LgF_Carlos_Stachissini_Matheus_Debtil.ipynb

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  "metadata": {
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Stachiss/LogicaFuzzy-AnalisaFinanceiraCredito/blob/main/LgF_Carlos_Stachissini_Matheus_Debtil.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": "1Zt1EQ1TzfPr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "770d8f30-965f-4ecc-c7bd-12887ebcad45"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting networkx==2.3\n",
            "  Downloading networkx-2.3.zip (1.7 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.7 MB 33.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: decorator>=4.3.0 in /usr/local/lib/python3.7/dist-packages (from networkx==2.3) (4.4.2)\n",
            "Building wheels for collected packages: networkx\n",
            "  Building wheel for networkx (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for networkx: filename=networkx-2.3-py2.py3-none-any.whl size=1556008 sha256=5ddcacc635924b23306c757be6e2e8ca96873e8f64e95b19232f7c28862d2327\n",
            "  Stored in directory: /root/.cache/pip/wheels/44/e6/b8/4efaab31158e9e9ca9ed80b11f6b11130bac9a9672b3cbbeaf\n",
            "Successfully built networkx\n",
            "Installing collected packages: networkx\n",
            "  Attempting uninstall: networkx\n",
            "    Found existing installation: networkx 2.6.3\n",
            "    Uninstalling networkx-2.6.3:\n",
            "      Successfully uninstalled networkx-2.6.3\n",
            "Successfully installed networkx-2.3\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting imgaug==0.2.5\n",
            "  Downloading imgaug-0.2.5.tar.gz (562 kB)\n",
            "\u001b[K     |████████████████████████████████| 562 kB 33.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from imgaug==0.2.5) (1.7.3)\n",
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            "Requirement already satisfied: tifffile>=2019.7.26 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.11.0->imgaug==0.2.5) (2021.11.2)\n",
            "Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.11.0->imgaug==0.2.5) (2.3)\n",
            "Requirement already satisfied: matplotlib!=3.0.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.11.0->imgaug==0.2.5) (3.2.2)\n",
            "Requirement already satisfied: pillow!=7.1.0,!=7.1.1,>=4.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.11.0->imgaug==0.2.5) (7.1.2)\n",
            "Requirement already satisfied: imageio>=2.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.11.0->imgaug==0.2.5) (2.9.0)\n",
            "Requirement already satisfied: PyWavelets>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.11.0->imgaug==0.2.5) (1.3.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.11.0->imgaug==0.2.5) (3.0.9)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.11.0->imgaug==0.2.5) (2.8.2)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.11.0->imgaug==0.2.5) (0.11.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.11.0->imgaug==0.2.5) (1.4.4)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.11.0->imgaug==0.2.5) (4.1.1)\n",
            "Requirement already satisfied: decorator>=4.3.0 in /usr/local/lib/python3.7/dist-packages (from networkx>=2.0->scikit-image>=0.11.0->imgaug==0.2.5) (4.4.2)\n",
            "Building wheels for collected packages: imgaug\n",
            "  Building wheel for imgaug (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for imgaug: filename=imgaug-0.2.5-py3-none-any.whl size=561438 sha256=3b12daf5de8fc825ef6785dfc075965c445abb7156382c3134ada625e3d8e3f7\n",
            "  Stored in directory: /root/.cache/pip/wheels/60/dd/38/d1dc2cad2b6a66dc0249261004990bccb0f27985c74ba26e49\n",
            "Successfully built imgaug\n",
            "Installing collected packages: imgaug\n",
            "  Attempting uninstall: imgaug\n",
            "    Found existing installation: imgaug 0.4.0\n",
            "    Uninstalling imgaug-0.4.0:\n",
            "      Successfully uninstalled imgaug-0.4.0\n",
            "Successfully installed imgaug-0.2.5\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting scikit-fuzzy\n",
            "  Downloading scikit-fuzzy-0.4.2.tar.gz (993 kB)\n",
            "\u001b[K     |████████████████████████████████| 993 kB 32.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from scikit-fuzzy) (1.21.6)\n",
            "Requirement already satisfied: scipy>=0.9.0 in /usr/local/lib/python3.7/dist-packages (from scikit-fuzzy) (1.7.3)\n",
            "Requirement already satisfied: networkx>=1.9.0 in /usr/local/lib/python3.7/dist-packages (from scikit-fuzzy) (2.3)\n",
            "Requirement already satisfied: decorator>=4.3.0 in /usr/local/lib/python3.7/dist-packages (from networkx>=1.9.0->scikit-fuzzy) (4.4.2)\n",
            "Building wheels for collected packages: scikit-fuzzy\n",
            "  Building wheel for scikit-fuzzy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for scikit-fuzzy: filename=scikit_fuzzy-0.4.2-py3-none-any.whl size=894089 sha256=24485d7dd9082d8bf155cb9f8866391867d25926e08dc7b9497b51957d7e903c\n",
            "  Stored in directory: /root/.cache/pip/wheels/d5/74/fc/38588a3d2e3f34f74588e6daa3aa5b0a322bd6f9420a707131\n",
            "Successfully built scikit-fuzzy\n",
            "Installing collected packages: scikit-fuzzy\n",
            "Successfully installed scikit-fuzzy-0.4.2\n"
          ]
        }
      ],
      "source": [
        "!pip install networkx==2.3\n",
        "!pip install imgaug==0.2.5\n",
        "!pip install scikit-fuzzy"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from skfuzzy import control as ctrl \n",
        "import matplotlib as plt \n",
        "import skfuzzy as fuzz \n",
        "import pandas as pd \n",
        "import numpy as np\n",
        "\n",
        "# Variáveis ​​Linguisticas. Termos Linguisticos \n",
        "score_mercado = ctrl.Antecedent (np.arange (0, 1001, 1), 'score_mercado') \n",
        "score_interno = ctrl.Antecedent (np.arange (0, 1001, 1), 'score_interno') \n",
        "engajamento = ctrl.Antecedent (np. arange (0, 5000, 1), 'engajamento') \n",
        "politica_cliente = ctrl.Consequent (np.arange (0, 1001, 1), 'politica_de_risco')\n",
        "\n",
        "# Fuzzificação \n",
        "score_mercado ['RATING 1'] = fuzz.trimf (score_mercado.universe, [890, 900, 1000]) \n",
        "score_mercado ['RATING 2'] = fuzz.trimf (score_mercado.universe, [750, 800, 890])\n",
        "score_mercado ['RATING 3'] = fuzz.trimf (score_mercado.universe, [570, 700, 750]) \n",
        "score_mercado ['RATING 4'] = fuzz.trimf (score_mercado.universe, [390, 500, 570]) \n",
        "score_mercado [ 'RATING 5'] = fuzz.trimf (score_mercado.universe, [240, 300, 390]) \n",
        "score_mercado ['RATING 6'] = fuzz.trimf (score_mercado.universe, [0, 200, 240])\n",
        "score_interno ['RATING 1'] = fuzz.trimf (score_interno.universe, [890, 900, 1000]) \n",
        "score_interno ['RATING 2'] = fuzz.trimf (score_interno.universe, [750, 800, 890]) \n",
        "score_interno [ 'RATING 3'] = fuzz.trimf (score_interno.universe, [570, 700, 750])\n",
        "score_interno ['RATING 4'] = fuzz.trimf (score_interno.universe, [390, 500, 570]) \n",
        "score_interno ['RATING 5'] = fuzz.trimf (score_interno.universe, [240, 300, 390]) \n",
        "score_interno [ 'RATING 6'] = fuzz.trimf (score_interno.universe, [0, 200, 240])\n",
        "engajamento ['alto'] = fuzz.trimf (engajamento.universe, [300, 1000, 5000]) \n",
        "engajamento ['medio'] = fuzz.trimf (engajamento.universe, [100, 200, 300]) \n",
        "engajamento ['baixo'] = fuzz.trimf (engajamento.universe, [0, 50, 100])\n",
        "\n",
        "politica_cliente ['grau de risco 1'] = fuzz.trimf (politica_cliente.universe, [850, 950, 1000]) \n",
        "politica_cliente ['grau de risco 2'] = fuzz.trimf (politica_cliente.universe, [750, 850, 900]) \n",
        "politica_cliente ['grau de risco 3'] = fuzz.trimf (politica_cliente.universe, [650, 750, 800]) \n",
        "politica_cliente ['grau de risco 4'] = fuzz.trimf (politica_cliente.universe, [250, 500, 700]) \n",
        "politica_cliente ['grau de risco 5'] = fuzz.trimf (politica_cliente.universe, [0, 250, 300])\n",
        "\n",
        "# Maquina de Inferência \n",
        "regra1 = ctrl.Rule (engajamento ['baixo'] & (score_mercado ['RATING 6'] | score_interno ['RATING 6']) | (score_mercado ['RATING 5'] | score_interno ['RATING 5' ]) | (score_mercado ['RATING 4'] | score_interno ['RATING 4']) | (score_mercado ['RATING 3'] | score_interno ['RATING 3']), politica_cliente ['grau de risco 5'])\n",
        "regra2 = ctrl.Rule (engajamento ['baixo'] & (score_mercado ['RATING 1'] | score_interno ['RATING 1']) | (score_mercado ['RATING 2'] | score_interno ['RATING 2']), politica_cliente ['grau de risco 4'])\n",
        "regra3 = ctrl.Rule (engajamento ['medio'] & (score_mercado ['RATING 6'] | score_interno ['RATING 6']) | (score_mercado ['RATING 5'] | score_interno ['RATING 5']) | ( score_mercado ['RATING 4'] | score_interno ['RATING 4']), politica_cliente ['grau de risco 5'])\n",
        "regra4 = ctrl.Rule (engajamento ['medio'] & (score_mercado ['RATING 1'] | score_interno ['RATING 1']) | (score_mercado ['RATING 2'] | score_interno ['RATING 2']) | ( score_mercado ['RATING 3'] | score_interno ['RATING 3']), politica_cliente ['grau de risco 4'])\n",
        "regra5 = ctrl.Rule (engajamento ['alto'] & (score_mercado ['RATING 6'] | score_interno ['RATING 6']), politica_cliente ['grau de risco 4'])\n",
        "regra6 = ctrl.Rule (engajamento ['alto'] & (score_mercado ['RATING 5'] | score_interno ['RATING 5']) | (score_mercado ['RATING 4'] | score_interno ['RATING 4']), politica_cliente ['grau de risco 3'])\n",
        "regra7 = ctrl.Rule (engajamento ['alto'] & (score_mercado ['RATING 3'] | score_interno ['RATING 3']), politica_cliente ['grau de risco 2'])\n",
        "regra8 = ctrl.Rule (engajamento ['alto'] & (score_mercado ['RATING 1'] | score_interno ['RATING 1']) | (score_mercado ['RATING 2'] | score_interno ['RATING 2']), politica_cliente ['grau de risco 1'])\n",
        "politica_ctrl = ctrl.ControlSystem ([regra1, regra2, regra3, regra4, regra5, regra6, regra7, regra8]) \n",
        "politica_simulador = ctrl.ControlSystemSimulation (politica_ctrl)\n",
        "\n",
        "### Simulação\n",
        "politica_simulador.input ['score_mercado'] = 5 \n",
        "politica_simulador.input ['score_interno'] = 100 \n",
        "politica_simulador.input ['engajamento'] = 90\n",
        "# Deffuzificação \n",
        "politica_simulador.compute () \n",
        "print (politica_simulador.output ['politica_de_risco']) \n",
        "politica_cliente.view (sim = politica_simulador)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "bH6GCK7b0p6m",
        "outputId": "77c8f1b5-4342-4905-ee38-0cdf1953b177"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "159.62962962962902\n"
          ]
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}