 209 210 211 213 214 215\n",
            " 216 217 218 219 220 221 222 223 224 226 228 229 231 232 233 234 235 236\n",
            " 237 239 240 241 242 244 245 246 247 248 249 250 251 252 255 257 258 259\n",
            " 260 261 262 263 264 265 267 268 269 270 271 272 273 274 276 277 278 280\n",
            " 281 282 283 284 285 286 287 288 289 291 292 293 294 295 296 297 298 299\n",
            " 302 303 305 307 308 309 310 311 312 313 314 317 318 319 320 321 322 323\n",
            " 324 325 326 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342\n",
            " 343 344 345 346 347 348 349 350 351 352 353 354 356 357 358 359 360 362\n",
            " 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380\n",
            " 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398\n",
            " 399 400 401 402 403 404 405 406 407 408 411 412 413 414 415 416 417 418\n",
            " 419 420 421 422 424 425 426 427 429 430 432 433 434 435 436 437 439 440\n",
            " 441 442 443 444 446 447 448 449 450 451 452 453 454 455 456 457 458 459\n",
            " 460 461 462 463 464 465 467 469 470 471 472 473 474 475 476 477 478 479\n",
            " 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497\n",
            " 498 499 500 501 503 504 505 506 507 508 509 512 513 515 516 517 518 519\n",
            " 520 521 522 523 524 525 526 527 528 529 531 533 534 535 537 538 540 542\n",
            " 543 544 545 546 547 548 549 551 552 553 554 555 556 557 558 559 560 562\n",
            " 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 580 581\n",
            " 582 583 584 586 587 588 589 590 591 592] TEST: [  1  16  42  43  49  52  72  78  98 101 103 105 109 141 149 204 205 212\n",
            " 225 227 230 238 243 253 254 256 266 275 279 290 300 301 304 306 315 316\n",
            " 327 355 361 409 410 423 428 431 438 445 466 468 502 510 511 514 530 532\n",
            " 536 539 541 550 561 579 585]\n",
            "TRAIN: [  0   1   2   3   5   6   7   9  10  11  12  13  14  15  16  18  19  20\n",
            "  21  23  24  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40\n",
            "  41  42  43  44  45  46  47  48  49  50  51  52  53  54  55  56  58  59\n",
            "  61  62  63  64  65  66  67  68  69  70  71  72  73  74  75  77  78  79\n",
            "  80  81  82  83  84  85  86  87  88  89  90  91  92  93  94  95  96  97\n",
            "  98  99 100 101 102 103 104 105 106 108 109 110 111 112 113 114 115 116\n",
            " 117 118 120 121 122 124 125 126 127 128 129 130 132 133 134 135 136 137\n",
            " 138 141 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158\n",
            " 159 160 161 162 163 164 165 166 167 168 169 170 171 173 174 175 176 177\n",
            " 178 179 180 181 182 183 184 185 186 187 188 189 190 191 193 194 195 196\n",
            " 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214\n",
            " 215 216 217 218 219 220 221 223 224 225 226 227 228 229 230 231 232 233\n",
            " 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251\n",
            " 253 254 255 256 258 260 261 262 264 265 266 267 268 269 270 271 272 273\n",
            " 274 275 276 277 278 279 281 282 283 284 285 286 287 288 289 290 291 292\n",
            " 293 294 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311\n",
            " 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329\n",
            " 330 331 332 333 334 335 336 337 339 340 341 342 343 344 345 347 349 350\n",
            " 352 353 354 355 356 357 358 359 360 361 362 363 365 367 368 369 371 372\n",
            " 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390\n",
            " 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409\n",
            " 410 411 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428\n",
            " 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 449 450\n",
            " 451 453 455 456 457 458 459 461 462 463 464 465 466 467 468 469 470 471\n",
            " 472 474 475 476 477 478 480 481 482 484 485 486 487 488 489 490 491 492\n",
            " 493 494 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511\n",
            " 512 513 514 515 516 517 518 520 521 522 523 524 526 528 529 530 532 533\n",
            " 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551\n",
            " 553 555 556 557 558 559 560 561 563 564 566 568 569 570 571 572 573 574\n",
            " 575 576 578 579 580 581 584 585 586 587 588 589 590 591 592] TEST: [  4   8  17  22  25  57  60  76 107 119 123 131 139 140 142 172 192 222\n",
            " 252 257 259 263 280 295 338 346 348 351 364 366 370 391 412 429 430 447\n",
            " 448 452 454 460 473 479 483 495 519 525 527 531 552 554 562 565 567 577\n",
            " 582 583]\n",
            "TRAIN: [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  16  17  18  20\n",
            "  21  22  23  24  25  26  28  29  30  31  33  34  35  36  37  38  39  40\n",
            "  42  43  44  45  46  47  48  49  50  52  53  54  55  56  57  58  59  60\n",
            "  61  62  63  66  67  69  70  71  72  74  75  76  77  78  79  80  81  82\n",
            "  83  84  85  86  87  88  89  91  93  94  95  96  97  98  99 100 101 102\n",
            " 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120\n",
            " 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138\n",
            " 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156\n",
            " 157 158 159 161 162 163 165 166 167 168 169 170 171 172 173 174 175 176\n",
            " 177 178 179 181 183 184 185 186 188 189 190 191 192 193 196 197 199 200\n",
            " 201 202 204 205 206 207 208 209 210 211 212 213 217 218 219 220 221 222\n",
            " 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242\n",
            " 243 244 245 246 247 248 249 250 251 252 253 254 256 257 258 259 260 261\n",
            " 263 264 265 266 267 268 270 271 272 273 274 275 276 277 278 279 280 281\n",
            " 282 283 284 285 286 287 288 290 291 292 293 294 295 296 297 298 299 300\n",
            " 301 302 303 304 305 306 307 308 309 310 311 312 313 315 316 317 318 319\n",
            " 320 321 322 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339\n",
            " 340 341 342 343 345 346 347 348 349 350 351 353 354 355 356 357 358 359\n",
            " 360 361 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378\n",
            " 379 380 381 382 383 384 385 386 387 388 389 390 391 393 394 395 396 397\n",
            " 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415\n",
            " 416 417 418 419 422 423 425 426 428 429 430 431 432 433 434 435 436 437\n",
            " 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455\n",
            " 456 457 458 459 460 462 463 464 465 466 467 468 470 471 472 473 474 475\n",
            " 476 477 478 479 480 481 482 483 484 485 487 488 489 490 491 492 493 495\n",
            " 496 497 498 499 500 501 502 503 504 506 507 509 510 511 512 513 514 515\n",
            " 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533\n",
            " 534 535 536 537 538 539 541 542 543 544 546 547 548 549 550 551 552 553\n",
            " 554 555 557 558 559 561 562 563 564 565 566 567 568 569 570 571 572 573\n",
            " 574 575 577 578 579 580 581 582 583 584 585 586 587 588 590 591] TEST: [ 14  15  19  27  32  41  51  64  65  68  73  90  92 160 164 180 182 187\n",
            " 194 195 198 203 214 215 216 223 224 255 262 269 289 314 323 324 344 352\n",
            " 362 392 420 421 424 427 461 469 486 494 505 508 540 545 556 560 576 589\n",
            " 592]\n",
            "TRAIN: [  1   2   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18  19\n",
            "  20  22  23  24  25  26  27  28  30  31  32  33  34  35  36  37  39  41\n",
            "  42  43  44  45  46  47  48  49  51  52  54  55  56  57  58  59  60  61\n",
            "  62  63  64  65  66  68  69  70  71  72  73  74  75  76  77  78  79  80\n",
            "  81  83  84  85  86  87  88  90  91  92  93  94  95  96  97  98  99 100\n",
            " 101 102 103 104 105 107 108 109 110 112 113 114 115 116 117 118 119 120\n",
            " 121 123 124 125 127 128 129 130 131 132 133 134 135 136 137 138 139 140\n",
            " 141 142 143 144 146 147 148 149 150 151 152 153 155 156 157 158 160 162\n",
            " 163 164 165 167 168 169 170 171 172 173 174 175 177 178 179 180 181 182\n",
            " 183 184 185 186 187 188 189 190 192 194 195 196 197 198 200 203 204 205\n",
            " 206 207 208 209 210 211 212 213 214 215 216 217 218 220 221 222 223 224\n",
            " 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 243\n",
            " 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261\n",
            " 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279\n",
            " 280 281 282 283 284 285 286 289 290 291 292 294 295 296 297 299 300 301\n",
            " 302 303 304 305 306 308 309 310 311 313 314 315 316 317 318 319 320 321\n",
            " 322 323 324 325 326 327 329 330 331 332 333 334 335 336 337 338 340 341\n",
            " 342 343 344 346 347 348 349 350 351 352 353 354 355 356 357 358 360 361\n",
            " 362 363 364 365 366 368 369 370 371 372 373 374 375 376 377 378 379 380\n",
            " 381 382 383 384 386 387 388 390 391 392 393 394 395 396 398 401 402 403\n",
            " 404 405 407 408 409 410 412 413 414 415 417 418 419 420 421 422 423 424\n",
            " 425 427 428 429 430 431 432 434 436 437 438 439 440 442 443 444 445 446\n",
            " 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464\n",
            " 465 466 467 468 469 470 471 472 473 474 475 476 478 479 480 481 482 483\n",
            " 484 485 486 487 488 489 490 491 492 493 494 495 497 498 499 500 501 502\n",
            " 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520\n",
            " 521 522 523 524 525 526 527 528 530 531 532 533 534 535 536 537 538 539\n",
            " 540 541 542 544 545 546 547 548 550 551 552 553 554 555 556 557 558 559\n",
            " 560 561 562 563 564 565 566 567 568 569 570 571 574 576 577 578 579 580\n",
            " 581 582 583 584 585 586 587 589 590 591 592] TEST: [  0   3  21  29  38  40  50  53  67  82  89 106 111 122 126 145 154 159\n",
            " 161 166 176 191 193 199 201 202 219 242 287 288 293 298 307 312 328 339\n",
            " 345 359 367 385 389 397 399 400 406 411 416 426 433 435 441 477 496 529\n",
            " 543 549 572 573 575 588]\n",
            "TRAIN: [  0   1   2   3   4   5   6   8   9  10  11  12  13  14  15  16  17  18\n",
            "  19  20  21  22  23  24  25  26  27  29  30  32  33  34  35  36  38  40\n",
            "  41  42  43  45  46  47  48  49  50  51  52  53  54  55  56  57  58  60\n",
            "  62  63  64  65  66  67  68  69  72  73  74  75  76  77  78  79  81  82\n",
            "  83  85  86  87  88  89  90  91  92  93  94  95  96  97  98  99 100 101\n",
            " 102 103 104 105 106 107 109 110 111 112 114 115 116 117 118 119 120 122\n",
            " 123 124 125 126 127 128 130 131 132 133 134 135 136 137 138 139 140 141\n",
            " 142 143 144 145 146 147 148 149 150 151 153 154 157 159 160 161 163 164\n",
            " 165 166 167 168 169 171 172 173 174 175 176 177 178 179 180 181 182 184\n",
            " 185 186 187 190 191 192 193 194 195 196 197 198 199 201 202 203 204 205\n",
            " 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223\n",
            " 224 225 226 227 228 229 230 231 232 233 234 235 237 238 239 240 241 242\n",
            " 243 244 245 247 248 249 250 251 252 253 254 255 256 257 258 259 261 262\n",
            " 263 264 265 266 267 269 270 271 272 273 274 275 278 279 280 281 282 283\n",
            " 284 285 286 287 288 289 290 291 292 293 295 297 298 299 300 301 302 303\n",
            " 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 320 321 322\n",
            " 323 324 325 326 327 328 329 330 331 332 334 335 337 338 339 340 341 342\n",
            " 343 344 345 346 347 348 349 350 351 352 353 354 355 356 358 359 361 362\n",
            " 363 364 365 366 367 368 369 370 371 373 374 375 376 377 378 379 380 381\n",
            " 382 383 384 385 388 389 390 391 392 393 394 396 397 398 399 400 402 403\n",
            " 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421\n",
            " 422 423 424 425 426 427 428 429 430 431 432 433 435 436 437 438 439 440\n",
            " 441 443 444 445 446 447 448 449 450 452 453 454 455 456 457 458 459 460\n",
            " 461 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 479 480\n",
            " 481 482 483 484 486 487 489 490 491 492 493 494 495 496 498 499 500 501\n",
            " 502 503 504 505 506 507 508 509 510 511 512 513 514 515 518 519 520 521\n",
            " 522 523 524 525 526 527 528 529 530 531 532 533 535 536 537 538 539 540\n",
            " 541 542 543 544 545 546 547 549 550 551 552 553 554 555 556 557 558 559\n",
            " 560 561 562 563 564 565 567 568 569 570 571 572 573 574 575 576 577 579\n",
            " 581 582 583 584 585 586 587 588 589 590 591 592] TEST: [  7  28  31  37  39  44  59  61  70  71  80  84 108 113 121 129 152 155\n",
            " 156 158 162 170 183 188 189 200 236 246 260 268 276 277 294 296 319 333\n",
            " 336 357 360 372 386 387 395 401 434 442 451 462 478 485 488 497 516 517\n",
            " 534 548 566 578 580]\n",
            "TRAIN: [  0   1   2   3   4   5   6   7   8   9  11  12  13  14  15  16  17  18\n",
            "  19  21  22  24  25  27  28  29  31  32  33  34  35  36  37  38  39  40\n",
            "  41  42  43  44  45  46  47  48  49  50  51  52  53  54  55  56  57  58\n",
            "  59  60  61  62  63  64  65  66  67  68  69  70  71  72  73  74  75  76\n",
            "  77  78  79  80  81  82  83  84  85  86  87  89  90  91  92  93  94  95\n",
            "  96  97  98  99 100 101 102 103 104 105 106 107 108 109 111 112 113 115\n",
            " 116 117 118 119 120 121 122 123 124 126 127 128 129 130 131 132 133 134\n",
            " 135 136 137 139 140 141 142 143 144 145 146 147 148 149 150 152 153 154\n",
            " 155 156 157 158 159 160 161 162 164 165 166 167 168 169 170 171 172 173\n",
            " 175 176 178 179 180 181 182 183 185 187 188 189 190 191 192 193 194 195\n",
            " 196 198 199 200 201 202 203 204 205 207 208 210 211 212 213 214 215 216\n",
            " 217 218 219 221 222 223 224 225 226 227 230 231 232 233 234 235 236 237\n",
            " 238 239 240 241 242 243 244 245 246 247 248 249 251 252 253 254 255 256\n",
            " 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 273 274 275\n",
            " 276 277 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294\n",
            " 295 296 297 298 299 300 301 302 303 304 305 306 307 308 311 312 313 314\n",
            " 315 316 317 318 319 320 321 323 324 325 326 327 328 329 330 331 332 333\n",
            " 334 335 336 337 338 339 340 341 342 344 345 346 348 349 350 351 352 353\n",
            " 354 355 356 357 359 360 361 362 364 365 366 367 368 369 370 371 372 373\n",
            " 375 376 377 378 379 381 382 384 385 386 387 388 389 390 391 392 393 394\n",
            " 395 397 398 399 400 401 403 406 407 408 409 410 411 412 413 414 415 416\n",
            " 417 418 419 420 421 423 424 425 426 427 428 429 430 431 432 433 434 435\n",
            " 436 438 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455\n",
            " 456 459 460 461 462 464 465 466 468 469 470 471 472 473 474 475 477 478\n",
            " 479 480 481 482 483 484 485 486 488 489 493 494 495 496 497 498 499 500\n",
            " 501 502 504 505 506 508 509 510 511 514 515 516 517 518 519 520 521 523\n",
            " 524 525 527 528 529 530 531 532 533 534 535 536 538 539 540 541 542 543\n",
            " 544 545 546 547 548 549 550 551 552 554 555 556 557 558 560 561 562 563\n",
            " 565 566 567 568 569 570 572 573 574 575 576 577 578 579 580 581 582 583\n",
            " 584 585 586 587 588 589 590 591 592] TEST: [ 10  20  23  26  30  88 110 114 125 138 151 163 174 177 184 186 197 206\n",
            " 209 220 228 229 250 272 278 309 310 322 343 347 358 363 374 380 383 396\n",
            " 402 404 405 422 437 439 457 458 463 467 476 487 490 491 492 503 507 512\n",
            " 513 522 526 537 553 559 564 571]\n",
            "TRAIN: [  0   1   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  19\n",
            "  20  21  22  23  24  25  26  27  28  29  30  31  32  33  35  36  37  38\n",
            "  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54  57  58\n",
            "  59  60  61  62  64  65  66  67  68  70  71  72  73  74  76  77  78  80\n",
            "  81  82  83  84  87  88  89  90  91  92  94  95  96  98  99 100 101 102\n",
            " 103 104 105 106 107 108 109 110 111 112 113 114 116 117 119 120 121 122\n",
            " 123 124 125 126 127 129 131 132 133 135 136 138 139 140 141 142 143 144\n",
            " 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162\n",
            " 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180\n",
            " 181 182 183 184 185 186 187 188 189 191 192 193 194 195 197 198 199 200\n",
            " 201 202 203 204 205 206 208 209 210 212 213 214 215 216 217 218 219 220\n",
            " 221 222 223 224 225 227 228 229 230 231 232 234 235 236 237 238 240 242\n",
            " 243 245 246 247 248 249 250 252 253 254 255 256 257 258 259 260 261 262\n",
            " 263 265 266 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282\n",
            " 283 284 285 287 288 289 290 291 293 294 295 296 297 298 299 300 301 302\n",
            " 303 304 306 307 308 309 310 312 314 315 316 317 318 319 320 321 322 323\n",
            " 324 325 327 328 329 330 331 332 333 334 336 337 338 339 340 341 342 343\n",
            " 344 345 346 347 348 351 352 353 354 355 356 357 358 359 360 361 362 363\n",
            " 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381\n",
            " 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399\n",
            " 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 418\n",
            " 419 420 421 422 423 424 426 427 428 429 430 431 433 434 435 436 437 438\n",
            " 439 440 441 442 444 445 446 447 448 449 450 451 452 453 454 456 457 458\n",
            " 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 476 477 478\n",
            " 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496\n",
            " 497 499 500 502 503 504 505 507 508 509 510 511 512 513 514 515 516 517\n",
            " 518 519 521 522 524 525 526 527 528 529 530 531 532 533 534 535 536 537\n",
            " 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 556\n",
            " 559 560 561 562 563 564 565 566 567 571 572 573 574 575 576 577 578 579\n",
            " 580 581 582 583 584 585 586 587 588 589 590 591 592] TEST: [  2  18  34  55  56  63  69  75  79  85  86  93  97 115 118 128 130 134\n",
            " 137 190 196 207 211 226 233 239 241 244 251 264 267 286 292 305 311 313\n",
            " 326 335 349 350 417 425 432 443 455 459 475 498 501 506 520 523 555 557\n",
            " 558 568 569 570]\n",
            "TRAIN: [  0   1   2   3   4   5   6   7   8  10  11  12  14  15  16  17  18  19\n",
            "  20  21  22  23  25  26  27  28  29  30  31  32  33  34  35  37  38  39\n",
            "  40  41  42  43  44  48  49  50  51  52  53  54  55  56  57  58  59  60\n",
            "  61  62  63  64  65  66  67  68  69  70  71  72  73  75  76  77  78  79\n",
            "  80  81  82  83  84  85  86  87  88  89  90  92  93  94  95  96  97  98\n",
            " 100 101 102 103 105 106 107 108 109 110 111 112 113 114 115 117 118 119\n",
            " 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137\n",
            " 138 139 140 141 142 143 145 146 147 149 150 151 152 153 154 155 156 157\n",
            " 158 159 160 161 162 163 164 165 166 168 169 170 172 173 174 176 177 178\n",
            " 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196\n",
            " 197 198 199 200 201 202 203 204 205 206 207 208 209 211 212 213 214 215\n",
            " 216 217 219 220 222 223 224 225 226 227 228 229 230 231 233 234 236 237\n",
            " 238 239 240 241 242 243 244 245 246 247 250 251 252 253 254 255 256 257\n",
            " 258 259 260 261 262 263 264 265 266 267 268 269 270 272 274 275 276 277\n",
            " 278 279 280 281 282 283 284 286 287 288 289 290 291 292 293 294 295 296\n",
            " 298 299 300 301 304 305 306 307 308 309 310 311 312 313 314 315 316 318\n",
            " 319 320 322 323 324 325 326 327 328 329 330 331 332 333 335 336 337 338\n",
            " 339 340 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358\n",
            " 359 360 361 362 363 364 366 367 368 369 370 371 372 373 374 375 376 377\n",
            " 378 380 381 383 385 386 387 388 389 390 391 392 393 394 395 396 397 398\n",
            " 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416\n",
            " 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434\n",
            " 435 436 437 438 439 440 441 442 443 445 446 447 448 449 450 451 452 453\n",
            " 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 472\n",
            " 473 475 476 477 478 479 480 481 483 485 486 487 488 490 491 492 493 494\n",
            " 495 496 497 498 499 501 502 503 504 505 506 507 508 510 511 512 513 514\n",
            " 516 517 519 520 521 522 523 524 525 526 527 529 530 531 532 534 535 536\n",
            " 537 538 539 540 541 543 545 546 548 549 550 551 552 553 554 555 556 557\n",
            " 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 575 576\n",
            " 577 578 579 580 582 583 584 585 587 588 589 590 591 592] TEST: [  9  13  24  36  45  46  47  74  91  99 104 116 144 148 167 171 175 210\n",
            " 218 221 232 235 248 249 271 273 285 297 302 303 317 321 334 341 342 365\n",
            " 379 382 384 444 471 474 482 484 489 500 509 515 518 528 533 542 544 547\n",
            " 574 581 586]\n",
            "TRAIN: [  0   1   2   3   4   7   8   9  10  11  12  13  14  15  16  17  18  19\n",
            "  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37\n",
            "  38  39  40  41  42  43  44  45  46  47  49  50  51  52  53  55  56  57\n",
            "  59  60  61  63  64  65  67  68  69  70  71  72  73  74  75  76  78  79\n",
            "  80  82  83  84  85  86  88  89  90  91  92  93  94  95  97  98  99 100\n",
            " 101 103 104 105 106 107 108 109 110 111 113 114 115 116 118 119 121 122\n",
            " 123 125 126 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142\n",
            " 144 145 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163\n",
            " 164 165 166 167 168 170 171 172 174 175 176 177 178 180 181 182 183 184\n",
            " 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202\n",
            " 203 204 205 206 207 209 210 211 212 214 215 216 218 219 220 221 222 223\n",
            " 224 225 226 227 228 229 230 232 233 234 235 236 238 239 240 241 242 243\n",
            " 244 245 246 248 249 250 251 252 253 254 255 256 257 258 259 260 262 263\n",
            " 264 265 266 267 268 269 270 271 272 273 275 276 277 278 279 280 283 284\n",
            " 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302\n",
            " 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 321\n",
            " 322 323 324 325 326 327 328 329 330 331 333 334 335 336 338 339 341 342\n",
            " 343 344 345 346 347 348 349 350 351 352 355 356 357 358 359 360 361 362\n",
            " 363 364 365 366 367 370 372 373 374 376 378 379 380 381 382 383 384 385\n",
            " 386 387 388 389 390 391 392 395 396 397 399 400 401 402 403 404 405 406\n",
            " 407 408 409 410 411 412 415 416 417 420 421 422 423 424 425 426 427 428\n",
            " 429 430 431 432 433 434 435 437 438 439 441 442 443 444 445 446 447 448\n",
            " 449 450 451 452 454 455 457 458 459 460 461 462 463 464 466 467 468 469\n",
            " 470 471 473 474 475 476 477 478 479 482 483 484 485 486 487 488 489 490\n",
            " 491 492 494 495 496 497 498 500 501 502 503 505 506 507 508 509 510 511\n",
            " 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529\n",
            " 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547\n",
            " 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 564 565 566\n",
            " 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584\n",
            " 585 586 587 588 589 590 591 592] TEST: [  5   6  48  54  58  62  66  77  81  87  96 102 112 117 120 124 127 143\n",
            " 146 147 169 173 179 208 213 217 231 237 247 261 274 281 282 320 332 337\n",
            " 340 353 354 368 369 371 375 377 393 394 398 413 414 418 419 436 440 453\n",
            " 456 465 472 480 481 493 499 504 563]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8vvGRMiHF_SU"
      },
      "source": [
        "#Exibindo informações do dataset trabalhado"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QsESXBp1C1O3",
        "outputId": "b27bcd4f-8692-443d-9bbe-0a5e4e0629e0"
      },
      "source": [
        "f =np.array(feature_names)\n",
        "features = pd.DataFrame(f)\n",
        "features_x = features = features.iloc[:,0]\n",
        "features_x\n"
      ],
      "execution_count": 693,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     Mean_Acc1298_Mean_Mem40_Centroid\n",
              "1      Mean_Acc1298_Mean_Mem40_Rolloff\n",
              "2         Mean_Acc1298_Mean_Mem40_Flux\n",
              "3       Mean_Acc1298_Mean_Mem40_MFCC_0\n",
              "4       Mean_Acc1298_Mean_Mem40_MFCC_1\n",
              "                    ...               \n",
              "67                      BH_HighPeakBPM\n",
              "68                     BH_HighLowRatio\n",
              "69                              BHSUM1\n",
              "70                              BHSUM2\n",
              "71                              BHSUM3\n",
              "Name: 0, Length: 72, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 693
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eJ-Njs7TISoW",
        "outputId": "a7cc09dc-9c37-4d1b-c22b-155e2d7a0879"
      },
      "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": 694,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.034741, 0.089665, 0.091225, ..., 0.245457, 0.105065, 0.405399],\n",
              "       [0.081374, 0.272747, 0.085733, ..., 0.343547, 0.276366, 0.710924],\n",
              "       [0.110545, 0.273567, 0.08441 , ..., 0.188693, 0.045941, 0.457372],\n",
              "       ...,\n",
              "       [0.035169, 0.065403, 0.075227, ..., 0.184313, 0.247136, 0.476993],\n",
              "       [0.054276, 0.238158, 0.095935, ..., 0.547126, 0.183494, 1.25582 ],\n",
              "       [0.073194, 0.140733, 0.080545, ..., 0.087328, 0.236815, 0.451701]])"
            ]
          },
          "metadata": {},
          "execution_count": 694
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "OqtLuuoLJVjV",
        "outputId": "27fa2d9f-625d-4e58-9aa0-6904999ff6aa"
      },
      "source": [
        "df = pd.DataFrame(dX_train)\n",
        "dfy = pd.DataFrame(dy_train)\n",
        "df_origin = df\n",
        "df"
      ],
      "execution_count": 695,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>47</th>\n",
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              "      <th>49</th>\n",
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              "      <th>51</th>\n",
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              "      <th>55</th>\n",
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              "      <th>57</th>\n",
              "      <th>58</th>\n",
              "      <th>59</th>\n",
              "      <th>60</th>\n",
              "      <th>61</th>\n",
              "      <th>62</th>\n",
              "      <th>63</th>\n",
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              "      <th>65</th>\n",
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              "      <th>0</th>\n",
              "      <td>0.034741</td>\n",
              "      <td>0.089665</td>\n",
              "      <td>0.091225</td>\n",
              "      <td>-73.302422</td>\n",
              "      <td>6.215179</td>\n",
              "      <td>0.615074</td>\n",
              "      <td>2.037160</td>\n",
              "      <td>0.804065</td>\n",
              "      <td>1.301409</td>\n",
              "      <td>0.558576</td>\n",
              "      <td>0.672063</td>\n",
              "      <td>0.783788</td>\n",
              "      <td>0.766640</td>\n",
              "      <td>0.458712</td>\n",
              "      <td>0.530384</td>\n",
              "      <td>0.812429</td>\n",
              "      <td>0.028851</td>\n",
              "      <td>0.129039</td>\n",
              "      <td>0.039614</td>\n",
              "      <td>5.762173</td>\n",
              "      <td>1.636819</td>\n",
              "      <td>1.170034</td>\n",
              "      <td>1.051511</td>\n",
              "      <td>0.764163</td>\n",
              "      <td>0.642705</td>\n",
              "      <td>0.617868</td>\n",
              "      <td>0.510265</td>\n",
              "      <td>0.566213</td>\n",
              "      <td>0.509149</td>\n",
              "      <td>0.477275</td>\n",
              "      <td>0.505073</td>\n",
              "      <td>0.463535</td>\n",
              "      <td>0.013519</td>\n",
              "      <td>0.050591</td>\n",
              "      <td>0.009025</td>\n",
              "      <td>8.156257</td>\n",
              "      <td>1.077167</td>\n",
              "      <td>0.624711</td>\n",
              "      <td>0.810244</td>\n",
              "      <td>0.399568</td>\n",
              "      <td>0.279947</td>\n",
              "      <td>0.314215</td>\n",
              "      <td>0.231439</td>\n",
              "      <td>0.345401</td>\n",
              "      <td>0.285389</td>\n",
              "      <td>0.210613</td>\n",
              "      <td>0.321896</td>\n",
              "      <td>0.290551</td>\n",
              "      <td>0.022774</td>\n",
              "      <td>0.095801</td>\n",
              "      <td>0.015057</td>\n",
              "      <td>4.748694</td>\n",
              "      <td>0.536378</td>\n",
              "      <td>0.296306</td>\n",
              "      <td>0.273210</td>\n",
              "      <td>0.175800</td>\n",
              "      <td>0.105508</td>\n",
              "      <td>0.168246</td>\n",
              "      <td>0.115849</td>\n",
              "      <td>0.136020</td>\n",
              "      <td>0.110514</td>\n",
              "      <td>0.100517</td>\n",
              "      <td>0.118630</td>\n",
              "      <td>0.094923</td>\n",
              "      <td>0.051035</td>\n",
              "      <td>68.0</td>\n",
              "      <td>0.014937</td>\n",
              "      <td>136.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.245457</td>\n",
              "      <td>0.105065</td>\n",
              "      <td>0.405399</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.081374</td>\n",
              "      <td>0.272747</td>\n",
              "      <td>0.085733</td>\n",
              "      <td>-62.584437</td>\n",
              "      <td>3.183163</td>\n",
              "      <td>-0.218145</td>\n",
              "      <td>0.163038</td>\n",
              "      <td>0.620251</td>\n",
              "      <td>0.458514</td>\n",
              "      <td>0.041426</td>\n",
              "      <td>0.308287</td>\n",
              "      <td>0.538152</td>\n",
              "      <td>0.594871</td>\n",
              "      <td>0.734332</td>\n",
              "      <td>0.415489</td>\n",
              "      <td>0.761508</td>\n",
              "      <td>0.066288</td>\n",
              "      <td>0.262370</td>\n",
              "      <td>0.034438</td>\n",
              "      <td>3.480874</td>\n",
              "      <td>1.596532</td>\n",
              "      <td>0.943803</td>\n",
              "      <td>0.804444</td>\n",
              "      <td>0.511229</td>\n",
              "      <td>0.498670</td>\n",
              "      <td>0.523039</td>\n",
              "      <td>0.480916</td>\n",
              "      <td>0.488657</td>\n",
              "      <td>0.483166</td>\n",
              "      <td>0.445187</td>\n",
              "      <td>0.415994</td>\n",
              "      <td>0.405593</td>\n",
              "      <td>0.013621</td>\n",
              "      <td>0.073041</td>\n",
              "      <td>0.010094</td>\n",
              "      <td>1.243981</td>\n",
              "      <td>0.829790</td>\n",
              "      <td>0.252972</td>\n",
              "      <td>0.347831</td>\n",
              "      <td>0.205087</td>\n",
              "      <td>0.168601</td>\n",
              "      <td>0.178009</td>\n",
              "      <td>0.144080</td>\n",
              "      <td>0.178703</td>\n",
              "      <td>0.146937</td>\n",
              "      <td>0.125580</td>\n",
              "      <td>0.128202</td>\n",
              "      <td>0.107007</td>\n",
              "      <td>0.020028</td>\n",
              "      <td>0.066940</td>\n",
              "      <td>0.029483</td>\n",
              "      <td>3.963534</td>\n",
              "      <td>0.382360</td>\n",
              "      <td>0.168389</td>\n",
              "      <td>0.117525</td>\n",
              "      <td>0.098341</td>\n",
              "      <td>0.087046</td>\n",
              "      <td>0.057991</td>\n",
              "      <td>0.059393</td>\n",
              "      <td>0.059457</td>\n",
              "      <td>0.053439</td>\n",
              "      <td>0.067684</td>\n",
              "      <td>0.070075</td>\n",
              "      <td>0.041565</td>\n",
              "      <td>0.295031</td>\n",
              "      <td>70.0</td>\n",
              "      <td>0.276366</td>\n",
              "      <td>140.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.343547</td>\n",
              "      <td>0.276366</td>\n",
              "      <td>0.710924</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.110545</td>\n",
              "      <td>0.273567</td>\n",
              "      <td>0.084410</td>\n",
              "      <td>-65.235325</td>\n",
              "      <td>2.794964</td>\n",
              "      <td>0.639047</td>\n",
              "      <td>1.281297</td>\n",
              "      <td>0.757896</td>\n",
              "      <td>0.489412</td>\n",
              "      <td>0.627636</td>\n",
              "      <td>0.469322</td>\n",
              "      <td>0.644336</td>\n",
              "      <td>0.441556</td>\n",
              "      <td>0.335964</td>\n",
              "      <td>0.290713</td>\n",
              "      <td>0.158538</td>\n",
              "      <td>0.082743</td>\n",
              "      <td>0.215373</td>\n",
              "      <td>0.035970</td>\n",
              "      <td>4.834742</td>\n",
              "      <td>1.213443</td>\n",
              "      <td>0.864034</td>\n",
              "      <td>0.909222</td>\n",
              "      <td>0.780572</td>\n",
              "      <td>0.550833</td>\n",
              "      <td>0.639740</td>\n",
              "      <td>0.573309</td>\n",
              "      <td>0.526312</td>\n",
              "      <td>0.562622</td>\n",
              "      <td>0.538407</td>\n",
              "      <td>0.492292</td>\n",
              "      <td>0.455562</td>\n",
              "      <td>0.029112</td>\n",
              "      <td>0.070433</td>\n",
              "      <td>0.008525</td>\n",
              "      <td>2.759906</td>\n",
              "      <td>0.592634</td>\n",
              "      <td>0.761852</td>\n",
              "      <td>0.568740</td>\n",
              "      <td>0.589827</td>\n",
              "      <td>0.281181</td>\n",
              "      <td>0.437752</td>\n",
              "      <td>0.479889</td>\n",
              "      <td>0.227320</td>\n",
              "      <td>0.296224</td>\n",
              "      <td>0.273855</td>\n",
              "      <td>0.191804</td>\n",
              "      <td>0.198025</td>\n",
              "      <td>0.038119</td>\n",
              "      <td>0.065427</td>\n",
              "      <td>0.029622</td>\n",
              "      <td>3.371796</td>\n",
              "      <td>0.430373</td>\n",
              "      <td>0.172862</td>\n",
              "      <td>0.177523</td>\n",
              "      <td>0.184333</td>\n",
              "      <td>0.095718</td>\n",
              "      <td>0.139323</td>\n",
              "      <td>0.109279</td>\n",
              "      <td>0.090650</td>\n",
              "      <td>0.117886</td>\n",
              "      <td>0.100852</td>\n",
              "      <td>0.079917</td>\n",
              "      <td>0.085821</td>\n",
              "      <td>0.161574</td>\n",
              "      <td>61.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>183.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.188693</td>\n",
              "      <td>0.045941</td>\n",
              "      <td>0.457372</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.042481</td>\n",
              "      <td>0.199281</td>\n",
              "      <td>0.093447</td>\n",
              "      <td>-80.305152</td>\n",
              "      <td>5.824409</td>\n",
              "      <td>0.648848</td>\n",
              "      <td>1.754870</td>\n",
              "      <td>1.495532</td>\n",
              "      <td>0.739909</td>\n",
              "      <td>0.809644</td>\n",
              "      <td>0.460945</td>\n",
              "      <td>0.409566</td>\n",
              "      <td>0.680122</td>\n",
              "      <td>0.590405</td>\n",
              "      <td>0.481380</td>\n",
              "      <td>0.621956</td>\n",
              "      <td>0.049939</td>\n",
              "      <td>0.281616</td>\n",
              "      <td>0.044727</td>\n",
              "      <td>6.719538</td>\n",
              "      <td>1.377811</td>\n",
              "      <td>1.265771</td>\n",
              "      <td>0.986178</td>\n",
              "      <td>0.710955</td>\n",
              "      <td>0.706904</td>\n",
              "      <td>0.710147</td>\n",
              "      <td>0.688825</td>\n",
              "      <td>0.699573</td>\n",
              "      <td>0.577976</td>\n",
              "      <td>0.533882</td>\n",
              "      <td>0.501818</td>\n",
              "      <td>0.495368</td>\n",
              "      <td>0.020749</td>\n",
              "      <td>0.106318</td>\n",
              "      <td>0.009108</td>\n",
              "      <td>3.992357</td>\n",
              "      <td>0.656429</td>\n",
              "      <td>0.927692</td>\n",
              "      <td>0.569916</td>\n",
              "      <td>0.378919</td>\n",
              "      <td>0.530714</td>\n",
              "      <td>0.317807</td>\n",
              "      <td>0.308447</td>\n",
              "      <td>0.324934</td>\n",
              "      <td>0.263444</td>\n",
              "      <td>0.359477</td>\n",
              "      <td>0.274257</td>\n",
              "      <td>0.233287</td>\n",
              "      <td>0.032678</td>\n",
              "      <td>0.119480</td>\n",
              "      <td>0.028707</td>\n",
              "      <td>4.125111</td>\n",
              "      <td>0.461304</td>\n",
              "      <td>0.280751</td>\n",
              "      <td>0.246108</td>\n",
              "      <td>0.142805</td>\n",
              "      <td>0.183657</td>\n",
              "      <td>0.124399</td>\n",
              "      <td>0.155513</td>\n",
              "      <td>0.167114</td>\n",
              "      <td>0.113774</td>\n",
              "      <td>0.112815</td>\n",
              "      <td>0.129145</td>\n",
              "      <td>0.122330</td>\n",
              "      <td>0.043012</td>\n",
              "      <td>66.0</td>\n",
              "      <td>0.206562</td>\n",
              "      <td>132.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.102839</td>\n",
              "      <td>0.241934</td>\n",
              "      <td>0.351009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.074550</td>\n",
              "      <td>0.140880</td>\n",
              "      <td>0.079789</td>\n",
              "      <td>-93.697749</td>\n",
              "      <td>5.543229</td>\n",
              "      <td>1.064262</td>\n",
              "      <td>0.899152</td>\n",
              "      <td>0.890336</td>\n",
              "      <td>0.702328</td>\n",
              "      <td>0.490685</td>\n",
              "      <td>0.796904</td>\n",
              "      <td>0.745373</td>\n",
              "      <td>0.911234</td>\n",
              "      <td>0.594429</td>\n",
              "      <td>0.454186</td>\n",
              "      <td>0.384836</td>\n",
              "      <td>0.035751</td>\n",
              "      <td>0.085592</td>\n",
              "      <td>0.029413</td>\n",
              "      <td>4.755293</td>\n",
              "      <td>1.116290</td>\n",
              "      <td>0.926772</td>\n",
              "      <td>0.634988</td>\n",
              "      <td>0.639660</td>\n",
              "      <td>0.552653</td>\n",
              "      <td>0.527708</td>\n",
              "      <td>0.584705</td>\n",
              "      <td>0.696173</td>\n",
              "      <td>0.648611</td>\n",
              "      <td>0.689096</td>\n",
              "      <td>0.643595</td>\n",
              "      <td>0.578063</td>\n",
              "      <td>0.047014</td>\n",
              "      <td>0.136984</td>\n",
              "      <td>0.010356</td>\n",
              "      <td>7.713140</td>\n",
              "      <td>1.592642</td>\n",
              "      <td>1.027190</td>\n",
              "      <td>0.591399</td>\n",
              "      <td>0.565654</td>\n",
              "      <td>0.524420</td>\n",
              "      <td>0.554501</td>\n",
              "      <td>0.606200</td>\n",
              "      <td>0.616760</td>\n",
              "      <td>0.596926</td>\n",
              "      <td>0.524291</td>\n",
              "      <td>0.637971</td>\n",
              "      <td>0.637960</td>\n",
              "      <td>0.036151</td>\n",
              "      <td>0.087741</td>\n",
              "      <td>0.030180</td>\n",
              "      <td>5.085385</td>\n",
              "      <td>0.551937</td>\n",
              "      <td>0.257562</td>\n",
              "      <td>0.159950</td>\n",
              "      <td>0.175855</td>\n",
              "      <td>0.150907</td>\n",
              "      <td>0.142092</td>\n",
              "      <td>0.222804</td>\n",
              "      <td>0.329188</td>\n",
              "      <td>0.251668</td>\n",
              "      <td>0.265049</td>\n",
              "      <td>0.284196</td>\n",
              "      <td>0.189988</td>\n",
              "      <td>0.029308</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.144039</td>\n",
              "      <td>200.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.195196</td>\n",
              "      <td>0.310801</td>\n",
              "      <td>0.683817</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>525</th>\n",
              "      <td>0.027142</td>\n",
              "      <td>0.047551</td>\n",
              "      <td>0.072043</td>\n",
              "      <td>-79.881347</td>\n",
              "      <td>8.119313</td>\n",
              "      <td>1.927310</td>\n",
              "      <td>1.696017</td>\n",
              "      <td>0.397888</td>\n",
              "      <td>0.857559</td>\n",
              "      <td>0.302742</td>\n",
              "      <td>0.672649</td>\n",
              "      <td>0.748163</td>\n",
              "      <td>0.412952</td>\n",
              "      <td>0.852625</td>\n",
              "      <td>0.597127</td>\n",
              "      <td>0.456126</td>\n",
              "      <td>0.013099</td>\n",
              "      <td>0.029163</td>\n",
              "      <td>0.030049</td>\n",
              "      <td>3.076905</td>\n",
              "      <td>0.862406</td>\n",
              "      <td>0.640101</td>\n",
              "      <td>0.615202</td>\n",
              "      <td>0.557800</td>\n",
              "      <td>0.514684</td>\n",
              "      <td>0.577850</td>\n",
              "      <td>0.600797</td>\n",
              "      <td>0.544352</td>\n",
              "      <td>0.540142</td>\n",
              "      <td>0.553717</td>\n",
              "      <td>0.493653</td>\n",
              "      <td>0.451593</td>\n",
              "      <td>0.012848</td>\n",
              "      <td>0.044107</td>\n",
              "      <td>0.008805</td>\n",
              "      <td>2.560837</td>\n",
              "      <td>1.008119</td>\n",
              "      <td>0.523135</td>\n",
              "      <td>0.802130</td>\n",
              "      <td>0.694118</td>\n",
              "      <td>0.412457</td>\n",
              "      <td>0.461475</td>\n",
              "      <td>0.500786</td>\n",
              "      <td>0.492573</td>\n",
              "      <td>0.454346</td>\n",
              "      <td>0.437815</td>\n",
              "      <td>0.345676</td>\n",
              "      <td>0.366906</td>\n",
              "      <td>0.028604</td>\n",
              "      <td>0.069883</td>\n",
              "      <td>0.030413</td>\n",
              "      <td>5.042254</td>\n",
              "      <td>0.455528</td>\n",
              "      <td>0.177015</td>\n",
              "      <td>0.165939</td>\n",
              "      <td>0.124718</td>\n",
              "      <td>0.118118</td>\n",
              "      <td>0.194543</td>\n",
              "      <td>0.146845</td>\n",
              "      <td>0.139167</td>\n",
              "      <td>0.161785</td>\n",
              "      <td>0.159234</td>\n",
              "      <td>0.129552</td>\n",
              "      <td>0.106260</td>\n",
              "      <td>0.089991</td>\n",
              "      <td>87.0</td>\n",
              "      <td>0.564842</td>\n",
              "      <td>174.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.261742</td>\n",
              "      <td>0.002657</td>\n",
              "      <td>1.149211</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>526</th>\n",
              "      <td>0.094829</td>\n",
              "      <td>0.204498</td>\n",
              "      <td>0.082824</td>\n",
              "      <td>-61.364436</td>\n",
              "      <td>2.966229</td>\n",
              "      <td>0.627740</td>\n",
              "      <td>1.440352</td>\n",
              "      <td>0.856243</td>\n",
              "      <td>1.110282</td>\n",
              "      <td>0.394450</td>\n",
              "      <td>0.726323</td>\n",
              "      <td>0.885339</td>\n",
              "      <td>0.517613</td>\n",
              "      <td>-0.186371</td>\n",
              "      <td>0.531083</td>\n",
              "      <td>0.312116</td>\n",
              "      <td>0.052306</td>\n",
              "      <td>0.131783</td>\n",
              "      <td>0.029793</td>\n",
              "      <td>3.512784</td>\n",
              "      <td>0.918634</td>\n",
              "      <td>1.043679</td>\n",
              "      <td>0.778114</td>\n",
              "      <td>0.613929</td>\n",
              "      <td>0.598554</td>\n",
              "      <td>0.566087</td>\n",
              "      <td>0.568431</td>\n",
              "      <td>0.541677</td>\n",
              "      <td>0.552420</td>\n",
              "      <td>0.546411</td>\n",
              "      <td>0.580214</td>\n",
              "      <td>0.608745</td>\n",
              "      <td>0.021612</td>\n",
              "      <td>0.047508</td>\n",
              "      <td>0.007370</td>\n",
              "      <td>1.419983</td>\n",
              "      <td>0.529261</td>\n",
              "      <td>0.388876</td>\n",
              "      <td>0.434100</td>\n",
              "      <td>0.318737</td>\n",
              "      <td>0.265518</td>\n",
              "      <td>0.210297</td>\n",
              "      <td>0.169365</td>\n",
              "      <td>0.189288</td>\n",
              "      <td>0.247443</td>\n",
              "      <td>0.228670</td>\n",
              "      <td>0.249573</td>\n",
              "      <td>0.221631</td>\n",
              "      <td>0.023890</td>\n",
              "      <td>0.047186</td>\n",
              "      <td>0.030264</td>\n",
              "      <td>4.043908</td>\n",
              "      <td>0.362876</td>\n",
              "      <td>0.210280</td>\n",
              "      <td>0.213201</td>\n",
              "      <td>0.114466</td>\n",
              "      <td>0.158569</td>\n",
              "      <td>0.100762</td>\n",
              "      <td>0.115185</td>\n",
              "      <td>0.092511</td>\n",
              "      <td>0.093992</td>\n",
              "      <td>0.108324</td>\n",
              "      <td>0.128090</td>\n",
              "      <td>0.167160</td>\n",
              "      <td>0.035827</td>\n",
              "      <td>65.0</td>\n",
              "      <td>0.003942</td>\n",
              "      <td>130.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.282122</td>\n",
              "      <td>0.052218</td>\n",
              "      <td>0.335371</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>527</th>\n",
              "      <td>0.035169</td>\n",
              "      <td>0.065403</td>\n",
              "      <td>0.075227</td>\n",
              "      <td>-81.750533</td>\n",
              "      <td>10.311701</td>\n",
              "      <td>0.092224</td>\n",
              "      <td>0.818851</td>\n",
              "      <td>1.569606</td>\n",
              "      <td>1.831909</td>\n",
              "      <td>0.057216</td>\n",
              "      <td>0.419342</td>\n",
              "      <td>0.482342</td>\n",
              "      <td>0.745151</td>\n",
              "      <td>0.403275</td>\n",
              "      <td>0.838195</td>\n",
              "      <td>0.711755</td>\n",
              "      <td>0.013232</td>\n",
              "      <td>0.035040</td>\n",
              "      <td>0.029861</td>\n",
              "      <td>4.111372</td>\n",
              "      <td>0.911620</td>\n",
              "      <td>1.057301</td>\n",
              "      <td>0.496378</td>\n",
              "      <td>0.666827</td>\n",
              "      <td>0.556064</td>\n",
              "      <td>0.465213</td>\n",
              "      <td>0.499597</td>\n",
              "      <td>0.422874</td>\n",
              "      <td>0.516194</td>\n",
              "      <td>0.375284</td>\n",
              "      <td>0.473949</td>\n",
              "      <td>0.386111</td>\n",
              "      <td>0.016030</td>\n",
              "      <td>0.035553</td>\n",
              "      <td>0.009759</td>\n",
              "      <td>5.776610</td>\n",
              "      <td>1.123743</td>\n",
              "      <td>0.830796</td>\n",
              "      <td>0.339320</td>\n",
              "      <td>1.003937</td>\n",
              "      <td>0.441073</td>\n",
              "      <td>0.563689</td>\n",
              "      <td>0.474611</td>\n",
              "      <td>0.358808</td>\n",
              "      <td>0.543528</td>\n",
              "      <td>0.340304</td>\n",
              "      <td>0.396751</td>\n",
              "      <td>0.496262</td>\n",
              "      <td>0.027062</td>\n",
              "      <td>0.053435</td>\n",
              "      <td>0.030620</td>\n",
              "      <td>4.801035</td>\n",
              "      <td>0.632917</td>\n",
              "      <td>0.351023</td>\n",
              "      <td>0.133192</td>\n",
              "      <td>0.348863</td>\n",
              "      <td>0.161952</td>\n",
              "      <td>0.129868</td>\n",
              "      <td>0.137076</td>\n",
              "      <td>0.118185</td>\n",
              "      <td>0.120515</td>\n",
              "      <td>0.083030</td>\n",
              "      <td>0.147194</td>\n",
              "      <td>0.098238</td>\n",
              "      <td>0.033681</td>\n",
              "      <td>88.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>176.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.184313</td>\n",
              "      <td>0.247136</td>\n",
              "      <td>0.476993</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>528</th>\n",
              "      <td>0.054276</td>\n",
              "      <td>0.238158</td>\n",
              "      <td>0.095935</td>\n",
              "      <td>-71.009724</td>\n",
              "      <td>3.181340</td>\n",
              "      <td>1.547197</td>\n",
              "      <td>2.407780</td>\n",
              "      <td>0.618838</td>\n",
              "      <td>0.997950</td>\n",
              "      <td>0.825143</td>\n",
              "      <td>0.758816</td>\n",
              "      <td>0.727540</td>\n",
              "      <td>0.500074</td>\n",
              "      <td>0.402084</td>\n",
              "      <td>0.539242</td>\n",
              "      <td>0.326410</td>\n",
              "      <td>0.052974</td>\n",
              "      <td>0.283645</td>\n",
              "      <td>0.047596</td>\n",
              "      <td>6.714642</td>\n",
              "      <td>1.600006</td>\n",
              "      <td>1.309167</td>\n",
              "      <td>0.878546</td>\n",
              "      <td>0.712208</td>\n",
              "      <td>0.595806</td>\n",
              "      <td>0.516834</td>\n",
              "      <td>0.578153</td>\n",
              "      <td>0.519924</td>\n",
              "      <td>0.483610</td>\n",
              "      <td>0.478800</td>\n",
              "      <td>0.470226</td>\n",
              "      <td>0.415839</td>\n",
              "      <td>0.016953</td>\n",
              "      <td>0.091381</td>\n",
              "      <td>0.008863</td>\n",
              "      <td>3.245796</td>\n",
              "      <td>0.754218</td>\n",
              "      <td>0.787304</td>\n",
              "      <td>0.442779</td>\n",
              "      <td>0.310832</td>\n",
              "      <td>0.323179</td>\n",
              "      <td>0.247374</td>\n",
              "      <td>0.257608</td>\n",
              "      <td>0.222008</td>\n",
              "      <td>0.232946</td>\n",
              "      <td>0.233224</td>\n",
              "      <td>0.232351</td>\n",
              "      <td>0.163934</td>\n",
              "      <td>0.026424</td>\n",
              "      <td>0.088106</td>\n",
              "      <td>0.028770</td>\n",
              "      <td>4.094957</td>\n",
              "      <td>0.584713</td>\n",
              "      <td>0.312613</td>\n",
              "      <td>0.214629</td>\n",
              "      <td>0.169042</td>\n",
              "      <td>0.115469</td>\n",
              "      <td>0.111847</td>\n",
              "      <td>0.112750</td>\n",
              "      <td>0.090504</td>\n",
              "      <td>0.091698</td>\n",
              "      <td>0.090272</td>\n",
              "      <td>0.080006</td>\n",
              "      <td>0.058181</td>\n",
              "      <td>0.155650</td>\n",
              "      <td>84.0</td>\n",
              "      <td>0.276695</td>\n",
              "      <td>168.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.547126</td>\n",
              "      <td>0.183494</td>\n",
              "      <td>1.255820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>529</th>\n",
              "      <td>0.073194</td>\n",
              "      <td>0.140733</td>\n",
              "      <td>0.080545</td>\n",
              "      <td>-74.517081</td>\n",
              "      <td>6.945590</td>\n",
              "      <td>-1.047953</td>\n",
              "      <td>1.952278</td>\n",
              "      <td>0.341936</td>\n",
              "      <td>0.770582</td>\n",
              "      <td>0.144989</td>\n",
              "      <td>0.405832</td>\n",
              "      <td>0.186847</td>\n",
              "      <td>0.496161</td>\n",
              "      <td>0.389096</td>\n",
              "      <td>0.324653</td>\n",
              "      <td>0.193508</td>\n",
              "      <td>0.020042</td>\n",
              "      <td>0.043408</td>\n",
              "      <td>0.028217</td>\n",
              "      <td>3.012740</td>\n",
              "      <td>0.804194</td>\n",
              "      <td>0.524353</td>\n",
              "      <td>0.508190</td>\n",
              "      <td>0.485743</td>\n",
              "      <td>0.518681</td>\n",
              "      <td>0.481357</td>\n",
              "      <td>0.482828</td>\n",
              "      <td>0.514857</td>\n",
              "      <td>0.508133</td>\n",
              "      <td>0.516179</td>\n",
              "      <td>0.474873</td>\n",
              "      <td>0.447746</td>\n",
              "      <td>0.015537</td>\n",
              "      <td>0.034044</td>\n",
              "      <td>0.007524</td>\n",
              "      <td>5.018377</td>\n",
              "      <td>0.852230</td>\n",
              "      <td>0.410106</td>\n",
              "      <td>0.275035</td>\n",
              "      <td>0.300508</td>\n",
              "      <td>0.248464</td>\n",
              "      <td>0.362048</td>\n",
              "      <td>0.491118</td>\n",
              "      <td>0.446384</td>\n",
              "      <td>0.482079</td>\n",
              "      <td>0.438563</td>\n",
              "      <td>0.356211</td>\n",
              "      <td>0.254899</td>\n",
              "      <td>0.024653</td>\n",
              "      <td>0.052302</td>\n",
              "      <td>0.030290</td>\n",
              "      <td>4.244648</td>\n",
              "      <td>0.350933</td>\n",
              "      <td>0.149544</td>\n",
              "      <td>0.122719</td>\n",
              "      <td>0.063955</td>\n",
              "      <td>0.078072</td>\n",
              "      <td>0.068832</td>\n",
              "      <td>0.081025</td>\n",
              "      <td>0.139319</td>\n",
              "      <td>0.103590</td>\n",
              "      <td>0.114416</td>\n",
              "      <td>0.073605</td>\n",
              "      <td>0.081219</td>\n",
              "      <td>0.056101</td>\n",
              "      <td>94.0</td>\n",
              "      <td>0.007080</td>\n",
              "      <td>188.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.087328</td>\n",
              "      <td>0.236815</td>\n",
              "      <td>0.451701</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>530 rows × 72 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           0         1         2          3   ...   68        69        70        71\n",
              "0    0.034741  0.089665  0.091225 -73.302422  ...  2.0  0.245457  0.105065  0.405399\n",
              "1    0.081374  0.272747  0.085733 -62.584437  ...  2.0  0.343547  0.276366  0.710924\n",
              "2    0.110545  0.273567  0.084410 -65.235325  ...  3.0  0.188693  0.045941  0.457372\n",
              "3    0.042481  0.199281  0.093447 -80.305152  ...  2.0  0.102839  0.241934  0.351009\n",
              "4    0.074550  0.140880  0.079789 -93.697749  ...  2.0  0.195196  0.310801  0.683817\n",
              "..        ...       ...       ...        ...  ...  ...       ...       ...       ...\n",
              "525  0.027142  0.047551  0.072043 -79.881347  ...  2.0  0.261742  0.002657  1.149211\n",
              "526  0.094829  0.204498  0.082824 -61.364436  ...  2.0  0.282122  0.052218  0.335371\n",
              "527  0.035169  0.065403  0.075227 -81.750533  ...  2.0  0.184313  0.247136  0.476993\n",
              "528  0.054276  0.238158  0.095935 -71.009724  ...  2.0  0.547126  0.183494  1.255820\n",
              "529  0.073194  0.140733  0.080545 -74.517081  ...  2.0  0.087328  0.236815  0.451701\n",
              "\n",
              "[530 rows x 72 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 695
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VVZdHXfNFoOb",
        "outputId": "fc7cea42-c9a9-47a7-d7d1-88cdd6fad1e8"
      },
      "source": [
        "va = (df.isna().sum()).sum()\n",
        "va"
      ],
      "execution_count": 696,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 696
        }
      ]
    },
    {
      "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": 697,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R3gRnSNF6BnZ",
        "outputId": "d8b92a4a-f071-4046-a080-bb7e081991b7"
      },
      "source": [
        "X_test"
      ],
      "execution_count": 698,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.52751657, -0.7163883 ,  1.21315006, ..., -0.04586578,\n",
              "        -0.22651953, -0.60392646],\n",
              "       [-0.15986713, -0.37935696, -0.83883174, ...,  1.14280483,\n",
              "         0.3797663 ,  0.38762244],\n",
              "       [ 2.04640878,  1.25442176,  1.24451415, ..., -1.00563697,\n",
              "        -0.19717983, -0.87608304],\n",
              "       ...,\n",
              "       [-0.96235614, -1.02193939, -0.6029958 , ..., -0.76221794,\n",
              "        -0.88382307, -0.78601863],\n",
              "       [ 1.4356929 ,  0.61769484,  1.20874808, ...,  0.29080106,\n",
              "         1.34725769,  0.49765303],\n",
              "       [-0.89368062, -1.08026503, -0.36782015, ..., -0.88947997,\n",
              "         0.12370162, -0.67435006]])"
            ]
          },
          "metadata": {},
          "execution_count": 698
        }
      ]
    },
    {
      "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": 699,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0sh7m7mI2SFM",
        "outputId": "c75ea8d7-2dab-4c28-fbe4-8e434823abb8"
      },
      "source": [
        "dX_train.size"
      ],
      "execution_count": 700,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "38160"
            ]
          },
          "metadata": {},
          "execution_count": 700
        }
      ]
    },
    {
      "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": 701,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-BXupnbMhj82",
        "outputId": "4b72d8f3-fcb5-4625-fe74-b7b1cfa79d61"
      },
      "source": [
        "taxa_de_missing_values = 0.10\n",
        "missing_values = amount_missing_values(taxa_de_missing_values, dX_train.size)"
      ],
      "execution_count": 702,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total de dados:  38160 \n",
            "Total de Valores Ausentes:  3816\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EGMtsgfm4SiY",
        "outputId": "7fc49cdb-480e-4f1c-d4a4-a2ba5b203bdc"
      },
      "source": [
        "type(missing_values)"
      ],
      "execution_count": 703,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "int"
            ]
          },
          "metadata": {},
          "execution_count": 703
        }
      ]
    },
    {
      "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": 704,
      "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": 705,
      "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": 706,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2HGLJ44z0-QO"
      },
      "source": [
        "list_positions_missing_values = ampute(rows,cols)"
      ],
      "execution_count": 707,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oWNk210nu74B",
        "outputId": "b5145b9e-60d4-4467-ab12-a429dc5f1c93"
      },
      "source": [
        "len(indexList), len(list_positions_missing_values)"
      ],
      "execution_count": 708,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3816, 3816)"
            ]
          },
          "metadata": {},
          "execution_count": 708
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bu-xX7GH9yQI",
        "outputId": "5a7d3a5a-0cf1-4a58-907a-b4798e073b52"
      },
      "source": [
        "list_positions_missing_values"
      ],
      "execution_count": 709,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[104, 71],\n",
              " [281, 45],\n",
              " [273, 18],\n",
              " [390, 42],\n",
              " [113, 36],\n",
              " [312, 23],\n",
              " [269, 11],\n",
              " [409, 71],\n",
              " [382, 47],\n",
              " [256, 67],\n",
              " [356, 58],\n",
              " [415, 21],\n",
              " [55, 53],\n",
              " [320, 24],\n",
              " [403, 54],\n",
              " [234, 71],\n",
              " [12, 62],\n",
              " [207, 61],\n",
              " [368, 2],\n",
              " [226, 7],\n",
              " [431, 36],\n",
              " [427, 25],\n",
              " [443, 54],\n",
              " [441, 38],\n",
              " [24, 46],\n",
              " [355, 17],\n",
              " [186, 48],\n",
              " [187, 18],\n",
              " [315, 6],\n",
              " [441, 62],\n",
              " [51, 45],\n",
              " [56, 28],\n",
              " [479, 59],\n",
              " [181, 29],\n",
              " [24, 27],\n",
              " [234, 63],\n",
              " [389, 24],\n",
              " [527, 39],\n",
              " [290, 8],\n",
              " [178, 43],\n",
              " [84, 8],\n",
              " [311, 43],\n",
              " [47, 30],\n",
              " [368, 55],\n",
              " [418, 48],\n",
              " [68, 7],\n",
              " [514, 8],\n",
              " [369, 5],\n",
              " [74, 56],\n",
              " [464, 29],\n",
              " [277, 19],\n",
              " [494, 62],\n",
              " [234, 5],\n",
              " [54, 14],\n",
              " [140, 28],\n",
              " [267, 23],\n",
              " [401, 61],\n",
              " [99, 19],\n",
              " [162, 44],\n",
              " [224, 48],\n",
              " [338, 15],\n",
              " [108, 56],\n",
              " [523, 37],\n",
              " [60, 67],\n",
              " [50, 42],\n",
              " [305, 23],\n",
              " [408, 52],\n",
              " [207, 18],\n",
              " [426, 44],\n",
              " [293, 10],\n",
              " [235, 6],\n",
              " [112, 16],\n",
              " [63, 51],\n",
              " [59, 43],\n",
              " [178, 54],\n",
              " [306, 44],\n",
              " [221, 70],\n",
              " [108, 25],\n",
              " [10, 50],\n",
              " [366, 45],\n",
              " [500, 6],\n",
              " [203, 17],\n",
              " [488, 41],\n",
              " [428, 3],\n",
              " [500, 10],\n",
              " [493, 51],\n",
              " [455, 4],\n",
              " [349, 59],\n",
              " [67, 63],\n",
              " [452, 31],\n",
              " [136, 47],\n",
              " [511, 2],\n",
              " [383, 61],\n",
              " [271, 63],\n",
              " [219, 70],\n",
              " [165, 26],\n",
              " [6, 48],\n",
              " [22, 47],\n",
              " [62, 16],\n",
              " [396, 56],\n",
              " [285, 1],\n",
              " [235, 37],\n",
              " [23, 21],\n",
              " [483, 29],\n",
              " [378, 43],\n",
              " [288, 54],\n",
              " [471, 21],\n",
              " [72, 65],\n",
              " [416, 50],\n",
              " [402, 25],\n",
              " [389, 21],\n",
              " [415, 56],\n",
              " [181, 62],\n",
              " [43, 46],\n",
              " [78, 17],\n",
              " [438, 5],\n",
              " [178, 49],\n",
              " [25, 5],\n",
              " [146, 54],\n",
              " [253, 8],\n",
              " [266, 19],\n",
              " [498, 43],\n",
              " [443, 37],\n",
              " [381, 1],\n",
              " [355, 1],\n",
              " [336, 37],\n",
              " [197, 66],\n",
              " [272, 2],\n",
              " [430, 16],\n",
              " [285, 2],\n",
              " [329, 63],\n",
              " [421, 33],\n",
              " [226, 25],\n",
              " [244, 27],\n",
              " [205, 20],\n",
              " [429, 54],\n",
              " [273, 12],\n",
              " [423, 45],\n",
              " [403, 18],\n",
              " [352, 37],\n",
              " [468, 18],\n",
              " [31, 37],\n",
              " [44, 63],\n",
              " [180, 51],\n",
              " [5, 29],\n",
              " [369, 18],\n",
              " [259, 14],\n",
              " [113, 55],\n",
              " [373, 39],\n",
              " [371, 62],\n",
              " [412, 12],\n",
              " [382, 63],\n",
              " [97, 21],\n",
              " [298, 29],\n",
              " [399, 4],\n",
              " [465, 23],\n",
              " [260, 50],\n",
              " [303, 5],\n",
              " [37, 21],\n",
              " [172, 20],\n",
              " [84, 58],\n",
              " [237, 60],\n",
              " [366, 13],\n",
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              " [318, 51],\n",
              " [162, 32],\n",
              " [236, 68],\n",
              " [162, 41],\n",
              " [145, 42],\n",
              " [228, 19],\n",
              " [480, 29],\n",
              " [518, 1],\n",
              " [366, 47],\n",
              " [385, 54],\n",
              " [83, 40],\n",
              " [243, 21],\n",
              " [407, 51],\n",
              " [174, 15],\n",
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              " [198, 45],\n",
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              " [264, 41],\n",
              " [291, 68],\n",
              " [374, 41],\n",
              " [496, 32],\n",
              " [239, 13],\n",
              " [126, 31],\n",
              " [137, 34],\n",
              " [328, 39],\n",
              " [204, 34],\n",
              " [237, 43],\n",
              " [463, 21],\n",
              " [136, 44],\n",
              " [90, 64],\n",
              " [478, 32],\n",
              " [156, 54],\n",
              " [290, 29],\n",
              " [228, 37],\n",
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              " [50, 59],\n",
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              " [419, 46],\n",
              " [503, 42],\n",
              " [448, 45],\n",
              " [308, 19],\n",
              " [399, 70],\n",
              " [38, 33],\n",
              " [245, 10],\n",
              " [272, 62],\n",
              " ...]"
            ]
          },
          "metadata": {},
          "execution_count": 709
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "WhTjlES08e00",
        "outputId": "17126898-c60e-4a94-f18c-2b38b4b78135"
      },
      "source": [
        "df"
      ],
      "execution_count": 710,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>0.783788</td>\n",
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              "      <td>0.530384</td>\n",
              "      <td>0.812429</td>\n",
              "      <td>0.028851</td>\n",
              "      <td>0.129039</td>\n",
              "      <td>0.039614</td>\n",
              "      <td>5.762173</td>\n",
              "      <td>1.636819</td>\n",
              "      <td>1.170034</td>\n",
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              "      <td>0.118630</td>\n",
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              "      <td>3.183163</td>\n",
              "      <td>-0.218145</td>\n",
              "      <td>0.163038</td>\n",
              "      <td>0.620251</td>\n",
              "      <td>0.458514</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.308287</td>\n",
              "      <td>0.538152</td>\n",
              "      <td>0.594871</td>\n",
              "      <td>0.734332</td>\n",
              "      <td>0.415489</td>\n",
              "      <td>0.761508</td>\n",
              "      <td>0.066288</td>\n",
              "      <td>0.262370</td>\n",
              "      <td>0.034438</td>\n",
              "      <td>3.480874</td>\n",
              "      <td>1.596532</td>\n",
              "      <td>0.943803</td>\n",
              "      <td>0.804444</td>\n",
              "      <td>0.511229</td>\n",
              "      <td>0.498670</td>\n",
              "      <td>0.523039</td>\n",
              "      <td>0.480916</td>\n",
              "      <td>0.488657</td>\n",
              "      <td>0.483166</td>\n",
              "      <td>0.445187</td>\n",
              "      <td>0.415994</td>\n",
              "      <td>0.405593</td>\n",
              "      <td>0.013621</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.010094</td>\n",
              "      <td>1.243981</td>\n",
              "      <td>0.829790</td>\n",
              "      <td>0.252972</td>\n",
              "      <td>0.347831</td>\n",
              "      <td>0.205087</td>\n",
              "      <td>0.168601</td>\n",
              "      <td>0.178009</td>\n",
              "      <td>0.144080</td>\n",
              "      <td>0.178703</td>\n",
              "      <td>0.146937</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.128202</td>\n",
              "      <td>0.107007</td>\n",
              "      <td>0.020028</td>\n",
              "      <td>0.066940</td>\n",
              "      <td>0.029483</td>\n",
              "      <td>3.963534</td>\n",
              "      <td>0.382360</td>\n",
              "      <td>0.168389</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.098341</td>\n",
              "      <td>0.087046</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.059393</td>\n",
              "      <td>0.059457</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.067684</td>\n",
              "      <td>0.070075</td>\n",
              "      <td>0.041565</td>\n",
              "      <td>0.295031</td>\n",
              "      <td>70.0</td>\n",
              "      <td>0.276366</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.343547</td>\n",
              "      <td>0.276366</td>\n",
              "      <td>0.710924</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.110545</td>\n",
              "      <td>0.273567</td>\n",
              "      <td>0.084410</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.794964</td>\n",
              "      <td>0.639047</td>\n",
              "      <td>1.281297</td>\n",
              "      <td>0.757896</td>\n",
              "      <td>0.489412</td>\n",
              "      <td>0.627636</td>\n",
              "      <td>0.469322</td>\n",
              "      <td>0.644336</td>\n",
              "      <td>0.441556</td>\n",
              "      <td>0.335964</td>\n",
              "      <td>0.290713</td>\n",
              "      <td>0.158538</td>\n",
              "      <td>0.082743</td>\n",
              "      <td>0.215373</td>\n",
              "      <td>0.035970</td>\n",
              "      <td>4.834742</td>\n",
              "      <td>1.213443</td>\n",
              "      <td>0.864034</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.780572</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.639740</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.526312</td>\n",
              "      <td>0.562622</td>\n",
              "      <td>0.538407</td>\n",
              "      <td>0.492292</td>\n",
              "      <td>0.455562</td>\n",
              "      <td>0.029112</td>\n",
              "      <td>0.070433</td>\n",
              "      <td>0.008525</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.592634</td>\n",
              "      <td>0.761852</td>\n",
              "      <td>0.568740</td>\n",
              "      <td>0.589827</td>\n",
              "      <td>0.281181</td>\n",
              "      <td>0.437752</td>\n",
              "      <td>0.479889</td>\n",
              "      <td>0.227320</td>\n",
              "      <td>0.296224</td>\n",
              "      <td>0.273855</td>\n",
              "      <td>0.191804</td>\n",
              "      <td>0.198025</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.065427</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.430373</td>\n",
              "      <td>0.172862</td>\n",
              "      <td>0.177523</td>\n",
              "      <td>0.184333</td>\n",
              "      <td>0.095718</td>\n",
              "      <td>0.139323</td>\n",
              "      <td>0.109279</td>\n",
              "      <td>0.090650</td>\n",
              "      <td>0.117886</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.079917</td>\n",
              "      <td>0.085821</td>\n",
              "      <td>0.161574</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>183.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.045941</td>\n",
              "      <td>0.457372</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.042481</td>\n",
              "      <td>0.199281</td>\n",
              "      <td>0.093447</td>\n",
              "      <td>-80.305152</td>\n",
              "      <td>5.824409</td>\n",
              "      <td>0.648848</td>\n",
              "      <td>1.754870</td>\n",
              "      <td>1.495532</td>\n",
              "      <td>0.739909</td>\n",
              "      <td>0.809644</td>\n",
              "      <td>0.460945</td>\n",
              "      <td>0.409566</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.590405</td>\n",
              "      <td>0.481380</td>\n",
              "      <td>0.621956</td>\n",
              "      <td>0.049939</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.044727</td>\n",
              "      <td>6.719538</td>\n",
              "      <td>1.377811</td>\n",
              "      <td>1.265771</td>\n",
              "      <td>0.986178</td>\n",
              "      <td>0.710955</td>\n",
              "      <td>0.706904</td>\n",
              "      <td>0.710147</td>\n",
              "      <td>0.688825</td>\n",
              "      <td>0.699573</td>\n",
              "      <td>0.577976</td>\n",
              "      <td>0.533882</td>\n",
              "      <td>0.501818</td>\n",
              "      <td>0.495368</td>\n",
              "      <td>0.020749</td>\n",
              "      <td>0.106318</td>\n",
              "      <td>0.009108</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.927692</td>\n",
              "      <td>0.569916</td>\n",
              "      <td>0.378919</td>\n",
              "      <td>0.530714</td>\n",
              "      <td>0.317807</td>\n",
              "      <td>0.308447</td>\n",
              "      <td>0.324934</td>\n",
              "      <td>0.263444</td>\n",
              "      <td>0.359477</td>\n",
              "      <td>0.274257</td>\n",
              "      <td>0.233287</td>\n",
              "      <td>0.032678</td>\n",
              "      <td>0.119480</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.125111</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.280751</td>\n",
              "      <td>0.246108</td>\n",
              "      <td>0.142805</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.124399</td>\n",
              "      <td>0.155513</td>\n",
              "      <td>0.167114</td>\n",
              "      <td>0.113774</td>\n",
              "      <td>0.112815</td>\n",
              "      <td>0.129145</td>\n",
              "      <td>0.122330</td>\n",
              "      <td>0.043012</td>\n",
              "      <td>66.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>132.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.102839</td>\n",
              "      <td>0.241934</td>\n",
              "      <td>0.351009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.074550</td>\n",
              "      <td>0.140880</td>\n",
              "      <td>0.079789</td>\n",
              "      <td>-93.697749</td>\n",
              "      <td>5.543229</td>\n",
              "      <td>1.064262</td>\n",
              "      <td>0.899152</td>\n",
              "      <td>0.890336</td>\n",
              "      <td>0.702328</td>\n",
              "      <td>0.490685</td>\n",
              "      <td>0.796904</td>\n",
              "      <td>0.745373</td>\n",
              "      <td>0.911234</td>\n",
              "      <td>0.594429</td>\n",
              "      <td>0.454186</td>\n",
              "      <td>0.384836</td>\n",
              "      <td>0.035751</td>\n",
              "      <td>0.085592</td>\n",
              "      <td>0.029413</td>\n",
              "      <td>4.755293</td>\n",
              "      <td>1.116290</td>\n",
              "      <td>0.926772</td>\n",
              "      <td>0.634988</td>\n",
              "      <td>0.639660</td>\n",
              "      <td>0.552653</td>\n",
              "      <td>0.527708</td>\n",
              "      <td>0.584705</td>\n",
              "      <td>0.696173</td>\n",
              "      <td>0.648611</td>\n",
              "      <td>0.689096</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.047014</td>\n",
              "      <td>0.136984</td>\n",
              "      <td>0.010356</td>\n",
              "      <td>7.713140</td>\n",
              "      <td>1.592642</td>\n",
              "      <td>1.027190</td>\n",
              "      <td>0.591399</td>\n",
              "      <td>0.565654</td>\n",
              "      <td>0.524420</td>\n",
              "      <td>0.554501</td>\n",
              "      <td>0.606200</td>\n",
              "      <td>0.616760</td>\n",
              "      <td>0.596926</td>\n",
              "      <td>0.524291</td>\n",
              "      <td>0.637971</td>\n",
              "      <td>0.637960</td>\n",
              "      <td>0.036151</td>\n",
              "      <td>0.087741</td>\n",
              "      <td>0.030180</td>\n",
              "      <td>5.085385</td>\n",
              "      <td>0.551937</td>\n",
              "      <td>0.257562</td>\n",
              "      <td>0.159950</td>\n",
              "      <td>0.175855</td>\n",
              "      <td>0.150907</td>\n",
              "      <td>0.142092</td>\n",
              "      <td>0.222804</td>\n",
              "      <td>0.329188</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.265049</td>\n",
              "      <td>0.284196</td>\n",
              "      <td>0.189988</td>\n",
              "      <td>0.029308</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.144039</td>\n",
              "      <td>200.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.195196</td>\n",
              "      <td>0.310801</td>\n",
              "      <td>0.683817</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>525</th>\n",
              "      <td>0.027142</td>\n",
              "      <td>0.047551</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.119313</td>\n",
              "      <td>1.927310</td>\n",
              "      <td>1.696017</td>\n",
              "      <td>0.397888</td>\n",
              "      <td>0.857559</td>\n",
              "      <td>0.302742</td>\n",
              "      <td>0.672649</td>\n",
              "      <td>0.748163</td>\n",
              "      <td>0.412952</td>\n",
              "      <td>0.852625</td>\n",
              "      <td>0.597127</td>\n",
              "      <td>0.456126</td>\n",
              "      <td>0.013099</td>\n",
              "      <td>0.029163</td>\n",
              "      <td>0.030049</td>\n",
              "      <td>3.076905</td>\n",
              "      <td>0.862406</td>\n",
              "      <td>0.640101</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.514684</td>\n",
              "      <td>0.577850</td>\n",
              "      <td>0.600797</td>\n",
              "      <td>0.544352</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.553717</td>\n",
              "      <td>0.493653</td>\n",
              "      <td>0.451593</td>\n",
              "      <td>0.012848</td>\n",
              "      <td>0.044107</td>\n",
              "      <td>0.008805</td>\n",
              "      <td>2.560837</td>\n",
              "      <td>1.008119</td>\n",
              "      <td>0.523135</td>\n",
              "      <td>0.802130</td>\n",
              "      <td>0.694118</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.461475</td>\n",
              "      <td>0.500786</td>\n",
              "      <td>0.492573</td>\n",
              "      <td>0.454346</td>\n",
              "      <td>0.437815</td>\n",
              "      <td>0.345676</td>\n",
              "      <td>0.366906</td>\n",
              "      <td>0.028604</td>\n",
              "      <td>0.069883</td>\n",
              "      <td>0.030413</td>\n",
              "      <td>5.042254</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.177015</td>\n",
              "      <td>0.165939</td>\n",
              "      <td>0.124718</td>\n",
              "      <td>0.118118</td>\n",
              "      <td>0.194543</td>\n",
              "      <td>0.146845</td>\n",
              "      <td>0.139167</td>\n",
              "      <td>0.161785</td>\n",
              "      <td>0.159234</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.106260</td>\n",
              "      <td>0.089991</td>\n",
              "      <td>87.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>174.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.261742</td>\n",
              "      <td>0.002657</td>\n",
              "      <td>1.149211</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>526</th>\n",
              "      <td>0.094829</td>\n",
              "      <td>0.204498</td>\n",
              "      <td>0.082824</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.966229</td>\n",
              "      <td>0.627740</td>\n",
              "      <td>1.440352</td>\n",
              "      <td>0.856243</td>\n",
              "      <td>1.110282</td>\n",
              "      <td>0.394450</td>\n",
              "      <td>0.726323</td>\n",
              "      <td>0.885339</td>\n",
              "      <td>0.517613</td>\n",
              "      <td>-0.186371</td>\n",
              "      <td>0.531083</td>\n",
              "      <td>0.312116</td>\n",
              "      <td>0.052306</td>\n",
              "      <td>0.131783</td>\n",
              "      <td>0.029793</td>\n",
              "      <td>3.512784</td>\n",
              "      <td>0.918634</td>\n",
              "      <td>1.043679</td>\n",
              "      <td>0.778114</td>\n",
              "      <td>0.613929</td>\n",
              "      <td>0.598554</td>\n",
              "      <td>0.566087</td>\n",
              "      <td>0.568431</td>\n",
              "      <td>0.541677</td>\n",
              "      <td>0.552420</td>\n",
              "      <td>0.546411</td>\n",
              "      <td>0.580214</td>\n",
              "      <td>0.608745</td>\n",
              "      <td>0.021612</td>\n",
              "      <td>0.047508</td>\n",
              "      <td>0.007370</td>\n",
              "      <td>1.419983</td>\n",
              "      <td>0.529261</td>\n",
              "      <td>0.388876</td>\n",
              "      <td>0.434100</td>\n",
              "      <td>0.318737</td>\n",
              "      <td>0.265518</td>\n",
              "      <td>0.210297</td>\n",
              "      <td>0.169365</td>\n",
              "      <td>0.189288</td>\n",
              "      <td>0.247443</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.249573</td>\n",
              "      <td>0.221631</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.047186</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.043908</td>\n",
              "      <td>0.362876</td>\n",
              "      <td>0.210280</td>\n",
              "      <td>0.213201</td>\n",
              "      <td>0.114466</td>\n",
              "      <td>0.158569</td>\n",
              "      <td>0.100762</td>\n",
              "      <td>0.115185</td>\n",
              "      <td>0.092511</td>\n",
              "      <td>0.093992</td>\n",
              "      <td>0.108324</td>\n",
              "      <td>0.128090</td>\n",
              "      <td>0.167160</td>\n",
              "      <td>NaN</td>\n",
              "      <td>65.0</td>\n",
              "      <td>0.003942</td>\n",
              "      <td>130.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.282122</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.335371</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>527</th>\n",
              "      <td>0.035169</td>\n",
              "      <td>0.065403</td>\n",
              "      <td>0.075227</td>\n",
              "      <td>-81.750533</td>\n",
              "      <td>10.311701</td>\n",
              "      <td>0.092224</td>\n",
              "      <td>0.818851</td>\n",
              "      <td>1.569606</td>\n",
              "      <td>1.831909</td>\n",
              "      <td>0.057216</td>\n",
              "      <td>0.419342</td>\n",
              "      <td>0.482342</td>\n",
              "      <td>0.745151</td>\n",
              "      <td>0.403275</td>\n",
              "      <td>0.838195</td>\n",
              "      <td>0.711755</td>\n",
              "      <td>0.013232</td>\n",
              "      <td>0.035040</td>\n",
              "      <td>0.029861</td>\n",
              "      <td>4.111372</td>\n",
              "      <td>0.911620</td>\n",
              "      <td>1.057301</td>\n",
              "      <td>0.496378</td>\n",
              "      <td>0.666827</td>\n",
              "      <td>0.556064</td>\n",
              "      <td>0.465213</td>\n",
              "      <td>0.499597</td>\n",
              "      <td>0.422874</td>\n",
              "      <td>0.516194</td>\n",
              "      <td>0.375284</td>\n",
              "      <td>0.473949</td>\n",
              "      <td>0.386111</td>\n",
              "      <td>0.016030</td>\n",
              "      <td>0.035553</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.776610</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.830796</td>\n",
              "      <td>0.339320</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.441073</td>\n",
              "      <td>0.563689</td>\n",
              "      <td>0.474611</td>\n",
              "      <td>0.358808</td>\n",
              "      <td>0.543528</td>\n",
              "      <td>0.340304</td>\n",
              "      <td>0.396751</td>\n",
              "      <td>0.496262</td>\n",
              "      <td>0.027062</td>\n",
              "      <td>0.053435</td>\n",
              "      <td>0.030620</td>\n",
              "      <td>4.801035</td>\n",
              "      <td>0.632917</td>\n",
              "      <td>0.351023</td>\n",
              "      <td>0.133192</td>\n",
              "      <td>0.348863</td>\n",
              "      <td>0.161952</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.137076</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.120515</td>\n",
              "      <td>0.083030</td>\n",
              "      <td>0.147194</td>\n",
              "      <td>0.098238</td>\n",
              "      <td>0.033681</td>\n",
              "      <td>88.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>176.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.184313</td>\n",
              "      <td>0.247136</td>\n",
              "      <td>0.476993</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>528</th>\n",
              "      <td>0.054276</td>\n",
              "      <td>0.238158</td>\n",
              "      <td>0.095935</td>\n",
              "      <td>-71.009724</td>\n",
              "      <td>3.181340</td>\n",
              "      <td>1.547197</td>\n",
              "      <td>2.407780</td>\n",
              "      <td>0.618838</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.825143</td>\n",
              "      <td>0.758816</td>\n",
              "      <td>0.727540</td>\n",
              "      <td>0.500074</td>\n",
              "      <td>0.402084</td>\n",
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              "      <td>0.326410</td>\n",
              "      <td>0.052974</td>\n",
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              "      <td>0.047596</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>0.595806</td>\n",
              "      <td>0.516834</td>\n",
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              "      <td>0.478800</td>\n",
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              "      <td>0.415839</td>\n",
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              "      <td>0.787304</td>\n",
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              "      <td>0.088106</td>\n",
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              "      <td>0.112750</td>\n",
              "      <td>0.090504</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>84.0</td>\n",
              "      <td>0.276695</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.547126</td>\n",
              "      <td>0.183494</td>\n",
              "      <td>1.255820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>529</th>\n",
              "      <td>0.073194</td>\n",
              "      <td>0.140733</td>\n",
              "      <td>0.080545</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.945590</td>\n",
              "      <td>-1.047953</td>\n",
              "      <td>1.952278</td>\n",
              "      <td>0.341936</td>\n",
              "      <td>0.770582</td>\n",
              "      <td>0.144989</td>\n",
              "      <td>0.405832</td>\n",
              "      <td>0.186847</td>\n",
              "      <td>0.496161</td>\n",
              "      <td>0.389096</td>\n",
              "      <td>0.324653</td>\n",
              "      <td>0.193508</td>\n",
              "      <td>0.020042</td>\n",
              "      <td>0.043408</td>\n",
              "      <td>0.028217</td>\n",
              "      <td>3.012740</td>\n",
              "      <td>0.804194</td>\n",
              "      <td>0.524353</td>\n",
              "      <td>0.508190</td>\n",
              "      <td>0.485743</td>\n",
              "      <td>0.518681</td>\n",
              "      <td>0.481357</td>\n",
              "      <td>0.482828</td>\n",
              "      <td>0.514857</td>\n",
              "      <td>0.508133</td>\n",
              "      <td>0.516179</td>\n",
              "      <td>0.474873</td>\n",
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              "      <td>0.034044</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.018377</td>\n",
              "      <td>0.852230</td>\n",
              "      <td>0.410106</td>\n",
              "      <td>0.275035</td>\n",
              "      <td>0.300508</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>0.482079</td>\n",
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              "      <td>0.356211</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>0.052302</td>\n",
              "      <td>0.030290</td>\n",
              "      <td>4.244648</td>\n",
              "      <td>0.350933</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>0.081025</td>\n",
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              "      <td>0.103590</td>\n",
              "      <td>0.114416</td>\n",
              "      <td>0.073605</td>\n",
              "      <td>0.081219</td>\n",
              "      <td>0.056101</td>\n",
              "      <td>94.0</td>\n",
              "      <td>0.007080</td>\n",
              "      <td>188.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.087328</td>\n",
              "      <td>0.236815</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>530 rows × 72 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           0         1         2          3   ...   68        69        70        71\n",
              "0    0.034741  0.089665  0.091225 -73.302422  ...  2.0  0.245457  0.105065  0.405399\n",
              "1    0.081374  0.272747  0.085733 -62.584437  ...  NaN  0.343547  0.276366  0.710924\n",
              "2    0.110545  0.273567  0.084410        NaN  ...  3.0       NaN  0.045941  0.457372\n",
              "3    0.042481  0.199281  0.093447 -80.305152  ...  2.0  0.102839  0.241934  0.351009\n",
              "4    0.074550  0.140880  0.079789 -93.697749  ...  NaN  0.195196  0.310801  0.683817\n",
              "..        ...       ...       ...        ...  ...  ...       ...       ...       ...\n",
              "525  0.027142  0.047551       NaN        NaN  ...  2.0  0.261742  0.002657  1.149211\n",
              "526  0.094829  0.204498  0.082824        NaN  ...  2.0  0.282122       NaN  0.335371\n",
              "527  0.035169  0.065403  0.075227 -81.750533  ...  2.0  0.184313  0.247136  0.476993\n",
              "528  0.054276  0.238158  0.095935 -71.009724  ...  2.0  0.547126  0.183494  1.255820\n",
              "529  0.073194  0.140733  0.080545        NaN  ...  2.0  0.087328  0.236815       NaN\n",
              "\n",
              "[530 rows x 72 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 710
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V1a-5SqH8o-f",
        "outputId": "9f6f9707-0b23-40c9-b24f-ee59ca457a96"
      },
      "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": 711,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3631, 185)"
            ]
          },
          "metadata": {},
          "execution_count": 711
        }
      ]
    },
    {
      "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": 712,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WwFZdRyx7Cop"
      },
      "source": [
        "#df com missiing values\n",
        "df\n",
        "df_amp = df.copy()"
      ],
      "execution_count": 713,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 379
        },
        "id": "tXZXkvNE8SD-",
        "outputId": "da4792ca-a3bb-42ae-ee33-e4d453eb3484"
      },
      "source": [
        "#visualizando missing values\n",
        "import missingno as msno\n",
        "msno.matrix(df)"
      ],
      "execution_count": 714,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f45e4e39690>"
            ]
          },
          "metadata": {},
          "execution_count": 714
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 1800x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 829
        },
        "id": "CrlNN_CS89IZ",
        "outputId": "48d3396e-2737-4763-83e0-306c37115705"
      },
      "source": [
        "msno.bar(df)"
      ],
      "execution_count": 715,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f45e44fd6d0>"
            ]
          },
          "metadata": {},
          "execution_count": 715
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x1692 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GnhXBOpE9l7D",
        "outputId": "a164c1c4-2f71-45cf-e3c6-cf0b8fc60dce"
      },
      "source": [
        "missing_values = (df.isnull().sum()).sum()\n",
        "missing_values"
      ],
      "execution_count": 716,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3631"
            ]
          },
          "metadata": {},
          "execution_count": 716
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lEUwlKyGRMNE"
      },
      "source": [
        "missing_values = df.isnull().sum()"
      ],
      "execution_count": 717,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "Yovav_L--cfg",
        "outputId": "2902a4b4-8dbe-4890-8010-17b2de6a4c42"
      },
      "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": 718,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cOTTWvTOS74c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "outputId": "500c2f10-79bd-45e6-ee00-dd9319db406f"
      },
      "source": [
        "df.describe()\n"
      ],
      "execution_count": 719,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "      <th>13</th>\n",
              "      <th>14</th>\n",
              "      <th>15</th>\n",
              "      <th>16</th>\n",
              "      <th>17</th>\n",
              "      <th>18</th>\n",
              "      <th>19</th>\n",
              "      <th>20</th>\n",
              "      <th>21</th>\n",
              "      <th>22</th>\n",
              "      <th>23</th>\n",
              "      <th>24</th>\n",
              "      <th>25</th>\n",
              "      <th>26</th>\n",
              "      <th>27</th>\n",
              "      <th>28</th>\n",
              "      <th>29</th>\n",
              "      <th>30</th>\n",
              "      <th>31</th>\n",
              "      <th>32</th>\n",
              "      <th>33</th>\n",
              "      <th>34</th>\n",
              "      <th>35</th>\n",
              "      <th>36</th>\n",
              "      <th>37</th>\n",
              "      <th>38</th>\n",
              "      <th>39</th>\n",
              "      <th>40</th>\n",
              "      <th>41</th>\n",
              "      <th>42</th>\n",
              "      <th>43</th>\n",
              "      <th>44</th>\n",
              "      <th>45</th>\n",
              "      <th>46</th>\n",
              "      <th>47</th>\n",
              "      <th>48</th>\n",
              "      <th>49</th>\n",
              "      <th>50</th>\n",
              "      <th>51</th>\n",
              "      <th>52</th>\n",
              "      <th>53</th>\n",
              "      <th>54</th>\n",
              "      <th>55</th>\n",
              "      <th>56</th>\n",
              "      <th>57</th>\n",
              "      <th>58</th>\n",
              "      <th>59</th>\n",
              "      <th>60</th>\n",
              "      <th>61</th>\n",
              "      <th>62</th>\n",
              "      <th>63</th>\n",
              "      <th>64</th>\n",
              "      <th>65</th>\n",
              "      <th>66</th>\n",
              "      <th>67</th>\n",
              "      <th>68</th>\n",
              "      <th>69</th>\n",
              "      <th>70</th>\n",
              "      <th>71</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>530.000000</td>\n",
              "      <td>484.000000</td>\n",
              "      <td>479.000000</td>\n",
              "      <td>483.000000</td>\n",
              "      <td>487.000000</td>\n",
              "      <td>475.000000</td>\n",
              "      <td>483.000000</td>\n",
              "      <td>490.000000</td>\n",
              "      <td>486.000000</td>\n",
              "      <td>473.000000</td>\n",
              "      <td>474.000000</td>\n",
              "      <td>498.000000</td>\n",
              "      <td>490.000000</td>\n",
              "      <td>478.000000</td>\n",
              "      <td>471.000000</td>\n",
              "      <td>476.000000</td>\n",
              "      <td>474.000000</td>\n",
              "      <td>472.000000</td>\n",
              "      <td>459.000000</td>\n",
              "      <td>472.000000</td>\n",
              "      <td>479.000000</td>\n",
              "      <td>468.000000</td>\n",
              "      <td>479.000000</td>\n",
              "      <td>479.000000</td>\n",
              "      <td>487.000000</td>\n",
              "      <td>481.000000</td>\n",
              "      <td>482.000000</td>\n",
              "      <td>475.000000</td>\n",
              "      <td>472.000000</td>\n",
              "      <td>478.000000</td>\n",
              "      <td>493.000000</td>\n",
              "      <td>486.000000</td>\n",
              "      <td>471.000000</td>\n",
              "      <td>475.000000</td>\n",
              "      <td>479.000000</td>\n",
              "      <td>468.000000</td>\n",
              "      <td>477.000000</td>\n",
              "      <td>473.000000</td>\n",
              "      <td>492.000000</td>\n",
              "      <td>485.000000</td>\n",
              "      <td>465.000000</td>\n",
              "      <td>483.000000</td>\n",
              "      <td>476.000000</td>\n",
              "      <td>467.000000</td>\n",
              "      <td>480.000000</td>\n",
              "      <td>474.000000</td>\n",
              "      <td>477.000000</td>\n",
              "      <td>486.000000</td>\n",
              "      <td>475.000000</td>\n",
              "      <td>488.000000</td>\n",
              "      <td>474.000000</td>\n",
              "      <td>487.000000</td>\n",
              "      <td>475.000000</td>\n",
              "      <td>482.000000</td>\n",
              "      <td>487.000000</td>\n",
              "      <td>483.000000</td>\n",
              "      <td>474.000000</td>\n",
              "      <td>480.000000</td>\n",
              "      <td>484.000000</td>\n",
              "      <td>484.000000</td>\n",
              "      <td>473.000000</td>\n",
              "      <td>469.000000</td>\n",
              "      <td>469.000000</td>\n",
              "      <td>479.000000</td>\n",
              "      <td>482.000000</td>\n",
              "      <td>482.000000</td>\n",
              "      <td>471.000000</td>\n",
              "      <td>482.000000</td>\n",
              "      <td>479.000000</td>\n",
              "      <td>479.000000</td>\n",
              "      <td>485.000000</td>\n",
              "      <td>485.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.069331</td>\n",
              "      <td>0.195598</td>\n",
              "      <td>0.085790</td>\n",
              "      <td>-72.937440</td>\n",
              "      <td>4.982590</td>\n",
              "      <td>0.581764</td>\n",
              "      <td>1.713443</td>\n",
              "      <td>0.644742</td>\n",
              "      <td>0.839220</td>\n",
              "      <td>0.508456</td>\n",
              "      <td>0.541678</td>\n",
              "      <td>0.467497</td>\n",
              "      <td>0.509731</td>\n",
              "      <td>0.482203</td>\n",
              "      <td>0.435583</td>\n",
              "      <td>0.438831</td>\n",
              "      <td>0.046332</td>\n",
              "      <td>0.157030</td>\n",
              "      <td>0.035969</td>\n",
              "      <td>4.562929</td>\n",
              "      <td>1.296457</td>\n",
              "      <td>0.953128</td>\n",
              "      <td>0.780204</td>\n",
              "      <td>0.656790</td>\n",
              "      <td>0.577898</td>\n",
              "      <td>0.554491</td>\n",
              "      <td>0.536146</td>\n",
              "      <td>0.519601</td>\n",
              "      <td>0.509956</td>\n",
              "      <td>0.494207</td>\n",
              "      <td>0.482769</td>\n",
              "      <td>0.468299</td>\n",
              "      <td>0.023892</td>\n",
              "      <td>0.064740</td>\n",
              "      <td>0.008138</td>\n",
              "      <td>3.657370</td>\n",
              "      <td>0.850763</td>\n",
              "      <td>0.624042</td>\n",
              "      <td>0.498093</td>\n",
              "      <td>0.385601</td>\n",
              "      <td>0.332398</td>\n",
              "      <td>0.314634</td>\n",
              "      <td>0.307940</td>\n",
              "      <td>0.304328</td>\n",
              "      <td>0.297401</td>\n",
              "      <td>0.283815</td>\n",
              "      <td>0.275984</td>\n",
              "      <td>0.272512</td>\n",
              "      <td>0.029024</td>\n",
              "      <td>0.066498</td>\n",
              "      <td>0.029544</td>\n",
              "      <td>4.157900</td>\n",
              "      <td>0.411794</td>\n",
              "      <td>0.241387</td>\n",
              "      <td>0.196438</td>\n",
              "      <td>0.150861</td>\n",
              "      <td>0.122909</td>\n",
              "      <td>0.115913</td>\n",
              "      <td>0.114794</td>\n",
              "      <td>0.113208</td>\n",
              "      <td>0.112648</td>\n",
              "      <td>0.109563</td>\n",
              "      <td>0.109879</td>\n",
              "      <td>0.104725</td>\n",
              "      <td>0.226159</td>\n",
              "      <td>74.155602</td>\n",
              "      <td>0.253168</td>\n",
              "      <td>154.910788</td>\n",
              "      <td>2.106472</td>\n",
              "      <td>0.401875</td>\n",
              "      <td>0.361759</td>\n",
              "      <td>0.958998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.031252</td>\n",
              "      <td>0.118073</td>\n",
              "      <td>0.009427</td>\n",
              "      <td>7.502866</td>\n",
              "      <td>2.068497</td>\n",
              "      <td>1.017642</td>\n",
              "      <td>0.604918</td>\n",
              "      <td>0.454194</td>\n",
              "      <td>0.370825</td>\n",
              "      <td>0.307596</td>\n",
              "      <td>0.295518</td>\n",
              "      <td>0.282619</td>\n",
              "      <td>0.239519</td>\n",
              "      <td>0.217336</td>\n",
              "      <td>0.208454</td>\n",
              "      <td>0.191430</td>\n",
              "      <td>0.030078</td>\n",
              "      <td>0.108255</td>\n",
              "      <td>0.008959</td>\n",
              "      <td>1.584019</td>\n",
              "      <td>0.399773</td>\n",
              "      <td>0.271528</td>\n",
              "      <td>0.209547</td>\n",
              "      <td>0.153361</td>\n",
              "      <td>0.106152</td>\n",
              "      <td>0.091643</td>\n",
              "      <td>0.088636</td>\n",
              "      <td>0.084820</td>\n",
              "      <td>0.077124</td>\n",
              "      <td>0.074289</td>\n",
              "      <td>0.073188</td>\n",
              "      <td>0.077771</td>\n",
              "      <td>0.010513</td>\n",
              "      <td>0.032622</td>\n",
              "      <td>0.001533</td>\n",
              "      <td>1.795568</td>\n",
              "      <td>0.368134</td>\n",
              "      <td>0.259077</td>\n",
              "      <td>0.203795</td>\n",
              "      <td>0.153780</td>\n",
              "      <td>0.136568</td>\n",
              "      <td>0.141008</td>\n",
              "      <td>0.143197</td>\n",
              "      <td>0.153887</td>\n",
              "      <td>0.154044</td>\n",
              "      <td>0.162271</td>\n",
              "      <td>0.151923</td>\n",
              "      <td>0.166512</td>\n",
              "      <td>0.007515</td>\n",
              "      <td>0.023594</td>\n",
              "      <td>0.001496</td>\n",
              "      <td>0.524159</td>\n",
              "      <td>0.140009</td>\n",
              "      <td>0.087522</td>\n",
              "      <td>0.076562</td>\n",
              "      <td>0.061118</td>\n",
              "      <td>0.041726</td>\n",
              "      <td>0.037265</td>\n",
              "      <td>0.038491</td>\n",
              "      <td>0.043601</td>\n",
              "      <td>0.046533</td>\n",
              "      <td>0.048262</td>\n",
              "      <td>0.051958</td>\n",
              "      <td>0.058411</td>\n",
              "      <td>0.293190</td>\n",
              "      <td>14.581289</td>\n",
              "      <td>0.374615</td>\n",
              "      <td>30.136114</td>\n",
              "      <td>0.328461</td>\n",
              "      <td>0.336351</td>\n",
              "      <td>0.365417</td>\n",
              "      <td>0.669752</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.010201</td>\n",
              "      <td>0.038286</td>\n",
              "      <td>0.070932</td>\n",
              "      <td>-98.890602</td>\n",
              "      <td>0.051474</td>\n",
              "      <td>-2.277307</td>\n",
              "      <td>-0.604609</td>\n",
              "      <td>-1.143864</td>\n",
              "      <td>-0.237114</td>\n",
              "      <td>-0.444623</td>\n",
              "      <td>-0.895966</td>\n",
              "      <td>-0.351568</td>\n",
              "      <td>-0.294770</td>\n",
              "      <td>-0.258025</td>\n",
              "      <td>-0.275685</td>\n",
              "      <td>-0.129515</td>\n",
              "      <td>0.006918</td>\n",
              "      <td>0.017434</td>\n",
              "      <td>0.025297</td>\n",
              "      <td>1.665689</td>\n",
              "      <td>0.544490</td>\n",
              "      <td>0.426085</td>\n",
              "      <td>0.438436</td>\n",
              "      <td>0.375337</td>\n",
              "      <td>0.376085</td>\n",
              "      <td>0.329479</td>\n",
              "      <td>0.322254</td>\n",
              "      <td>0.327975</td>\n",
              "      <td>0.296855</td>\n",
              "      <td>0.297832</td>\n",
              "      <td>0.281625</td>\n",
              "      <td>0.273264</td>\n",
              "      <td>0.010684</td>\n",
              "      <td>0.018333</td>\n",
              "      <td>0.005696</td>\n",
              "      <td>1.103874</td>\n",
              "      <td>0.181559</td>\n",
              "      <td>0.107836</td>\n",
              "      <td>0.113009</td>\n",
              "      <td>0.057821</td>\n",
              "      <td>0.054569</td>\n",
              "      <td>0.050367</td>\n",
              "      <td>0.071779</td>\n",
              "      <td>0.055301</td>\n",
              "      <td>0.059252</td>\n",
              "      <td>0.048837</td>\n",
              "      <td>0.042072</td>\n",
              "      <td>0.033739</td>\n",
              "      <td>0.009299</td>\n",
              "      <td>0.015678</td>\n",
              "      <td>0.009090</td>\n",
              "      <td>1.908520</td>\n",
              "      <td>0.105014</td>\n",
              "      <td>0.053278</td>\n",
              "      <td>0.054015</td>\n",
              "      <td>0.043679</td>\n",
              "      <td>0.039299</td>\n",
              "      <td>0.024210</td>\n",
              "      <td>0.036153</td>\n",
              "      <td>0.032399</td>\n",
              "      <td>0.035843</td>\n",
              "      <td>0.038657</td>\n",
              "      <td>0.035496</td>\n",
              "      <td>0.035386</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.045049</td>\n",
              "      <td>0.108132</td>\n",
              "      <td>0.079193</td>\n",
              "      <td>-77.024349</td>\n",
              "      <td>3.397924</td>\n",
              "      <td>-0.118830</td>\n",
              "      <td>1.314463</td>\n",
              "      <td>0.385422</td>\n",
              "      <td>0.612727</td>\n",
              "      <td>0.306204</td>\n",
              "      <td>0.379360</td>\n",
              "      <td>0.289373</td>\n",
              "      <td>0.353262</td>\n",
              "      <td>0.359896</td>\n",
              "      <td>0.306588</td>\n",
              "      <td>0.311005</td>\n",
              "      <td>0.023073</td>\n",
              "      <td>0.065139</td>\n",
              "      <td>0.030082</td>\n",
              "      <td>3.479967</td>\n",
              "      <td>1.004941</td>\n",
              "      <td>0.743459</td>\n",
              "      <td>0.599379</td>\n",
              "      <td>0.534892</td>\n",
              "      <td>0.495954</td>\n",
              "      <td>0.485177</td>\n",
              "      <td>0.473820</td>\n",
              "      <td>0.456147</td>\n",
              "      <td>0.456184</td>\n",
              "      <td>0.441640</td>\n",
              "      <td>0.432230</td>\n",
              "      <td>0.418017</td>\n",
              "      <td>0.017711</td>\n",
              "      <td>0.043884</td>\n",
              "      <td>0.007328</td>\n",
              "      <td>2.404727</td>\n",
              "      <td>0.602470</td>\n",
              "      <td>0.430930</td>\n",
              "      <td>0.361550</td>\n",
              "      <td>0.280676</td>\n",
              "      <td>0.241819</td>\n",
              "      <td>0.221437</td>\n",
              "      <td>0.210444</td>\n",
              "      <td>0.200097</td>\n",
              "      <td>0.195735</td>\n",
              "      <td>0.180603</td>\n",
              "      <td>0.175208</td>\n",
              "      <td>0.171926</td>\n",
              "      <td>0.024813</td>\n",
              "      <td>0.051713</td>\n",
              "      <td>0.029053</td>\n",
              "      <td>3.759824</td>\n",
              "      <td>0.311654</td>\n",
              "      <td>0.173762</td>\n",
              "      <td>0.137803</td>\n",
              "      <td>0.102941</td>\n",
              "      <td>0.092208</td>\n",
              "      <td>0.089430</td>\n",
              "      <td>0.087736</td>\n",
              "      <td>0.083018</td>\n",
              "      <td>0.082175</td>\n",
              "      <td>0.077812</td>\n",
              "      <td>0.074664</td>\n",
              "      <td>0.070386</td>\n",
              "      <td>0.046469</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>0.006946</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.176574</td>\n",
              "      <td>0.103414</td>\n",
              "      <td>0.506870</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.065049</td>\n",
              "      <td>0.169385</td>\n",
              "      <td>0.083775</td>\n",
              "      <td>-72.429150</td>\n",
              "      <td>4.709617</td>\n",
              "      <td>0.545329</td>\n",
              "      <td>1.742567</td>\n",
              "      <td>0.645847</td>\n",
              "      <td>0.826170</td>\n",
              "      <td>0.525120</td>\n",
              "      <td>0.529148</td>\n",
              "      <td>0.453553</td>\n",
              "      <td>0.512171</td>\n",
              "      <td>0.495466</td>\n",
              "      <td>0.434881</td>\n",
              "      <td>0.446233</td>\n",
              "      <td>0.037567</td>\n",
              "      <td>0.125433</td>\n",
              "      <td>0.034150</td>\n",
              "      <td>4.353893</td>\n",
              "      <td>1.261375</td>\n",
              "      <td>0.943021</td>\n",
              "      <td>0.752505</td>\n",
              "      <td>0.635010</td>\n",
              "      <td>0.561165</td>\n",
              "      <td>0.548324</td>\n",
              "      <td>0.523810</td>\n",
              "      <td>0.513161</td>\n",
              "      <td>0.502568</td>\n",
              "      <td>0.487045</td>\n",
              "      <td>0.476286</td>\n",
              "      <td>0.461836</td>\n",
              "      <td>0.021480</td>\n",
              "      <td>0.055083</td>\n",
              "      <td>0.007831</td>\n",
              "      <td>3.273989</td>\n",
              "      <td>0.781418</td>\n",
              "      <td>0.581794</td>\n",
              "      <td>0.471313</td>\n",
              "      <td>0.362743</td>\n",
              "      <td>0.313402</td>\n",
              "      <td>0.288212</td>\n",
              "      <td>0.285995</td>\n",
              "      <td>0.277824</td>\n",
              "      <td>0.268058</td>\n",
              "      <td>0.243480</td>\n",
              "      <td>0.242969</td>\n",
              "      <td>0.231994</td>\n",
              "      <td>0.027263</td>\n",
              "      <td>0.062315</td>\n",
              "      <td>0.029859</td>\n",
              "      <td>4.082622</td>\n",
              "      <td>0.393556</td>\n",
              "      <td>0.234721</td>\n",
              "      <td>0.183158</td>\n",
              "      <td>0.141551</td>\n",
              "      <td>0.118553</td>\n",
              "      <td>0.112627</td>\n",
              "      <td>0.109083</td>\n",
              "      <td>0.105904</td>\n",
              "      <td>0.103189</td>\n",
              "      <td>0.101798</td>\n",
              "      <td>0.096355</td>\n",
              "      <td>0.087826</td>\n",
              "      <td>0.099758</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>0.075470</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.292914</td>\n",
              "      <td>0.247380</td>\n",
              "      <td>0.788581</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.086249</td>\n",
              "      <td>0.257307</td>\n",
              "      <td>0.091324</td>\n",
              "      <td>-68.240320</td>\n",
              "      <td>6.450138</td>\n",
              "      <td>1.291943</td>\n",
              "      <td>2.075876</td>\n",
              "      <td>0.929751</td>\n",
              "      <td>1.056526</td>\n",
              "      <td>0.710612</td>\n",
              "      <td>0.714323</td>\n",
              "      <td>0.646493</td>\n",
              "      <td>0.680279</td>\n",
              "      <td>0.611211</td>\n",
              "      <td>0.574293</td>\n",
              "      <td>0.563795</td>\n",
              "      <td>0.061766</td>\n",
              "      <td>0.239699</td>\n",
              "      <td>0.039417</td>\n",
              "      <td>5.537363</td>\n",
              "      <td>1.518116</td>\n",
              "      <td>1.126444</td>\n",
              "      <td>0.941617</td>\n",
              "      <td>0.766146</td>\n",
              "      <td>0.641411</td>\n",
              "      <td>0.613560</td>\n",
              "      <td>0.594454</td>\n",
              "      <td>0.570819</td>\n",
              "      <td>0.553050</td>\n",
              "      <td>0.538398</td>\n",
              "      <td>0.521585</td>\n",
              "      <td>0.502739</td>\n",
              "      <td>0.027251</td>\n",
              "      <td>0.075409</td>\n",
              "      <td>0.008710</td>\n",
              "      <td>4.504108</td>\n",
              "      <td>1.061585</td>\n",
              "      <td>0.770607</td>\n",
              "      <td>0.594998</td>\n",
              "      <td>0.479044</td>\n",
              "      <td>0.403181</td>\n",
              "      <td>0.370891</td>\n",
              "      <td>0.371065</td>\n",
              "      <td>0.376468</td>\n",
              "      <td>0.364578</td>\n",
              "      <td>0.333516</td>\n",
              "      <td>0.336113</td>\n",
              "      <td>0.330376</td>\n",
              "      <td>0.031140</td>\n",
              "      <td>0.077539</td>\n",
              "      <td>0.030294</td>\n",
              "      <td>4.459551</td>\n",
              "      <td>0.501601</td>\n",
              "      <td>0.296445</td>\n",
              "      <td>0.247321</td>\n",
              "      <td>0.186267</td>\n",
              "      <td>0.145681</td>\n",
              "      <td>0.142517</td>\n",
              "      <td>0.138649</td>\n",
              "      <td>0.133610</td>\n",
              "      <td>0.129537</td>\n",
              "      <td>0.129188</td>\n",
              "      <td>0.128090</td>\n",
              "      <td>0.119385</td>\n",
              "      <td>0.271551</td>\n",
              "      <td>84.000000</td>\n",
              "      <td>0.335367</td>\n",
              "      <td>180.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.510709</td>\n",
              "      <td>0.483341</td>\n",
              "      <td>1.255286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.195412</td>\n",
              "      <td>0.698277</td>\n",
              "      <td>0.159460</td>\n",
              "      <td>-56.297652</td>\n",
              "      <td>11.853471</td>\n",
              "      <td>3.910873</td>\n",
              "      <td>4.382370</td>\n",
              "      <td>2.252710</td>\n",
              "      <td>2.358098</td>\n",
              "      <td>1.484489</td>\n",
              "      <td>1.722420</td>\n",
              "      <td>1.846221</td>\n",
              "      <td>1.064865</td>\n",
              "      <td>1.146151</td>\n",
              "      <td>1.021535</td>\n",
              "      <td>1.092881</td>\n",
              "      <td>0.168399</td>\n",
              "      <td>0.471795</td>\n",
              "      <td>0.116190</td>\n",
              "      <td>15.262381</td>\n",
              "      <td>3.664476</td>\n",
              "      <td>2.045210</td>\n",
              "      <td>1.373682</td>\n",
              "      <td>1.321038</td>\n",
              "      <td>1.016693</td>\n",
              "      <td>0.825064</td>\n",
              "      <td>0.829619</td>\n",
              "      <td>1.098111</td>\n",
              "      <td>0.839606</td>\n",
              "      <td>0.852488</td>\n",
              "      <td>0.772496</td>\n",
              "      <td>1.001184</td>\n",
              "      <td>0.117437</td>\n",
              "      <td>0.234718</td>\n",
              "      <td>0.022730</td>\n",
              "      <td>13.447154</td>\n",
              "      <td>2.925425</td>\n",
              "      <td>2.202609</td>\n",
              "      <td>1.281424</td>\n",
              "      <td>1.025277</td>\n",
              "      <td>1.038158</td>\n",
              "      <td>1.155625</td>\n",
              "      <td>1.334833</td>\n",
              "      <td>1.075426</td>\n",
              "      <td>1.169477</td>\n",
              "      <td>1.281840</td>\n",
              "      <td>1.160050</td>\n",
              "      <td>1.809666</td>\n",
              "      <td>0.063173</td>\n",
              "      <td>0.186134</td>\n",
              "      <td>0.032048</td>\n",
              "      <td>6.536928</td>\n",
              "      <td>1.046212</td>\n",
              "      <td>0.618704</td>\n",
              "      <td>0.528945</td>\n",
              "      <td>0.415798</td>\n",
              "      <td>0.271085</td>\n",
              "      <td>0.241432</td>\n",
              "      <td>0.304417</td>\n",
              "      <td>0.329188</td>\n",
              "      <td>0.296556</td>\n",
              "      <td>0.321645</td>\n",
              "      <td>0.471506</td>\n",
              "      <td>0.415202</td>\n",
              "      <td>1.598533</td>\n",
              "      <td>115.000000</td>\n",
              "      <td>1.762948</td>\n",
              "      <td>237.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.795128</td>\n",
              "      <td>1.797765</td>\n",
              "      <td>3.422899</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               0           1           2   ...          69          70          71\n",
              "count  530.000000  484.000000  479.000000  ...  479.000000  485.000000  485.000000\n",
              "mean     0.069331    0.195598    0.085790  ...    0.401875    0.361759    0.958998\n",
              "std      0.031252    0.118073    0.009427  ...    0.336351    0.365417    0.669752\n",
              "min      0.010201    0.038286    0.070932  ...    0.000000    0.000000    0.000000\n",
              "25%      0.045049    0.108132    0.079193  ...    0.176574    0.103414    0.506870\n",
              "50%      0.065049    0.169385    0.083775  ...    0.292914    0.247380    0.788581\n",
              "75%      0.086249    0.257307    0.091324  ...    0.510709    0.483341    1.255286\n",
              "max      0.195412    0.698277    0.159460  ...    1.795128    1.797765    3.422899\n",
              "\n",
              "[8 rows x 72 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 719
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 132
        },
        "id": "J0MTDI2WeTG_",
        "outputId": "02a6c37a-ff04-4e97-e6e3-083041b09759"
      },
      "source": [
        "df.head(2)"
      ],
      "execution_count": 720,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>0</th>\n",
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              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "      <th>13</th>\n",
              "      <th>14</th>\n",
              "      <th>15</th>\n",
              "      <th>16</th>\n",
              "      <th>17</th>\n",
              "      <th>18</th>\n",
              "      <th>19</th>\n",
              "      <th>20</th>\n",
              "      <th>21</th>\n",
              "      <th>22</th>\n",
              "      <th>23</th>\n",
              "      <th>24</th>\n",
              "      <th>25</th>\n",
              "      <th>26</th>\n",
              "      <th>27</th>\n",
              "      <th>28</th>\n",
              "      <th>29</th>\n",
              "      <th>30</th>\n",
              "      <th>31</th>\n",
              "      <th>32</th>\n",
              "      <th>33</th>\n",
              "      <th>34</th>\n",
              "      <th>35</th>\n",
              "      <th>36</th>\n",
              "      <th>37</th>\n",
              "      <th>38</th>\n",
              "      <th>39</th>\n",
              "      <th>40</th>\n",
              "      <th>41</th>\n",
              "      <th>42</th>\n",
              "      <th>43</th>\n",
              "      <th>44</th>\n",
              "      <th>45</th>\n",
              "      <th>46</th>\n",
              "      <th>47</th>\n",
              "      <th>48</th>\n",
              "      <th>49</th>\n",
              "      <th>50</th>\n",
              "      <th>51</th>\n",
              "      <th>52</th>\n",
              "      <th>53</th>\n",
              "      <th>54</th>\n",
              "      <th>55</th>\n",
              "      <th>56</th>\n",
              "      <th>57</th>\n",
              "      <th>58</th>\n",
              "      <th>59</th>\n",
              "      <th>60</th>\n",
              "      <th>61</th>\n",
              "      <th>62</th>\n",
              "      <th>63</th>\n",
              "      <th>64</th>\n",
              "      <th>65</th>\n",
              "      <th>66</th>\n",
              "      <th>67</th>\n",
              "      <th>68</th>\n",
              "      <th>69</th>\n",
              "      <th>70</th>\n",
              "      <th>71</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.034741</td>\n",
              "      <td>0.089665</td>\n",
              "      <td>0.091225</td>\n",
              "      <td>-73.302422</td>\n",
              "      <td>6.215179</td>\n",
              "      <td>0.615074</td>\n",
              "      <td>2.037160</td>\n",
              "      <td>0.804065</td>\n",
              "      <td>1.301409</td>\n",
              "      <td>0.558576</td>\n",
              "      <td>0.672063</td>\n",
              "      <td>0.783788</td>\n",
              "      <td>0.766640</td>\n",
              "      <td>0.458712</td>\n",
              "      <td>0.530384</td>\n",
              "      <td>0.812429</td>\n",
              "      <td>0.028851</td>\n",
              "      <td>0.129039</td>\n",
              "      <td>0.039614</td>\n",
              "      <td>5.762173</td>\n",
              "      <td>1.636819</td>\n",
              "      <td>1.170034</td>\n",
              "      <td>1.051511</td>\n",
              "      <td>0.764163</td>\n",
              "      <td>0.642705</td>\n",
              "      <td>0.617868</td>\n",
              "      <td>0.510265</td>\n",
              "      <td>0.566213</td>\n",
              "      <td>0.509149</td>\n",
              "      <td>0.477275</td>\n",
              "      <td>0.505073</td>\n",
              "      <td>0.463535</td>\n",
              "      <td>0.013519</td>\n",
              "      <td>0.050591</td>\n",
              "      <td>0.009025</td>\n",
              "      <td>8.156257</td>\n",
              "      <td>1.077167</td>\n",
              "      <td>0.624711</td>\n",
              "      <td>0.810244</td>\n",
              "      <td>0.399568</td>\n",
              "      <td>0.279947</td>\n",
              "      <td>0.314215</td>\n",
              "      <td>0.231439</td>\n",
              "      <td>0.345401</td>\n",
              "      <td>0.285389</td>\n",
              "      <td>0.210613</td>\n",
              "      <td>0.321896</td>\n",
              "      <td>0.290551</td>\n",
              "      <td>0.022774</td>\n",
              "      <td>0.095801</td>\n",
              "      <td>0.015057</td>\n",
              "      <td>4.748694</td>\n",
              "      <td>0.536378</td>\n",
              "      <td>0.296306</td>\n",
              "      <td>0.27321</td>\n",
              "      <td>0.175800</td>\n",
              "      <td>0.105508</td>\n",
              "      <td>0.168246</td>\n",
              "      <td>0.115849</td>\n",
              "      <td>0.136020</td>\n",
              "      <td>0.110514</td>\n",
              "      <td>0.100517</td>\n",
              "      <td>0.118630</td>\n",
              "      <td>0.094923</td>\n",
              "      <td>0.051035</td>\n",
              "      <td>68.0</td>\n",
              "      <td>0.014937</td>\n",
              "      <td>136.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.245457</td>\n",
              "      <td>0.105065</td>\n",
              "      <td>0.405399</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.081374</td>\n",
              "      <td>0.272747</td>\n",
              "      <td>0.085733</td>\n",
              "      <td>-62.584437</td>\n",
              "      <td>3.183163</td>\n",
              "      <td>-0.218145</td>\n",
              "      <td>0.163038</td>\n",
              "      <td>0.620251</td>\n",
              "      <td>0.458514</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.308287</td>\n",
              "      <td>0.538152</td>\n",
              "      <td>0.594871</td>\n",
              "      <td>0.734332</td>\n",
              "      <td>0.415489</td>\n",
              "      <td>0.761508</td>\n",
              "      <td>0.066288</td>\n",
              "      <td>0.262370</td>\n",
              "      <td>0.034438</td>\n",
              "      <td>3.480874</td>\n",
              "      <td>1.596532</td>\n",
              "      <td>0.943803</td>\n",
              "      <td>0.804444</td>\n",
              "      <td>0.511229</td>\n",
              "      <td>0.498670</td>\n",
              "      <td>0.523039</td>\n",
              "      <td>0.480916</td>\n",
              "      <td>0.488657</td>\n",
              "      <td>0.483166</td>\n",
              "      <td>0.445187</td>\n",
              "      <td>0.415994</td>\n",
              "      <td>0.405593</td>\n",
              "      <td>0.013621</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.010094</td>\n",
              "      <td>1.243981</td>\n",
              "      <td>0.829790</td>\n",
              "      <td>0.252972</td>\n",
              "      <td>0.347831</td>\n",
              "      <td>0.205087</td>\n",
              "      <td>0.168601</td>\n",
              "      <td>0.178009</td>\n",
              "      <td>0.144080</td>\n",
              "      <td>0.178703</td>\n",
              "      <td>0.146937</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.128202</td>\n",
              "      <td>0.107007</td>\n",
              "      <td>0.020028</td>\n",
              "      <td>0.066940</td>\n",
              "      <td>0.029483</td>\n",
              "      <td>3.963534</td>\n",
              "      <td>0.382360</td>\n",
              "      <td>0.168389</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.098341</td>\n",
              "      <td>0.087046</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.059393</td>\n",
              "      <td>0.059457</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.067684</td>\n",
              "      <td>0.070075</td>\n",
              "      <td>0.041565</td>\n",
              "      <td>0.295031</td>\n",
              "      <td>70.0</td>\n",
              "      <td>0.276366</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.343547</td>\n",
              "      <td>0.276366</td>\n",
              "      <td>0.710924</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         0         1         2          3   ...   68        69        70        71\n",
              "0  0.034741  0.089665  0.091225 -73.302422  ...  2.0  0.245457  0.105065  0.405399\n",
              "1  0.081374  0.272747  0.085733 -62.584437  ...  NaN  0.343547  0.276366  0.710924\n",
              "\n",
              "[2 rows x 72 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 720
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BIcDtz3iR647"
      },
      "source": [
        "#Instalando o panda profile\n",
        "#!pip install pandas-profiling\n",
        "#from pandas_profiling import ProfileReport"
      ],
      "execution_count": 721,
      "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": "a2863b78-7e9d-4ed7-9a44-ce7be1e2df72"
      },
      "source": [
        "#installing\n",
        "!pip install missingpy"
      ],
      "execution_count": 722,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: missingpy in /usr/local/lib/python3.7/dist-packages (0.2.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WkyeF0EcTuKZ"
      },
      "source": [
        "# Criação da popoulação inicial"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AzeUjJXcrELk"
      },
      "source": [
        "imputando os indivíduos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NAfA-01Bfkwb"
      },
      "source": [
        "#!pip install -U scikit-learn"
      ],
      "execution_count": 723,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dl6nOtFdgfAo"
      },
      "source": [
        "#!pip install sklearn\n",
        "#import sklearn\n",
        "#from sklearn import neighbors"
      ],
      "execution_count": 724,
      "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": 725,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WhGruSVm7qL3"
      },
      "source": [
        "from sklearn.impute import SimpleImputer"
      ],
      "execution_count": 726,
      "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": 727,
      "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": 728,
      "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": 729,
      "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": 730,
      "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": 731,
      "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": 732,
      "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": 733,
      "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": 734,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tG5Bd0jkrt9x"
      },
      "source": [
        "ind4 = knnImp(df_kNN, 3)\n",
        "ind5 = knnImp(df_kNN, 5)\n",
        "ind6 = knnImp(df_kNN, 10)"
      ],
      "execution_count": 735,
      "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, ind6]\n"
      ],
      "execution_count": 736,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CVNMKwXTymeK",
        "outputId": "3fe7747f-6333-4458-9271-bf48f39e71f5"
      },
      "source": [
        "len(individuos)"
      ],
      "execution_count": 737,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6"
            ]
          },
          "metadata": {},
          "execution_count": 737
        }
      ]
    },
    {
      "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": 738,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I8iRKpnyzW0t",
        "outputId": "c99c8196-3994-4e58-835d-bbef9bafce09"
      },
      "source": [
        "populationInitial"
      ],
      "execution_count": 739,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([[-1.10787768e+00, -9.39820598e-01,  6.07059664e-01, ...,\n",
              "         -4.89687773e-01, -7.35093468e-01, -8.64961164e-01],\n",
              "        [ 3.85709262e-01,  6.84453373e-01, -6.39910551e-03, ...,\n",
              "         -1.82604493e-01, -2.44539886e-01, -3.87598862e-01],\n",
              "        [ 1.32001383e+00,  6.91728281e-01, -1.54178776e-01, ...,\n",
              "          1.73784753e-15, -9.04406490e-01, -7.83756840e-01],\n",
              "        ...,\n",
              "        [-1.09416947e+00, -1.15506915e+00, -1.17992384e+00, ...,\n",
              "         -6.81106878e-01, -3.28245651e-01, -7.53100345e-01],\n",
              "        [-4.82200136e-01,  3.77585345e-01,  1.13316870e+00, ...,\n",
              "          4.54725581e-01, -5.10496841e-01,  4.63764558e-01],\n",
              "        [ 1.23715799e-01, -4.86753540e-01, -5.85900944e-01, ...,\n",
              "         -9.84730814e-01, -3.57801833e-01,  1.38771916e-15]]),\n",
              " array([[-1.10787768, -0.91767161,  0.62734024, ..., -0.45456902,\n",
              "         -0.70435328, -0.84004326],\n",
              "        [ 0.38570926,  0.70313221,  0.01522876, ..., -0.14902782,\n",
              "         -0.2158316 , -0.36399032],\n",
              "        [ 1.32001383,  0.71039157, -0.13222635, ..., -0.30674489,\n",
              "         -0.872965  , -0.75906167],\n",
              "        ...,\n",
              "        [-1.09416947, -1.1324603 , -1.15571865, ..., -0.64502687,\n",
              "         -0.29919065, -0.72848926],\n",
              "        [-0.48220014,  0.39691978,  1.15229383, ...,  0.48510177,\n",
              "         -0.48068695,  0.48503789],\n",
              "        [ 0.1237158 , -0.4655725 , -0.56300036, ..., -0.9471261 ,\n",
              "         -0.32862441, -0.24298928]]),\n",
              " array([[-1.10787768, -0.76198037,  0.67984031, ..., -0.34560251,\n",
              "         -0.62173101, -0.68075205],\n",
              "        [ 0.38570926,  0.74978405,  0.09913162, ..., -0.0576967 ,\n",
              "         -0.15043389, -0.24026503],\n",
              "        [ 1.32001383,  0.75655504, -0.0407587 , ..., -1.06604796,\n",
              "         -0.78439772, -0.6058206 ],\n",
              "        ...,\n",
              "        [-1.09416947, -0.96231918, -1.01174339, ..., -0.52506741,\n",
              "         -0.23085382, -0.57753226],\n",
              "        [-0.48220014,  0.46417203,  1.17786251, ...,  0.53983184,\n",
              "         -0.40595082,  0.54533226],\n",
              "        [ 0.1237158 , -0.34029618, -0.44943297, ..., -0.8097299 ,\n",
              "         -0.25924979, -1.26523121]]),\n",
              " array([[-1.10787768, -0.91858017,  0.59579725, ..., -0.48233857,\n",
              "         -0.70921427, -0.83643554],\n",
              "        [ 0.38570926,  0.67053952, -0.00633585, ..., -0.18679078,\n",
              "         -0.23055801, -0.36556597],\n",
              "        [ 1.32001383,  0.67765698, -0.15138721, ..., -0.98912163,\n",
              "         -0.87442096, -0.7563357 ],\n",
              "        ...,\n",
              "        [-1.09416947, -1.12917008, -1.15819498, ..., -0.66656708,\n",
              "         -0.31223367, -0.72609617],\n",
              "        [-0.48220014,  0.37031306,  1.11219327, ...,  0.42659819,\n",
              "         -0.49006475,  0.47421783],\n",
              "        [ 0.1237158 , -0.47531887, -0.57513893, ..., -0.95878547,\n",
              "         -0.34107303, -0.38806825]]),\n",
              " array([[-1.10787768, -0.91998849,  0.59563466, ..., -0.47726289,\n",
              "         -0.71382496, -0.84217418],\n",
              "        [ 0.38570926,  0.66936455, -0.00887808, ..., -0.18003975,\n",
              "         -0.23264877, -0.37043274],\n",
              "        [ 1.32001383,  0.67648305, -0.15450269, ..., -0.51938137,\n",
              "         -0.87990141, -0.76192603],\n",
              "        ...,\n",
              "        [-1.09416947, -1.13060932, -1.16528937, ..., -0.66253572,\n",
              "         -0.31475442, -0.73163051],\n",
              "        [-0.48220014,  0.369094  ,  1.11407148, ...,  0.43682629,\n",
              "         -0.49352171,  0.47090601],\n",
              "        [ 0.1237158 , -0.47666211, -0.57992908, ..., -0.9564106 ,\n",
              "         -0.34374561, -0.35596668]]),\n",
              " array([[-1.10787768, -0.92759868,  0.59747798, ..., -0.48686071,\n",
              "         -0.71852824, -0.85112659],\n",
              "        [ 0.38570926,  0.66859573, -0.00945655, ..., -0.18838163,\n",
              "         -0.23664939, -0.37759364],\n",
              "        [ 1.32001383,  0.67574487, -0.15566456, ..., -0.41104294,\n",
              "         -0.88484721, -0.77057368],\n",
              "        ...,\n",
              "        [-1.09416947, -1.13912613, -1.17050066, ..., -0.67291642,\n",
              "         -0.31887494, -0.74016311],\n",
              "        [-0.48220014,  0.36703266,  1.11799176, ...,  0.43109103,\n",
              "         -0.49790328,  0.46694023],\n",
              "        [ 0.1237158 , -0.482364  , -0.5827953 , ..., -0.96803309,\n",
              "         -0.34790846, -0.40354624]])]"
            ]
          },
          "metadata": {},
          "execution_count": 739
        }
      ]
    },
    {
      "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  F1"
      ],
      "execution_count": 740,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oCZgVleqW-wI"
      },
      "source": [
        ""
      ],
      "execution_count": 740,
      "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": 741,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ia_BbBmaYRGk",
        "outputId": "2a7602f1-8a47-4bd3-a3e0-29fe4b0b7011"
      },
      "source": [
        "populacao_inicial_avaliada = fitness(populationInitial)"
      ],
      "execution_count": 742,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tam_pop_atual:  6\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.64  melhor: 0.6990291262135923  escolhido:  0.6990291262135923\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.6311111111111112  melhor: 0.6960784313725491  escolhido:  0.6960784313725491\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.6339285714285715  melhor: 0.6851851851851852  escolhido:  0.6851851851851852\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.6272727272727273  melhor: 0.7085201793721972  escolhido:  0.7085201793721972\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.6396396396396397  melhor: 0.7009345794392523  escolhido:  0.7009345794392523\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.6133333333333334  melhor: 0.711111111111111  escolhido:  0.711111111111111\n",
            "----------------------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mqh_QMw0sbt8"
      },
      "source": [
        "def fitness_population(populationInitial):\n",
        "  acc = []\n",
        "  f1 = []\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",
        "\n",
        "    f1.append(evaluation_binaryRelevance('svm', ind_atual[1]))\n",
        "    f1.append(evaluation_binaryRelevance('randomForest', ind_atual[1]))\n",
        "    f1.append(evaluation_binaryRelevance('3nn', ind_atual[1]))\n",
        "    f1.append(evaluation_binaryRelevance('5nn', ind_atual[1]))\n",
        "    f1.append(evaluation_binaryRelevance('10nn', ind_atual[1]))\n",
        "\n",
        "    indexx = indexx+1\n",
        "    #print(populationInitialScale[i])\n",
        "    aval_lista_descrecente = sorted(f1, reverse=True)\n",
        "    #print(aval_lista_descrecente)\n",
        "    aval_lista_crescente = sorted(f1)\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",
        "    f1 =[]\n",
        "  return populacao"
      ],
      "execution_count": 743,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_jmojq6fKLe1",
        "outputId": "551a7fb7-4270-46ee-a0bb-e0260abbf02e"
      },
      "source": [
        "populacao_inicial_avaliada"
      ],
      "execution_count": 744,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[0.6990291262135923,\n",
              "  array([[-1.10787768e+00, -9.39820598e-01,  6.07059664e-01, ...,\n",
              "          -4.89687773e-01, -7.35093468e-01, -8.64961164e-01],\n",
              "         [ 3.85709262e-01,  6.84453373e-01, -6.39910551e-03, ...,\n",
              "          -1.82604493e-01, -2.44539886e-01, -3.87598862e-01],\n",
              "         [ 1.32001383e+00,  6.91728281e-01, -1.54178776e-01, ...,\n",
              "           1.73784753e-15, -9.04406490e-01, -7.83756840e-01],\n",
              "         ...,\n",
              "         [-1.09416947e+00, -1.15506915e+00, -1.17992384e+00, ...,\n",
              "          -6.81106878e-01, -3.28245651e-01, -7.53100345e-01],\n",
              "         [-4.82200136e-01,  3.77585345e-01,  1.13316870e+00, ...,\n",
              "           4.54725581e-01, -5.10496841e-01,  4.63764558e-01],\n",
              "         [ 1.23715799e-01, -4.86753540e-01, -5.85900944e-01, ...,\n",
              "          -9.84730814e-01, -3.57801833e-01,  1.38771916e-15]])],\n",
              " [0.6960784313725491,\n",
              "  array([[-1.10787768, -0.91767161,  0.62734024, ..., -0.45456902,\n",
              "          -0.70435328, -0.84004326],\n",
              "         [ 0.38570926,  0.70313221,  0.01522876, ..., -0.14902782,\n",
              "          -0.2158316 , -0.36399032],\n",
              "         [ 1.32001383,  0.71039157, -0.13222635, ..., -0.30674489,\n",
              "          -0.872965  , -0.75906167],\n",
              "         ...,\n",
              "         [-1.09416947, -1.1324603 , -1.15571865, ..., -0.64502687,\n",
              "          -0.29919065, -0.72848926],\n",
              "         [-0.48220014,  0.39691978,  1.15229383, ...,  0.48510177,\n",
              "          -0.48068695,  0.48503789],\n",
              "         [ 0.1237158 , -0.4655725 , -0.56300036, ..., -0.9471261 ,\n",
              "          -0.32862441, -0.24298928]])],\n",
              " [0.6851851851851852,\n",
              "  array([[-1.10787768, -0.76198037,  0.67984031, ..., -0.34560251,\n",
              "          -0.62173101, -0.68075205],\n",
              "         [ 0.38570926,  0.74978405,  0.09913162, ..., -0.0576967 ,\n",
              "          -0.15043389, -0.24026503],\n",
              "         [ 1.32001383,  0.75655504, -0.0407587 , ..., -1.06604796,\n",
              "          -0.78439772, -0.6058206 ],\n",
              "         ...,\n",
              "         [-1.09416947, -0.96231918, -1.01174339, ..., -0.52506741,\n",
              "          -0.23085382, -0.57753226],\n",
              "         [-0.48220014,  0.46417203,  1.17786251, ...,  0.53983184,\n",
              "          -0.40595082,  0.54533226],\n",
              "         [ 0.1237158 , -0.34029618, -0.44943297, ..., -0.8097299 ,\n",
              "          -0.25924979, -1.26523121]])],\n",
              " [0.7085201793721972,\n",
              "  array([[-1.10787768, -0.91858017,  0.59579725, ..., -0.48233857,\n",
              "          -0.70921427, -0.83643554],\n",
              "         [ 0.38570926,  0.67053952, -0.00633585, ..., -0.18679078,\n",
              "          -0.23055801, -0.36556597],\n",
              "         [ 1.32001383,  0.67765698, -0.15138721, ..., -0.98912163,\n",
              "          -0.87442096, -0.7563357 ],\n",
              "         ...,\n",
              "         [-1.09416947, -1.12917008, -1.15819498, ..., -0.66656708,\n",
              "          -0.31223367, -0.72609617],\n",
              "         [-0.48220014,  0.37031306,  1.11219327, ...,  0.42659819,\n",
              "          -0.49006475,  0.47421783],\n",
              "         [ 0.1237158 , -0.47531887, -0.57513893, ..., -0.95878547,\n",
              "          -0.34107303, -0.38806825]])],\n",
              " [0.7009345794392523,\n",
              "  array([[-1.10787768, -0.91998849,  0.59563466, ..., -0.47726289,\n",
              "          -0.71382496, -0.84217418],\n",
              "         [ 0.38570926,  0.66936455, -0.00887808, ..., -0.18003975,\n",
              "          -0.23264877, -0.37043274],\n",
              "         [ 1.32001383,  0.67648305, -0.15450269, ..., -0.51938137,\n",
              "          -0.87990141, -0.76192603],\n",
              "         ...,\n",
              "         [-1.09416947, -1.13060932, -1.16528937, ..., -0.66253572,\n",
              "          -0.31475442, -0.73163051],\n",
              "         [-0.48220014,  0.369094  ,  1.11407148, ...,  0.43682629,\n",
              "          -0.49352171,  0.47090601],\n",
              "         [ 0.1237158 , -0.47666211, -0.57992908, ..., -0.9564106 ,\n",
              "          -0.34374561, -0.35596668]])],\n",
              " [0.711111111111111,\n",
              "  array([[-1.10787768, -0.92759868,  0.59747798, ..., -0.48686071,\n",
              "          -0.71852824, -0.85112659],\n",
              "         [ 0.38570926,  0.66859573, -0.00945655, ..., -0.18838163,\n",
              "          -0.23664939, -0.37759364],\n",
              "         [ 1.32001383,  0.67574487, -0.15566456, ..., -0.41104294,\n",
              "          -0.88484721, -0.77057368],\n",
              "         ...,\n",
              "         [-1.09416947, -1.13912613, -1.17050066, ..., -0.67291642,\n",
              "          -0.31887494, -0.74016311],\n",
              "         [-0.48220014,  0.36703266,  1.11799176, ...,  0.43109103,\n",
              "          -0.49790328,  0.46694023],\n",
              "         [ 0.1237158 , -0.482364  , -0.5827953 , ..., -0.96803309,\n",
              "          -0.34790846, -0.40354624]])]]"
            ]
          },
          "metadata": {},
          "execution_count": 744
        }
      ]
    },
    {
      "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": 745,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xltBmsu9Vab1"
      },
      "source": [
        "elitista = elitismo(populacao_inicial_avaliada)"
      ],
      "execution_count": 746,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PKolrnNSNQC_",
        "outputId": "b21fc2cb-0e7a-49ca-9b70-57f54beb93c9"
      },
      "source": [
        "elitista"
      ],
      "execution_count": 747,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.711111111111111,\n",
              " array([[-1.10787768, -0.92759868,  0.59747798, ..., -0.48686071,\n",
              "         -0.71852824, -0.85112659],\n",
              "        [ 0.38570926,  0.66859573, -0.00945655, ..., -0.18838163,\n",
              "         -0.23664939, -0.37759364],\n",
              "        [ 1.32001383,  0.67574487, -0.15566456, ..., -0.41104294,\n",
              "         -0.88484721, -0.77057368],\n",
              "        ...,\n",
              "        [-1.09416947, -1.13912613, -1.17050066, ..., -0.67291642,\n",
              "         -0.31887494, -0.74016311],\n",
              "        [-0.48220014,  0.36703266,  1.11799176, ...,  0.43109103,\n",
              "         -0.49790328,  0.46694023],\n",
              "        [ 0.1237158 , -0.482364  , -0.5827953 , ..., -0.96803309,\n",
              "         -0.34790846, -0.40354624]])]"
            ]
          },
          "metadata": {},
          "execution_count": 747
        }
      ]
    },
    {
      "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": 748,
      "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": 749,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kcba-uAIsF91",
        "outputId": "854ac8b9-523f-4419-f6cb-d58490dd150c"
      },
      "source": [
        "import numpy\n",
        "ss = index_vas\n",
        "final_list = numpy.array_split(ss,2);\n",
        "final_list"
      ],
      "execution_count": 750,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([[104,  71],\n",
              "        [281,  45],\n",
              "        [273,  18],\n",
              "        ...,\n",
              "        [288,  12],\n",
              "        [446,  27],\n",
              "        [523,  53]]), array([[ 64,  32],\n",
              "        [318,  23],\n",
              "        [448,  32],\n",
              "        ...,\n",
              "        [ 50,  58],\n",
              "        [356,  55],\n",
              "        [ 13,  49]])]"
            ]
          },
          "metadata": {},
          "execution_count": 750
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "spXPuxaWsdaY",
        "outputId": "8b623af4-1fe9-4252-c04e-5aaced7d4831"
      },
      "source": [
        "len(ss), len(final_list[0]),len(final_list[1]) "
      ],
      "execution_count": 751,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3816, 1908, 1908)"
            ]
          },
          "metadata": {},
          "execution_count": 751
        }
      ]
    },
    {
      "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": 752,
      "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": 753,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SrTV9X19pUOO"
      },
      "source": [
        "#nova_populacao_recombinada = crossover(ss,2,atual,4)"
      ],
      "execution_count": 754,
      "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": 755,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hcWIluclhe8l"
      },
      "source": [
        "#ind = mutacao(df_amp)"
      ],
      "execution_count": 756,
      "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": 757,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ESP4G7Fj9-V7",
        "outputId": "cc84638d-3f7b-439a-ce94-1d6a307ad9b3"
      },
      "source": [
        "populationInitial[0]"
      ],
      "execution_count": 758,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.10787768e+00, -9.39820598e-01,  6.07059664e-01, ...,\n",
              "        -4.89687773e-01, -7.35093468e-01, -8.64961164e-01],\n",
              "       [ 3.85709262e-01,  6.84453373e-01, -6.39910551e-03, ...,\n",
              "        -1.82604493e-01, -2.44539886e-01, -3.87598862e-01],\n",
              "       [ 1.32001383e+00,  6.91728281e-01, -1.54178776e-01, ...,\n",
              "         1.73784753e-15, -9.04406490e-01, -7.83756840e-01],\n",
              "       ...,\n",
              "       [-1.09416947e+00, -1.15506915e+00, -1.17992384e+00, ...,\n",
              "        -6.81106878e-01, -3.28245651e-01, -7.53100345e-01],\n",
              "       [-4.82200136e-01,  3.77585345e-01,  1.13316870e+00, ...,\n",
              "         4.54725581e-01, -5.10496841e-01,  4.63764558e-01],\n",
              "       [ 1.23715799e-01, -4.86753540e-01, -5.85900944e-01, ...,\n",
              "        -9.84730814e-01, -3.57801833e-01,  1.38771916e-15]])"
            ]
          },
          "metadata": {},
          "execution_count": 758
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8ZQv6VWu6NaI"
      },
      "source": [
        "a = populationInitial.pop(0)"
      ],
      "execution_count": 759,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "opUvT2THJtIP",
        "outputId": "5e6162ae-5028-414b-d4fa-fa66aa26473a"
      },
      "source": [
        "a"
      ],
      "execution_count": 760,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.10787768e+00, -9.39820598e-01,  6.07059664e-01, ...,\n",
              "        -4.89687773e-01, -7.35093468e-01, -8.64961164e-01],\n",
              "       [ 3.85709262e-01,  6.84453373e-01, -6.39910551e-03, ...,\n",
              "        -1.82604493e-01, -2.44539886e-01, -3.87598862e-01],\n",
              "       [ 1.32001383e+00,  6.91728281e-01, -1.54178776e-01, ...,\n",
              "         1.73784753e-15, -9.04406490e-01, -7.83756840e-01],\n",
              "       ...,\n",
              "       [-1.09416947e+00, -1.15506915e+00, -1.17992384e+00, ...,\n",
              "        -6.81106878e-01, -3.28245651e-01, -7.53100345e-01],\n",
              "       [-4.82200136e-01,  3.77585345e-01,  1.13316870e+00, ...,\n",
              "         4.54725581e-01, -5.10496841e-01,  4.63764558e-01],\n",
              "       [ 1.23715799e-01, -4.86753540e-01, -5.85900944e-01, ...,\n",
              "        -9.84730814e-01, -3.57801833e-01,  1.38771916e-15]])"
            ]
          },
          "metadata": {},
          "execution_count": 760
        }
      ]
    },
    {
      "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": 761,
      "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",
        "      populacao_atual_avaliada.pop(len(populacao_atual_avaliada)-1)\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": 762,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LgibKIEbAurT"
      },
      "source": [
        "# Running"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9OysaJjwBFO3",
        "outputId": "03b46e97-63d0-4c5e-e515-8e156603ac72"
      },
      "source": [
        "gen_sizes = [8,10,12,14,16,18]#[10,20,40,20,10,5]\n",
        "result,p = main(gen_sizes)\n",
        "result"
      ],
      "execution_count": 763,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tamanho da população atual 6\n",
            "pop_cruzada 8\n",
            "tam_pop_atual:  8\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.6311111111111112  melhor: 0.6902654867256638  escolhido:  0.6902654867256638\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.6311111111111112  melhor: 0.6919431279620852  escolhido:  0.6919431279620852\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.6311111111111112  melhor: 0.6902654867256638  escolhido:  0.6902654867256638\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.6457399103139014  melhor: 0.6981132075471698  escolhido:  0.6981132075471698\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.6291079812206574  melhor: 0.704225352112676  escolhido:  0.704225352112676\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.6311111111111112  melhor: 0.6893203883495145  escolhido:  0.6893203883495145\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.6291079812206574  melhor: 0.704225352112676  escolhido:  0.704225352112676\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.6291079812206574  melhor: 0.6944444444444443  escolhido:  0.6944444444444443\n",
            "----------------------------------------------------\n",
            "melhor anterior foi escolhido:  0.711111111111111\n",
            "8\n",
            "tamanho da população atual 8\n",
            "pop_cruzada 10\n",
            "tam_pop_atual:  10\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.6311111111111112  melhor: 0.6948356807511737  escolhido:  0.6948356807511737\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.6311111111111112  melhor: 0.6915887850467289  escolhido:  0.6915887850467289\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.6311111111111112  melhor: 0.7169811320754716  escolhido:  0.7169811320754716\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.6363636363636364  melhor: 0.6976744186046512  escolhido:  0.6976744186046512\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.6363636363636364  melhor: 0.7009345794392523  escolhido:  0.7009345794392523\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.6311111111111112  melhor: 0.7196261682242991  escolhido:  0.7196261682242991\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.6311111111111112  melhor: 0.6981132075471698  escolhido:  0.6981132075471698\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.6363636363636364  melhor: 0.7075471698113208  escolhido:  0.7075471698113208\n",
            "----------------------------------------------------\n",
            "Individual -  8\n",
            "Pior: 0.6311111111111112  melhor: 0.6857142857142857  escolhido:  0.6857142857142857\n",
            "----------------------------------------------------\n",
            "Individual -  9\n",
            "Pior: 0.6311111111111112  melhor: 0.7037037037037037  escolhido:  0.7037037037037037\n",
            "----------------------------------------------------\n",
            "Novo melhor: 0.7196261682242991\n",
            "10\n",
            "tamanho da população atual 10\n",
            "pop_cruzada 12\n",
            "tam_pop_atual:  12\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.6283185840707964  melhor: 0.6857142857142857  escolhido:  0.6857142857142857\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.6285714285714286  melhor: 0.7238095238095238  escolhido:  0.7238095238095238\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.6283185840707964  melhor: 0.7102803738317757  escolhido:  0.7102803738317757\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.6285714285714286  melhor: 0.6760563380281691  escolhido:  0.6760563380281691\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.6283185840707964  melhor: 0.7004608294930875  escolhido:  0.7004608294930875\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.6285714285714286  melhor: 0.6976744186046512  escolhido:  0.6976744186046512\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.6283185840707964  melhor: 0.6976744186046512  escolhido:  0.6976744186046512\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.6283185840707964  melhor: 0.6919431279620852  escolhido:  0.6919431279620852\n",
            "----------------------------------------------------\n",
            "Individual -  8\n",
            "Pior: 0.6283185840707964  melhor: 0.7142857142857143  escolhido:  0.7142857142857143\n",
            "----------------------------------------------------\n",
            "Individual -  9\n",
            "Pior: 0.6285714285714286  melhor: 0.6729857819905214  escolhido:  0.6729857819905214\n",
            "----------------------------------------------------\n",
            "Individual -  10\n",
            "Pior: 0.6285714285714286  melhor: 0.704225352112676  escolhido:  0.704225352112676\n",
            "----------------------------------------------------\n",
            "Individual -  11\n",
            "Pior: 0.6283185840707964  melhor: 0.708133971291866  escolhido:  0.708133971291866\n",
            "----------------------------------------------------\n",
            "Novo melhor: 0.7238095238095238\n",
            "12\n",
            "tamanho da população atual 12\n",
            "pop_cruzada 14\n",
            "tam_pop_atual:  14\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.6285714285714286  melhor: 0.7203791469194313  escolhido:  0.7203791469194313\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.6160714285714286  melhor: 0.6886792452830189  escolhido:  0.6886792452830189\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.6160714285714286  melhor: 0.7115384615384616  escolhido:  0.7115384615384616\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.6160714285714286  melhor: 0.6915887850467289  escolhido:  0.6915887850467289\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.6285714285714286  melhor: 0.7102803738317757  escolhido:  0.7102803738317757\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.6285714285714286  melhor: 0.7047619047619048  escolhido:  0.7047619047619048\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.6160714285714286  melhor: 0.6883720930232559  escolhido:  0.6883720930232559\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.6285714285714286  melhor: 0.7019230769230769  escolhido:  0.7019230769230769\n",
            "----------------------------------------------------\n",
            "Individual -  8\n",
            "Pior: 0.6285714285714286  melhor: 0.7047619047619048  escolhido:  0.7047619047619048\n",
            "----------------------------------------------------\n",
            "Individual -  9\n",
            "Pior: 0.6285714285714286  melhor: 0.7037037037037037  escolhido:  0.7037037037037037\n",
            "----------------------------------------------------\n",
            "Individual -  10\n",
            "Pior: 0.6285714285714286  melhor: 0.6968325791855203  escolhido:  0.6968325791855203\n",
            "----------------------------------------------------\n",
            "Individual -  11\n",
            "Pior: 0.6285714285714286  melhor: 0.6790697674418604  escolhido:  0.6790697674418604\n",
            "----------------------------------------------------\n",
            "Individual -  12\n",
            "Pior: 0.6160714285714286  melhor: 0.7075471698113208  escolhido:  0.7075471698113208\n",
            "----------------------------------------------------\n",
            "Individual -  13\n",
            "Pior: 0.6160714285714286  melhor: 0.7109004739336493  escolhido:  0.7109004739336493\n",
            "----------------------------------------------------\n",
            "melhor anterior foi escolhido:  0.7238095238095238\n",
            "14\n",
            "tamanho da população atual 14\n",
            "pop_cruzada 16\n",
            "tam_pop_atual:  16\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.6320346320346322  melhor: 0.7014218009478673  escolhido:  0.7014218009478673\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.6320346320346322  melhor: 0.6952380952380953  escolhido:  0.6952380952380953\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.6320346320346322  melhor: 0.7102803738317757  escolhido:  0.7102803738317757\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.6320346320346322  melhor: 0.7069767441860465  escolhido:  0.7069767441860465\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.6301369863013698  melhor: 0.6889952153110048  escolhido:  0.6889952153110048\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.6320346320346322  melhor: 0.6886792452830189  escolhido:  0.6886792452830189\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.6301369863013698  melhor: 0.704225352112676  escolhido:  0.704225352112676\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.6320346320346322  melhor: 0.6859903381642511  escolhido:  0.6859903381642511\n",
            "----------------------------------------------------\n",
            "Individual -  8\n",
            "Pior: 0.6301369863013698  melhor: 0.7109004739336493  escolhido:  0.7109004739336493\n",
            "----------------------------------------------------\n",
            "Individual -  9\n",
            "Pior: 0.6320346320346322  melhor: 0.7129629629629629  escolhido:  0.7129629629629629\n",
            "----------------------------------------------------\n",
            "Individual -  10\n",
            "Pior: 0.6320346320346322  melhor: 0.6952380952380953  escolhido:  0.6952380952380953\n",
            "----------------------------------------------------\n",
            "Individual -  11\n",
            "Pior: 0.6301369863013698  melhor: 0.6948356807511737  escolhido:  0.6948356807511737\n",
            "----------------------------------------------------\n",
            "Individual -  12\n",
            "Pior: 0.6301369863013698  melhor: 0.6915887850467289  escolhido:  0.6915887850467289\n",
            "----------------------------------------------------\n",
            "Individual -  13\n",
            "Pior: 0.6320346320346322  melhor: 0.6820276497695852  escolhido:  0.6820276497695852\n",
            "----------------------------------------------------\n",
            "Individual -  14\n",
            "Pior: 0.6301369863013698  melhor: 0.7009345794392523  escolhido:  0.7009345794392523\n",
            "----------------------------------------------------\n",
            "Individual -  15\n",
            "Pior: 0.6301369863013698  melhor: 0.7014218009478673  escolhido:  0.7014218009478673\n",
            "----------------------------------------------------\n",
            "melhor anterior foi escolhido:  0.7238095238095238\n",
            "16\n",
            "tamanho da população atual 16\n",
            "pop_cruzada 18\n",
            "tam_pop_atual:  18\n",
            "Avaliações com Binary Relevance\n",
            "\n",
            "Individual -  0\n",
            "Pior: 0.6133333333333334  melhor: 0.6981132075471698  escolhido:  0.6981132075471698\n",
            "----------------------------------------------------\n",
            "Individual -  1\n",
            "Pior: 0.6133333333333334  melhor: 0.6976744186046512  escolhido:  0.6976744186046512\n",
            "----------------------------------------------------\n",
            "Individual -  2\n",
            "Pior: 0.6411483253588518  melhor: 0.6952380952380953  escolhido:  0.6952380952380953\n",
            "----------------------------------------------------\n",
            "Individual -  3\n",
            "Pior: 0.6411483253588518  melhor: 0.7264150943396225  escolhido:  0.7264150943396225\n",
            "----------------------------------------------------\n",
            "Individual -  4\n",
            "Pior: 0.6133333333333334  melhor: 0.6981132075471698  escolhido:  0.6981132075471698\n",
            "----------------------------------------------------\n",
            "Individual -  5\n",
            "Pior: 0.6411483253588518  melhor: 0.6886792452830189  escolhido:  0.6886792452830189\n",
            "----------------------------------------------------\n",
            "Individual -  6\n",
            "Pior: 0.6133333333333334  melhor: 0.6948356807511737  escolhido:  0.6948356807511737\n",
            "----------------------------------------------------\n",
            "Individual -  7\n",
            "Pior: 0.6133333333333334  melhor: 0.6981132075471698  escolhido:  0.6981132075471698\n",
            "----------------------------------------------------\n",
            "Individual -  8\n",
            "Pior: 0.6411483253588518  melhor: 0.7009345794392523  escolhido:  0.7009345794392523\n",
            "----------------------------------------------------\n",
            "Individual -  9\n",
            "Pior: 0.6133333333333334  melhor: 0.6790697674418604  escolhido:  0.6790697674418604\n",
            "----------------------------------------------------\n",
            "Individual -  10\n",
            "Pior: 0.6133333333333334  melhor: 0.6886792452830189  escolhido:  0.6886792452830189\n",
            "----------------------------------------------------\n",
            "Individual -  11\n",
            "Pior: 0.6133333333333334  melhor: 0.7136150234741784  escolhido:  0.7136150234741784\n",
            "----------------------------------------------------\n",
            "Individual -  12\n",
            "Pior: 0.6133333333333334  melhor: 0.7203791469194313  escolhido:  0.7203791469194313\n",
            "----------------------------------------------------\n",
            "Individual -  13\n",
            "Pior: 0.6133333333333334  melhor: 0.6886792452830189  escolhido:  0.6886792452830189\n",
            "----------------------------------------------------\n",
            "Individual -  14\n",
            "Pior: 0.6411483253588518  melhor: 0.7019230769230769  escolhido:  0.7019230769230769\n",
            "----------------------------------------------------\n",
            "Individual -  15\n",
            "Pior: 0.6133333333333334  melhor: 0.6915887850467289  escolhido:  0.6915887850467289\n",
            "----------------------------------------------------\n",
            "Individual -  16\n",
            "Pior: 0.6133333333333334  melhor: 0.6948356807511737  escolhido:  0.6948356807511737\n",
            "----------------------------------------------------\n",
            "Individual -  17\n",
            "Pior: 0.6411483253588518  melhor: 0.7129629629629629  escolhido:  0.7129629629629629\n",
            "----------------------------------------------------\n",
            "Novo melhor: 0.7264150943396225\n",
            "18\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[0.711111111111111,\n",
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              "         [ 1.32001383,  0.67574487, -0.15566456, ...,  0.435863  ,\n",
              "          -0.88484721, -0.77057368],\n",
              "         ...,\n",
              "         [-1.09416947, -1.13912613, -1.17050066, ..., -0.67291642,\n",
              "          -0.31887494, -0.74016311],\n",
              "         [-0.48220014,  0.36703266,  1.11799176, ...,  0.43109103,\n",
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              "         [ 0.1237158 , -0.482364  , -0.5827953 , ..., -0.96803309,\n",
              "          -0.34790846,  0.881401  ]])],\n",
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              "          -0.23664939, -0.37759364],\n",
              "         [ 1.32001383,  0.67574487, -0.15566456, ...,  0.435863  ,\n",
              "          -0.88484721, -0.77057368],\n",
              "         ...,\n",
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              "         [-0.48220014,  0.36703266,  1.11799176, ...,  0.43109103,\n",
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              "         [ 0.1237158 , -0.482364  , -0.5827953 , ..., -0.96803309,\n",
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              " [0.7196261682242991,\n",
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              "         [ 0.1237158 , -0.482364  , -0.5827953 , ..., -0.96803309,\n",
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              "          -0.23664939, -0.37759364],\n",
              "         [ 1.32001383,  0.67574487, -0.15566456, ...,  0.435863  ,\n",
              "          -0.88484721, -0.77057368],\n",
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              "          -0.49790328,  0.46694023],\n",
              "         [ 0.1237158 , -0.482364  , -0.5827953 , ..., -0.96803309,\n",
              "          -0.34790846,  0.881401  ]])],\n",
              " [0.7238095238095238,\n",
              "  array([[-1.10787768, -0.92759868,  0.59747798, ..., -0.48686071,\n",
              "          -0.71852824, -0.85112659],\n",
              "         [ 0.38570926,  0.66859573, -0.00945655, ..., -0.18838163,\n",
              "          -0.23664939, -0.37759364],\n",
              "         [ 1.32001383,  0.67574487, -0.15566456, ...,  0.435863  ,\n",
              "          -0.88484721, -0.77057368],\n",
              "         ...,\n",
              "         [-1.09416947, -1.13912613, -1.17050066, ..., -0.67291642,\n",
              "          -0.31887494, -0.74016311],\n",
              "         [-0.48220014,  0.36703266,  1.11799176, ...,  0.43109103,\n",
              "          -0.49790328,  0.46694023],\n",
              "         [ 0.1237158 , -0.482364  , -0.5827953 , ..., -0.96803309,\n",
              "          -0.34790846,  0.881401  ]])],\n",
              " [0.7238095238095238,\n",
              "  array([[-1.10787768, -0.92759868,  0.59747798, ..., -0.48686071,\n",
              "          -0.71852824, -0.85112659],\n",
              "         [ 0.38570926,  0.66859573, -0.00945655, ..., -0.18838163,\n",
              "          -0.23664939, -0.37759364],\n",
              "         [ 1.32001383,  0.67574487, -0.15566456, ...,  0.435863  ,\n",
              "          -0.88484721, -0.77057368],\n",
              "         ...,\n",
              "         [-1.09416947, -1.13912613, -1.17050066, ..., -0.67291642,\n",
              "          -0.31887494, -0.74016311],\n",
              "         [-0.48220014,  0.36703266,  1.11799176, ...,  0.43109103,\n",
              "          -0.49790328,  0.46694023],\n",
              "         [ 0.1237158 , -0.482364  , -0.5827953 , ..., -0.96803309,\n",
              "          -0.34790846,  0.881401  ]])],\n",
              " [0.7264150943396225,\n",
              "  array([[-1.10787768, -0.9398206 ,  0.60705966, ..., -0.48968777,\n",
              "          -0.73509347, -0.86496116],\n",
              "         [ 0.38570926,  0.68445337, -0.00639911, ..., -0.18260449,\n",
              "          -0.24453989, -0.38759886],\n",
              "         [ 1.32001383,  0.69172828, -0.15417878, ...,  0.097802  ,\n",
              "          -0.90440649, -0.78375684],\n",
              "         ...,\n",
              "         [-1.09416947, -1.15506915, -1.17992384, ..., -0.68110688,\n",
              "          -0.32824565, -0.75310034],\n",
              "         [-0.48220014,  0.37758534,  1.1331687 , ...,  0.45472558,\n",
              "          -0.51049684,  0.46376456],\n",
              "         [ 0.1237158 , -0.48675354, -0.58590094, ..., -0.98473081,\n",
              "          -0.35780183,  0.821039  ]])]]"
            ]
          },
          "metadata": {},
          "execution_count": 763
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DHiPqqv754Jn",
        "outputId": "497439c4-8eaa-4cfd-e236-a9d2b2cdcf69"
      },
      "source": [
        "p"
      ],
      "execution_count": 764,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "18"
            ]
          },
          "metadata": {},
          "execution_count": 764
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2WjQSIcIeXWr"
      },
      "source": [
        "dados = []\n",
        "for i in result:\n",
        "  dados.append(i[0])"
      ],
      "execution_count": 765,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dados"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VI2HZYbm18IA",
        "outputId": "3613cc60-a7d7-4c40-8805-a8f487594b06"
      },
      "execution_count": 766,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.711111111111111,\n",
              " 0.711111111111111,\n",
              " 0.7196261682242991,\n",
              " 0.7238095238095238,\n",
              " 0.7238095238095238,\n",
              " 0.7238095238095238,\n",
              " 0.7264150943396225]"
            ]
          },
          "metadata": {},
          "execution_count": 766
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SK_JnhgtmyTr"
      },
      "source": [
        "geracoes = pd.DataFrame({'geracoes':dados})"
      ],
      "execution_count": 767,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "5hBXWVyZnZ3_",
        "outputId": "b0c1d4d0-94f0-435a-c96f-56736135b578"
      },
      "source": [
        "geracoes"
      ],
      "execution_count": 768,
      "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>geracoes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.711111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.711111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.719626</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.723810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.723810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.723810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.726415</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   geracoes\n",
              "0  0.711111\n",
              "1  0.711111\n",
              "2  0.719626\n",
              "3  0.723810\n",
              "4  0.723810\n",
              "5  0.723810\n",
              "6  0.726415"
            ]
          },
          "metadata": {},
          "execution_count": 768
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "t65Beac9oHgQ",
        "outputId": "d8fff51f-a078-466e-8ac7-f1f6a7139298"
      },
      "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": 769,
      "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": "4e82aed7-5708-4ac1-ff1a-a51d3b2657b4"
      },
      "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": 770,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVM:            0.6434782608695652\n",
            "Random Forest:  0.7547169811320754\n"
          ]
        }
      ]
    }
  ]
}