CTGan_augmentation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iEF4Vcp7ZcyF",
    "outputId": "18173bc2-1dc1-47bf-cf92-63710829f0df"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 't81_558_deep_learning'...\n",
      "remote: Enumerating objects: 4745, done.\u001b[K\n",
      "remote: Counting objects: 100% (60/60), done.\u001b[K\n",
      "remote: Compressing objects: 100% (42/42), done.\u001b[K\n",
      "remote: Total 4745 (delta 29), reused 48 (delta 18), pack-reused 4685\u001b[K\n",
      "Receiving objects: 100% (4745/4745), 242.65 MiB | 36.80 MiB/s, done.\n",
      "Resolving deltas: 100% (3182/3182), done.\n"
     ]
    }
   ],
   "source": [
    "!git clone https://github.com/jeffheaton/t81_558_deep_learning.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "gcKcMK2tZgpA",
    "outputId": "0964bfdf-8a01-4a76-89ea-36ce2f3f9aa6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2022-06-11 12:28:20--  https://raw.githubusercontent.com/Diyago/GAN-for-tabular-data/master/requirements.txt\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.108.133, 185.199.109.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 197 [text/plain]\n",
      "Saving to: ‘requirements.txt’\n",
      "\n",
      "\r",
      "requirements.txt      0%[                    ]       0  --.-KB/s               \r",
      "requirements.txt    100%[===================>]     197  --.-KB/s    in 0s      \n",
      "\n",
      "2022-06-11 12:28:20 (9.73 MB/s) - ‘requirements.txt’ saved [197/197]\n",
      "\n",
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
      "Requirement already satisfied: scipy==1.4.1 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 1)) (1.4.1)\n",
      "Collecting category_encoders==2.1.0\n",
      "  Downloading category_encoders-2.1.0-py2.py3-none-any.whl (100 kB)\n",
      "\u001b[K     |████████████████████████████████| 100 kB 4.2 MB/s \n",
      "\u001b[?25hCollecting numpy==1.18.1\n",
      "  Downloading numpy-1.18.1-cp37-cp37m-manylinux1_x86_64.whl (20.1 MB)\n",
      "\u001b[K     |████████████████████████████████| 20.1 MB 1.2 MB/s \n",
      "\u001b[?25hCollecting torch==1.6.0\n",
      "  Downloading torch-1.6.0-cp37-cp37m-manylinux1_x86_64.whl (748.8 MB)\n",
      "\u001b[K     |████████████████████████████████| 748.8 MB 19 kB/s \n",
      "\u001b[?25hCollecting pandas==1.2.2\n",
      "  Downloading pandas-1.2.2-cp37-cp37m-manylinux1_x86_64.whl (9.9 MB)\n",
      "\u001b[K     |████████████████████████████████| 9.9 MB 28.1 MB/s \n",
      "\u001b[?25hCollecting lightgbm==2.3.1\n",
      "  Downloading lightgbm-2.3.1-py2.py3-none-manylinux1_x86_64.whl (1.2 MB)\n",
      "\u001b[K     |████████████████████████████████| 1.2 MB 50.7 MB/s \n",
      "\u001b[?25hCollecting scikit_learn==0.23.2\n",
      "  Downloading scikit_learn-0.23.2-cp37-cp37m-manylinux1_x86_64.whl (6.8 MB)\n",
      "\u001b[K     |████████████████████████████████| 6.8 MB 60.1 MB/s \n",
      "\u001b[?25hRequirement already satisfied: torchvision>=0.4.2 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 8)) (0.12.0+cu113)\n",
      "Collecting python-dateutil==2.8.1\n",
      "  Downloading python_dateutil-2.8.1-py2.py3-none-any.whl (227 kB)\n",
      "\u001b[K     |████████████████████████████████| 227 kB 75.9 MB/s \n",
      "\u001b[?25hCollecting tqdm==4.61.1\n",
      "  Downloading tqdm-4.61.1-py2.py3-none-any.whl (75 kB)\n",
      "\u001b[K     |████████████████████████████████| 75 kB 4.7 MB/s \n",
      "\u001b[?25hRequirement already satisfied: patsy>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from category_encoders==2.1.0->-r requirements.txt (line 2)) (0.5.2)\n",
      "Requirement already satisfied: statsmodels>=0.6.1 in /usr/local/lib/python3.7/dist-packages (from category_encoders==2.1.0->-r requirements.txt (line 2)) (0.10.2)\n",
      "Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from torch==1.6.0->-r requirements.txt (line 4)) (0.16.0)\n",
      "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas==1.2.2->-r requirements.txt (line 5)) (2022.1)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit_learn==0.23.2->-r requirements.txt (line 7)) (3.1.0)\n",
      "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit_learn==0.23.2->-r requirements.txt (line 7)) (1.1.0)\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil==2.8.1->-r requirements.txt (line 11)) (1.15.0)\n",
      "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.7/dist-packages (from torchvision>=0.4.2->-r requirements.txt (line 8)) (7.1.2)\n",
      "Collecting torchvision>=0.4.2\n",
      "  Downloading torchvision-0.12.0-cp37-cp37m-manylinux1_x86_64.whl (21.0 MB)\n",
      "\u001b[K     |████████████████████████████████| 21.0 MB 1.3 MB/s \n",
      "\u001b[?25h  Downloading torchvision-0.11.3-cp37-cp37m-manylinux1_x86_64.whl (23.2 MB)\n",
      "\u001b[K     |████████████████████████████████| 23.2 MB 54 kB/s \n",
      "\u001b[?25h  Downloading torchvision-0.11.2-cp37-cp37m-manylinux1_x86_64.whl (23.3 MB)\n",
      "\u001b[K     |████████████████████████████████| 23.3 MB 1.4 MB/s \n",
      "\u001b[?25h  Downloading torchvision-0.11.1-cp37-cp37m-manylinux1_x86_64.whl (23.3 MB)\n",
      "\u001b[K     |████████████████████████████████| 23.3 MB 1.3 MB/s \n",
      "\u001b[?25h  Downloading torchvision-0.10.1-cp37-cp37m-manylinux1_x86_64.whl (22.1 MB)\n",
      "\u001b[K     |████████████████████████████████| 22.1 MB 56.5 MB/s \n",
      "\u001b[?25h  Downloading torchvision-0.10.0-cp37-cp37m-manylinux1_x86_64.whl (22.1 MB)\n",
      "\u001b[K     |████████████████████████████████| 22.1 MB 1.3 MB/s \n",
      "\u001b[?25h  Downloading torchvision-0.9.1-cp37-cp37m-manylinux1_x86_64.whl (17.4 MB)\n",
      "\u001b[K     |████████████████████████████████| 17.4 MB 855 kB/s \n",
      "\u001b[?25h  Downloading torchvision-0.9.0-cp37-cp37m-manylinux1_x86_64.whl (17.3 MB)\n",
      "\u001b[K     |████████████████████████████████| 17.3 MB 174 kB/s \n",
      "\u001b[?25h  Downloading torchvision-0.8.2-cp37-cp37m-manylinux1_x86_64.whl (12.8 MB)\n",
      "\u001b[K     |████████████████████████████████| 12.8 MB 49.7 MB/s \n",
      "\u001b[?25h  Downloading torchvision-0.8.1-cp37-cp37m-manylinux1_x86_64.whl (12.7 MB)\n",
      "\u001b[K     |████████████████████████████████| 12.7 MB 51.1 MB/s \n",
      "\u001b[?25h  Downloading torchvision-0.8.0-cp37-cp37m-manylinux1_x86_64.whl (11.8 MB)\n",
      "\u001b[K     |████████████████████████████████| 11.8 MB 52.7 MB/s \n",
      "\u001b[?25h  Downloading torchvision-0.7.0-cp37-cp37m-manylinux1_x86_64.whl (5.9 MB)\n",
      "\u001b[K     |████████████████████████████████| 5.9 MB 21.2 MB/s \n",
      "\u001b[?25hInstalling collected packages: python-dateutil, numpy, pandas, torch, scikit-learn, tqdm, torchvision, lightgbm, category-encoders\n",
      "  Attempting uninstall: python-dateutil\n",
      "    Found existing installation: python-dateutil 2.8.2\n",
      "    Uninstalling python-dateutil-2.8.2:\n",
      "      Successfully uninstalled python-dateutil-2.8.2\n",
      "  Attempting uninstall: numpy\n",
      "    Found existing installation: numpy 1.21.6\n",
      "    Uninstalling numpy-1.21.6:\n",
      "      Successfully uninstalled numpy-1.21.6\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 1.11.0+cu113\n",
      "    Uninstalling torch-1.11.0+cu113:\n",
      "      Successfully uninstalled torch-1.11.0+cu113\n",
      "  Attempting uninstall: scikit-learn\n",
      "    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: tqdm\n",
      "    Found existing installation: tqdm 4.64.0\n",
      "    Uninstalling tqdm-4.64.0:\n",
      "      Successfully uninstalled tqdm-4.64.0\n",
      "  Attempting uninstall: torchvision\n",
      "    Found existing installation: torchvision 0.12.0+cu113\n",
      "    Uninstalling torchvision-0.12.0+cu113:\n",
      "      Successfully uninstalled torchvision-0.12.0+cu113\n",
      "  Attempting uninstall: lightgbm\n",
      "    Found existing installation: lightgbm 2.2.3\n",
      "    Uninstalling lightgbm-2.2.3:\n",
      "      Successfully uninstalled lightgbm-2.2.3\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",
      "yellowbrick 1.4 requires scikit-learn>=1.0.0, but you have scikit-learn 0.23.2 which is incompatible.\n",
      "xarray-einstats 0.2.2 requires numpy>=1.21, but you have numpy 1.18.1 which is incompatible.\n",
      "torchtext 0.12.0 requires torch==1.11.0, but you have torch 1.6.0 which is incompatible.\n",
      "torchaudio 0.11.0+cu113 requires torch==1.11.0, but you have torch 1.6.0 which is incompatible.\n",
      "tensorflow 2.8.2+zzzcolab20220527125636 requires numpy>=1.20, but you have numpy 1.18.1 which is incompatible.\n",
      "tables 3.7.0 requires numpy>=1.19.0, but you have numpy 1.18.1 which is incompatible.\n",
      "kapre 0.3.7 requires numpy>=1.18.5, but you have numpy 1.18.1 which is incompatible.\n",
      "jaxlib 0.3.7+cuda11.cudnn805 requires numpy>=1.19, but you have numpy 1.18.1 which is incompatible.\n",
      "jax 0.3.8 requires numpy>=1.19, but you have numpy 1.18.1 which is incompatible.\n",
      "imbalanced-learn 0.8.1 requires scikit-learn>=0.24, but you have scikit-learn 0.23.2 which is incompatible.\n",
      "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\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 category-encoders-2.1.0 lightgbm-2.3.1 numpy-1.18.1 pandas-1.2.2 python-dateutil-2.8.1 scikit-learn-0.23.2 torch-1.6.0 torchvision-0.7.0 tqdm-4.61.1\n"
     ]
    },
    {
     "data": {
      "application/vnd.colab-display-data+json": {
       "pip_warning": {
        "packages": [
         "dateutil",
         "numpy"
        ]
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
      "Collecting tabgan\n",
      "  Downloading tabgan-1.2.0-py2.py3-none-any.whl (45 kB)\n",
      "\u001b[K     |████████████████████████████████| 45 kB 1.8 MB/s \n",
      "\u001b[?25hRequirement already satisfied: python-dateutil in /usr/local/lib/python3.7/dist-packages (from tabgan) (2.8.1)\n",
      "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from tabgan) (4.61.1)\n",
      "Requirement already satisfied: category-encoders in /usr/local/lib/python3.7/dist-packages (from tabgan) (2.1.0)\n",
      "Requirement already satisfied: torchvision in /usr/local/lib/python3.7/dist-packages (from tabgan) (0.7.0)\n",
      "Requirement already satisfied: lightgbm in /usr/local/lib/python3.7/dist-packages (from tabgan) (2.3.1)\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from tabgan) (1.18.1)\n",
      "Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from tabgan) (1.6.0)\n",
      "Requirement already satisfied: scikit-learn==0.23.2 in /usr/local/lib/python3.7/dist-packages (from tabgan) (0.23.2)\n",
      "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from tabgan) (1.2.2)\n",
      "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn==0.23.2->tabgan) (1.1.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn==0.23.2->tabgan) (3.1.0)\n",
      "Requirement already satisfied: scipy>=0.19.1 in /usr/local/lib/python3.7/dist-packages (from scikit-learn==0.23.2->tabgan) (1.4.1)\n",
      "Requirement already satisfied: patsy>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from category-encoders->tabgan) (0.5.2)\n",
      "Requirement already satisfied: statsmodels>=0.6.1 in /usr/local/lib/python3.7/dist-packages (from category-encoders->tabgan) (0.10.2)\n",
      "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->tabgan) (2022.1)\n",
      "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from patsy>=0.4.1->category-encoders->tabgan) (1.15.0)\n",
      "Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from torch->tabgan) (0.16.0)\n",
      "Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.7/dist-packages (from torchvision->tabgan) (7.1.2)\n",
      "Installing collected packages: tabgan\n",
      "Successfully installed tabgan-1.2.0\n"
     ]
    }
   ],
   "source": [
    "# HIDE OUTPUT\n",
    "CMD = \"wget https://raw.githubusercontent.com/Diyago/\"\\\n",
    "  \"GAN-for-tabular-data/master/requirements.txt\"\n",
    "\n",
    "!{CMD}\n",
    "!pip install -r requirements.txt\n",
    "!pip install tabgan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "Yz4pnN8TZhL2"
   },
   "outputs": [],
   "source": [
    "# HIDE OUTPUT\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Activation\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "from sklearn.model_selection import train_test_split\n",
    "import pandas as pd\n",
    "import io\n",
    "import os\n",
    "import requests\n",
    "import numpy as np\n",
    "from sklearn import metrics\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.metrics import balanced_accuracy_score\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YFSCanhxviU1"
   },
   "source": [
    "# Loading csv tabular data(Must process to get only the numerical features and Label for classification or regression)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "uIgZ90i0sd3_",
    "outputId": "54351dea-5890-49bb-dcef-fb6cc13cc444"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-83982692-1890-4c02-8ded-232edcfa1baf\">\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>s13</th>\n",
       "      <th>s48</th>\n",
       "      <th>s52</th>\n",
       "      <th>n1</th>\n",
       "      <th>n2</th>\n",
       "      <th>n3</th>\n",
       "      <th>n4</th>\n",
       "      <th>n5</th>\n",
       "      <th>n6</th>\n",
       "      <th>n7</th>\n",
       "      <th>...</th>\n",
       "      <th>x</th>\n",
       "      <th>~1</th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>op: A</th>\n",
       "      <th>op: B</th>\n",
       "      <th>op: C</th>\n",
       "      <th>op: D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.319978</td>\n",
       "      <td>1.071222</td>\n",
       "      <td>3</td>\n",
       "      <td>6.029996</td>\n",
       "      <td>-32.574983</td>\n",
       "      <td>0.007418</td>\n",
       "      <td>-9.283087</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6.667097</td>\n",
       "      <td>0.699584</td>\n",
       "      <td>6</td>\n",
       "      <td>2.352073</td>\n",
       "      <td>-32.394892</td>\n",
       "      <td>0.014828</td>\n",
       "      <td>-9.174026</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16.571195</td>\n",
       "      <td>2.504946</td>\n",
       "      <td>8</td>\n",
       "      <td>6.776169</td>\n",
       "      <td>-32.576455</td>\n",
       "      <td>0.011080</td>\n",
       "      <td>-8.851669</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.769811</td>\n",
       "      <td>1.908708</td>\n",
       "      <td>6</td>\n",
       "      <td>5.917154</td>\n",
       "      <td>-32.508091</td>\n",
       "      <td>0.018298</td>\n",
       "      <td>-8.879981</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>16.065690</td>\n",
       "      <td>2.463559</td>\n",
       "      <td>3</td>\n",
       "      <td>6.765048</td>\n",
       "      <td>-32.345645</td>\n",
       "      <td>0.023098</td>\n",
       "      <td>-9.042806</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 51 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-83982692-1890-4c02-8ded-232edcfa1baf')\"\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-83982692-1890-4c02-8ded-232edcfa1baf 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-83982692-1890-4c02-8ded-232edcfa1baf');\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": [
       "   s13  s48  s52         n1        n2  n3        n4         n5        n6  \\\n",
       "0    1    1    1   7.319978  1.071222   3  6.029996 -32.574983  0.007418   \n",
       "1    0    1    1   6.667097  0.699584   6  2.352073 -32.394892  0.014828   \n",
       "2    1    1    1  16.571195  2.504946   8  6.776169 -32.576455  0.011080   \n",
       "3    1    1    1  12.769811  1.908708   6  5.917154 -32.508091  0.018298   \n",
       "4    1    0    1  16.065690  2.463559   3  6.765048 -32.345645  0.023098   \n",
       "\n",
       "         n7  ...  x  ~1  a  b  c  d  op: A  op: B  op: C  op: D  \n",
       "0 -9.283087  ...  0   1  0  1  0  0      0      0      0      1  \n",
       "1 -9.174026  ...  0   1  0  0  1  0      0      0      1      0  \n",
       "2 -8.851669  ...  1   0  0  1  0  0      0      1      0      0  \n",
       "3 -8.879981  ...  0   1  0  0  1  0      0      0      0      1  \n",
       "4 -9.042806  ...  0   1  0  1  0  0      0      0      1      0  \n",
       "\n",
       "[5 rows x 51 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_label_all = pd.read_csv(\"/content/one_zerolittle.csv\")\n",
    "df_label_all.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Hdupxx7UwIAv"
   },
   "source": [
    "# Splitting Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "GEVZDgD7wHH_"
   },
   "outputs": [],
   "source": [
    "# Split into training and test sets\n",
    "df_x_train, df_x_test, df_y_train, df_y_test = train_test_split(\n",
    "    df_label_all.drop(\"label\", axis=1),     #here y label is denoted as 'label'\n",
    "    df_label_all[\"label\"],\n",
    "    test_size=0.20,\n",
    "    random_state=42,\n",
    ")\n",
    "\n",
    "# Create dataframe versions for tabular GAN\n",
    "df_x_test, df_y_test = df_x_test.reset_index(drop=True), \\\n",
    "  df_y_test.reset_index(drop=True)\n",
    "df_y_train = pd.DataFrame(df_y_train)\n",
    "df_y_test = pd.DataFrame(df_y_test)\n",
    "\n",
    "# Pandas to Numpy\n",
    "x_train = df_x_train.values\n",
    "x_test = df_x_test.values\n",
    "y_train = df_y_train.values\n",
    "y_test = df_y_test.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hucc4HI-wpfu"
   },
   "source": [
    "# Fitting Xgboost model to training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8PwyMcP_sD8H",
    "outputId": "0153fa11-c3de-4b6c-d2ae-8283901ec106"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Balance accuracy is 0.7364431942491014\n"
     ]
    }
   ],
   "source": [
    "\n",
    "model = XGBClassifier()\n",
    "model.fit(x_train, y_train)\n",
    "\n",
    "# Validating the model\n",
    "y_pred = model.predict(x_test)\n",
    "predictions = [round(value) for value in y_pred]\n",
    "print(f'Balance accuracy is {balanced_accuracy_score(y_test, predictions)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HeuEjCVLxryo"
   },
   "source": [
    "# Training the GAN and generate new data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 81,
     "referenced_widgets": [
      "a64671087a2444fc97416cf61b26e1d9",
      "8393b0ef520843769e0c85fe96ae9406",
      "c814ad12e9fb4b778ebbc9236a5421c5",
      "e376647fa5344fc98ba7c18b006135f5",
      "df6e2f8a5cf14e85acf3304dc6143411",
      "7d28e5e2eb89483dbb1413195f439937",
      "bd5cbf740ac64acea257b87d1c1bf649",
      "af739e70853d4f56a84fff19c69a500a",
      "49365fd04b3841e4a9168b1c08dac318",
      "ded80b94037c465fbbaacb53731049b2",
      "df00825bcd5e43e3a808be582b6230bd",
      "bd13ec14350a498aa2e15dfa2b62e560",
      "3972a0db7d5b4738ae2c1f7308a83ded",
      "5b1953c07d3a455db560f7825bca77b2",
      "bfc9ba79a482422db0f4dffbd842e659",
      "24e000a00bd64744bed3309ca6036423",
      "ce54fa5c02154f76981e6249107417b2",
      "6d4d40dca25b4644888dd35bac448c8a",
      "9056b7010518469a8c2730982e714c45",
      "740fd0eb50164aec9312c6723a65f62b",
      "52bed51e216645458c80f5e38bae621b",
      "233ffb634ca1475d8bb04089143dbfe8"
     ]
    },
    "id": "iFg8cInkZ2TO",
    "outputId": "cfdfe628-d125-4b46-83e2-2fba45838775"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a64671087a2444fc97416cf61b26e1d9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Fitting CTGAN transformers for each column:   0%|          | 0/51 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bd13ec14350a498aa2e15dfa2b62e560",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training CTGAN, epochs::   0%|          | 0/500 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tabgan.sampler import GANGenerator\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import re\n",
    "df_x_train = df_x_train.rename(columns = lambda x:re.sub('[^A-Za-z0-9_]+', '', x))    # to tackle an error\n",
    "df_x_test = df_x_test.rename(columns = lambda x:re.sub('[^A-Za-z0-9_]+', '', x))\n",
    "\n",
    "\n",
    "how_many_times_data_needed = 2.2   #how many data generation needed\n",
    "gen_x, gen_y = GANGenerator(gen_x_times=how_many_times_data_needed, cat_cols=None,   #turn on the gpu to process quickly\n",
    "           bot_filter_quantile=0.001, top_filter_quantile=0.999, \\\n",
    "              is_post_process=True,\n",
    "           adversarial_model_params={\n",
    "               \"metrics\": \"rmse\", \"max_depth\": 2, \"max_bin\": 120, \n",
    "               \"learning_rate\": 0.02, \"random_state\": \\\n",
    "                42, \"n_estimators\": 800,\n",
    "           }, pregeneration_frac=2, only_generated_data=False,\\\n",
    "           gan_params = {\"batch_size\": 500, \"patience\": 25, \\\n",
    "          \"epochs\" : 500,}).generate_data_pipe(df_x_train, df_y_train,\\\n",
    "          df_x_test, deep_copy=True, only_adversarial=False, \\\n",
    "          use_adversarial=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lCS9PhILbh4g",
    "outputId": "4d7df8f3-3d0a-4d82-e195-f24fb1174f6a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "previously original data was of size (6359, 51)\n",
      "Newly generated data is of size (9462, 50)\n"
     ]
    }
   ],
   "source": [
    "print(f'previously original data was of size {df_label_all.shape}')\n",
    "print(f'Newly generated data is of size {gen_x.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "l9_43hMIyR_H"
   },
   "source": [
    "# Checking Generated Data Label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LNssvHkDZ4zP",
    "outputId": "fd3949bc-cc58-4e70-afdf-c7371b8f4713"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1\n",
       "1       0\n",
       "2       0\n",
       "3       0\n",
       "4       0\n",
       "       ..\n",
       "9457    0\n",
       "9458    1\n",
       "9459    0\n",
       "9460    1\n",
       "9461    1\n",
       "Name: label, Length: 9462, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gen_y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LSCREbQgy9zg"
   },
   "source": [
    "# See generated data distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 312
    },
    "id": "eOnpmkT-PBiY",
    "outputId": "4168c192-a729-4570-974c-ee21dafb1e64"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# matplotlib histogram\n",
    "plt.hist(gen_y, color = 'red', edgecolor = 'black',\n",
    "         bins = 2)\n",
    "\n",
    "# seaborn histogram\n",
    "sns.distplot(gen_y, hist=True, kde=False, \n",
    "             bins=2, color = 'blue',\n",
    "             hist_kws={'edgecolor':'black'})\n",
    "# Add labels\n",
    "plt.title('Histogram of generated Data')\n",
    "plt.xlabel('X label')\n",
    "plt.ylabel('Frequency')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NAIa2sBizPNf"
   },
   "source": [
    "# Check the generated data quality (Less the difference between the previously score in original data better the quality)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pf26aedZZ7v7",
    "outputId": "320a2802-9b21-42ba-c81a-c5be01a6f073"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final score (Balanced acc): 0.5629858933717212\n"
     ]
    }
   ],
   "source": [
    "pred = model.predict(gen_x.values)\n",
    "pred = [np.round(value) for value in pred]\n",
    "\n",
    "\n",
    "from sklearn.metrics import balanced_accuracy_score\n",
    "score = balanced_accuracy_score(gen_y, pred)\n",
    "print(\"Final score (Balanced acc): {}\".format(score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "fhpqc7RCnmI8"
   },
   "outputs": [],
   "source": [
    "gen_x['label'] = gen_y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MkP4_9NZznvI"
   },
   "source": [
    "# Saving Synthetic Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Xd81evD4Kj_V"
   },
   "outputs": [],
   "source": [
    "gen_x.to_csv('New one_zero even_more.csv', index = False)"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "CyclicGan_augmentation.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "233ffb634ca1475d8bb04089143dbfe8": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "24e000a00bd64744bed3309ca6036423": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "3972a0db7d5b4738ae2c1f7308a83ded": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_ce54fa5c02154f76981e6249107417b2",
      "placeholder": "​",
      "style": "IPY_MODEL_6d4d40dca25b4644888dd35bac448c8a",
      "value": "Training CTGAN, epochs:: 100%"
     }
    },
    "49365fd04b3841e4a9168b1c08dac318": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "52bed51e216645458c80f5e38bae621b": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "5b1953c07d3a455db560f7825bca77b2": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_9056b7010518469a8c2730982e714c45",
      "max": 500,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_740fd0eb50164aec9312c6723a65f62b",
      "value": 500
     }
    },
    "6d4d40dca25b4644888dd35bac448c8a": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "740fd0eb50164aec9312c6723a65f62b": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "7d28e5e2eb89483dbb1413195f439937": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "8393b0ef520843769e0c85fe96ae9406": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_7d28e5e2eb89483dbb1413195f439937",
      "placeholder": "​",
      "style": "IPY_MODEL_bd5cbf740ac64acea257b87d1c1bf649",
      "value": "Fitting CTGAN transformers for each column: 100%"
     }
    },
    "9056b7010518469a8c2730982e714c45": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "a64671087a2444fc97416cf61b26e1d9": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_8393b0ef520843769e0c85fe96ae9406",
       "IPY_MODEL_c814ad12e9fb4b778ebbc9236a5421c5",
       "IPY_MODEL_e376647fa5344fc98ba7c18b006135f5"
      ],
      "layout": "IPY_MODEL_df6e2f8a5cf14e85acf3304dc6143411"
     }
    },
    "af739e70853d4f56a84fff19c69a500a": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "bd13ec14350a498aa2e15dfa2b62e560": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_3972a0db7d5b4738ae2c1f7308a83ded",
       "IPY_MODEL_5b1953c07d3a455db560f7825bca77b2",
       "IPY_MODEL_bfc9ba79a482422db0f4dffbd842e659"
      ],
      "layout": "IPY_MODEL_24e000a00bd64744bed3309ca6036423"
     }
    },
    "bd5cbf740ac64acea257b87d1c1bf649": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "bfc9ba79a482422db0f4dffbd842e659": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_52bed51e216645458c80f5e38bae621b",
      "placeholder": "​",
      "style": "IPY_MODEL_233ffb634ca1475d8bb04089143dbfe8",
      "value": " 500/500 [03:03&lt;00:00,  3.12it/s]"
     }
    },
    "c814ad12e9fb4b778ebbc9236a5421c5": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_af739e70853d4f56a84fff19c69a500a",
      "max": 51,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_49365fd04b3841e4a9168b1c08dac318",
      "value": 51
     }
    },
    "ce54fa5c02154f76981e6249107417b2": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "ded80b94037c465fbbaacb53731049b2": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "df00825bcd5e43e3a808be582b6230bd": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "df6e2f8a5cf14e85acf3304dc6143411": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "e376647fa5344fc98ba7c18b006135f5": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_ded80b94037c465fbbaacb53731049b2",
      "placeholder": "​",
      "style": "IPY_MODEL_df00825bcd5e43e3a808be582b6230bd",
      "value": " 51/51 [00:17&lt;00:00, 13.46it/s]"
     }
    }
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}