mlvu_asd_ctgan.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CTGAN in ABCD ASD\n",
    "* Model Paper: Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, Kalyan Veeramachaneni. Modeling Tabular data using Conditional GAN. NeurIPS, 2019.\n",
    "* CTGAN 0.2.2.dev1documentation: https://sdv.dev/CTGAN/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0. load modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fatal: destination path 'SDV' already exists and is not an empty directory.\r\n"
     ]
    }
   ],
   "source": [
    "### git clone\n",
    "!git clone https://github.com/sdv-dev/SDV.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "### import module\n",
    "\n",
    "## sys\n",
    "import os\n",
    "import sys\n",
    "sys.path.append(\"/home/ubuntu/anaconda3/lib/python3.6/site-packages\")\n",
    "sys.path.append(\"/home/ubuntu/anaconda3/lib/python3.6/site-packages/ctgan-0.2.2.dev2-py3.6.egg\")\n",
    "\n",
    "## basic module\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import itertools\n",
    "from tqdm.notebook import tqdm\n",
    "from pathlib import Path\n",
    "\n",
    "## sdv\n",
    "from sdmetrics import evaluate\n",
    "from ctgan import CTGANSynthesizer\n",
    "from sdv import load_demo, SDV, Metadata\n",
    "from sdv.evaluation import evaluate\n",
    "\n",
    "## sklearn\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.metrics import roc_auc_score, accuracy_score\n",
    "from sklearn.model_selection import KFold\n",
    "\n",
    "## torch\n",
    "import torch\n",
    "from torch import nn\n",
    "from torch import tensor \n",
    "from torch.autograd import Variable\n",
    "from pytorch_tabnet.tab_model import TabNetClassifier,TabNetRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/ubuntu/Betty/CTGAN'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. prepare the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "### load data\n",
    "dataset_name = 'mor.symp.demo.qc.z'\n",
    "train_out = Path(os.getcwd()+ \"/train_mor.symp.demo.qc.z.csv\")\n",
    "test_out = Path(os.getcwd()+ \"/test_mor.symp.demo.qc.z.csv\")\n",
    "total_out = Path(os.getcwd()+ \"/mor.symp.demo.qc.z.csv\")\n",
    "\n",
    "train_data = pd.read_csv(train_out)\n",
    "test_data = pd.read_csv(test_out)\n",
    "total_data = pd.read_csv(total_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "### [TRAIN] check the data\n",
    "nan_values = train_data.isna()\n",
    "nan_bool = nan_values.any()\n",
    "nan_columns = train_data.columns[nan_bool].tolist()\n",
    "train_data.drop(nan_columns, inplace = True, axis=1)\n",
    "train_data_keys = train_data.keys()\n",
    "    \n",
    "### [TEST] check the data\n",
    "nan_values = test_data.isna()\n",
    "nan_bool = nan_values.any()\n",
    "nan_columns = test_data.columns[nan_bool].tolist()\n",
    "test_data.drop(nan_columns, inplace = True, axis=1)\n",
    "test_data_keys = test_data.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['subjectkey', 'age', 'abcd_site', 'ASD', 'dl_speech',\n",
       "       'lh_bankssts_area._.1', 'lh_caudalanteriorcingulate_area._.1',\n",
       "       'lh_caudalmiddlefrontal_area._.1', 'lh_cuneus_area._.1',\n",
       "       'lh_entorhinal_area._.1',\n",
       "       ...\n",
       "       'wm.rh.supramarginal._.18', 'wm.rh.frontalpole._.18',\n",
       "       'wm.rh.temporalpole._.18', 'wm.rh.transversetemporal._.18',\n",
       "       'wm.rh.insula._.18', 'Left.UnsegmentedWhiteMatter._.18',\n",
       "       'Right.UnsegmentedWhiteMatter._.18', 'lhCerebralWhiteMatterVol._.18',\n",
       "       'rhCerebralWhiteMatterVol._.18', 'CerebralWhiteMatterVol._.18'],\n",
       "      dtype='object', length=1000)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### check the data structure again\n",
    "#test_data.shape\n",
    "#test_data.head()\n",
    "#print(len(train_data), len(train_data.columns))\n",
    "train_data_keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train_columns_ASDGen = train_data.keys()\n",
    "#del train_columns_ASDGan[3] #3; try to exclude ASD column \n",
    "#print(train_columns_ASDGan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "### set variable\n",
    "target = 'ASD'\n",
    "unused_feat = ['subjectkey', 'age', 'female', 'race', 'abcd_site',\n",
    "               'SRS', 'dl_speech', 'symp_eyePast', 'symp_eyePresent', 'sym_movPast', 'sym_movPresent',\n",
    "              'sym_rouPast', 'sym_rouPresent', 'PDD_NOS']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find index of mri feature start \n",
    "# np.where의 결과값이 array에 들어가기 때문에 방금 계산해 넣어놓은 [0]번째 값을 가져온다.\n",
    "start_brain_index = np.where(test_data.columns.values == \"lh_bankssts_area._.1\")[0][0]\n",
    "mor_brain = list(test_data.columns.values[start_brain_index:])\n",
    "\n",
    "# the number of feature that you want to show \n",
    "Num_FOLDS  = 5\n",
    "Num_feat = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "995\n"
     ]
    }
   ],
   "source": [
    "#mor_brain\n",
    "print(len(mor_brain)) #995"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ASD</th>\n",
       "      <th>lh_bankssts_area._.1</th>\n",
       "      <th>lh_caudalanteriorcingulate_area._.1</th>\n",
       "      <th>lh_caudalmiddlefrontal_area._.1</th>\n",
       "      <th>lh_cuneus_area._.1</th>\n",
       "      <th>lh_entorhinal_area._.1</th>\n",
       "      <th>lh_fusiform_area._.1</th>\n",
       "      <th>lh_inferiorparietal_area._.1</th>\n",
       "      <th>lh_inferiortemporal_area._.1</th>\n",
       "      <th>lh_isthmuscingulate_area._.1</th>\n",
       "      <th>...</th>\n",
       "      <th>wm.rh.supramarginal._.18</th>\n",
       "      <th>wm.rh.frontalpole._.18</th>\n",
       "      <th>wm.rh.temporalpole._.18</th>\n",
       "      <th>wm.rh.transversetemporal._.18</th>\n",
       "      <th>wm.rh.insula._.18</th>\n",
       "      <th>Left.UnsegmentedWhiteMatter._.18</th>\n",
       "      <th>Right.UnsegmentedWhiteMatter._.18</th>\n",
       "      <th>lhCerebralWhiteMatterVol._.18</th>\n",
       "      <th>rhCerebralWhiteMatterVol._.18</th>\n",
       "      <th>CerebralWhiteMatterVol._.18</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.025851</td>\n",
       "      <td>0.519113</td>\n",
       "      <td>0.347495</td>\n",
       "      <td>0.016790</td>\n",
       "      <td>0.268523</td>\n",
       "      <td>-0.834353</td>\n",
       "      <td>-0.167922</td>\n",
       "      <td>-0.753314</td>\n",
       "      <td>0.379927</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.879234</td>\n",
       "      <td>-0.454089</td>\n",
       "      <td>-1.661157</td>\n",
       "      <td>-1.292066</td>\n",
       "      <td>0.259451</td>\n",
       "      <td>-1.608799</td>\n",
       "      <td>-1.469811</td>\n",
       "      <td>-1.188557</td>\n",
       "      <td>-1.297889</td>\n",
       "      <td>-1.244560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2.502511</td>\n",
       "      <td>0.890463</td>\n",
       "      <td>2.063899</td>\n",
       "      <td>0.832845</td>\n",
       "      <td>0.678534</td>\n",
       "      <td>1.353124</td>\n",
       "      <td>2.247249</td>\n",
       "      <td>1.138800</td>\n",
       "      <td>1.666732</td>\n",
       "      <td>...</td>\n",
       "      <td>2.117144</td>\n",
       "      <td>-0.889764</td>\n",
       "      <td>-0.151850</td>\n",
       "      <td>2.051664</td>\n",
       "      <td>1.122743</td>\n",
       "      <td>0.416397</td>\n",
       "      <td>0.066510</td>\n",
       "      <td>1.433300</td>\n",
       "      <td>1.374887</td>\n",
       "      <td>1.405527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.778180</td>\n",
       "      <td>-0.157275</td>\n",
       "      <td>-0.350008</td>\n",
       "      <td>0.066752</td>\n",
       "      <td>0.659464</td>\n",
       "      <td>-0.343589</td>\n",
       "      <td>-1.022242</td>\n",
       "      <td>0.837025</td>\n",
       "      <td>-0.007253</td>\n",
       "      <td>...</td>\n",
       "      <td>0.488968</td>\n",
       "      <td>-0.290898</td>\n",
       "      <td>-0.417831</td>\n",
       "      <td>1.401047</td>\n",
       "      <td>0.202878</td>\n",
       "      <td>1.440708</td>\n",
       "      <td>1.423929</td>\n",
       "      <td>0.338072</td>\n",
       "      <td>0.449538</td>\n",
       "      <td>0.394261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.316237</td>\n",
       "      <td>-0.309794</td>\n",
       "      <td>0.602790</td>\n",
       "      <td>1.311652</td>\n",
       "      <td>-1.247566</td>\n",
       "      <td>0.682330</td>\n",
       "      <td>0.310445</td>\n",
       "      <td>-0.734800</td>\n",
       "      <td>0.949310</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.237514</td>\n",
       "      <td>-0.364259</td>\n",
       "      <td>-0.883743</td>\n",
       "      <td>0.169281</td>\n",
       "      <td>0.094184</td>\n",
       "      <td>-0.376163</td>\n",
       "      <td>-0.205434</td>\n",
       "      <td>-0.223385</td>\n",
       "      <td>-0.343072</td>\n",
       "      <td>-0.283573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.875348</td>\n",
       "      <td>-0.018019</td>\n",
       "      <td>0.370289</td>\n",
       "      <td>-0.770120</td>\n",
       "      <td>0.001538</td>\n",
       "      <td>-0.353453</td>\n",
       "      <td>0.801776</td>\n",
       "      <td>0.220514</td>\n",
       "      <td>-0.457065</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.121501</td>\n",
       "      <td>0.081896</td>\n",
       "      <td>-0.366062</td>\n",
       "      <td>-0.692279</td>\n",
       "      <td>-1.209566</td>\n",
       "      <td>-0.107533</td>\n",
       "      <td>-0.292677</td>\n",
       "      <td>-0.245230</td>\n",
       "      <td>-0.274355</td>\n",
       "      <td>-0.260075</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>6237</th>\n",
       "      <td>0</td>\n",
       "      <td>0.029223</td>\n",
       "      <td>-0.170537</td>\n",
       "      <td>0.386245</td>\n",
       "      <td>2.406664</td>\n",
       "      <td>0.287593</td>\n",
       "      <td>0.561489</td>\n",
       "      <td>-0.390901</td>\n",
       "      <td>0.052038</td>\n",
       "      <td>-0.280557</td>\n",
       "      <td>...</td>\n",
       "      <td>0.909970</td>\n",
       "      <td>-1.009538</td>\n",
       "      <td>0.195354</td>\n",
       "      <td>-0.659239</td>\n",
       "      <td>0.529242</td>\n",
       "      <td>-0.445809</td>\n",
       "      <td>-0.431223</td>\n",
       "      <td>-0.461179</td>\n",
       "      <td>-0.577056</td>\n",
       "      <td>-0.519705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6238</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.316237</td>\n",
       "      <td>-1.403950</td>\n",
       "      <td>-1.325600</td>\n",
       "      <td>-0.557779</td>\n",
       "      <td>0.783421</td>\n",
       "      <td>-0.757902</td>\n",
       "      <td>-0.258670</td>\n",
       "      <td>-0.325644</td>\n",
       "      <td>-1.208651</td>\n",
       "      <td>...</td>\n",
       "      <td>0.042686</td>\n",
       "      <td>0.760112</td>\n",
       "      <td>-0.044743</td>\n",
       "      <td>0.357349</td>\n",
       "      <td>-0.420948</td>\n",
       "      <td>-0.047291</td>\n",
       "      <td>-0.093791</td>\n",
       "      <td>-0.552586</td>\n",
       "      <td>-0.558935</td>\n",
       "      <td>-0.556340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6239</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.656689</td>\n",
       "      <td>-0.303162</td>\n",
       "      <td>-0.575670</td>\n",
       "      <td>-0.920007</td>\n",
       "      <td>-0.255911</td>\n",
       "      <td>-0.178356</td>\n",
       "      <td>-0.127734</td>\n",
       "      <td>-1.245782</td>\n",
       "      <td>-0.906878</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.883026</td>\n",
       "      <td>-0.481038</td>\n",
       "      <td>-0.574920</td>\n",
       "      <td>-0.375442</td>\n",
       "      <td>-1.140010</td>\n",
       "      <td>-1.163325</td>\n",
       "      <td>-1.269541</td>\n",
       "      <td>-1.336130</td>\n",
       "      <td>-1.323185</td>\n",
       "      <td>-1.331032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6240</th>\n",
       "      <td>0</td>\n",
       "      <td>2.287225</td>\n",
       "      <td>1.586744</td>\n",
       "      <td>0.725879</td>\n",
       "      <td>0.545560</td>\n",
       "      <td>-0.589641</td>\n",
       "      <td>1.150899</td>\n",
       "      <td>-0.078472</td>\n",
       "      <td>-0.538554</td>\n",
       "      <td>0.641843</td>\n",
       "      <td>...</td>\n",
       "      <td>0.623590</td>\n",
       "      <td>0.748135</td>\n",
       "      <td>0.453302</td>\n",
       "      <td>-1.504703</td>\n",
       "      <td>-0.102449</td>\n",
       "      <td>1.307773</td>\n",
       "      <td>1.025365</td>\n",
       "      <td>1.061030</td>\n",
       "      <td>1.092109</td>\n",
       "      <td>1.077700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6241</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.196077</td>\n",
       "      <td>-0.369475</td>\n",
       "      <td>-0.160816</td>\n",
       "      <td>-1.469595</td>\n",
       "      <td>-0.122418</td>\n",
       "      <td>-0.703647</td>\n",
       "      <td>0.035610</td>\n",
       "      <td>-0.570027</td>\n",
       "      <td>-0.622186</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.878391</td>\n",
       "      <td>-1.626370</td>\n",
       "      <td>-0.671316</td>\n",
       "      <td>0.440371</td>\n",
       "      <td>-0.637671</td>\n",
       "      <td>-0.393825</td>\n",
       "      <td>-0.408145</td>\n",
       "      <td>-0.547227</td>\n",
       "      <td>-0.543383</td>\n",
       "      <td>-0.545869</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6242 rows × 996 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ASD  lh_bankssts_area._.1  lh_caudalanteriorcingulate_area._.1  \\\n",
       "0       0             -0.025851                             0.519113   \n",
       "1       0              2.502511                             0.890463   \n",
       "2       0             -1.778180                            -0.157275   \n",
       "3       0             -0.316237                            -0.309794   \n",
       "4       0              0.875348                            -0.018019   \n",
       "...   ...                   ...                                  ...   \n",
       "6237    0              0.029223                            -0.170537   \n",
       "6238    0             -0.316237                            -1.403950   \n",
       "6239    0             -0.656689                            -0.303162   \n",
       "6240    0              2.287225                             1.586744   \n",
       "6241    0             -0.196077                            -0.369475   \n",
       "\n",
       "      lh_caudalmiddlefrontal_area._.1  lh_cuneus_area._.1  \\\n",
       "0                            0.347495            0.016790   \n",
       "1                            2.063899            0.832845   \n",
       "2                           -0.350008            0.066752   \n",
       "3                            0.602790            1.311652   \n",
       "4                            0.370289           -0.770120   \n",
       "...                               ...                 ...   \n",
       "6237                         0.386245            2.406664   \n",
       "6238                        -1.325600           -0.557779   \n",
       "6239                        -0.575670           -0.920007   \n",
       "6240                         0.725879            0.545560   \n",
       "6241                        -0.160816           -1.469595   \n",
       "\n",
       "      lh_entorhinal_area._.1  lh_fusiform_area._.1  \\\n",
       "0                   0.268523             -0.834353   \n",
       "1                   0.678534              1.353124   \n",
       "2                   0.659464             -0.343589   \n",
       "3                  -1.247566              0.682330   \n",
       "4                   0.001538             -0.353453   \n",
       "...                      ...                   ...   \n",
       "6237                0.287593              0.561489   \n",
       "6238                0.783421             -0.757902   \n",
       "6239               -0.255911             -0.178356   \n",
       "6240               -0.589641              1.150899   \n",
       "6241               -0.122418             -0.703647   \n",
       "\n",
       "      lh_inferiorparietal_area._.1  lh_inferiortemporal_area._.1  \\\n",
       "0                        -0.167922                     -0.753314   \n",
       "1                         2.247249                      1.138800   \n",
       "2                        -1.022242                      0.837025   \n",
       "3                         0.310445                     -0.734800   \n",
       "4                         0.801776                      0.220514   \n",
       "...                            ...                           ...   \n",
       "6237                     -0.390901                      0.052038   \n",
       "6238                     -0.258670                     -0.325644   \n",
       "6239                     -0.127734                     -1.245782   \n",
       "6240                     -0.078472                     -0.538554   \n",
       "6241                      0.035610                     -0.570027   \n",
       "\n",
       "      lh_isthmuscingulate_area._.1  ...  wm.rh.supramarginal._.18  \\\n",
       "0                         0.379927  ...                 -0.879234   \n",
       "1                         1.666732  ...                  2.117144   \n",
       "2                        -0.007253  ...                  0.488968   \n",
       "3                         0.949310  ...                 -0.237514   \n",
       "4                        -0.457065  ...                 -0.121501   \n",
       "...                            ...  ...                       ...   \n",
       "6237                     -0.280557  ...                  0.909970   \n",
       "6238                     -1.208651  ...                  0.042686   \n",
       "6239                     -0.906878  ...                 -0.883026   \n",
       "6240                      0.641843  ...                  0.623590   \n",
       "6241                     -0.622186  ...                 -0.878391   \n",
       "\n",
       "      wm.rh.frontalpole._.18  wm.rh.temporalpole._.18  \\\n",
       "0                  -0.454089                -1.661157   \n",
       "1                  -0.889764                -0.151850   \n",
       "2                  -0.290898                -0.417831   \n",
       "3                  -0.364259                -0.883743   \n",
       "4                   0.081896                -0.366062   \n",
       "...                      ...                      ...   \n",
       "6237               -1.009538                 0.195354   \n",
       "6238                0.760112                -0.044743   \n",
       "6239               -0.481038                -0.574920   \n",
       "6240                0.748135                 0.453302   \n",
       "6241               -1.626370                -0.671316   \n",
       "\n",
       "      wm.rh.transversetemporal._.18  wm.rh.insula._.18  \\\n",
       "0                         -1.292066           0.259451   \n",
       "1                          2.051664           1.122743   \n",
       "2                          1.401047           0.202878   \n",
       "3                          0.169281           0.094184   \n",
       "4                         -0.692279          -1.209566   \n",
       "...                             ...                ...   \n",
       "6237                      -0.659239           0.529242   \n",
       "6238                       0.357349          -0.420948   \n",
       "6239                      -0.375442          -1.140010   \n",
       "6240                      -1.504703          -0.102449   \n",
       "6241                       0.440371          -0.637671   \n",
       "\n",
       "      Left.UnsegmentedWhiteMatter._.18  Right.UnsegmentedWhiteMatter._.18  \\\n",
       "0                            -1.608799                          -1.469811   \n",
       "1                             0.416397                           0.066510   \n",
       "2                             1.440708                           1.423929   \n",
       "3                            -0.376163                          -0.205434   \n",
       "4                            -0.107533                          -0.292677   \n",
       "...                                ...                                ...   \n",
       "6237                         -0.445809                          -0.431223   \n",
       "6238                         -0.047291                          -0.093791   \n",
       "6239                         -1.163325                          -1.269541   \n",
       "6240                          1.307773                           1.025365   \n",
       "6241                         -0.393825                          -0.408145   \n",
       "\n",
       "      lhCerebralWhiteMatterVol._.18  rhCerebralWhiteMatterVol._.18  \\\n",
       "0                         -1.188557                      -1.297889   \n",
       "1                          1.433300                       1.374887   \n",
       "2                          0.338072                       0.449538   \n",
       "3                         -0.223385                      -0.343072   \n",
       "4                         -0.245230                      -0.274355   \n",
       "...                             ...                            ...   \n",
       "6237                      -0.461179                      -0.577056   \n",
       "6238                      -0.552586                      -0.558935   \n",
       "6239                      -1.336130                      -1.323185   \n",
       "6240                       1.061030                       1.092109   \n",
       "6241                      -0.547227                      -0.543383   \n",
       "\n",
       "      CerebralWhiteMatterVol._.18  \n",
       "0                       -1.244560  \n",
       "1                        1.405527  \n",
       "2                        0.394261  \n",
       "3                       -0.283573  \n",
       "4                       -0.260075  \n",
       "...                           ...  \n",
       "6237                    -0.519705  \n",
       "6238                    -0.556340  \n",
       "6239                    -1.331032  \n",
       "6240                     1.077700  \n",
       "6241                    -0.545869  \n",
       "\n",
       "[6242 rows x 996 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### ASD column + mor_brain => features_forargu\n",
    "features_forargu = [i for i in [target]+mor_brain if i not in unused_feat]\n",
    "\n",
    "### extract features_forargu from train_data\n",
    "data_forargu = train_data[features_forargu]\n",
    "data_forargu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6242, 996)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_forargu.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ASD']"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "discrete_columns =[]\n",
    "for i in range(len(data_forargu.dtypes)):\n",
    "    if data_forargu.dtypes[i]=='int64':\n",
    "        discrete_columns.append(data_forargu.columns[i])\n",
    "discrete_columns   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(discrete_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data_exp = test_data[features_forargu]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>ASD</th>\n",
       "      <th>lh_bankssts_area._.1</th>\n",
       "      <th>lh_caudalanteriorcingulate_area._.1</th>\n",
       "      <th>lh_caudalmiddlefrontal_area._.1</th>\n",
       "      <th>lh_cuneus_area._.1</th>\n",
       "      <th>lh_entorhinal_area._.1</th>\n",
       "      <th>lh_fusiform_area._.1</th>\n",
       "      <th>lh_inferiorparietal_area._.1</th>\n",
       "      <th>lh_inferiortemporal_area._.1</th>\n",
       "      <th>lh_isthmuscingulate_area._.1</th>\n",
       "      <th>...</th>\n",
       "      <th>wm.rh.supramarginal._.18</th>\n",
       "      <th>wm.rh.frontalpole._.18</th>\n",
       "      <th>wm.rh.temporalpole._.18</th>\n",
       "      <th>wm.rh.transversetemporal._.18</th>\n",
       "      <th>wm.rh.insula._.18</th>\n",
       "      <th>Left.UnsegmentedWhiteMatter._.18</th>\n",
       "      <th>Right.UnsegmentedWhiteMatter._.18</th>\n",
       "      <th>lhCerebralWhiteMatterVol._.18</th>\n",
       "      <th>rhCerebralWhiteMatterVol._.18</th>\n",
       "      <th>CerebralWhiteMatterVol._.18</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.162362</td>\n",
       "      <td>-0.674513</td>\n",
       "      <td>-1.038393</td>\n",
       "      <td>-1.465432</td>\n",
       "      <td>-0.694528</td>\n",
       "      <td>-1.769024</td>\n",
       "      <td>-1.923232</td>\n",
       "      <td>-1.538301</td>\n",
       "      <td>-0.713288</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.792435</td>\n",
       "      <td>-0.364259</td>\n",
       "      <td>-0.608837</td>\n",
       "      <td>-0.727012</td>\n",
       "      <td>-0.579676</td>\n",
       "      <td>-1.328681</td>\n",
       "      <td>-1.175095</td>\n",
       "      <td>-1.218899</td>\n",
       "      <td>-1.341326</td>\n",
       "      <td>-1.281493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1.861659</td>\n",
       "      <td>-0.144012</td>\n",
       "      <td>0.668893</td>\n",
       "      <td>0.849499</td>\n",
       "      <td>0.077820</td>\n",
       "      <td>1.059652</td>\n",
       "      <td>0.432305</td>\n",
       "      <td>-0.338604</td>\n",
       "      <td>0.841127</td>\n",
       "      <td>...</td>\n",
       "      <td>1.329707</td>\n",
       "      <td>1.664400</td>\n",
       "      <td>-0.084016</td>\n",
       "      <td>1.761089</td>\n",
       "      <td>-0.231706</td>\n",
       "      <td>-0.084393</td>\n",
       "      <td>-0.167806</td>\n",
       "      <td>0.678000</td>\n",
       "      <td>0.592754</td>\n",
       "      <td>0.636001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1.210794</td>\n",
       "      <td>-0.296531</td>\n",
       "      <td>0.386245</td>\n",
       "      <td>1.240872</td>\n",
       "      <td>-0.418008</td>\n",
       "      <td>1.049787</td>\n",
       "      <td>2.060569</td>\n",
       "      <td>0.711131</td>\n",
       "      <td>1.029024</td>\n",
       "      <td>...</td>\n",
       "      <td>1.495720</td>\n",
       "      <td>1.487735</td>\n",
       "      <td>1.618084</td>\n",
       "      <td>-0.651615</td>\n",
       "      <td>1.508997</td>\n",
       "      <td>1.481633</td>\n",
       "      <td>1.887554</td>\n",
       "      <td>2.253305</td>\n",
       "      <td>2.062633</td>\n",
       "      <td>2.160130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.349649</td>\n",
       "      <td>0.472694</td>\n",
       "      <td>-1.077143</td>\n",
       "      <td>0.079243</td>\n",
       "      <td>-0.990117</td>\n",
       "      <td>-0.385513</td>\n",
       "      <td>-0.389605</td>\n",
       "      <td>1.186936</td>\n",
       "      <td>-1.202957</td>\n",
       "      <td>...</td>\n",
       "      <td>0.346059</td>\n",
       "      <td>0.553503</td>\n",
       "      <td>0.612176</td>\n",
       "      <td>0.385306</td>\n",
       "      <td>-0.596260</td>\n",
       "      <td>0.452048</td>\n",
       "      <td>0.402334</td>\n",
       "      <td>-0.087955</td>\n",
       "      <td>-0.041242</td>\n",
       "      <td>-0.064646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.451416</td>\n",
       "      <td>-0.959656</td>\n",
       "      <td>-1.195673</td>\n",
       "      <td>0.449799</td>\n",
       "      <td>0.792956</td>\n",
       "      <td>0.161972</td>\n",
       "      <td>-0.692960</td>\n",
       "      <td>0.064998</td>\n",
       "      <td>-1.504730</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.845947</td>\n",
       "      <td>0.144777</td>\n",
       "      <td>-1.016734</td>\n",
       "      <td>-1.896936</td>\n",
       "      <td>-0.916180</td>\n",
       "      <td>0.090162</td>\n",
       "      <td>0.579627</td>\n",
       "      <td>-0.236562</td>\n",
       "      <td>-0.072682</td>\n",
       "      <td>-0.154714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1555</th>\n",
       "      <td>0</td>\n",
       "      <td>-2.058553</td>\n",
       "      <td>-1.118806</td>\n",
       "      <td>-0.117507</td>\n",
       "      <td>0.932770</td>\n",
       "      <td>-1.304777</td>\n",
       "      <td>1.094178</td>\n",
       "      <td>-0.787596</td>\n",
       "      <td>-0.551513</td>\n",
       "      <td>1.114431</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.358091</td>\n",
       "      <td>0.637345</td>\n",
       "      <td>0.646093</td>\n",
       "      <td>-0.359346</td>\n",
       "      <td>1.071192</td>\n",
       "      <td>0.250080</td>\n",
       "      <td>0.186252</td>\n",
       "      <td>0.135256</td>\n",
       "      <td>0.147156</td>\n",
       "      <td>0.141357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1556</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.191070</td>\n",
       "      <td>0.107975</td>\n",
       "      <td>0.251759</td>\n",
       "      <td>0.133369</td>\n",
       "      <td>1.365065</td>\n",
       "      <td>2.092969</td>\n",
       "      <td>0.698065</td>\n",
       "      <td>0.796294</td>\n",
       "      <td>0.118011</td>\n",
       "      <td>...</td>\n",
       "      <td>1.630904</td>\n",
       "      <td>-0.750528</td>\n",
       "      <td>0.094495</td>\n",
       "      <td>1.357842</td>\n",
       "      <td>1.674833</td>\n",
       "      <td>0.209339</td>\n",
       "      <td>0.040848</td>\n",
       "      <td>0.937421</td>\n",
       "      <td>0.867498</td>\n",
       "      <td>0.903367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1557</th>\n",
       "      <td>0</td>\n",
       "      <td>2.036892</td>\n",
       "      <td>3.430232</td>\n",
       "      <td>2.177870</td>\n",
       "      <td>1.927857</td>\n",
       "      <td>0.440155</td>\n",
       "      <td>1.059652</td>\n",
       "      <td>1.359223</td>\n",
       "      <td>2.075601</td>\n",
       "      <td>-0.371658</td>\n",
       "      <td>...</td>\n",
       "      <td>1.341013</td>\n",
       "      <td>1.082003</td>\n",
       "      <td>0.506855</td>\n",
       "      <td>1.949158</td>\n",
       "      <td>1.449770</td>\n",
       "      <td>-0.355578</td>\n",
       "      <td>-0.753168</td>\n",
       "      <td>1.802498</td>\n",
       "      <td>1.675731</td>\n",
       "      <td>1.740866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1558</th>\n",
       "      <td>0</td>\n",
       "      <td>1.611326</td>\n",
       "      <td>-0.760719</td>\n",
       "      <td>-0.087874</td>\n",
       "      <td>-1.248927</td>\n",
       "      <td>-1.209426</td>\n",
       "      <td>-1.043976</td>\n",
       "      <td>-1.052059</td>\n",
       "      <td>-1.125442</td>\n",
       "      <td>-1.117549</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.208370</td>\n",
       "      <td>-0.973606</td>\n",
       "      <td>-2.073517</td>\n",
       "      <td>0.231970</td>\n",
       "      <td>-0.197023</td>\n",
       "      <td>-0.124357</td>\n",
       "      <td>-0.193386</td>\n",
       "      <td>-0.513814</td>\n",
       "      <td>-0.626792</td>\n",
       "      <td>-0.570943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1559</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.032189</td>\n",
       "      <td>1.168975</td>\n",
       "      <td>0.632423</td>\n",
       "      <td>-1.744389</td>\n",
       "      <td>0.545042</td>\n",
       "      <td>-0.720910</td>\n",
       "      <td>0.135432</td>\n",
       "      <td>-0.221967</td>\n",
       "      <td>1.438979</td>\n",
       "      <td>...</td>\n",
       "      <td>0.046337</td>\n",
       "      <td>-1.298491</td>\n",
       "      <td>-0.217006</td>\n",
       "      <td>-0.408481</td>\n",
       "      <td>-0.361626</td>\n",
       "      <td>-1.111444</td>\n",
       "      <td>-1.052788</td>\n",
       "      <td>-0.871938</td>\n",
       "      <td>-0.957635</td>\n",
       "      <td>-0.915772</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1560 rows × 996 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ASD  lh_bankssts_area._.1  lh_caudalanteriorcingulate_area._.1  \\\n",
       "0       0             -1.162362                            -0.674513   \n",
       "1       0              1.861659                            -0.144012   \n",
       "2       0              1.210794                            -0.296531   \n",
       "3       0              0.349649                             0.472694   \n",
       "4       0             -0.451416                            -0.959656   \n",
       "...   ...                   ...                                  ...   \n",
       "1555    0             -2.058553                            -1.118806   \n",
       "1556    0             -0.191070                             0.107975   \n",
       "1557    0              2.036892                             3.430232   \n",
       "1558    0              1.611326                            -0.760719   \n",
       "1559    0             -1.032189                             1.168975   \n",
       "\n",
       "      lh_caudalmiddlefrontal_area._.1  lh_cuneus_area._.1  \\\n",
       "0                           -1.038393           -1.465432   \n",
       "1                            0.668893            0.849499   \n",
       "2                            0.386245            1.240872   \n",
       "3                           -1.077143            0.079243   \n",
       "4                           -1.195673            0.449799   \n",
       "...                               ...                 ...   \n",
       "1555                        -0.117507            0.932770   \n",
       "1556                         0.251759            0.133369   \n",
       "1557                         2.177870            1.927857   \n",
       "1558                        -0.087874           -1.248927   \n",
       "1559                         0.632423           -1.744389   \n",
       "\n",
       "      lh_entorhinal_area._.1  lh_fusiform_area._.1  \\\n",
       "0                  -0.694528             -1.769024   \n",
       "1                   0.077820              1.059652   \n",
       "2                  -0.418008              1.049787   \n",
       "3                  -0.990117             -0.385513   \n",
       "4                   0.792956              0.161972   \n",
       "...                      ...                   ...   \n",
       "1555               -1.304777              1.094178   \n",
       "1556                1.365065              2.092969   \n",
       "1557                0.440155              1.059652   \n",
       "1558               -1.209426             -1.043976   \n",
       "1559                0.545042             -0.720910   \n",
       "\n",
       "      lh_inferiorparietal_area._.1  lh_inferiortemporal_area._.1  \\\n",
       "0                        -1.923232                     -1.538301   \n",
       "1                         0.432305                     -0.338604   \n",
       "2                         2.060569                      0.711131   \n",
       "3                        -0.389605                      1.186936   \n",
       "4                        -0.692960                      0.064998   \n",
       "...                            ...                           ...   \n",
       "1555                     -0.787596                     -0.551513   \n",
       "1556                      0.698065                      0.796294   \n",
       "1557                      1.359223                      2.075601   \n",
       "1558                     -1.052059                     -1.125442   \n",
       "1559                      0.135432                     -0.221967   \n",
       "\n",
       "      lh_isthmuscingulate_area._.1  ...  wm.rh.supramarginal._.18  \\\n",
       "0                        -0.713288  ...                 -0.792435   \n",
       "1                         0.841127  ...                  1.329707   \n",
       "2                         1.029024  ...                  1.495720   \n",
       "3                        -1.202957  ...                  0.346059   \n",
       "4                        -1.504730  ...                 -0.845947   \n",
       "...                            ...  ...                       ...   \n",
       "1555                      1.114431  ...                 -0.358091   \n",
       "1556                      0.118011  ...                  1.630904   \n",
       "1557                     -0.371658  ...                  1.341013   \n",
       "1558                     -1.117549  ...                 -0.208370   \n",
       "1559                      1.438979  ...                  0.046337   \n",
       "\n",
       "      wm.rh.frontalpole._.18  wm.rh.temporalpole._.18  \\\n",
       "0                  -0.364259                -0.608837   \n",
       "1                   1.664400                -0.084016   \n",
       "2                   1.487735                 1.618084   \n",
       "3                   0.553503                 0.612176   \n",
       "4                   0.144777                -1.016734   \n",
       "...                      ...                      ...   \n",
       "1555                0.637345                 0.646093   \n",
       "1556               -0.750528                 0.094495   \n",
       "1557                1.082003                 0.506855   \n",
       "1558               -0.973606                -2.073517   \n",
       "1559               -1.298491                -0.217006   \n",
       "\n",
       "      wm.rh.transversetemporal._.18  wm.rh.insula._.18  \\\n",
       "0                         -0.727012          -0.579676   \n",
       "1                          1.761089          -0.231706   \n",
       "2                         -0.651615           1.508997   \n",
       "3                          0.385306          -0.596260   \n",
       "4                         -1.896936          -0.916180   \n",
       "...                             ...                ...   \n",
       "1555                      -0.359346           1.071192   \n",
       "1556                       1.357842           1.674833   \n",
       "1557                       1.949158           1.449770   \n",
       "1558                       0.231970          -0.197023   \n",
       "1559                      -0.408481          -0.361626   \n",
       "\n",
       "      Left.UnsegmentedWhiteMatter._.18  Right.UnsegmentedWhiteMatter._.18  \\\n",
       "0                            -1.328681                          -1.175095   \n",
       "1                            -0.084393                          -0.167806   \n",
       "2                             1.481633                           1.887554   \n",
       "3                             0.452048                           0.402334   \n",
       "4                             0.090162                           0.579627   \n",
       "...                                ...                                ...   \n",
       "1555                          0.250080                           0.186252   \n",
       "1556                          0.209339                           0.040848   \n",
       "1557                         -0.355578                          -0.753168   \n",
       "1558                         -0.124357                          -0.193386   \n",
       "1559                         -1.111444                          -1.052788   \n",
       "\n",
       "      lhCerebralWhiteMatterVol._.18  rhCerebralWhiteMatterVol._.18  \\\n",
       "0                         -1.218899                      -1.341326   \n",
       "1                          0.678000                       0.592754   \n",
       "2                          2.253305                       2.062633   \n",
       "3                         -0.087955                      -0.041242   \n",
       "4                         -0.236562                      -0.072682   \n",
       "...                             ...                            ...   \n",
       "1555                       0.135256                       0.147156   \n",
       "1556                       0.937421                       0.867498   \n",
       "1557                       1.802498                       1.675731   \n",
       "1558                      -0.513814                      -0.626792   \n",
       "1559                      -0.871938                      -0.957635   \n",
       "\n",
       "      CerebralWhiteMatterVol._.18  \n",
       "0                       -1.281493  \n",
       "1                        0.636001  \n",
       "2                        2.160130  \n",
       "3                       -0.064646  \n",
       "4                       -0.154714  \n",
       "...                           ...  \n",
       "1555                     0.141357  \n",
       "1556                     0.903367  \n",
       "1557                     1.740866  \n",
       "1558                    -0.570943  \n",
       "1559                    -0.915772  \n",
       "\n",
       "[1560 rows x 996 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data_exp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. fit CTGAN to the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Define a GAN\n",
    "#ctgan = CTGANSynthesizer(batch_size = 500)\n",
    "ctgan = CTGANSynthesizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ctgan.synthesizer.CTGANSynthesizer at 0x7fccac3d4048>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctgan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "### To check whether there is error, ctgan.fit for test with only epochs=10\n",
    "#ctgan.fit(df, discrete_columns, epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 11, Loss G: -1.5921, Loss D: 0.5629\n",
      "Epoch 12, Loss G: -0.3739, Loss D: -0.2175\n",
      "Epoch 13, Loss G: -1.3559, Loss D: 0.0677\n",
      "Epoch 14, Loss G: -1.9614, Loss D: 0.1669\n",
      "Epoch 15, Loss G: -0.6946, Loss D: -0.2136\n",
      "Epoch 16, Loss G: -1.7770, Loss D: 1.4243\n",
      "Epoch 17, Loss G: 1.2688, Loss D: -0.0115\n",
      "Epoch 18, Loss G: 4.4130, Loss D: 0.0357\n",
      "Epoch 19, Loss G: 4.9753, Loss D: -0.1639\n",
      "Epoch 20, Loss G: 6.0181, Loss D: 2.3310\n",
      "Epoch 21, Loss G: 10.8289, Loss D: -2.0992\n",
      "Epoch 22, Loss G: 7.4970, Loss D: -1.3385\n",
      "Epoch 23, Loss G: 6.1594, Loss D: 1.7419\n",
      "Epoch 24, Loss G: 10.4338, Loss D: -2.4107\n",
      "Epoch 25, Loss G: 8.2808, Loss D: -0.8633\n",
      "Epoch 26, Loss G: 8.8559, Loss D: -0.4205\n",
      "Epoch 27, Loss G: 9.8463, Loss D: 0.1283\n",
      "Epoch 28, Loss G: 10.3287, Loss D: -0.8931\n",
      "Epoch 29, Loss G: 6.3044, Loss D: 0.5819\n",
      "Epoch 30, Loss G: 10.9524, Loss D: -3.1806\n",
      "Epoch 31, Loss G: 8.3836, Loss D: -0.0012\n",
      "Epoch 32, Loss G: 8.5834, Loss D: 0.8668\n",
      "Epoch 33, Loss G: 10.0268, Loss D: -1.2543\n",
      "Epoch 34, Loss G: 7.4745, Loss D: 0.2741\n",
      "Epoch 35, Loss G: 7.1128, Loss D: -0.6176\n",
      "Epoch 36, Loss G: 7.9845, Loss D: -1.6359\n",
      "Epoch 37, Loss G: 7.1773, Loss D: 2.2762\n",
      "Epoch 38, Loss G: 5.6563, Loss D: 1.1836\n",
      "Epoch 39, Loss G: 7.2623, Loss D: -3.0047\n",
      "Epoch 40, Loss G: 5.0990, Loss D: 0.6866\n",
      "Epoch 41, Loss G: 8.2814, Loss D: -0.5454\n",
      "Epoch 42, Loss G: 5.7065, Loss D: 2.3703\n",
      "Epoch 43, Loss G: 9.8115, Loss D: -1.8136\n",
      "Epoch 44, Loss G: 7.5460, Loss D: -0.2465\n",
      "Epoch 45, Loss G: 9.1106, Loss D: 0.6580\n",
      "Epoch 46, Loss G: 8.8855, Loss D: -1.9740\n",
      "Epoch 47, Loss G: 6.5869, Loss D: 1.0490\n",
      "Epoch 48, Loss G: 8.5292, Loss D: 0.8667\n",
      "Epoch 49, Loss G: 8.1251, Loss D: -0.2261\n",
      "Epoch 50, Loss G: 6.3575, Loss D: -0.0752\n",
      "Epoch 51, Loss G: 9.7679, Loss D: -0.7931\n",
      "Epoch 52, Loss G: 7.7611, Loss D: 0.2177\n",
      "Epoch 53, Loss G: 5.5879, Loss D: 1.9304\n",
      "Epoch 54, Loss G: 5.5767, Loss D: -0.0054\n",
      "Epoch 55, Loss G: 5.5325, Loss D: 1.7255\n",
      "Epoch 56, Loss G: 3.3207, Loss D: -0.7653\n",
      "Epoch 57, Loss G: 7.9851, Loss D: -1.7016\n",
      "Epoch 58, Loss G: 5.4985, Loss D: 1.0063\n",
      "Epoch 59, Loss G: 3.9348, Loss D: 3.2052\n",
      "Epoch 60, Loss G: 10.6587, Loss D: -0.3142\n",
      "Epoch 61, Loss G: 4.6025, Loss D: 3.9406\n",
      "Epoch 62, Loss G: 6.4738, Loss D: -0.7206\n",
      "Epoch 63, Loss G: 4.4452, Loss D: -0.8277\n",
      "Epoch 64, Loss G: 6.8189, Loss D: 0.2866\n",
      "Epoch 65, Loss G: 5.7030, Loss D: -0.1836\n",
      "Epoch 66, Loss G: 5.5160, Loss D: 1.2342\n",
      "Epoch 67, Loss G: 5.0456, Loss D: -0.4845\n",
      "Epoch 68, Loss G: 5.3743, Loss D: -1.1244\n",
      "Epoch 69, Loss G: 8.1619, Loss D: -1.1385\n",
      "Epoch 70, Loss G: 7.3418, Loss D: 3.7631\n",
      "Epoch 71, Loss G: 7.9053, Loss D: -0.6315\n",
      "Epoch 72, Loss G: 8.7279, Loss D: 0.8692\n",
      "Epoch 73, Loss G: 8.2662, Loss D: 0.8630\n",
      "Epoch 74, Loss G: 4.1178, Loss D: -2.0660\n",
      "Epoch 75, Loss G: 1.4374, Loss D: -1.4808\n",
      "Epoch 76, Loss G: 3.7006, Loss D: -2.3212\n",
      "Epoch 77, Loss G: 3.0487, Loss D: 0.0250\n",
      "Epoch 78, Loss G: 6.7542, Loss D: -0.2497\n",
      "Epoch 79, Loss G: 1.9891, Loss D: -0.4187\n",
      "Epoch 80, Loss G: 6.3323, Loss D: -0.8412\n",
      "Epoch 81, Loss G: 4.7859, Loss D: -1.1168\n",
      "Epoch 82, Loss G: 4.5504, Loss D: 0.8245\n",
      "Epoch 83, Loss G: 3.9617, Loss D: 1.7414\n",
      "Epoch 84, Loss G: 2.2218, Loss D: 1.8845\n",
      "Epoch 85, Loss G: 1.4222, Loss D: 0.0449\n",
      "Epoch 86, Loss G: 0.9699, Loss D: 1.3969\n",
      "Epoch 87, Loss G: 0.4822, Loss D: -0.9461\n",
      "Epoch 88, Loss G: 1.2747, Loss D: 0.5462\n",
      "Epoch 89, Loss G: 1.9586, Loss D: -1.6728\n",
      "Epoch 90, Loss G: -1.0279, Loss D: -0.0820\n",
      "Epoch 91, Loss G: 1.8111, Loss D: -0.1249\n",
      "Epoch 92, Loss G: 1.7209, Loss D: 3.0096\n",
      "Epoch 93, Loss G: 1.5161, Loss D: 0.1029\n",
      "Epoch 94, Loss G: 0.4907, Loss D: -0.8578\n",
      "Epoch 95, Loss G: 5.9035, Loss D: 0.3633\n",
      "Epoch 96, Loss G: 3.8143, Loss D: -0.1400\n",
      "Epoch 97, Loss G: 2.8682, Loss D: 1.1873\n",
      "Epoch 98, Loss G: 3.6344, Loss D: 0.7046\n",
      "Epoch 99, Loss G: 2.2039, Loss D: -0.6750\n",
      "Epoch 100, Loss G: 3.4329, Loss D: -0.1572\n",
      "Epoch 101, Loss G: 2.5551, Loss D: 1.7811\n",
      "Epoch 102, Loss G: 2.0483, Loss D: 0.6760\n",
      "Epoch 103, Loss G: 3.1183, Loss D: 0.5296\n",
      "Epoch 104, Loss G: 4.4346, Loss D: -0.8750\n",
      "Epoch 105, Loss G: 3.7886, Loss D: 0.6205\n",
      "Epoch 106, Loss G: 3.6438, Loss D: 0.0191\n",
      "Epoch 107, Loss G: 3.9262, Loss D: 1.0578\n",
      "Epoch 108, Loss G: 2.5985, Loss D: 1.0322\n",
      "Epoch 109, Loss G: 2.1577, Loss D: 1.9408\n",
      "Epoch 110, Loss G: 0.5000, Loss D: 0.1383\n",
      "Epoch 111, Loss G: -1.8200, Loss D: -0.0543\n",
      "Epoch 112, Loss G: 0.9622, Loss D: -1.6958\n",
      "Epoch 113, Loss G: -1.1998, Loss D: 0.3160\n",
      "Epoch 114, Loss G: -1.6598, Loss D: 0.6613\n",
      "Epoch 115, Loss G: 0.4854, Loss D: -1.2603\n",
      "Epoch 116, Loss G: 0.0765, Loss D: 0.5290\n",
      "Epoch 117, Loss G: 0.8717, Loss D: 0.3772\n",
      "Epoch 118, Loss G: 2.0492, Loss D: -1.2157\n",
      "Epoch 119, Loss G: 2.2042, Loss D: -0.4460\n",
      "Epoch 120, Loss G: 1.5808, Loss D: -3.2431\n",
      "Epoch 121, Loss G: -1.1176, Loss D: 3.3523\n",
      "Epoch 122, Loss G: 2.3883, Loss D: 0.5950\n",
      "Epoch 123, Loss G: 1.4704, Loss D: 0.6879\n",
      "Epoch 124, Loss G: 2.2359, Loss D: 0.7177\n",
      "Epoch 125, Loss G: 3.9962, Loss D: -1.7190\n",
      "Epoch 126, Loss G: 2.9720, Loss D: -2.3923\n",
      "Epoch 127, Loss G: 0.7181, Loss D: -0.0122\n",
      "Epoch 128, Loss G: -0.0617, Loss D: -0.4214\n",
      "Epoch 129, Loss G: 2.3126, Loss D: 1.3956\n",
      "Epoch 130, Loss G: 1.5856, Loss D: -0.0336\n",
      "Epoch 131, Loss G: 1.0610, Loss D: 3.6710\n",
      "Epoch 132, Loss G: 2.7107, Loss D: -2.7150\n",
      "Epoch 133, Loss G: 1.8926, Loss D: 2.6466\n",
      "Epoch 134, Loss G: 1.2777, Loss D: 1.0718\n",
      "Epoch 135, Loss G: 1.3983, Loss D: -0.6460\n",
      "Epoch 136, Loss G: 1.2291, Loss D: 0.1752\n",
      "Epoch 137, Loss G: 5.1108, Loss D: -0.9640\n",
      "Epoch 138, Loss G: 2.7181, Loss D: 1.1536\n",
      "Epoch 139, Loss G: 1.3048, Loss D: -0.9858\n",
      "Epoch 140, Loss G: -0.1984, Loss D: 0.5756\n",
      "Epoch 141, Loss G: 3.6816, Loss D: 0.1013\n",
      "Epoch 142, Loss G: 3.2040, Loss D: -0.0135\n",
      "Epoch 143, Loss G: 0.8363, Loss D: -0.0551\n",
      "Epoch 144, Loss G: 1.6013, Loss D: 1.2440\n",
      "Epoch 145, Loss G: 2.1463, Loss D: -1.2537\n",
      "Epoch 146, Loss G: 0.5135, Loss D: -0.7925\n",
      "Epoch 147, Loss G: 1.0435, Loss D: -2.4363\n",
      "Epoch 148, Loss G: 0.9510, Loss D: 0.5019\n",
      "Epoch 149, Loss G: 1.5260, Loss D: -0.4742\n",
      "Epoch 150, Loss G: 0.4825, Loss D: 2.0298\n",
      "Epoch 151, Loss G: -0.0656, Loss D: -1.0400\n",
      "Epoch 152, Loss G: 4.5386, Loss D: -0.4274\n",
      "Epoch 153, Loss G: 1.9732, Loss D: 2.0216\n",
      "Epoch 154, Loss G: 5.2028, Loss D: -2.4554\n",
      "Epoch 155, Loss G: 6.1648, Loss D: 2.1536\n",
      "Epoch 156, Loss G: 0.7430, Loss D: 0.9415\n",
      "Epoch 157, Loss G: 2.4328, Loss D: -3.6097\n",
      "Epoch 158, Loss G: 2.9457, Loss D: -1.7507\n",
      "Epoch 159, Loss G: 4.2280, Loss D: -1.4762\n",
      "Epoch 160, Loss G: -0.7935, Loss D: 0.0217\n",
      "Epoch 161, Loss G: -0.5873, Loss D: -1.1571\n",
      "Epoch 162, Loss G: -2.3839, Loss D: -1.7045\n",
      "Epoch 163, Loss G: -0.9590, Loss D: 0.9469\n",
      "Epoch 164, Loss G: 1.3473, Loss D: -1.8386\n",
      "Epoch 165, Loss G: 2.5828, Loss D: 1.1941\n",
      "Epoch 166, Loss G: 1.3437, Loss D: -0.6864\n",
      "Epoch 167, Loss G: 1.3467, Loss D: -0.2913\n",
      "Epoch 168, Loss G: 1.9944, Loss D: -0.5486\n",
      "Epoch 169, Loss G: 1.8576, Loss D: 1.6098\n",
      "Epoch 170, Loss G: -1.3080, Loss D: -6.1376\n",
      "Epoch 171, Loss G: 2.1683, Loss D: -2.3948\n",
      "Epoch 172, Loss G: 2.8463, Loss D: -1.3725\n",
      "Epoch 173, Loss G: 2.2323, Loss D: -1.8139\n",
      "Epoch 174, Loss G: 0.3735, Loss D: -1.0760\n",
      "Epoch 175, Loss G: 3.0894, Loss D: -1.7933\n",
      "Epoch 176, Loss G: 3.4104, Loss D: 1.1262\n",
      "Epoch 177, Loss G: -1.1532, Loss D: -4.9645\n",
      "Epoch 178, Loss G: -0.4138, Loss D: -1.0036\n",
      "Epoch 179, Loss G: 3.5944, Loss D: -3.7219\n",
      "Epoch 180, Loss G: 0.1437, Loss D: -2.3762\n",
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      "Epoch 257, Loss G: -0.0805, Loss D: -2.1076\n",
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      "Epoch 262, Loss G: -0.1801, Loss D: 0.5517\n",
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      "Epoch 264, Loss G: -6.0063, Loss D: 1.8816\n",
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      "Epoch 283, Loss G: -2.4390, Loss D: -1.8222\n",
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      "Epoch 286, Loss G: 1.2046, Loss D: -1.0170\n",
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      "Epoch 288, Loss G: 3.3460, Loss D: 0.5282\n",
      "Epoch 289, Loss G: 0.5825, Loss D: -2.6277\n",
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      "Epoch 297, Loss G: -0.0131, Loss D: -0.2320\n",
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      "Epoch 300, Loss G: -2.3840, Loss D: -1.2324\n",
      "Epoch 301, Loss G: -1.4947, Loss D: 1.2262\n",
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      "Epoch 303, Loss G: 0.7554, Loss D: -1.0114\n",
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      "Epoch 308, Loss G: -0.3808, Loss D: 1.1374\n",
      "Epoch 309, Loss G: -0.8386, Loss D: -0.2911\n",
      "Epoch 310, Loss G: -2.4658, Loss D: -0.6554\n",
      "Epoch 311, Loss G: -1.8333, Loss D: -0.9597\n",
      "Epoch 312, Loss G: 0.7574, Loss D: 0.9890\n",
      "Epoch 313, Loss G: 1.7345, Loss D: 1.5572\n",
      "Epoch 314, Loss G: 0.3133, Loss D: -2.5691\n",
      "Epoch 315, Loss G: -4.1380, Loss D: -0.7208\n",
      "Epoch 316, Loss G: -3.3495, Loss D: -2.2975\n",
      "Epoch 317, Loss G: 0.0162, Loss D: 0.5429\n",
      "Epoch 318, Loss G: 1.1098, Loss D: 0.5536\n",
      "Epoch 319, Loss G: 0.2739, Loss D: 0.0037\n",
      "Epoch 320, Loss G: -0.7889, Loss D: 1.1206\n",
      "Epoch 321, Loss G: 1.1209, Loss D: -0.4968\n",
      "Epoch 322, Loss G: 0.8213, Loss D: -1.4652\n",
      "Epoch 323, Loss G: -1.3861, Loss D: -1.0012\n",
      "Epoch 324, Loss G: -1.4944, Loss D: -2.4462\n",
      "Epoch 325, Loss G: -4.6797, Loss D: -0.2547\n",
      "Epoch 326, Loss G: -0.2911, Loss D: 0.0222\n",
      "Epoch 327, Loss G: -2.8783, Loss D: 0.6165\n",
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      "Epoch 329, Loss G: -5.8724, Loss D: -1.2498\n",
      "Epoch 330, Loss G: -3.2480, Loss D: -1.8770\n",
      "Epoch 331, Loss G: -1.4258, Loss D: 0.0584\n",
      "Epoch 332, Loss G: -3.2122, Loss D: -0.5137\n",
      "Epoch 333, Loss G: -0.2397, Loss D: -1.8828\n",
      "Epoch 334, Loss G: -0.2609, Loss D: -1.6631\n",
      "Epoch 335, Loss G: -2.5233, Loss D: -2.8616\n",
      "Epoch 336, Loss G: -0.6823, Loss D: -0.5146\n",
      "Epoch 337, Loss G: -1.3196, Loss D: 0.1608\n",
      "Epoch 338, Loss G: -2.5338, Loss D: -0.0305\n",
      "Epoch 339, Loss G: -0.7842, Loss D: 0.9984\n",
      "Epoch 340, Loss G: -1.0586, Loss D: -2.2480\n",
      "Epoch 341, Loss G: -1.0678, Loss D: 1.9596\n",
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      "Epoch 344, Loss G: -1.8120, Loss D: 1.5768\n",
      "Epoch 345, Loss G: -3.0226, Loss D: -1.5236\n",
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      "Epoch 347, Loss G: 2.2941, Loss D: -1.1418\n",
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      "Epoch 355, Loss G: 1.3664, Loss D: 0.1805\n",
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      "Epoch 357, Loss G: 2.7502, Loss D: -1.0377\n",
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      "Epoch 361, Loss G: 2.2758, Loss D: -0.5268\n",
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      "Epoch 363, Loss G: 2.3283, Loss D: -2.1798\n",
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      "Epoch 368, Loss G: 0.1376, Loss D: -0.5190\n",
      "Epoch 369, Loss G: 0.9562, Loss D: -3.3032\n",
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      "Epoch 373, Loss G: 2.9503, Loss D: -1.7114\n",
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      "Epoch 377, Loss G: 1.0382, Loss D: -1.9640\n",
      "Epoch 378, Loss G: -0.6331, Loss D: -2.6979\n",
      "Epoch 379, Loss G: 1.9985, Loss D: 0.6292\n",
      "Epoch 380, Loss G: 0.8593, Loss D: -0.1778\n",
      "Epoch 381, Loss G: 0.9843, Loss D: -0.4710\n",
      "Epoch 382, Loss G: 0.8508, Loss D: -2.5134\n",
      "Epoch 383, Loss G: 0.9751, Loss D: -3.9799\n",
      "Epoch 384, Loss G: -0.2375, Loss D: -2.4796\n",
      "Epoch 385, Loss G: -0.4840, Loss D: -3.5216\n",
      "Epoch 386, Loss G: -0.1164, Loss D: -2.2371\n",
      "Epoch 387, Loss G: -0.4930, Loss D: -1.7580\n",
      "Epoch 388, Loss G: -1.2456, Loss D: -4.3383\n",
      "Epoch 389, Loss G: -1.8991, Loss D: -0.5041\n",
      "Epoch 390, Loss G: -0.0318, Loss D: -0.5020\n",
      "Epoch 391, Loss G: -1.1570, Loss D: -0.5929\n",
      "Epoch 392, Loss G: 0.6086, Loss D: 0.8397\n",
      "Epoch 393, Loss G: -2.0853, Loss D: -1.7185\n",
      "Epoch 394, Loss G: -1.2695, Loss D: -0.2632\n",
      "Epoch 395, Loss G: 0.2280, Loss D: 2.6332\n",
      "Epoch 396, Loss G: -2.5967, Loss D: -0.8352\n",
      "Epoch 397, Loss G: 1.2129, Loss D: -1.3713\n",
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      "Epoch 399, Loss G: -1.6717, Loss D: -1.6995\n",
      "Epoch 400, Loss G: -2.9970, Loss D: -0.8739\n",
      "Epoch 401, Loss G: -2.8922, Loss D: -1.2504\n",
      "Epoch 402, Loss G: -2.7011, Loss D: -1.1170\n",
      "Epoch 403, Loss G: -1.5405, Loss D: 0.6070\n",
      "Epoch 404, Loss G: -2.0926, Loss D: 0.7719\n",
      "Epoch 405, Loss G: 0.9081, Loss D: -0.7138\n",
      "Epoch 406, Loss G: 0.7586, Loss D: 1.7261\n",
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      "Epoch 408, Loss G: -1.7842, Loss D: 1.1527\n",
      "Epoch 409, Loss G: -4.0600, Loss D: 1.4323\n",
      "Epoch 410, Loss G: 1.4583, Loss D: -1.5333\n",
      "Epoch 411, Loss G: 0.1559, Loss D: 0.2802\n",
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      "Epoch 414, Loss G: -1.9855, Loss D: -1.2789\n",
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      "Epoch 416, Loss G: -1.7083, Loss D: -1.4071\n",
      "Epoch 417, Loss G: -2.5308, Loss D: 0.9894\n",
      "Epoch 418, Loss G: -3.1011, Loss D: 0.6009\n",
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      "Epoch 420, Loss G: 0.0108, Loss D: 0.0385\n",
      "Epoch 421, Loss G: -5.5193, Loss D: -2.0643\n",
      "Epoch 422, Loss G: -3.1698, Loss D: 0.8734\n",
      "Epoch 423, Loss G: -1.9792, Loss D: 2.3305\n",
      "Epoch 424, Loss G: -0.4671, Loss D: 0.5383\n",
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      "Epoch 429, Loss G: -0.6073, Loss D: 0.6920\n",
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      "Epoch 431, Loss G: 0.4130, Loss D: 0.1106\n",
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      "Epoch 433, Loss G: -5.6717, Loss D: -1.6389\n",
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      "Epoch 435, Loss G: -1.3486, Loss D: -2.5667\n",
      "Epoch 436, Loss G: -1.0846, Loss D: -0.4454\n",
      "Epoch 437, Loss G: -3.3879, Loss D: 0.6875\n",
      "Epoch 438, Loss G: -4.6571, Loss D: 0.7050\n",
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      "Epoch 441, Loss G: -1.1465, Loss D: -2.0258\n",
      "Epoch 442, Loss G: 0.1947, Loss D: -0.9763\n",
      "Epoch 443, Loss G: 2.6480, Loss D: 0.9726\n",
      "Epoch 444, Loss G: -2.0926, Loss D: -1.7416\n",
      "Epoch 445, Loss G: -3.1838, Loss D: -3.4625\n",
      "Epoch 446, Loss G: -0.6970, Loss D: -0.5729\n",
      "Epoch 447, Loss G: -0.9925, Loss D: -2.9502\n",
      "Epoch 448, Loss G: -1.8890, Loss D: -0.9073\n",
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      "Epoch 450, Loss G: -1.2953, Loss D: -0.4836\n",
      "Epoch 451, Loss G: 2.4489, Loss D: -1.4079\n",
      "Epoch 452, Loss G: 2.6930, Loss D: -1.9121\n",
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      "Epoch 455, Loss G: -0.7690, Loss D: -2.1743\n",
      "Epoch 456, Loss G: 0.1789, Loss D: 0.6758\n",
      "Epoch 457, Loss G: -0.7757, Loss D: -1.1058\n",
      "Epoch 458, Loss G: 2.3022, Loss D: 0.3860\n",
      "Epoch 459, Loss G: 2.7433, Loss D: 0.9614\n",
      "Epoch 460, Loss G: 2.5530, Loss D: -1.9643\n",
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      "Epoch 463, Loss G: 0.3965, Loss D: 1.1040\n",
      "Epoch 464, Loss G: 0.5910, Loss D: -3.1514\n",
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      "Epoch 467, Loss G: -0.3140, Loss D: -1.6567\n",
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      "Epoch 469, Loss G: -2.1967, Loss D: -1.4326\n",
      "Epoch 470, Loss G: -0.2596, Loss D: -1.3713\n",
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      "Epoch 472, Loss G: -3.1756, Loss D: -2.6250\n",
      "Epoch 473, Loss G: 0.3338, Loss D: -0.9672\n",
      "Epoch 474, Loss G: 3.3655, Loss D: 1.6532\n",
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      "Epoch 478, Loss G: -1.8858, Loss D: -1.3589\n",
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      "Epoch 514, Loss G: 0.4845, Loss D: -1.2135\n",
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      "Epoch 523, Loss G: -1.0106, Loss D: -1.9621\n",
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      "Epoch 525, Loss G: 2.1387, Loss D: -1.4425\n",
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      "Epoch 527, Loss G: 1.2897, Loss D: -1.8756\n",
      "Epoch 528, Loss G: -2.1642, Loss D: -2.3885\n",
      "Epoch 529, Loss G: -1.7970, Loss D: -0.4985\n",
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      "Epoch 532, Loss G: 1.3075, Loss D: 1.1585\n",
      "Epoch 533, Loss G: 3.4579, Loss D: -2.7481\n",
      "Epoch 534, Loss G: -1.5242, Loss D: 0.6187\n",
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      "Epoch 536, Loss G: -0.0155, Loss D: -1.5008\n",
      "Epoch 537, Loss G: -0.9626, Loss D: 0.4980\n",
      "Epoch 538, Loss G: -0.5608, Loss D: -4.6864\n",
      "Epoch 539, Loss G: -0.8839, Loss D: -0.2958\n",
      "Epoch 540, Loss G: -0.1332, Loss D: -2.4276\n",
      "Epoch 541, Loss G: -1.0794, Loss D: -3.0102\n",
      "Epoch 542, Loss G: -2.2765, Loss D: -0.5022\n",
      "Epoch 543, Loss G: -0.5328, Loss D: -3.0272\n",
      "Epoch 544, Loss G: -1.3833, Loss D: -0.0253\n",
      "Epoch 545, Loss G: -0.6846, Loss D: -4.3319\n",
      "Epoch 546, Loss G: -1.6596, Loss D: 0.2453\n",
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      "Epoch 548, Loss G: 0.2045, Loss D: -3.5909\n",
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      "Epoch 550, Loss G: 1.3046, Loss D: -2.7662\n",
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      "Epoch 553, Loss G: 1.4281, Loss D: -1.4251\n",
      "Epoch 554, Loss G: 1.9029, Loss D: 1.0755\n",
      "Epoch 555, Loss G: -1.2183, Loss D: -0.6726\n",
      "Epoch 556, Loss G: -0.7855, Loss D: -1.8469\n",
      "Epoch 557, Loss G: -0.8701, Loss D: -0.5821\n",
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      "Epoch 559, Loss G: 2.1472, Loss D: -1.8238\n",
      "Epoch 560, Loss G: -2.2592, Loss D: -3.1539\n",
      "Epoch 561, Loss G: -0.8714, Loss D: -1.5797\n",
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      "Epoch 564, Loss G: 1.2798, Loss D: -4.2335\n",
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      "Epoch 567, Loss G: 0.3663, Loss D: -2.6424\n",
      "Epoch 568, Loss G: -0.3375, Loss D: -2.6345\n",
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      "Epoch 572, Loss G: 0.4615, Loss D: -1.0020\n",
      "Epoch 573, Loss G: 0.3315, Loss D: -3.0683\n",
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      "Epoch 575, Loss G: 1.3078, Loss D: -5.4216\n",
      "Epoch 576, Loss G: -0.2839, Loss D: -3.3623\n",
      "Epoch 577, Loss G: -0.0376, Loss D: -2.7812\n",
      "Epoch 578, Loss G: 1.5390, Loss D: -3.3442\n",
      "Epoch 579, Loss G: -0.4606, Loss D: -1.4483\n",
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      "Epoch 581, Loss G: 0.0566, Loss D: -4.1638\n",
      "Epoch 582, Loss G: 0.0532, Loss D: -1.1039\n",
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      "Epoch 584, Loss G: -1.4396, Loss D: -4.3827\n",
      "Epoch 585, Loss G: -2.5959, Loss D: -2.3452\n",
      "Epoch 586, Loss G: -1.5141, Loss D: -2.3942\n",
      "Epoch 587, Loss G: -2.1855, Loss D: -4.2514\n",
      "Epoch 588, Loss G: -0.8613, Loss D: -1.8419\n",
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      "Epoch 590, Loss G: -0.0579, Loss D: -5.5779\n",
      "Epoch 591, Loss G: 2.2721, Loss D: -4.1475\n",
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      "Epoch 593, Loss G: -2.2876, Loss D: -2.8638\n",
      "Epoch 594, Loss G: -0.9676, Loss D: -2.5865\n",
      "Epoch 595, Loss G: -1.9802, Loss D: -2.1179\n",
      "Epoch 596, Loss G: -1.7906, Loss D: -4.4621\n",
      "Epoch 597, Loss G: -0.0872, Loss D: -2.0767\n",
      "Epoch 598, Loss G: -3.0695, Loss D: -1.8445\n",
      "Epoch 599, Loss G: -1.5491, Loss D: -3.5642\n",
      "Epoch 600, Loss G: -2.8292, Loss D: -3.3009\n",
      "Epoch 601, Loss G: -0.4577, Loss D: -1.6826\n",
      "Epoch 602, Loss G: -1.2476, Loss D: -0.7742\n",
      "Epoch 603, Loss G: 0.1632, Loss D: -2.1671\n",
      "Epoch 604, Loss G: -1.7870, Loss D: -4.4929\n",
      "Epoch 605, Loss G: -0.2428, Loss D: -3.1491\n",
      "Epoch 606, Loss G: -1.5615, Loss D: -2.3796\n",
      "Epoch 607, Loss G: -1.4030, Loss D: -3.3987\n",
      "Epoch 608, Loss G: -0.6447, Loss D: -0.9217\n",
      "Epoch 609, Loss G: -1.3104, Loss D: -2.6497\n",
      "Epoch 610, Loss G: -0.4278, Loss D: -1.9309\n",
      "Epoch 611, Loss G: -1.8567, Loss D: -3.0714\n",
      "Epoch 612, Loss G: -1.4631, Loss D: -3.5309\n",
      "Epoch 613, Loss G: -0.9184, Loss D: -2.2684\n",
      "Epoch 614, Loss G: -1.6958, Loss D: -2.5021\n",
      "Epoch 615, Loss G: -0.7676, Loss D: -2.4914\n",
      "Epoch 616, Loss G: 0.7403, Loss D: -4.8313\n",
      "Epoch 617, Loss G: 1.2395, Loss D: -4.5073\n",
      "Epoch 618, Loss G: 0.3373, Loss D: -2.4962\n",
      "Epoch 619, Loss G: 1.3847, Loss D: -2.4741\n",
      "Epoch 620, Loss G: -1.6802, Loss D: -1.9693\n",
      "Epoch 621, Loss G: -0.9897, Loss D: -3.1633\n",
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      "Epoch 624, Loss G: -1.6963, Loss D: -4.0162\n",
      "Epoch 625, Loss G: -1.9798, Loss D: -1.8754\n",
      "Epoch 626, Loss G: -0.4905, Loss D: -3.4246\n",
      "Epoch 627, Loss G: -1.4420, Loss D: -3.7707\n",
      "Epoch 628, Loss G: 0.0638, Loss D: -1.1782\n",
      "Epoch 629, Loss G: -1.7040, Loss D: -2.0896\n",
      "Epoch 630, Loss G: -0.9812, Loss D: -5.1507\n",
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      "Epoch 632, Loss G: -1.2334, Loss D: -3.0161\n",
      "Epoch 633, Loss G: 0.5076, Loss D: -3.4865\n",
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      "Epoch 636, Loss G: -2.9020, Loss D: -3.8811\n",
      "Epoch 637, Loss G: -0.3851, Loss D: -3.0054\n",
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      "Epoch 639, Loss G: -2.9506, Loss D: -2.9500\n",
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      "Epoch 641, Loss G: -1.0833, Loss D: -4.4335\n",
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      "Epoch 650, Loss G: -0.0151, Loss D: -3.4030\n",
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      "Epoch 660, Loss G: -2.1082, Loss D: -3.3697\n",
      "Epoch 661, Loss G: -0.5875, Loss D: -3.6484\n",
      "Epoch 662, Loss G: 0.0911, Loss D: -3.0431\n",
      "Epoch 663, Loss G: -0.7015, Loss D: -4.7060\n",
      "Epoch 664, Loss G: -2.0171, Loss D: -4.4495\n",
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      "Epoch 666, Loss G: -0.2998, Loss D: -2.4471\n",
      "Epoch 667, Loss G: -0.5110, Loss D: -2.5705\n",
      "Epoch 668, Loss G: 0.4675, Loss D: -3.6212\n",
      "Epoch 669, Loss G: -0.3163, Loss D: -3.6543\n",
      "Epoch 670, Loss G: 1.1160, Loss D: -3.1987\n",
      "Epoch 671, Loss G: -0.4933, Loss D: -3.9612\n",
      "Epoch 672, Loss G: -0.5647, Loss D: -3.8474\n",
      "Epoch 673, Loss G: -1.1417, Loss D: -4.6524\n",
      "Epoch 674, Loss G: -0.7992, Loss D: -2.6240\n",
      "Epoch 675, Loss G: -0.6871, Loss D: -3.4785\n",
      "Epoch 676, Loss G: -0.3920, Loss D: -3.4119\n",
      "Epoch 677, Loss G: -2.1387, Loss D: -3.0884\n",
      "Epoch 678, Loss G: -0.7077, Loss D: -3.8399\n",
      "Epoch 679, Loss G: -0.3004, Loss D: -1.7167\n",
      "Epoch 680, Loss G: -0.7122, Loss D: -2.5135\n",
      "Epoch 681, Loss G: -0.4775, Loss D: -3.4013\n",
      "Epoch 682, Loss G: 0.1815, Loss D: -4.1342\n",
      "Epoch 683, Loss G: -2.2763, Loss D: -3.3202\n",
      "Epoch 684, Loss G: -1.6423, Loss D: -4.5929\n",
      "Epoch 685, Loss G: -1.1822, Loss D: -3.3347\n",
      "Epoch 686, Loss G: -0.2807, Loss D: -3.1258\n",
      "Epoch 687, Loss G: -0.2485, Loss D: -3.8016\n",
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      "Epoch 689, Loss G: -1.7049, Loss D: -2.1760\n",
      "Epoch 690, Loss G: -0.3121, Loss D: -4.3913\n",
      "Epoch 691, Loss G: -1.4055, Loss D: -4.3252\n",
      "Epoch 692, Loss G: -0.8604, Loss D: -3.0439\n",
      "Epoch 693, Loss G: -1.7075, Loss D: -4.0746\n",
      "Epoch 694, Loss G: -1.3443, Loss D: -4.6240\n",
      "Epoch 695, Loss G: -1.3640, Loss D: -5.0276\n",
      "Epoch 696, Loss G: -1.3957, Loss D: -4.0118\n",
      "Epoch 697, Loss G: -1.7471, Loss D: -2.6744\n",
      "Epoch 698, Loss G: 1.0232, Loss D: -4.7718\n",
      "Epoch 699, Loss G: 0.6435, Loss D: -3.3499\n",
      "Epoch 700, Loss G: 0.6033, Loss D: -2.4228\n",
      "Epoch 701, Loss G: -2.4088, Loss D: -2.6710\n",
      "Epoch 702, Loss G: -0.6246, Loss D: -3.1624\n",
      "Epoch 703, Loss G: 0.1542, Loss D: -4.8399\n",
      "Epoch 704, Loss G: -2.0606, Loss D: -4.3711\n",
      "Epoch 705, Loss G: -1.1222, Loss D: -3.2845\n",
      "Epoch 706, Loss G: -0.9887, Loss D: -5.1426\n",
      "Epoch 707, Loss G: 1.2295, Loss D: -2.0122\n",
      "Epoch 708, Loss G: 0.8433, Loss D: -2.4616\n",
      "Epoch 709, Loss G: -0.2956, Loss D: -3.8330\n",
      "Epoch 710, Loss G: 0.0913, Loss D: -4.2747\n",
      "Epoch 711, Loss G: -0.8002, Loss D: -2.6940\n",
      "Epoch 712, Loss G: -0.3353, Loss D: -5.0080\n",
      "Epoch 713, Loss G: -1.1464, Loss D: -5.3755\n",
      "Epoch 714, Loss G: -1.2693, Loss D: -3.3314\n",
      "Epoch 715, Loss G: -1.7391, Loss D: -4.8220\n",
      "Epoch 716, Loss G: -3.3086, Loss D: -3.1799\n",
      "Epoch 717, Loss G: -0.6972, Loss D: -4.0626\n",
      "Epoch 718, Loss G: -0.6581, Loss D: -2.7770\n",
      "Epoch 719, Loss G: -0.1868, Loss D: -2.9397\n",
      "Epoch 720, Loss G: 0.7525, Loss D: -2.3921\n",
      "Epoch 721, Loss G: -0.1071, Loss D: -4.7436\n",
      "Epoch 722, Loss G: -2.2721, Loss D: -3.4324\n",
      "Epoch 723, Loss G: 1.1304, Loss D: -4.0147\n",
      "Epoch 724, Loss G: 1.0584, Loss D: -3.9543\n",
      "Epoch 725, Loss G: 0.3390, Loss D: -2.1004\n",
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      "Epoch 727, Loss G: -1.0603, Loss D: -5.4386\n",
      "Epoch 728, Loss G: -0.6306, Loss D: -3.9967\n",
      "Epoch 729, Loss G: -1.1665, Loss D: -5.0315\n",
      "Epoch 730, Loss G: 0.2440, Loss D: -4.5912\n",
      "Epoch 731, Loss G: -0.3995, Loss D: -4.4779\n",
      "Epoch 732, Loss G: -1.4273, Loss D: -3.7967\n",
      "Epoch 733, Loss G: -3.1198, Loss D: -1.9738\n",
      "Epoch 734, Loss G: -1.8644, Loss D: -3.9800\n",
      "Epoch 735, Loss G: -1.1333, Loss D: -2.8372\n",
      "Epoch 736, Loss G: -0.7937, Loss D: -5.1838\n",
      "Epoch 737, Loss G: -0.1286, Loss D: -5.2522\n",
      "Epoch 738, Loss G: -0.2685, Loss D: -4.5806\n",
      "Epoch 739, Loss G: -1.6730, Loss D: -2.7910\n",
      "Epoch 740, Loss G: -0.8589, Loss D: -5.4685\n",
      "Epoch 741, Loss G: -1.4998, Loss D: -3.2728\n",
      "Epoch 742, Loss G: 0.8430, Loss D: -3.3896\n",
      "Epoch 743, Loss G: -1.7302, Loss D: -3.3775\n",
      "Epoch 744, Loss G: -0.1096, Loss D: -3.0428\n",
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      "Epoch 746, Loss G: 0.4383, Loss D: -3.9470\n",
      "Epoch 747, Loss G: 0.9566, Loss D: -2.7731\n",
      "Epoch 748, Loss G: -0.2511, Loss D: -2.7672\n",
      "Epoch 749, Loss G: -0.3025, Loss D: -5.5027\n",
      "Epoch 750, Loss G: -2.2852, Loss D: -3.2384\n",
      "Epoch 751, Loss G: 0.3742, Loss D: -3.2615\n",
      "Epoch 752, Loss G: -1.0127, Loss D: -3.5312\n",
      "Epoch 753, Loss G: 1.3674, Loss D: -3.4058\n",
      "Epoch 754, Loss G: -0.7393, Loss D: -5.3970\n",
      "Epoch 755, Loss G: -2.6179, Loss D: -4.2446\n",
      "Epoch 756, Loss G: -3.7711, Loss D: -3.3062\n",
      "Epoch 757, Loss G: -4.6152, Loss D: -5.3909\n",
      "Epoch 758, Loss G: -1.1573, Loss D: -4.5360\n",
      "Epoch 759, Loss G: 0.1976, Loss D: -5.3209\n",
      "Epoch 760, Loss G: -0.2628, Loss D: -2.8705\n",
      "Epoch 761, Loss G: -1.0505, Loss D: -4.4643\n",
      "Epoch 762, Loss G: -1.0996, Loss D: -5.3476\n",
      "Epoch 763, Loss G: 0.9450, Loss D: -2.5487\n",
      "Epoch 764, Loss G: -0.1568, Loss D: -5.0027\n",
      "Epoch 765, Loss G: -1.5660, Loss D: -6.7864\n",
      "Epoch 766, Loss G: -2.6233, Loss D: -3.9494\n",
      "Epoch 767, Loss G: -3.2473, Loss D: -5.0716\n",
      "Epoch 768, Loss G: -2.6408, Loss D: -4.5825\n",
      "Epoch 769, Loss G: -0.5340, Loss D: -3.9811\n",
      "Epoch 770, Loss G: 3.2264, Loss D: -4.2233\n",
      "Epoch 771, Loss G: -0.1897, Loss D: -2.6484\n",
      "Epoch 772, Loss G: -0.5824, Loss D: -4.0049\n",
      "Epoch 773, Loss G: -3.6276, Loss D: -5.5061\n",
      "Epoch 774, Loss G: -0.5305, Loss D: -4.5188\n",
      "Epoch 775, Loss G: -0.3176, Loss D: -2.0146\n",
      "Epoch 776, Loss G: -0.7327, Loss D: -2.5072\n",
      "Epoch 777, Loss G: -2.0640, Loss D: -5.0106\n",
      "Epoch 778, Loss G: -1.0468, Loss D: -4.5904\n",
      "Epoch 779, Loss G: -0.4663, Loss D: -4.6005\n",
      "Epoch 780, Loss G: 0.1666, Loss D: -5.0810\n",
      "Epoch 781, Loss G: 0.3303, Loss D: -4.4099\n",
      "Epoch 782, Loss G: -1.7907, Loss D: -4.9475\n",
      "Epoch 783, Loss G: -2.6980, Loss D: -4.5301\n",
      "Epoch 784, Loss G: -2.3889, Loss D: -3.3958\n",
      "Epoch 785, Loss G: -1.9994, Loss D: -3.2620\n",
      "Epoch 786, Loss G: 0.0126, Loss D: -2.6403\n",
      "Epoch 787, Loss G: 1.2862, Loss D: -2.4880\n",
      "Epoch 788, Loss G: -2.6722, Loss D: -4.2121\n",
      "Epoch 789, Loss G: -0.7631, Loss D: -3.2207\n",
      "Epoch 790, Loss G: -2.3294, Loss D: -4.4716\n",
      "Epoch 791, Loss G: -1.4624, Loss D: -4.2558\n",
      "Epoch 792, Loss G: -2.2637, Loss D: -3.9625\n",
      "Epoch 793, Loss G: -0.8492, Loss D: -4.3114\n",
      "Epoch 794, Loss G: 1.0163, Loss D: -1.7039\n",
      "Epoch 795, Loss G: -2.1377, Loss D: -3.0470\n",
      "Epoch 796, Loss G: -0.3937, Loss D: -3.7055\n",
      "Epoch 797, Loss G: -1.7292, Loss D: -5.0485\n",
      "Epoch 798, Loss G: 0.5799, Loss D: -1.8936\n",
      "Epoch 799, Loss G: -0.8911, Loss D: -3.3541\n",
      "Epoch 800, Loss G: -0.8676, Loss D: -2.9979\n",
      "Epoch 801, Loss G: -1.6507, Loss D: -3.4273\n",
      "Epoch 802, Loss G: -1.7190, Loss D: -3.8889\n",
      "Epoch 803, Loss G: -2.4981, Loss D: -2.4719\n",
      "Epoch 804, Loss G: -2.5891, Loss D: -2.8411\n",
      "Epoch 805, Loss G: -2.8578, Loss D: -4.6347\n",
      "Epoch 806, Loss G: -2.3263, Loss D: -3.7072\n",
      "Epoch 807, Loss G: -1.1150, Loss D: -3.5863\n",
      "Epoch 808, Loss G: -1.2456, Loss D: -3.6798\n",
      "Epoch 809, Loss G: -2.3476, Loss D: -3.1324\n",
      "Epoch 810, Loss G: -2.4908, Loss D: -3.3890\n",
      "Epoch 811, Loss G: -2.2011, Loss D: -4.9122\n",
      "Epoch 812, Loss G: -2.1522, Loss D: -5.2617\n",
      "Epoch 813, Loss G: -1.9257, Loss D: -4.4058\n",
      "Epoch 814, Loss G: -0.9875, Loss D: -4.1023\n",
      "Epoch 815, Loss G: 0.0012, Loss D: -4.4772\n",
      "Epoch 816, Loss G: -2.2367, Loss D: -4.1752\n",
      "Epoch 817, Loss G: -2.5580, Loss D: -5.0965\n",
      "Epoch 818, Loss G: -2.1839, Loss D: -5.0211\n",
      "Epoch 819, Loss G: -2.9747, Loss D: -3.6513\n",
      "Epoch 820, Loss G: -1.4495, Loss D: -3.2316\n",
      "Epoch 821, Loss G: -0.1740, Loss D: -3.4683\n",
      "Epoch 822, Loss G: 0.5329, Loss D: -3.4810\n",
      "Epoch 823, Loss G: -1.8137, Loss D: -3.5604\n",
      "Epoch 824, Loss G: -2.6774, Loss D: -4.0467\n",
      "Epoch 825, Loss G: -2.4951, Loss D: -3.8437\n",
      "Epoch 826, Loss G: -0.5827, Loss D: -4.0028\n",
      "Epoch 827, Loss G: 0.4163, Loss D: -5.0039\n",
      "Epoch 828, Loss G: 1.3165, Loss D: -2.9537\n",
      "Epoch 829, Loss G: -0.3728, Loss D: -5.1909\n",
      "Epoch 830, Loss G: -1.7481, Loss D: -4.4662\n",
      "Epoch 831, Loss G: -1.9956, Loss D: -3.8885\n",
      "Epoch 832, Loss G: -0.2866, Loss D: -3.5034\n",
      "Epoch 833, Loss G: 0.0423, Loss D: -2.4484\n",
      "Epoch 834, Loss G: 0.0338, Loss D: -5.1469\n",
      "Epoch 835, Loss G: -2.7303, Loss D: -3.1947\n",
      "Epoch 836, Loss G: -2.3384, Loss D: -4.4371\n",
      "Epoch 837, Loss G: -0.4169, Loss D: -2.9053\n",
      "Epoch 838, Loss G: 1.4740, Loss D: -3.0744\n",
      "Epoch 839, Loss G: 0.9246, Loss D: -2.9486\n",
      "Epoch 840, Loss G: 0.8126, Loss D: -4.5612\n",
      "Epoch 841, Loss G: -0.8912, Loss D: -4.5095\n",
      "Epoch 842, Loss G: -2.2771, Loss D: -3.2091\n",
      "Epoch 843, Loss G: -3.3287, Loss D: -4.2353\n",
      "Epoch 844, Loss G: -2.6961, Loss D: -3.8818\n",
      "Epoch 845, Loss G: 0.1051, Loss D: -3.9828\n",
      "Epoch 846, Loss G: 0.1506, Loss D: -4.2567\n",
      "Epoch 847, Loss G: -0.8442, Loss D: -5.4794\n",
      "Epoch 848, Loss G: -1.5757, Loss D: -3.8717\n",
      "Epoch 849, Loss G: -2.7831, Loss D: -4.1467\n",
      "Epoch 850, Loss G: -2.9831, Loss D: -3.4825\n",
      "Epoch 851, Loss G: -1.4973, Loss D: -3.9899\n",
      "Epoch 852, Loss G: -0.9679, Loss D: -3.8480\n",
      "Epoch 853, Loss G: -1.8213, Loss D: -3.6235\n",
      "Epoch 854, Loss G: -2.5069, Loss D: -4.4863\n",
      "Epoch 855, Loss G: -2.5776, Loss D: -4.6590\n",
      "Epoch 856, Loss G: -0.6573, Loss D: -4.5880\n",
      "Epoch 857, Loss G: -0.6631, Loss D: -4.9246\n",
      "Epoch 858, Loss G: -0.9030, Loss D: -3.8814\n",
      "Epoch 859, Loss G: -1.6518, Loss D: -4.1695\n",
      "Epoch 860, Loss G: -2.7357, Loss D: -3.9408\n",
      "Epoch 861, Loss G: -3.0642, Loss D: -2.4816\n",
      "Epoch 862, Loss G: -1.7920, Loss D: -4.3908\n",
      "Epoch 863, Loss G: -0.0793, Loss D: -4.6967\n",
      "Epoch 864, Loss G: -0.0331, Loss D: -3.8853\n",
      "Epoch 865, Loss G: -0.7764, Loss D: -4.4652\n",
      "Epoch 866, Loss G: -1.6814, Loss D: -4.5164\n",
      "Epoch 867, Loss G: -2.3192, Loss D: -3.5059\n",
      "Epoch 868, Loss G: -2.1938, Loss D: -4.4441\n",
      "Epoch 869, Loss G: -1.4917, Loss D: -3.0130\n",
      "Epoch 870, Loss G: -1.8605, Loss D: -3.5007\n",
      "Epoch 871, Loss G: -2.7590, Loss D: -4.1469\n",
      "Epoch 872, Loss G: -3.8964, Loss D: -4.7260\n",
      "Epoch 873, Loss G: -1.8401, Loss D: -2.6579\n",
      "Epoch 874, Loss G: -0.7989, Loss D: -3.1756\n",
      "Epoch 875, Loss G: -1.0008, Loss D: -4.4713\n",
      "Epoch 876, Loss G: -2.5859, Loss D: -5.2521\n",
      "Epoch 877, Loss G: -2.3885, Loss D: -3.4037\n",
      "Epoch 878, Loss G: -3.1839, Loss D: -3.9178\n",
      "Epoch 879, Loss G: -1.7411, Loss D: -4.0805\n",
      "Epoch 880, Loss G: -1.1173, Loss D: -4.4754\n",
      "Epoch 881, Loss G: -1.6995, Loss D: -4.9789\n",
      "Epoch 882, Loss G: -3.8108, Loss D: -4.9004\n",
      "Epoch 883, Loss G: -4.7643, Loss D: -4.4448\n",
      "Epoch 884, Loss G: -3.7980, Loss D: -3.0748\n",
      "Epoch 885, Loss G: -2.1313, Loss D: -3.4332\n",
      "Epoch 886, Loss G: -1.7170, Loss D: -3.8365\n",
      "Epoch 887, Loss G: -0.9932, Loss D: -4.0372\n",
      "Epoch 888, Loss G: -2.3399, Loss D: -5.3069\n",
      "Epoch 889, Loss G: -3.1611, Loss D: -3.5218\n",
      "Epoch 890, Loss G: -1.2459, Loss D: -5.7946\n",
      "Epoch 891, Loss G: -0.6763, Loss D: -3.0751\n",
      "Epoch 892, Loss G: -2.2369, Loss D: -4.2633\n",
      "Epoch 893, Loss G: -2.7020, Loss D: -5.3530\n",
      "Epoch 894, Loss G: -3.6997, Loss D: -4.1680\n",
      "Epoch 895, Loss G: -0.8024, Loss D: -2.8479\n",
      "Epoch 896, Loss G: 0.2047, Loss D: -2.8961\n",
      "Epoch 897, Loss G: 0.1777, Loss D: -3.7640\n",
      "Epoch 898, Loss G: -2.5882, Loss D: -4.4420\n",
      "Epoch 899, Loss G: -2.4218, Loss D: -3.2799\n",
      "Epoch 900, Loss G: -1.4124, Loss D: -4.1006\n",
      "Epoch 901, Loss G: -2.5163, Loss D: -4.0163\n",
      "Epoch 902, Loss G: 0.6563, Loss D: -3.8443\n",
      "Epoch 903, Loss G: -1.3763, Loss D: -4.5343\n",
      "Epoch 904, Loss G: -2.5281, Loss D: -4.0484\n",
      "Epoch 905, Loss G: -3.9872, Loss D: -4.5635\n",
      "Epoch 906, Loss G: -1.4338, Loss D: -4.4971\n",
      "Epoch 907, Loss G: -1.4979, Loss D: -4.5928\n",
      "Epoch 908, Loss G: -1.1383, Loss D: -3.4097\n",
      "Epoch 909, Loss G: -3.6315, Loss D: -4.7798\n",
      "Epoch 910, Loss G: -3.8987, Loss D: -3.9662\n",
      "Epoch 911, Loss G: -2.2268, Loss D: -3.0470\n",
      "Epoch 912, Loss G: -0.8884, Loss D: -2.6427\n",
      "Epoch 913, Loss G: -1.1826, Loss D: -3.7613\n",
      "Epoch 914, Loss G: -3.4019, Loss D: -3.7923\n",
      "Epoch 915, Loss G: -3.3538, Loss D: -4.4097\n",
      "Epoch 916, Loss G: -3.4468, Loss D: -4.7546\n",
      "Epoch 917, Loss G: -0.4440, Loss D: -4.3916\n",
      "Epoch 918, Loss G: -0.1319, Loss D: -3.2935\n",
      "Epoch 919, Loss G: -2.1200, Loss D: -4.0008\n",
      "Epoch 920, Loss G: -3.6913, Loss D: -3.1040\n",
      "Epoch 921, Loss G: -2.1779, Loss D: -3.5938\n",
      "Epoch 922, Loss G: -1.0897, Loss D: -4.5902\n",
      "Epoch 923, Loss G: -0.1473, Loss D: -3.9706\n",
      "Epoch 924, Loss G: -2.0117, Loss D: -4.8775\n",
      "Epoch 925, Loss G: -3.6760, Loss D: -3.6828\n",
      "Epoch 926, Loss G: -1.2867, Loss D: -3.5245\n",
      "Epoch 927, Loss G: -0.3209, Loss D: -4.2080\n",
      "Epoch 928, Loss G: -1.0659, Loss D: -3.4111\n",
      "Epoch 929, Loss G: -2.9261, Loss D: -5.0720\n",
      "Epoch 930, Loss G: -3.7173, Loss D: -4.8556\n",
      "Epoch 931, Loss G: -0.9407, Loss D: -3.5358\n",
      "Epoch 932, Loss G: 0.0060, Loss D: -3.6300\n",
      "Epoch 933, Loss G: -0.9603, Loss D: -3.5550\n",
      "Epoch 934, Loss G: -2.9341, Loss D: -4.7259\n",
      "Epoch 935, Loss G: -2.3651, Loss D: -4.7249\n",
      "Epoch 936, Loss G: 0.2929, Loss D: -4.7559\n",
      "Epoch 937, Loss G: -1.2841, Loss D: -3.9987\n",
      "Epoch 938, Loss G: -3.2649, Loss D: -3.6646\n",
      "Epoch 939, Loss G: -5.5878, Loss D: -4.2990\n",
      "Epoch 940, Loss G: -1.2327, Loss D: -3.7852\n",
      "Epoch 941, Loss G: 0.4877, Loss D: -2.1954\n",
      "Epoch 942, Loss G: -1.6596, Loss D: -4.1571\n",
      "Epoch 943, Loss G: -5.3753, Loss D: -4.0966\n",
      "Epoch 944, Loss G: -1.1116, Loss D: -3.9465\n",
      "Epoch 945, Loss G: 0.4374, Loss D: -5.1833\n",
      "Epoch 946, Loss G: -1.6335, Loss D: -3.7312\n",
      "Epoch 947, Loss G: -4.7506, Loss D: -4.9055\n",
      "Epoch 948, Loss G: -2.6559, Loss D: -4.2253\n",
      "Epoch 949, Loss G: 0.2891, Loss D: -4.4776\n",
      "Epoch 950, Loss G: -2.2661, Loss D: -3.0964\n",
      "Epoch 951, Loss G: -5.0252, Loss D: -3.7506\n",
      "Epoch 952, Loss G: -2.2349, Loss D: -3.1826\n",
      "Epoch 953, Loss G: 0.6008, Loss D: -4.6473\n",
      "Epoch 954, Loss G: -2.8966, Loss D: -3.7131\n",
      "Epoch 955, Loss G: -6.5477, Loss D: -5.2283\n",
      "Epoch 956, Loss G: -2.4718, Loss D: -4.0434\n",
      "Epoch 957, Loss G: 1.1965, Loss D: -4.1869\n",
      "Epoch 958, Loss G: -3.2324, Loss D: -3.3306\n",
      "Epoch 959, Loss G: -7.3420, Loss D: -5.1413\n",
      "Epoch 960, Loss G: -1.0073, Loss D: -4.4530\n",
      "Epoch 961, Loss G: 1.1394, Loss D: -4.0410\n",
      "Epoch 962, Loss G: -2.4376, Loss D: -4.4181\n",
      "Epoch 963, Loss G: -3.5356, Loss D: -4.8565\n",
      "Epoch 964, Loss G: 0.3052, Loss D: -4.3230\n",
      "Epoch 965, Loss G: 0.4109, Loss D: -3.6364\n",
      "Epoch 966, Loss G: -2.7054, Loss D: -5.2848\n",
      "Epoch 967, Loss G: -4.1303, Loss D: -3.6911\n",
      "Epoch 968, Loss G: -0.7002, Loss D: -4.4807\n",
      "Epoch 969, Loss G: -2.5139, Loss D: -3.8580\n",
      "Epoch 970, Loss G: -3.1264, Loss D: -4.1182\n",
      "Epoch 971, Loss G: -0.8282, Loss D: -4.8737\n",
      "Epoch 972, Loss G: -1.4889, Loss D: -3.7685\n",
      "Epoch 973, Loss G: -1.8721, Loss D: -4.6389\n",
      "Epoch 974, Loss G: -3.4068, Loss D: -3.5735\n",
      "Epoch 975, Loss G: -1.8594, Loss D: -5.0112\n",
      "Epoch 976, Loss G: -0.4116, Loss D: -4.3872\n",
      "Epoch 977, Loss G: -0.5637, Loss D: -4.0013\n",
      "Epoch 978, Loss G: -2.1092, Loss D: -3.8075\n",
      "Epoch 979, Loss G: -1.7740, Loss D: -3.9875\n",
      "Epoch 980, Loss G: -0.5816, Loss D: -4.6111\n",
      "Epoch 981, Loss G: -2.6314, Loss D: -4.2979\n",
      "Epoch 982, Loss G: -3.2276, Loss D: -4.8672\n",
      "Epoch 983, Loss G: -0.8321, Loss D: -3.7225\n",
      "Epoch 984, Loss G: -1.1741, Loss D: -3.1572\n",
      "Epoch 985, Loss G: -3.7257, Loss D: -4.5657\n",
      "Epoch 986, Loss G: -2.4162, Loss D: -4.1460\n",
      "Epoch 987, Loss G: -1.2382, Loss D: -4.3649\n",
      "Epoch 988, Loss G: -1.9367, Loss D: -4.1137\n",
      "Epoch 989, Loss G: -4.9067, Loss D: -4.4811\n",
      "Epoch 990, Loss G: -1.4787, Loss D: -3.1864\n",
      "Epoch 991, Loss G: 0.3939, Loss D: -4.6085\n",
      "Epoch 992, Loss G: -2.6462, Loss D: -4.5303\n",
      "Epoch 993, Loss G: -3.6386, Loss D: -3.9713\n",
      "Epoch 994, Loss G: 0.6927, Loss D: -4.9590\n",
      "Epoch 995, Loss G: -2.1116, Loss D: -3.6434\n",
      "Epoch 996, Loss G: -2.9644, Loss D: -3.1088\n",
      "Epoch 997, Loss G: -2.7139, Loss D: -4.3276\n",
      "Epoch 998, Loss G: -1.7924, Loss D: -3.3908\n",
      "Epoch 999, Loss G: -2.2077, Loss D: -4.2601\n",
      "Epoch 1000, Loss G: -3.7685, Loss D: -3.7106\n",
      "Epoch 1001, Loss G: -1.2349, Loss D: -3.9355\n",
      "Epoch 1002, Loss G: 1.0227, Loss D: -3.8818\n",
      "Epoch 1003, Loss G: -1.7634, Loss D: -3.6221\n",
      "Epoch 1004, Loss G: -4.8031, Loss D: -3.6195\n",
      "Epoch 1005, Loss G: -0.9982, Loss D: -3.5262\n",
      "Epoch 1006, Loss G: 0.8898, Loss D: -3.9209\n",
      "Epoch 1007, Loss G: -0.9006, Loss D: -3.9887\n",
      "Epoch 1008, Loss G: -2.5235, Loss D: -4.3804\n",
      "Epoch 1009, Loss G: -0.8972, Loss D: -3.4688\n",
      "Epoch 1010, Loss G: 1.0348, Loss D: -4.3600\n"
     ]
    }
   ],
   "source": [
    "### fit ctgan to the data \n",
    "#data_forargu; ASD + mor extracted from train data\n",
    "ctgan.fit(data_forargu, discrete_columns = discrete_columns, epochs = 1000)\n",
    "\n",
    "### generate synthetic data\n",
    "#the number of rows that I want to generate = len(data_forargu)*2\n",
    "new_data = ctgan.sample(len(data_forargu)*2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. save the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "### write csv\n",
    "new_data.to_csv('aug_train_asd_ep1000.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "### save ctgan\n",
    "ctgan.save('ctgan_asd_ep1000.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "### resession -> so i read the saved data\n",
    "new_data = pd.read_csv('/home/ubuntu/Betty/CTGAN/aug_train_asd_ep1000.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "#new_data\n",
    "new_data = new_data.drop(columns=['Unnamed: 0'], axis =1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2+. test which model perfroms highest for tabnet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### testing code for searching best model\n",
    "\"\"\"\n",
    "import glob\n",
    "\n",
    "model_choice = glob.glob('./models/batch500' + '/*')\n",
    "\n",
    "# of inference\n",
    "n_inference = 5\n",
    "n_sample = 1000\n",
    "for m in model_choice:\n",
    "    for i in range(n_inference):\n",
    "        ctgan = CTGANSynthesizer(batch_size=batch_size)\n",
    "        ctgan = ctgan.load(m)\n",
    "\n",
    "        # Sample datapoints\n",
    "        smri_generated = ctgan.sample(n_sample)\n",
    "\n",
    "        # Add additional marker for visualization denoting generated data\n",
    "        df_generated ['Gan'] = 1\n",
    "        df_generated [['subjectkey', 'age', 'female', 'race', 'abcd_site',\n",
    "               'SRS', 'dl_speech', 'symp_eyePast', 'symp_eyePresent', 'sym_movPast', 'sym_movPresent',\n",
    "              'sym_rouPast', 'sym_rouPresent', 'PDD_NOS']] = df_generated [['subjectkey', 'age', 'female', 'race', 'abcd_site',\n",
    "               'SRS', 'dl_speech', 'symp_eyePast', 'symp_eyePresent', 'sym_movPast', 'sym_movPresent',\n",
    "              'sym_rouPast', 'sym_rouPresent', 'PDD_NOS']].astype('int32')\n",
    "\n",
    "        # Prepare augmented data which merges original + generated data\n",
    "        df_augmented = df.copy()\n",
    "        df_augmented['Gan']=0\n",
    "        df_augmented = df_augmented.append(df_generated)\n",
    "        \"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. evaluate the model\n",
    "* output number will be between 0 and 1 which indicate how similar the two tables are, 0=worst and 1=best possible score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/anaconda3/lib/python3.6/site-packages/numpy/core/fromnumeric.py:3373: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/home/ubuntu/anaconda3/lib/python3.6/site-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/home/ubuntu/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "/home/ubuntu/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "/home/ubuntu/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\"\\nepoch10\\n{'cstest': nan,\\n 'kstest': 0.001060880286018847,\\n 'logistic_detection': 0.9998776163919899,\\n 'svc_detection': 0.9960339811172877}\\n \""
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#from sdv.evaluation import evaluate\n",
    "evaluate(new_data, data_forargu, aggregate = False)\n",
    "'''\n",
    "epoch10\n",
    "{'cstest': nan,\n",
    " 'kstest': 0.001060880286018847,\n",
    " 'logistic_detection': 0.9998776163919899,\n",
    " 'svc_detection': 0.9960339811172877}\n",
    " '''\n",
    "\n",
    "'''\n",
    "\"\\nepoch10\\n{'cstest': nan,\\n \n",
    "'kstest': 0.001060880286018847,\\n \n",
    "'logistic_detection': 0.9998776163919899,\\n \n",
    "'svc_detection': 0.9960339811172877}\\n \"\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/anaconda3/lib/python3.6/site-packages/numpy/core/fromnumeric.py:3373: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/home/ubuntu/anaconda3/lib/python3.6/site-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/home/ubuntu/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "/home/ubuntu/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "/home/ubuntu/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.6655326433824514"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluate(new_data, data_forargu, aggregate = True) \n",
    "#epoch10 ->0.6656085582619684\n",
    "#epoch1000 ->0.6655326433824514"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=12484, step=1)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=6242, step=1)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_forargu.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n"
     ]
    }
   ],
   "source": [
    "## Sample datapoints\n",
    "df_generated = new_data[new_data['ASD'] == 1]\n",
    "\n",
    "## Additional markers for visualization denoting generated data\n",
    "df_generated['Gen'] = 1\n",
    "\n",
    "df_augmented = data_forargu.copy()\n",
    "df_augmented['Gen'] = 0\n",
    "df_augmented = df_augmented.append(df_generated)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    6149\n",
       "1      93\n",
       "Name: ASD, dtype: int64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_forargu['ASD'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    6149\n",
       "1     628\n",
       "Name: ASD, dtype: int64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_augmented['ASD'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    535\n",
       "Name: ASD, dtype: int64"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_generated['ASD'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_augmented['ASD'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>ASD</th>\n",
       "      <th>lh_bankssts_area._.1</th>\n",
       "      <th>lh_caudalanteriorcingulate_area._.1</th>\n",
       "      <th>lh_caudalmiddlefrontal_area._.1</th>\n",
       "      <th>lh_cuneus_area._.1</th>\n",
       "      <th>lh_entorhinal_area._.1</th>\n",
       "      <th>lh_fusiform_area._.1</th>\n",
       "      <th>lh_inferiorparietal_area._.1</th>\n",
       "      <th>lh_inferiortemporal_area._.1</th>\n",
       "      <th>lh_isthmuscingulate_area._.1</th>\n",
       "      <th>...</th>\n",
       "      <th>wm.rh.frontalpole._.18</th>\n",
       "      <th>wm.rh.temporalpole._.18</th>\n",
       "      <th>wm.rh.transversetemporal._.18</th>\n",
       "      <th>wm.rh.insula._.18</th>\n",
       "      <th>Left.UnsegmentedWhiteMatter._.18</th>\n",
       "      <th>Right.UnsegmentedWhiteMatter._.18</th>\n",
       "      <th>lhCerebralWhiteMatterVol._.18</th>\n",
       "      <th>rhCerebralWhiteMatterVol._.18</th>\n",
       "      <th>CerebralWhiteMatterVol._.18</th>\n",
       "      <th>Gen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.025851</td>\n",
       "      <td>0.519113</td>\n",
       "      <td>0.347495</td>\n",
       "      <td>0.016790</td>\n",
       "      <td>0.268523</td>\n",
       "      <td>-0.834353</td>\n",
       "      <td>-0.167922</td>\n",
       "      <td>-0.753314</td>\n",
       "      <td>0.379927</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.454089</td>\n",
       "      <td>-1.661157</td>\n",
       "      <td>-1.292066</td>\n",
       "      <td>0.259451</td>\n",
       "      <td>-1.608799</td>\n",
       "      <td>-1.469811</td>\n",
       "      <td>-1.188557</td>\n",
       "      <td>-1.297889</td>\n",
       "      <td>-1.244560</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2.502511</td>\n",
       "      <td>0.890463</td>\n",
       "      <td>2.063899</td>\n",
       "      <td>0.832845</td>\n",
       "      <td>0.678534</td>\n",
       "      <td>1.353124</td>\n",
       "      <td>2.247249</td>\n",
       "      <td>1.138800</td>\n",
       "      <td>1.666732</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.889764</td>\n",
       "      <td>-0.151850</td>\n",
       "      <td>2.051664</td>\n",
       "      <td>1.122743</td>\n",
       "      <td>0.416397</td>\n",
       "      <td>0.066510</td>\n",
       "      <td>1.433300</td>\n",
       "      <td>1.374887</td>\n",
       "      <td>1.405527</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.778180</td>\n",
       "      <td>-0.157275</td>\n",
       "      <td>-0.350008</td>\n",
       "      <td>0.066752</td>\n",
       "      <td>0.659464</td>\n",
       "      <td>-0.343589</td>\n",
       "      <td>-1.022242</td>\n",
       "      <td>0.837025</td>\n",
       "      <td>-0.007253</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.290898</td>\n",
       "      <td>-0.417831</td>\n",
       "      <td>1.401047</td>\n",
       "      <td>0.202878</td>\n",
       "      <td>1.440708</td>\n",
       "      <td>1.423929</td>\n",
       "      <td>0.338072</td>\n",
       "      <td>0.449538</td>\n",
       "      <td>0.394261</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.316237</td>\n",
       "      <td>-0.309794</td>\n",
       "      <td>0.602790</td>\n",
       "      <td>1.311652</td>\n",
       "      <td>-1.247566</td>\n",
       "      <td>0.682330</td>\n",
       "      <td>0.310445</td>\n",
       "      <td>-0.734800</td>\n",
       "      <td>0.949310</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.364259</td>\n",
       "      <td>-0.883743</td>\n",
       "      <td>0.169281</td>\n",
       "      <td>0.094184</td>\n",
       "      <td>-0.376163</td>\n",
       "      <td>-0.205434</td>\n",
       "      <td>-0.223385</td>\n",
       "      <td>-0.343072</td>\n",
       "      <td>-0.283573</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.875348</td>\n",
       "      <td>-0.018019</td>\n",
       "      <td>0.370289</td>\n",
       "      <td>-0.770120</td>\n",
       "      <td>0.001538</td>\n",
       "      <td>-0.353453</td>\n",
       "      <td>0.801776</td>\n",
       "      <td>0.220514</td>\n",
       "      <td>-0.457065</td>\n",
       "      <td>...</td>\n",
       "      <td>0.081896</td>\n",
       "      <td>-0.366062</td>\n",
       "      <td>-0.692279</td>\n",
       "      <td>-1.209566</td>\n",
       "      <td>-0.107533</td>\n",
       "      <td>-0.292677</td>\n",
       "      <td>-0.245230</td>\n",
       "      <td>-0.274355</td>\n",
       "      <td>-0.260075</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12383</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.689314</td>\n",
       "      <td>-0.104179</td>\n",
       "      <td>3.576644</td>\n",
       "      <td>-0.914229</td>\n",
       "      <td>0.271626</td>\n",
       "      <td>-0.961258</td>\n",
       "      <td>-2.255318</td>\n",
       "      <td>0.451910</td>\n",
       "      <td>-2.140220</td>\n",
       "      <td>...</td>\n",
       "      <td>0.609701</td>\n",
       "      <td>-1.294610</td>\n",
       "      <td>-0.693026</td>\n",
       "      <td>-1.151336</td>\n",
       "      <td>-0.882418</td>\n",
       "      <td>-0.751517</td>\n",
       "      <td>-0.206150</td>\n",
       "      <td>0.939904</td>\n",
       "      <td>-1.087117</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12418</th>\n",
       "      <td>1</td>\n",
       "      <td>0.075582</td>\n",
       "      <td>0.313313</td>\n",
       "      <td>0.328503</td>\n",
       "      <td>1.684575</td>\n",
       "      <td>3.219488</td>\n",
       "      <td>1.958033</td>\n",
       "      <td>-1.384416</td>\n",
       "      <td>-1.093816</td>\n",
       "      <td>1.289984</td>\n",
       "      <td>...</td>\n",
       "      <td>1.157062</td>\n",
       "      <td>-0.401550</td>\n",
       "      <td>-1.775251</td>\n",
       "      <td>-0.300932</td>\n",
       "      <td>3.045821</td>\n",
       "      <td>1.960018</td>\n",
       "      <td>0.206361</td>\n",
       "      <td>1.184375</td>\n",
       "      <td>1.273626</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12454</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.019479</td>\n",
       "      <td>0.785006</td>\n",
       "      <td>-1.907257</td>\n",
       "      <td>-2.189647</td>\n",
       "      <td>-2.519724</td>\n",
       "      <td>-1.279863</td>\n",
       "      <td>-1.056551</td>\n",
       "      <td>0.553968</td>\n",
       "      <td>-1.561046</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.611311</td>\n",
       "      <td>-1.866694</td>\n",
       "      <td>-0.876288</td>\n",
       "      <td>1.243759</td>\n",
       "      <td>-2.635206</td>\n",
       "      <td>-0.672330</td>\n",
       "      <td>0.191981</td>\n",
       "      <td>4.587037</td>\n",
       "      <td>-1.081285</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12457</th>\n",
       "      <td>1</td>\n",
       "      <td>-2.624089</td>\n",
       "      <td>-0.943895</td>\n",
       "      <td>-1.803735</td>\n",
       "      <td>-1.057847</td>\n",
       "      <td>-0.775262</td>\n",
       "      <td>0.582163</td>\n",
       "      <td>-0.442459</td>\n",
       "      <td>0.191262</td>\n",
       "      <td>-0.076612</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.862383</td>\n",
       "      <td>0.721966</td>\n",
       "      <td>-0.646719</td>\n",
       "      <td>-0.412038</td>\n",
       "      <td>-1.524784</td>\n",
       "      <td>-1.582460</td>\n",
       "      <td>0.463219</td>\n",
       "      <td>0.496497</td>\n",
       "      <td>1.143149</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12468</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.821406</td>\n",
       "      <td>-0.594061</td>\n",
       "      <td>0.952135</td>\n",
       "      <td>0.101341</td>\n",
       "      <td>1.256406</td>\n",
       "      <td>0.752440</td>\n",
       "      <td>-0.964531</td>\n",
       "      <td>0.311057</td>\n",
       "      <td>1.031803</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.387726</td>\n",
       "      <td>3.184086</td>\n",
       "      <td>1.045918</td>\n",
       "      <td>1.620922</td>\n",
       "      <td>-0.211697</td>\n",
       "      <td>-0.546469</td>\n",
       "      <td>1.867257</td>\n",
       "      <td>-0.178778</td>\n",
       "      <td>1.182909</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6777 rows × 997 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ASD  lh_bankssts_area._.1  lh_caudalanteriorcingulate_area._.1  \\\n",
       "0        0             -0.025851                             0.519113   \n",
       "1        0              2.502511                             0.890463   \n",
       "2        0             -1.778180                            -0.157275   \n",
       "3        0             -0.316237                            -0.309794   \n",
       "4        0              0.875348                            -0.018019   \n",
       "...    ...                   ...                                  ...   \n",
       "12383    1             -0.689314                            -0.104179   \n",
       "12418    1              0.075582                             0.313313   \n",
       "12454    1             -0.019479                             0.785006   \n",
       "12457    1             -2.624089                            -0.943895   \n",
       "12468    1             -0.821406                            -0.594061   \n",
       "\n",
       "       lh_caudalmiddlefrontal_area._.1  lh_cuneus_area._.1  \\\n",
       "0                             0.347495            0.016790   \n",
       "1                             2.063899            0.832845   \n",
       "2                            -0.350008            0.066752   \n",
       "3                             0.602790            1.311652   \n",
       "4                             0.370289           -0.770120   \n",
       "...                                ...                 ...   \n",
       "12383                         3.576644           -0.914229   \n",
       "12418                         0.328503            1.684575   \n",
       "12454                        -1.907257           -2.189647   \n",
       "12457                        -1.803735           -1.057847   \n",
       "12468                         0.952135            0.101341   \n",
       "\n",
       "       lh_entorhinal_area._.1  lh_fusiform_area._.1  \\\n",
       "0                    0.268523             -0.834353   \n",
       "1                    0.678534              1.353124   \n",
       "2                    0.659464             -0.343589   \n",
       "3                   -1.247566              0.682330   \n",
       "4                    0.001538             -0.353453   \n",
       "...                       ...                   ...   \n",
       "12383                0.271626             -0.961258   \n",
       "12418                3.219488              1.958033   \n",
       "12454               -2.519724             -1.279863   \n",
       "12457               -0.775262              0.582163   \n",
       "12468                1.256406              0.752440   \n",
       "\n",
       "       lh_inferiorparietal_area._.1  lh_inferiortemporal_area._.1  \\\n",
       "0                         -0.167922                     -0.753314   \n",
       "1                          2.247249                      1.138800   \n",
       "2                         -1.022242                      0.837025   \n",
       "3                          0.310445                     -0.734800   \n",
       "4                          0.801776                      0.220514   \n",
       "...                             ...                           ...   \n",
       "12383                     -2.255318                      0.451910   \n",
       "12418                     -1.384416                     -1.093816   \n",
       "12454                     -1.056551                      0.553968   \n",
       "12457                     -0.442459                      0.191262   \n",
       "12468                     -0.964531                      0.311057   \n",
       "\n",
       "       lh_isthmuscingulate_area._.1  ...  wm.rh.frontalpole._.18  \\\n",
       "0                          0.379927  ...               -0.454089   \n",
       "1                          1.666732  ...               -0.889764   \n",
       "2                         -0.007253  ...               -0.290898   \n",
       "3                          0.949310  ...               -0.364259   \n",
       "4                         -0.457065  ...                0.081896   \n",
       "...                             ...  ...                     ...   \n",
       "12383                     -2.140220  ...                0.609701   \n",
       "12418                      1.289984  ...                1.157062   \n",
       "12454                     -1.561046  ...               -1.611311   \n",
       "12457                     -0.076612  ...               -1.862383   \n",
       "12468                      1.031803  ...               -0.387726   \n",
       "\n",
       "       wm.rh.temporalpole._.18  wm.rh.transversetemporal._.18  \\\n",
       "0                    -1.661157                      -1.292066   \n",
       "1                    -0.151850                       2.051664   \n",
       "2                    -0.417831                       1.401047   \n",
       "3                    -0.883743                       0.169281   \n",
       "4                    -0.366062                      -0.692279   \n",
       "...                        ...                            ...   \n",
       "12383                -1.294610                      -0.693026   \n",
       "12418                -0.401550                      -1.775251   \n",
       "12454                -1.866694                      -0.876288   \n",
       "12457                 0.721966                      -0.646719   \n",
       "12468                 3.184086                       1.045918   \n",
       "\n",
       "       wm.rh.insula._.18  Left.UnsegmentedWhiteMatter._.18  \\\n",
       "0               0.259451                         -1.608799   \n",
       "1               1.122743                          0.416397   \n",
       "2               0.202878                          1.440708   \n",
       "3               0.094184                         -0.376163   \n",
       "4              -1.209566                         -0.107533   \n",
       "...                  ...                               ...   \n",
       "12383          -1.151336                         -0.882418   \n",
       "12418          -0.300932                          3.045821   \n",
       "12454           1.243759                         -2.635206   \n",
       "12457          -0.412038                         -1.524784   \n",
       "12468           1.620922                         -0.211697   \n",
       "\n",
       "       Right.UnsegmentedWhiteMatter._.18  lhCerebralWhiteMatterVol._.18  \\\n",
       "0                              -1.469811                      -1.188557   \n",
       "1                               0.066510                       1.433300   \n",
       "2                               1.423929                       0.338072   \n",
       "3                              -0.205434                      -0.223385   \n",
       "4                              -0.292677                      -0.245230   \n",
       "...                                  ...                            ...   \n",
       "12383                          -0.751517                      -0.206150   \n",
       "12418                           1.960018                       0.206361   \n",
       "12454                          -0.672330                       0.191981   \n",
       "12457                          -1.582460                       0.463219   \n",
       "12468                          -0.546469                       1.867257   \n",
       "\n",
       "       rhCerebralWhiteMatterVol._.18  CerebralWhiteMatterVol._.18  Gen  \n",
       "0                          -1.297889                    -1.244560    0  \n",
       "1                           1.374887                     1.405527    0  \n",
       "2                           0.449538                     0.394261    0  \n",
       "3                          -0.343072                    -0.283573    0  \n",
       "4                          -0.274355                    -0.260075    0  \n",
       "...                              ...                          ...  ...  \n",
       "12383                       0.939904                    -1.087117    1  \n",
       "12418                       1.184375                     1.273626    1  \n",
       "12454                       4.587037                    -1.081285    1  \n",
       "12457                       0.496497                     1.143149    1  \n",
       "12468                      -0.178778                     1.182909    1  \n",
       "\n",
       "[6777 rows x 997 columns]"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_augmented"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "## all evaluation\n",
    "real_ = {'samples': data_forargu[features_forargu]} #features_forargu; asd + mor_brain\n",
    "synthetic_ = {'samples': df_generated[features_forargu]}\n",
    "\n",
    "metadata = Metadata()\n",
    "metadata.add_table('samples', real_['samples'])\n",
    "report = evaluate(metadata, real_, synthetic_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-875971.3402337931\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/anaconda3/lib/python3.6/site-packages/sdmetrics/report.py:247: UserWarning: This figure was using constrained_layout==True, but that is incompatible with subplots_adjust and or tight_layout: setting constrained_layout==False. \n",
      "  fig.tight_layout(pad=2.0)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x864 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(report.overall())\n",
    "figure = report.visualize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-875971.3402337931]\n"
     ]
    }
   ],
   "source": [
    "score_agg = []\n",
    "score_agg.append(report.overall())\n",
    "print(score_agg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Either both or neither of `x` and `y` must be specified (but try passing to `data`, which is more flexible).",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-132-8059643260a3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlineplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscore_agg\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[0m\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/seaborn/relational.py\u001b[0m in \u001b[0;36mlineplot\u001b[0;34m(x, y, hue, size, style, data, palette, hue_order, hue_norm, sizes, size_order, size_norm, dashes, markers, style_order, units, estimator, ci, n_boot, seed, sort, err_style, err_kws, legend, ax, **kwargs)\u001b[0m\n\u001b[1;32m   1124\u001b[0m         \u001b[0mdashes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdashes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmarkers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmarkers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstyle_order\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstyle_order\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1125\u001b[0m         \u001b[0munits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mestimator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mci\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mci\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_boot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_boot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1126\u001b[0;31m         \u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr_style\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merr_style\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr_kws\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merr_kws\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlegend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlegend\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   1127\u001b[0m     )\n\u001b[1;32m   1128\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/seaborn/relational.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, x, y, hue, size, style, data, palette, hue_order, hue_norm, sizes, size_order, size_norm, dashes, markers, style_order, units, estimator, ci, n_boot, seed, sort, err_style, err_kws, legend)\u001b[0m\n\u001b[1;32m    693\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    694\u001b[0m         plot_data = self.establish_variables(\n\u001b[0;32m--> 695\u001b[0;31m             \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstyle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    696\u001b[0m         )\n\u001b[1;32m    697\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/seaborn/relational.py\u001b[0m in \u001b[0;36mestablish_variables\u001b[0;34m(self, x, y, hue, size, style, units, data)\u001b[0m\n\u001b[1;32m    159\u001b[0m             err = (\"Either both or neither of `x` and `y` must be specified \"\n\u001b[1;32m    160\u001b[0m                    \"(but try passing to `data`, which is more flexible).\")\n\u001b[0;32m--> 161\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\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    162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    163\u001b[0m         \u001b[0;31m# ---- Post-processing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Either both or neither of `x` and `y` must be specified (but try passing to `data`, which is more flexible)."
     ]
    }
   ],
   "source": [
    "#sns.lineplot(np.array(score_agg))\n",
    "#plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Visualization of generated data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[t-SNE] Computing 91 nearest neighbors...\n",
      "[t-SNE] Indexed 6777 samples in 0.945s...\n",
      "[t-SNE] Computed neighbors for 6777 samples in 65.905s...\n",
      "[t-SNE] Computed conditional probabilities for sample 1000 / 6777\n",
      "[t-SNE] Computed conditional probabilities for sample 2000 / 6777\n",
      "[t-SNE] Computed conditional probabilities for sample 3000 / 6777\n",
      "[t-SNE] Computed conditional probabilities for sample 4000 / 6777\n",
      "[t-SNE] Computed conditional probabilities for sample 5000 / 6777\n",
      "[t-SNE] Computed conditional probabilities for sample 6000 / 6777\n",
      "[t-SNE] Computed conditional probabilities for sample 6777 / 6777\n",
      "[t-SNE] Mean sigma: 7.459000\n",
      "[t-SNE] KL divergence after 250 iterations with early exaggeration: 75.868980\n",
      "[t-SNE] KL divergence after 1000 iterations: 1.545578\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.manifold import TSNE\n",
    "import seaborn as sns\n",
    "\n",
    "#dat_to_vis = df_augmented[features_forargu].values\n",
    "#tsne = TSNE(n_componen ts=2)\n",
    "#tsne_results = tsne.fit_transform(dat_to_vis)\n",
    "tsne = TSNE(n_components=2, verbose=1, random_state=1234)\n",
    "tsne_result = tsne.fit_transform(df_augmented)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_augmented['tsne-2d-one'] = tsne_results[:,0]\n",
    "df_augmented['tsne-2d-two'] = tsne_results[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 26.735195 , -44.819637 ,  10.4932165, ...,   4.8879924,\n",
       "         8.833127 , -37.45092  ], dtype=float32)"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " tsne_results[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcd47323908>"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,10))\n",
    "sns.scatterplot(x=tsne_results[:,0], y=tsne_results[:,1],\n",
    "               hue=y,\n",
    "               palette=sns.color_palette(\"hls\", 2),\n",
    "               data = df_augmented,\n",
    "               legend = \"full\")"
   ]
  }
 ],
 "metadata": {
  "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": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}