codigofinal

{
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  "metadata": {
    "colab": {
      "name": "Untitled4.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/astrodatos/proyecto_ps2/blob/master/codigofinal\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GMK3SffZnnW2",
        "colab_type": "code",
        "outputId": "cce9da5f-8f9d-4cfa-9802-368a32cf76c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        }
      },
      "source": [
        "import numpy as np\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import sklearn as sk\n",
        "import pandas as pd\n",
        "from math import *\n",
        "import random\n",
        "from astropy.constants import G\n",
        "import seaborn as sb\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn import datasets\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import pairwise_distances_argmin_min\n",
        "from sklearn.datasets.samples_generator import make_blobs\n",
        "!pip install astroquery"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: astroquery in /usr/local/lib/python3.6/dist-packages (0.3.9)\n",
            "Requirement already satisfied: html5lib>=0.999 in /usr/local/lib/python3.6/dist-packages (from astroquery) (1.0.1)\n",
            "Requirement already satisfied: astropy>=1.0 in /usr/local/lib/python3.6/dist-packages (from astroquery) (3.0.5)\n",
            "Requirement already satisfied: beautifulsoup4>=4.3.2 in /usr/local/lib/python3.6/dist-packages (from astroquery) (4.6.3)\n",
            "Requirement already satisfied: requests>=2.4.3 in /usr/local/lib/python3.6/dist-packages (from astroquery) (2.21.0)\n",
            "Requirement already satisfied: keyring>=4.0 in /usr/local/lib/python3.6/dist-packages (from astroquery) (19.0.2)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.6/dist-packages (from html5lib>=0.999->astroquery) (0.5.1)\n",
            "Requirement already satisfied: six>=1.9 in /usr/local/lib/python3.6/dist-packages (from html5lib>=0.999->astroquery) (1.12.0)\n",
            "Requirement already satisfied: numpy>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from astropy>=1.0->astroquery) (1.16.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.4.3->astroquery) (2019.6.16)\n",
            "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.4.3->astroquery) (2.8)\n",
            "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.4.3->astroquery) (1.24.3)\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.4.3->astroquery) (3.0.4)\n",
            "Requirement already satisfied: secretstorage; sys_platform == \"linux\" in /usr/local/lib/python3.6/dist-packages (from keyring>=4.0->astroquery) (3.1.1)\n",
            "Requirement already satisfied: entrypoints in /usr/local/lib/python3.6/dist-packages (from keyring>=4.0->astroquery) (0.3)\n",
            "Requirement already satisfied: jeepney in /usr/local/lib/python3.6/dist-packages (from secretstorage; sys_platform == \"linux\"->keyring>=4.0->astroquery) (0.4)\n",
            "Requirement already satisfied: cryptography in /usr/local/lib/python3.6/dist-packages (from secretstorage; sys_platform == \"linux\"->keyring>=4.0->astroquery) (2.7)\n",
            "Requirement already satisfied: asn1crypto>=0.21.0 in /usr/local/lib/python3.6/dist-packages (from cryptography->secretstorage; sys_platform == \"linux\"->keyring>=4.0->astroquery) (0.24.0)\n",
            "Requirement already satisfied: cffi!=1.11.3,>=1.8 in /usr/local/lib/python3.6/dist-packages (from cryptography->secretstorage; sys_platform == \"linux\"->keyring>=4.0->astroquery) (1.12.3)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.6/dist-packages (from cffi!=1.11.3,>=1.8->cryptography->secretstorage; sys_platform == \"linux\"->keyring>=4.0->astroquery) (2.19)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CKcfKArinnxy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "planetas=pd.read_csv(\"https://raw.githubusercontent.com/astrodatos/proyecto_ps2/master/compositepars_2019.07.07_14.34.43.csv\", comment = '#')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dqOW0tVNnn18",
        "colab_type": "code",
        "outputId": "5ceb7e27-06b4-4061-b303-ddfe5529df25",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "planetas = pd.DataFrame(planetas)\n",
        "Ts = 5700 #K\n",
        "ai = 2.7619 * 10**(-5)\n",
        "bi = 3.8095 * 10**(-9)\n",
        "ao = 1.3786 * 10**(-4)\n",
        "bo = 1.4286 * 10**(-9)\n",
        "ris = 0.72\n",
        "ros = 1.77\n",
        "planetas = planetas.filter(['fpl_orbper','fpl_bmasse','fst_mass','fst_teff','fst_lum'], axis=1)\n",
        "planetas = planetas.dropna()\n",
        "Teff = planetas['fst_teff']\n",
        "lum = 10**(planetas['fst_lum'])\n",
        "T = planetas['fpl_orbper']\n",
        "m = planetas['fpl_bmasse']\n",
        "M = planetas['fst_mass']\n",
        "suma = M + m\n",
        "r = np.array(((G*suma*(T)**2)/(4*(pi)**2))**(1/3))\n",
        "resta = Teff - Ts\n",
        "\n",
        "#Distancia del borde más lejano de la zona habitable a la estrella, en UA\n",
        "Ri = (ris - ai*resta - bi*(resta)**2)/np.sqrt(lum)\n",
        "#Distancia del borde más cercano de la zona habitable a la estrella, en UA\n",
        "Ro = (ros - ao*resta - bo*(resta)**2)/np.sqrt(lum)\n",
        "#índice de habitabilidad \n",
        "resta1 = Ro - Ri\n",
        "resta2 = 2*r - Ro - Ri\n",
        "HDZ = resta2/resta1\n",
        "\n",
        "\n",
        "planetas['HDZ'] = HDZ\n",
        "mask_1 = -1<=planetas['HDZ'] \n",
        "mask_2 = planetas['HDZ'] <= 1\n",
        "booleanos = np.logical_and(mask_1, mask_2)\n",
        "booleanos = booleanos.astype(int)\n",
        "planetas['Habitabilidad'] = booleanos\n",
        "print(planetas)\n",
        "\n",
        "habitables = planetas[(-1<=(planetas['HDZ']))|((planetas['HDZ'])<=1)]\n"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "        fpl_orbper  fpl_bmasse  fst_mass  ...  fst_lum       HDZ  Habitabilidad\n",
            "0       326.030000   6165.6000      2.70  ...    2.243  0.081433              1\n",
            "1       516.219970   4684.8142      2.78  ...    2.430  1.218163              0\n",
            "2       185.840000   1525.5000      2.20  ...    1.763 -1.698284              0\n",
            "3      1773.400020   1481.0878      0.90  ...   -0.151 -2.031467              0\n",
            "4       798.500000    565.7374      1.08  ...    0.100 -2.193690              0\n",
            "5       993.300000   3273.5000      2.30  ...    1.602 -0.335040              1\n",
            "7        30.350600    289.2253      0.99  ...    1.774 -2.193579              0\n",
            "8       452.800000    632.4600      1.54  ...    1.164 -1.903594              0\n",
            "9       883.000000    273.3200      1.54  ...    1.164 -1.828770              0\n",
            "14      416.000000    603.8770      0.48  ...    0.168 -2.498228              0\n",
            "15     7336.500000   6657.4800      0.08  ...   -3.480 -2.038969              0\n",
            "17      335.100010   4392.4106      1.93  ...    0.381 -2.131232              0\n",
            "18      269.300000   2256.5000      1.23  ...    2.062 -1.082408              0\n",
            "19      479.100000   1233.1300      0.98  ...    2.155 -0.701225              1\n",
            "20     1078.000000    804.0800      1.03  ...    0.216 -2.098526              0\n",
            "21     2391.000000    171.6210      1.03  ...    0.216 -2.093300              0\n",
            "22    14002.000000    521.2200      1.03  ...    0.216 -0.999808              1\n",
            "23    14965.000000    635.6600      1.75  ...    0.850  1.213543              0\n",
            "24        4.230785    146.2018      1.12  ...    0.147 -2.376171              0\n",
            "25       14.651520    263.9785      0.91  ...   -0.235 -2.318893              0\n",
            "26       44.417500     54.4738      0.91  ...   -0.235 -2.317356              0\n",
            "27     4825.000000   1232.4930      0.91  ...   -0.235 -1.821997              0\n",
            "28        0.736539      8.0800      0.91  ...   -0.235 -2.324933              0\n",
            "29      262.000000     44.8120      0.91  ...   -0.235 -2.301152              0\n",
            "30      934.300000    638.8383      1.44  ...    1.156 -1.647317              0\n",
            "31        4.215000      5.1000      0.94  ...   -0.095 -2.359223              0\n",
            "32       38.021000     18.2000      0.94  ...   -0.095 -2.354104              0\n",
            "33      123.010000     22.9000      0.94  ...   -0.095 -2.345883              0\n",
            "34      796.000000    781.8618      1.52  ...    1.053 -1.737634              0\n",
            "35      116.688000   2380.4500      1.09  ...    0.487 -2.228586              0\n",
            "...            ...         ...       ...  ...      ...       ...            ...\n",
            "3978    522.300000   1938.7000      1.40  ...    2.730  1.406043              0\n",
            "3979    417.900000   2129.4610      1.70  ...    2.235 -0.375297              1\n",
            "3980   2502.000000    492.6365      0.83  ...   -0.419 -2.127230              0\n",
            "3981    594.900000   2415.4060      2.70  ...    1.987 -0.342632              1\n",
            "3982    428.500000   2790.4300      1.23  ...    2.510  0.599863              1\n",
            "3983    903.300000    587.9600      1.40  ...    1.073 -1.728955              0\n",
            "3984    415.200000    324.1866      1.47  ...    1.850 -1.622509              0\n",
            "3985    964.600000   1455.6614      1.47  ...    1.850 -0.359142              1\n",
            "3986    511.098000   2803.1000      1.05  ...    1.820 -0.787242              1\n",
            "3988   1285.000000    689.6911      1.50  ...    1.105 -1.517941              0\n",
            "3989    357.800000    762.8000      1.50  ...    1.797 -1.528930              0\n",
            "3990    530.320000   7627.9200      3.04  ...    2.090  0.698708              1\n",
            "3991   3186.000000   8581.4100      3.04  ...    2.090  8.007972              0\n",
            "3992    277.020000    540.3000      2.17  ...    1.850 -1.684241              0\n",
            "3993    187.830000    476.7450      2.13  ...    1.709 -1.906081              0\n",
            "3994   1630.000000   1303.1030      3.09  ...    2.140  1.519886              0\n",
            "3995      6.267900      4.8200      1.09  ...    0.160 -2.400387              0\n",
            "3996   3117.000000    486.2799      1.19  ...    0.326 -1.832218              0\n",
            "3997     39.845800    332.1000      0.89  ...    0.232 -2.340971              0\n",
            "3998    102.540000     25.0000      0.89  ...    0.232 -2.345692              0\n",
            "3999      3.312457   1891.0000      1.34  ...    0.535 -2.422463              0\n",
            "4000    162.870000      3.9300      0.78  ...   -0.305 -2.328021              0\n",
            "4001    636.130000      3.9300      0.78  ...   -0.305 -2.316672              0\n",
            "4002     20.000000      1.7500      0.78  ...   -0.305 -2.334149              0\n",
            "4003     49.410000      1.8300      0.78  ...   -0.305 -2.332846              0\n",
            "4004    305.500000   6547.0000      2.30  ...    2.519  0.815107              1\n",
            "4005      4.617033    218.5310      1.30  ...    0.561 -2.407725              0\n",
            "4006    241.258000    629.5950      1.30  ...    0.561 -2.263873              0\n",
            "4007   1276.460000   1313.2200      1.30  ...    0.561 -1.827901              0\n",
            "4008    136.750000    890.0000      2.20  ...    1.839 -1.848394              0\n",
            "\n",
            "[3849 rows x 7 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yLrWP2Mmnn4o",
        "colab_type": "code",
        "outputId": "675d8a7c-1649-479e-cfe2-7af28ac4d3ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 638
        }
      },
      "source": [
        "#Hisotgrama de índice de habitabilidad \n",
        "plt.figure(figsize = (10,10))\n",
        "plt.hist(planetas['HDZ'])\n",
        "plt.xlabel('índice de habitabilidad')\n",
        "plt.ylabel('Cantidad de planetas')\n",
        "plt.title('Planetas y su habitabilidad')"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Planetas y su habitabilidad')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fBz6d5DbgnmHLkiRJ0lSTnHE7GPgZ8KfAPwHfB140ZFGSJEla1YzBLckTkzyz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\nJPQRoXAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EzCEXR2pnn67",
        "colab_type": "code",
        "outputId": "3b78ae48-0b9b-4b71-9129-21a49d9f264a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 638
        }
      },
      "source": [
        "#Histograma de los planetas habitables\n",
        "plt.figure(figsize = (10,10))\n",
        "plt.hist(booleanos)\n",
        "plt.xlabel('índice de habitabilidad')\n",
        "plt.ylabel('Cantidad de planetas')\n",
        "plt.title('Planetas habitables')"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Planetas habitables')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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C4CZJktQJg5skSVInDG6SJEmdGFtwS7JzkrOTXJ7ksiRvae3vT7ImycXtddDQNu9KsjrJ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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VUDb3PtoshTJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "planetaslimpio=planetas.dropna()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sqE852XPcLjO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Acá empieza ml KNN\n",
        "X = np.array(planetas.iloc[:,:-1])\n",
        "y = np.array(planetas[\"Habitabilidad\"])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mnuoZjj3D3CT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "rs = 1800 #Guardamos la semilla \n",
        "train_X, test_X, train_y, test_y = train_test_split(X, y, test_size = 0.3, random_state = rs)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z-OiprCZD7Sn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "knn_clf = KNeighborsClassifier(n_neighbors=3)\n",
        "knn_clf.fit(train_X,train_y)\n",
        "pred_test = knn_clf.predict(test_X)\n",
        "pred_test.sum()\n",
        "clases = planetas[\"Habitabilidad\"].unique()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dDSSmXWrD98d",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1e4557e0-9f69-4204-e715-ae90b1b345a5"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "pred_test.sum()"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SC6siM6XC0vW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "confusion_matrix(test_y, pred_test)\n",
        "from sklearn.utils.multiclass import unique_labels\n",
        "\n",
        "\n",
        "def plot_confusion_matrix(y_true, y_pred, classes,\n",
        "                          normalize=False,\n",
        "                          title=None,\n",
        "                          cmap=plt.cm.Blues):\n",
        "    \"\"\"\n",
        "    This function prints and plots the confusion matrix.\n",
        "    Normalization can be applied by setting `normalize=True`.\n",
        "    \"\"\"\n",
        "    if not title:\n",
        "        if normalize:\n",
        "            title = 'Matriz de confusion normalizada'\n",
        "        else:\n",
        "            title = 'Matriz de confusion, sin normalizacion'\n",
        "\n",
        "    # Compute confusion matrix\n",
        "    cm = confusion_matrix(y_true, y_pred)\n",
        "    # Only use the labels that appear in the data\n",
        "    classes = classes[unique_labels(y_true, y_pred)]\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        print(\"Matriz de confusion normalizada\")\n",
        "    else:\n",
        "        print('Matriz de confusion, sin normalizacion')\n",
        "\n",
        "    print(cm)\n",
        "\n",
        "    fig, ax = plt.subplots(figsize = (8,8))\n",
        "    im = ax.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    ax.figure.colorbar(im, ax=ax)\n",
        "    # We want to show all ticks...\n",
        "    ax.set(xticks=np.arange(cm.shape[1]),\n",
        "           yticks=np.arange(cm.shape[0]),\n",
        "           # ... and label them with the respective list entries\n",
        "           xticklabels=classes, yticklabels=classes,\n",
        "           title=title,\n",
        "           ylabel='Etiqueta Real',\n",
        "           xlabel='Etiqueta Predicha')\n",
        "           \n",
        "    # Rotate the tick labels and set their alignment.\n",
        "    plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
        "             rotation_mode=\"anchor\", fontsize=20)\n",
        "\n",
        "    # Loop over data dimensions and create text annotations.\n",
        "    fmt = '.2f' if normalize else 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i in range(cm.shape[0]):\n",
        "        for j in range(cm.shape[1]):\n",
        "            ax.text(j, i, format(cm[i, j], fmt), fontsize=20,\n",
        "                    ha=\"center\", va=\"center\",\n",
        "                    color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "            \n",
        "    ax.grid(False)\n",
        "    fig.tight_layout()\n",
        "    return ax\n",
        "np.set_printoptions(precision=2)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZLxCMtwgJOGJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 654
        },
        "outputId": "271f8d3a-53b0-4bf4-c642-5649bb11d768"
      },
      "source": [
        "# Plot non-normalized confusion matrix\n",
        "plot_confusion_matrix(test_y, pred_test, classes=clases,\n",
        "                      title='Matriz de confusion, sin normalizacion')\n",
        "\n"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Matriz de confusion, sin normalizacion\n",
            "[[1137    5]\n",
            " [   4    9]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f21564de9e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 113
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TEkmSVAiTEkmSCmBSYlIiSZIKYVIiSVIBTEpMSiRJUiFMSiRJKoFBiUmJJEkqg0mJJEkF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\n/j/ptnwG8JbYEgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3R6DtJIYJOMp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 654
        },
        "outputId": "420d1239-518b-4e4b-c0ad-bc1519bed9b0"
      },
      "source": [
        "plot_confusion_matrix(test_y, pred_test, classes=clases, normalize = True,\n",
        "                      title='Matriz de confusion, con normalizacion')"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Matriz de confusion normalizada\n",
            "[[1.   0.  ]\n",
            " [0.31 0.69]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f21559beda0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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iok+uvZIkqZVpU73BRrPGo6xIKa2seRER7YCUX5ckSVI1aU6y8UhEfBvoFBGH\nA18G/jffbkmS1Lq09mTjYuA94GXgLOBu4JI8OyVJkqpHk8lGSmkN8IfsIUmSNrLCTdKqN9postiI\niDepZ4xGSmnbXHokSZKqSnPGbAwvet4ROB7YIp/uSJLUOrXqMRsppblFj5kppV8BH2uBvkmSpCrQ\nnNMoexW9bEMh6WhOIiJJkpqpiodsNKtoKL4vyipgKvCZXHojSZKqTnOuRhnZEh2RJEnVqTmnUS5o\nbH1K6RcbrzuSJLU+AbSp4vMozb0aZW9gVPb648CzwKS8OiVJkqpHc4qN/sBeKaXFABFxBXBXSunU\nPDsmSVJr0pwpvTdVzTm2rYCVRa9XZsskSZKa1Jxk40bg2Yi4I3v9CeDPufVIkqRWqIqHbDTrapQf\nRsQ9wEHZoi+klF7It1uSJKlaNFhsRET3lNKiiNiCwtwaU4vWbZFSmpd/9yRJqn4R0WqvRrkFOAZ4\njto3YovstTdikyRJTWqw2EgpHZP9/5CW644kSa1TFQcbTV+NEhEPNWeZJElSfRobs9ER6Az0jojN\nKZw+AegO9GuBvkmS1GpU8y3mGxuzcRZwPtAXeL5o+SLg6jw7JUmSqkdjYzZ+Dfw6Is5LKV3Vgn2S\nJKlVqfZ7ozQ4ZiMiLgJIKV0VEcfXWfejvDsmSZKqQ2MDRE8sev6tOuuOzKEvkiSpCjU2ZiMaeF7f\na0mStAGq+CxKo8lGauB5fa8lSZLq1ViysXtELKKQYnTKnpO97ph7zyRJai2ilV76mlJq25IdkSRJ\n1ak5t5iXJEk5iyoeDtnkdOWSJEkbwmRDkqQyK0zqVe5e5MdkQ5Ik5cpkQ5KkCmCyIUmSVCKTDUmS\nKkBU8RSiJhuSJClXJhuSJJWZV6NIkiRtAJMNSZLKLVrvXV8lSZI2mMWGJEnKladRJEmqAG2q+DyK\nyYYkScqVyYYkSWXmpa+SJEkbwGJDkqQKENEyj6b7EUdGxISIeCMiLm5ku/+IiBQRw5tq02JDkiQB\nEBFtgWuAo4CdgZMiYud6tusGfA14pjntWmxIklR2QZsWejRhBPBGSmlKSmklcCtwXD3bfR/4CbC8\nOUdnsSFJUuvSOyLGFj2+VLSuHzC96PWMbNlaEbEXMCCldFdzd+jVKJIklVnQotOVz0kpNTnOoj4R\n0Qb4BXDa+rzPZEOSJNWYCQwoet0/W1ajG7Ar8HBETAX2BUY1NUjUZEOSpHKLiplnYwwwNCKGUCgy\nTgROrlmZUloI9K55HREPAxemlMY21qjJhiRJAiCltAo4F7gPeB24PaX0akR8LyKOLbVdkw1JkipA\npdwbJaV0N3B3nWWXNbDtIc0OOA94AAAXRElEQVRp02RDkiTlymJDkiTlytMokiSVWQtf+triTDYk\nSVKuTDYkSaoAlTJANA8mG5IkKVcmG5IkVYAqDjZMNiRJUr5MNiRJKrOguv/6r+ZjkyRJFcBkQ5Kk\ncguIKh60YbIhSZJyZbIhSVIFqN5cw2RDkiTlzGRDkqQyC5xBVJIkqWQmG5IkVYDqzTVMNiRJUs4s\nNiRJUq48jSJJUgWo4vGhJhuSJClfJhuSJJVdOF25JElSqUw2JEkqM28xL0mStAFMNiRJqgCO2ZAk\nSSqRyYYkSRWgenMNkw1JkpQzkw1JksotHLMhSZJUMpMNSZLKzHk2JEmSNoDFhiRJypWnUSRJqgAO\nEJUkSSqRyYYkSRWgenMNi41WYc7bs/jLNVfy/BOjWbRgPlts2Yd9Dz2Kk8/+Bl179GxWG//40zWM\ne/YJpk+ZyKL584g2beizTX/22O9gPvm5s+i9dd913nP/P29h4ssvMGXCK0ybNJ4Vy5dxwpnn89mv\nXryxD1FqEX26d+DLI7dl/+170bPTZsx5fwWjx7/H7x9+k8XLV61XWztu043P7z+QvQb1ZPPO7Vm8\nfBVvzlnCnS/M4l8vvV1r23Ztg1P3HcBRH9qagb06s3pNYtI77/PXZ6Zz/6vvbsxDlHJhsVHlZk+f\nyn+eegwL5s1h35FH0n/I9kx85QVG/eUPPP/4aK68aRTde27RZDv3/u0mOnbuwq7D96Nnry1Z/cEq\nJo9/mf+56VoeuOMW/uuGf7LdTh+q9Z4//uwKlixeRNfuPdliy62YPX1qTkcp5a//5p3479OH06tr\ne0aPf4835yxh137dOWXfgey/fS9O++NYFi5rXsFxwoj+XHTkMBYt/4DHJ87l3cUr6N6pHdv36cqB\nQ3vXKjbatQ1+d+qe7D1kc2bOX8aoF2YTAQcO7cWVx3+I7fq8ye9GT8nrsNWCqnjIhsVGtfvtDy5m\nwbw5nHXxD/j4KWesXf6HKy/nf266lht/82POvezKJtu55o6Had+h4zrL7/37X7j6uxdy42/+i+/+\n7pZa6y668vcM2HYoffoO4ME7b+VXl56/4Qcklcm3P7YDvbq258d3T+DWZ2esXf6Njw7ls/sN5Nz/\ntx0//NeEJtvZb7st+OZRw3h68jwuvP1llq5cXWt9uza1f+OcuHd/9h6yOS9NX8BZN77A8g/WANCp\nfVuuP20vzvzIYB6Z8B6vzVq8EY5SyocDRKvY7OlTeeHJh9mq3wA+dtIXa6075Sv/ScdOnRn9r7+x\nfOmSJtuqr9AAOOijxwIw660311n34QMPpU/fAevfcanC9N+8E/tv34uZ85dx25gZtdb9bvQUlq5c\nxTG7bUPHzZr+kXr+4duz4oM1fOsfr6xTaACsWpNqvR6505YAXP/o1LWFBsCylau5/tE3aRPBZ/bu\nX8phqYIUJvWKFnmUQ67FRkQcGRETIuKNiPBEfQsb9+wTAOy53yG0aVP7n7pzl67stOcIVixbxvhx\nz5e8j2cfvh+AIcN2Lr2jUoXbe8jmADw1eR6pdi3A0pWrefGthXRq35bd+vdotJ3t+nRhh6278dTk\nuSxctorhgzfnc/sP5LP7DWTEkM3rjdF7d+0AwIz5y9ZZN2P+cgBGZP2TKlVup1Eioi1wDXA4MAMY\nExGjUkqv5bVP1TZz6hsA9B28bb3r+w4cwgtPPsysaZPZY9+DmtXmff+4mTnvzGL50iVMnTSel55+\nlD59+/P587+zsbotVZzBvToDMG3u0nrXvzVvKfvTi0G9OvPsm/MbbGfXvt0BmLfkA64/bS+GD65d\nJEx8ZzHfuO1lps/7d2GxYOlKBvXqTL/NO/HmnNr77795IXHs27MTHdq1YcWqNWjT5ZiN0owA3kgp\nTQGIiFuB4wCLjRayZHHhHG6Xrt3rXd+lW2H5+4sXNbvN+/95MxOKkpChu+7Bf/7kd/QdOGQDeipV\ntq4dCz8q319R/wDQ97MrUbp1bPxH6uZd2gPwib224d1FKzj35hd54a0F9OrSni8dPIRjdt+Gq07e\nnU//7hlWrS5EKI9NnMvuA3pyxkGDGfPm/LUFRcfN2nD6QYPXtt2tYztWvL9yg45TykuexUY/YHrR\n6xnAPjnuTy3g5zffDcCiBfOY/NrL3HjVf3H+CUfwzZ9dx4cPGFnm3kmVrWbsZ7s2bbj4768wbkah\n0F+yYhmX3PEaQ3p3YZd+3Tlspz7c+8o7ANz8zHQO36UPewzsyT++si+PT5pDEBw0rBcpweLlH9Ct\n42bUGeqhTU4QVTzTRtkHiEbElyJibESMXTh/Xrm7U1W6dOsGwJL3608ulmSJRtdu9Scfjenecwv2\n3P9gvn/tbbTv0JFffPtcVixf95yyVA1qkouuHer/+6wm+Whqro2a9e8tXrG20Cg2esJ7AOza79//\nTS5buZrTbniOPz42ldVrEp/aqx9H7NqH56Yt4As3PEebCD5YvYZFyz5Y/wOTWkieycZMoPhShP7Z\nslpSStcB1wEM3WV3a/ONqN/g7QGYNbX+a/BrriDpO2i7kvfRtXsPdtx9OE//3z28NXkCQ3fZo+S2\npEo1NRurMSgbu1HXwC0aH9NRt52GipLF2TwdHepc1bJs5WquemgyVz00udbyfpt3pEuHdrw6a9E6\nV7Fo01PNYzbyTDbGAEMjYkhEtAdOBEbluD/VsduIAwB44amHWbOm9sCxpUve5/UXnqVDp07suNte\nG7Sfue/OBqBtW6dtUXUakw363G+7Ldb5hdC5fVv2GNiDZStXM27GwkbbeXnGQpauXEXfnh3rvUx2\nuz5dAJiZXWXSlI/vvg0A97z8dhNbSuWVW7GRUloFnAvcB7wO3J5SejWv/Wld2wwYzJ77H8I7M6dz\n119vqLXu5mt+yvJlSxl5zPF07Nxl7fLpUyYxfcqkWtu+O3sG8+e8V+8+7rn9Ria98iJbbt2PQUN3\n2vgHIVWAGfOX8eQbc+m3eSdOqDOnxTkjt6Vz+3b8a9zsWvNgDO7dmcG9aychyz9Yw53Pz6bjZm05\n99DaieL2fbpw7B7b8MHqNTz4Wu0pyLt0aLtOn/bddgtOO2AQb81byt/HrhMaaxNT7fNs5PqnaErp\nbuDuPPehxn35kh/zn6cew7U/voSXnnmc/tsOZeLLzzPu2SfoN3g7PlfnPiXnHFe4BPZfRX8pTX7t\nZX584ZnsuNtwthk4mJ69tmTxgvlMGPccUye9TqfOXbjgR1fRtm3tH4j3/eNmXnv+GYC1U5U/88j9\nzHlnFgD9hwzl+DPOy+vQpY3qR3dN4L9PH87FR+/APttuwZT3lvCh/t0ZMWQLps5ZwtV1TnHcee5+\nAOxxxUO1ll8zejJ7DerJqfsNZLcBPXjxrQX06tqeQ3fqQ8fN2nLlPRPXmVPjjnP3Y9I77zN1zhJW\nrFrDTtt0Y59tt2DO+yv5+l/H1SpypEpk7l3lthkwmF/edh83X30lzz0xmrGPPcTmW/bh2FPPbPaN\n2Lbb+UMce8oZvPr8M4x99EEWL1pA+/Yd2Kr/ID75+bM59tQz2XLrfuu877Xnn+GhUbfXWjZ14mtM\nnVi4+nnX4ftZbGiTMWP+Mk6+7tm1N2I7cGgv3lu8gpuffmu9bsS2ZMVqvvCn5zj9wEEcvstWnDhi\nACtWrebFtxZw45Nv8dTkdQfK3zPubfbfvhe7D+hBuzbB7IXL+fMT0/jzE9NY1Mz7sUjlFKnudHhl\nNHSX3dOvbru/3N2QNnnf+fsr5e6CtMmbeN2XWTprQoucdxi26x7pqtsfaIldceQufZ5LKQ1vkZ1l\nyn7pqyRJqm6eRpEkqQJ46askSVKJTDYkSaoATlcuSZJUIpMNSZLKLPj3jfqqkcmGJEnKlcmGJEkV\nwDEbkiRJJTLZkCSpAjjPhiRJUolMNiRJqgCO2ZAkSSqRxYYkScqVp1EkSSozJ/WSJEnaACYbkiSV\nXThAVJIkqVQmG5IklVs4qZckSVLJTDYkSaoAVRxsmGxIkqR8mWxIklRmhXk2qjfbMNmQJEm5MtmQ\nJKkCVG+uYbIhSZJyZrIhSVIlqOJow2RDkiTlymJDkiTlytMokiRVAG/EJkmSVCKTDUmSKkAVz+ll\nsiFJkvJlsiFJUgWo4mDDZEOSJOXLZEOSpEpQxdGGyYYkScqVyYYkSWUWOM+GJElSyUw2JEkqt3Ce\nDUmSpJKZbEiSVAGqONgw2ZAkSfky2ZAkqRJUcbRhsiFJknJlsSFJknLlaRRJksounNRLkiSpVCYb\nkiRVACf1kiRJrUJEHBkREyLijYi4uJ71F0TEaxExLiIeiohBTbVpsSFJUplFCz4a7UdEW+Aa4Chg\nZ+CkiNi5zmYvAMNTSrsBfweubOr4LDYkSVKNEcAbKaUpKaWVwK3AccUbpJRGp5SWZi+fBvo31ahj\nNiRJqgQtN2ajd0SMLXp9XUrpuux5P2B60boZwD6NtHU6cE9TO7TYkCSpdZmTUhq+oY1ExKnAcODg\npra12JAkqQJUyDwbM4EBRa/7Z8tqiYjDgO8AB6eUVjTVqGM2JElSjTHA0IgYEhHtgROBUcUbRMSe\nwLXAsSmld5vTqMmGJEkVoBLm2UgprYqIc4H7gLbADSmlVyPie8DYlNIo4KdAV+BvUej0WymlYxtr\n12JDkiStlVK6G7i7zrLLip4ftr5tWmxIklQBKiDYyI1jNiRJUq4sNiRJUq48jSJJUrk1Zy7xTZjJ\nhiRJypXJhiRJFaBCJvXKhcmGJEnKlcmGJEllFlTGpF55MdmQJEm5MtmQJKkCVHGwYbIhSZLyZbIh\nSVIlqOJow2RDkiTlymRDkqQK4DwbkiRJJTLZkCSpAjjPhiRJUolMNiRJqgBVHGyYbEiSpHxZbEiS\npFx5GkWSpEpQxedRTDYkSVKuTDYkSSqzwEm9JEmSSmayIUlSuYWTekmSJJXMZEOSpApQxcGGyYYk\nScqXyYYkSZWgiqMNkw1JkpQrkw1JksounGdDkiSpVCYbkiRVAOfZkCRJKpHFhiRJypWnUSRJKrOg\nqq98NdmQJEn5MtmQJKkSVHG0YbIhSZJyZbIhSVIFqOZJvSqq2HjjtXFzjvnQ1tPK3Q81qjcwp9yd\nkKqA/y1VvkHl7kC1qKhiI6W0Zbn7oMZFxNiU0vBy90Pa1PnfkupyUi9JkqQSVVSyIUlSa1XFwYbJ\nhtbbdeXugFQl/G9JrYbJhtZLSskfkNJG4H9LqiUcsyFJklQykw1JkipC9UYbJhuSJClXJhuSJJVZ\n4JgNSdJGFBH+7FWr4hdeklpe53J3QGpJnkZRs0VEm5TSmnL3Q9pURcRFwD7A3hHxDDAG+HlKaXV5\ne6ZKUMVnUUw21LSIOCUiuqeU1hj/SqWJiDuBHwDDgeXAJ4EfA/dGxNER0amc/ZPy5C8ONSr7AXkj\n8M2I6GrBIa2/iPgN8FHgexSKjd2BA4CHgEOAXwKnRkSXcvVR5RfRMo9y8JeGGhQRPwaOpfBX2HnA\nxRYc0vqJiD7AUcCjwG9SSu8Bq1JKzwBnUCg0tgK+DRwfER3K1lkpJ/7CUL0i4hPAl4DxwNnA28AF\nWHBI62sAsB3wSkppUUS0Tyl9AJBSmgb8HPgV0IvCf2N7AkRU84WQqk+00P/KwV8WWkdEdAROADoB\nX0gp3QR8FZgNfAMLDml9vAcsAXYESCmtLC4kUkrvAL+ncLpyVwr/jZFSSi3fVSkf/qLQOlJKy4Er\ngYuyqBfgQQoJxyzqKTj8K0xq0PvATOCoiPg8FAqJOgXH28CvgYnAf0TEp8rSU5VXtNCjDCw2VK+U\n0gsU/tqqeb0K+D8KBcdM6hQcNX+FRcSW5eivVKlSSvOAy4EPgNMiYt9s+dqCIyIipTSJwtUqicKp\nF6lqWGyoQTXnlYter6ZQcJzDvxOOb0dEO4CIOAO4JyL2aem+ShXuHuBvwMHA1yNiT6hVcNT8LJ5O\n4W/PQWXppcqqioMNJ/XS+kkprY6ImoTj98D5wOqImAl8B9gCWFDGLkoVJxsY+i2gL3A80C4irkop\nPZylgjWTeu1B4eqvMWXqqpQLiw2tt6zgeAQ4Hbge+DqFwaSLgH1SShPK2T+pEqWUpkfEF4HrgE8A\n20bEX4Grs02OAs4EplG4TFatSDnnwGgJnkbResumLV8JPAL8k8J9HhYAB6aUXilr56QKllKaCpwG\nXAPsRGEG0ReAccAfKVz+enxKaWaZuijlwmJD663o/iinAacAC4GDUkqvlq1T0iYipVQz3ukwCsX6\nImAOhUtfLdhbsWqeZ8PTKCpJRBwIXEFhjMbwlNJr5e2RtOnIBl8/HhFP1dyELbsixbk1VJVMNlSq\nScDdFAoNEw2pNN5FWa2CyYZKklJ6JyLOy+bfkFSC4iTDVENluy61BZhsqGQWGpKk5jDZkCSpAlRx\nsGGyIUmS8mWyIUlSBXBSL0mSpBKZbEiSVHblm3CrJZhsSM0QEasj4sWix8XZ8vMjonPRdndHRM+N\nvO/BEXHyBvT5lYj4W3E/S2jrkIj4V/b82Jrjb6S/zoIpaS2TDal5lqWU9qhn+fnAX4ClACmlo3PY\n92DgZOCW9Xzf2j5HxM0U7tT7i5qV2a3No2j6+WZJKY0CRq1nXyQ1InDMhqR6RMRXKdwyfHREjM6W\nTY2I3tnz70TExIh4PCL+GhEXZssfjojh2fPeETE1e942In4aEWMiYlxEnJXt6sfAQVlK8fUsOXgs\nIp7PHvs3o7uPAdtn750QETcCrwADIuKIiHgqa+tvEdE168+RETE+Ip4HPlV03KdFxNXZ860i4o6I\neCl71PSlbUT8ISJejYj7I6JTtv2Z2fG9FBH/2JC0RdKmw2JDap5OdU6jnJBS+g0wCxiZUhpZvHFE\nfBg4EdgDOBrYuxn7OB1YmFLaO9v+zIgYAlwMPJZS2iOl9EvgXeDwlNJewAnAbxprNCLaUbh9+cvZ\noqHAb1NKuwBLgEuAw7L2xgIXRERH4A/Ax4EPA1s30PxvgEdSSrsDewE1U9cPBa7J9rEA+I9s+T9T\nSntn27+eHbOkKudpFKl5GjqN0pCDgDtSSksBIqI5px2OAHaLiE9nr3tQ+KW9ss52mwFXR8QewGpg\nWAPtdYqIF7Pnj1G4hXlfYFpK6els+b7AzsAThbMqtAeeAnYE3kwpTcr6/xfgS/Xs41DgcwDZDcUW\nRsTm2Xtr9v0chVNBALtGxA+AnkBX4L4GPw1JVcNiQ2p5q/h3qtixaHkA56WUav0CjohD6rz/68A7\nwO5ZO8sb2M86BVJWUCyps88HUkon1dlufQqr+qwoer4a6JQ9/zPwiZTSSxFxGnDIBu5HqhqO2ZDU\nkMVAt3qWPwp8IiI6RUQ3CqcjakylcGoC4NNFy+8DzomIzQAiYlhEdKlnHz2A2dnAzs8CbTeg/08D\nB0TE9tk+u0TEMGA8MDgitsu2O6mB9z8EnJO9t21E9Ghif92A2dkxnrIB/Za0CbHYkJqn7piNH2fL\nrwPurRkgWiOl9DxwG/AScA8wpmj1zygUFS8AvYuWXw+8BjyfXTp6LYX0cRywOhtU+XXgt8DnI+Il\nCqc7ipOK9ZJSeg84DfhrRIwjO4WSUlpO4bTJXdkA0XcbaOJrwMiIeJnC6ZKdm9jlpcAzwBMUChpJ\nmWih/5Xl2LyrsZS/iLgCeD+l9LNy90VS5dlzr+Hp4SeebZF99ezc9rmU0vAW2VnGZEOSJOXKAaJS\nC0gpXVHuPkiqYOEAUUmSpJKZbEiSVGaRPaqVyYYkScqVyYYkSZWgiqMNkw1JkpQrkw1JkipAuSbc\nagkmG5IkKVcmG5IkVQDn2ZAkSSqRyYYkSRWgioMNkw1JkpQvkw1JkipBFUcbJhuSJClXFhuSJClX\nnkaRJKkCOKmXJElSiUw2JEkqs6C6J/WKlFK5+yBJUqsWEfcCvVtod3NSSke20L4Aiw1JkpQzx2xI\nkqRcWWxIkqRcWWxIkqRcWWxIkqRcWWxIkqRcWWxIkqRcWWxIkqRcWWxIkqRcWWxIkqRc/X/SwjxR\neDqF9wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s512jCf7OGqC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "b2f7e5a7-8ab4-497a-bed2-05d16574a617"
      },
      "source": [
        "# Ahora con sklearn\n",
        "from sklearn.metrics import precision_score, recall_score, accuracy_score\n",
        "\n",
        "# Debemos usar las opciones \"average='binary'\" y  \"pos_label=0\" para obtener los resultados correctos\n",
        "precision_sk = precision_score(test_y, pred_test, average='binary', pos_label=0)\n",
        "recall_sk = recall_score(test_y, pred_test, average='binary', pos_label=0)\n",
        "\n",
        "accuracy_sk = accuracy_score(test_y, pred_test)\n",
        "\n",
        "print('Precision (sklearn): ', format(precision_sk, '.3f'))\n",
        "print('Recall (sklearn):    ', format(recall_sk, '.3f'))\n",
        "print('Accuracy (sklearn):  ', format(accuracy_sk, '.3f'))\n"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Precision (sklearn):  0.996\n",
            "Recall (sklearn):     0.996\n",
            "Accuracy (sklearn):   0.992\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yFZ0FLp2OZdP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "outputId": "eacd26bf-0e2a-48eb-9ca6-6a28a8b443f6"
      },
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(test_y, pred_test, target_names=[\"No Habitable\",\"Habitable\"]))\n",
        "\n"
      ],
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "No Habitable       1.00      1.00      1.00      1142\n",
            "   Habitable       0.64      0.69      0.67        13\n",
            "\n",
            "    accuracy                           0.99      1155\n",
            "   macro avg       0.82      0.84      0.83      1155\n",
            "weighted avg       0.99      0.99      0.99      1155\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4BOry5EkObCo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "1d452107-694b-4f28-804e-386f825caf48"
      },
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "# Creamos un nuevo clasificador para hacer las pruebas de cross-validation\n",
        "cv_knn_clf = KNeighborsClassifier(n_neighbors=25)\n",
        "\n",
        "# Calculamos el puntaje tomando 10 subconjuntos del conjunto de entrenamiento\n",
        "# Usamos el accuracy como métrica\n",
        "scores = cross_val_score(cv_knn_clf, train_X, train_y, cv=10, scoring=None)\n",
        "\n",
        "print('Cross validation promedio: ', format(scores.mean(), '.3f'))\n",
        "print('Cross validation std:      ', format(scores.std(), '.3f'))\n",
        "# Primer ejemplo, iterando\n",
        "neighbors = np.arange(1,25,1)\n",
        "scores = np.zeros(np.shape(neighbors))\n",
        "stds = np.zeros(np.shape(neighbors))\n",
        "\n",
        "\n"
      ],
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cross validation promedio:  0.987\n",
            "Cross validation std:       0.002\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iwicuF6POdpA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "d9ae0332-6d4c-42a8-98c2-61fcfe4da52b"
      },
      "source": [
        "for s, n in enumerate(neighbors):\n",
        "  clf = KNeighborsClassifier(n_neighbors=n)\n",
        "  cv_scores = cross_val_score(clf, train_X, train_y, cv=10, scoring=\"accuracy\")\n",
        "  scores[s] = cv_scores.mean()\n",
        "  stds[s] = cv_scores.std()\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.fill_between(neighbors, scores-stds, scores+stds, alpha=0.3)\n",
        "plt.plot(neighbors, scores)\n",
        "plt.xlabel('Número de vecinos')\n",
        "plt.ylabel('Puntaje de Cross-Validation')\n",
        "plt.title('Puntaje en función del número de vecinos')\n",
        "from sklearn.svm import SVC\n",
        "scores.max()"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9870149664728813"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 118
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EGzNdeAkm+ar6ENCnqs+p6sdxuu0aM6STrT3WJdiYacTL4lgDqykdF5F34cyJ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\nEkIIoWbRmIQQQqjZ/wCiHJPqlYd8RwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ckkzMO72cLmZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Acá viene el random forest\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "import sklearn.metrics\n",
        "from sklearn.ensemble import ExtraTreesClassifier\n",
        "\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.63, random_state=9)  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B1Kln6hpOocj",
        "colab_type": "code",
        "outputId": "74600733-1a4f-4ed0-8b5c-ff6d89540ece",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "regressor = RandomForestClassifier()  \n",
        "regressor.fit(X_train, y_train)  \n",
        "y_pred = regressor.predict(X_test)\n"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
            "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BmsA6RmqOsWA",
        "colab_type": "code",
        "outputId": "83bcc77c-95fb-431e-992e-5dae861dc4ec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "from sklearn import metrics\n",
        "\n",
        "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))  \n",
        "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))  \n",
        "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred))) "
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error: 0.004123711340206186\n",
            "Mean Squared Error: 0.004123711340206186\n",
            "Root Mean Squared Error: 0.06421612990679355\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OzzC7D0dR1kO",
        "colab_type": "code",
        "outputId": "e71bfa54-78da-409a-dd05-b7d4faa9dc93",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "confusion_matrix(y_test,y_pred)"
      ],
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2383,   10],\n",
              "       [   0,   32]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sEE24TZUebv8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "from sklearn.utils.multiclass import unique_labels\n",
        "\n",
        "\n",
        "def plot_confusion_matrix(y_true, y_pred, classes,\n",
        "                          normalize=False,\n",
        "                          title=None,\n",
        "                          cmap=plt.cm.Blues):\n",
        "    \"\"\"\n",
        "    This function prints and plots the confusion matrix.\n",
        "    Normalization can be applied by setting `normalize=True`.\n",
        "    \"\"\"\n",
        "    if not title:\n",
        "        if normalize:\n",
        "            title = 'Matriz de confusion normalizada'\n",
        "        else:\n",
        "            title = 'Matriz de confusion, sin normalizacion'\n",
        "\n",
        "    # Compute confusion matrix\n",
        "    cm = confusion_matrix(y_true, y_pred)\n",
        "    # Only use the labels that appear in the data\n",
        "    classes = classes[unique_labels(y_true, y_pred)]\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        print(\"Matriz de confusion normalizada\")\n",
        "    else:\n",
        "        print('Matriz de confusion, sin normalizacion')\n",
        "\n",
        "    print(cm)\n",
        "\n",
        "    fig, ax = plt.subplots(figsize = (8,8))\n",
        "    im = ax.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    ax.figure.colorbar(im, ax=ax)\n",
        "    # We want to show all ticks...\n",
        "    ax.set(xticks=np.arange(cm.shape[1]),\n",
        "           yticks=np.arange(cm.shape[0]),\n",
        "           # ... and label them with the respective list entries\n",
        "           xticklabels=classes, yticklabels=classes,\n",
        "           title=title,\n",
        "           ylabel='Etiqueta Real',\n",
        "           xlabel=\"Etiqueta Predicha\")\n",
        "\n",
        "    # Rotate the tick labels and set their alignment.\n",
        "    plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
        "             rotation_mode=\"anchor\", fontsize=15)\n",
        "\n",
        "    # Loop over data dimensions and create text annotations.\n",
        "    fmt = '.2f' if normalize else 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i in range(cm.shape[0]):\n",
        "        for j in range(cm.shape[1]):\n",
        "            ax.text(j, i, format(cm[i, j], fmt), fontsize=25,\n",
        "                    ha=\"center\", va=\"center\",\n",
        "                    color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "            \n",
        "    ax.grid(False)\n",
        "    fig.tight_layout()\n",
        "    return ax\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IOueJdk4eibJ",
        "colab_type": "code",
        "outputId": "0137087d-bfe8-4204-d516-5848d4fc9ab8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 655
        }
      },
      "source": [
        "np.set_printoptions(precision=2)\n",
        "\n",
        "# Plot non-normalized confusion matrix\n",
        "plot_confusion_matrix(y_test, y_pred, classes=planetas[\"Habitabilidad\"],\n",
        "                      title='Matriz de confusion, sin normalizacion')"
      ],
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Matriz de confusion, sin normalizacion\n",
            "[[2383   10]\n",
            " [   0   32]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f21551e4fd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 124
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PiP9UlZ0ZERMjYnTx+GTVvm9FxJiIeC4i9qkq37coGxMRp7embpMSSZLqrVwr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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aSoeaKWDekWh",
        "colab_type": "code",
        "outputId": "b88bb581-daa3-42c8-ac92-9cc9c2194884",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 655
        }
      },
      "source": [
        "plot_confusion_matrix(y_test, y_pred, classes=planetas[\"Habitabilidad\"], normalize = True,\n",
        "                      title='Matriz de confusion, con normalizacion')"
      ],
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Matriz de confusion normalizada\n",
            "[[1. 0.]\n",
            " [0. 1.]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f215594b630>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 125
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BPBYRo1NKz+WVGQCcCQxLKb0XEZs0167JhiRJqvMpYGpK6ZWU0hLgNuCAVcoc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\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}