cap7_1_avaliacao_performance_classificacao.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "cap7_1_avaliacao_performance_classificacao.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPDDqyoKdjCK0txrnTYXayb",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/viniciusrpb/cic0193_machinelearning/blob/main/cap7_1_avaliacao_performance_classificacao.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Y0ZDogPM9vH"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import itertools"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nPmZKT33M55G"
      },
      "source": [
        "def plot_confusion_matrix(cm,\n",
        "                          target_names,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=None,\n",
        "                          normalize=False):\n",
        "    \n",
        "    \n",
        "\n",
        "    accuracy = np.trace(cm) / float(np.sum(cm))\n",
        "    misclass = 1 - accuracy\n",
        "\n",
        "    if cmap is None:\n",
        "        cmap = plt.get_cmap('Blues')\n",
        "\n",
        "    plt.figure(figsize=(8, 6))\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "\n",
        "    if target_names is not None:\n",
        "        tick_marks = np.arange(len(target_names))\n",
        "        plt.xticks(tick_marks, target_names, rotation=45)\n",
        "        plt.yticks(tick_marks, target_names)\n",
        "\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "\n",
        "\n",
        "    thresh = cm.max() / 1.5 if normalize else cm.max() / 2\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        if normalize:\n",
        "            plt.text(j, i, \"{:0.4f}\".format(cm[i, j]),\n",
        "                     horizontalalignment=\"center\",\n",
        "                     color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "        else:\n",
        "            plt.text(j, i, \"{:,}\".format(cm[i, j]),\n",
        "                     horizontalalignment=\"center\",\n",
        "                     color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label\\naccuracy={:0.4f}; misclass={:0.4f}'.format(accuracy, misclass))\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wQXJssk4LX4Q"
      },
      "source": [
        "\n",
        "#problema de classificacao binaria\n",
        "# Positiva (1) e Negativa (0)\n",
        "# obs: de um conjunto de testes\n",
        "labels = [1,0,1,0,0,0,1,1,1,0]\n",
        "pred1 = [1,1,0,0,1,1,0,1,1,0]\n",
        "\n",
        "# predicao de um modelo 1 de classificacao\n",
        "\n",
        "\n",
        "# predicao de um modelo 2 de classificacao\n",
        "pred2 = [1,1,1,1,1,1,1,1,1,1]\n",
        "\n",
        "# predicao de um modelo 2 de classificacao\n",
        "pred3 = [0,0,0,0,0,0,0,0,0,0]\n",
        "\n",
        "# acuracia: taxa de classificacao correta"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iJrCEXF9LgUt"
      },
      "source": [
        "tp = 0.00000001\n",
        "fn = 0.00000001\n",
        "fp = 0.00000001\n",
        "tn = 0.00000001\n",
        "\n",
        "for i in range(N):\n",
        "    if labels[i] == 1 and pred1[i] ==1:\n",
        "        tp+=1\n",
        "    elif labels[i] == 1 and pred1[i] == 0:\n",
        "        fn+=1\n",
        "    elif labels[i] == 0 and pred1[i] == 1:\n",
        "        fp+=1\n",
        "    else:\n",
        "        tn+=1\n",
        "\n",
        "acc = (tp + tn)/(tp + tn+fp+fn)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "9dY_1oQIMtng",
        "outputId": "10cd4622-6354-45ff-81dc-d1b259cac393"
      },
      "source": [
        "plot_confusion_matrix(np.array(conf_matrix),[\"Classe P\",\"Classe N\"])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "01P7H1lhNdA9"
      },
      "source": [
        "# curva roc receiving operator characteristics\n",
        "tpr = tp/(tp+fp)\n",
        "\n",
        "fpr = fp/(tp+fp)\n",
        "\n",
        "# focar apenas na classe positiva\n",
        "precision = tpr\n",
        "\n",
        "recall = tp/(tp+fn)\n",
        "\n",
        "print(\"precisao: \"+str(precision))\n",
        "print(\"revocacao: \"+str(recall))\n",
        "\n",
        "# f measure ou f1-score\n",
        "f1 = 2*recall*precision/(recall+precision)\n",
        "print(\"f1-score: \"+str(f1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 130
        },
        "id": "b_NhHDxliBHS",
        "outputId": "6b978e3e-0373-44b1-b943-c7283dcab080"
      },
      "source": [
        "labels = [1,0,1,0,0,0,1,0,1,1]\n",
        "\n",
        "probs = [0.25,0.43,0.53,0.76,0.85,0.85,0.85]\n",
        "\n",
        "histograma = {0 : 0, 1 : 0}\n",
        "\n",
        "tp=fp=fn=tn=0\n",
        "\n",
        "for label in labels:\n",
        "  histograma[label]+=1\n",
        "\n",
        "\n",
        "tp+= histograma[1]\n",
        "fp+= histograma[0]\n",
        "tn = 0\n",
        "fn = 0\n",
        "\n",
        "tpr = tp/(tp+fp)\n",
        "fpr = tp/(tp+fn)\n",
        "\n",
        "pos = 1\n",
        "neg = 0\n",
        "\n",
        "for i in range(1,N):\n",
        "  \n",
        "  if labels[i] == 1:\n",
        "    tp+= histograma[1]\n",
        "    #histograma[1]-=1\n",
        "    #fn+= histograma[0]-tp\n",
        "    #fp-= histograma[0]\n",
        "    #histograma[0]-=1\n",
        "    tn+= histograma[0]-fp\n",
        "  else:\n",
        "    fp-= histograma[0]\n",
        "    #histograma[0]-=1\n",
        "    #tn+= histograma[0]-fp\n",
        "\n",
        "  print(tp)\n",
        "  print(fn)\n",
        "  print(fp)\n",
        "  print(tn)\n",
        "  \n",
        "  tpr = tp/(tp+fp)\n",
        "  fpr = tp/(tp+fn)\n",
        "\n",
        "N = len(labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "IndentationError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-72-b0586502dc77>\"\u001b[0;36m, line \u001b[0;32m23\u001b[0m\n\u001b[0;31m    print(tp)\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m unexpected indent\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QscZQdJXTdmF"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "   \n",
        "Year = [1920,1930,1940,1950,1960,1970,1980,1990,2000,2010]\n",
        "Unemployment_Rate = [9.8,12,8,7.2,6.9,7,6.5,6.2,5.5,6.3]\n",
        "  \n",
        "plt.plot(Year, Unemployment_Rate)\n",
        "plt.title('Unemployment Rate Vs Year')\n",
        "plt.xlabel('Year')\n",
        "plt.ylabel('Unemployment Rate')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}