overview_on_the_method.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/krylowicz/synthetic-data/blob/master/overview_on_the_method.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OLg_po1qcTRF"
      },
      "source": [
        "## Installations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-HzvMh0fAfEO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d722c9dd-4e43-4c04-8641-47d306322116"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting sdv\n",
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            "  Downloading psutil-5.9.0-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (280 kB)\n",
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            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from numba>=0.48.0->pyts<0.13.0,>=0.12.0->sdmetrics<0.5,>=0.4.1->sdv) (57.4.0)\n",
            "Requirement already satisfied: pillow!=8.3.0,>=5.3.0 in /usr/local/lib/python3.7/dist-packages (from torchvision<1,>=0.9.0->ctgan<0.6,>=0.5.0->sdv) (7.1.2)\n",
            "Installing collected packages: scipy, pyyaml, psutil, rdt, pyts, copulas, sdmetrics, graphviz, Faker, deepecho, ctgan, sdv\n",
            "  Attempting uninstall: scipy\n",
            "    Found existing installation: scipy 1.4.1\n",
            "    Uninstalling scipy-1.4.1:\n",
            "      Successfully uninstalled scipy-1.4.1\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "  Attempting uninstall: psutil\n",
            "    Found existing installation: psutil 5.4.8\n",
            "    Uninstalling psutil-5.4.8:\n",
            "      Successfully uninstalled psutil-5.4.8\n",
            "  Attempting uninstall: graphviz\n",
            "    Found existing installation: graphviz 0.10.1\n",
            "    Uninstalling graphviz-0.10.1:\n",
            "      Successfully uninstalled graphviz-0.10.1\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "Successfully installed Faker-9.9.1 copulas-0.6.0 ctgan-0.5.0 deepecho-0.3.0.post1 graphviz-0.19.1 psutil-5.9.0 pyts-0.12.0 pyyaml-5.4.1 rdt-0.6.3 scipy-1.7.3 sdmetrics-0.4.1 sdv-0.13.1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "psutil"
                ]
              }
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "!pip install sdv"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LK3MEZDvAi5b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "6bd3d87f-42da-4c2f-ed7e-f14809885594"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[?25hInstalling collected packages: pandas, torch, statsmodels, scikit-learn, torchvision, sktime, rdt, copulas, sdmetrics, pomegranate, Faker, deepecho, ctgan, sdv\n",
            "  Attempting uninstall: pandas\n",
            "    Found existing installation: pandas 1.3.5\n",
            "    Uninstalling pandas-1.3.5:\n",
            "      Successfully uninstalled pandas-1.3.5\n",
            "  Attempting uninstall: torch\n",
            "    Found existing installation: torch 1.10.0+cu111\n",
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            "    Found existing installation: statsmodels 0.10.2\n",
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            "    Found existing installation: scikit-learn 1.0.2\n",
            "    Uninstalling scikit-learn-1.0.2:\n",
            "      Successfully uninstalled scikit-learn-1.0.2\n",
            "  Attempting uninstall: torchvision\n",
            "    Found existing installation: torchvision 0.11.1+cu111\n",
            "    Uninstalling torchvision-0.11.1+cu111:\n",
            "      Successfully uninstalled torchvision-0.11.1+cu111\n",
            "  Attempting uninstall: rdt\n",
            "    Found existing installation: rdt 0.6.3\n",
            "    Uninstalling rdt-0.6.3:\n",
            "      Successfully uninstalled rdt-0.6.3\n",
            "  Attempting uninstall: copulas\n",
            "    Found existing installation: copulas 0.6.0\n",
            "    Uninstalling copulas-0.6.0:\n",
            "      Successfully uninstalled copulas-0.6.0\n",
            "  Attempting uninstall: sdmetrics\n",
            "    Found existing installation: sdmetrics 0.4.1\n",
            "    Uninstalling sdmetrics-0.4.1:\n",
            "      Successfully uninstalled sdmetrics-0.4.1\n",
            "  Attempting uninstall: Faker\n",
            "    Found existing installation: Faker 9.9.1\n",
            "    Uninstalling Faker-9.9.1:\n",
            "      Successfully uninstalled Faker-9.9.1\n",
            "  Attempting uninstall: deepecho\n",
            "    Found existing installation: deepecho 0.3.0.post1\n",
            "    Uninstalling deepecho-0.3.0.post1:\n",
            "      Successfully uninstalled deepecho-0.3.0.post1\n",
            "  Attempting uninstall: ctgan\n",
            "    Found existing installation: ctgan 0.5.0\n",
            "    Uninstalling ctgan-0.5.0:\n",
            "      Successfully uninstalled ctgan-0.5.0\n",
            "  Attempting uninstall: sdv\n",
            "    Found existing installation: sdv 0.13.1\n",
            "    Uninstalling sdv-0.13.1:\n",
            "      Successfully uninstalled sdv-0.13.1\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "torchtext 0.11.0 requires torch==1.10.0, but you have torch 1.7.1 which is incompatible.\n",
            "torchaudio 0.10.0+cu111 requires torch==1.10.0, but you have torch 1.7.1 which is incompatible.\u001b[0m\n",
            "Successfully installed Faker-4.14.2 copulas-0.5.1 ctgan-0.4.3 deepecho-0.2.1 pandas-1.1.4 pomegranate-0.14.1 rdt-0.5.3 scikit-learn-0.24.2 sdmetrics-0.3.2 sdv-0.12.0 sktime-0.5.3 statsmodels-0.13.2 torch-1.7.1 torchvision-0.8.2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "pandas"
                ]
              }
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "%pip install sdv==0.12.0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iNWyHHMbGIBq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fe4a3846-aed9-4de2-9186-4abdb198f6d0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: sdmetrics in /usr/local/lib/python3.7/dist-packages (0.3.2)\n",
            "Requirement already satisfied: copulas<0.6,>=0.5.0 in /usr/local/lib/python3.7/dist-packages (from sdmetrics) (0.5.1)\n",
            "Requirement already satisfied: scipy<2,>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from sdmetrics) (1.7.3)\n",
            "Requirement already satisfied: scikit-learn<1,>=0.23 in /usr/local/lib/python3.7/dist-packages (from sdmetrics) (0.24.2)\n",
            "Requirement already satisfied: pandas<1.1.5,>=1.1 in /usr/local/lib/python3.7/dist-packages (from sdmetrics) (1.1.4)\n",
            "Requirement already satisfied: rdt<0.6,>=0.5.0 in /usr/local/lib/python3.7/dist-packages (from sdmetrics) (0.5.3)\n",
            "Requirement already satisfied: torch<2,>=1.4 in /usr/local/lib/python3.7/dist-packages (from sdmetrics) (1.7.1)\n",
            "Requirement already satisfied: numpy<2,>=1.18.0 in /usr/local/lib/python3.7/dist-packages (from sdmetrics) (1.21.5)\n",
            "Requirement already satisfied: sktime<0.6,>=0.4 in /usr/local/lib/python3.7/dist-packages (from sdmetrics) (0.5.3)\n",
            "Requirement already satisfied: matplotlib<4,>=3.2.0 in /usr/local/lib/python3.7/dist-packages (from copulas<0.6,>=0.5.0->sdmetrics) (3.2.2)\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<4,>=3.2.0->copulas<0.6,>=0.5.0->sdmetrics) (3.0.7)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib<4,>=3.2.0->copulas<0.6,>=0.5.0->sdmetrics) (2.8.2)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib<4,>=3.2.0->copulas<0.6,>=0.5.0->sdmetrics) (0.11.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib<4,>=3.2.0->copulas<0.6,>=0.5.0->sdmetrics) (1.3.2)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas<1.1.5,>=1.1->sdmetrics) (2018.9)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib<4,>=3.2.0->copulas<0.6,>=0.5.0->sdmetrics) (1.15.0)\n",
            "Requirement already satisfied: psutil<6,>=5.7 in /usr/local/lib/python3.7/dist-packages (from rdt<0.6,>=0.5.0->sdmetrics) (5.9.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn<1,>=0.23->sdmetrics) (1.1.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn<1,>=0.23->sdmetrics) (3.1.0)\n",
            "Requirement already satisfied: wheel in /usr/local/lib/python3.7/dist-packages (from sktime<0.6,>=0.4->sdmetrics) (0.37.1)\n",
            "Requirement already satisfied: numba>=0.50 in /usr/local/lib/python3.7/dist-packages (from sktime<0.6,>=0.4->sdmetrics) (0.51.2)\n",
            "Requirement already satisfied: statsmodels>=0.12.1 in /usr/local/lib/python3.7/dist-packages (from sktime<0.6,>=0.4->sdmetrics) (0.13.2)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from numba>=0.50->sktime<0.6,>=0.4->sdmetrics) (57.4.0)\n",
            "Requirement already satisfied: llvmlite<0.35,>=0.34.0.dev0 in /usr/local/lib/python3.7/dist-packages (from numba>=0.50->sktime<0.6,>=0.4->sdmetrics) (0.34.0)\n",
            "Requirement already satisfied: patsy>=0.5.2 in /usr/local/lib/python3.7/dist-packages (from statsmodels>=0.12.1->sktime<0.6,>=0.4->sdmetrics) (0.5.2)\n",
            "Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.7/dist-packages (from statsmodels>=0.12.1->sktime<0.6,>=0.4->sdmetrics) (21.3)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch<2,>=1.4->sdmetrics) (3.10.0.2)\n"
          ]
        }
      ],
      "source": [
        "!pip install sdmetrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6c41tvEJHvFR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fad1f000-e836-4320-a6e1-b06ec39f3851"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: ctgan in /usr/local/lib/python3.7/dist-packages (0.4.3)\n",
            "Requirement already satisfied: scikit-learn<1,>=0.23 in /usr/local/lib/python3.7/dist-packages (from ctgan) (0.24.2)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from ctgan) (21.3)\n",
            "Requirement already satisfied: numpy<2,>=1.18.0 in /usr/local/lib/python3.7/dist-packages (from ctgan) (1.21.5)\n",
            "Requirement already satisfied: rdt<0.6,>=0.5.0 in /usr/local/lib/python3.7/dist-packages (from ctgan) (0.5.3)\n",
            "Requirement already satisfied: pandas<1.1.5,>=1.1 in /usr/local/lib/python3.7/dist-packages (from ctgan) (1.1.4)\n",
            "Requirement already satisfied: torchvision<1,>=0.5.0 in /usr/local/lib/python3.7/dist-packages (from ctgan) (0.8.2)\n",
            "Requirement already satisfied: torch<2,>=1.4 in /usr/local/lib/python3.7/dist-packages (from ctgan) (1.7.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas<1.1.5,>=1.1->ctgan) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas<1.1.5,>=1.1->ctgan) (2018.9)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas<1.1.5,>=1.1->ctgan) (1.15.0)\n",
            "Requirement already satisfied: scipy<2,>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from rdt<0.6,>=0.5.0->ctgan) (1.7.3)\n",
            "Requirement already satisfied: psutil<6,>=5.7 in /usr/local/lib/python3.7/dist-packages (from rdt<0.6,>=0.5.0->ctgan) (5.9.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn<1,>=0.23->ctgan) (1.1.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn<1,>=0.23->ctgan) (3.1.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch<2,>=1.4->ctgan) (3.10.0.2)\n",
            "Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.7/dist-packages (from torchvision<1,>=0.5.0->ctgan) (7.1.2)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->ctgan) (3.0.7)\n"
          ]
        }
      ],
      "source": [
        "!pip install ctgan"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The latest Version of table_evaluatore doesnt work properly. Should be downgraded to the specific version"
      ],
      "metadata": {
        "id": "z9vghiua114L"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hJq-hQwhGZkM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "218df7b3-2ab8-43fa-e788-82fb5dbce608"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting table_evaluator==1.2.2.post1\n",
            "  Downloading table-evaluator-1.2.2.post1.tar.gz (18 kB)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from table_evaluator==1.2.2.post1) (1.1.4)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from table_evaluator==1.2.2.post1) (1.21.5)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from table_evaluator==1.2.2.post1) (4.62.3)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.7/dist-packages (from table_evaluator==1.2.2.post1) (5.9.0)\n",
            "Requirement already satisfied: dython==0.5.1 in /usr/local/lib/python3.7/dist-packages (from table_evaluator==1.2.2.post1) (0.5.1)\n",
            "Requirement already satisfied: seaborn in /usr/local/lib/python3.7/dist-packages (from table_evaluator==1.2.2.post1) (0.11.2)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from table_evaluator==1.2.2.post1) (3.2.2)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from table_evaluator==1.2.2.post1) (0.24.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from table_evaluator==1.2.2.post1) (1.7.3)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->table_evaluator==1.2.2.post1) (0.11.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->table_evaluator==1.2.2.post1) (1.3.2)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->table_evaluator==1.2.2.post1) (2.8.2)\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->table_evaluator==1.2.2.post1) (3.0.7)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib->table_evaluator==1.2.2.post1) (1.15.0)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas->table_evaluator==1.2.2.post1) (2018.9)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->table_evaluator==1.2.2.post1) (1.1.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->table_evaluator==1.2.2.post1) (3.1.0)\n",
            "Building wheels for collected packages: table-evaluator\n",
            "  Building wheel for table-evaluator (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for table-evaluator: filename=table_evaluator-1.2.2.post1-py3-none-any.whl size=17051 sha256=2ac0c1103f7f48db9cb165991bc48ab2c6092fcd46143010fa5d407bd77a0533\n",
            "  Stored in directory: /root/.cache/pip/wheels/45/c7/20/47a3f8b044d705f3c088d9577968a805dded4b83096fabd2ac\n",
            "Successfully built table-evaluator\n",
            "Installing collected packages: table-evaluator\n",
            "  Attempting uninstall: table-evaluator\n",
            "    Found existing installation: table-evaluator 1.3.2\n",
            "    Uninstalling table-evaluator-1.3.2:\n",
            "      Successfully uninstalled table-evaluator-1.3.2\n",
            "Successfully installed table-evaluator-1.2.2.post1\n"
          ]
        }
      ],
      "source": [
        "!pip install table_evaluator==1.2.2.post1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-TUiEbPYg9rf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bd1dbc11-4c73-41bb-aa32-f547dbcd6625"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: tensorflow in /usr/local/lib/python3.7/dist-packages (2.8.0)\n",
            "Collecting tf-estimator-nightly==2.8.0.dev2021122109\n",
            "  Downloading tf_estimator_nightly-2.8.0.dev2021122109-py2.py3-none-any.whl (462 kB)\n",
            "\u001b[K     |████████████████████████████████| 462 kB 8.0 MB/s \n",
            "\u001b[?25hRequirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (3.3.0)\n",
            "Requirement already satisfied: h5py>=2.9.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (3.1.0)\n",
            "Requirement already satisfied: gast>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (0.5.3)\n",
            "Requirement already satisfied: absl-py>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (1.0.0)\n",
            "Requirement already satisfied: libclang>=9.0.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (13.0.0)\n",
            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (1.43.0)\n",
            "Requirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (1.13.3)\n",
            "Requirement already satisfied: keras-preprocessing>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (1.1.2)\n",
            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (0.2.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from tensorflow) (57.4.0)\n",
            "Requirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (1.21.5)\n",
            "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (1.6.3)\n",
            "Requirement already satisfied: protobuf>=3.9.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (3.17.3)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (1.15.0)\n",
            "Requirement already satisfied: flatbuffers>=1.12 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (2.0)\n",
            "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (0.24.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (1.1.0)\n",
            "Requirement already satisfied: keras<2.9,>=2.8.0rc0 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (2.8.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (3.10.0.2)\n",
            "Requirement already satisfied: tensorboard<2.9,>=2.8 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (2.8.0)\n",
            "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.7/dist-packages (from astunparse>=1.6.0->tensorflow) (0.37.1)\n",
            "Requirement already satisfied: cached-property in /usr/local/lib/python3.7/dist-packages (from h5py>=2.9.0->tensorflow) (1.5.2)\n",
            "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow) (0.6.1)\n",
            "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow) (1.35.0)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow) (1.0.1)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow) (3.3.6)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow) (0.4.6)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow) (2.23.0)\n",
            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow) (1.8.1)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.9,>=2.8->tensorflow) (4.2.4)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.9,>=2.8->tensorflow) (0.2.8)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.9,>=2.8->tensorflow) (4.8)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.9,>=2.8->tensorflow) (1.3.1)\n",
            "Requirement already satisfied: importlib-metadata>=4.4 in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard<2.9,>=2.8->tensorflow) (4.11.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard<2.9,>=2.8->tensorflow) (3.7.0)\n",
            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.9,>=2.8->tensorflow) (0.4.8)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.9,>=2.8->tensorflow) (2021.10.8)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.9,>=2.8->tensorflow) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.9,>=2.8->tensorflow) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.9,>=2.8->tensorflow) (3.0.4)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.9,>=2.8->tensorflow) (3.2.0)\n",
            "Installing collected packages: tf-estimator-nightly\n",
            "Successfully installed tf-estimator-nightly-2.8.0.dev2021122109\n"
          ]
        }
      ],
      "source": [
        "!pip install tensorflow"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install table-evaluator"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S-ZVRRxruRsY",
        "outputId": "a343df8d-6ef2-49c9-bd64-fbb00d9a1f55"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: table-evaluator in /usr/local/lib/python3.7/dist-packages (1.2.2.post1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from table-evaluator) (1.21.5)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.7/dist-packages (from table-evaluator) (5.9.0)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from table-evaluator) (0.24.2)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from table-evaluator) (1.1.4)\n",
            "Requirement already satisfied: seaborn in /usr/local/lib/python3.7/dist-packages (from table-evaluator) (0.11.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from table-evaluator) (1.7.3)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from table-evaluator) (4.62.3)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from table-evaluator) (3.2.2)\n",
            "Requirement already satisfied: dython==0.5.1 in /usr/local/lib/python3.7/dist-packages (from table-evaluator) (0.5.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->table-evaluator) (1.3.2)\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->table-evaluator) (3.0.7)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->table-evaluator) (2.8.2)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->table-evaluator) (0.11.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib->table-evaluator) (1.15.0)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas->table-evaluator) (2018.9)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->table-evaluator) (3.1.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->table-evaluator) (1.1.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4-vYfOIgTSCN"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "from ctgan import CTGANSynthesizer\n",
        "from ctgan import load_demo\n",
        "\n",
        "from table_evaluator import load_data, TableEvaluator\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# SDV PACKAGE\n",
        "from sdv import SDV\n",
        "from sdv.tabular import TVAE\n",
        "\n",
        "from sdv import load_demo\n",
        "from sdv.evaluation import evaluate\n",
        "\n",
        "from sdv import Metadata\n",
        "\n",
        "from table_evaluator import load_data, TableEvaluator\n",
        "\n",
        "import sdmetrics\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.impute import SimpleImputer, KNNImputer\n",
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.metrics import roc_curve, roc_auc_score, confusion_matrix, classification_report\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "from sklearn import metrics"
      ],
      "metadata": {
        "id": "GB-vhirVTFjK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7OgsUR_ZddFg"
      },
      "source": [
        "## Import dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fLSdfWaudmdB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 398
        },
        "outputId": "90870ece-88da-4ff0-99d6-6e6e6d3688fa"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-3-6667a57192a7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive/MyDrive/Capstone/_data/dataset_09022022.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    686\u001b[0m     )\n\u001b[1;32m    687\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 688\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    689\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    690\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    452\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    453\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 454\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    455\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    456\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    946\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    947\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 948\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    949\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    950\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1178\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"c\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1179\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"c\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1180\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1181\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1182\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"python\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m   2008\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"usecols\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2009\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2010\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2011\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2012\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/content/drive/MyDrive/Capstone/_data/dataset_09022022.csv'"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv('/content/drive/MyDrive/Capstone/_data/dataset_09022022.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a_wXZmeuDvkx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165
        },
        "outputId": "5a934ba1-895a-42f4-8de2-d620fe363ea2"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-4-ebe1ea663a6b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m2000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m17\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
          ]
        }
      ],
      "source": [
        "df = df.sample(n = 2000, random_state=17)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "W2BhU8qL1jTR"
      },
      "outputs": [],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MMpy9gOx4o33"
      },
      "outputs": [],
      "source": [
        "df.head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yMVGcDUhIIpL"
      },
      "source": [
        "## Data Cleaning\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dJCWxXFjIHtt"
      },
      "outputs": [],
      "source": [
        "df = df.dropna()\n",
        "df = df.drop('Unnamed: 0', 1)\n",
        "df.date = pd.to_datetime(df.date)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9HAweUbQ2b1U"
      },
      "outputs": [],
      "source": [
        "df['postcode']=df['postcode'].astype(str)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xs_52mH7tKeA"
      },
      "outputs": [],
      "source": [
        "df.loan_status.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EUkeKLPb6xkd"
      },
      "outputs": [],
      "source": [
        "# df['dates']=df['dates'].astype(str)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dividing dataset to categorical & numerical columns; used for table evaluation"
      ],
      "metadata": {
        "id": "cmV9Is34yMlA"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zoKC9wzvHFZf"
      },
      "outputs": [],
      "source": [
        "df_cat = df[['customer_id', \n",
        "                    'city',\n",
        "                    'street',\n",
        "                    'date', 'loan_status']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0Ml5cUPGfjWG"
      },
      "outputs": [],
      "source": [
        "df_num = df[['current_loan_amount',   \n",
        " 'annual_income',\n",
        " 'monthly_debt',  \n",
        " 'current_credit_balance',\n",
        " 'bankruptcies',\n",
        " 'postcode']]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv('/content/drive/MyDrive/Capstone/_data/df_sample.csv')"
      ],
      "metadata": {
        "id": "oHPPQwB6pDa9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5arLVl-0kaUL"
      },
      "source": [
        "## CTGAN"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A GAN-based Deep Learning data synthesizer that can generate synthetic tabular data with high fidelity"
      ],
      "metadata": {
        "id": "sJo3VlvOqAoR"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "keCQfAgOOPNY"
      },
      "source": [
        "### Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XIEeeInl3_rd"
      },
      "outputs": [],
      "source": [
        "discrete_columns = ['customer_id',\n",
        "                    'bankruptcies', \n",
        "                    'city',\n",
        "                    'street',\n",
        "                    'date', 'loan_status','current_loan_amount',   \n",
        " 'annual_income',\n",
        " 'monthly_debt',  \n",
        " 'current_credit_balance',\n",
        " 'bankruptcies',\n",
        " 'postcode']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lHJ_YydVSD70"
      },
      "outputs": [],
      "source": [
        "ctgan = CTGANSynthesizer(epochs=500, verbose=True)\n",
        "ctgan.fit(df, discrete_columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dsMkFmp4Spai"
      },
      "outputs": [],
      "source": [
        "# Synthetic copy\n",
        "sample = ctgan.sample(2000)\n",
        "sample"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AE_l8rP6Oa4E"
      },
      "outputs": [],
      "source": [
        "sample.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "r6ZDlbCwKYPE"
      },
      "outputs": [],
      "source": [
        "sample.to_csv('/content/drive/MyDrive/Capstone/_STD/CTGAN_500epochs_2000entries.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vpo2ZgJEOErO"
      },
      "source": [
        "### Evaluation q = 0.65"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "\n",
        "sns.lineplot(data=df, x=\"date\", y=\"annual_income\")"
      ],
      "metadata": {
        "id": "ZBIo3kTUEHDW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.lineplot(data=sample, x=\"date\", y=\"annual_income\")"
      ],
      "metadata": {
        "id": "kYPZfmAqGgn8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/Capstone/_data/df_sample.csv')"
      ],
      "metadata": {
        "id": "ac7GGsKd0nkg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sample = pd.read_csv('/content/drive/MyDrive/Capstone/_STD/CTGAN_500epochs_2000entries.csv')"
      ],
      "metadata": {
        "id": "_YHyiUbr00Q1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OzNgw96gVlj4"
      },
      "outputs": [],
      "source": [
        "from table_evaluator import load_data, TableEvaluator\n",
        "table_evaluator = TableEvaluator(df, sample, cat_cols=df_cat.columns.values.tolist())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YZ0kadSnFDfN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "30de3e4b-70e5-4470-fefa-1e7d420174ef"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x1512 with 12 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x2016 with 12 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1728x504 with 6 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "table_evaluator.visual_evaluation()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sT30GSG7z1tZ"
      },
      "source": [
        "The output of this function EVALUATE() call will be a number between 0 and 1 that will indicate how similar the two tables are, being 0 the worst and 1 the best possible score."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fetK25yefj4_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "83ccc1c7-4e45-40ce-f22b-06448b1a3e52"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.47798152235059743"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "evaluate(sample, df)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate(sample, df, aggregate=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "oo1yHZao1TNW",
        "outputId": "7a91c4d6-d760-4588-f810-c18f485fb5e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-54b11edc-a5a7-4c95-8493-07bd0c13484d\">\n",
              "    <div 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>metric</th>\n",
              "      <th>name</th>\n",
              "      <th>raw_score</th>\n",
              "      <th>normalized_score</th>\n",
              "      <th>min_value</th>\n",
              "      <th>max_value</th>\n",
              "      <th>goal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>BNLogLikelihood</td>\n",
              "      <td>BayesianNetwork Log Likelihood</td>\n",
              "      <td>-18.420681</td>\n",
              "      <td>1.000000e-08</td>\n",
              "      <td>-inf</td>\n",
              "      <td>0.0</td>\n",
              "      <td>MAXIMIZE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>LogisticDetection</td>\n",
              "      <td>LogisticRegression Detection</td>\n",
              "      <td>0.028195</td>\n",
              "      <td>2.819516e-02</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>MAXIMIZE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>SVCDetection</td>\n",
              "      <td>SVC Detection</td>\n",
              "      <td>0.207707</td>\n",
              "      <td>2.077073e-01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>MAXIMIZE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>GMLogLikelihood</td>\n",
              "      <td>GaussianMixture Log Likelihood</td>\n",
              "      <td>-68.583249</td>\n",
              "      <td>1.639356e-30</td>\n",
              "      <td>-inf</td>\n",
              "      <td>inf</td>\n",
              "      <td>MAXIMIZE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>CSTest</td>\n",
              "      <td>Chi-Squared</td>\n",
              "      <td>0.970614</td>\n",
              "      <td>9.706135e-01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>MAXIMIZE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>KSTest</td>\n",
              "      <td>Inverted Kolmogorov-Smirnov D statistic</td>\n",
              "      <td>0.836643</td>\n",
              "      <td>8.366429e-01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>MAXIMIZE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>KSTestExtended</td>\n",
              "      <td>Inverted Kolmogorov-Smirnov D statistic</td>\n",
              "      <td>0.883875</td>\n",
              "      <td>8.838750e-01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>MAXIMIZE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>ContinuousKLDivergence</td>\n",
              "      <td>Continuous Kullback–Leibler Divergence</td>\n",
              "      <td>0.756836</td>\n",
              "      <td>7.568357e-01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>MAXIMIZE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>DiscreteKLDivergence</td>\n",
              "      <td>Discrete Kullback–Leibler Divergence</td>\n",
              "      <td>0.138317</td>\n",
              "      <td>1.383175e-01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>MAXIMIZE</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-54b11edc-a5a7-4c95-8493-07bd0c13484d')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-54b11edc-a5a7-4c95-8493-07bd0c13484d 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-54b11edc-a5a7-4c95-8493-07bd0c13484d');\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",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                    metric  ...      goal\n",
              "0          BNLogLikelihood  ...  MAXIMIZE\n",
              "1        LogisticDetection  ...  MAXIMIZE\n",
              "2             SVCDetection  ...  MAXIMIZE\n",
              "11         GMLogLikelihood  ...  MAXIMIZE\n",
              "12                  CSTest  ...  MAXIMIZE\n",
              "13                  KSTest  ...  MAXIMIZE\n",
              "14          KSTestExtended  ...  MAXIMIZE\n",
              "27  ContinuousKLDivergence  ...  MAXIMIZE\n",
              "28    DiscreteKLDivergence  ...  MAXIMIZE\n",
              "\n",
              "[9 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sZ_5OknpAYgQ"
      },
      "outputs": [],
      "source": [
        "df.hist(figsize=(12,12), sharey=True);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hFszhhiM9Z0L"
      },
      "outputs": [],
      "source": [
        "sample.hist(figsize=(12,12), sharey=True);"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wtsb7G_4v7KB"
      },
      "source": [
        "#### Utility metrics (TRTR *etc*)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lAxTB3DddHhi"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "sample = pd.read_csv('/content/drive/MyDrive/Capstone/_STD/CTGAN_500epochs_2000entries.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cZNcwK1SgwHy"
      },
      "outputs": [],
      "source": [
        "df['date_year'] = df['date'].dt.year\n",
        "df['date_month'] = df['date'].dt.month\n",
        "df['date_day'] = df['date'].dt.day"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0JF340Ah2Fej"
      },
      "outputs": [],
      "source": [
        "df['date_month']=df['date_month'].astype(str)\n",
        "df['date_day'] = df['date'].astype(str)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QSWQmXgF6Lsv"
      },
      "outputs": [],
      "source": [
        "df = df.drop('postcode', 1)\n",
        "df = df.drop('city', 1)\n",
        "df = df.drop('date_year', 1)\n",
        "df = df.drop('customer_id', 1)\n",
        "df = df.drop('date', 1)\n",
        "df = df.drop('date_day', 1)\n",
        "df = df.drop('street', 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RZ80g5jzg9nA"
      },
      "outputs": [],
      "source": [
        "sample.date = pd.to_datetime(sample.date)\n",
        "sample['date_year'] = sample['date'].dt.year\n",
        "sample['date_month'] = sample['date'].dt.month\n",
        "sample['date_day'] = sample['date'].dt.day"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RyroRF__2MuH"
      },
      "outputs": [],
      "source": [
        "sample['date_month']=sample['date_month'].astype(str)\n",
        "sample['postcode']=sample['postcode'].astype(str)\n",
        "sample['date_day'] = sample['date'].astype(str)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7tEyUXgrvuuq"
      },
      "outputs": [],
      "source": [
        "sample.loan_status.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "liLL7S2MhncQ"
      },
      "outputs": [],
      "source": [
        "sample = sample.drop('Unnamed: 0', 1)\n",
        "sample = sample.drop('date_year', 1)\n",
        "sample = sample.drop('customer_id', 1)\n",
        "sample = sample.drop('postcode', 1)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sample = sample.drop('city', 1)\n",
        "sample = sample.drop('street', 1)\n",
        "sample = sample.drop('date_month', 1)\n",
        "sample = sample.drop('date_day', 1)\n",
        "sample = sample.drop('date', 1)"
      ],
      "metadata": {
        "id": "pkppmEfe7Axt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "id": "XfvmYfGWt21N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sample"
      ],
      "metadata": {
        "id": "JJZspgO36cN1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PeN2ZPv5wFr-"
      },
      "outputs": [],
      "source": [
        "X_train_RD, X_test_RD, y_train_RD, y_test_RD = train_test_split(df.drop(columns=['loan_status']), # X\n",
        "                                                    df['loan_status'], # y\n",
        "                                                    test_size=0.3, # %age of test split\n",
        "                                                    random_state=17, stratify = df['loan_status'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tHrBZoQ9KF5w"
      },
      "outputs": [],
      "source": [
        "X_train_STD, X_test_STD, y_train_STD, y_test_STD = train_test_split(sample.drop(columns=['loan_status']), # X\n",
        "                                                    sample['loan_status'], # y\n",
        "                                                    test_size=0.3, # %age of test split\n",
        "                                                    random_state=17, stratify = sample['loan_status'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8rPrjYH1daOX"
      },
      "outputs": [],
      "source": [
        "categorical_features_RD = X_train_RD.select_dtypes(include=['object', 'category']).columns.tolist()\n",
        "numeric_features_RD = X_train_RD.select_dtypes(include=['int', 'float']).columns.tolist()\n",
        "\n",
        "categorical_features_RD, numeric_features_RD"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "343XIutwdsPy"
      },
      "outputs": [],
      "source": [
        "categorical_features_STD = X_train_STD.select_dtypes(include=['object', 'category']).columns.tolist()\n",
        "numeric_features_STD = X_train_STD.select_dtypes(include=['int', 'float']).columns.tolist()\n",
        "\n",
        "categorical_features_STD, numeric_features_STD"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "categorical_transformer = Pipeline(steps=[\n",
        "                                          (\"cat_imputer\", SimpleImputer(strategy='constant',\n",
        "                                                                        fill_value='Not Available')),\n",
        "                                          (\"onehot\", OneHotEncoder(handle_unknown=\"ignore\"))\n",
        "                                          ])\n",
        "categorical_transformer"
      ],
      "metadata": {
        "id": "2gq4-XJ5pToU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "numeric_transformer = Pipeline(steps=[\n",
        "                                      (\"knn_imputer\", KNNImputer(n_neighbors=5)),\n",
        "                                      (\"scaler\", StandardScaler())\n",
        "                                      ])\n",
        "\n",
        "numeric_transformer"
      ],
      "metadata": {
        "id": "h1J6AKKLpXu-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "numeric_transformer = Pipeline(steps=[\n",
        "                                      (\"knn_imputer\", KNNImputer(n_neighbors=5))\n",
        "                                      ])\n",
        "\n",
        "numeric_transformer"
      ],
      "metadata": {
        "id": "BUZDCYmGNQMc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HmXe-ojUiHtW"
      },
      "outputs": [],
      "source": [
        "preprocessor_RD = ColumnTransformer(transformers=[\n",
        "                                               (\"num\", numeric_transformer, \n",
        "                                                       numeric_features_RD),\n",
        "                                               (\"cat\", categorical_transformer, \n",
        "                                                       categorical_features_RD)\n",
        "                                               ])\n",
        "preprocessor_RD"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0d0joPiDiOlt"
      },
      "outputs": [],
      "source": [
        "preprocessor_STD = ColumnTransformer(transformers=[\n",
        "                                               (\"num\", numeric_transformer, \n",
        "                                                       numeric_features_STD),\n",
        "                                               (\"cat\", categorical_transformer, \n",
        "                                                       categorical_features_STD)\n",
        "                                               ])\n",
        "preprocessor_STD"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "56DtiXeSqgfk"
      },
      "source": [
        "##### TRTR"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "foNRAJ_Rq14n"
      },
      "outputs": [],
      "source": [
        "scores_train = []\n",
        "scores_test = []\n",
        "f1_scores = []\n",
        "\n",
        "models = {RandomForestClassifier(random_state=17, class_weight='balanced')}\n",
        "\n",
        "for i in models:\n",
        "    model = i\n",
        "    pipeline = Pipeline(steps=[\n",
        "                              (\"pre_process\", preprocessor_RD), \n",
        "                              (\"model\", model)\n",
        "                              ])\n",
        "    pipeline.fit(X_train_RD, y_train_RD)\n",
        "    y_pred = pipeline.predict(X_test_RD)\n",
        "    scores_train.append([i, np.mean(cross_val_score(pipeline, X_train_RD, y_train_RD))]) #scoring='f1_weighted'\n",
        "    #scores_test.append(cross_val_score(pipeline_lr, X_test, y_test))\n",
        "    f1_scores.append([i,f1_score(y_test_RD, y_pred,average=\"weighted\")])\n",
        "scores_train, f1_scores"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "target_names = ['Charged Off', 'Fully Paid']\n",
        "print(classification_report(y_test_RD, y_pred, target_names=target_names))"
      ],
      "metadata": {
        "id": "6YWbA_qZJ97X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "conf_mat = confusion_matrix(y_test_RD, y_pred)\n",
        "conf_mat"
      ],
      "metadata": {
        "id": "B8ZWyHBIQAL8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KYayLQtT5fYh"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "# {'Charged Off': 3.5,\n",
        "#                                            'Fully Paid': 1}\n",
        "\n",
        "model = LogisticRegression(random_state=17, solver='liblinear', class_weight= 'balanced')\n",
        "pipeline = Pipeline(steps=[\n",
        "                              (\"pre_process\", preprocessor_RD), \n",
        "                              (\"model\", model)\n",
        "                              ])\n",
        "pipeline.fit(X_train_RD, y_train_RD)\n",
        "y_pred = pipeline.predict(X_test_RD)\n",
        "scores_train = np.mean(cross_val_score(pipeline, X_train_RD, y_train_RD)) #scoring='f1_weighted'\n",
        "f1_scores = f1_score(y_test_RD, y_pred,average=\"weighted\")\n",
        "scores_train, f1_scores "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_Uj_fqEhvVTC"
      },
      "outputs": [],
      "source": [
        "conf_mat = confusion_matrix(y_test_RD, y_pred)\n",
        "conf_mat"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3kk7BUxFvcor"
      },
      "outputs": [],
      "source": [
        "target_names = ['Charged Off', 'Fully Paid']\n",
        "print(classification_report(y_test_RD, y_pred, target_names=target_names))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "# {'Charged Off': 3.5,\n",
        "#                                            'Fully Paid': 1}\n",
        "\n",
        "model = LogisticRegression(random_state=17, C = 1.0,\n",
        " class_weight =None, dual = False, fit_intercept = True, intercept_scaling = 1, l1_ratio = None,\n",
        " max_iter = 1000,n_jobs = None, penalty = 'l2', solver = 'lbfgs',\n",
        " tol = 0.0001, verbose = 1, warm_start = False)\n",
        "pipeline = Pipeline(steps=[\n",
        "                              (\"pre_process\", preprocessor_RD), \n",
        "                              (\"model\", model)\n",
        "                              ])\n",
        "pipeline.fit(X_train_RD, y_train_RD)\n",
        "y_pred = pipeline.predict(X_test_RD)\n",
        "scores_train = np.mean(cross_val_score(pipeline, X_train_RD, y_train_RD)) #scoring='f1_weighted'\n",
        "f1_scores = f1_score(y_test_RD, y_pred,average=\"weighted\")\n",
        "scores_train, f1_scores "
      ],
      "metadata": {
        "id": "HiEl2XGa4LtK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "conf_mat = confusion_matrix(y_test_RD, y_pred)\n",
        "conf_mat"
      ],
      "metadata": {
        "id": "I1SM8Bgb5L-W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "target_names = ['Charged Off', 'Fully Paid']\n",
        "print(classification_report(y_test_RD, y_pred, target_names=target_names))"
      ],
      "metadata": {
        "id": "wbLGPlxa5PQQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "GridSearch"
      ],
      "metadata": {
        "id": "kYcC94dawaVH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "# Hyperparameter Optimization\n",
        "parameters = {'model__C' : [1, 10, 100, 1000],\n",
        "              #'penalty': ['none', 'l1', 'l2', 'elasticnet'],\n",
        "              'model__solver': ['liblinear', 'lbfgs']\n",
        "             }\n",
        "\n",
        "# Run the grid search\n",
        "lr = LogisticRegression(random_state=17)\n",
        "pipeline = Pipeline(steps=[\n",
        "                              (\"pre_process\", preprocessor_RD), \n",
        "                              (\"model\", lr)\n",
        "                              ])\n",
        "grid_obj = GridSearchCV(pipeline, parameters, cv=5)\n",
        "grid_obj = grid_obj.fit(X_train_RD, y_train_RD)\n",
        "\n",
        "# Set the clf to the best combination of parameters\n",
        "clf = grid_obj.best_estimator_\n",
        "\n",
        "# Train the model using the training sets \n",
        "clf.fit(X_train_RD, y_train_RD)"
      ],
      "metadata": {
        "id": "Lj07xg1Rzmsr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "grid_obj.best_params_"
      ],
      "metadata": {
        "id": "4E9bphWAz6G5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        " # Prediction on test data\n",
        "y_pred = clf.predict(X_test_RD)\n",
        "\n",
        "# Calculating the accuracy\n",
        "acc_logreg = round( metrics.accuracy_score(y_test_RD, y_pred) * 100, 2 )\n",
        "print( 'Accuracy of Logistic Regression model : ', acc_logreg )"
      ],
      "metadata": {
        "id": "EURNq7Ks0jra"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "target_names = ['Charged Off', 'Fully Paid']\n",
        "print(classification_report(y_test_RD, y_pred, target_names=target_names))"
      ],
      "metadata": {
        "id": "ga8POm211Akq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "# Hyperparameter Optimization\n",
        "parameters = {'criterion': ['entropy', 'gini'],\n",
        "                 'max_depth': [10, 20, None],\n",
        "                 'max_features': ['sqrt', 'log2'],\n",
        "                 'n_estimators': [50, 100, 200]}\n",
        "\n",
        "# Run the grid search\n",
        "rf = RandomForestClassifier(random_state=17, class_weight='balanced')\n",
        "pipeline = Pipeline(steps=[\n",
        "                              (\"pre_process\", preprocessor_RD), \n",
        "                              (\"model\", rf)\n",
        "                              ])\n",
        "grid_obj = GridSearchCV(pipeline, parameters, scoring='f1', cv=3, verbose=5, n_jobs=-1)\n",
        "grid_obj = grid_obj.fit(X_train_RD, y_train_RD)\n",
        "\n",
        "# Set the clf to the best combination of parameters\n",
        "clf = grid_obj.best_estimator_\n",
        "\n",
        "# Train the model using the training sets \n",
        "clf.fit(X_train_RD, y_train_RD)"
      ],
      "metadata": {
        "id": "6iflpYUkLDgs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# from sklearn.model_selection import RandomizedSearchCV\n",
        "# from sklearn.model_selection import cross_val_score\n",
        "\n",
        "# random_search = {'criterion': ['entropy', 'gini'],\n",
        "#                  'max_depth': [10, 20, None],\n",
        "#                  'max_features': ['sqrt', 'log2'],\n",
        "#                  'n_estimators': [50, 100, 200]}\n",
        "\n",
        "#                # 2 x 3 x 2 x 3 = 36 => randomly selects 10 combinations (models) from 36 possible model hyperparameter configs\n",
        "\n",
        "# rf = RandomForestClassifier(random_state=17, class_weight='balanced')\n",
        "# clf = Pipeline(steps=[\n",
        "#                               (\"pre_process\", preprocessor_RD), \n",
        "#                               (\"model\", rf)\n",
        "#                               ])\n",
        "\n",
        "# random_search_obj = RandomizedSearchCV(estimator=clf,\n",
        "#                            param_distributions=random_search,\n",
        "#                            n_iter=10,\n",
        "#                            scoring='f1',\n",
        "#                            cv=3, verbose=1, random_state=42, n_jobs=-1)\n",
        "\n",
        "# random_search_obj.fit(X_train_RD, y_train_RD)"
      ],
      "metadata": {
        "id": "leY1uAqqMVKB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from pycaret.classification import *\n",
        "\n",
        "reg_new = setup(data=pd.concat([X_train_RD, y_train_RD], axis=1), \n",
        "            target = 'loan_status', \n",
        "            session_id=13, \n",
        "            log_experiment=True, \n",
        "            experiment_name='Challenge')"
      ],
      "metadata": {
        "id": "vCo4iTJaoDuq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "best_model = compare_models(fold=5)"
      ],
      "metadata": {
        "id": "sWh6fiJIoQS0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "low AUCs indicates that models perform like random guesses, with little prediction value."
      ],
      "metadata": {
        "id": "ameOlUgPmYMD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_model(best_model, plot=\"auc\")"
      ],
      "metadata": {
        "id": "7GeoFzyzReeR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tuned = tune_model(best_model)"
      ],
      "metadata": {
        "id": "c3FsIL33TiVz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_model(best_model, plot = 'feature_all', use_train_data = False, plot_kwargs = {'percent': True})"
      ],
      "metadata": {
        "id": "WgGa4K2xXUhv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "best_model.get_params()"
      ],
      "metadata": {
        "id": "YBfnSSGLwKxw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2d-89SOoq5sc"
      },
      "source": [
        "##### TSTS"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KttqKWQ2rjli"
      },
      "outputs": [],
      "source": [
        "models = {LogisticRegression(random_state=17, C = 1.0,\n",
        " class_weight =None, dual = False, fit_intercept = True, intercept_scaling = 1, l1_ratio = None,\n",
        " max_iter = 1000,n_jobs = None, penalty = 'l2', solver = 'lbfgs',\n",
        " tol = 0.0001, verbose = 1, warm_start = False)}\n",
        "scores_train = []\n",
        "scores_test = []\n",
        "f1_scores = []\n",
        "\n",
        "for i in models:\n",
        "    model = i\n",
        "    pipeline = Pipeline(steps=[\n",
        "                              (\"pre_process\", preprocessor_STD), \n",
        "                              (\"model\", model)\n",
        "                              ])\n",
        "    pipeline.fit(X_train_STD, y_train_STD)\n",
        "    y_pred = pipeline.predict(X_test_STD)\n",
        "    scores_train.append([i, np.mean(cross_val_score(pipeline, X_train_STD, y_train_STD))]) #scoring='f1_weighted'\n",
        "    #scores_test.append(cross_val_score(pipeline_lr, X_test, y_test))\n",
        "    f1_scores.append([i,f1_score(y_test_STD, y_pred,average=\"binary\", pos_label='Fully Paid')])\n",
        "scores_train, f1_scores"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "conf_mat = confusion_matrix(y_test_STD, y_pred)\n",
        "conf_mat"
      ],
      "metadata": {
        "id": "69tGCjhT7lr4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HwrAvVWgyjAK"
      },
      "outputs": [],
      "source": [
        "target_names = ['Charged Off', 'Fully Paid']\n",
        "print(classification_report(y_test_STD, y_pred, target_names=target_names))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cad5C-yjrC-k"
      },
      "source": [
        "##### TRTS"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XgE65Wa5iUnA"
      },
      "outputs": [],
      "source": [
        "models = {LogisticRegression(random_state=17, C = 1.0,\n",
        " class_weight =None, dual = False, fit_intercept = True, intercept_scaling = 1, l1_ratio = None,\n",
        " max_iter = 1000,n_jobs = None, penalty = 'l2', solver = 'lbfgs',\n",
        " tol = 0.0001, verbose = 1, warm_start = False)}\n",
        " \n",
        "scores_train = []\n",
        "scores_test = []\n",
        "f1_scores = []\n",
        "\n",
        "for i in models:\n",
        "    model = i\n",
        "    pipeline = Pipeline(steps=[\n",
        "                              (\"pre_process\", preprocessor_RD), \n",
        "                              (\"model\", model)\n",
        "                              ])\n",
        "    pipeline.fit(X_train_RD, y_train_RD)\n",
        "    y_pred = pipeline.predict(X_test_STD)\n",
        "    scores_train.append([i, np.mean(cross_val_score(pipeline, X_train_RD, y_train_RD))]) #scoring='f1_weighted'\n",
        "    #scores_test.append(cross_val_score(pipeline_lr, X_test, y_test))\n",
        "    f1_scores.append([i,f1_score(y_test_STD, y_pred,average=\"binary\", pos_label='Fully Paid')])\n",
        "scores_train, f1_scores"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G-Bk5edMioZX"
      },
      "outputs": [],
      "source": [
        "conf_mat = confusion_matrix(y_test_STD, y_pred)\n",
        "conf_mat"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AckZuUNGkvGm"
      },
      "outputs": [],
      "source": [
        "target_names = ['Fully Paid', 'Charged Off']\n",
        "print(classification_report(y_test_STD, y_pred, target_names=target_names))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tYwdCA6brLSc"
      },
      "source": [
        "##### TSTR"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "I5Yk9TJ8lIhQ"
      },
      "outputs": [],
      "source": [
        "models = {LogisticRegression(random_state=17, C = 1.0,\n",
        " class_weight =None, dual = False, fit_intercept = True, intercept_scaling = 1, l1_ratio = None,\n",
        " max_iter = 1000,n_jobs = None, penalty = 'l2', solver = 'lbfgs',\n",
        " tol = 0.0001, verbose = 1, warm_start = False)}\n",
        "\n",
        "for i in models:\n",
        "    model = i\n",
        "    pipeline = Pipeline(steps=[\n",
        "                              (\"pre_process\", preprocessor_STD), \n",
        "                              (\"model\", model)\n",
        "                              ])\n",
        "    pipeline.fit(X_train_STD, y_train_STD)\n",
        "    y_pred = pipeline.predict(X_test_RD)\n",
        "    scores_train.append([i, np.mean(cross_val_score(pipeline, X_train_STD, y_train_STD))]) #scoring='f1_weighted'\n",
        "    #scores_test.append(cross_val_score(pipeline_lr, X_test, y_test))\n",
        "    f1_scores.append([i,f1_score(y_test_RD, y_pred,average=\"binary\", pos_label='Fully Paid')])\n",
        "scores_train, f1_scores"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sgV6VIBinPTs"
      },
      "outputs": [],
      "source": [
        "conf_mat = confusion_matrix(y_test_RD, y_pred)\n",
        "conf_mat"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Tr26gu1yysDj"
      },
      "outputs": [],
      "source": [
        "target_names = ['Charged Off', 'Fully Paid']\n",
        "print(classification_report(y_test_RD, y_pred, target_names=target_names))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EB3Hce3bdDSv"
      },
      "source": [
        "#### Data Labelling Analysis (DLA) 66%"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4ORf6J9QdF-r"
      },
      "outputs": [],
      "source": [
        "df['Label'] = df.apply(lambda x: 0, axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dgOljchZhiY5"
      },
      "outputs": [],
      "source": [
        "sample['Label'] = sample.apply(lambda x: 1, axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rNuy0LDThywb"
      },
      "outputs": [],
      "source": [
        "df_DLA = pd.concat([df, sample])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RtaWbqSbiOjU"
      },
      "outputs": [],
      "source": [
        "df_DLA = df_DLA.sample(frac = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3pgOgO0wkdLw"
      },
      "outputs": [],
      "source": [
        "df_DLA"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AMi36WxZjkTr"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(df_DLA.drop(columns=['Label']), # X\n",
        "                                                    df_DLA['Label'], # y\n",
        "                                                    test_size=0.2, # %age of test split\n",
        "                                                    random_state=17)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fwyl4cDvk8OP"
      },
      "outputs": [],
      "source": [
        "categorical_features = X_train.select_dtypes(include=['object', 'category']).columns.tolist()\n",
        "numeric_features = X_train.select_dtypes(include=['int', 'float']).columns.tolist()\n",
        "\n",
        "categorical_features, numeric_features"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OwLUHCSVlF9I"
      },
      "source": [
        "Define Preprocessing Pipeline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9qOOpVbilBAj"
      },
      "outputs": [],
      "source": [
        "categorical_transformer = Pipeline(steps=[\n",
        "                                          (\"cat_imputer\", SimpleImputer(strategy='constant',\n",
        "                                                                        fill_value='Not Available')),\n",
        "                                          (\"onehot\", OneHotEncoder(handle_unknown=\"ignore\"))\n",
        "                                          ])\n",
        "categorical_transformer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_Jjy22g2lonz"
      },
      "outputs": [],
      "source": [
        "numeric_transformer = Pipeline(steps=[(\"scaler\", StandardScaler())\n",
        "                                      ])\n",
        "\n",
        "numeric_transformer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SQsTJFVgmU4H"
      },
      "outputs": [],
      "source": [
        "preprocessor = ColumnTransformer(transformers=[\n",
        "                                               (\"num\", numeric_transformer, \n",
        "                                                       numeric_features),\n",
        "                                               (\"cat\", categorical_transformer, \n",
        "                                                       categorical_features)\n",
        "                                               ])\n",
        "preprocessor"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HzcUYVJUmQpn"
      },
      "source": [
        "##### Model\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1NtsPDirmatT"
      },
      "outputs": [],
      "source": [
        "models = {RandomForestClassifier(random_state=17)}\n",
        "scores_train = []\n",
        "scores_test = []\n",
        "f1_scores = []\n",
        "\n",
        "for i in models:\n",
        "    model = i\n",
        "    pipeline = Pipeline(steps=[\n",
        "                              (\"pre_process\", preprocessor), \n",
        "                              (\"model\", model)\n",
        "                              ])\n",
        "    pipeline.fit(X_train, y_train)\n",
        "    y_pred = pipeline.predict(X_test)\n",
        "    scores_train.append([i, np.mean(cross_val_score(pipeline, X_train, y_train))]) #scoring='f1_weighted'\n",
        "    #scores_test.append(cross_val_score(pipeline_lr, X_test, y_test))\n",
        "    f1_scores.append([i,f1_score(y_test, y_pred,average=\"binary\", pos_label=1)])\n",
        "scores_train, f1_scores"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5NZn28oMnkdC"
      },
      "outputs": [],
      "source": [
        "conf_mat = confusion_matrix(\n",
        "        y_test, y_pred)\n",
        "conf_mat "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LaMmK7uAnChJ"
      },
      "outputs": [],
      "source": [
        "target_names = ['Fully Paid', 'Charged Off']\n",
        "print(classification_report(y_test, y_pred, target_names=target_names))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5MjE1wPaxeoI"
      },
      "outputs": [],
      "source": [
        "!pip install catboost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qyOvvtYGxpmc"
      },
      "outputs": [],
      "source": [
        "from catboost import CatBoostClassifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xKRfG8QSwwsv"
      },
      "outputs": [],
      "source": [
        "model = CatBoostClassifier(iterations=1000, task_type=\"GPU\")\n",
        "pipeline = Pipeline(steps=[\n",
        "                              (\"pre_process\", preprocessor), \n",
        "                              (\"model\", model)\n",
        "                              ])\n",
        "pipeline.fit(X_train, y_train)\n",
        "y_pred = pipeline.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "05bGs-AC0BRi"
      },
      "outputs": [],
      "source": [
        "conf_mat = confusion_matrix(\n",
        "        y_test, y_pred)\n",
        "conf_mat "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U7-W6KwX0C5m"
      },
      "outputs": [],
      "source": [
        "target_names = ['0', '1']\n",
        "print(classification_report(y_test, y_pred, target_names=target_names))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3hwtrUvJ04Fv"
      },
      "outputs": [],
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "model = KNeighborsClassifier(n_neighbors=10)\n",
        "pipeline = Pipeline(steps=[\n",
        "                              (\"pre_process\", preprocessor), \n",
        "                              (\"model\", model)\n",
        "                              ])\n",
        "pipeline.fit(X_train, y_train)\n",
        "y_pred = pipeline.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HrsYcaZv1Grm"
      },
      "outputs": [],
      "source": [
        "conf_mat = confusion_matrix(y_test, y_pred)\n",
        "conf_mat "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ip7IFAcR1Kfh"
      },
      "outputs": [],
      "source": [
        "target_names = ['0', '1']\n",
        "print(classification_report(y_test, y_pred, target_names=target_names))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g7xWMwmSqMcx"
      },
      "outputs": [],
      "source": [
        "!pip install pycaret[full]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BjZwkWT6qYwc"
      },
      "outputs": [],
      "source": [
        "!pip install pyyaml==5.4.1 --ignore-installed"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aCB-MwhEp-Wi"
      },
      "outputs": [],
      "source": [
        "from pycaret.classification import *\n",
        "\n",
        "reg_new = setup(data=pd.concat([X_train, y_train], axis=1), \n",
        "            target = 'Label', \n",
        "            session_id=13, \n",
        "            log_experiment=True, \n",
        "            experiment_name='Challenge')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fNJhWCSfp-fD"
      },
      "outputs": [],
      "source": [
        "best_model = compare_models(fold=5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Be2wOFhzKQW3"
      },
      "source": [
        "##TVAE: Triplet-Based Variational Autoencoder \n",
        "https://sdv.dev/SDV/user_guides/single_table/tvae.html"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A2aYqOmdZHM5"
      },
      "source": [
        "### Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dE9CwIGTKgmz"
      },
      "outputs": [],
      "source": [
        "model = TVAE(epochs=1000)\n",
        "\n",
        "model.fit(df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2_ru1RPBKgz1"
      },
      "outputs": [],
      "source": [
        "new_data = model.sample(1000)\n",
        "new_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NNCOxbZaayh2"
      },
      "outputs": [],
      "source": [
        "new_data.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "azPmXTp5BoGN"
      },
      "outputs": [],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uX7Eet_IZ5O0"
      },
      "source": [
        "### Evaluation q = 0.50"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hEfzL4nKBK6R"
      },
      "outputs": [],
      "source": [
        "table_evaluator = TableEvaluator(df, new_data, cat_cols=df_cat.columns.values.tolist())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DKgP-M3iA1Qp"
      },
      "outputs": [],
      "source": [
        "table_evaluator.visual_evaluation()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dKdabGn4CADf"
      },
      "outputs": [],
      "source": [
        "evaluate(new_data, df, aggregate=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4zgSgEOM4fA0"
      },
      "outputs": [],
      "source": [
        "evaluate(new_data, df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TKxYXzJ_dsa7"
      },
      "source": [
        "### Data Labelling Analysis (DLA)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HcwSBxzBdzlA"
      },
      "outputs": [],
      "source": [
        "df['Label'] = df.apply(lambda x: 0, axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wXZLHsVBeJvg"
      },
      "outputs": [],
      "source": [
        "new_data['Label'] = new_data.apply(lambda x: 1, axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hh8DughZeT_w"
      },
      "outputs": [],
      "source": [
        "df_DLA = pd.concat([df, new_data])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aRfHiZkQeXVA"
      },
      "outputs": [],
      "source": [
        "df_DLA = df_DLA.sample(frac = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GtoghYZZebxS"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(df_DLA.drop(columns=['Label']), # X\n",
        "                                                    df_DLA['Label'], # y\n",
        "                                                    test_size=0.2, # %age of test split\n",
        "                                                    random_state=17)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = KNeighborsClassifier(n_neighbors=10)\n",
        "pipeline = Pipeline(steps=[\n",
        "                              (\"pre_process\", preprocessor), \n",
        "                              (\"model\", model)\n",
        "                              ])\n",
        "pipeline.fit(X_train, y_train)\n",
        "y_pred = pipeline.predict(X_test)"
      ],
      "metadata": {
        "id": "waf1ad4V-Vnm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QX_o00dDem8L"
      },
      "outputs": [],
      "source": [
        "from pycaret.classification import *\n",
        "\n",
        "reg_new = setup(data=pd.concat([X_train, y_train], axis=1), \n",
        "            target = 'Label', \n",
        "            session_id=13, \n",
        "            log_experiment=True, \n",
        "            experiment_name='Challenge')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "20FCzegDepN_"
      },
      "outputs": [],
      "source": [
        "\n",
        "best_model = compare_models(fold=5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0Su_5IaVJqDs"
      },
      "source": [
        "## GaussianCopula Model\n",
        "https://sdv.dev/SDV/user_guides/single_table/gaussian_copula.html\n",
        "\n",
        "A tool to model multivariate distributions using copula functions"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Attention: update to the latest SDV version"
      ],
      "metadata": {
        "id": "9PT_PqjMRXUp"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1C8vwOe9WAzd"
      },
      "source": [
        "### Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d4phGn7LLEBL"
      },
      "outputs": [],
      "source": [
        "from sdv.tabular import GaussianCopula\n",
        "\n",
        "model = GaussianCopula()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UVRZ7mL6LrA1"
      },
      "outputs": [],
      "source": [
        "model.fit(df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "epzQrFKtLzvC"
      },
      "outputs": [],
      "source": [
        "new_data = model.sample(2000)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9w2B-AENVjvZ"
      },
      "outputs": [],
      "source": [
        "new_data"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_data.to_csv('/content/drive/MyDrive/Capstone/_STD/GaussianCopula_std_2000entries.csv')"
      ],
      "metadata": {
        "id": "kzPNeCtHcy90"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5F941ucEXiuw"
      },
      "source": [
        "### Evaluation q = 0.48"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "r9gc9Es-XqIV"
      },
      "outputs": [],
      "source": [
        "table_evaluator = TableEvaluator(df, new_data, cat_cols=df_cat.columns.values.tolist())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Cpl27oXfXwj5"
      },
      "outputs": [],
      "source": [
        "table_evaluator.visual_evaluation()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rAD8PSrYFiPV"
      },
      "outputs": [],
      "source": [
        "ax = df['postcode'].value_counts().plot(kind='bar',\n",
        "                                    figsize=(14,8),\n",
        "                                    title=\"____\")\n",
        "ax.set_xlabel(\"_____\")\n",
        "ax.set_ylabel(\"_____\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IgYvFlWCFrJb"
      },
      "outputs": [],
      "source": [
        "ax = new_data['postcode'].value_counts().plot(kind='bar',\n",
        "                                    figsize=(14,8),\n",
        "                                    title=\"____\")\n",
        "ax.set_xlabel(\"_____\")\n",
        "ax.set_ylabel(\"_____\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z3aY3zeZF7a6"
      },
      "outputs": [],
      "source": [
        "df['Postal Code'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z1INqqP6F1NO"
      },
      "outputs": [],
      "source": [
        "new_data['Postal Code'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b7PzC1MgepWL"
      },
      "outputs": [],
      "source": [
        "metrics = sdmetrics.multi_table.MultiTableMetric.get_subclasses()\n",
        "sdmetrics.compute_metrics(metrics, df, new_data, metadata=None)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kQHHdNTtoCDB"
      },
      "outputs": [],
      "source": [
        "from sdv.evaluation import evaluate\n",
        "\n",
        "evaluate(new_data, df)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## CopulaGAN\n",
        "https://sdv.dev/SDV/user_guides/single_table/copulagan.html"
      ],
      "metadata": {
        "id": "bU-63cIiLpVn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "id": "d2XbdCYKOPmc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model"
      ],
      "metadata": {
        "id": "qvbCuxmXiFgG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sdv.tabular import CopulaGAN\n",
        "# transformers_dict = {'current_loan_amount': 'float', 'annual_income': 'float', 'monthly_debt': 'float','current_credit_balance': 'float', 'bankruptcies': 'float', 'date': 'datetime', 'loan_status': 'one_hot_encoding', 'city': 'one_hot_encoding'} \n",
        "model = CopulaGAN(epochs = 500)"
      ],
      "metadata": {
        "id": "_1aMHaTyLycm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_data = model.sample(200)"
      ],
      "metadata": {
        "id": "xawR8ZxfL7lP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_data"
      ],
      "metadata": {
        "id": "0SSl3zKYh8gO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_data.to_csv('/content/drive/MyDrive/Capstone/_STD/CopulaGAN_std_2000entries.csv')"
      ],
      "metadata": {
        "id": "gLXMKvsyrKKG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Evaluation q = 0.53"
      ],
      "metadata": {
        "id": "DtIWGlJSiJfE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "table_evaluator = TableEvaluator(df, new_data, cat_cols=df_cat.columns.values.tolist())"
      ],
      "metadata": {
        "id": "BTvzsn_biItn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "table_evaluator.visual_evaluation()"
      ],
      "metadata": {
        "id": "jzd5ZuEMiPEN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate(new_data, df)"
      ],
      "metadata": {
        "id": "K_55KPfkiUGN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.street.nunique()"
      ],
      "metadata": {
        "id": "5-VYbyyfH6-m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rd = pd.read_csv('/content/drive/MyDrive/Capstone/_data/dataset_09022022.csv')"
      ],
      "metadata": {
        "id": "vM_fpuO6IMVK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rd.street.nunique()"
      ],
      "metadata": {
        "id": "W6XEXZNCIhcD"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "overview on the method.ipynb",
      "provenance": [],
      "toc_visible": true,
      "mount_file_id": "1TbluX2ji7GoFiLiiGxZ2_FkheRrGn7RF",
      "authorship_tag": "ABX9TyPJj4lUyM14ASAQvZcMRaAG",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}