tmpt.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled30.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOT9vCtCUbRn+pK1tvtaYMj",
      "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/Vaycold/Python_ML/blob/main/tmpt.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KzRdiryijAy8"
      },
      "source": [
        "## Package & Data, model Load\n",
        "\n",
        "    - eli5 : 머신러닝 모델 설명(Explain Like I am 5)\n",
        "    - pdpbox : partial dependence plot 학습 모델 설명 및 시각화\n",
        "    - lime   : 학습한 결과를 대리분석으로 설명"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zeyoA8K9jaFK",
        "outputId": "5321b47a-6318-44d5-cf8e-e7329f9123dd"
      },
      "source": [
        "! pip install eli5\n",
        "! pip install pdpbox\n",
        "! pip install lime"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1kq4E3JYjocv"
      },
      "source": [
        "  [머신러닝 모델, 데이터셋을 저장, 불러오기 방법]\n",
        "\n",
        "  ____\n",
        "  1. 외부 모델 저장 후, 불러오기\n",
        "  2. 나의 모델 저장 후, 불러오기\n",
        "  3. 세션 내에서 학습시킨 모델 이용하기.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nwH9GjMYidBE",
        "outputId": "27a340a1-14b9-4253-b9a4-bccb2593d2c7"
      },
      "source": [
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import pickle      ## 데이터 모델 저장, 불러오기\n",
        "import gzip        ## 파일압축, 풀기\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/Drive')"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/Drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s9xl9KGsisFr"
      },
      "source": [
        "# ML model\n",
        "datapath = 'https://github.com/mchoimis/financialML/raw/main/xai/'"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NVrQ92u6oTjI"
      },
      "source": [
        "```\n",
        "모델 및 데이터 설명\n",
        " - data00 : 이진분류용 데이터\n",
        " - data01 : 다중분류용 데이터\n",
        " - logreg : Logistic Regression Classifier( 00 )\n",
        " - rfc    : Random Forest Classifier ( 00, 01 )\n",
        " - gbc    : Gradient Boosting Classifier ( 00 )\n",
        " - lgbm   : LightGBM Classifier (00, 01 )\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2sjD4aAjoQq7"
      },
      "source": [
        "import os"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "89lKNJS5pVsY",
        "outputId": "de7e8428-79a3-48cc-b84d-b8042ba71370"
      },
      "source": [
        "# Get present working Directory\n",
        "! pwd"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eqk2SgF2ppyy"
      },
      "source": [
        "# Create New Folder\n",
        "! mkdir financialml"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wRIvg787pxE4"
      },
      "source": [
        "# 저장한 데이터와 모델 불러오기\n",
        "\n",
        "modelpath = '/content/financialml/'\n",
        "with gzip.open(modelpath + 'data00.pickle', 'rb') as f:\n",
        "    data00 = pickle.load(f)\n",
        "with gzip.open(modelpath + 'data00_X_train.pickle', 'rb') as f:\n",
        "    X_train = pickle.load(f)\n",
        "with gzip.open(modelpath + 'data00_X_test.pickle', 'rb') as f:\n",
        "    X_test = pickle.load(f)\n",
        "with gzip.open(modelpath + 'data00_y_train.pickle', 'rb') as f:\n",
        "    y_train = pickle.load(f)\n",
        "with gzip.open(modelpath + 'data00_y_test.pickle', 'rb') as f:\n",
        "    y_test = pickle.load(f)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-KKPSoTbtecv"
      },
      "source": [
        "## Interpretation Logistic Regression model's coef"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IIbqWDL0s6Ct"
      },
      "source": [
        "# 로지스틱 모델 회귀계수 해석\n",
        "# 이진분류 모델 불러오기\n",
        "with open(modelpath + 'logreg00.pkl', 'rb') as file :\n",
        "    logreg = pickle.load(file)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2_vNQPxatMcG",
        "outputId": "f6a96340-3c47-4466-af97-8c487d729594"
      },
      "source": [
        "X_test.columns"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['age', 'workclass', 'education.num', 'marital.status', 'occupation',\n",
              "       'relationship', 'race', 'sex', 'capital.gain', 'capital.loss',\n",
              "       'hours.per.week', 'native.country'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tx3BU6njtN4J",
        "outputId": "bdf7c229-da24-4230-ae9b-435d69a490ab"
      },
      "source": [
        "logreg.coef_"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.45435118, -0.08929983,  0.88011855, -0.37428934, -0.01347698,\n",
              "        -0.19718355,  0.08082303,  0.43380857,  2.34127172,  0.27435968,\n",
              "         0.37244795,  0.01498249]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gXKoHRHntxHs",
        "outputId": "cb666814-9843-4328-d8bb-a0b118c68188"
      },
      "source": [
        "# Change the shape\n",
        "coef = sum(logreg.coef_)\n",
        "coef"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.45435118, -0.08929983,  0.88011855, -0.37428934, -0.01347698,\n",
              "       -0.19718355,  0.08082303,  0.43380857,  2.34127172,  0.27435968,\n",
              "        0.37244795,  0.01498249])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 535
        },
        "id": "ZU24ymg7t5Vx",
        "outputId": "418a5457-4681-4c75-b260-0c7a48d2db3c"
      },
      "source": [
        "# Visualization\n",
        "\n",
        "indices = np.argsort(coef)\n",
        "\n",
        "plt.figure(figsize=(12,8))\n",
        "plt.barh(X_test.columns, coef[indices],\n",
        "         .35,\n",
        "         color = 'orange',\n",
        "         align = 'center')\n",
        "plt.yticks(X_test.columns, X_test.columns[indices],\n",
        "           fontsize=12)\n",
        "plt.xlabel('Coefficients(beta)', fontsize = 12)\n",
        "plt.title('Feature Importances - Logistic Regression Coefficients\\n', \n",
        "          fontsize = 15)\n",
        "plt.show()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FWiX8uVTusnh"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}