Espirometria_regressionN.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gustavosr8/SpiroPhone/blob/master/Espirometria_regressionN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "i8pdpl_FMhx1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e9a4fca7-e493-44ac-a33b-2f4512f59d4f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting scikeras\n",
            "  Downloading scikeras-0.11.0-py3-none-any.whl (27 kB)\n",
            "Requirement already satisfied: packaging>=0.21 in /usr/local/lib/python3.10/dist-packages (from scikeras) (23.1)\n",
            "Requirement already satisfied: scikit-learn>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from scikeras) (1.2.2)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.0.0->scikeras) (1.23.5)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.0.0->scikeras) (1.10.1)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.0.0->scikeras) (1.3.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.0.0->scikeras) (3.2.0)\n",
            "Installing collected packages: scikeras\n",
            "Successfully installed scikeras-0.11.0\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "from google.colab import drive\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "\n",
        "import librosa\n",
        "import librosa.display\n",
        "\n",
        "import scipy\n",
        "\n",
        "from scipy import signal\n",
        "from scipy.signal import butter, filtfilt\n",
        "\n",
        "import sklearn\n",
        "from sklearn import preprocessing, decomposition, model_selection, ensemble, metrics, pipeline\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.pipeline import Pipeline\n",
        "\n",
        "from sklearn.model_selection import RandomizedSearchCV, train_test_split, KFold, cross_val_score\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.metrics import precision_score, accuracy_score, explained_variance_score\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers, models, callbacks\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Input, Activation\n",
        "\n",
        "!pip install scikeras\n",
        "from scikeras.wrappers import KerasRegressor\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9tp6i9yWlCYy"
      },
      "source": [
        "#Leitura dos dados\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZUZs7oGiNrvw",
        "outputId": "b8272d18-2915-4074-fdbf-5629141fbb57"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n",
            "1 /content/drive/MyDrive/IC/codigo/assets/4306486.wav\n",
            "11 /content/drive/MyDrive/IC/codigo/assets/10348393.wav\n",
            "21 /content/drive/MyDrive/IC/codigo/assets/4340095.wav\n",
            "31 /content/drive/MyDrive/IC/codigo/assets/10305974n.wav\n",
            "41 /content/drive/MyDrive/IC/codigo/assets/863418n.wav\n",
            "Encontrei esse numero de arquivos: 46\n"
          ]
        }
      ],
      "source": [
        "drive.mount('/content/drive')\n",
        "path='/content/drive/MyDrive/IC/codigo/assets/'\n",
        "dataset = 'datasetn.csv'\n",
        "\n",
        "df = pd.read_csv(path+dataset)\n",
        "df.head()\n",
        "\n",
        "# Checando se arquivos existem, numero de canais e taxa de amostragem\n",
        "n_files = 0\n",
        "missing_files = 0\n",
        "taxas_de_amostragem = []\n",
        "duracoes = []\n",
        "amostras = []\n",
        "classe = []\n",
        "for index, row in df.iterrows():\n",
        "  full_fname = path + str(row['REGISTRO'])+'.wav'\n",
        "  if (n_files % 10 == 0):\n",
        "    print(str(n_files+1) + \" \" + full_fname)\n",
        "  if not os.path.exists(full_fname):\n",
        "    print(str(row['REGISTRO']) + \" (index=\" + str(n_files) + \") does not exist!\")\n",
        "    df.drop(df.index[n_files])\n",
        "    missing_files += 1\n",
        "    n_files += 1\n",
        "  else:\n",
        "    y, sr = librosa.load(full_fname, sr=None, mono=True)\n",
        "    taxas_de_amostragem.append(sr)\n",
        "    duracoes.append(len(y)/sr)\n",
        "    amostras.append(y)\n",
        "    n_files += 1\n",
        "print(\"Encontrei esse numero de arquivos: \" + str(n_files-missing_files))\n",
        "df['sr'] = taxas_de_amostragem\n",
        "df['len'] = duracoes"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "teste1, sr = librosa.load(path+'10509684.wav', sr=None, mono=True)\n",
        "teste2, sr = librosa.load(path+'10523145.wav', sr=None, mono=True)"
      ],
      "metadata": {
        "id": "k_Gs3RNjLVfu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PA-3q_QIKthJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "7bf8bf49-1a8c-4a8b-80ab-a7128ffa5f26"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      REGISTRO SEXO  IDADE   PESO  ALTURA   CVF  CVF%  VEF1  VEF1%  VEF1/CVF  \\\n",
              "0      4306486    F     71   76.9   160.0  2.58    92  1.63     76      0.63   \n",
              "1     10429556    M     63   91.6   187.5  4.33    85  2.52     63      0.58   \n",
              "2      3724996    F     65   58.5   162.0  2.50    83  1.77     75      0.71   \n",
              "3     10433358    F     45   61.6   146.0  2.12    80  1.16     52      0.54   \n",
              "4     10425372    F     56   65.8   149.0  1.48    55  0.58     26      0.39   \n",
              "5      4481868    F     69   81.1   174.0  3.41    82  2.57     81      0.75   \n",
              "6     10082908    F     39   66.9   153.0  3.25   107  2.46     95      0.75   \n",
              "7     10305974    M     70   71.0   172.0  2.98    74  1.66     54      0.56   \n",
              "8      4432769    F     60   61.4   146.0  1.87    78  1.48     78      0.79   \n",
              "9       522460    F     70   74.4   151.0  2.05    84  1.58     85      0.77   \n",
              "10    10348393    M     55   59.8   157.0  3.03    88  2.26     82      0.75   \n",
              "11     4544683    F     63   53.5   150.0  3.18   127  2.22    113      0.70   \n",
              "12     7109657    M     45   75.5   183.0  4.60    88  3.02     71      0.66   \n",
              "13     4548116    F     68   64.0   160.0  2.53    88  1.35     61      0.53   \n",
              "14      893629    M     58  105.2   168.5  3.30    81  1.91     59      0.58   \n",
              "15     4589993    M     66  101.5   172.0  3.30    81  1.54     48      0.47   \n",
              "16    10137282    F     88   77.0   147.0  2.00   101  1.70    115      0.85   \n",
              "17      863418    M     45   75.5   183.0  4.60    88  3.02     71      0.66   \n",
              "18     4569469    M     81   81.1   166.0  2.14    57  1.82     65      0.85   \n",
              "19     4027607    F     50  101.8   159.0  1.90    61  1.68     66      0.88   \n",
              "20     4340095    F     28   77.0   168.0  4.30   107  3.21     96      0.75   \n",
              "21    10462710    F     52   75.4   155.0  2.42    83  2.04     86      0.84   \n",
              "22   101549938    F     43   77.8   156.0  3.71   115  2.80    105      0.75   \n",
              "23    4306486n    F     71   76.9   160.0  2.58    92  1.63     76      0.63   \n",
              "24   10429556n    M     63   91.6   187.5  4.33    85  2.52     63      0.58   \n",
              "25    3724996n    F     65   58.5   162.0  2.50    83  1.77     75      0.71   \n",
              "26   10433358n    F     45   61.6   146.0  2.12    80  1.16     52      0.54   \n",
              "27   10425372n    F     56   65.8   149.0  1.48    55  0.58     26      0.39   \n",
              "28    4481868n    F     69   81.1   174.0  3.41    82  2.57     81      0.75   \n",
              "29   10082908n    F     39   66.9   153.0  3.25   107  2.46     95      0.75   \n",
              "30   10305974n    M     70   71.0   172.0  2.98    74  1.66     54      0.56   \n",
              "31    4432769n    F     60   61.4   146.0  1.87    78  1.48     78      0.79   \n",
              "32     522460n    F     70   74.4   151.0  2.05    84  1.58     85      0.77   \n",
              "33   10348393n    M     55   59.8   157.0  3.03    88  2.26     82      0.75   \n",
              "34    4544683n    F     63   53.5   150.0  3.18   127  2.22    113      0.70   \n",
              "35    7109657n    M     45   75.5   183.0  4.60    88  3.02     71      0.66   \n",
              "36    4548116n    F     68   64.0   160.0  2.53    88  1.35     61      0.53   \n",
              "37     893629n    M     58  105.2   168.5  3.30    81  1.91     59      0.58   \n",
              "38    4589993n    M     66  101.5   172.0  3.30    81  1.54     48      0.47   \n",
              "39   10137282n    F     88   77.0   147.0  2.00   101  1.70    115      0.85   \n",
              "40     863418n    M     45   75.5   183.0  4.60    88  3.02     71      0.66   \n",
              "41    4569469n    M     81   81.1   166.0  2.14    57  1.82     65      0.85   \n",
              "42    4027607n    F     50  101.8   159.0  1.90    61  1.68     66      0.88   \n",
              "43    4340095n    F     28   77.0   168.0  4.30   107  3.21     96      0.75   \n",
              "44   10462710n    F     52   75.4   155.0  2.42    83  2.04     86      0.84   \n",
              "45  101549938n    F     43   77.8   156.0  3.71   115  2.80    105      0.75   \n",
              "\n",
              "    VEF1/CVF%   FEF  FEF%    PF  PF%  DISTÚRBIO     sr        len  \n",
              "0          81  1.17    57  4.30   74  OBSTRUÇÃO  48000  11.677125  \n",
              "1          74  2.05    58  7.21   72  OBSTRUÇÃO  48000  12.490104  \n",
              "2          90  1.21    55  4.84   80  OBSTRUÇÃO  48000  17.440042  \n",
              "3          66  0.50    20  2.91   50  OBSTRUÇÃO  48000  14.128250  \n",
              "4          47  0.19     8  2.02   31  OBSTRUÇÃO  48000  14.616604  \n",
              "5          97  1.97    69  8.09   95     NORMAL  48000   9.181437  \n",
              "6          89  1.95    68  6.72  107  OBSTRUÇÃO  48000  12.798333  \n",
              "7          72  0.58    20  4.77   57  OBSTRUÇÃO  48000  13.218063  \n",
              "8          99  1.48    73  4.54   85  RESTRIÇÃO  48000  12.182771  \n",
              "9          98  1.45    76  5.18   98     NORMAL  48000  11.457896  \n",
              "10         93  1.67    61  4.03  108     NORMAL  48000  12.974771  \n",
              "11         88  1.90    93  6.51  120  OBSTRUÇÃO  48000   8.989313  \n",
              "12         81  1.88    44  9.08   88  OBSTRUÇÃO  48000  15.126271  \n",
              "13         68  0.63    30  4.00   68  OBSTRUÇÃO  48000  11.562354  \n",
              "14         73  0.82    29  5.01   59  OBSTRUÇÃO  48000  11.580187  \n",
              "15         59  0.62    21  3.99   47  OBSTRUÇÃO  48000   9.113896  \n",
              "16        110  2.41   146  4.92   83     NORMAL  48000   8.223729  \n",
              "17         81  1.88    44  9.08   88  OBSTRUÇÃO  48000  15.161437  \n",
              "18        113  2.34    94  5.57   55  RESTRIÇÃO  48000  10.131042  \n",
              "19        109  2.88   113  7.44  118  RESTRIÇÃO  48000  19.102667  \n",
              "20         89  2.57    70  6.31   77  OBSTRUÇÃO  48000   6.894146  \n",
              "21        103  2.57   107  7.20  119     NORMAL  48000   7.130063  \n",
              "22         91  2.30    87  7.01   84     NORMAL  48000  11.336562  \n",
              "23         81  1.17    57  4.30   74  OBSTRUÇÃO  48000  11.677125  \n",
              "24         74  2.05    58  7.21   72  OBSTRUÇÃO  48000  12.490104  \n",
              "25         90  1.21    55  4.84   80  OBSTRUÇÃO  48000  17.440042  \n",
              "26         66  0.50    20  2.91   50  OBSTRUÇÃO  48000  14.128250  \n",
              "27         47  0.19     8  2.02   31  OBSTRUÇÃO  48000  14.616604  \n",
              "28         97  1.97    69  8.09   95     NORMAL  48000   9.181437  \n",
              "29         89  1.95    68  6.72  107  OBSTRUÇÃO  48000  12.798333  \n",
              "30         72  0.58    20  4.77   57  OBSTRUÇÃO  48000  13.218063  \n",
              "31         99  1.48    73  4.54   85  RESTRIÇÃO  48000  12.182771  \n",
              "32         98  1.45    76  5.18   98     NORMAL  48000  11.457896  \n",
              "33         93  1.67    61  4.03  108     NORMAL  48000  12.974771  \n",
              "34         88  1.90    93  6.51  120  OBSTRUÇÃO  48000   8.989313  \n",
              "35         81  1.88    44  9.08   88  OBSTRUÇÃO  48000  15.126271  \n",
              "36         68  0.63    30  4.00   68  OBSTRUÇÃO  48000  11.562354  \n",
              "37         73  0.82    29  5.01   59  OBSTRUÇÃO  48000  11.580187  \n",
              "38         59  0.62    21  3.99   47  OBSTRUÇÃO  48000   9.113896  \n",
              "39        110  2.41   146  4.92   83     NORMAL  48000   8.223729  \n",
              "40         81  1.88    44  9.08   88  OBSTRUÇÃO  48000  15.161437  \n",
              "41        113  2.34    94  5.57   55  RESTRIÇÃO  48000  10.131042  \n",
              "42        109  2.88   113  7.44  118  RESTRIÇÃO  48000  19.102667  \n",
              "43         89  2.57    70  6.31   77  OBSTRUÇÃO  48000   6.894146  \n",
              "44        103  2.57   107  7.20  119     NORMAL  48000   7.130063  \n",
              "45         91  2.30    87  7.01   84     NORMAL  48000  11.336562  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e1fb853c-dabc-46c6-81e4-a81ed680cd6d\" class=\"colab-df-container\">\n",
              "    <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>REGISTRO</th>\n",
              "      <th>SEXO</th>\n",
              "      <th>IDADE</th>\n",
              "      <th>PESO</th>\n",
              "      <th>ALTURA</th>\n",
              "      <th>CVF</th>\n",
              "      <th>CVF%</th>\n",
              "      <th>VEF1</th>\n",
              "      <th>VEF1%</th>\n",
              "      <th>VEF1/CVF</th>\n",
              "      <th>VEF1/CVF%</th>\n",
              "      <th>FEF</th>\n",
              "      <th>FEF%</th>\n",
              "      <th>PF</th>\n",
              "      <th>PF%</th>\n",
              "      <th>DISTÚRBIO</th>\n",
              "      <th>sr</th>\n",
              "      <th>len</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4306486</td>\n",
              "      <td>F</td>\n",
              "      <td>71</td>\n",
              "      <td>76.9</td>\n",
              "      <td>160.0</td>\n",
              "      <td>2.58</td>\n",
              "      <td>92</td>\n",
              "      <td>1.63</td>\n",
              "      <td>76</td>\n",
              "      <td>0.63</td>\n",
              "      <td>81</td>\n",
              "      <td>1.17</td>\n",
              "      <td>57</td>\n",
              "      <td>4.30</td>\n",
              "      <td>74</td>\n",
              "      <td>OBSTRUÇÃO</td>\n",
              "      <td>48000</td>\n",
              "      <td>11.677125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10429556</td>\n",
              "      <td>M</td>\n",
              "      <td>63</td>\n",
              "      <td>91.6</td>\n",
              "      <td>187.5</td>\n",
              "      <td>4.33</td>\n",
              "      <td>85</td>\n",
              "      <td>2.52</td>\n",
              "      <td>63</td>\n",
              "      <td>0.58</td>\n",
              "      <td>74</td>\n",
              "      <td>2.05</td>\n",
              "      <td>58</td>\n",
              "      <td>7.21</td>\n",
              "      <td>72</td>\n",
              "      <td>OBSTRUÇÃO</td>\n",
              "      <td>48000</td>\n",
              "      <td>12.490104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3724996</td>\n",
              "      <td>F</td>\n",
              "      <td>65</td>\n",
              "      <td>58.5</td>\n",
              "      <td>162.0</td>\n",
              "      <td>2.50</td>\n",
              "      <td>83</td>\n",
              "      <td>1.77</td>\n",
              "      <td>75</td>\n",
              "      <td>0.71</td>\n",
              "      <td>90</td>\n",
              "      <td>1.21</td>\n",
              "      <td>55</td>\n",
              "      <td>4.84</td>\n",
              "      <td>80</td>\n",
              "      <td>OBSTRUÇÃO</td>\n",
              "      <td>48000</td>\n",
              "      <td>17.440042</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10433358</td>\n",
              "      <td>F</td>\n",
              "      <td>45</td>\n",
              "      <td>61.6</td>\n",
              "      <td>146.0</td>\n",
              "      <td>2.12</td>\n",
              "      <td>80</td>\n",
              "      <td>1.16</td>\n",
              "      <td>52</td>\n",
              "      <td>0.54</td>\n",
              "      <td>66</td>\n",
              "      <td>0.50</td>\n",
              "      <td>20</td>\n",
              "      <td>2.91</td>\n",
              "      <td>50</td>\n",
              "      <td>OBSTRUÇÃO</td>\n",
              "      <td>48000</td>\n",
              "      <td>14.128250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10425372</td>\n",
              "      <td>F</td>\n",
              "      <td>56</td>\n",
              "      <td>65.8</td>\n",
              "      <td>149.0</td>\n",
              "      <td>1.48</td>\n",
              "      <td>55</td>\n",
              "      <td>0.58</td>\n",
              "      <td>26</td>\n",
              "      <td>0.39</td>\n",
              "      <td>47</td>\n",
              "      <td>0.19</td>\n",
              "      <td>8</td>\n",
              "      <td>2.02</td>\n",
              "      <td>31</td>\n",
              "      <td>OBSTRUÇÃO</td>\n",
              "      <td>48000</td>\n",
              "      <td>14.616604</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>4481868</td>\n",
              "      <td>F</td>\n",
              "      <td>69</td>\n",
              "      <td>81.1</td>\n",
              "      <td>174.0</td>\n",
              "      <td>3.41</td>\n",
              "      <td>82</td>\n",
              "      <td>2.57</td>\n",
              "      <td>81</td>\n",
              "      <td>0.75</td>\n",
              "      <td>97</td>\n",
              "      <td>1.97</td>\n",
              "      <td>69</td>\n",
              "      <td>8.09</td>\n",
              "      <td>95</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>48000</td>\n",
              "      <td>9.181437</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>10082908</td>\n",
              "      <td>F</td>\n",
              "      <td>39</td>\n",
              "      <td>66.9</td>\n",
              "      <td>153.0</td>\n",
              "      <td>3.25</td>\n",
              "      <td>107</td>\n",
              "      <td>2.46</td>\n",
              "      <td>95</td>\n",
              "      <td>0.75</td>\n",
              "      <td>89</td>\n",
              "      <td>1.95</td>\n",
              "      <td>68</td>\n",
              "      <td>6.72</td>\n",
              "      <td>107</td>\n",
              "      <td>OBSTRUÇÃO</td>\n",
              "      <td>48000</td>\n",
              "      <td>12.798333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>10305974</td>\n",
              "      <td>M</td>\n",
              "      <td>70</td>\n",
              "      <td>71.0</td>\n",
              "      <td>172.0</td>\n",
              "      <td>2.98</td>\n",
              "      <td>74</td>\n",
              "      <td>1.66</td>\n",
              "      <td>54</td>\n",
              "      <td>0.56</td>\n",
              "      <td>72</td>\n",
              "      <td>0.58</td>\n",
              "      <td>20</td>\n",
              "      <td>4.77</td>\n",
              "      <td>57</td>\n",
              "      <td>OBSTRUÇÃO</td>\n",
              "      <td>48000</td>\n",
              "      <td>13.218063</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>4432769</td>\n",
              "      <td>F</td>\n",
              "      <td>60</td>\n",
              "      <td>61.4</td>\n",
              "      <td>146.0</td>\n",
              "      <td>1.87</td>\n",
              "      <td>78</td>\n",
              "      <td>1.48</td>\n",
              "      <td>78</td>\n",
              "      <td>0.79</td>\n",
              "      <td>99</td>\n",
              "      <td>1.48</td>\n",
              "      <td>73</td>\n",
              "      <td>4.54</td>\n",
              "      <td>85</td>\n",
              "      <td>RESTRIÇÃO</td>\n",
              "      <td>48000</td>\n",
              "      <td>12.182771</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>522460</td>\n",
              "      <td>F</td>\n",
              "      <td>70</td>\n",
              "      <td>74.4</td>\n",
              "      <td>151.0</td>\n",
              "      <td>2.05</td>\n",
              "      <td>84</td>\n",
              "      <td>1.58</td>\n",
              "      <td>85</td>\n",
              "      <td>0.77</td>\n",
              "      <td>98</td>\n",
              "      <td>1.45</td>\n",
              "      <td>76</td>\n",
              "      <td>5.18</td>\n",
              "      <td>98</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>48000</td>\n",
              "      <td>11.457896</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>10348393</td>\n",
              "      <td>M</td>\n",
              "      <td>55</td>\n",
              "      <td>59.8</td>\n",
              "      <td>157.0</td>\n",
              "      <td>3.03</td>\n",
              "      <td>88</td>\n",
              "      <td>2.26</td>\n",
              "      <td>82</td>\n",
              "      <td>0.75</td>\n",
              "      <td>93</td>\n",
              "      <td>1.67</td>\n",
              "      <td>61</td>\n",
              "      <td>4.03</td>\n",
              "      <td>108</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>48000</td>\n",
              "      <td>12.974771</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>4544683</td>\n",
              "      <td>F</td>\n",
              "      <td>63</td>\n",
              "      <td>53.5</td>\n",
              "      <td>150.0</td>\n",
              "      <td>3.18</td>\n",
              "      <td>127</td>\n",
              "      <td>2.22</td>\n",
              "      <td>113</td>\n",
              "      <td>0.70</td>\n",
              "      <td>88</td>\n",
              "      <td>1.90</td>\n",
              "      <td>93</td>\n",
              "      <td>6.51</td>\n",
              "      <td>120</td>\n",
              "      <td>OBSTRUÇÃO</td>\n",
              "      <td>48000</td>\n",
              "      <td>8.989313</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>7109657</td>\n",
              "      <td>M</td>\n",
              "      <td>45</td>\n",
              "      <td>75.5</td>\n",
              "      <td>183.0</td>\n",
              "      <td>4.60</td>\n",
              "      <td>88</td>\n",
              "      <td>3.02</td>\n",
              "      <td>71</td>\n",
              "      <td>0.66</td>\n",
              "      <td>81</td>\n",
              "      <td>1.88</td>\n",
              "      <td>44</td>\n",
              "      <td>9.08</td>\n",
              "      <td>88</td>\n",
              "      <td>OBSTRUÇÃO</td>\n",
              "      <td>48000</td>\n",
              "      <td>15.126271</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>4548116</td>\n",
              "      <td>F</td>\n",
              "      <td>68</td>\n",
              "      <td>64.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>2.53</td>\n",
              "      <td>88</td>\n",
              "      <td>1.35</td>\n",
              "      <td>61</td>\n",
              "      <td>0.53</td>\n",
              "      <td>68</td>\n",
              "      <td>0.63</td>\n",
              "      <td>30</td>\n",
              "      <td>4.00</td>\n",
              "      <td>68</td>\n",
              "      <td>OBSTRUÇÃO</td>\n",
              "      <td>48000</td>\n",
              "      <td>11.562354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>893629</td>\n",
              "      <td>M</td>\n",
              "      <td>58</td>\n",
              "      <td>105.2</td>\n",
              "      <td>168.5</td>\n",
              "      <td>3.30</td>\n",
              "      <td>81</td>\n",
              "      <td>1.91</td>\n",
              "      <td>59</td>\n",
              "      <td>0.58</td>\n",
              "      <td>73</td>\n",
              "      <td>0.82</td>\n",
              "      <td>29</td>\n",
              "      <td>5.01</td>\n",
              "      <td>59</td>\n",
              "      <td>OBSTRUÇÃO</td>\n",
              "      <td>48000</td>\n",
              "      <td>11.580187</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>4589993</td>\n",
              "      <td>M</td>\n",
              "      <td>66</td>\n",
              "      <td>101.5</td>\n",
              "      <td>172.0</td>\n",
              "      <td>3.30</td>\n",
              "      <td>81</td>\n",
              "      <td>1.54</td>\n",
              "      <td>48</td>\n",
              "      <td>0.47</td>\n",
              "      <td>59</td>\n",
              "      <td>0.62</td>\n",
              "      <td>21</td>\n",
              "      <td>3.99</td>\n",
              "      <td>47</td>\n",
              "      <td>OBSTRUÇÃO</td>\n",
              "      <td>48000</td>\n",
              "      <td>9.113896</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>10137282</td>\n",
              "      <td>F</td>\n",
              "      <td>88</td>\n",
              "      <td>77.0</td>\n",
              "      <td>147.0</td>\n",
              "      <td>2.00</td>\n",
              "      <td>101</td>\n",
              "      <td>1.70</td>\n",
              "      <td>115</td>\n",
              "      <td>0.85</td>\n",
              "      <td>110</td>\n",
              "      <td>2.41</td>\n",
              "      <td>146</td>\n",
              "      <td>4.92</td>\n",
              "      <td>83</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>48000</td>\n",
              "      <td>8.223729</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>863418</td>\n",
              "      <td>M</td>\n",
              "      <td>45</td>\n",
              "      <td>75.5</td>\n",
              "      <td>183.0</td>\n",
              "      <td>4.60</td>\n",
              "      <td>88</td>\n",
              "      <td>3.02</td>\n",
              "      <td>71</td>\n",
              "      <td>0.66</td>\n",
              "      <td>81</td>\n",
              "      <td>1.88</td>\n",
              "      <td>44</td>\n",
              "      <td>9.08</td>\n",
              "      <td>88</td>\n",
              "      <td>OBSTRUÇÃO</td>\n",
              "      <td>48000</td>\n",
              "      <td>15.161437</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>4569469</td>\n",
              "      <td>M</td>\n",
              "      <td>81</td>\n",
              "      <td>81.1</td>\n",
              "      <td>166.0</td>\n",
              "      <td>2.14</td>\n",
              "      <td>57</td>\n",
              "      <td>1.82</td>\n",
              "      <td>65</td>\n",
              "      <td>0.85</td>\n",
              "      <td>113</td>\n",
              "      <td>2.34</td>\n",
              "      <td>94</td>\n",
              "      <td>5.57</td>\n",
              "      <td>55</td>\n",
              "      <td>RESTRIÇÃO</td>\n",
              "      <td>48000</td>\n",
              "      <td>10.131042</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>4027607</td>\n",
              "      <td>F</td>\n",
              "      <td>50</td>\n",
              "      <td>101.8</td>\n",
              "      <td>159.0</td>\n",
              "      <td>1.90</td>\n",
              "      <td>61</td>\n",
              "      <td>1.68</td>\n",
              "      <td>66</td>\n",
              "      <td>0.88</td>\n",
              "      <td>109</td>\n",
              "      <td>2.88</td>\n",
              "      <td>113</td>\n",
              "      <td>7.44</td>\n",
              "      <td>118</td>\n",
              "      <td>RESTRIÇÃO</td>\n",
              "      <td>48000</td>\n",
              "      <td>19.102667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>4340095</td>\n",
              "      <td>F</td>\n",
              "      <td>28</td>\n",
              "      <td>77.0</td>\n",
              "      <td>168.0</td>\n",
              "      <td>4.30</td>\n",
              "      <td>107</td>\n",
              "      <td>3.21</td>\n",
              "      <td>96</td>\n",
              "      <td>0.75</td>\n",
              "      <td>89</td>\n",
              "      <td>2.57</td>\n",
              "      <td>70</td>\n",
              "      <td>6.31</td>\n",
              "      <td>77</td>\n",
              "      <td>OBSTRUÇÃO</td>\n",
              "      <td>48000</td>\n",
              "      <td>6.894146</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>10462710</td>\n",
              "      <td>F</td>\n",
              "      <td>52</td>\n",
              "      <td>75.4</td>\n",
              "      <td>155.0</td>\n",
              "      <td>2.42</td>\n",
              "      <td>83</td>\n",
              "      <td>2.04</td>\n",
              "      <td>86</td>\n",
              "      <td>0.84</td>\n",
              "      <td>103</td>\n",
              "      <td>2.57</td>\n",
              "      <td>107</td>\n",
              "      <td>7.20</td>\n",
              "      <td>119</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>48000</td>\n",
              "      <td>7.130063</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>101549938</td>\n",
              "      <td>F</td>\n",
              "      <td>43</td>\n",
              "      <td>77.8</td>\n",
              "      <td>156.0</td>\n",
              "      <td>3.71</td>\n",
              "      <td>115</td>\n",
              "      <td>2.80</td>\n",
              "      <td>105</td>\n",
              "      <td>0.75</td>\n",
              "      <td>91</td>\n",
              "      <td>2.30</td>\n",
              "      <td>87</td>\n",
              "      <td>7.01</td>\n",
              "      <td>84</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>48000</td>\n",
              "      <td>11.336562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>4306486n</td>\n",
              "      <td>F</td>\n",
              "      <td>71</td>\n",
              "      <td>76.9</td>\n",
              "      <td>160.0</td>\n",
              "      <td>2.58</td>\n",
              "      <td>92</td>\n",
              "      <td>1.63</td>\n",
              "      <td>76</td>\n",
              "      <td>0.63</td>\n",
              "      <td>81</td>\n",
              "      <td>1.17</td>\n",
              "      <td>57</td>\n",
              "      <td>4.30</td>\n",
              "      <td>74</td>\n",
              "      <td>OBSTRUÇÃO</td>\n",
              "      <td>48000</td>\n",
              "      <td>11.677125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>10429556n</td>\n",
              "      <td>M</td>\n",
              "      <td>63</td>\n",
              "      <td>91.6</td>\n",
              "      <td>187.5</td>\n",
              "      <td>4.33</td>\n",
              "      <td>85</td>\n",
              "      <td>2.52</td>\n",
              "      <td>63</td>\n",
              "      <td>0.58</td>\n",
              "      <td>74</td>\n",
              "      <td>2.05</td>\n",
              "      <td>58</td>\n",
              "      <td>7.21</td>\n",
              "      <td>72</td>\n",
              "      <td>OBSTRUÇÃO</td>\n",
              "      <td>48000</td>\n",
              "      <td>12.490104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>3724996n</td>\n",
              "      <td>F</td>\n",
              "      <td>65</td>\n",
              "      <td>58.5</td>\n",
              "      <td>162.0</td>\n",
              "      <td>2.50</td>\n",
              "      <td>83</td>\n",
              "      <td>1.77</td>\n",
              "      <td>75</td>\n",
              "      <td>0.71</td>\n",
              "      <td>90</td>\n",
              "      <td>1.21</td>\n",
              "      <td>55</td>\n",
              "      <td>4.84</td>\n",
              "      <td>80</td>\n",
              "      <td>OBSTRUÇÃO</td>\n",
              "      <td>48000</td>\n",
              "      <td>17.440042</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>10433358n</td>\n",
              "      <td>F</td>\n",
              "      <td>45</td>\n",
              "      <td>61.6</td>\n",
              "      <td>146.0</td>\n",
              "      <td>2.12</td>\n",
              "      <td>80</td>\n",
              "      <td>1.16</td>\n",
              "      <td>52</td>\n",
              "      <td>0.54</td>\n",
              "      <td>66</td>\n",
              "      <td>0.50</td>\n",
              "      <td>20</td>\n",
              "      <td>2.91</td>\n",
              "      <td>50</td>\n",
              "      <td>OBSTRUÇÃO</td>\n",
              "      <td>48000</td>\n",
              "      <td>14.128250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>10425372n</td>\n",
              "      <td>F</td>\n",
              "      <td>56</td>\n",
              "      <td>65.8</td>\n",
              "      <td>149.0</td>\n",
              "      <td>1.48</td>\n",
              "      <td>55</td>\n",
              "      <td>0.58</td>\n",
              "      <td>26</td>\n",
              "      <td>0.39</td>\n",
              "      <td>47</td>\n",
              "      <td>0.19</td>\n",
              "      <td>8</td>\n",
              "      <td>2.02</td>\n",
              "      <td>31</td>\n",
              "      <td>OBSTRUÇÃO</td>\n",
              "      <td>48000</td>\n",
              "      <td>14.616604</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>4481868n</td>\n",
              "      <td>F</td>\n",
              "      <td>69</td>\n",
              "      <td>81.1</td>\n",
              "      <td>174.0</td>\n",
              "      <td>3.41</td>\n",
              "      <td>82</td>\n",
              "      <td>2.57</td>\n",
              "      <td>81</td>\n",
              "      <td>0.75</td>\n",
              "      <td>97</td>\n",
              "      <td>1.97</td>\n",
              "      <td>69</td>\n",
              "      <td>8.09</td>\n",
              "      <td>95</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>48000</td>\n",
              "      <td>9.181437</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>10082908n</td>\n",
              "      <td>F</td>\n",
              "      <td>39</td>\n",
              "      <td>66.9</td>\n",
              "      <td>153.0</td>\n",
              "      <td>3.25</td>\n",
              "      <td>107</td>\n",
              "      <td>2.46</td>\n",
              "      <td>95</td>\n",
              "      <td>0.75</td>\n",
              "      <td>89</td>\n",
              "      <td>1.95</td>\n",
              "      <td>68</td>\n",
              "      <td>6.72</td>\n",
              "      <td>107</td>\n",
              "      <td>OBSTRUÇÃO</td>\n",
              "      <td>48000</td>\n",
              "      <td>12.798333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>10305974n</td>\n",
              "      <td>M</td>\n",
              "      <td>70</td>\n",
              "      <td>71.0</td>\n",
              "      <td>172.0</td>\n",
              "      <td>2.98</td>\n",
              "      <td>74</td>\n",
              "      <td>1.66</td>\n",
              "      <td>54</td>\n",
              "      <td>0.56</td>\n",
              "      <td>72</td>\n",
              "      <td>0.58</td>\n",
              "      <td>20</td>\n",
              "      <td>4.77</td>\n",
              "      <td>57</td>\n",
              "      <td>OBSTRUÇÃO</td>\n",
              "      <td>48000</td>\n",
              "      <td>13.218063</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>4432769n</td>\n",
              "      <td>F</td>\n",
              "      <td>60</td>\n",
              "      <td>61.4</td>\n",
              "      <td>146.0</td>\n",
              "      <td>1.87</td>\n",
              "      <td>78</td>\n",
              "      <td>1.48</td>\n",
              "      <td>78</td>\n",
              "      <td>0.79</td>\n",
              "      <td>99</td>\n",
              "      <td>1.48</td>\n",
              "      <td>73</td>\n",
              "      <td>4.54</td>\n",
              "      <td>85</td>\n",
              "      <td>RESTRIÇÃO</td>\n",
              "      <td>48000</td>\n",
              "      <td>12.182771</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>522460n</td>\n",
              "      <td>F</td>\n",
              "      <td>70</td>\n",
              "      <td>74.4</td>\n",
              "      <td>151.0</td>\n",
              "      <td>2.05</td>\n",
              "      <td>84</td>\n",
              "      <td>1.58</td>\n",
              "      <td>85</td>\n",
              "      <td>0.77</td>\n",
              "      <td>98</td>\n",
              "      <td>1.45</td>\n",
              "      <td>76</td>\n",
              "      <td>5.18</td>\n",
              "      <td>98</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>48000</td>\n",
              "      <td>11.457896</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>10348393n</td>\n",
              "      <td>M</td>\n",
              "      <td>55</td>\n",
              "      <td>59.8</td>\n",
              "      <td>157.0</td>\n",
              "      <td>3.03</td>\n",
              "      <td>88</td>\n",
              "      <td>2.26</td>\n",
              "      <td>82</td>\n",
              "      <td>0.75</td>\n",
              "      <td>93</td>\n",
              "      <td>1.67</td>\n",
              "      <td>61</td>\n",
              "      <td>4.03</td>\n",
              "      <td>108</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>48000</td>\n",
              "      <td>12.974771</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>4544683n</td>\n",
              "      <td>F</td>\n",
              "      <td>63</td>\n",
              "      <td>53.5</td>\n",
              "      <td>150.0</td>\n",
              "      <td>3.18</td>\n",
              "      <td>127</td>\n",
              "      <td>2.22</td>\n",
              "      <td>113</td>\n",
              "      <td>0.70</td>\n",
              "      <td>88</td>\n",
              "      <td>1.90</td>\n",
              "      <td>93</td>\n",
              "      <td>6.51</td>\n",
              "      <td>120</td>\n",
              "      <td>OBSTRUÇÃO</td>\n",
              "      <td>48000</td>\n",
              "      <td>8.989313</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>7109657n</td>\n",
              "      <td>M</td>\n",
              "      <td>45</td>\n",
              "      <td>75.5</td>\n",
              "      <td>183.0</td>\n",
              "      <td>4.60</td>\n",
              "      <td>88</td>\n",
              "      <td>3.02</td>\n",
              "      <td>71</td>\n",
              "      <td>0.66</td>\n",
              "      <td>81</td>\n",
              "      <td>1.88</td>\n",
              "      <td>44</td>\n",
              "      <td>9.08</td>\n",
              "      <td>88</td>\n",
              "      <td>OBSTRUÇÃO</td>\n",
              "      <td>48000</td>\n",
              "      <td>15.126271</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>4548116n</td>\n",
              "      <td>F</td>\n",
              "      <td>68</td>\n",
              "      <td>64.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>2.53</td>\n",
              "      <td>88</td>\n",
              "      <td>1.35</td>\n",
              "      <td>61</td>\n",
              "      <td>0.53</td>\n",
              "      <td>68</td>\n",
              "      <td>0.63</td>\n",
              "      <td>30</td>\n",
              "      <td>4.00</td>\n",
              "      <td>68</td>\n",
              "      <td>OBSTRUÇÃO</td>\n",
              "      <td>48000</td>\n",
              "      <td>11.562354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>893629n</td>\n",
              "      <td>M</td>\n",
              "      <td>58</td>\n",
              "      <td>105.2</td>\n",
              "      <td>168.5</td>\n",
              "      <td>3.30</td>\n",
              "      <td>81</td>\n",
              "      <td>1.91</td>\n",
              "      <td>59</td>\n",
              "      <td>0.58</td>\n",
              "      <td>73</td>\n",
              "      <td>0.82</td>\n",
              "      <td>29</td>\n",
              "      <td>5.01</td>\n",
              "      <td>59</td>\n",
              "      <td>OBSTRUÇÃO</td>\n",
              "      <td>48000</td>\n",
              "      <td>11.580187</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>4589993n</td>\n",
              "      <td>M</td>\n",
              "      <td>66</td>\n",
              "      <td>101.5</td>\n",
              "      <td>172.0</td>\n",
              "      <td>3.30</td>\n",
              "      <td>81</td>\n",
              "      <td>1.54</td>\n",
              "      <td>48</td>\n",
              "      <td>0.47</td>\n",
              "      <td>59</td>\n",
              "      <td>0.62</td>\n",
              "      <td>21</td>\n",
              "      <td>3.99</td>\n",
              "      <td>47</td>\n",
              "      <td>OBSTRUÇÃO</td>\n",
              "      <td>48000</td>\n",
              "      <td>9.113896</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>10137282n</td>\n",
              "      <td>F</td>\n",
              "      <td>88</td>\n",
              "      <td>77.0</td>\n",
              "      <td>147.0</td>\n",
              "      <td>2.00</td>\n",
              "      <td>101</td>\n",
              "      <td>1.70</td>\n",
              "      <td>115</td>\n",
              "      <td>0.85</td>\n",
              "      <td>110</td>\n",
              "      <td>2.41</td>\n",
              "      <td>146</td>\n",
              "      <td>4.92</td>\n",
              "      <td>83</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>48000</td>\n",
              "      <td>8.223729</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>863418n</td>\n",
              "      <td>M</td>\n",
              "      <td>45</td>\n",
              "      <td>75.5</td>\n",
              "      <td>183.0</td>\n",
              "      <td>4.60</td>\n",
              "      <td>88</td>\n",
              "      <td>3.02</td>\n",
              "      <td>71</td>\n",
              "      <td>0.66</td>\n",
              "      <td>81</td>\n",
              "      <td>1.88</td>\n",
              "      <td>44</td>\n",
              "      <td>9.08</td>\n",
              "      <td>88</td>\n",
              "      <td>OBSTRUÇÃO</td>\n",
              "      <td>48000</td>\n",
              "      <td>15.161437</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>4569469n</td>\n",
              "      <td>M</td>\n",
              "      <td>81</td>\n",
              "      <td>81.1</td>\n",
              "      <td>166.0</td>\n",
              "      <td>2.14</td>\n",
              "      <td>57</td>\n",
              "      <td>1.82</td>\n",
              "      <td>65</td>\n",
              "      <td>0.85</td>\n",
              "      <td>113</td>\n",
              "      <td>2.34</td>\n",
              "      <td>94</td>\n",
              "      <td>5.57</td>\n",
              "      <td>55</td>\n",
              "      <td>RESTRIÇÃO</td>\n",
              "      <td>48000</td>\n",
              "      <td>10.131042</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>4027607n</td>\n",
              "      <td>F</td>\n",
              "      <td>50</td>\n",
              "      <td>101.8</td>\n",
              "      <td>159.0</td>\n",
              "      <td>1.90</td>\n",
              "      <td>61</td>\n",
              "      <td>1.68</td>\n",
              "      <td>66</td>\n",
              "      <td>0.88</td>\n",
              "      <td>109</td>\n",
              "      <td>2.88</td>\n",
              "      <td>113</td>\n",
              "      <td>7.44</td>\n",
              "      <td>118</td>\n",
              "      <td>RESTRIÇÃO</td>\n",
              "      <td>48000</td>\n",
              "      <td>19.102667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>4340095n</td>\n",
              "      <td>F</td>\n",
              "      <td>28</td>\n",
              "      <td>77.0</td>\n",
              "      <td>168.0</td>\n",
              "      <td>4.30</td>\n",
              "      <td>107</td>\n",
              "      <td>3.21</td>\n",
              "      <td>96</td>\n",
              "      <td>0.75</td>\n",
              "      <td>89</td>\n",
              "      <td>2.57</td>\n",
              "      <td>70</td>\n",
              "      <td>6.31</td>\n",
              "      <td>77</td>\n",
              "      <td>OBSTRUÇÃO</td>\n",
              "      <td>48000</td>\n",
              "      <td>6.894146</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>10462710n</td>\n",
              "      <td>F</td>\n",
              "      <td>52</td>\n",
              "      <td>75.4</td>\n",
              "      <td>155.0</td>\n",
              "      <td>2.42</td>\n",
              "      <td>83</td>\n",
              "      <td>2.04</td>\n",
              "      <td>86</td>\n",
              "      <td>0.84</td>\n",
              "      <td>103</td>\n",
              "      <td>2.57</td>\n",
              "      <td>107</td>\n",
              "      <td>7.20</td>\n",
              "      <td>119</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>48000</td>\n",
              "      <td>7.130063</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>101549938n</td>\n",
              "      <td>F</td>\n",
              "      <td>43</td>\n",
              "      <td>77.8</td>\n",
              "      <td>156.0</td>\n",
              "      <td>3.71</td>\n",
              "      <td>115</td>\n",
              "      <td>2.80</td>\n",
              "      <td>105</td>\n",
              "      <td>0.75</td>\n",
              "      <td>91</td>\n",
              "      <td>2.30</td>\n",
              "      <td>87</td>\n",
              "      <td>7.01</td>\n",
              "      <td>84</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>48000</td>\n",
              "      <td>11.336562</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e1fb853c-dabc-46c6-81e4-a81ed680cd6d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e1fb853c-dabc-46c6-81e4-a81ed680cd6d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e1fb853c-dabc-46c6-81e4-a81ed680cd6d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-0a325262-a68c-44d6-9596-99bd6ece08e8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0a325262-a68c-44d6-9596-99bd6ece08e8')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0a325262-a68c-44d6-9596-99bd6ece08e8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fey3qu_6oBUw"
      },
      "source": [
        "#Processamento de Features\n",
        "\n",
        "##Pressão e fluxo de ar aproximados"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lynCkn5AlmZR"
      },
      "source": [
        "A pressão proporcional de ar de saída é dada por uma função de transferência do áudio original."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RSnzo-EdI17f"
      },
      "outputs": [],
      "source": [
        "def butter_lowpass_filter(data, cutoff, fs, order):\n",
        "    normal_cutoff = cutoff / (0.5*fs)\n",
        "    # Pega os coeficientes do filtro\n",
        "    b, a = butter(order, normal_cutoff, btype='low', analog=False)\n",
        "    y = filtfilt(b, a, data)\n",
        "    return y\n",
        "\n",
        "def butter_highpass_filter(data, cutoff, fs, order):\n",
        "    normal_cutoff = cutoff / (0.5*fs)\n",
        "    # Pega os coeficientes do filtro\n",
        "    b, a = butter(order, normal_cutoff, btype='highpass', analog=False)\n",
        "    y = filtfilt(b, a, data)\n",
        "    return y\n",
        "\n",
        "# Requerimentos do filtro\n",
        "fs = sr         # sample rate, Hz\n",
        "cutoff = 1000      # Frequência de corte do filtro, em Hz\n",
        "order = 5       # Ordem do filtro\n",
        "\n",
        "plips=[]\n",
        "for i in amostras:\n",
        "  plips.append(butter_highpass_filter(i, cutoff, fs, order))\n",
        "\n",
        "rlips=  0.05                                    #raio de abertura da boca de 5cm\n",
        "ulips=[]\n",
        "\n",
        "for p in plips:\n",
        "  ulips.append(2*np.pi*rlips**2*np.sqrt(2*abs(p)))   #proporcional ao fluxo de ar saindo da boca\n",
        "\n",
        "amplitude_envelope_plips = []\n",
        "amplitude_envelope_ulips = []\n",
        "\n",
        "for i in range(len(plips)):\n",
        "  amplitude_envelope_plips.append(librosa.feature.rms(y=plips[i])[0,:])\n",
        "  amplitude_envelope_ulips.append(librosa.feature.rms(y=ulips[i])[0,:])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RcFZTMGGsX0Z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4e4a72eb-d39b-4801-be41-fbdb37d7c9fb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processei esse número de arquivos: 46\n",
            "(46, 6)\n"
          ]
        }
      ],
      "source": [
        "def descritores(ulips, plips, df_row ):\n",
        "  return np.array([np.max(ulips), np.sum(ulips), np.sum(ulips[0:df_row['sr']]), df_row['IDADE'], df_row['PESO'], df_row['ALTURA']])\n",
        "\n",
        "n_files = 0\n",
        "vetores = []\n",
        "for i in range(len(ulips)):\n",
        "  vetores.append(descritores(amplitude_envelope_ulips[i], amplitude_envelope_plips[i], df.iloc[i]))\n",
        "  n_files +=1\n",
        "print(\"Processei esse número de arquivos: \" + str(n_files))\n",
        "vetores = np.array(vetores)\n",
        "print(vetores.shape)\n",
        "\n",
        "scaler = StandardScaler()\n",
        "scaler.fit(vetores)\n",
        "dados = scaler.transform(vetores)\n",
        "params = ['CVF', 'VEF1','PF']\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plips_test=[butter_highpass_filter(teste1, cutoff, fs, order),\n",
        "            butter_highpass_filter(teste2, cutoff, fs, order)]\n",
        "\n",
        "ulips_test=[]\n",
        "for p in plips_test:\n",
        "  ulips_test.append(2*np.pi*rlips**2*np.sqrt(2*abs(p)))   #proporcional ao fluxo de ar saindo da boca\n",
        "\n",
        "amplitude_envelope_plips_test = []\n",
        "amplitude_envelope_ulips_test = []\n",
        "\n",
        "for i in range(len(plips_test)):\n",
        "  amplitude_envelope_plips_test.append(librosa.feature.rms(y=plips_test[i])[0,:])\n",
        "  amplitude_envelope_ulips_test.append(librosa.feature.rms(y=ulips_test[i])[0,:])"
      ],
      "metadata": {
        "id": "Kj1qsV3WLbFq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vetores_test = [np.array([np.max(amplitude_envelope_ulips[0]), np.sum(amplitude_envelope_ulips[0]), np.sum(amplitude_envelope_ulips[0][0:48000]), 55, 78.6, 158]),\n",
        "                np.array([np.max(amplitude_envelope_ulips[1]), np.sum(amplitude_envelope_ulips[1]), np.sum(amplitude_envelope_ulips[1][0:48000]), 66, 78.6, 171])]\n",
        "scaler = StandardScaler()\n",
        "scaler.fit(vetores_test)\n",
        "dados_test = scaler.transform(vetores_test)"
      ],
      "metadata": {
        "id": "WUSdZ925L7Wa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cZIglef4B7w2"
      },
      "source": [
        "#Random forest regressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QA77a-2oDEqS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d2b9b9e1-862e-4b07-fb2b-43ac243dd0de"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CVF\n",
            "predicted:\n",
            "[2.45405 3.1606 ]\n",
            "0.85 accuracy with a standard deviation of 0.07\n",
            ".\n",
            ".\n",
            ".\n",
            "VEF1\n",
            "predicted:\n",
            "[2.30971 2.392  ]\n",
            "0.83 accuracy with a standard deviation of 0.08\n",
            ".\n",
            ".\n",
            ".\n",
            "PF\n",
            "predicted:\n",
            "[6.86392 7.35353]\n",
            "0.82 accuracy with a standard deviation of 0.06\n",
            ".\n",
            ".\n",
            ".\n"
          ]
        }
      ],
      "source": [
        "def RandomForest(param):\n",
        "  X = dados\n",
        "  y = df[param].to_numpy()\n",
        "\n",
        "  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "  # Fitting Random Forest Regression to the dataset\n",
        "\n",
        "  # create regressor object\n",
        "  regressor = RandomForestRegressor(n_estimators=1000, random_state=42, verbose=0, n_jobs=-1, oob_score=True)\n",
        "\n",
        "  scores = cross_val_score(regressor, X, y, cv=5, scoring='explained_variance')\n",
        "  # fit the regressor with x and y data\n",
        "  regressor.fit(X, y)\n",
        "\n",
        "  y_pred = regressor.predict(dados_test)\n",
        "  print(param)\n",
        "  print(\"predicted:\")\n",
        "  print(y_pred)\n",
        "  #print(\"goal:\")\n",
        "  #print(y_test)\n",
        "  #print(\"score:\")\n",
        "  #print(explained_variance_score(y_test, y_pred))\n",
        "  print(\"%0.2f accuracy with a standard deviation of %0.2f\" % (scores.mean(), scores.std()))\n",
        "  print(\".\\n.\\n.\")\n",
        "\n",
        "for param in params:\n",
        "  RandomForest(param)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zOKD7nhLHkkb"
      },
      "source": [
        "#Regressor linear\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Iaa2S5Ihx2vW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9912927e-6b1b-4dbc-8e14-2980b721b250"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CVF\n",
            "predicted:\n",
            "[[2.9122713]\n",
            " [2.9454024]]\n",
            "0.68 accuracy with a standard deviation of 0.21\n",
            ".\n",
            ".\n",
            ".\n",
            "VEF1\n",
            "predicted:\n",
            "[[2.3023515]\n",
            " [1.6954584]]\n",
            "0.46 accuracy with a standard deviation of 0.17\n",
            ".\n",
            ".\n",
            ".\n",
            "PF\n",
            "predicted:\n",
            "[[6.4953814]\n",
            " [4.8460846]]\n",
            "0.24 accuracy with a standard deviation of 0.54\n",
            ".\n",
            ".\n",
            ".\n"
          ]
        }
      ],
      "source": [
        "def RegressorLinear(param):\n",
        "  X = dados\n",
        "  y = df[param].to_numpy()\n",
        "\n",
        "  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "  # Fitting Random Forest Regression to the dataset\n",
        "\n",
        "  # Fix seed to make training deterministic.\n",
        "  SEED_VALUE = 42\n",
        "  np.random.seed(SEED_VALUE)\n",
        "  tf.random.set_seed(SEED_VALUE)\n",
        "\n",
        "  # create regressor object\n",
        "  model = Sequential()\n",
        "  # Define the model consisting of a single neuron.\n",
        "  model.add(Dense(units=1, input_shape=(X.shape[1],)))\n",
        "  model.compile(optimizer=tf.keras.optimizers.RMSprop(learning_rate=.005), loss='mse')\n",
        "  es = EarlyStopping(monitor='loss', mode='min', verbose=0, patience=20, restore_best_weights=True)\n",
        "\n",
        "\n",
        "  scores = []\n",
        "  #scores = cross_val_score(model, X, y, cv=5, scoring='explained_variance')\n",
        "  fold_no = 1\n",
        "  kfold = KFold(n_splits=5, shuffle=True)\n",
        "  for train, test in kfold.split(X, y):\n",
        "\n",
        "    model.fit(X[train], y[train], epochs=1000, callbacks = es, verbose = 0)\n",
        "    y_pred = model.predict(X[test], verbose = 0)\n",
        "    scores.append(explained_variance_score(y[test],y_pred))\n",
        "    #print(f'Score for fold {fold_no}: {model.metrics_names[0]} of {scores[0]}; {model.metrics_names[1]} of {scores[1]*100}%')\n",
        "    #acc_per_fold.append(scores[1] * 100)\n",
        "    #loss_per_fold.append(scores[0])\n",
        "    #fold_no += 1\n",
        "  scores = np.array(scores)\n",
        "  model.fit(X, y, epochs=1000, callbacks = es, verbose = 0)\n",
        "  y_pred = model.predict(dados_test, verbose = 0)\n",
        "\n",
        "\n",
        "  #y_pred_=[]\n",
        "  #for i in y_pred:\n",
        "  #  y_pred_.append(i[0])\n",
        "  #y_pred = y_pred_\n",
        "  print(param)\n",
        "  print(\"predicted:\")\n",
        "  print(y_pred)\n",
        "  #print(\"goal:\")\n",
        "  #print(y_test)\n",
        "  #print(\"score:\")\n",
        "  #print(explained_variance_score(y_test,y_pred))\n",
        "  #print(scores)\n",
        "  print(\"%0.2f accuracy with a standard deviation of %0.2f\" % (scores.mean(), scores.std()))\n",
        "  print(\".\\n.\\n.\")\n",
        "for param in params:\n",
        "  RegressorLinear(param)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "y5NxawM87b7T",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d6eb4871-2101-42a1-dbe0-fb2339ccc70b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 38ms/step\n",
            "CVF\n",
            "predicted:\n",
            "[[2.690206]\n",
            " [3.259498]]\n",
            "0.90 accuracy with a standard deviation of 0.05\n",
            ".\n",
            ".\n",
            ".\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "VEF1\n",
            "predicted:\n",
            "[[2.2280874]\n",
            " [1.7304606]]\n",
            "0.69 accuracy with a standard deviation of 0.17\n",
            ".\n",
            ".\n",
            ".\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "PF\n",
            "predicted:\n",
            "[[6.7346163]\n",
            " [5.222809 ]]\n",
            "0.58 accuracy with a standard deviation of 0.22\n",
            ".\n",
            ".\n",
            ".\n"
          ]
        }
      ],
      "source": [
        "def MLP(param):\n",
        "  X = dados\n",
        "  y = df[param].to_numpy()\n",
        "\n",
        "  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "  # Fitting Random Forest Regression to the dataset\n",
        "\n",
        "  # Fix seed to make training deterministic.\n",
        "  SEED_VALUE = 42\n",
        "  np.random.seed(SEED_VALUE)\n",
        "  tf.random.set_seed(SEED_VALUE)\n",
        "\n",
        "  model = Sequential()\n",
        "  # create regressor object\n",
        "  model.add(Dense(3, input_shape=(X_train.shape[1],), kernel_initializer='normal', activation='relu'))\n",
        "  model.add(Dense(6, kernel_initializer='normal'))\n",
        "  model.add(Dense(1, kernel_initializer='normal'))\n",
        "  model.add(Dense(units=1, input_shape=(X.shape[1],)))\n",
        "  model.compile(optimizer=tf.keras.optimizers.RMSprop(learning_rate=.005), loss='mse')\n",
        "  es = EarlyStopping(monitor='loss', mode='min', verbose=0, patience=20, restore_best_weights=True)\n",
        "\n",
        "  scores = []\n",
        "  #scores = cross_val_score(model, X, y, cv=5, scoring='explained_variance')\n",
        "  fold_no = 1\n",
        "  kfold = KFold(n_splits=5, shuffle=True)\n",
        "  for train, test in kfold.split(X, y):\n",
        "\n",
        "    model.fit(X_train, y_train, epochs=1000, callbacks = es, verbose = 0)\n",
        "    y_pred = model.predict(X[test], verbose = 0)\n",
        "    scores.append(explained_variance_score(y[test],y_pred))\n",
        "    #print(f'Score for fold {fold_no}: {model.metrics_names[0]} of {scores[0]}; {model.metrics_names[1]} of {scores[1]*100}%')\n",
        "    #acc_per_fold.append(scores[1] * 100)\n",
        "    #loss_per_fold.append(scores[0])\n",
        "    #fold_no += 1\n",
        "  scores = np.array(scores)\n",
        "  model.fit(X, y, epochs=1000, callbacks = es, verbose = 0)\n",
        "  y_pred = model.predict(dados_test)\n",
        "\n",
        "  print(param)\n",
        "  print(\"predicted:\")\n",
        "  print(y_pred)\n",
        "  #print(\"goal:\")\n",
        "  #print(y_test)\n",
        "  #print(\"score:\")\n",
        "  #print(explained_variance_score(y_test,y_pred))\n",
        "  print(\"%0.2f accuracy with a standard deviation of %0.2f\" % (scores.mean(), scores.std()))\n",
        "  print(\".\\n.\\n.\")\n",
        "for param in params:\n",
        "  MLP(param)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zz_aF5J9qbeM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a8b2d4fa-0904-465c-8bb4-27a86c593b38"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 23ms/step\n",
            "CVF\n",
            "predicted:\n",
            "[[2.2880404]\n",
            " [3.2133238]]\n",
            "0.80 accuracy with a standard deviation of 0.09\n",
            ".\n",
            ".\n",
            ".\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "VEF1\n",
            "predicted:\n",
            "[[2.0019526]\n",
            " [1.6716396]]\n",
            "0.68 accuracy with a standard deviation of 0.16\n",
            ".\n",
            ".\n",
            ".\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "PF\n",
            "predicted:\n",
            "[[6.273751]\n",
            " [5.085983]]\n",
            "0.70 accuracy with a standard deviation of 0.20\n",
            ".\n",
            ".\n",
            ".\n"
          ]
        }
      ],
      "source": [
        "def MLP(param):\n",
        "  X = dados\n",
        "  y = df[param].to_numpy()\n",
        "\n",
        "  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "  # Fitting Random Forest Regression to the dataset\n",
        "\n",
        "  # Fix seed to make training deterministic.\n",
        "  SEED_VALUE = 42\n",
        "  np.random.seed(SEED_VALUE)\n",
        "  tf.random.set_seed(SEED_VALUE)\n",
        "\n",
        "  model = Sequential()\n",
        "  # create regressor object\n",
        "  model.add(Dense(6, input_shape=(X_train.shape[1],), kernel_initializer='normal', activation='relu'))\n",
        "  model.add(Dense(3, kernel_initializer='normal'))\n",
        "  model.add(Dense(1, kernel_initializer='normal'))\n",
        "  model.add(Dense(units=1, input_shape=(X.shape[1],)))\n",
        "  model.compile(optimizer=tf.keras.optimizers.RMSprop(learning_rate=.005), loss='mse')\n",
        "  es = EarlyStopping(monitor='loss', mode='min', verbose=0, patience=20, restore_best_weights=True)\n",
        "\n",
        "  scores = []\n",
        "  #scores = cross_val_score(model, X, y, cv=5, scoring='explained_variance')\n",
        "  fold_no = 1\n",
        "  kfold = KFold(n_splits=5, shuffle=True)\n",
        "  for train, test in kfold.split(X, y):\n",
        "\n",
        "    model.fit(X_train, y_train, epochs=1000, callbacks = es, verbose = 0)\n",
        "    y_pred = model.predict(X[test], verbose = 0)\n",
        "    scores.append(explained_variance_score(y[test],y_pred))\n",
        "    #print(f'Score for fold {fold_no}: {model.metrics_names[0]} of {scores[0]}; {model.metrics_names[1]} of {scores[1]*100}%')\n",
        "    #acc_per_fold.append(scores[1] * 100)\n",
        "    #loss_per_fold.append(scores[0])\n",
        "    #fold_no += 1\n",
        "  scores = np.array(scores)\n",
        "  model.fit(X, y, epochs=1000, callbacks = es, verbose = 0)\n",
        "  y_pred = model.predict(dados_test)\n",
        "\n",
        "  print(param)\n",
        "  print(\"predicted:\")\n",
        "  print(y_pred)\n",
        "  #print(\"goal:\")\n",
        "  #print(y_test)\n",
        "  #print(\"score:\")\n",
        "  #print(explained_variance_score(y_test,y_pred))\n",
        "  print(\"%0.2f accuracy with a standard deviation of %0.2f\" % (scores.mean(), scores.std()))\n",
        "  print(\".\\n.\\n.\")\n",
        "for param in params:\n",
        "  MLP(param)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hJTVzQryqkWH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "49b515e7-2e98-46f5-a632-f1ac2b19c787"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 30ms/step\n",
            "CVF\n",
            "predicted:\n",
            "[[2.6319528]\n",
            " [3.1102068]]\n",
            "0.85 accuracy with a standard deviation of 0.07\n",
            ".\n",
            ".\n",
            ".\n",
            "1/1 [==============================] - 0s 36ms/step\n",
            "VEF1\n",
            "predicted:\n",
            "[[1.989645 ]\n",
            " [1.8057649]]\n",
            "0.73 accuracy with a standard deviation of 0.17\n",
            ".\n",
            ".\n",
            ".\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "PF\n",
            "predicted:\n",
            "[[6.457251 ]\n",
            " [5.1127567]]\n",
            "0.66 accuracy with a standard deviation of 0.22\n",
            ".\n",
            ".\n",
            ".\n"
          ]
        }
      ],
      "source": [
        "def MLP(param):\n",
        "  X = dados\n",
        "  y = df[param].to_numpy()\n",
        "\n",
        "  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "  # Fitting Random Forest Regression to the dataset\n",
        "\n",
        "  # Fix seed to make training deterministic.\n",
        "  SEED_VALUE = 42\n",
        "  np.random.seed(SEED_VALUE)\n",
        "  tf.random.set_seed(SEED_VALUE)\n",
        "\n",
        "  model = Sequential()\n",
        "  # create regressor object\n",
        "  model.add(Dense(10, input_shape=(X_train.shape[1],), kernel_initializer='normal', activation='relu'))\n",
        "  model.add(Dense(8, kernel_initializer='normal'))\n",
        "  model.add(Dense(6, kernel_initializer='normal'))\n",
        "  model.add(Dense(4, kernel_initializer='normal'))\n",
        "  model.add(Dense(2, kernel_initializer='normal'))\n",
        "  model.add(Dense(1, kernel_initializer='normal'))\n",
        "  model.add(Dense(units=1, input_shape=(X.shape[1],)))\n",
        "  model.compile(optimizer=tf.keras.optimizers.RMSprop(learning_rate=.005), loss='mse')\n",
        "  es = EarlyStopping(monitor='loss', mode='min', verbose=0, patience=20, restore_best_weights=True)\n",
        "\n",
        "  scores = []\n",
        "  #scores = cross_val_score(model, X, y, cv=5, scoring='explained_variance')\n",
        "  fold_no = 1\n",
        "  kfold = KFold(n_splits=5, shuffle=True)\n",
        "  for train, test in kfold.split(X, y):\n",
        "\n",
        "    model.fit(X_train, y_train, epochs=1000, callbacks = es, verbose = 0)\n",
        "    y_pred = model.predict(X[test], verbose = 0)\n",
        "    scores.append(explained_variance_score(y[test],y_pred))\n",
        "    #print(f'Score for fold {fold_no}: {model.metrics_names[0]} of {scores[0]}; {model.metrics_names[1]} of {scores[1]*100}%')\n",
        "    #acc_per_fold.append(scores[1] * 100)\n",
        "    #loss_per_fold.append(scores[0])\n",
        "    #fold_no += 1\n",
        "  scores = np.array(scores)\n",
        "  model.fit(X, y, epochs=1000, callbacks = es, verbose = 0)\n",
        "  y_pred = model.predict(dados_test)\n",
        "\n",
        "  print(param)\n",
        "  print(\"predicted:\")\n",
        "  print(y_pred)\n",
        "  #print(\"goal:\")\n",
        "  #print(y_test)\n",
        "  #print(\"score:\")\n",
        "  #print(explained_variance_score(y_test,y_pred))\n",
        "  print(\"%0.2f accuracy with a standard deviation of %0.2f\" % (scores.mean(), scores.std()))\n",
        "  print(\".\\n.\\n.\")\n",
        "for param in params:\n",
        "  MLP(param)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gWlotUh_Mbrc"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPZVKGr9tfEETPUPSbhVm9R",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}