code/baselines/SimCLR/evaluate_model_fairness_globem.ipynb

{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "© 2024 Nokia\n",
    "Licensed under the BSD 3 Clause Clear License  \n",
    "SPDX-License-Identifier: BSD-3-Clause-Clear"
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:No module named 'tempeh': LawSchoolGPADataset will be unavailable. To install, run:\n",
      "pip install 'aif360[LawSchoolGPA]'\n",
      "WARNING:root:No module named 'fairlearn': ExponentiatedGradientReduction will be unavailable. To install, run:\n",
      "pip install 'aif360[Reductions]'\n",
      "WARNING:root:No module named 'fairlearn': GridSearchReduction will be unavailable. To install, run:\n",
      "pip install 'aif360[Reductions]'\n",
      "WARNING:root:No module named 'fairlearn': GridSearchReduction will be unavailable. To install, run:\n",
      "pip install 'aif360[Reductions]'\n"
     ]
    }
   ],
   "execution_count": 1,
   "source": [
    "import os\n",
    "import pickle\n",
    "import re\n",
    "\n",
    "import pandas as pd\n",
    "import scipy\n",
    "import datetime\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from fairness_utilities import *\n",
    "from definitions import ROOT_DIR\n",
    "\n",
    "from sklearn import metrics\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set(font_scale = 1.5)\n",
    "\n",
    "seed = 2\n",
    "tf.random.set_seed(seed)\n",
    "np.random.seed(seed)\n",
    "\n",
    "import simclr_models\n",
    "import simclr_utitlities\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n",
    "\n",
    "sns.set(style='white', context=\"poster\", font='Work Sans Regural')"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## MODEL SUMMARY"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "# Dataset-specific - Updated implementation with hyper-parameter tuning\n",
    "\n",
    "# data folder structure\n",
    "data_folder = 'GLOBEM'\n",
    "# model folder structure\n",
    "working_directory = 'GLOBEM/'\n",
    "\n",
    "# SimCLR finetuned models\n",
    "# FROZEN: 3\n",
    "# subfolder = '20231128-152654_e200_esTrue_bs128_wTrue_f3'\n",
    "# image_folder = os.path.join(data_folder, 'img', subfolder)\n",
    "# model_name = 'simclr.frozen.1.71.hdf5'\n",
    "# frozen_layers = 3\n",
    "# added_layers = 2\n",
    "# tag = 'frozen3'\n",
    "# FROZEN: 2 (f-m)\n",
    "# subfolder = '20231128-152630_e200_esTrue_bs128_wTrue_f2'\n",
    "# image_folder = os.path.join(data_folder, 'img', subfolder)\n",
    "# model_name = 'simclr.frozen.1.19.hdf5'\n",
    "# frozen_layers = 2\n",
    "# added_layers = 2\n",
    "# tag = 'frozen2'\n",
    "# # FROZEN: 2 (f-l)\n",
    "# subfolder = '20231128-152529_e200_esTrue_bs128_wTrue_f2_fl'\n",
    "# image_folder = os.path.join(data_folder, 'img', subfolder)\n",
    "# model_name = 'simclr.frozen.1.41.hdf5'\n",
    "# frozen_layers = 2\n",
    "# added_layers = 2\n",
    "# tag = 'frozen2fl'\n",
    "# # FROZEN: 1 (f)\n",
    "# subfolder = '20231128-152601_e200_esTrue_bs128_wTrue_f1'\n",
    "# image_folder = os.path.join(data_folder, 'img', subfolder)\n",
    "# model_name = 'simclr.frozen.1.08.hdf5'\n",
    "# frozen_layers = 1\n",
    "# added_layers = 2\n",
    "# tag = 'frozen1'\n",
    "# FROZEN: 1 (m)\n",
    "subfolder = '20231128-152400_e200_esTrue_bs128_wTrue_f1_m'\n",
    "image_folder = os.path.join(data_folder, 'img', subfolder)\n",
    "model_name = 'simclr.frozen.1.58.hdf5'\n",
    "frozen_layers = 1\n",
    "added_layers = 2\n",
    "tag = 'frozen1m'\n",
    "# FROZEN: 0\n",
    "# subfolder = '20231128-152548_e200_esTrue_bs128_wTrue_f0'\n",
    "# image_folder = os.path.join(data_folder, 'img', subfolder)\n",
    "# model_name = 'simclr.frozen.1.56.hdf5'\n",
    "# frozen_layers = 0\n",
    "# added_layers = 2\n",
    "# tag = 'frozen0'\n",
    "# # SUPERVISED\n",
    "# working_directory = '..\\Supervised\\GLOBEM'\n",
    "# subfolder = '20231115-091914_l2_e200_esTrue_bs64_wTrue'\n",
    "# image_folder = os.path.join(working_directory, 'img', subfolder)\n",
    "# model_name = \"supervised.finetuned.hdf5\"\n",
    "# frozen_layers = ''\n",
    "# added_layers = 2\n",
    "# tag = 'supervised'\n",
    "#\n",
    "#\n",
    "# pretrained_model = tf.keras.models.load_model(os.path.join(working_directory, subfolder, model_name), compile=False)  # compile=False as we use the model only for inference\n",
    "# if not os.path.exists(image_folder):\n",
    "#     os.makedirs(image_folder)\n",
    "# pretrained_model.summary()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-22T09:24:37.077810700Z",
     "start_time": "2024-01-22T09:24:37.030289600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"TPN\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input (InputLayer)           [(None, 28, 1390)]        0         \n",
      "_________________________________________________________________\n",
      "conv1d (Conv1D)              (None, 28, 32)            1067552   \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 28, 32)            0         \n",
      "_________________________________________________________________\n",
      "conv1d_1 (Conv1D)            (None, 28, 64)            32832     \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 28, 64)            0         \n",
      "_________________________________________________________________\n",
      "conv1d_2 (Conv1D)            (None, 28, 96)            49248     \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 28, 96)            0         \n",
      "_________________________________________________________________\n",
      "global_max_pooling1d (Global (None, 96)                0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 128)               12416     \n",
      "_________________________________________________________________\n",
      "drop (Dropout)               (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 2)                 258       \n",
      "_________________________________________________________________\n",
      "softmax (Softmax)            (None, 2)                 0         \n",
      "=================================================================\n",
      "Total params: 1,162,306\n",
      "Trainable params: 1,129,474\n",
      "Non-trainable params: 32,832\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Dataset-specific - MARIA'S IMPLEMENTATION\n",
    "\n",
    "# # data folder structure\n",
    "# data_folder = 'GLOBEM'\n",
    "# # model folder structure\n",
    "# working_directory = 'GLOBEM/'\n",
    "\n",
    "# SimCLR finetuned models\n",
    "# FROZEN: 3\n",
    "# subfolder = '20231114-113050_e100_esFalse_bs128_wFalse_f3'\n",
    "# image_folder = os.path.join(data_folder, 'img', subfolder)\n",
    "# model_name = 'simclr.frozen.1.05.hdf5'\n",
    "# frozen_layers = 3\n",
    "# added_layers = 2\n",
    "# tag = 'frozen3'\n",
    "# FROZEN: 2 (f-m)\n",
    "# subfolder = '20231114-112937_e100_esFalse_bs128_wFalse_f2'\n",
    "# image_folder = os.path.join(data_folder, 'img', subfolder)\n",
    "# model_name = 'simclr.frozen.0.97.hdf5'\n",
    "# frozen_layers = 2\n",
    "# added_layers = 2\n",
    "# tag = 'frozen2'\n",
    "# # FROZEN: 2 (f-l)\n",
    "# subfolder = '20231114-112324_e100_esFalse_bs128_wFalse_f2_fl'\n",
    "# image_folder = os.path.join(data_folder, 'img', subfolder)\n",
    "# model_name = 'simclr.frozen.0.96.hdf5'\n",
    "# frozen_layers = 2\n",
    "# added_layers = 2\n",
    "# tag = 'frozen2fl'\n",
    "# # FROZEN: 1 (f)\n",
    "# subfolder = '20231114-112818_e100_esFalse_bs128_wFalse_f1'\n",
    "# image_folder = os.path.join(data_folder, 'img', subfolder)\n",
    "# model_name = 'simclr.frozen.0.92.hdf5'\n",
    "# frozen_layers = 1\n",
    "# added_layers = 2\n",
    "# tag = 'frozen1'\n",
    "# # FROZEN: 1 (m)\n",
    "# subfolder = '20231114-112507_e100_esFalse_bs128_wFalse_f1_m'\n",
    "# image_folder = os.path.join(data_folder, 'img', subfolder)\n",
    "# model_name = 'simclr.frozen.1.12.hdf5'\n",
    "# frozen_layers = 1\n",
    "# added_layers = 2\n",
    "# tag = 'frozen1m'\n",
    "# # FROZEN: 0\n",
    "# subfolder = '20231114-112650_e100_esFalse_bs128_wFalse_f0'\n",
    "# image_folder = os.path.join(data_folder, 'img', subfolder)\n",
    "# model_name = 'simclr.frozen.1.23.hdf5'\n",
    "# frozen_layers = 0\n",
    "# added_layers = 2\n",
    "# tag = 'frozen0'\n",
    "# # SUPERVISED\n",
    "# working_directory = '..\\Supervised\\GLOBEM'\n",
    "# # subfolder = '20231114-141632_l2_e100_esFalse_bs128_wFalse'\n",
    "# subfolder = '20231115-091914_l2_e200_esTrue_bs64_wTrue'\n",
    "# image_folder = os.path.join(working_directory, 'img', subfolder)\n",
    "# model_name = \"supervised.finetuned.hdf5\"\n",
    "# frozen_layers = ''\n",
    "# added_layers = 2\n",
    "# tag = 'supervised'\n",
    "\n",
    "\n",
    "pretrained_model = tf.keras.models.load_model(os.path.join(working_directory, subfolder, model_name), compile=False)  # compile=False as we use the model only for inference\n",
    "if not os.path.exists(image_folder):\n",
    "    os.makedirs(image_folder)\n",
    "pretrained_model.summary()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-22T09:24:37.200211400Z",
     "start_time": "2024-01-22T09:24:37.046298100Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## DATA SHAPES"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "# Load preprocessed data\n",
    "np_train = (np.load(os.path.join(data_folder, 'train_x.npy')),\n",
    "           np.load(os.path.join(data_folder, 'train_y.npy')))\n",
    "np_val = (np.load(os.path.join(data_folder, 'val_x.npy')),\n",
    "           np.load(os.path.join(data_folder, 'val_y.npy')))\n",
    "np_test = (np.load(os.path.join(data_folder, 'test_x.npy')),\n",
    "           np.load(os.path.join(data_folder, 'test_y.npy')))\n",
    "\n",
    "probs = pretrained_model.predict(np_test[0])\n",
    "predictions = np.argmax(probs, axis=1)\n",
    "# print(simclr_utitlities.evaluate_model_simple(pretrained_model.predict(np_test[0]), np_test[1], return_dict=True))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-22T09:24:52.425618700Z",
     "start_time": "2024-01-22T09:24:37.206210900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train - Validation - Test Set Shapes:\n",
      "Train X: (5957, 28, 1390) - Val X: (1082, 28, 1390) - Test X: (1083, 28, 1390)\n",
      "Train y: (5957, 2) - Val y: (1082, 2) - Test y: (1083, 2)\n"
     ]
    }
   ],
   "source": [
    "print(\"Train - Validation - Test Set Shapes:\")\n",
    "print(\"Train X: {} - Val X: {} - Test X: {}\".format(np_train[0].shape, np_val[0].shape, np_test[0].shape))\n",
    "print(\"Train y: {} - Val y: {} - Test y: {}\".format(np_train[1].shape, np_val[1].shape, np_test[1].shape))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-22T09:24:52.445617200Z",
     "start_time": "2024-01-22T09:24:52.428617900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "0.0    676\n1.0    407\ndtype: int64"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The test set values agree with the ones reported in the original paper: https://arxiv.org/pdf/1703.07771.pdf\n",
    "pd.Series(np_test[1][:,1]).value_counts()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-22T09:24:52.489138800Z",
     "start_time": "2024-01-22T09:24:52.453617800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "   PID  gender   age      race  generation  disability\n0    1     1.0  18.0     Asian         2.0         0.0\n1    2     2.0  18.0     White         4.0         0.0\n2    3     2.0  18.0  Biracial         4.0         0.0\n3    4     2.0  18.0     Asian         2.0         0.0\n4    5     2.0  18.0     Asian         2.0         0.0",
      "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>PID</th>\n      <th>gender</th>\n      <th>age</th>\n      <th>race</th>\n      <th>generation</th>\n      <th>disability</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1.0</td>\n      <td>18.0</td>\n      <td>Asian</td>\n      <td>2.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>2.0</td>\n      <td>18.0</td>\n      <td>White</td>\n      <td>4.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>2.0</td>\n      <td>18.0</td>\n      <td>Biracial</td>\n      <td>4.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>2.0</td>\n      <td>18.0</td>\n      <td>Asian</td>\n      <td>2.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>2.0</td>\n      <td>18.0</td>\n      <td>Asian</td>\n      <td>2.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ROOT_DIR = \"../../../\"\n",
    "os.chdir(ROOT_DIR)\n",
    "subjects = pd.read_csv(os.path.join('datasets', data_folder, 'demographics.csv'), delimiter=',')\n",
    "# subject IDs per train-validation-test set\n",
    "train_listfile = pd.read_csv(os.path.join('datasets', data_folder, 'train_listfile.csv'))\n",
    "val_listfile = pd.read_csv(os.path.join('datasets', data_folder, 'val_listfile.csv'))\n",
    "test_listfile = pd.read_csv(os.path.join('datasets', data_folder, 'test_listfile.csv'))\n",
    "# change back the working directory\n",
    "os.chdir(os.path.join('code', 'baselines', 'SimCLR'))\n",
    "subjects.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-22T09:24:52.576686Z",
     "start_time": "2024-01-22T09:24:52.476139300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "(5957, 7)"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_listfile.shape"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-22T09:24:52.593665300Z",
     "start_time": "2024-01-22T09:24:52.522144600Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## PROTECTED ATTRIBUTE DISTRIBUTIONS"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total rows: 8122\n",
      "    PID  gender   age   race  generation  disability  year\n",
      "0   1.0     1.0  18.0  Asian         2.0         0.0     1\n",
      "1   1.0     1.0  18.0  Asian         2.0         0.0     1\n",
      "2   1.0     1.0  18.0  Asian         2.0         0.0     1\n",
      "3   1.0     1.0  18.0  Asian         2.0         0.0     1\n",
      "4   1.0     1.0  18.0  Asian         2.0         0.0     1\n",
      "..  ...     ...   ...    ...         ...         ...   ...\n",
      "95  6.0     2.0  18.0  Asian         2.0         0.0     1\n",
      "96  7.0     2.0  18.0  White         4.0         0.0     1\n",
      "97  7.0     2.0  18.0  White         4.0         0.0     1\n",
      "98  7.0     2.0  18.0  White         4.0         0.0     1\n",
      "99  7.0     2.0  18.0  White         4.0         0.0     1\n",
      "\n",
      "[100 rows x 7 columns]\n"
     ]
    }
   ],
   "source": [
    "df = pd.concat([train_listfile, val_listfile, test_listfile])\n",
    "print(\"Total rows: {}\".format(df.shape[0]))\n",
    "print(df.head(100))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-22T09:24:52.651663800Z",
     "start_time": "2024-01-22T09:24:52.538139200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "(5957, 7)"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_listfile.shape"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-22T09:24:52.654694700Z",
     "start_time": "2024-01-22T09:24:52.553138600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "699"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.PID.nunique()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-22T09:24:52.679807Z",
     "start_time": "2024-01-22T09:24:52.567144400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x187a78f8e20>"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:matplotlib.font_manager:findfont: Font family ['Work Sans Regural'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.disability.hist()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-22T09:24:52.894092700Z",
     "start_time": "2024-01-22T09:24:52.581668500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [],
   "source": [
    "df.gender = df.gender.apply(lambda v: 'Male' if v==1 else ('Female' if v==2 else ('Transgender' if v==3 else 'Genderqueer ' if v==4 else 'Other')))\n",
    "df.disability = df.disability.apply(lambda v: 'Yes' if v==1 else 'No')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-22T09:24:52.897092500Z",
     "start_time": "2024-01-22T09:24:52.795389200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "        PID gender   age   race  generation disability  year\n0       1.0   Male  18.0  Asian         2.0         No     1\n1       1.0   Male  18.0  Asian         2.0         No     1\n2       1.0   Male  18.0  Asian         2.0         No     1\n3       1.0   Male  18.0  Asian         2.0         No     1\n4       1.0   Male  18.0  Asian         2.0         No     1\n...     ...    ...   ...    ...         ...        ...   ...\n1078  999.0   Male  21.0  Asian         1.0         No     4\n1079  999.0   Male  21.0  Asian         1.0         No     4\n1080  999.0   Male  21.0  Asian         1.0         No     4\n1081  999.0   Male  21.0  Asian         1.0         No     4\n1082  999.0   Male  21.0  Asian         1.0         No     4\n\n[8122 rows x 7 columns]",
      "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>PID</th>\n      <th>gender</th>\n      <th>age</th>\n      <th>race</th>\n      <th>generation</th>\n      <th>disability</th>\n      <th>year</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.0</td>\n      <td>Male</td>\n      <td>18.0</td>\n      <td>Asian</td>\n      <td>2.0</td>\n      <td>No</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1.0</td>\n      <td>Male</td>\n      <td>18.0</td>\n      <td>Asian</td>\n      <td>2.0</td>\n      <td>No</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1.0</td>\n      <td>Male</td>\n      <td>18.0</td>\n      <td>Asian</td>\n      <td>2.0</td>\n      <td>No</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1.0</td>\n      <td>Male</td>\n      <td>18.0</td>\n      <td>Asian</td>\n      <td>2.0</td>\n      <td>No</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1.0</td>\n      <td>Male</td>\n      <td>18.0</td>\n      <td>Asian</td>\n      <td>2.0</td>\n      <td>No</td>\n      <td>1</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    </tr>\n    <tr>\n      <th>1078</th>\n      <td>999.0</td>\n      <td>Male</td>\n      <td>21.0</td>\n      <td>Asian</td>\n      <td>1.0</td>\n      <td>No</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>1079</th>\n      <td>999.0</td>\n      <td>Male</td>\n      <td>21.0</td>\n      <td>Asian</td>\n      <td>1.0</td>\n      <td>No</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>1080</th>\n      <td>999.0</td>\n      <td>Male</td>\n      <td>21.0</td>\n      <td>Asian</td>\n      <td>1.0</td>\n      <td>No</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>1081</th>\n      <td>999.0</td>\n      <td>Male</td>\n      <td>21.0</td>\n      <td>Asian</td>\n      <td>1.0</td>\n      <td>No</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>1082</th>\n      <td>999.0</td>\n      <td>Male</td>\n      <td>21.0</td>\n      <td>Asian</td>\n      <td>1.0</td>\n      <td>No</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n<p>8122 rows × 7 columns</p>\n</div>"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-22T09:24:52.967101Z",
     "start_time": "2024-01-22T09:24:52.813389700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "data": {
      "text/plain": "           index    variable   value\n0         Female      gender  4857.0\n1           Male      gender  3177.0\n2          Other      gender    54.0\n3   Genderqueer       gender    22.0\n4    Transgender      gender    12.0\n5          Asian        race  4062.0\n6          White        race  2477.0\n7       Biracial        race   901.0\n8         Latinx        race   472.0\n9          Black        race   178.0\n10            No  disability  7547.0\n11           Yes  disability   575.0",
      "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>index</th>\n      <th>variable</th>\n      <th>value</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Female</td>\n      <td>gender</td>\n      <td>4857.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Male</td>\n      <td>gender</td>\n      <td>3177.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Other</td>\n      <td>gender</td>\n      <td>54.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Genderqueer</td>\n      <td>gender</td>\n      <td>22.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Transgender</td>\n      <td>gender</td>\n      <td>12.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Asian</td>\n      <td>race</td>\n      <td>4062.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>White</td>\n      <td>race</td>\n      <td>2477.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Biracial</td>\n      <td>race</td>\n      <td>901.0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Latinx</td>\n      <td>race</td>\n      <td>472.0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Black</td>\n      <td>race</td>\n      <td>178.0</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>No</td>\n      <td>disability</td>\n      <td>7547.0</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Yes</td>\n      <td>disability</td>\n      <td>575.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "protected_attributes = ['gender', 'race', 'disability']\n",
    "privileged_classes = [['Male'], ['White'], ['No']]\n",
    "\n",
    "count_df = pd.concat(axis=0, ignore_index=True, objs=[\n",
    "    df[protected_attribute].value_counts().reset_index(drop=False) for protected_attribute in protected_attributes\n",
    "])\n",
    "count_df = count_df.melt('index')\n",
    "count_df.dropna(axis=0, inplace=True)\n",
    "count_df.reset_index(inplace=True, drop=True)\n",
    "# count_df[\"majority\"] = np.where(count_df.index % 2, 0, 1)\n",
    "# count_df.loc[:, \"label\"] = count_df.apply(lambda row: FRIENDLY_LABELS.get(row['variable']).get(row['index']), axis=1)\n",
    "count_df.head(20)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-22T09:24:52.970621400Z",
     "start_time": "2024-01-22T09:24:52.821390700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [],
   "source": [
    "# sns.set_style('whitegrid')\n",
    "# sns.set(font_scale = 1.5)\n",
    "count_df = count_df[count_df.variable.isin(['gender', 'race', 'disability'])]\n",
    "# g = sns.catplot(data=count_df[count_df.index.notnull()], x=\"label\", col=\"x_label\", y=\"value\", kind='bar', hue=\"Segment\", palette=['#1c9e77', '#cccccc'], sharex=False, ci=None, aspect=.5)\n",
    "# ratios = count_df.groupby('x_label')['label'].nunique().values\n",
    "ratios = [5, 5, 2]\n",
    "count_df.loc[:, \"variable\"] = count_df.loc[:, \"variable\"].apply(lambda l: 'GENDER' if l=='gender' else ('ETHNICITY' if l=='race' else 'DISABILITY'))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-22T09:24:52.988622900Z",
     "start_time": "2024-01-22T09:24:52.855394200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "           index    variable   value   Segment\n0         Female      GENDER  4857.0  Majority\n1           Male      GENDER  3177.0  Minority\n2          Other      GENDER    54.0  Minority\n3   Genderqueer       GENDER    22.0  Minority\n4    Transgender      GENDER    12.0  Minority\n5          Asian   ETHNICITY  4062.0  Majority\n6          White   ETHNICITY  2477.0  Minority\n7       Biracial   ETHNICITY   901.0  Minority\n8         Latinx   ETHNICITY   472.0  Minority\n9          Black   ETHNICITY   178.0  Minority\n10            No  DISABILITY  7547.0  Majority\n11           Yes  DISABILITY   575.0  Minority",
      "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>index</th>\n      <th>variable</th>\n      <th>value</th>\n      <th>Segment</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Female</td>\n      <td>GENDER</td>\n      <td>4857.0</td>\n      <td>Majority</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Male</td>\n      <td>GENDER</td>\n      <td>3177.0</td>\n      <td>Minority</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Other</td>\n      <td>GENDER</td>\n      <td>54.0</td>\n      <td>Minority</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Genderqueer</td>\n      <td>GENDER</td>\n      <td>22.0</td>\n      <td>Minority</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Transgender</td>\n      <td>GENDER</td>\n      <td>12.0</td>\n      <td>Minority</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Asian</td>\n      <td>ETHNICITY</td>\n      <td>4062.0</td>\n      <td>Majority</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>White</td>\n      <td>ETHNICITY</td>\n      <td>2477.0</td>\n      <td>Minority</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Biracial</td>\n      <td>ETHNICITY</td>\n      <td>901.0</td>\n      <td>Minority</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Latinx</td>\n      <td>ETHNICITY</td>\n      <td>472.0</td>\n      <td>Minority</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Black</td>\n      <td>ETHNICITY</td>\n      <td>178.0</td>\n      <td>Minority</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>No</td>\n      <td>DISABILITY</td>\n      <td>7547.0</td>\n      <td>Majority</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Yes</td>\n      <td>DISABILITY</td>\n      <td>575.0</td>\n      <td>Minority</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_df.loc[:, \"Segment\"] = count_df[\"index\"].apply(lambda i: \"Majority\" if (i==\"Female\" or i==\"Asian\" or i==\"No\") else \"Minority\")\n",
    "count_df"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-22T09:24:52.991620500Z",
     "start_time": "2024-01-22T09:24:52.876091200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "data": {
      "text/plain": "           index    variable   value   Segment  order\n5          Asian   ETHNICITY  4062.0  Majority      0\n6          White   ETHNICITY  2477.0  Minority      0\n7       Biracial   ETHNICITY   901.0  Minority      0\n8         Latinx   ETHNICITY   472.0  Minority      0\n9          Black   ETHNICITY   178.0  Minority      0\n0         Female      GENDER  4857.0  Majority      1\n1           Male      GENDER  3177.0  Minority      1\n2          Other      GENDER    54.0  Minority      1\n3   Genderqueer       GENDER    22.0  Minority      1\n4    Transgender      GENDER    12.0  Minority      1\n10            No  DISABILITY  7547.0  Majority      2\n11           Yes  DISABILITY   575.0  Minority      2",
      "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>index</th>\n      <th>variable</th>\n      <th>value</th>\n      <th>Segment</th>\n      <th>order</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>5</th>\n      <td>Asian</td>\n      <td>ETHNICITY</td>\n      <td>4062.0</td>\n      <td>Majority</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>White</td>\n      <td>ETHNICITY</td>\n      <td>2477.0</td>\n      <td>Minority</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Biracial</td>\n      <td>ETHNICITY</td>\n      <td>901.0</td>\n      <td>Minority</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Latinx</td>\n      <td>ETHNICITY</td>\n      <td>472.0</td>\n      <td>Minority</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Black</td>\n      <td>ETHNICITY</td>\n      <td>178.0</td>\n      <td>Minority</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Female</td>\n      <td>GENDER</td>\n      <td>4857.0</td>\n      <td>Majority</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Male</td>\n      <td>GENDER</td>\n      <td>3177.0</td>\n      <td>Minority</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Other</td>\n      <td>GENDER</td>\n      <td>54.0</td>\n      <td>Minority</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Genderqueer</td>\n      <td>GENDER</td>\n      <td>22.0</td>\n      <td>Minority</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Transgender</td>\n      <td>GENDER</td>\n      <td>12.0</td>\n      <td>Minority</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>No</td>\n      <td>DISABILITY</td>\n      <td>7547.0</td>\n      <td>Majority</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Yes</td>\n      <td>DISABILITY</td>\n      <td>575.0</td>\n      <td>Minority</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_df.loc[:, 'order'] = count_df.variable.apply(lambda v: 0 if v=='ETHNICITY' else (1 if v=='GENDER' else 2))\n",
    "count_df.sort_values(by='order', ascending=True, inplace=True)\n",
    "count_df"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-22T09:24:53.023616800Z",
     "start_time": "2024-01-22T09:24:52.884127900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:matplotlib.font_manager:findfont: Font family ['Work Sans Regural'] not found. Falling back to DejaVu Sans.\n",
      "C:\\Users\\syfan\\PycharmProjects\\TFC-pretraining\\venv\\lib\\site-packages\\seaborn\\axisgrid.py:88: UserWarning: Tight layout not applied. tight_layout cannot make axes width small enough to accommodate all axes decorations\n",
      "  self._figure.tight_layout(*args, **kwargs)\n",
      "WARNING:matplotlib.font_manager:findfont: Font family ['Work Sans Regural'] not found. Falling back to DejaVu Sans.\n",
      "WARNING:matplotlib.font_manager:findfont: Font family ['Work Sans Regural'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 984.125x500 with 3 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style='white', context=\"poster\", font='Work Sans Regural')\n",
    "\n",
    "g = sns.catplot(data=count_df[count_df.index.notnull()], x=\"index\", col=\"variable\", ci=None, y=\"value\", kind='bar', sharex=False, aspect=.5, dodge=False,\n",
    "                # palette=['#1c9e77'],\n",
    "                hue=\"Segment\", palette=['#1c9e77', '#cccccc'],\n",
    "                facet_kws={'gridspec_kws':{'width_ratios': ratios}})\n",
    "\n",
    "g.set_titles(\"{col_name}\")\n",
    "g.set_ylabels('Number of samples')\n",
    "g.set_xlabels('Protected Attribute')\n",
    "g.set_xticklabels(rotation=90)\n",
    "\n",
    "# plt.suptitle(\"Privileged vs. Unprivileged Group Representation (#Samples)\")\n",
    "for ax in g.axes.flat[1:]:\n",
    "    sns.despine(ax=ax, left=True)\n",
    "for ax in g.axes.flat:\n",
    "    ax.set_xlabel(ax.get_title(), fontdict={'weight': 'bold'})\n",
    "    ax.set_title('')\n",
    "    ax.margins(x=0.1) # slightly more margin as a separation\n",
    "    ax.xaxis.set_label_position('top')\n",
    "    ylabels = ['{:}'.format(x) + 'K' for x in ax.get_yticks()/1000]\n",
    "    ax.set_ylabel(ax.get_ylabel(), fontdict={'weight': 'bold'})\n",
    "    ax.set_yticklabels(ylabels)\n",
    "plt.subplots_adjust(wspace=0, bottom=0.18, left=0.06)\n",
    "\n",
    "# plt.legend(labels=['Majority Segment', 'Minority Segment'], labelcolor=['#1c9e77', '#e72a8a'])\n",
    "# plt.show()\n",
    "\n",
    "plt.savefig(os.path.join(image_folder, 'representationBar.pdf'), format='pdf', bbox_inches=\"tight\")\n",
    "plt.savefig(os.path.join(image_folder, 'representationBar.png'))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-22T09:24:53.876321400Z",
     "start_time": "2024-01-22T09:24:52.906090Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [
    {
     "data": {
      "text/plain": "           index    variable   value   Segment  order\n5          Asian   ETHNICITY  4062.0  Majority      0\n6          White   ETHNICITY  2477.0  Minority      0\n7       Biracial   ETHNICITY   901.0  Minority      0\n8         Latinx   ETHNICITY   472.0  Minority      0\n9          Black   ETHNICITY   178.0  Minority      0\n0         Female      GENDER  4857.0  Majority      1\n1           Male      GENDER  3177.0  Minority      1\n2          Other      GENDER    54.0  Minority      1\n3   Genderqueer       GENDER    22.0  Minority      1\n4    Transgender      GENDER    12.0  Minority      1\n10            No  DISABILITY  7547.0  Majority      2\n11           Yes  DISABILITY   575.0  Minority      2",
      "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>index</th>\n      <th>variable</th>\n      <th>value</th>\n      <th>Segment</th>\n      <th>order</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>5</th>\n      <td>Asian</td>\n      <td>ETHNICITY</td>\n      <td>4062.0</td>\n      <td>Majority</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>White</td>\n      <td>ETHNICITY</td>\n      <td>2477.0</td>\n      <td>Minority</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Biracial</td>\n      <td>ETHNICITY</td>\n      <td>901.0</td>\n      <td>Minority</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Latinx</td>\n      <td>ETHNICITY</td>\n      <td>472.0</td>\n      <td>Minority</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Black</td>\n      <td>ETHNICITY</td>\n      <td>178.0</td>\n      <td>Minority</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Female</td>\n      <td>GENDER</td>\n      <td>4857.0</td>\n      <td>Majority</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Male</td>\n      <td>GENDER</td>\n      <td>3177.0</td>\n      <td>Minority</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Other</td>\n      <td>GENDER</td>\n      <td>54.0</td>\n      <td>Minority</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Genderqueer</td>\n      <td>GENDER</td>\n      <td>22.0</td>\n      <td>Minority</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Transgender</td>\n      <td>GENDER</td>\n      <td>12.0</td>\n      <td>Minority</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>No</td>\n      <td>DISABILITY</td>\n      <td>7547.0</td>\n      <td>Majority</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Yes</td>\n      <td>DISABILITY</td>\n      <td>575.0</td>\n      <td>Minority</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_df"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-22T09:24:53.925319100Z",
     "start_time": "2024-01-22T09:24:53.875320600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [
    {
     "data": {
      "text/plain": "'GLOBEM\\\\img\\\\20231128-152400_e200_esTrue_bs128_wTrue_f1_m'"
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_folder"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-22T09:24:53.927340300Z",
     "start_time": "2024-01-22T09:24:53.891341900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [
    {
     "data": {
      "text/plain": "           index    variable   value   Segment  order  percentage\n5          Asian   ETHNICITY  4062.0  Majority      0    0.502101\n6          White   ETHNICITY  2477.0  Minority      0    0.306180\n7       Biracial   ETHNICITY   901.0  Minority      0    0.111372\n8         Latinx   ETHNICITY   472.0  Minority      0    0.058344\n9          Black   ETHNICITY   178.0  Minority      0    0.022002\n0         Female      GENDER  4857.0  Majority      1    0.598005\n1           Male      GENDER  3177.0  Minority      1    0.391160\n2          Other      GENDER    54.0  Minority      1    0.006649\n3   Genderqueer       GENDER    22.0  Minority      1    0.002709\n4    Transgender      GENDER    12.0  Minority      1    0.001477\n10            No  DISABILITY  7547.0  Majority      2    0.929205\n11           Yes  DISABILITY   575.0  Minority      2    0.070795",
      "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>index</th>\n      <th>variable</th>\n      <th>value</th>\n      <th>Segment</th>\n      <th>order</th>\n      <th>percentage</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>5</th>\n      <td>Asian</td>\n      <td>ETHNICITY</td>\n      <td>4062.0</td>\n      <td>Majority</td>\n      <td>0</td>\n      <td>0.502101</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>White</td>\n      <td>ETHNICITY</td>\n      <td>2477.0</td>\n      <td>Minority</td>\n      <td>0</td>\n      <td>0.306180</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Biracial</td>\n      <td>ETHNICITY</td>\n      <td>901.0</td>\n      <td>Minority</td>\n      <td>0</td>\n      <td>0.111372</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Latinx</td>\n      <td>ETHNICITY</td>\n      <td>472.0</td>\n      <td>Minority</td>\n      <td>0</td>\n      <td>0.058344</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Black</td>\n      <td>ETHNICITY</td>\n      <td>178.0</td>\n      <td>Minority</td>\n      <td>0</td>\n      <td>0.022002</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Female</td>\n      <td>GENDER</td>\n      <td>4857.0</td>\n      <td>Majority</td>\n      <td>1</td>\n      <td>0.598005</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Male</td>\n      <td>GENDER</td>\n      <td>3177.0</td>\n      <td>Minority</td>\n      <td>1</td>\n      <td>0.391160</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Other</td>\n      <td>GENDER</td>\n      <td>54.0</td>\n      <td>Minority</td>\n      <td>1</td>\n      <td>0.006649</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Genderqueer</td>\n      <td>GENDER</td>\n      <td>22.0</td>\n      <td>Minority</td>\n      <td>1</td>\n      <td>0.002709</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Transgender</td>\n      <td>GENDER</td>\n      <td>12.0</td>\n      <td>Minority</td>\n      <td>1</td>\n      <td>0.001477</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>No</td>\n      <td>DISABILITY</td>\n      <td>7547.0</td>\n      <td>Majority</td>\n      <td>2</td>\n      <td>0.929205</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Yes</td>\n      <td>DISABILITY</td>\n      <td>575.0</td>\n      <td>Minority</td>\n      <td>2</td>\n      <td>0.070795</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get numbers\n",
    "# count_df_merged.groupby('variable').\n",
    "count_df['percentage'] = count_df['value'] / count_df.groupby('variable')['value'].transform('sum')\n",
    "count_df"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-22T09:24:53.971459900Z",
     "start_time": "2024-01-22T09:24:53.907320100Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## OVERALL PERFORMANCE"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 799,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- Train Performance Overall ---\n",
      "{'Accuracy': 0.5554809467852946, 'Balanced Accuracy': 0.5534386689627545, 'AUROC': 0.582319068288405, 'AUPRC Macro': 0.55367662083067, 'AUPRC Micro': 0.55367662083067, 'Confusion Matrix': array([[1855, 1296],\n",
      "       [1352, 1454]], dtype=int64), 'F1 Macro': 0.5534574786706589, 'F1 Micro': 0.5554809467852946, 'F1 Weighted': 0.5551983676969664, 'Precision': 0.5535747370808175, 'Recall': 0.5534386689627545, 'Kappa': 0.1069942344050292}\n",
      "\n",
      "--- Validation Performance Overall ---\n",
      "{'Accuracy': 0.48613678373382624, 'Balanced Accuracy': 0.4866422711726579, 'AUROC': 0.4690597484599063, 'AUPRC Macro': 0.4800898737612037, 'AUPRC Micro': 0.4800898737612037, 'Confusion Matrix': array([[336, 203],\n",
      "       [353, 190]], dtype=int64), 'F1 Macro': 0.4766070881706061, 'F1 Micro': 0.48613678373382624, 'F1 Weighted': 0.47634600062092886, 'Precision': 0.48556191995627396, 'Recall': 0.4866422711726579, 'Kappa': -0.02668810179638692}\n",
      "\n",
      "--- Test Performance Overall ---\n",
      "{'Accuracy': 0.5078485687903971, 'Balanced Accuracy': 0.4796443161827777, 'AUROC': 0.4823175784714246, 'AUPRC Macro': 0.3686948688724948, 'AUPRC Micro': 0.3686948688724948, 'Confusion Matrix': array([[401, 275],\n",
      "       [258, 149]], dtype=int64), 'F1 Macro': 0.47967657756324455, 'F1 Micro': 0.5078485687903971, 'F1 Weighted': 0.50974906026207, 'Precision': 0.47995640908179915, 'Recall': 0.4796443161827777, 'Kappa': -0.04037582208828905}\n"
     ]
    }
   ],
   "source": [
    "# Overall performance\n",
    "print(\"\\n--- Train Performance Overall ---\")\n",
    "train_predictions = pretrained_model.predict(np_train[0])\n",
    "train_performance = simclr_utitlities.evaluate_model_simple(train_predictions, np_train[1], return_dict=True)\n",
    "print(train_performance)\n",
    "print(\"\\n--- Validation Performance Overall ---\")\n",
    "validation_predictions = pretrained_model.predict(np_val[0])\n",
    "validation_performance = simclr_utitlities.evaluate_model_simple(validation_predictions, np_val[1], return_dict=True)\n",
    "print(validation_performance)\n",
    "print(\"\\n--- Test Performance Overall ---\")\n",
    "test_predictions = pretrained_model.predict(np_test[0])\n",
    "test_performance = simclr_utitlities.evaluate_model_simple(test_predictions, np_test[1], return_dict=True)\n",
    "print(test_performance)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-11-28T17:41:43.369282Z",
     "end_time": "2023-11-28T17:41:48.751020Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 800,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlAAAAHoCAYAAACGk9HAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzde1yUZd4/8M9wPgsCBoiUykE8L7p4KDINyrTNNE+r2T5tm5Y/D7lRmpZrutWalubumllZ5pOataam27OJUZqaqISRmLgKJggyznA+DDBz//6Y5mYGhjncMwwz+Hm/XvM8981c13VfsCTfuQ7fSyYIggAiIiIisphbZ3eAiIiIyNUwgCIiIiKyEgMoIiIiIisxgCIiIiKyEgMoIiIiIisxgCIiIiKyEgMoIiIiIisxgCIiIiKyEgMoIiIiIisxgCIiIiKykkdnd4CIiIi6jqVLl2Lfvn3i/YIFC7Bw4UKz9crKyrB7925kZmaiuLgYKpUKYWFhGD58OB555BEkJydb1Y+MjAwcOHAAubm5uHnzJgICAhATE4O0tDTMmDEDgYGBVn9v+mQ8C4+IiIjs4ejRo3jyyScNvmZJAHX48GEsX74cVVVV7ZaZPn06Vq1aBXd3d5Nt1dTUID09HZmZme2WiYiIwIYNG5CUlGSyLVM4AkVEREQ2q6mpwcqVKwEAfn5+qKurs6jeiRMnsGTJEjQ1NQEA7rnnHowbNw6+vr7Iy8vDp59+ipqaGuzZswcAsGbNmnbbam5uxqJFi3D8+HEAQFhYGKZNm4bY2FhUVlbi4MGDyM7ORmlpKebNm4edO3ciLi5O0vfLESgiIiKy2cqVK/HJJ58gIiICDzzwAD744AMApkegVCoVxo8fj+vXrwMAXnrpJTz66KMGZQoKCjBnzhzI5XIAwLZt23DnnXcabW/nzp14+eWXAQCxsbHYvn07wsLCDMqsXbsW27ZtAwAkJSVh165dkr5fLiInIiIim3z//ffiCNGqVavg7+9vUb3PPvtMDJ7Gjh3bJngCgN69e4sjWwCwadMmo22p1Wps3rxZvH/99dfbBE8AkJ6ejsTERABAdnY2jh07ZlFfW2MARURERJLV19fjxRdfhCAImDBhAsaOHWtx3X//+9/i9eOPP95uudTUVPTs2RMAkJOTg6KiojZlsrKyxFGq5ORkDBgwwGhb7u7umDNnjnh/6NAhi/urj2ugOlhJSQm+/vpr8T4mJga+vr6d2CMiIrJUfX09fvnlF/F+3LhxiIyMtOszqqurcfHiRbu2CQAJCQk27zSzxBtvvIFr166hW7duWLFihcX1ampqkJ2dDQDw9/fH8OHD2y3r5uaGlJQU7N69G4B2sfqsWbMMyuiPJN19990mnz1mzBjx+ujRoxb3WR8DqA729ddfY/Xq1Z3dDSIispPZs2fbtb2LFy/avU0A+Pjjj00GJfaQnZ2Njz/+GIA2fYGxKbP2XL58GRqNBgDQv39/s7vrBg0aJAZQly5davN+fn6+QVlTwsLCEBkZiZKSEigUCiiVSnTv3t3ivgMMoIiIiLokW0a1oqKiEBUVZbKMSqXC8uXLodFoMGrUKDzyyCNWPaOgoEC8jo6ONltev4x+XVvaKykpAQBcuXKFARQRERHBptkPS3I3vfXWWygoKICPj4+kZ+nnfAoJCTFbPjg42Ghdnerqasnt6de1FAOoDtarVy+D+5ee0SChbyd1hqgDzfj64c7uApHdyW4q4HHsuHjf+t/0jvDMXA2SBlmfYSg7V4aNWx2zNyw3NxcffvghAGDhwoWIiYmxug39PFFeXl5my/v4+IjXtbW1Jtvz9vY2255+GWPtmcMAqoP5+fkZ3Cf0BYYN7qTOEHUg4UJEZ3eBqMO1/je9IyQNEiT+nXBMWsfGxkYsX74carUa/fv3N7l7zlIymcwOPXMsBlBERERORCMIUEvIca0RDIOQlStXIiEhQVIfTK1/evvtt5Gfnw93d3f89a9/Nbv4uz36wahKpTJbvqGhQbw2lmfKz88PlZWVYnseHqZDHP1nWpq3Sh8DKCIiIiciQIBGwmhS6xoJCQl234X3888/49133wUA/M///E+7uZYsERQUJF6Xl5ebLV9RUWG0rk5gYKAYQJWXl5sNivTbk5LugQEUERERWWTv3r1oamqCm5sbPD09DTJ/6zt9+rTBta5c79698cADD4jXOsYSY7amX0a/rv7XdGWKiorM7sTTb69Pnz5mn98aAygiIiInooFG0giUBh2/jkh3fK5Go8GWLVssqnPq1CmcOnUKAHDvvfeKAVTfvn3h5uYGjUaDvLw8qNVqk9OBubm54rWxA4Dj4+PFZJq5ubkYOXJku23dvHlTTGEQGhpqdQoDgEe5EBERORUNAPWv66CseWk6u+NWCggIQFJSEgDtLrizZ8+2W1aj0eC7774T741lGk9JSRGvzWUX//bbb8Vr/azk1uAIFBERkRPRSFwDJaWOtVasWGHRcS1///vf8Y9//AOA6ZxSEyZMwJkzZwAA27ZtQ3JystFyGRkZ4pTb0KFDjU7PJScnIzw8HHK5HFlZWTh//rzRNVpqtRo7duww6IMUHIEiIiKiTjF16lRxx19mZqZ4LIy+wsJCg0SdixcvNtqWu7s75s+fL94vXboUCoWiTbn169fjwoULAICkpCSDkStrcASKiIjIiWggQO2kI1D25u3tjVdeeQVz585FU1MTVq9ejWPHjmHcuHHw9fVFXl4ePv30UzFT+PTp0zF69Oh225s+fToyMjJw/PhxXLp0CZMmTcK0adMQGxuLiooKHDp0SJwqDAwMtClbOwMoIiIiJ+LMU3gdYfTo0diwYQOWL1+OqqoqZGZmIjMzs0256dOnY9WqVSbb8vDwwKZNm5Ceno7MzEzI5XKjOwUjIiKwYcMGo4vRLcUAioiIiDpVWloahgwZgl27diEzMxPFxcVQqVQIDw/HsGHDMHXq1HbXR7UWEBCALVu2ICMjA/v370dubi4UCgX8/f0RExODtLQ0zJw5U1LuJ30MoIiIiJyIRoDETOQd0BmJFi5caPYw4tZ69OiBxYsXt7vGyVqpqalITU21S1vGMIAiIiJyIgIgKSWBE8VPtwTuwiMiIiKyEkegiIiInIha4i48KXVIOgZQRERETkS7BkpaPXIcTuERERERWYkjUERERE5EA2mLyF3tLDxXxwCKiIjIiWgggxoySfXIcRhAERERORFBkLaeSULqKLIB10ARERERWYkjUERERE5EDUiawlPbvytkAgMoIiIiJ8I1UK6BU3hEREREVuIIFBERkRMRIINGsH40SeAIlEMxgCIiInIiaolTeFLqkHScwiMiIiKyEkegiIiInIh2Ebn14xtcRO5YDKCIiIiciEaQSUqkKWXdFEnHKTwiIiIiK3EEioiIyIlop/Ck1SPHYQBFRETkRDSCDGoJ03FSpv1IOgZQRERETkQDN2gk1SNH4hooIiIiIitxBIqIiMiJSF8DRY7EAIqIiMiJSF8DJQDgQihH4RQeERERkZU4AkVERORENJBJSkmgncLjCJSjMIAiIiJyItKPcuEqKEfiFB4RERGRlTgCRURE5EQ0ghvUgoQRKAGApP17JAUDKCIiIieiXQMlZQqP658ciVN4RERERFbiCBQREZET0UBiHigeJuxQDKCIiIiciBpuknbhqTmF51AMoIiIiJyIIMigkbCIXBCYxsCRuAaKiIiIyEocgSIiInIiGolTeFJ27pF0DKCIiIiciFriYcJS6pB0DFeJiIiIrMQRKCIiIiciSEykKTCNgUMxgCIiInIi0o9y4aSSI/GnTURERGQljkARERE5EQ2kZRVnFijHYgBFRETkRDSQOIXHSSWH4k+biIiIyEocgSIiInIiasgknoXHXXiOxACKiIjIiWjPwrM+GBKYSNOhGEARERE5ER7l4hr40yYiIiKyEkegiIiInIhGkElKiill2o+kYwBFRETkRDSQSVoQLiV3FEnHAIqIiIis8uOPPyI3Nxe5ubm4dOkSysvLUV5ejqamJgQFBaFv374YMWIEpkyZgqioqHbbOXXqFB577DGLnzt58mT87W9/s6hsRkYGDhw4gNzcXNy8eRMBAQGIiYlBWloaZsyYgcDAQIufawwDKCIiIieiEdwkTuE5blnzH/7wB9TV1Rl9T6FQQKFQICsrC++88w4WLFiAefPmOaxvNTU1SE9PR2ZmpsHXlUollEolcnJysGPHDmzYsAFJSUmSn8MAioiIyIloIC2nk6OPcgkNDcXgwYMRGxuLsLAwhIeHQxAEFBcX45tvvkF2djYaGxvx5ptvoqmpCQsWLDDZ3oQJEzBx4kSTZSIjI02+39zcjEWLFuH48eMAgLCwMEybNg2xsbGorKzEwYMHkZ2djdLSUsybNw87d+5EXFycdd/4rxhAERERkVU++eQTxMXFQSYzHujNmzcP+/btw7JlyyAIAt5++21MmzYNt912W7tt9unTB6mpqTb1a8+ePWLwFBsbi+3btyMsLEx8f/bs2Vi7di22bduGqqoqrFy5Ert27ZL0LKYxICIiciIauInTeFa9HPgnPT4+vt3gSefhhx/GPffcA0A7MnTs2LEO7ZNarcbmzZvF+9dff90geNJJT09HYmIiACA7O1tyvxhAERERORGNIINacLP65YxpDPSnxxQKRYc+KysrC3K5HACQnJyMAQMGGC3n7u6OOXPmiPeHDh2S9DwGUERERNQhrl69Kl4bGw2yJ/2RpLvvvttk2TFjxojXR48elfQ8roEiIiJyIgJkknI6CU6WByojIwOHDx8GAPj4+IjTee356quvkJGRgWvXrqGpqQndunVDfHw8Ro8ejalTpyI4ONhk/fz8fPF60KBBJsuGhYUhMjISJSUlUCgUUCqV6N69u2Xf2K8YQBERETkR3ZSclHr6Ll68KLkPUVFRJvM36Tt9+jQqKysBAI2NjSgtLcV3330nLub29PTE6tWrERoaarId/QAIAORyOeRyOY4fP45//vOfWLFiBaZOndpu/YKCAvE6OjrabL+jo6NRUlICALhy5QoDKCIiIlcmCNKOZREEw/vVq1dL7sOCBQuwcOFCi8quW7cO586da/N1mUyGESNGYNGiRRg2bFi79WUyGfr3748RI0agT58+CAwMRF1dHfLz8/Hll1+itLQUdXV1WLFiBZRKJebOnWu0nerqavE6JCTEbL/1R7T061qKARQRERHZXUREBEaOHGlyNKh379748ssv0bt3b6PvP/vss3jzzTexbds2AMCbb76J5ORkDB06tE1Z/cSe3t7eZvunX6a2ttZs+da4iJyIiMiJqOEm+dUZ9uzZg4sXL+LixYv44YcfsG/fPixcuBBVVVXYuHEjHnrooXZTBfTo0aPd4AnQTv8tXboUU6ZMAQAIgmCQqqAzcQSKiIjIiQiQSZvCa7WIfOXKlUhISJDUB0vXP7Xm5+eHxMREJCYm4qGHHsKsWbMgl8vx9NNP47PPPkO/fv0ktfvMM8/g888/hyAIOHnyJBoaGuDj49Pm2bq1WCqVCh4epkMclUolXvv7+1vdJwZQ5BS2/CUKn7/bQ7y/LboRH2XlWVz/h2MBOPxpd/yc7YebJZ7w9BYQHtmEYWOqcf/vFYiJU5lv5FeVCnfk/+iH/JxfXz/6QXnDU3z/9c/+iyGjayxuj24NHsoG+F2shO/laniV1MGjXAU3lQYaHzc0d/NGwx0BqE4KQ0NskNm2Ypd8L7kfVb8NQ9msWKPv3b46G57ljRa3JbgBl98YKbkv1LkSEhIwfPjwTnt+TEwMnn32WSxbtgxNTU3YsmULNm7cKKmt2267DbfffjsKCwvR2NiIoqIixMYa/p4HBgaKAVR5ebnZoKiiosKgrrUYQFGn+/kHP+x/P1xS3dpqN7z1XC98e8BwwaCqHqip8EDBBV/sez8Mc9JLMXNhmdn2nvldHC6ctf6TCN26vIpq0ePTK/D5xfgaCvc6Ndzr6uBdUoduJ8tQFxuEsll90Rxifo2GFOpArw5plxxHA5mkrOJSUh90NP18S1lZWTa1FRISgsLCQgBAVVVVm/d79+6NoqIiAEBRUZHZnXi6soD2GBlrMYCiTtXcBGxM7wWNxvr/8JubgNVP9EbOdy2fHO7oV4+4wfVQ1cvw06kAKMs80dzkhg9ei0JzkwyP/vmGyTb1R5qILOFVVt8meGoM90FjpB/U/h5wq2+GT2ENPCu0Iz9+/61C9Fs/oWjBADSH+RhrEhV3tX9eWGveJXXwvdyyg6h6uGXJCquGh0Hj4266kJmjOqhjaDORSzhM2AkzkQcEBIjXxoIea5SXl4vXQUFtR3Lj4+PFtVa5ubkYObL90dObN2+KKQxCQ0OtTmEAMICiTvbJP25DwQVfAMDYyUpkfm75L/HHGyLE4MnLR4Nn3/wF9zzcMiTb1CjD9rUR+PRt7R+jHesjMXhUDQaPMr3bwsNTgzsSGhA/tA7xQ+oQP7QO89OkzdvTraMxzAdVI8NRPSwc6uBWo0AaAYFZcoR/Xgi3Rg08KpsQ8b//RdHiAUaDlJuPtL+otrWID1py5zRE+6Mx0s+iesoHotHc3XgAR2QvuhEjAJKCFJ3S0lIxq7mXlxd69uzZpkxKSgref/99ANrs4k8++WS77X377bfitf4omTUYQFGn+eWSN3Zt0gY346Yo8ZuUGosDqIqbHti7tWXa76mXiw2CJwDw9BLwp5dKUFbsJU7xbXs1Chu/uNRuu6s+vILoPip4+QjtliHS1xzkhRu/74Pq4eGAWzsjAG4yVI/sAY2fByJ/DXh8rtbA72Il6vqZzq5siltdM/zyWj6VV/+2Y4/KIMcQBImLyJ1wBGr37t3idVJSkuR23nrrLQi/JroaMWIEfH1925RJTk5GeHg45HI5srKycP78eaPn4anVauzYsUO8nzBhgqQ+dek0BhkZGVi0aBHGjh2LQYMGYdSoUZgxYwbee+89SUmzyH4EQTt116RyQ0BwM+atum5V/cN7QtBQp51+iO7TgAmPtn9I5Z9evA43N+1/eBfO+uO/uW3/w9Pp07+BwRNZpSE2CNXJPdoPnvTUDu6OhpiWNXZ+eRUmSpsX8MNNuDVrf18FdxmqkxhAdQUaQQaN4Cbh5ZgAateuXfj+++/FgMYYtVqNrVu3YufOneLXZs2aZVDm6tWrePfdd1FT0/6mnKamJqxduxZ79+4VvzZ//nyjZd3d3Q3eW7p0qdEDjNevX48LFy4A0AZ1KSkp7T7flC45AlVTU4P09HRkZmYafF2pVEKpVCInJwc7duzAhg0bbIqISbovPgzD+dPaufEnX7yO4LBmq+qf+L9u4nXaDKXJpRo9opsw9K5qZB/Vzpkf/7IbYgfVW99pIjto6B0orpnyVFq+O9SYoNM3xeva/sHQBHANH3W8c+fOYdWqVYiMjMTo0aMRHx+P0NBQeHp6orq6Gvn5+Thy5AiKi4vFOvPmzUNycrJBO3V1dVi/fj02bdqEkSNHYtCgQYiOjoa/v79BJnLdWiUAWLJkicm/29OnT0dGRgaOHz+OS5cuYdKkSZg2bRpiY2NRUVGBQ4cO4ezZswC0O+9sydbe5QKo5uZmLFq0SDyDJywsTPzhVVZW4uDBg8jOzkZpaSnmzZuHnTt3Ii4urpN7fWuRX/fEB69FAgAGjqjB/b9XWlW/sUGGn7NbPsUPHmU+pcDg0TViAHXueICZ0kQdR9CP9jXSRzs9y+rhc7Xld7/6t9J2spLz0UAGtYQddY7ehVdSUoJ//etfJssEBgbiz3/+c5vRJ32NjY04evQojh492m6ZgIAAvPDCCybPwgMADw8PbNq0SRxEkcvlRhNvRkREYMOGDTb9/e9yAdSePXvE4Ck2Nhbbt29HWFjLsPbs2bOxdu1abNu2DVVVVVi5ciV27drVWd29Jf19WTTqatzh6aXB4tevWb3R59plb3HXnkwmIHag+dEk/RGnX/7LhbPUebyvtxw30dx6sbkVAk/LxWu1vwdq+1u3lsr7Wi38c8vhUdkIyAC1nycao3xR3zsQgk+X+9PgUjQS10A5agrvpZdewsSJE3H69Gnk5OSgrKwMSqUStbW18PX1RWhoKBISEpCSkoLx48e3m2Opb9++eO+993Du3DmcO3cOxcXFqKioQFVVFTw9PRESEoJ+/frhzjvvxKRJkwx29JkSEBCALVu2ICMjA/v370dubi4UCgX8/f0RExODtLQ0zJw5U1LuJ31d6r8StVptEGm+/vrrBsGTTnp6Ok6ePIkLFy4gOzsbx44dkzwHStbJ/DwYpzK002/T5pdZleBSp+hySwAUHNZs0ZqlHj1bkgdWl3ugQuGO4FC11c8msoVHuQq+/60U7+viu5kobYIgIPBsy/RddVIY4G7dktbID41vptB4ylA9PBzK8dFQBzGnVGfQZiK3foly60zkHcXf3x8pKSk2/9308vKySzvtSU1NRWpqaoe0DXSxReRZWVmQy7WfypKTk42uvge0C83mzJkj3h86dMgh/bvVVSndseUv2q2nPfs04PeLTedkared8pbcNcHhlq2dCmlVrrq8S312IBcRtu8qZBrtdVOIF2oHmj8x3hjfS1UGGcXtOX3n1iSg28ky9FqfC58r3GxD1J4u9VdE/7DCu+++22RZ/bwPpuZdyX62rOqJipvaRa6L1hbBy1va+o+G2pa439tHY1Gd1uUa6rrUZwdyAYFZcgT82LLeTzExBvCQ9nuoP32nivSDqpdl2fMFdzfUDAxB7cAQqHoFoKm7NwRPN22yz2u1CDwtR0COAjIB8KhuQuR7P6PomYFo6tH+zlWyP20mcudfA3Wr61IBVH5+S0K5QYMGmSwbFhaGyMhIlJSUQKFQQKlU2pTki0w7+00gjnym/fmmTVdi6J3Sz5JrVLX80fHwtCwI82wVrKnqGUCR43j/UoPwz66I99W/CUXNMGkpB2QqtUEgZk3up6JnBkLj3/affU2AJ+oSg1GXGIzq34Yh4oN8uDUJcK9XI/yzAlyf319SX0marpSJvCvrUn9FCgoKxGtzZ+C0LnPlyhUTJckWDXVueGup9mcdFNKMJ18qNlPDNC/vltGk5ibL/sFoUhmW8/a1bOSKyFYeigZEvncRbk3aIF4V6Qf5NMszjbcWcE4Jt0bt76/gBlRbEYgZC55aq0sMgXxKS//8LlXB+xcenk3UWpcKoPSTY4aEmF9bEBzcsmuFiTU7zgd/i8SNa9qDU+f+pRjdbFy87ePfEvyoGiz7FW5dzsePARR1PPfKRkRtuQCP6iYAQFOoN67P6weNr/TBf/3pu7p+wR2y0Ls6ORxNIS3t+v1sW8JPso6zJ9IkrS4VQNXVtWwP9vY2f9K5fpnaWtPno5E0l370xYFt2k/IQ+6sRtr0cjM1zAsKaQnAKuSW/SEqb1UuMMS6xJ1E1nKrbULUlgvwuqndadoc5InipxOh7iY94PEoV8H3csuBrFUdlfvJTYb6uJYdgl43mHjWkXRHuVj7csajXLqyLrUGipxPwQVfMWdTWbEXFj/YftKySkXLr6OyzMOg7KxnbmBEqvYPR3TfBvHrFTc90NggM5vKoKy45Y9WYEgzUxhQh5I1NCPqnZ/hXaoNPNT+Hrj+VCKaQ23LQRZ45iZkv/6qq/3cJe/is0RzUEtWc/dafuAgaq1LBVB+fn6orNTmWFGpVPDwMP3tqVQtOYj8/S3bxULSlRR6o6TQ/MggADQ1uhlkG9cPrnr1VcHNTYBGo/3Edfm8LxKH1RlrRqR//l1MbIOJkkS2kanUiNp6ET7XtKPaah93XJ/XD42Rfja3rT99V/ObMMm7+CzhpmqZ5tZ4danJCqfHXXiuwaIAat++fR3dDwDAww8/bFP9wMBAMYAqLy83GxRVVLTM69uakZQcx8tHQL+kWuSd0Wal/fFEgNkAKvdkSwbbITbsACQyRdakQeT7F+FboF1TqfFyQ8mT/aDqZfvxQd6F1fCStwT/VVbsvpP0vOKWZQ3NTKjpUNpEmtYHQ45KpElaFgVQy5Ytg8za8zYksDWA6t27N4qKigAARUVFZnfi6coCQJ8+fWx6Nhl33wwl7pth2Vl3X33SHW8siQEA3BbdiI+y8totO3p8pRhAfbWnO2YsLGu3rPy6J374riVAHj2+st2yRJKpNYj4IB9+l7RTzRoPGUr+mICGPvb5cBakN/rU2MMHqts77kOfZ1k9fApbNtY09A3qsGcRuSqrxmUFQeiwlz3Ex8eL17m5uSbL3rx5UzzhOTQ0lDmgXEza9HL4+GnXMRVd9sGXH7f/v997f42CRq39AJA4rBZxg7kgluxMIyBix3/hf0E7qi24yVD6hzjUJ0g8qqW1Zg0CchTirZTF4zKVZev+ZE0a9Nh1WcyYrvbzQG2idefskW24C881WBVAyWSyDnnZi/55Ouayi3/77bfitX5WcnINwWHNmDK35RP52y9F49sDhv/INzXK8P4rkfhmX8tC2z8uv+6wPtItQhDQ45MrCDinHWkVZMCN2X1RN9B+H8r8fyqHe51abL96uPXTd9Ebf0LYvkJ4FbW/49i7sBo93/oJvoUt09zK+6Mh+Li3W4fsj7vwXEOXWkSenJyM8PBwyOVyZGVl4fz580bPw1Or1dixY4d4P2HCBEd2k+xk9pJS5J3xR853gVA1uOHVp+7ArrfqETuoHo0NMuSeCoDyRstOojnpJRg8ynS6ipP/CcJH6yJNltmQ3gu+rfJIjbyvEn94vlT6N0MuK+j4DQRltQTzTWE+8Cmohk+B+dxyGj8PKB/oZf4ZetN39fHdoA62bDOGPjeVGsHfliL421I0B3iiMcoPzUGe2qNcGtTwLqo1WGMFAJUje6Dy7girn0W24SJy12BxAGWvabaO5O7ujvnz5+Pll18GACxduhTbt29HaGioQbn169fjwoULAICkpKQOOwmaOpaHJ7DyvQJsfK4Xjn6hHWUquOCLggu+rcpp8Oizpfj9ovbXSelUV3jgSp7pc7+M7STsO5DTgrcqj5omg3sveUObQKQ9TSFeZgMo9+om+P3csm7PHrmfPGqa4JHf/lpAtY87lBN7ofIuBk9E7bEogFqwYEFH98Nupk+fjoyMDBw/fhyXLl3CpLcWsOAAACAASURBVEmTMG3aNMTGxqKiogKHDh3C2bNnAWh33q1evbqTe0y28A/SYMU7V/HAbAUyPu2OvLP+UN7wgIcnEB7ViGFjqjF+lgIxcSrzjRE5oYCzNyHTaD/Aqn3cUTtI2tRg8cIB8Cmshk9BDbyLauFe0wT32ma4Naih8XKDOsATqmg/1Md1Q/WwMAjenLbrLLopPCn1yHG6XADl4eGBTZs2IT09HZmZmZDL5di8eXObchEREdiwYQPi4tpP7EiOZc2OvdaS7q5B0t22pyewpQ90a1KO7wXlePPTcFJV3hOJyntMTytbojnEGzUh3tr8UeTUNJB2MDAPqHKsLrUGSicgIABbtmxBRkYG9u/fj9zcXCgUCvj7+yMmJgZpaWmYOXMmcz8RERGRJF0ygNJJTU1FampqZ3eDiIjIYpzCcw0dHkCpVCpUVFRArVYjPDwcnp6e5isRERHdojQSAyjmgXIsuwdQarUa+/btw5EjR3DmzBlUV7ds5f34448xZMgQZGdnG9SJi4tDcDATtREREZFrsGsAderUKbzwwgtihm/91Ae6hJnu7u545ZVXcPHiRfG9xx9/HM8//7w9u0JEROSSBIl5oHgWnmPZ7YjtvXv34oknnsD169fF41nayzQ+c+ZMg2NcDh06ZK9uEBERuTSNAEmZyDXOn66xS7FLAHX69GmsXLkSzc3NFh3Rcv/998PNzU0sU1ZWhry89g+OJSIiInImNgdQgiBg1apVYvDU+j1jQkJCMGTIEIP3v//+e1u7QkRE5PI0kHYWHo9ycSybA6iMjAxcvnzZIHgSBAHe3qbPahoyZAiAlrVR58+ft7UrRERELo+HCbsGmwOozMxM8Vq37mnFihU4ffq0yXr6GcAFQcDly5dt7QoREZHLk7b+SVrqA5LO5l14OTk5kMlkYvB0//33Y86cOWbrtT7gt6zM/EGvRERERM7A5gBKoVAY3N97770W1fP1NTzxvqbG9nPMiIiIXJ4gMas4d+E5lM0BVG1trcF965Gl9rQOvEzt2iMiIrpVaCTmgeIicseyeQ1UUFCQwb1cLreo3i+//GKyHSIiIiJnZXMA1b17d4P7I0eOmK0jCAL27t1rMOoUHh5ua1eIiIhcHnfhuQabA6hBgwaJC8gFQcCRI0dw+PBhk3U2btwojkDp6g4ePNjWrhAREbk8AdpgyOoXp/AcyuYAauTIkeK1TCZDc3MzFi9ejCeeeKJN2Z07d2LSpEnYunVrmzVPo0aNsrUrRERERA5hcwD1wAMPGCwcl8lk0Gg0OHHiBICWbOS6M+8uXrzYJkN5jx49kJqaamtXiIiIXB7PwnMNNgdQXl5emD9/vkFQpJvOa033Nd3ok276bsGCBXB3d7e1K0RERK5PyvSdIAO4Bsqh7HKY8OzZs3H//fe3CaJaM3bI8Pjx4zFt2jR7dIOIiIjIIewSQAHAG2+8gUceeaRNEKX/0hEEAYIgYPLkyVi3bp29ukBEROTyeJSLa7BbAOXh4YFXXnkF7777rnhQsC5Q0n8B2oOEt27ditdeew0eHjbn8iQiIuoyBACCIOHV2R2/xdg9eklJSUFKSgqUSiXOnDkDuVyO6upqBAYGIjw8HMOGDbM4WzkREdGtRpCYiZxpDByrw4Z/unfvjvvuu6+jmiciIiLqNJw/IyIiciLirjoJ9chxOiSAUqvVOH/+PH766ScoFArU1tbC398foaGhGDhwIAYMGMC0BUREREZoBEAmIRhiHijHsmsAVV1djffeew+ffvopysvL2y0XEhKCadOm4U9/+hMCAwPt2QUiIiKiDme3XXinTp3CxIkTsXXrViiVSqM78HQvpVKJrVu34sEHH8SpU6fs1QUiIiLXJ2UHngBuw3MwuwRQJ06cwNy5c1FWViZmFzf3EgQBN27cwNy5c3Hy5El7dIOIiMjl8TBh12BzAFVZWYnnnnsOKpXKaKbx9ujKqlQqPPfcc6iqqrK1K0REREQOYXMAtXPnTigUCoPz7XTaS6TZupxCocDOnTtt7QoREZHLkzT6JHHnHkln8yLyjIwMg3vd9Jyvry+Sk5PRu3dv+Pr6or6+HoWFhTh16hTq6+vbHO3y1Vdf4amnnrK1O0RERC5NkHgsCwMox7I5gLp27ZrB6JNMJsP06dPx/PPPIyAgoE352tparFu3Drt37zZYD1VUVGRrV4iIiIgcwuYpvKamJgAtwdOYMWOwevVqo8ETAPj7+2PVqlW45557DKbxdO0QERHdyiSdg6fbiUcOY3MAddtttxncT5482aJ6rcv16NHD1q4QERG5PO1hwlJ24ZEj2TyFN2rUKBQWFor33bp1s6iefjmZTIbf/va3tnaFiIjI5QmCDHDyNVA//vgjcnNzkZubi0uXLqG8vBzl5eVoampCUFAQ+vbtixEjRmDKlCmIioqyqM2ysjLs3r0bmZmZKC4uhkqlQlhYGIYPH45HHnkEycnJVvUxIyMDBw4cQG5uLm7evImAgADExMQgLS0NM2bMsDmRt80B1IwZM7Bnzx5oNBoAwKVLlzBy5Eiz9S5dugSgZepv6tSptnaFiIiIHOAPf/gD6urqjL6nUCigUCiQlZWFd955BwsWLMC8efNMtnf48GEsX768TUqjoqIiFBUVYd++fZg+fTpWrVpl9ii4mpoapKenIzMz0+DrSqUSSqUSOTk52LFjBzZs2ICkpCQLvlvjbA6g+vXrhz/96U945513AADbtm3DxIkT0b1793brKJVKbNu2TbyfOXMmhg4damtXiIiIugRXmI4LDQ3F4MGDERsbi7CwMISHh0MQBBQXF+Obb75BdnY2Ghsb8eabb6KpqQkLFiww2s6JEyewZMkScS30Pffcg3HjxsHX1xd5eXn49NNPUVNTgz179gAA1qxZ026fmpubsWjRIhw/fhwAEBYWhmnTpiE2NhaVlZU4ePAgsrOzUVpainnz5mHnzp2Ii4uT9P3b5Sy8JUuWoK6uDjt27EBpaSmmTp2KBQsWYNy4cQgODhbLVVRU4MiRI/jnP/+JkpISyGQyjB8/Hi+++KI9ukFEROTypE7hQZA5LBf5J598gri4uHaTZ8+bNw/79u3DsmXLIAgC3n77bUybNq3NummVSoUVK1aIwdNLL72ERx99VHz/oYcewowZMzBnzhzI5XLs2bMH48ePx5133mn0uXv27BGDp9jYWGzfvh1hYWHi+7Nnz8batWuxbds2VFVVYeXKldi1a5ekn4FFAdS9995rUWO6H+T169exYsUKANq1Tro8UJWVlQAMk2jm5OTgvvvug0wma5NTioiIiJxPfHy82TIPP/ww/u///g+ZmZlobm7GsWPH2izX+eyzz3D9+nUAwNixYw2CJ53evXtj5cqVWLhwIQBg06ZNRgMotVqNzZs3i/evv/66QfCkk56ejpMnT+LChQvIzs7GsWPHkJKSYvb7ac2iAKq4uFjM19Qe/ShUv2xFRQUqKiqMlhUEAaWlpeI6KCIiolue1IOBnXDeLy4uTlyLpFAo2rz/73//W7x+/PHH220nNTUVPXv2RHFxMXJyclBUVITo6GiDMllZWZDL5QCA5ORkDBgwwGhb7u7umDNnDpYvXw4AOHTokKQAyqo0BuYOB9Y/rsVUWQAmgzEiIqJbVVc6yuXq1avidevRoJqaGmRnZwPQ5ogcPnx4u+24ubkZBDlHjx5tU+bYsWPi9d13322yX2PGjDHZliXssgYKMBxV0g+OWh/ZQkRERB3v4sWLkutGRUVZnH6gPRkZGTh8+DAAwMfHB/fcc4/B+5cvXxZ38Pfv39/s7rpBgwZh9+7dAFp28uvLz883KGtKWFgYIiMjUVJSAoVCAaVSaXLzmzF2C6B0TE3FcZqOiIjINEH8P7ZZvXq15LoLFiwQ1xyZc/r0aXGNc2NjI0pLS/Hdd9+Ji7k9PT2xevVqhIaGGtQrKCgQr1tPxxmjX0a/ri3tlZSUAACuXLnScQEUR4+IiIg6niBA4i48u3fFIuvWrcO5c+fafF0mk2HEiBFYtGgRhg0b1uZ9/ZxPISEhZp+jv6u/db4oAKiurpbcnn5dS1kUQP38889WN0xERES3roiICIwcObLd0SD9RJxeXl5m2/Px8RGva2trTbbn7e1ttj39MsbaM8fuU3hERERkAxvyQOlbuXIlEhISJHXBmvVPugSXgDaIuXr1Ko4cOYJt27Zh48aN+PDDD7F+/XqTO91ccYkPAygiIiJnIvw6jWclWas6CQkJJne2dQQ/Pz8kJiYiMTERDz30EGbNmgW5XI6nn34an332Gfr162dQVkelUpltu6GhQbz29/c3+mzdWiyVSgUPD9Mhjv4zjbVnjlVpDIiIiMgBBAkvJxMTE4Nnn30WANDU1IQtW7YYvB8UFCRel5eXm21PP6ekfl0d/cOBrW1PysHCDKCIiIioQ+jnW8rKyjJ4r3fv3uJ1UVGR2bb0y+jXtUd7ffr0MVu+NbtP4dXW1iI7Oxt5eXkoLy9HdXW1mOfBFJlMhldffdXe3SEiInIpkpNiOmEizYCAAPG69c65vn37ws3NDRqNBnl5eVCr1SZzQeXm5orXxg4Ajo+PF5Np5ubmYuTIke22dfPmTTGFQWhoqNUpDAA7BlANDQ1466238Mknn6C+vt6qurqjXBhAERHRLa8LHeVSWFgoXrcOUgICApCUlIQzZ86gtrYWZ8+eRXJystF2NBoNvvvuO/HeWKbxlJQUvP/++wC02cWffPLJdvv17bffitf6o2TWsMsUXlVVFWbNmoUPP/wQdXV1Bse6WPIiIiKirkeXORwAkpKS2rw/YcIE8Xrbtm3ttpORkSFOuQ0dOtRoaoTk5GSEh4cD0E4Xnj9/3mhbarUaO3bsMNoHa9glgPrrX/+KvLw8cSTJ2hcRERFpOftZeLt27cL3339vcgBErVZj69at2Llzp/i1WbNmtSk3depUMWVCZmYmPv744zZlCgsLDbKqL1682Ogz3d3dMX/+fPF+6dKlRg8wXr9+PS5cuABAG9RJOUgYsMMU3rVr1/DFF1+0CYRa/2CNvc/giYiIyAgnnpw5d+4cVq1ahcjISIwePRrx8fEIDQ2Fp6cnqqurkZ+fjyNHjqC4uFisM2/ePKPTc97e3njllVcwd+5cNDU1YfXq1Th27BjGjRsHX19f5OXl4dNPPxUzhU+fPh2jR49ut2/Tp09HRkYGjh8/jkuXLmHSpEmYNm0aYmNjUVFRgUOHDuHs2bMAtDvvbDnuxuYA6osvvhCDIf3/31rrA4bbK0dERETOr6SkBP/6179MlgkMDMSf//xno6NPOqNHj8aGDRuwfPlyVFVVITMzE5mZmW3KTZ8+HatWrTL5PA8PD2zatAnp6enIzMyEXC7H5s2b25SLiIjAhg0bjC5Gt5TNAdSPP/5ocB8QEIA1a9bgmWeeMQiqPvzwQygUCnz11Vf4z3/+I5Z98cUXbT7xmYiIqGtx3hmal156CRMnTsTp06eRk5ODsrIyKJVK1NbWwtfXF6GhoUhISEBKSgrGjx9vUY6ltLQ0DBkyBLt27UJmZiaKi4uhUqkQHh6OYcOGYerUqe0uMG8tICAAW7ZsQUZGBvbv34/c3FwoFAr4+/sjJiYGaWlpmDlzpqTcT/psDqAuX75sEChNmTIF48ePb1NuxIgRALSLtQ4ePIjnn38etbW1WLduHT788EObokAiIqIuw8l34fn7+yMlJUXy2qH29OjRA4sXL253jZO1UlNTkZqaape2jLF5EbkubbrOwIEDzdZ58MEHMWnSJAiCAIVCgcWLF1ud+oCIiIios9gcQOmffgxoE1IZo1arDe7T0tIAaNdDFRQU4PPPP7e1K0RERK5PyjEuTnqcS1dmcwDl5mbYhJeXFwDAx8fH4Os3b940Wk7nyy+/tLUrRERErk+QSX+Rw9gcQPn6+hrc605L1j9lGQBOnDhhcK+/+FwQBINspURERLcqAYAgSHh1dsdvMTYHUGFhYQb3urNuIiIiDNIabNy4EWfOnEF9fT0yMzPxwQcfGOSB0j8VmYiIiMiZ2bwLLyIiApcvXxbvdcmu+vbti7y8PPHrN27cwJw5c8T71ok0g4KCbO0KERGR63PyXXikZfMIVGJiosG9XC4HAIwaNapNWf3z73TBk+7akt17REREXR7XQLkEmwOoAQMGAGg5qiU3NxeANv+Cv7+/+J658+8mT55sa1eIiIiIHMLmAEo3cqQbWfrpp58AaKfk5s+fb9FxLbpspURERLc6mSD9RY5j8xqoXr164b333jMIlHTTck888QTkcjm2b98uvt/6DLxx48Zh3bp1tnaDiIioa+AaKJdgcwAFAHfddVe77y1btgwTJ07E/v378dNPP6G8vBz+/v6Ij4/Hgw8+aLIuERERkTOySwBlzqBBgzBo0CBHPIqIiMjFSV0QzkXkjuSQAIqIiIgsxCk8l2DzInIiIiKiWw1HoIiIiJwJR6BcgkUBVOtkmR1BJpMZZC4nIiK6JTGAcgkWBVCW5HIiIiIie+Aicldg8RSesezh9sIAjYiIiFwJ10ARERE5EZnEKTxmIncsBlBERETOhGugXIJFAVRUVFRH94OIiIjIZVgUQH399dcd3Q8iIiIil8EpPCIiIifCNVCugQGUgz26fzJwIrKzu0Fkd7GrT3R2F4jsrsGnFmW9OrsX5IwYQBERETkbSXmgyJEYQBERETkT7sJzCTxMmIiIiMhKHIEiIiJyJhyBcgkMoIiIiJyJIHFHHQMoh2IARURE5GwYDDk9roEiIiIishJHoIiIiJwJ10C5BAZQRERETkRqJnIGUI7FKTwiIiIiK3EEioiIyJkIMmmZyJm93KEYQBERETkTTuG5hA4PoFQqFSoqKqBWqxEeHg5PT8+OfiQRERFRh7J7AKVWq7Fv3z4cOXIEZ86cQXV1tfjexx9/jCFDhiA7O9ugTlxcHIKDg+3dFSIiIpcjAzia5ALsGkCdOnUKL7zwAkpKSgAAgtDyGyCTaedm3d3d8corr+DixYvie48//jief/55e3aFiIjINXEKzyXYbRfe3r178cQTT+D69esQBAGCIEAmk4mBk76ZM2eKZQRBwKFDh+zVDSIiIpcmE6S/yHHsEkCdPn0aK1euRHNzsxg0GQucdO6//364ubmJZcrKypCXl2ePrhARERF1OJsDKEEQsGrVKjF4av2eMSEhIRgyZIjB+99//72tXSEiInJ9gg0vchibA6iMjAxcvnzZIHgSBAHe3t4m6w0ZMgRAy9qo8+fP29oVIiKiroHBk9OzOYDKzMwUr3XrnlasWIHTp0+brBcXF2dQ7/Lly7Z2hYiIiMghbN6Fl5OTA5lMJgZP999/P+bMmWO2XmhoqMF9WVmZrV0hIiJyeTwLzzXYPAKlUCgM7u+9916L6vn6+hrc19TU2NoVIiIiIoewOYCqra01uG89stSe1oGXqV17RERERM7E5gAqKCjI4F4ul1tU75dffjHZDhER0S2Ju/Bcgs0BVPfu3Q3ujxw5YraOIAjYu3evwahTeHi4rV0hIiJyfVKTaDKAciibA6hBgwaJC8gFQcCRI0dw+PBhk3U2btwojkDp6g4ePNjWrhARERE5hM278EaOHInPP/8cgHYdU3NzMxYvXoxRo0a1Kbtz5068/PLLyM/Pb7PmyVh5IiKiW5KTjybV1NTg+PHjOHXqFPLy8lBYWIjq6mp4e3ujR48eGDx4MB588EGkpKSYXON86tQpPPbYYxY/d/Lkyfjb3/5mUdmMjAwcOHAAubm5uHnzJgICAhATE4O0tDTMmDEDgYGBFj/XGJsDqAceeACvv/46lEolAG0QpdFocOLECQAt2ch1Z97p7vV/oD169EBqaqqtXSEiInJ9Tp7G4IMPPsCGDRugUqnavNfc3IyCggIUFBRg//79GD58ONatW4eoqCjHdA7a4C49Pd0gTyUAKJVKKJVK5OTkYMeOHdiwYQOSkpIkP8fmAMrLywvz58/HmjVrxKBIN53XWuvgSTd9t2DBAri7u9vaFSIiIpcn+WBgwTExVEFBgRg8RUREYNSoURg4cCC6d++OhoYGnDt3DgcOHEBdXR3OnDmDOXPmYM+ePWZ36U+YMAETJ040WSYyMtLk+83NzVi0aBGOHz8OAAgLC8O0adMQGxuLyspKHDx4ENnZ2SgtLcW8efOwc+dOg8Te1rA5gAKA2bNnIysrC//5z39MBlHGhvHGjx+PadOm2aMbRERE1MFkMhnuuusu/PGPf8SoUaPg5ma4nHrKlCmYO3cunnjiCRQUFKCoqAjr16/Ha6+9ZrLdPn362DwbtWfPHjF4io2Nxfbt2xEWFia+P3v2bKxduxbbtm1DVVUVVq5ciV27dkl6ls2LyHXeeOMNPPLIIwZBk0wmM3jpCIIAQRAwefJkrFu3zl5dICIicn1OnsZgyZIleP/993HnnXe2CZ50evbsiY0bN4r3X375Jerr6zu0X2q1Gps3bxbvX3/9dYPgSSc9PR2JiYkAgOzsbBw7dkzS8+wWQHl4eOCVV17Bu+++Kx4UrAuU9F+A9iDhrVu34rXXXoOHh10GwYiIiLoEGaSlMXBUOurg4GCLyvXr1w99+vQBANTX1+Pq1asd2S1kZWWJuSiTk5MxYMAAo+Xc3d0Njpw7dOiQpOfZPXpJSUlBSkoKlEolzpw5A7lcjurqagQGBiI8PBzDhg2zOFs5ERERuS5/f3/x2tiic3vSH0m6++67TZYdM2aMeH306FFJz+uw4Z/u3bvjvvvu66jmiYiIuiYn34VnqcbGRhQWFor35nbiffXVV8jIyMC1a9fQ1NSEbt26IT4+HqNHj8bUqVPNjnzl5+eL14MGDTJZNiwsDJGRkSgpKYFCoYBSqWyTGNwczp8RERE5EzsFUBcvXpTchaioKJtTDxw8eBDV1dUAgAEDBpg9cUQ/AAK0R8PJ5XIcP34c//znP7FixQpMnTq13foFBQXidXR0tNn+RUdHo6SkBABw5coVBlBEREQErF69WnLdBQsWYOHChZLrK5VKg01iTz/9dLtlZTIZ+vfvjxEjRqBPnz4IDAxEXV0d8vPz8eWXX6K0tBR1dXVYsWIFlEol5s6da7QdXbAGACEhIWb7qD+ipV/XUjYHUPv27bO1CdHDDz9st7aIiIhckS15oJxBY2MjFi5cKCbYTk1NRVpamtGyvXv3xpdffonevXsbff/ZZ5/Fm2++iW3btgEA3nzzTSQnJ2Po0KFtytbV1YnX3t7eZvupX6a2ttZs+dZsDqCWLVtmMk27NRhAERHRLc+F10BpNBosX74cZ86cAQDExMTg1Vdfbbd8jx49TLbn6emJpUuXoqKiAnv37oUgCNi8eTO2bt1q135LYbcpPGOZx61hryCMiIiIgJUrVyIhIUFSXSnrnwRBwF/+8hd88cUXYhsffPABunXrJqkP+p555hl8/vnnEAQBJ0+eRENDA3x8fAzK+Pn5obKyEoB2x5+5NEn6uwL1dwtaym4BlC0BkK3BFxERUZdihz+LCQkJGD58uO0NWUAQBKxatQp79uwBoD3iZfv27RYt5rbEbbfdhttvvx2FhYVobGxEUVERYmNjDcoEBgaKAVR5ebnZoKiiosKgrrXslkjTGq0TaxIREdGvpCTRdGAm8jbdFQS8/PLL2L17NwBtsPPRRx8hJibGrs/RXxheVVXV5n39dVRFRUVm29Mvo0v4aQ27BFDGMo639wLQ5lgXIiIi+pWTH+Vi0NVfgyfdeXI9evTARx99hNtvv93uzyovLxevg4KC2rwfHx8vXufm5pps6+bNm2IKg9DQUKtTGAB2mMJbsGCB2TJqtRpVVVW4ePEifvjhB6jVagDaQGrMmDEYOHCgrd0gIiIiB2odPIWHh+Ojjz7CHXfcYfdnlZaWikfBeHl5oWfPnm3KpKSk4P333wegzS7+5JNPttvet99+K17rZyW3hkMCKH0lJSV4+eWX8c0330AQBPzwww947rnn0LdvX1u7QkRE5PKkpjGQlPrABqtXr24TPLWXjsBWb731ljhjNWLECPj6+rYpk5ycjPDwcMjlcmRlZeH8+fNGz8NTq9XYsWOHeD9hwgRJfXL4GqjIyEhs3rxZzOFQVVWF5557jlN5REREOk4+fbdmzRrs3LkTgDZ42r59u9XriK5evYp3330XNTU17ZZpamrC2rVrsXfvXvFr8+fPN1rW3d3d4L2lS5dCoVC0Kbd+/XpcuHABAJCUlISUlBSr+q3TKZnI3dzcMGvWLOTk5EAQBFy4cAFffPEFHnrooc7oDhEREVlow4YN+N///V8A2qU4jz32GAoKCgyOUjGmf//+BukR6urqsH79emzatAkjR47EoEGDEB0dDX9/f4NM5Lq1SgCwZMkSJCUltfuM6dOnIyMjA8ePH8elS5cwadIkTJs2DbGxsaioqMChQ4dw9uxZANqdd7Zka++0o1x0Z+LoFpQfOnSIARQREZGTJ9LMzs5ueaQg4I033rCo3muvvYYpU6a0+XpjYyOOHj2Ko0ePtls3ICAAL7zwgsmz8ADAw8MDmzZtQnp6OjIzMyGXy7F58+Y25SIiIrBhwwbExcVZ1Hejz5Jc00anT58Wr3WjUERERLc62a8vKfVcSd++ffHee+/h3LlzOHfuHIqLi1FRUYGqqip4enoiJCQE/fr1w5133olJkyYhICDAonYDAgKwZcsWZGRkYP/+/cjNzYVCoYC/vz9iYmKQlpaGmTNnSsr9pM/hAVRNTQ3279+PLVu2GKQz0N+eSERERM5JfwG2Lby8vJCSkiJ5DZI5qampSE1N7ZC2ATsEUPfee69F5Zqbm1FfXy+eeCwIgkEAZWlkSURE1KU5+RQeadkcQBUXF0Mmk1m9i04XPOkCqX79+tnaFSIiIpfnKmkMbnVOcRYeAEycONFOPSEiIiLqWJ22iFzfb37zGzzyyCOd3Q0iIiLnwNEkp9fphwmPHTu2YNVRFAAAIABJREFUzYJyIiKiW5YLnYV3K7PLCJSl65/c3d3h7++PqKgoDBkyBL/73e8wfPhwe3SBiIioa5C4BooBlGPZHED9/PPP9ugHERERkctwijVQRERE9CumMXAJNgdQ+hnFAW0a9d/85je2NktERHRLkkFiGgO794RMsTmAmjNnjsEC8LFjxxo9d4aIiIioq7B5F56HhzYG0y0kHzt2rK1NEhER3bq4C88l2BxAhYWFGdz37NnT1iaJiIhuWbpM5FJe5Dg2B1CxsbEGaQxUKpWtTRIRERE5NZsDqPHjxwNoOcql9aJyIiIisgKn8FyCzQHUww8/jMTERDGz+K5du1BQUGCPvhEREd16GEC5BLssIt+0aRN69eoFmUyG+vp6PProozhw4ACam5vt0UciIiIip2JzGoMXXngBAHDHHXfg2rVrkMlkUCgUWLp0KdasWYPExESEh4fDy8vLZDsymQyvvvqqrd0hIiJyacwD5RpsDqA+//zzNgcBy2QyCIKA6upqi9ZECYLAAIqIiAhgJnIXYbejXHQ78XTBkC6osvSgYSIiIgIgCJBJ+dvJv7cOZbcASjfqBLQETfqBlCkMsoiIiMiV2PUwYUuCJSIiIjKBU3guweIA6h//+IfB/dSpUxEREQGAI0hERET2wkXkrsGqAEp/hGn06NGIiIjARx991CEdIyIiInJWVk/h6RaJ6yQnJ9u1Q0RERLc0TuG5BLuugSIiIiLbSD0YmIcJO5bNmciJiIiIbjUcgSIiInImnMJzCQygiIiInAh34bkGBlDkMFGBVbgzugjDI68jvrsSkYHV8PNoRm2TJ27U+iPnRgQOXYrD6ZIoi9o6Mvtjq55/sqgn/njwIYvKBnip8Lu4S0jp9QsSQhUI8WmAu5sG1Y1eKKwIRnZpJD6/mICCihCr+kBdm5ubgNsTGhA/tA7xg+sRP7QOvRMb4Oml/Wt47oQ/np8aa5dnjbyvEi9/WGjwtceSE3GjyPS5ozp+gWo8MEuB4WOrcXt8AwJD1KircYP8uhfOfB2Iw592R/EVb7v0lagrkhxA/etf/8KJEyfs2RcsWLDAru2Rc0gMleMvdx/FkNvKjL4f7K5CsI8KCaFKzOifh1PFUXghcxxKagId3FOttN6Xseruo+ju29DmvVDfBoT6lmJYZCn+OCQHu/MG4G8nRqNZ494JPSVnMmp8JZb94xf4+Gk6/Fl+AWoseLVYcv27f1eBRWuLEBisNvi6l7cawaH1iBtUj0eekuPjN2/D7r/3AMc2HIxTeC5BUgAlCAL27t1r774wgOqiegdXtAmeCiq64ZKyO8obfBHkpcLQiFJEBtQCAEb0vI5dkz/Ho/seRlF1kNn2axo9sT8/wWy5K+XBZss80Pe/WHdvBtzdWv4l+qksHNeqgqBSuyM6sBqDepTB20MNdzcBswf+hDDfOjxz+H6zbVPXFhCkdkjwBAB/erEE4VFNkupOnHMTi9a2BF+NDTLknvJHWZEXfPw16D+8FrdFN8HLW8DjL5TCP0iN918xPypM9sNdeK5B8giUvbOP8xiYru9qZTd8diERBy7Foaw2wOA9GQRM6fczlt/5Hfw8m3Gbfy3W3ZuB3++bDHOffitVPvjrdyk29y/QS4WVKUfF4ClPHoaVR8fgvLyHQbkI/xq8cOd3uK9PAQDg/r5XkHrpCjIK+9jcB3J9yjIP5J/zQ36OLy7m+GH4PdWY/ORNu7U/MLkGDzyqAAB8vTcY46ZUWFy3z4B6PL3munh/JjMQb/y5F5Q3PMWvyWQCJs5R4KnV1+HpJWD6/5Mj74w/Tv6nm92+BzJDgLSDgRlAOZTkAMqeAQ+Pguna5HX+eCFzLA7kx0MjGM+cIUCGf/2ciMoGb/x9/H8AAEMjbuDO6Gs4XhTjkH6Ou6MAwT4qAEBdkwee+nIC5HX+bcqV1gZgyeH78NkjnyExTPuH7Hfx+QygbnFnMgPx6G8TIS82XIPUL6nObs/w9NbgmfVFcHMDrhd4YefG26wKoB5LLxXXY10+74O//M8daG4y/G9SEGQ4+FEYIAMWvqYdqfrj8hKcygiCRs0PukQ6XS4PlFqtRn5+Pvbu3Ys1a9ZgxowZGDJkCBISEpCQkIBly5Z1dhdvOadLorDvYr92gyd9GYV9cO5Gy4jPmNt/6ciuGegXqhCvz5REGQ2edDSCG/7935bFwHd0q+zQvpHzK5d7tgme7O3RJTfQK1Yb5G9aFo3GBsv/CffxU2P42GrxfscbEW2CJ32HPgpF6S/a7ycmToVhY6rbLUv2p5vGs+ZFjuU0U3j28swzz+Crr77q7G6QDX4ojRDXTPUMrHLYc/08W9aUVKnM/yGsUrXsUHLjv17Uwfr0r8fUp7X/XWR8FoIfjgXituhGi+vHDa4XR5/UzUD2t6Y3aQiCDGe/CcTEx7QfLO6aWInTX5tfk0h2wEXkLkFSACWTydCvXz8EBASYL+xgarXhrpLg4GAEBwejsLCwczpEVhP01jy5OzAw0d/1FxtSbrZ8XHeleP2zIrRD+kQEaNMjPLP+Gjw8gSqlO7ausn5Rd0h4s3hdqfSAqt786NWNopa1UUPv4ggUkT7JI1AvvfQSkpKS7NkXuxg8eDD69u2LAQMGYMCAAejVqxf27t2LF154obO7RhaK794ylVZSYz5Id5dpMDr6GgaEyxHi0wBVszuUDb7ILeuBn+ThFqcY+LrwDixOzgIA9AtTYELfS/j35TijZfuGKDG5388AAI0A7D4/wKJnEEkxZa4cCUPrAQDv/jUSlUrr/+mW6X0YkTKB0KNnE3z81GioY8qOjibTaF9S6pHjdLlEmk899VRnd4FsEOFfgxE9W7ZYnyyONl8noBbvP3jQ6Hs363zxUe5gfPj/27vzuKir/X/gL3ZZRQRERZRFMQk19KLpVdTACrWkRC3TfllXu2apN77XtDKXSlyKW11NKy0ll9RrLiilGC6hQUoYKorKIiAo+74O8/uD/DjjfIaZzwjDoK/n40GPz3I+53NmAuc955zP+5wfgHoNgVRqUWfsTnkEoY+kAABWPXEMw3pkY/elR5BZ1hF1DabobleGsR5p+H/9z8ParAGNcuCT34biXB4f86bW4eJWi+lhtwAAf56xxpGdDjrVU1J49597+84NsLBs1NgL1cX17rC2sTHQw6sWV/+00un+JAGH8NqFBy6AovbtnWFxMP0rjcDNchvEZvS6r/ocrarxryHxCHRPx9yfnmp2YjgAfHAiAJV1Zvh/A/6EqbEcz/e9jOf/6mm6V1JeF3yZOAgnb/S8rzYSNWf+mmx0sGpEXa0RPl/oCl2TWl5LtoJMBpiYACamgF9AOc78pD41gZGRHINGKQ/b2XaSqSlN9PBhAEUG49k+l/GkZ5qw/2n8kGZ7jcpqzfHTdU/8muWGlAJHFFRbQtZoDEerKvi55OKlRy9goEvTN/f+zrfx5dOH8dL+iahpMFNbpxxGWHVmOPZcfgTv/f0Uhna/KVruZrkNfkrzxNmb7Hmi1jN2aiEeG1EBANi1zhlZ1zroXFdlmQkuxFtjwLCmhLXT387D78ds1T6JN25GIVzclCepW1kzgNIHroXXPjxwaQyoffJxuo2lI08K+4eveeHQtT5qy+dXWmNU5Ax8cHIUjqZ7ILvcDjUNZqhvNEFuhS0OXeuDF/aF4IvfByvcowCv9D+vsS1PelzHf5/8CUO730RDoxES81yw97I39qT0xW853VDbYIJuthV4Z9hpHJq6AwOc8+7rtROJsXesx6z3cwEAWdctsPNzZw1XaLbjsy7CtqdPDZZ9lwEHZ+WM5k2JNAvw+jLVLw/mHThGpBdyue4/pDfsgaI21922DF8+FY0Opk3fbq8UOuCDkyObvaa+0UTjnCbACOvP/Q09O5bhmT6pAICXB5zHV3/4QaYmJ9XbQ87gtceSAABnb3bFotgxKsvJOFpWYcmIkwjySIeLTSW+GncIk/c+j8xSzUvFEGnrjY9yhCGzz//tivq6+/+++8cpW+z+0gmh/8wHAAweXY7vzqQIS7lYWjei398qhblPF3+3Qg/PWtg5NLWjupLfuYnu4F8DtSknq0psGn8QTtZN2ZpvlNrhH4fGo6Ku5VaB/+L3vwnbHS3qMLCLeI9RsOdVIXi6WtQJ/zg8TnQtvoJqK8w/Oha/5TQN39lZ1GHx8LgWay/R40+WYuSEpuSsR37ohD/PtFzKmG9WdMO3K11QX9c04GNhKcfgURUIfqkIo0NKhOAp8aQNls3sBWu7u8N2FaV8Ak8fdEmiyWSa+qd1D1S3bspzPSwsWu4Djh5O9hY12DT+IHp2bEqWebvSCjOjJmic6C1Vdrkdssts4WrXNCHWo1OJ6FNzcwafFbY3nBvU7FypRrkxPksYgqEhPwIA/t7jBpysKlu87fTwsbBsxNyPswEAJYUm+Gp5y8+z2/lFFxzb2wnjpxdi4N8r0LVXLaxsGlFaaILrFyxxdLcDTkV1RBfXepgofEoU5Kr/m6AWxKfw2gWtA6hffvmlNdtBDxlrszp8NS4KvR2aElYWV3fAq1ETkCPS49MS8qushADKvkONyvluNuXw7HR3TbH4m9011nn+VhdU15vC0qwBxkZAP8cCnLjBAIruj33nBjh2/SvppdwIK7amqy1rZqGc+GfJ5nTU1zYNLCQcs8P2/3QRuwwAkJ9jjm/Duzbblj4D767jV1ZkgtxMfnHWF/YmGT7OgSK9szStx8bgQ/B1bpqHUV5rjtcOjce1Yt3y22h3z7tZmKvrVX/tna0rlfZLajQ/7SSHEcrqLGBp1lS3jXntfbaSSJm9YwPsHRs0F/yL16N3vxxkXbv/YGfAsAphO+Uc8z8RKeIcKNIrc5MGrHsqGoO6Ns1Dqqo3xezoYFwqcGq1e1qYNKCX/d3eJbFhttoG5bkdHS1Ue6nuZQQ57BSCpvIWnLdF1NbMzBsR8Mzdv5tj/+vUhq15yPApvHaBPVCkN6bGMnw+9mc87tqUaby2wQRzf3oaf+Q1P4xwv8b3vio84dcoB87mqt4vt9IGjXLA+K9EKkO756hdxuWOAV1uCb1PAHCjVH1SQiJt3co2x5PdBmhVtotrHbYmpAj7M/wfwa1szQtha+O5WfnC03fF+aaIi+bvt77oOiGcw376xR4o0gtjo0aseSIGAT1vAADqZcZYcDRIq6Va7mVmLIOZsXYJ/XrYleJfQ34T9s9ku6KwWnUooqTGEpcLHYX92X6JsDBRP3RibNSIef7xwn52mS0ymMaAHhA+/hV4ccEtYf+rZd3UJtwkeljxL4L0QI4VAcfx1F9ZxmWNRnjnlzGIzXTXqTYn60r8/OI2vDIgCS7WFaJljCBHsOdV7AjZCwfLpuG4OpkxPo0fqrbeLef7C9t9Ohfh63FRcLUtU72/VSU+G3tEKUv5pqSBOr0WIn1746NsDAksg6mZ6sqzZuaNeGZmPj78Ph0dLJu6M+Ki7fDLXg7f6ZX8Pn5IbziER63uBZ+LeK7vFWE/q8wOfl3z4NdVcwbvkhoL/Pesv8rxrjaV+PfjZxA29Ayyy+xwtcgBJbUd0NBoDEfLKgzocguOVtVCeVmjEZacGNXsXKsDV/tgVK9MPO15HQDwt265iH5hO/683QXpJfaQywFXuzI81uUWLEzv9oD9ktETu1L6afVe0INtRWQaOrsoZ/bu5HS3J7PPgGqsP3rl3svw3kseKLqlnxQBj42owDOvFKKyzBhX/7TC7RwzyGRGcHCux6P+lbC2uxtYnY21xco5XOtR39rDUi4VFRWIi4tDfHw8Ll26hIyMDJSXl8PCwgLOzs7o378/xo8fjxEjRsDISLuW3b59Gzt37kRsbCxycnJQW1sLR0dHDB48GM8//zz8/VU/C5oTExODAwcOIDk5GQUFBbCxsYGbmxuCgoIwZcoU2Nra6vLSBQygqNU5WFYr7feyL0Uv+1Ktrs0ptxUNoO4wNgLcOpbBraNqT9EdN0rt8N7x0fg9V1M+HSMsPPYEcstt8HL/P2FiLIepsRx+Lnnwc1EN9hrlwPYLj2LNb4+jUU1mc3q4uPWpgUuPerXnLa0b4emj+oCCmZn+uw6s7Rox8O/iPbh1NUbYtd4ZOz93bpEM6PRg+fbbbxEREYHaWtUnjxsaGpCeno709HTs378fgwcPxpo1a1RySd7r6NGjWLx4McrKlP8tz87ORnZ2Nvbt24fJkydj6dKlMDFpPqFrRUUFwsLCEBsbq3S8qKgIRUVFSEpKQmRkJCIiIuDn56flq1b1wAVQWVlZ2LNnj9KxK1fufuO7dOkSIiIilM77+Phg7Nixemkf3b+b5bZ45ofJGOhyCwO75MHLoRgOHaph36EG5iYyVNaZ43aVFf683QWxGT1xPLMX5Fp+N6tvNMGa34Zh28VH8Zz3ZQzqmgtP+xLYWTT9Q1FWZ47M0o44l9sVey8/ghtlnFhL7cuKf/TEoIAKDBhege7utbDv3AALq0aUFpoi74Y54mPscHy/PfJzWmYyOumgUd70o8t1epCeni4ETy4uLnj88cfx6KOPwsHBATU1NTh//jwOHDiAqqoqnD17FtOnT8euXbvQuXNn0fpOnz6NBQsWoL6+6cvHqFGjMGbMGFhaWuLSpUvYvXs3KioqsGvXLgDAihUr1LatoaEBb731FuLimlaHcHR0RGhoKLy8vFBaWoqoqCgkJiYiLy8Ps2fPxvbt29G7d/MPDKljJJc/WM89xsfHY8aMGZKuCQkJQXh4eKu05+zZs5g2bdrdA4HPAk6t+9QZUVtwW366rZtA1OJqOlTido/rwv62bdswePDgZq6Q7t7PCYdu42Bu6SK5nrrqPBTdPCTst0ZbAeCDDz5AdnY2Zs6ciccffxzGxqq9lDk5OXj11VeRnt6UCPa5557DypUrVcrV1tbiqaeews2bTXNK33//fbz00ktKZdLT0zF9+nTk5zflDty8eTOGDx8u2rbt27dj2bJlAAAvLy9s2bIFjo6OSmVWrVqFzZs3AwD8/PywY8cOKS9fwL5ZIiIi0tqCBQuwadMmDB8+XDR4AoDu3bvjP//5j7AfHR2N6upqlXJ79uwRgqfRo0erBE8A4O7ujiVLlgj7n3/+ueg9ZTIZ1q9fL+yvXr1aJXgCgLCwMDzyyCMAgMTERJw6dUq0Pk0euCG8IUOGKA3ZERERtSeGPonc3l67lC19+/aFh4cH0tLSUF1djczMTPTt21epzOHDh4XtV155RW1dgYGB6N69O3JycpCUlITs7Gy4uiqnwUlISBB6qfz9/eHj4yNal4mJCaZPn47FixcDAA4dOoQRI0Zo9ZoUsQeKiIjIkDxAmcitre+u/HDvpPOKigokJiYK5ZobbjQ2NlYKck6ePKlSRrEnaeTIkc22KyAgoNm6tMEAioiIyJDI72Yjl/JjaHmg6urqkJGRIezf+yTe9evX0djYlDajX79+Gp+u8/X1FbavXr2qcj41NVW0rBhHR0d07do0H7mwsBBFRUXNlhfzwA3hEREREe5rOku3bt00ph7QJCoqCuXl5QCannZ3clLOw3dngjkAleE4MYplFK+9n/pyc3MBAGlpaXBwkLagPQMoIiIiQ6Jrb9I91yxfvlznJsydOxdvvvmmztcXFRVhzZo1wv4///lPlTKKOZ86ddKc7V5x7tW9+aIACMGaLvUpXqstBlBEREQGpGlITnoEZSiLCdfV1eHNN98UhsUCAwMRFBSkUq6qqkrYNjfXnHesQ4cOwnZlZWWz9VlYWGisT7GMWH2acA4UERERtYjGxkYsXrwYZ8+eBQC4ubnh448/1nidtsu9GBL2QBERERkSOQDVtZ61u07BkiVL4O3trVMTdJn/JJfL8cEHH+DgwYNCHd9++y06dhRfscHKykrYFlsW5l41NXeXQVJ8uk+xvtLSUqE+U9PmQxzFe4rVpwkDKCIiIgNiJJfrOISnfI23t3erZCIXI5fLsXTpUmG5FRcXF2zZsqXZydx2dnbCdnFxscZ7lJSUiF57h62trRBAFRcXawyKFOvTZWFhDuERERGRzuRyOZYtW4adO3cCALp06YKtW7fCzc2t2evc3d2F7ezsbI33USyjeG1L1Ofh4aGx/L0YQBERERkS+X386LupfwVPd9aTc3Z2xtatW9GzZ0+N13p6egpLwVy6dAkymazZ8snJycK22ALAffr0ES0rpqCgQEhh0LlzZ8kpDAAGUERERAZG1yzk+o2g7g2enJycsHXrVvTq1Uur621sbODn5weg6Sm4c+fOqS3b2NiIX3/9VdgXyzSuKVO5ohMnTgjbilnJpWAARURERJItX75cJXgSG1prTnBwsLC9efNmteViYmKEIbeBAweKzq3y9/cXknUmJCTg4sWLonXJZDJERkaKtkEKBlBEREQGRJdlXITlXPRkxYoV2L59O4Cm4GnLli06zSOaNGmS8MRfbGwstm3bplImIyNDKSnovHnzROsyMTHBnDlzhP2FCxeisLBQpdzatWuRkpICAPDz89NpIWGAT+EREREZFl0XBtbTYsIRERH4/vvvATTlb5oxYwbS09NFl1dR1K9fP5X0CBYWFvjoo48wa9Ys1NfXY/ny5Th16hTGjBkDS0tLXLp0Cbt37xYyhU+ePBnDhg1Te4/JkycjJiYGcXFxuHr1Kp599lmEhobCy8sLJSUlOHTokDBUaGtre1/Z2hlAERERGRI5YNQCeaBaS2Ji4t1byuX45JNPtLpu5cqVeO6551SODxs2DBEREVi8eDHKysoQGxuL2NhYlXKTJ0/G0qVLm72HqakpPv/8c4SFhSE2Nhb5+flYv369SjkXFxdERESITkbXFgMoIiIialNBQUEYMGAAduzYgdjYWOTk5KC2thZOTk4YNGgQJk2aBH9/f63qsrGxwYYNGxATE4P9+/cjOTkZhYWFsLa2hpubG4KCgjB16lSdcj8pYgBFRERkSOTQcQivxVsiSnECdktydnbGvHnz1M5xkiowMBCBgYEtUpcYBlBERESGRNeMBAaymPDDgk/hEREREUnEHigiIiID0lJr4VHrYgBFRERkUHRMY8AxPL3iEB4RERGRROyBIiIiMiSNf/3och3pDQMoIiIiA8I5UO0Dh/CIiIiIJGIPFBERkSEx8ESa1IQBFBERkUHhU3jtAQMoIiIiQ9IIwEjH60hvOAeKiIiISCL2QBERERkQPoXXPjCAIiIiMiicA9UecAiPiIiISCL2QBERERkSpjFoFxhAERERGRK5jkN4nAOlVxzCIyIiIpKIPVBERESGRA7dcjqxA0qvGEAREREZECO5HEY6RENMY6BfHMIjIiIikog9UERERIZELodO43HsgdIrBlBERESGRNen8DgJSq8YQBERERkSufAf6XRZhJh0wjlQRERERBKxB4qIiMiQ6DoHCnL2QOkRAygiIiJDcj8BFOkNh/CIiIiIJGIPFBERkSHR9Sk8I/ZA6RMDKCIiIkMilwNyruVi6DiER0RERCQRe6CIiIgMCRNptgsMoIiIiAyJXA406hAMGTOA0icO4RERERFJxB4oIiIiQyKHbkN47IDSKwZQREREhkTXOVA6zZsiXTGAIiIiMiQMoNoFzoEiIiIikog9UERERIZELgcadUikyUzkesUAioiIyJBwCK9d4BAeERERkUTsgSIiIjIk7IFqFxhAERERGRJmIm8XOIRHREREJBF7oIiIiAyIXC6HXC79KTw5h/D0igEUERGRIWnUcQhPl2tIZxzCIyIiIpKIPVBEREQGRcen8LiasF4xgCIiIjIkjTpmIucQnl4xgCIiIjIkzAPVLnAOFBEREZFE7IEiIiIyIHJ5I+Q6DOHpkvpAVzKZDNevX8eFCxdw8eJFXLhwAZcvX0ZNTQ0AICQkBOHh4RrriY+Px4wZM7S+r7b1AkBMTAwOHDiA5ORkFBQUwMbGBm5ubggKCsKUKVNga2ur9X3FMIAiIiIyJHLoOITX4i1Ra/78+Thy5Ij+bihBRUUFwsLCEBsbq3S8qKgIRUVFSEpKQmRkJCIiIuDn56fzfRhAERERkSQymUxp397eHvb29sjIyNC5zuDgYIwbN67ZMl27dm32fENDA9566y3ExcUBABwdHREaGgovLy+UlpYiKioKiYmJyMvLw+zZs7F9+3b07t1bp/YygCIiIjIkuq6Fp8dJ5P3794enpyd8fHzg4+ODHj16YO/evVi0aJHOdXp4eCAwMPC+2rVr1y4hePLy8sKWLVvg6OgonJ82bRpWrVqFzZs3o6ysDEuWLMGOHTt0uhcDKCIiIkMib2z60eU6PXn99df1di9tyWQyrF+/XthfvXq1UvB0R1hYGM6cOYOUlBQkJibi1KlTGDFihOT78Sk8IiIiavcSEhKQn58PAPD394ePj49oORMTE0yfPl3YP3TokE73Yw8UERGRAWnqgJI+HKfHDiiDdOrUKWF75MiRzZYNCAgQtk+ePKnT/RhAERERGZIWGsK7cuWKzk3o1q0bunXrpvP1ujhy5AhiYmKQlZWF+vp6dOzYEX369MGwYcMwadIk2NvbN3t9amqqsO3r69tsWUdHR3Tt2hW5ubkoLCxEUVERHBwcJLWXARQREdEDaPny5TpfO3fuXLz55pst2BrNFAMgAMjPz0d+fj7i4uKwbt06vPvuu5g0aZLa69PT04VtV1dXjfdzdXVFbm4uACAtLY0BlKGpqqpSPlBS2DYNIWplNR0q27oJRC2uzrxaaV/l3/RWUGNRAbkOT9TVWrTPv0EjIyP069cPQ4YMgYeHB2xtbVFVVYXU1FRER0cjLy8PVVVVePfdd1FUVIRZs2aJ1lNeXi5sd+rUSeN9FXu0FK/VFgOoVpaVlaV84OyvbdMQolZ2u0dbt4Co9an8m94KSh1zW/0ehsLd3R3R0dFwd3cXPf/222/j008/xebNmwEAn376Kfz9/TFw4ECVsorBrYWFhcZ7K5aprJQefDKAIiIiegAeQkdfAAAaq0lEQVQtWbIE3t7eOl2rr/lPzs7OzZ43MzPDwoULUVJSgr1790Iul2P9+vX46quv9NK+5jCAIiIiegB5e3tj8ODBbd2MFjF//nz8+OOPkMvlOHPmDGpqatChQwelMlZWVigtLQUA1NbWwtS0+RCntrZW2La2tpbcJgZQrWzMmDFK+25ubrC0tGyj1hARkRTV1dW4ceOGsH/vv+ktwdvbG9u2bWuVeh8UXbp0Qc+ePZGRkYG6ujpkZ2fDy8tLqYytra0QQBUXF2sMikpKSpSulYoBVCvr2rUrpk2b1tbNICIiA2Vra/vA9BS1pk6dOglr7ZWVlamcd3d3R3Z2NgAgOztb45N4d8oCTcvISMVM5ERERGTwiouLhW07OzuV83369BG2k5OTm62roKBASGHQuXNnySkMAAZQREREZODy8vKQmZkJADA3N0f37t1VyiiuZ6cpu/iJEyeEbcWs5FIwgCIiIiKD9tlnnwm5sYYMGSI6l9jf3x9OTk4AmtbFu3jxomhdMpkMkZGRwn5wcLBObWIARURERHqXmZmJr7/+GhUVFWrL1NfXY9WqVdi7d69wbM6cOaJlTUxMlM4tXLgQhYWqyavXrl2LlJQUAICfn59Sz5UURnJd0p0SERHRQysrKwt79uxROnblyhXExsYCaHoCcPTo0UrnfXx8MHbsWGE/JSUFEydOhLm5OYYOHQpfX1+4urrC2tpaKRP5nblKALBgwQK8/vrratvV0NCAWbNmIS4uDgDg5OSE0NBQeHl5oaSkBIcOHcK5c+cANE3e37FjB3r37q3Te8AAioiIiCSJj4/HjBkzJF0TEhKC8PBwYf9OAKUNGxsbLFq0qNm18O6oqKhAWFiYEMyJcXFxQUREBPz8/LS6vximMSAiIiK98/T0xDfffIPz58/j/PnzyMnJQUlJCcrKymBmZoZOnTqhb9++GD58OJ599lnY2NhoVa+NjQ02bNiAmJgY7N+/H8nJySgsLIS1tTXc3NwQFBSEqVOn6pT7SRF7oIiIiIgk4iRyIiIiIokYQBERERFJxACKiIiISCIGUEREREQSMYAiIiIikogBFBEREZFEDKCIiIiIJGIARURERCQRAygiIiIiiRhAEREREUnEAIqIiIhIIi4mTA+EmJgYHDhwAMnJySgoKICNjY2waOSUKVPue9FIorYgk8lw/fp1XLhwARcvXsSFCxdw+fJl1NTUAFBd3Z6I9IcBFLVrFRUVCAsLQ2xsrNLxoqIiFBUVISkpCZGRkYiIiICfn18btZJIN/Pnz8eRI0fauhlEJIIBFLVbDQ0NeOuttxAXFwcAcHR0RGhoKLy8vFBaWoqoqCgkJiYiLy8Ps2fPxvbt29G7d+82bjWR9mQymdK+vb097O3tkZGR0TYNIiIBAyhqt3bt2iUET15eXtiyZQscHR2F89OmTcOqVauwefNmlJWVYcmSJdixY0dbNZdIsv79+8PT0xM+Pj7w8fFBjx49sHfvXixatKitm0b00GMARe2STCbD+vXrhf3Vq1crBU93hIWF4cyZM0hJSUFiYiJOnTqFESNG6LOpRDp7/fXX27oJRKQGn8KjdikhIQH5+fkAAH9/f/j4+IiWMzExwfTp04X9Q4cO6aV9RET0YGMARe3SqVOnhO2RI0c2WzYgIEDYPnnyZKu1iYiIHh4MoKhdSk1NFbZ9fX2bLevo6IiuXbsCAAoLC1FUVNSqbSMiogcfAyhql9LT04VtV1dXjeUVy6SlpbVKm4iI6OHBAIrapfLycmG7U6dOGsvb29uLXktERKQLBlDULlVVVQnbFhYWGssrlqmsrGyVNhER0cODARQRERGRRAygqF2ysrIStmtrazWWVyxjbW3dKm0iIqKHBwMoapcUFwcuLi7WWL6kpET0WiIiIl0wgKJ2yd3dXdjOzs7WWF6xjIeHR6u0iYiIHh4MoKhd6tOnj7CdnJzcbNmCggLk5uYCADp37gwHB4dWbRsRET34GEBRu6S4np2m7OInTpwQthWzkhMREemKARS1S/7+/nBycgLQtC7exYsXRcvJZDJERkYK+8HBwXppHxERPdgYQFG7ZGJigjlz5gj7CxcuRGFhoUq5tWvXIiUlBQDg5+en1HNFRESkKyO5XC5v60YQ6aKhoQGzZs1CXFwcAMDJyQmhoaHw8vJCSUkJDh06hHPnzgFoevJux44d6N27d1s2mUiSrKws7NmzR+nYlStXEBsbCwDw9vbG6NGjlc77+Phg7Nixemsj0cOKARS1axUVFQgLCxM+UMS4uLggIiICfn5+emwZ0f2Lj4/HjBkzJF0TEhKC8PDwVmoREd1h2tYNILofNjY22LBhA2JiYrB//34kJyejsLAQ1tbWcHNzQ1BQEKZOncrcT0RE1KLYA0VEREQkESeRExEREUnEAIqIiIhIIgZQRERERBIxgCIiIiKSiAEUERERkUQMoIiIiIgkYgBFREREJBEDKCIiIiKJGEARERERScQAioiIiEgiBlBEREREEnExYaIWsHfvXixatEjl+Ny5c/Hmm2+qHPf29lY51r17d/zyyy+t0r4H1fTp05GQkKBy/NixY3B1dTXYuqUYM2YMcnJyVI5fuXJFb23QhdS/CaL2hgEUqSX2IS/G2NgYtra2sLe3h7e3N/z8/DBhwgQ4Ojq2cguJiIjaBofw6L41NjaitLQUmZmZOHLkCMLDwzFq1Ch8+OGHqK6ubuvm0T3i4+Ph7e2t8vPOO++0ddOIiNoNBlDUKurr6xEZGYkXXngB5eXlbd0cIiKiFsUhPGpVKSkp+Ne//oWvv/66rZtiULZt26ZyzMLCog1aQkREumAARZK99957eOSRR4T9kpISnDx5Env27IFMJlMpf/LkScTFxWH48OH6bKZBGzx4cFs3gYiI7gMDKJKsT58+KgFAYGAg/Pz8sHDhQtFroqKiGEAREdEDgwEUtZiJEyciMjISFy5cUDmXmJiockzT49lJSUn43//+h4SEBNy+fRtVVVUICQlBeHi46P2zsrJw+PBhnD17FtevX0dpaSlqa2thb28PV1dXDBkyBOPHj0fv3r0lva4TJ05g7969OH/+PAoLC9GxY0e4u7tj/PjxCAkJgbm5uaT6gPtLY3Dz5k1ER0cjMTERV69eRWlpKSoqKmBubg5nZ2d4eHjgsccew+jRo4XXmp2djSeeeKLZen/88Uf8+OOPKsebe88B4Ndff8WJEyeQmJiIW7duobS0FKampnBwcEDfvn0xfPhwPPPMM7CxsdH42u6oqqrCzp07ceTIEaSnp6OmpgbOzs7w8/NDaGioQfbg5eXl4cKFC7h48SIuXbqEmzdv4vbt26iurkZDQwOsrKxgZ2cHd3d3+Pj4IDg4GH379r3v+2ZmZuKHH37AyZMnkZubC7lcju7du2PUqFF48cUX0bVrV63rkslkOHbsGOLi4pCUlISCggKUlpaiQ4cOcHBwgK+vL0aMGIHg4GCdfu+JHiQMoKhF+fj4iAZQ+fn5WtfR2NiIlStXIjIyEnK5XGP54uJifPTRRzh8+LDoEGJ+fj7y8/Pxxx9/YOPGjRg3bhyWLFmCjh07NltvVVUV/v3vf+Po0aOi9SUkJCAyMhKffvqp1q/tfhQWFuLjjz9GdHS06OtsaGhARkYGMjIy8Msvv+CTTz7BL7/8gu7du7dKe3777Td89NFHSE1NVTlXV1eHqqoqZGdnIyYmBp999hnmzZuHF198UWO9SUlJWLBgAW7evKl0/MaNG7hx4wb27duHKVOm4L333mux13K/0tLS8PTTTzdbpry8HOXl5cjJycGvv/6KjRs3YsSIEfjwww/h4uKi0323bduG1atXo6amRul4amoqUlNTsW3bNnzwwQd49tlnNdYVHR2NtWvXIjs7W+VcfX09ysvLkZmZiaioKPznP//BokWL8OSTT+rUbqIHAZ/CoxbVoUMH0eNiH/jqfPjhh9i6datWwdOVK1cQEhKCgwcPanUPuVyOqKgohIaGIisrS225uro6zJ49WyV4utfVq1fx8ssvIyMjQ+O970diYiImTJiAqKgoSe+lNu+hLr799lvMnDlTNHgSU1JSgmXLluG9995rtk3JycmYOXOmSvB0rx9++AFvv/22pPeiNen6Pp86dQpTp05FQUGB5Gu/++47LF++XCV4UlRZWYmFCxdi//79zdb18ccfY/78+aLBk5jc3Fy89dZb+O9//yupzUQPEgZQ1KKuX78uetzBwUHrOsSeUBNz+/ZtzJ49G7m5uVrXfUdmZibmzJmDqqoq0fPr1q0TzUItpqioCF999ZXkNmgrLS0N//znP1FYWNhq95Bi//79CA8P1yl42b17N9atWyd6rqamBmFhYaisrNSqriNHjogODbc3ubm5WLZsmeTrmhtWVSSXy7F06VK1XxjWr1+PLVu2SL4/AHzxxRfYt2+fTtcStXccwqMWk5ycjNOnT4ue69evn+T6/v73v2P8+PFwcXFBYWEh/vzzT6V5F+Hh4aLBk4ODAyZPnox+/frBysoKGRkZ2LZtG9LT05XKpaam4ssvv8Tbb7+tdPz27dvYtGmTaJscHBzw6quvol+/fqisrMRPP/2EqKioVuvpAYD3338fJSUloud69eqF5557Dt7e3rCwsEBhYSH++OMPREdHKwVczs7OQmCakpKCDz/8UKWukSNHYvbs2SrHFTPKFxUVqf2wHzBgACZOnIgePXqguroaiYmJ2Llzp0oy1XXr1uHpp5+Gp6en0vFt27ap7cnz9/dHaGgonJyccOPGDWzduhXXrl1r1fddKjs7O/j4+MDX1xcuLi6ws7ODnZ0drKysUF9fj6KiIly5cgV79+5V6XE6evQoMjMz0bNnT63vJ5fL0bFjR8ycORO+vr6orq7Gzz//jAMHDqiUraqqQkREhMpw8/Xr1/HFF1+I1j98+HA8/fTT6NatG0pLS3H69Gns27cP9fX1SuWWLVuGgIAAdOrUSeu2Ez0IGEDRfZHL5bh58yZOnz6NTz75BI2NjaLlAgMDJdX773//G6+++qrSsfHjxwvbaWlpOHz4sMp1vXr1wvfffw8nJyfh2IgRIzBlyhRMnz4dSUlJSuW///57zJo1C7a2tsKxXbt2qXxIAIC9vT12796ttA5aUFAQevfujYiICEmvT1tnzpzB2bNnRc+FhoZi6dKlMDVV/jMeP3483nnnHezcuVMYUjU3NxcmXqvrOercubPGydnfffedaA/R9OnTVeYkjR07FsHBwZg2bRrq6uqE442NjdiwYQPWrFmjVH7Hjh2i93zyySfx2WefwcjICADw+OOPY8KECZg2bRouXbrUbHv1xd3dHQkJCUIb1Rk/fjyCgoIQGhqqdFwulyMuLk5SAGVtbY2dO3fCw8NDOBYYGAgvLy/ReXlHjhxBYWEhOnfuLBxbv3696N/sO++8g1deeUXpWHBwMEaNGoU33nhD6XhVVRW2bNmC+fPna912ogcBAyiSbMaMGZLKe3h4YMKECVqXHzJkiErwdK9jx46J9j7MnTtXKXi6w9zcHC+++KJKAFVVVYUzZ85g7NixwrG4uDjRe7722muii8j+4x//wK5du0SfKLxfR44cET3+6KOPYtmyZTAxMRE9b2ZmhunTp7d4e2JiYlSO2draIiwsTLR8//79MWjQIJw5c0bp+PHjxyGTyYT2Z2Zmig4xmZiY4N1331UJTKysrPB///d/Kh/ybcXYuGk2RFVVFY4fP45Tp07h+vXryMnJQWVlJWpqajT2lkkNBmfMmKEUPN3x2muv4YcfflD5fayvr0d8fDyCg4MBND10cPz4cZXre/XqpfZ9DQwMhJubG27cuKF0/NixYwyg6KHDAIpalb29PT7//HOVXpLmTJkyRWOZc+fOiR4PCwtT+2Guzu+//y4EUHK5HBcvXhQtN2bMGNHjJiYmCAgIwPbt2yXdVxvqep9eeukltcFTaykuLhad41ZeXo4BAwZIqqusrAypqalCQlaxJzeBpkCxS5cuoueGDh0Ka2trredMtbZ9+/Zh9erVOs9VKy4ullReXVqK5n4fk5OThQDq8uXLqKioUCmTkZGh9ULid9xJp6HpyVaiBwknkVOr8fX1xfbt2yXnXdLmw/j27du6NqvZuioqKlBbW6tSxsjICL169VJbh7u7e4u1R5G69A9SA5aWICUVhTYU33d1T6E1974aGxtLGvJqTZGRkVi4cOF9TfSXumZkc++NunOK7WvJ/59yubzFfz+IDB17oKhFde7cGX5+fggJCcGoUaN06iURG4K7l9g3Z12VlpYK2+p6M8zNzZt9LZaWli3WHkXqPlQV52zpS0svCq04MV7d+64uLcYdrfW+S5Gbm4u1a9fedz1SJ8Q399rVnVN8n1vz/yfRw4ABFEl271p4JiYmsLGxgb29vVbBjybaLKrbkgGE4qRqa2tr0TJ1dXVKc3bude+TZi3F1tZWdGinvLy8Rd5rKezs7Fq0Pm3e9+ZyHAGt975LER0dLdpOa2trzJs3D2PGjIGzs7Pwe52VlSX5oQox1dXVarO7q3tfFN/n1vz/SfQwYABFkomthadv6oKHVatWiU70bo5iMGZjYwMLCwuVYTy5XI6MjAyVR+/vaK1Emk5OTqIB1J9//ik6gbg1KaYzUOTh4YEVK1ZIrk9xmEnxyTBF96aeUNTY2IjMzEzJ921p6uZvvf/++wgJCVE53lL5vNLT0+Hr6yt6Tt3vo+L7rO7/p7+/P+bNmye5PVLnTRG1dwygqF3y8/NDbGysyvGioiJMnDhR63oaGhqUJrgbGRnBx8dHNEFjbGysaAAlk8lEn2ZqCYMHDxbN9v3999/jmWeeEZ7+kkJdL5qmHoROnTrBw8MDaWlpSsezsrLg5uYGZ2dnrdtw7/uuLhC4cOECbt++LVp3fHy8QUwgVzd05ePjI3pcU3Z7bR07dkz0fWvu91GxfN++fUUn4V+7dg2+vr5a9QTfce//T6KHASeRU7v0xBNPiObcWbduHc6fP6/x+suXL2PlypUq+XiApgSCYr755hvRJUY2bdrUKikMgKY8U2KSk5OxbNkytUGPTCbD9u3bRSf2WllZiV7T3NI2d4g9+VVfX4+FCxdqDGZqa2sRExODWbNmYePGjUrnevbsiR49eqhcI5PJsHLlSpX5QdXV1Sp5pNqKuvfzt99+UzmWkJCgc9bve23dulUlmAWAzZs3i/4+mpmZYciQIcK+qakpAgICVMoVFRXh/fffF82FpqiiogIHDx7Eiy++iIMHD+rwCojaN35loHbJ09MTTz31FKKjo5WOV1RUYMqUKQgICEBAQABcXFxgaWmJ8vJy3Lx5E5cvX0Z8fLwQCIkttBsaGooNGzaofIAUFxcjNDQUM2fOVMpE3pofHsOGDcOgQYNE0zbs3LkTCQkJCAkJETKRFxUVITk5GdHR0cjNzcXIkSNVruvWrZvovf744w+sWrUKQ4YMUZpb079/fyED/MyZM7Ft2zaVJXBOnz6N0aNHIyQkBI888gicnZ3R0NCA0tJSXL9+HZcuXUJCQoIwN0es52Tq1KmiQdHhw4dRWFiokon86tWrzbxz+tOnTx/RXqU1a9YgLy8P/v7+MDIywqlTp7Bz506NgYm2Kisr8cILL+CVV15B//79UVVVhaNHj6pd9y4oKEhlqHTOnDn46aefVJJp7t+/H6dPn8bzzz8PLy8vODo6oq6uDsXFxbh27RqSk5Nx7tw54bVMmjSpRV4TUXvCAIrarUWLFiExMRG3bt1SOi6Xy3H8+HGdh9W6dOmCV199FRs2bFA5V1BQgNWrV+tUr65WrFiBF154QelpwTvS0tLwySefSKrP3t4evXr1Ep0ns3nzZmzevFnp2LFjx4R5ZQ4ODliyZAneeecdlWtLS0vx3XffSWqLomnTpmH37t2i7YqPj0d8fLzOdbemcePG4csvv1QJQurq6rBp0yaVZYFaMndVSUmJVlnwrayssGDBApXjvXv3xhtvvCG6nEt+fr7o3wARNeEQHrVbXbp0wddff42uXbu2eN1vvPEG/P39tSprZWWlVfJPXXl6emLDhg1qJ1rrYvLkyTpfGxISgoULF7Z4Ik9LS0usXbtW7RN59xo0aJDauVP65OnpiWnTpmlVtkOHDvj4449b5L7aZmE3MjLCBx98ADc3N9Hzc+fOxcsvv9wibSJ6mDCAonbN29sb+/fvx3PPPQczMzNJ1z722GOYM2eO6Dlzc3Ns3LhR4+Pm3bt3xzfffIOBAwdKurdUfn5+OHDgAMaNGycpcFG3NtvLL78sOv9FWzNnzsTWrVslLxJtZWWFCRMmqJ3b5evri02bNmkMiseOHYuvvvrKIPJAAU1rx4k9cafIyckJGzduxKOPPtpi93zzzTebnbxtZWWF8PBwjQ9WLF68GOvWrWs2WawYe3t7TJkyBX/7298kXUf0IOAQHrV7HTt2xMqVKzF//nwcPnwYiYmJuHLlCkpKSlBRUQELCwvY2tqiR48e8PT0xKBBgzB06FC1S4TcYWVlhXXr1uHEiRP43//+h6SkJBQVFcHOzg49e/bEk08+iUmTJsHGxkYvj9M7Ojri008/RVhYmPA6U1NTUVpaiqqqKtjZ2cHR0RGurq4YNmwYRowYITrHC2iaQLxhwwZER0fj4MGDSElJQXFxsWgWdnUGDx6MH3/8EWfPnkVsbCySkpKQnZ2NsrIy1NXVwcrKCg4ODnB3d4e3tzeGDBmCQYMGaXy667HHHsOhQ4ewc+dO/Pzzz8jIyEB1dTWcnJwwYMAATJw48b6Cv9ZgamqK8PBwjB8/Hj/88AP++OMPlJSUwNbWFq6urggKCsKkSZPg4OCA7OzsFrvv3LlzERQUhN27dyMuLg55eXkAmgL7gIAAvPTSS1r30AYGBuKJJ57AyZMn8euvvyIpKQl5eXkoKyuDTCaDtbU1HB0d4eHhgb59+2Lo0KEYMGAAn76jh5aRXGr6WyIiIqKHHIfwiIiIiCRiAEVEREQkEQMoIiIiIokYQBERERFJxACKiIiISCIGUEREREQSMYAiIiIikogBFBEREZFEDKCIiIiIJGIARURERCQRAygiIiIiiRhAEREREUnEAIqIiIhIIgZQRERERBIxgCIiIiKS6P8DsTVj9j0yiY8AAAAASUVORK5CYII=\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "\n",
    "cm = test_performance.get(\"Confusion Matrix\")\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=[0,1])\n",
    "disp.plot()\n",
    "disp.figure_.savefig(os.path.join(image_folder, 'confusion_matrix.pdf'))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-11-28T17:41:48.760018Z",
     "end_time": "2023-11-28T17:41:49.119427Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 801,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Set AUC-ROC:\n",
      "AUC=0.4823175784714246 [CI 0.4475437376897231-0.5198050631444793]\n",
      "Test Set AUC-PRC:\n",
      "AUC-PRC=None [CI 0.3333747552971241-0.41051320504172123]\n"
     ]
    }
   ],
   "source": [
    "# overall\n",
    "low_CI_binary, high_CI_binary = get_ci_auc(np_test[1], test_predictions, metric='AUROC')\n",
    "print(\"Test Set AUC-ROC:\\nAUC={} [CI {}-{}]\".format(\n",
    "    test_performance.get(\"AUROC\"),\n",
    "    low_CI_binary,\n",
    "    high_CI_binary\n",
    "))\n",
    "low_CI_binary, high_CI_binary = get_ci_auc(np_test[1], test_predictions, metric='AUPRC Micro')\n",
    "print(\"Test Set AUC-PRC:\\nAUC-PRC={} [CI {}-{}]\".format(\n",
    "    test_performance.get(\"AUPRC\"),\n",
    "    low_CI_binary,\n",
    "    high_CI_binary\n",
    "))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-11-28T17:41:49.018885Z",
     "end_time": "2023-11-28T17:41:51.310121Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 802,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating CI for Accuracy...\n",
      "Calculating CI for Balanced Accuracy...\n",
      "Calculating CI for AUROC...\n",
      "Calculating CI for AUPRC Macro...\n",
      "Calculating CI for AUPRC Micro...\n",
      "Calculating CI for F1 Macro...\n",
      "Calculating CI for F1 Micro...\n",
      "Calculating CI for F1 Weighted...\n",
      "Calculating CI for Precision...\n",
      "Calculating CI for Recall...\n",
      "['Accuracy', 'Balanced Accuracy', 'AUROC', 'AUPRC Macro', 'AUPRC Micro', 'F1 Macro', 'F1 Micro', 'F1 Weighted', 'Precision', 'Recall']\n"
     ]
    }
   ],
   "source": [
    "import tensorflow.keras.backend as K\n",
    "\n",
    "trainable_count = np.sum([K.count_params(w) for w in pretrained_model.trainable_weights])\n",
    "metrics_ci = []\n",
    "averages_ci = []\n",
    "lows_ci = []\n",
    "highs_ci = []\n",
    "for metric in test_performance:\n",
    "    if (metric == \"Confusion Matrix\") or (metric == \"Kappa\"):\n",
    "        continue\n",
    "    print(\"Calculating CI for {}...\".format(metric))\n",
    "    low_CI_binary, high_CI_binary = get_ci_auc(np_test[1], test_predictions, metric=metric)\n",
    "    metrics_ci.append(metric)\n",
    "    averages_ci.append(test_performance.get(metric))\n",
    "    highs_ci.append(high_CI_binary)\n",
    "    lows_ci.append(low_CI_binary)\n",
    "print(metrics_ci)\n",
    "all_metrics_ci = pd.DataFrame({'metric': metrics_ci, 'value': averages_ci, 'low_ci': lows_ci, 'high_ci': highs_ci, 'parameters': trainable_count, 'model_name': model_name, 'subfolder': subfolder, 'added_layers': added_layers, 'frozen_layers': frozen_layers, 'tag': tag})"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-11-28T17:41:51.316216Z",
     "end_time": "2023-11-28T17:42:00.217649Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 803,
   "outputs": [
    {
     "data": {
      "text/plain": "              metric     value    low_ci   high_ci  parameters  \\\n0           Accuracy  0.507849  0.476454  0.535549       94754   \n1  Balanced Accuracy  0.479644  0.450776  0.512481       94754   \n2              AUROC  0.482318  0.444956  0.519244       94754   \n3        AUPRC Macro  0.368695  0.332993  0.409805       94754   \n4        AUPRC Micro  0.368695  0.332312  0.408620       94754   \n5           F1 Macro  0.479677  0.449987  0.508653       94754   \n6           F1 Micro  0.507849  0.478301  0.537396       94754   \n7        F1 Weighted  0.509749  0.480796  0.543793       94754   \n8          Precision  0.479956  0.448080  0.511156       94754   \n9             Recall  0.479644  0.451586  0.510136       94754   \n\n                model_name                                   subfolder  \\\n0  simclr.frozen.1.08.hdf5  20231128-152601_e200_esTrue_bs128_wTrue_f1   \n1  simclr.frozen.1.08.hdf5  20231128-152601_e200_esTrue_bs128_wTrue_f1   \n2  simclr.frozen.1.08.hdf5  20231128-152601_e200_esTrue_bs128_wTrue_f1   \n3  simclr.frozen.1.08.hdf5  20231128-152601_e200_esTrue_bs128_wTrue_f1   \n4  simclr.frozen.1.08.hdf5  20231128-152601_e200_esTrue_bs128_wTrue_f1   \n5  simclr.frozen.1.08.hdf5  20231128-152601_e200_esTrue_bs128_wTrue_f1   \n6  simclr.frozen.1.08.hdf5  20231128-152601_e200_esTrue_bs128_wTrue_f1   \n7  simclr.frozen.1.08.hdf5  20231128-152601_e200_esTrue_bs128_wTrue_f1   \n8  simclr.frozen.1.08.hdf5  20231128-152601_e200_esTrue_bs128_wTrue_f1   \n9  simclr.frozen.1.08.hdf5  20231128-152601_e200_esTrue_bs128_wTrue_f1   \n\n   added_layers  frozen_layers      tag  \n0             2              1  frozen1  \n1             2              1  frozen1  \n2             2              1  frozen1  \n3             2              1  frozen1  \n4             2              1  frozen1  \n5             2              1  frozen1  \n6             2              1  frozen1  \n7             2              1  frozen1  \n8             2              1  frozen1  \n9             2              1  frozen1  ",
      "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>metric</th>\n      <th>value</th>\n      <th>low_ci</th>\n      <th>high_ci</th>\n      <th>parameters</th>\n      <th>model_name</th>\n      <th>subfolder</th>\n      <th>added_layers</th>\n      <th>frozen_layers</th>\n      <th>tag</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Accuracy</td>\n      <td>0.507849</td>\n      <td>0.476454</td>\n      <td>0.535549</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Balanced Accuracy</td>\n      <td>0.479644</td>\n      <td>0.450776</td>\n      <td>0.512481</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>AUROC</td>\n      <td>0.482318</td>\n      <td>0.444956</td>\n      <td>0.519244</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>AUPRC Macro</td>\n      <td>0.368695</td>\n      <td>0.332993</td>\n      <td>0.409805</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>AUPRC Micro</td>\n      <td>0.368695</td>\n      <td>0.332312</td>\n      <td>0.408620</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>F1 Macro</td>\n      <td>0.479677</td>\n      <td>0.449987</td>\n      <td>0.508653</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>F1 Micro</td>\n      <td>0.507849</td>\n      <td>0.478301</td>\n      <td>0.537396</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>F1 Weighted</td>\n      <td>0.509749</td>\n      <td>0.480796</td>\n      <td>0.543793</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Precision</td>\n      <td>0.479956</td>\n      <td>0.448080</td>\n      <td>0.511156</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Recall</td>\n      <td>0.479644</td>\n      <td>0.451586</td>\n      <td>0.510136</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 803,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# working_directory = os.path.join(\"../Supervised\", 'MESA')\n",
    "all_metrics_ci.to_csv(os.path.join(working_directory, subfolder, model_name.replace('.hdf5', '_ci_metrics.csv')), index=False)\n",
    "all_metrics_ci.head(10)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-11-28T17:42:00.220649Z",
     "end_time": "2023-11-28T17:42:00.273277Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 804,
   "outputs": [],
   "source": [
    "# logging results\n",
    "# results = pd.DataFrame(columns=['model', 'aucroc', 'gender_best', 'gender_best_auc', 'gender_best_delta', 'gender_worst', 'gender_worst_auc', 'gender_worst_delta'])\n",
    "results = {'model': subfolder, 'auc': test_performance.get('AUROC'), 'protected_attributes': {}}"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-11-28T17:42:00.267163Z",
     "end_time": "2023-11-28T17:42:00.288162Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## BIAS ANALYSIS"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 805,
   "outputs": [],
   "source": [
    "sns.set(style='white', font='Work Sans Regural')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-11-28T17:42:00.284162Z",
     "end_time": "2023-11-28T17:42:00.295163Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 806,
   "outputs": [
    {
     "data": {
      "text/plain": "         PID  gender   age   race  generation  disability  year\n0     1221.0     1.0  21.0  Asian         1.0         1.0     4\n1     1221.0     1.0  21.0  Asian         1.0         1.0     4\n2     1221.0     1.0  21.0  Asian         1.0         1.0     4\n3     1221.0     1.0  21.0  Asian         1.0         1.0     4\n4     1221.0     1.0  21.0  Asian         1.0         1.0     4\n...      ...     ...   ...    ...         ...         ...   ...\n1078   999.0     1.0  21.0  Asian         1.0         0.0     4\n1079   999.0     1.0  21.0  Asian         1.0         0.0     4\n1080   999.0     1.0  21.0  Asian         1.0         0.0     4\n1081   999.0     1.0  21.0  Asian         1.0         0.0     4\n1082   999.0     1.0  21.0  Asian         1.0         0.0     4\n\n[1083 rows x 7 columns]",
      "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>PID</th>\n      <th>gender</th>\n      <th>age</th>\n      <th>race</th>\n      <th>generation</th>\n      <th>disability</th>\n      <th>year</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1221.0</td>\n      <td>1.0</td>\n      <td>21.0</td>\n      <td>Asian</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1221.0</td>\n      <td>1.0</td>\n      <td>21.0</td>\n      <td>Asian</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1221.0</td>\n      <td>1.0</td>\n      <td>21.0</td>\n      <td>Asian</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1221.0</td>\n      <td>1.0</td>\n      <td>21.0</td>\n      <td>Asian</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1221.0</td>\n      <td>1.0</td>\n      <td>21.0</td>\n      <td>Asian</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>4</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    </tr>\n    <tr>\n      <th>1078</th>\n      <td>999.0</td>\n      <td>1.0</td>\n      <td>21.0</td>\n      <td>Asian</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>1079</th>\n      <td>999.0</td>\n      <td>1.0</td>\n      <td>21.0</td>\n      <td>Asian</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>1080</th>\n      <td>999.0</td>\n      <td>1.0</td>\n      <td>21.0</td>\n      <td>Asian</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>1081</th>\n      <td>999.0</td>\n      <td>1.0</td>\n      <td>21.0</td>\n      <td>Asian</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>1082</th>\n      <td>999.0</td>\n      <td>1.0</td>\n      <td>21.0</td>\n      <td>Asian</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n<p>1083 rows × 7 columns</p>\n</div>"
     },
     "execution_count": 806,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_listfile"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-11-28T17:42:00.298165Z",
     "end_time": "2023-11-28T17:42:00.333166Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 807,
   "outputs": [
    {
     "data": {
      "text/plain": "         PID gender   race disability\n0     1221.0   Male  Asian        Yes\n1     1221.0   Male  Asian        Yes\n2     1221.0   Male  Asian        Yes\n3     1221.0   Male  Asian        Yes\n4     1221.0   Male  Asian        Yes\n...      ...    ...    ...        ...\n1078   999.0   Male  Asian         No\n1079   999.0   Male  Asian         No\n1080   999.0   Male  Asian         No\n1081   999.0   Male  Asian         No\n1082   999.0   Male  Asian         No\n\n[1083 rows x 4 columns]",
      "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>PID</th>\n      <th>gender</th>\n      <th>race</th>\n      <th>disability</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1221.0</td>\n      <td>Male</td>\n      <td>Asian</td>\n      <td>Yes</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1221.0</td>\n      <td>Male</td>\n      <td>Asian</td>\n      <td>Yes</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1221.0</td>\n      <td>Male</td>\n      <td>Asian</td>\n      <td>Yes</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1221.0</td>\n      <td>Male</td>\n      <td>Asian</td>\n      <td>Yes</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1221.0</td>\n      <td>Male</td>\n      <td>Asian</td>\n      <td>Yes</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1078</th>\n      <td>999.0</td>\n      <td>Male</td>\n      <td>Asian</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>1079</th>\n      <td>999.0</td>\n      <td>Male</td>\n      <td>Asian</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>1080</th>\n      <td>999.0</td>\n      <td>Male</td>\n      <td>Asian</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>1081</th>\n      <td>999.0</td>\n      <td>Male</td>\n      <td>Asian</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>1082</th>\n      <td>999.0</td>\n      <td>Male</td>\n      <td>Asian</td>\n      <td>No</td>\n    </tr>\n  </tbody>\n</table>\n<p>1083 rows × 4 columns</p>\n</div>"
     },
     "execution_count": 807,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_listfile.gender = test_listfile.gender.apply(lambda v: 'Male' if v==1 else ('Female' if v==2 else ('Transgender' if v==3 else 'Genderqueer ' if v==4 else 'Other')))\n",
    "test_listfile.disability = test_listfile.disability.apply(lambda v: 'Yes' if v==1 else 'No')\n",
    "test_listfile.drop(columns=['generation', 'age', 'year'], inplace=True)\n",
    "test_listfile"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-11-28T17:42:00.312164Z",
     "end_time": "2023-11-28T17:42:00.422333Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 808,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test rows with null demograpphics: 11 (0.010156971375807941%)\n"
     ]
    },
    {
     "data": {
      "text/plain": "    gender   race disability\n0     Male  Asian        Yes\n1     Male  Asian        Yes\n2     Male  Asian        Yes\n3     Male  Asian        Yes\n4     Male  Asian        Yes\n5     Male  Asian        Yes\n6     Male  Asian        Yes\n7     Male  Asian        Yes\n8     Male  Asian        Yes\n9   Female  Asian        Yes\n10  Female  Asian        Yes\n11  Female  Asian        Yes\n12  Female  Asian        Yes\n13  Female  Asian        Yes\n14  Female  Asian        Yes\n15  Female  Asian        Yes\n16  Female  Asian        Yes\n17  Female  Asian        Yes\n18  Female  Asian        Yes\n19  Female  Asian        Yes",
      "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>gender</th>\n      <th>race</th>\n      <th>disability</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Male</td>\n      <td>Asian</td>\n      <td>Yes</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Male</td>\n      <td>Asian</td>\n      <td>Yes</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Male</td>\n      <td>Asian</td>\n      <td>Yes</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Male</td>\n      <td>Asian</td>\n      <td>Yes</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Male</td>\n      <td>Asian</td>\n      <td>Yes</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Male</td>\n      <td>Asian</td>\n      <td>Yes</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Male</td>\n      <td>Asian</td>\n      <td>Yes</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Male</td>\n      <td>Asian</td>\n      <td>Yes</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Male</td>\n      <td>Asian</td>\n      <td>Yes</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Female</td>\n      <td>Asian</td>\n      <td>Yes</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Female</td>\n      <td>Asian</td>\n      <td>Yes</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Female</td>\n      <td>Asian</td>\n      <td>Yes</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Female</td>\n      <td>Asian</td>\n      <td>Yes</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Female</td>\n      <td>Asian</td>\n      <td>Yes</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Female</td>\n      <td>Asian</td>\n      <td>Yes</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Female</td>\n      <td>Asian</td>\n      <td>Yes</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Female</td>\n      <td>Asian</td>\n      <td>Yes</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Female</td>\n      <td>Asian</td>\n      <td>Yes</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Female</td>\n      <td>Asian</td>\n      <td>Yes</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Female</td>\n      <td>Asian</td>\n      <td>Yes</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 808,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Test rows with null demograpphics: {} ({}%)\".format(test_listfile.PID.isna().sum(), test_listfile.PID.isna().sum()/test_listfile.shape[0]))\n",
    "test_listfile.drop(columns=['PID'], inplace=True)\n",
    "test_listfile.head(20)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-11-28T17:42:00.346336Z",
     "end_time": "2023-11-28T17:42:00.478855Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 809,
   "outputs": [
    {
     "data": {
      "text/plain": "  gender   race disability  y_pred\n0   Male  Asian        Yes       0\n1   Male  Asian        Yes       1\n2   Male  Asian        Yes       1\n3   Male  Asian        Yes       0\n4   Male  Asian        Yes       0",
      "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>gender</th>\n      <th>race</th>\n      <th>disability</th>\n      <th>y_pred</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Male</td>\n      <td>Asian</td>\n      <td>Yes</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Male</td>\n      <td>Asian</td>\n      <td>Yes</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Male</td>\n      <td>Asian</td>\n      <td>Yes</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Male</td>\n      <td>Asian</td>\n      <td>Yes</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Male</td>\n      <td>Asian</td>\n      <td>Yes</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 809,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_listfile.loc[:, \"y_pred\"] = predictions\n",
    "test_listfile.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-11-28T17:42:00.378336Z",
     "end_time": "2023-11-28T17:42:00.478855Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 810,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- Train Performance Overall ---\n",
      "{'Accuracy': 0.5554809467852946, 'Balanced Accuracy': 0.5534386689627545, 'AUROC': 0.582319068288405, 'AUPRC Macro': 0.55367662083067, 'AUPRC Micro': 0.55367662083067, 'Confusion Matrix': array([[1855, 1296],\n",
      "       [1352, 1454]], dtype=int64), 'F1 Macro': 0.5534574786706589, 'F1 Micro': 0.5554809467852946, 'F1 Weighted': 0.5551983676969664, 'Precision': 0.5535747370808175, 'Recall': 0.5534386689627545, 'Kappa': 0.1069942344050292}\n",
      "\n",
      "--- Validation Performance Overall ---\n",
      "{'Accuracy': 0.48613678373382624, 'Balanced Accuracy': 0.4866422711726579, 'AUROC': 0.4690597484599063, 'AUPRC Macro': 0.4800898737612037, 'AUPRC Micro': 0.4800898737612037, 'Confusion Matrix': array([[336, 203],\n",
      "       [353, 190]], dtype=int64), 'F1 Macro': 0.4766070881706061, 'F1 Micro': 0.48613678373382624, 'F1 Weighted': 0.47634600062092886, 'Precision': 0.48556191995627396, 'Recall': 0.4866422711726579, 'Kappa': -0.02668810179638692}\n",
      "\n",
      "--- Test Performance Overall ---\n",
      "{'Accuracy': 0.5078485687903971, 'Balanced Accuracy': 0.4796443161827777, 'AUROC': 0.4823175784714246, 'AUPRC Macro': 0.3686948688724948, 'AUPRC Micro': 0.3686948688724948, 'Confusion Matrix': array([[401, 275],\n",
      "       [258, 149]], dtype=int64), 'F1 Macro': 0.47967657756324455, 'F1 Micro': 0.5078485687903971, 'F1 Weighted': 0.50974906026207, 'Precision': 0.47995640908179915, 'Recall': 0.4796443161827777, 'Kappa': -0.04037582208828905}\n"
     ]
    }
   ],
   "source": [
    "# Overall performance\n",
    "print(\"\\n--- Train Performance Overall ---\")\n",
    "train_listfile.loc[:, \"y_pred\"] = np.argmax(train_predictions,axis=1)\n",
    "print(train_performance)\n",
    "print(\"\\n--- Validation Performance Overall ---\")\n",
    "val_listfile.loc[:, \"y_pred\"] = np.argmax(validation_predictions,axis=1)\n",
    "print(validation_performance)\n",
    "print(\"\\n--- Test Performance Overall ---\")\n",
    "print(test_performance)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-11-28T17:42:00.390334Z",
     "end_time": "2023-11-28T17:42:00.480857Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 811,
   "outputs": [],
   "source": [
    "# preparing for performance conditioned on protected attributes logging\n",
    "con_metrics = []\n",
    "con_protected_attributes = []\n",
    "con_max_groups = []\n",
    "con_max_values = []\n",
    "con_min_groups = []\n",
    "con_min_values = []\n",
    "con_max_deltas = []\n",
    "con_min_deltas = []"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-11-28T17:42:00.405342Z",
     "end_time": "2023-11-28T17:42:00.480857Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 812,
   "outputs": [],
   "source": [
    "# ROC curves\n",
    "MAX_AUC = -1\n",
    "MAX_GROUP = ''\n",
    "MIN_AUC = 1.1\n",
    "MIN_GROUP = ''"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-11-28T17:42:00.421337Z",
     "end_time": "2023-11-28T17:42:00.481856Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 813,
   "outputs": [],
   "source": [
    "def check_min_max(auc, group):\n",
    "    global MAX_AUC, MAX_GROUP, MIN_AUC, MIN_GROUP\n",
    "    if MAX_AUC < auc:\n",
    "        MAX_AUC = auc\n",
    "        MAX_GROUP = group\n",
    "    if MIN_AUC > auc:\n",
    "        MIN_AUC = auc\n",
    "        MIN_GROUP = group\n",
    "\n",
    "# preparing for performance conditioned on protected attributes logging\n",
    "def append_row(general_auc, protected_attribute, metric='AUROC'):\n",
    "    con_metrics.append(metric)\n",
    "    con_protected_attributes.append(protected_attribute)\n",
    "    con_max_groups.append(MAX_GROUP)\n",
    "    con_max_values.append(MAX_AUC)\n",
    "    con_min_groups.append(MIN_GROUP)\n",
    "    con_min_values.append(MIN_AUC)\n",
    "    con_max_deltas.append(MAX_AUC-general_auc)\n",
    "    con_min_deltas.append(MIN_AUC-general_auc)\n",
    "\n",
    "def reset_min_max():\n",
    "    global MAX_AUC, MAX_GROUP, MIN_AUC, MIN_GROUP\n",
    "    MAX_AUC = -1\n",
    "    MAX_GROUP = ''\n",
    "    MIN_AUC = 1.1\n",
    "    MIN_GROUP = ''"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-11-28T17:42:00.444856Z",
     "end_time": "2023-11-28T17:42:00.526859Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Performance conditioned on Gender"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 814,
   "outputs": [
    {
     "data": {
      "text/plain": "Female          590\nMale            459\nTransgender      12\nGenderqueer      11\nOther            11\nName: gender, dtype: int64"
     },
     "execution_count": 814,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_listfile.gender.value_counts()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-11-28T17:42:00.452857Z",
     "end_time": "2023-11-28T17:42:00.601393Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 815,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC curves\n",
    "# ALL\n",
    "fpr, tpr, _ = metrics.roc_curve(\n",
    "    np_test[1][:,1],\n",
    "    test_predictions[:,1]\n",
    ")\n",
    "general_auc = np.round(metrics.roc_auc_score(np_test[1][:, 1], test_predictions[:, 1], average='micro'), 3)\n",
    "low_CI_binary, high_CI_binary = get_ci_auc(np_test[1], test_predictions, metric='AUROC')\n",
    "plt.plot(fpr, tpr, label=\"All, AUC={} [CI {}-{}]\".format(str(general_auc), round(low_CI_binary, 2), round(high_CI_binary, 2)))\n",
    "\n",
    "# MALE\n",
    "fpr, tpr, _ = metrics.roc_curve(\n",
    "    np_test[1][test_listfile.gender == \"Male\"][:,1],\n",
    "    pretrained_model.predict(np_test[0][test_listfile.gender == \"Male\"])[:,1]\n",
    ")\n",
    "auc = np.round(metrics.roc_auc_score(np_test[1][test_listfile.gender == \"Male\", 1], pretrained_model.predict(np_test[0][test_listfile.gender == \"Male\"])[:, 1], average='micro'), 3)\n",
    "low_CI_binary, high_CI_binary = get_ci_auc(np_test[1][test_listfile.gender == \"Male\"], pretrained_model.predict(np_test[0][test_listfile.gender == \"Male\"]), metric='AUROC')\n",
    "plt.plot(fpr, tpr, label=\"Male, AUC={} [CI {}-{}]\".format(str(auc), round(low_CI_binary, 2), round(high_CI_binary, 2)))\n",
    "check_min_max(auc, \"Male\")\n",
    "\n",
    "# FEMALE\n",
    "fpr, tpr, _ = metrics.roc_curve(\n",
    "    np_test[1][test_listfile.gender == \"Female\"][:,1],\n",
    "    pretrained_model.predict(np_test[0][test_listfile.gender == \"Female\"])[:,1]\n",
    ")\n",
    "auc = np.round(metrics.roc_auc_score(np_test[1][test_listfile.gender == \"Female\", 1], pretrained_model.predict(np_test[0][test_listfile.gender == \"Female\"])[:, 1], average='micro'), 3)\n",
    "low_CI_binary, high_CI_binary = get_ci_auc(np_test[1][test_listfile.gender == \"Female\"], pretrained_model.predict(np_test[0][test_listfile.gender == \"Female\"]), metric='AUROC')\n",
    "plt.plot(fpr, tpr, label=\"Female, AUC={} [CI {}-{}]\".format(str(auc), round(low_CI_binary, 2), round(high_CI_binary, 2)))\n",
    "check_min_max(auc, \"Female\")\n",
    "\n",
    "# TRANSGENDER\n",
    "fpr, tpr, _ = metrics.roc_curve(\n",
    "    np_test[1][test_listfile.gender == \"Transgender\"][:,1],\n",
    "    pretrained_model.predict(np_test[0][test_listfile.gender == \"Transgender\"])[:,1]\n",
    ")\n",
    "auc = np.round(metrics.roc_auc_score(np_test[1][test_listfile.gender == \"Transgender\", 1], pretrained_model.predict(np_test[0][test_listfile.gender == \"Transgender\"])[:, 1], average='micro'), 3)\n",
    "low_CI_binary, high_CI_binary = get_ci_auc(np_test[1][test_listfile.gender == \"Transgender\"], pretrained_model.predict(np_test[0][test_listfile.gender == \"Transgender\"]), metric='AUROC')\n",
    "plt.plot(fpr, tpr, label=\"Transgender, AUC={} [CI {}-{}]\".format(str(auc), round(low_CI_binary, 2), round(high_CI_binary, 2)))\n",
    "check_min_max(auc, \"Transgender\")\n",
    "\n",
    "# QUEER\n",
    "# fpr, tpr, _ = metrics.roc_curve(\n",
    "#     np_test[1][test_listfile.gender == \"Genderqueer\"][:,1],\n",
    "#     pretrained_model.predict(np_test[0][test_listfile.gender == \"Genderqueer\"])[:,1]\n",
    "# )\n",
    "# auc = np.round(metrics.roc_auc_score(np_test[1][test_listfile.gender == \"Genderqueer\"], pretrained_model.predict(np_test[0][test_listfile.gender == \"Genderqueer\"]), average='micro'), 3)\n",
    "# low_CI_binary, high_CI_binary = get_ci_auc(np_test[1][test_listfile.gender == \"Genderqueer\"], pretrained_model.predict(np_test[0][test_listfile.gender == \"Genderqueer\"]), metric='AUROC')\n",
    "# plt.plot(fpr, tpr, label=\"Genderqueer, AUC={} [CI {}-{}]\".format(str(auc), round(low_CI_binary, 2), round(high_CI_binary, 2)))\n",
    "# check_min_max(auc, \"Genderqueer\")\n",
    "\n",
    "# OTHER\n",
    "fpr, tpr, _ = metrics.roc_curve(\n",
    "    np_test[1][test_listfile.gender == \"Other\"][:,1],\n",
    "    pretrained_model.predict(np_test[0][test_listfile.gender == \"Other\"])[:,1]\n",
    ")\n",
    "auc = np.round(metrics.roc_auc_score(np_test[1][test_listfile.gender == \"Other\", 1], pretrained_model.predict(np_test[0][test_listfile.gender == \"Other\"])[:, 1], average='micro'), 3)\n",
    "low_CI_binary, high_CI_binary = get_ci_auc(np_test[1][test_listfile.gender == \"Other\"], pretrained_model.predict(np_test[0][test_listfile.gender == \"Other\"]), metric='AUROC')\n",
    "plt.plot(fpr, tpr, label=\"Other, AUC={} [CI {}-{}]\".format(str(auc), round(low_CI_binary, 2), round(high_CI_binary, 2)))\n",
    "check_min_max(auc, \"Other\")\n",
    "\n",
    "plt.plot([0, 1], [0, 1], 'k--', label='chance')  # random predictions curve\n",
    "\n",
    "#create ROC curve\n",
    "plt.title('AUC-ROC conditioned on Gender')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.grid(False)\n",
    "sns.despine()\n",
    "plt.legend(loc='lower right')\n",
    "plt.savefig(os.path.join(image_folder, 'gender.pdf'), bbox_inches='tight')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-11-28T17:42:00.482859Z",
     "end_time": "2023-11-28T17:42:06.935300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 816,
   "outputs": [],
   "source": [
    "# append row to log file arrays\n",
    "append_row(general_auc, 'Gender')\n",
    "reset_min_max()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-11-28T17:42:06.938289Z",
     "end_time": "2023-11-28T17:42:06.964156Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Performance conditioned on Ethnicity"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 817,
   "outputs": [
    {
     "data": {
      "text/plain": "Asian       2915\nWhite       1916\nBiracial     704\nLatinx       281\nBlack        130\nName: race, dtype: int64"
     },
     "execution_count": 817,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_listfile.race.value_counts()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-11-28T17:42:06.952299Z",
     "end_time": "2023-11-28T17:42:07.003024Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 818,
   "outputs": [
    {
     "data": {
      "text/plain": "Asian       606\nWhite       253\nBiracial    114\nLatinx       99\nName: race, dtype: int64"
     },
     "execution_count": 818,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_listfile.race.value_counts()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-11-28T17:42:06.968035Z",
     "end_time": "2023-11-28T17:42:07.004112Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 819,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# test_listfile.race.value_counts()[:10].index.tolist()\n",
    "# test_listfile.race.value_counts()\n",
    "# ROC curves\n",
    "# ALL\n",
    "fpr, tpr, _ = metrics.roc_curve(\n",
    "    np_test[1][:,1],\n",
    "    test_predictions[:,1]\n",
    ")\n",
    "general_auc = np.round(metrics.roc_auc_score(np_test[1][:, 1], test_predictions[:, 1], average='micro'), 3)\n",
    "low_CI_binary, high_CI_binary = get_ci_auc(np_test[1], test_predictions, metric='AUROC')\n",
    "plt.plot(fpr, tpr, label=\"All, AUC={} [CI {}-{}]\".format(str(general_auc), round(low_CI_binary, 2), round(high_CI_binary, 2)))\n",
    "\n",
    "# WHITE\n",
    "fpr, tpr, _ = metrics.roc_curve(\n",
    "    np_test[1][test_listfile.race == \"White\"][:,1],\n",
    "    pretrained_model.predict(np_test[0][test_listfile.race == \"White\"])[:,1]\n",
    ")\n",
    "auc = np.round(metrics.roc_auc_score(np_test[1][test_listfile.race == \"White\", 1], pretrained_model.predict(np_test[0][test_listfile.race == \"White\"])[:, 1], average='micro'), 3)\n",
    "low_CI_binary, high_CI_binary = get_ci_auc(np_test[1][test_listfile.race == \"White\"], pretrained_model.predict(np_test[0][test_listfile.race == \"White\"]), metric='AUROC')\n",
    "plt.plot(fpr, tpr, label=\"White, AUC={} [CI {}-{}]\".format(str(auc), round(low_CI_binary, 2), round(high_CI_binary, 2)))\n",
    "check_min_max(auc, \"White\")\n",
    "\n",
    "# Biracial\n",
    "fpr, tpr, _ = metrics.roc_curve(\n",
    "    np_test[1][test_listfile.race == \"Biracial\"][:,1],\n",
    "    pretrained_model.predict(np_test[0][test_listfile.race == \"Biracial\"])[:,1]\n",
    ")\n",
    "auc = np.round(metrics.roc_auc_score(np_test[1][test_listfile.race == \"Biracial\", 1], pretrained_model.predict(np_test[0][test_listfile.race == \"Biracial\"])[:, 1], average='micro'), 3)\n",
    "low_CI_binary, high_CI_binary = get_ci_auc(np_test[1][test_listfile.race == \"Biracial\"], pretrained_model.predict(np_test[0][test_listfile.race == \"Biracial\"]), metric='AUROC')\n",
    "plt.plot(fpr, tpr, label=\"Biracial, AUC={} [CI {}-{}]\".format(str(auc), round(low_CI_binary, 2), round(high_CI_binary, 2)))\n",
    "check_min_max(auc, \"Biracial\")\n",
    "\n",
    "# ASIAN\n",
    "fpr, tpr, _ = metrics.roc_curve(\n",
    "    np_test[1][test_listfile.race == \"Asian\"][:,1],\n",
    "    pretrained_model.predict(np_test[0][test_listfile.race == \"Asian\"])[:,1]\n",
    ")\n",
    "auc = np.round(metrics.roc_auc_score(np_test[1][test_listfile.race == \"Asian\", 1], pretrained_model.predict(np_test[0][test_listfile.race == \"Asian\"])[:, 1], average='micro'), 3)\n",
    "low_CI_binary, high_CI_binary = get_ci_auc(np_test[1][test_listfile.race == \"Asian\"], pretrained_model.predict(np_test[0][test_listfile.race == \"Asian\"]), metric='AUROC')\n",
    "plt.plot(fpr, tpr, label=\"Asian, AUC={} [CI {}-{}]\".format(str(auc), round(low_CI_binary, 2), round(high_CI_binary, 2)))\n",
    "check_min_max(auc, \"Asian\")\n",
    "\n",
    "# HISPANIC/LATINO\n",
    "fpr, tpr, _ = metrics.roc_curve(\n",
    "    np_test[1][test_listfile.race == \"Latinx\"][:,1],\n",
    "    pretrained_model.predict(np_test[0][test_listfile.race == \"Latinx\"])[:,1]\n",
    ")\n",
    "auc = np.round(metrics.roc_auc_score(np_test[1][test_listfile.race == \"Latinx\", 1], pretrained_model.predict(np_test[0][test_listfile.race == \"Latinx\"])[:, 1], average='micro'), 3)\n",
    "low_CI_binary, high_CI_binary = get_ci_auc(np_test[1][test_listfile.race == \"Latinx\"], pretrained_model.predict(np_test[0][test_listfile.race == \"Latinx\"]), metric='AUROC')\n",
    "plt.plot(fpr, tpr, label=\"Latinx, AUC={} [CI {}-{}]\".format(str(auc), round(low_CI_binary, 2), round(high_CI_binary, 2)))\n",
    "check_min_max(auc, \"Latinx\")\n",
    "\n",
    "plt.plot([0, 1], [0, 1], 'k--', label='chance')  # random predictions curve\n",
    "\n",
    "#create ROC curve\n",
    "plt.title('AUC-ROC conditioned on Ethnicity')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.grid(False)\n",
    "sns.despine()\n",
    "plt.legend(loc='lower right')\n",
    "plt.savefig(os.path.join(image_folder, 'ethnicity.pdf'), bbox_inches='tight')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-11-28T17:42:06.982023Z",
     "end_time": "2023-11-28T17:42:12.848074Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 820,
   "outputs": [],
   "source": [
    "# append row to log file arrays\n",
    "append_row(general_auc, 'ETHNICITY')\n",
    "reset_min_max()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-11-28T17:42:12.849076Z",
     "end_time": "2023-11-28T17:42:12.885374Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Performance conditioned on Disability"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 821,
   "outputs": [
    {
     "data": {
      "text/plain": "No     1040\nYes      43\nName: disability, dtype: int64"
     },
     "execution_count": 821,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_listfile.disability.value_counts()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-11-28T17:42:12.863600Z",
     "end_time": "2023-11-28T17:42:12.886398Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 822,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC curves\n",
    "# ALL\n",
    "fpr, tpr, _ = metrics.roc_curve(\n",
    "    np_test[1][:,1],\n",
    "    test_predictions[:,1]\n",
    ")\n",
    "general_auc = np.round(metrics.roc_auc_score(np_test[1][:, 1], test_predictions[:, 1], average='micro'), 3)\n",
    "low_CI_binary, high_CI_binary = get_ci_auc(np_test[1], test_predictions, metric='AUROC')\n",
    "plt.plot(fpr, tpr, label=\"All, AUC={} [CI {}-{}]\".format(str(general_auc), round(low_CI_binary, 2), round(high_CI_binary, 2)))\n",
    "\n",
    "# No\n",
    "fpr, tpr, _ = metrics.roc_curve(\n",
    "    np_test[1][test_listfile.disability == \"No\"][:,1],\n",
    "    pretrained_model.predict(np_test[0][test_listfile.disability == \"No\"])[:,1]\n",
    ")\n",
    "auc = np.round(metrics.roc_auc_score(np_test[1][test_listfile.disability == \"No\", 1], pretrained_model.predict(np_test[0][test_listfile.disability == \"No\"])[:, 1], average='micro'), 3)\n",
    "low_CI_binary, high_CI_binary = get_ci_auc(np_test[1][test_listfile.disability == \"No\"], pretrained_model.predict(np_test[0][test_listfile.disability == \"No\"]), metric='AUROC')\n",
    "plt.plot(fpr, tpr, label=\"No disability, AUC={} [CI {}-{}]\".format(str(auc), round(low_CI_binary, 2), round(high_CI_binary, 2)))\n",
    "check_min_max(auc, \"No\")\n",
    "\n",
    "# Yes\n",
    "fpr, tpr, _ = metrics.roc_curve(\n",
    "    np_test[1][test_listfile.disability == \"Yes\"][:,1],\n",
    "    pretrained_model.predict(np_test[0][test_listfile.disability == \"Yes\"])[:,1]\n",
    ")\n",
    "auc = np.round(metrics.roc_auc_score(np_test[1][test_listfile.disability == \"Yes\", 1], pretrained_model.predict(np_test[0][test_listfile.disability == \"Yes\"])[:, 1], average='micro'), 3)\n",
    "low_CI_binary, high_CI_binary = get_ci_auc(np_test[1][test_listfile.disability == \"Yes\"], pretrained_model.predict(np_test[0][test_listfile.disability == \"Yes\"]), metric='AUROC')\n",
    "plt.plot(fpr, tpr, label=\"Disability, AUC={} [CI {}-{}]\".format(str(auc), round(low_CI_binary, 2), round(high_CI_binary, 2)))\n",
    "check_min_max(auc, \"Yes\")\n",
    "\n",
    "plt.plot([0, 1], [0, 1], 'k--', label='chance')  # random predictions curve\n",
    "\n",
    "#create ROC curve\n",
    "plt.title('AUC-ROC conditioned on Disability')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.grid(False)\n",
    "sns.despine()\n",
    "plt.legend(loc='lower right')\n",
    "plt.savefig(os.path.join(image_folder, 'disability.pdf'), bbox_inches='tight')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-11-28T17:42:12.888292Z",
     "end_time": "2023-11-28T17:42:18.106776Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 823,
   "outputs": [],
   "source": [
    "# append row to log file arrays\n",
    "append_row(general_auc, 'DISABILITY')\n",
    "reset_min_max()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-11-28T17:42:18.107772Z",
     "end_time": "2023-11-28T17:42:18.159301Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 824,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  metric protected_attribute max_group  max_value  max_delta    min_group  \\\n",
      "0  AUROC              Gender      Male      0.510      0.028  Transgender   \n",
      "1  AUROC           ETHNICITY    Latinx      0.520      0.038        White   \n",
      "2  AUROC          DISABILITY        No      0.486      0.004          Yes   \n",
      "\n",
      "   min_value  min_delta  parameters               model_name  \\\n",
      "0      0.000     -0.482       94754  simclr.frozen.1.08.hdf5   \n",
      "1      0.421     -0.061       94754  simclr.frozen.1.08.hdf5   \n",
      "2      0.382     -0.100       94754  simclr.frozen.1.08.hdf5   \n",
      "\n",
      "                                    subfolder  added_layers  frozen_layers  \\\n",
      "0  20231128-152601_e200_esTrue_bs128_wTrue_f1             2              1   \n",
      "1  20231128-152601_e200_esTrue_bs128_wTrue_f1             2              1   \n",
      "2  20231128-152601_e200_esTrue_bs128_wTrue_f1             2              1   \n",
      "\n",
      "       tag  \n",
      "0  frozen1  \n",
      "1  frozen1  \n",
      "2  frozen1  \n"
     ]
    }
   ],
   "source": [
    "df_fairness_auc = pd.DataFrame({'metric': con_metrics, 'protected_attribute': con_protected_attributes, 'max_group': con_max_groups, 'max_value': con_max_values, 'max_delta': con_max_deltas, 'min_group': con_min_groups, 'min_value': con_min_values, 'min_delta': con_min_deltas, 'parameters': trainable_count, 'model_name': model_name, 'subfolder': subfolder, 'added_layers': added_layers, 'frozen_layers': frozen_layers, 'tag': tag})\n",
    "print(df_fairness_auc.head())\n",
    "df_fairness_auc.to_csv(os.path.join(working_directory, subfolder, model_name.replace('.hdf5', '_conditioned_metrics.csv')), index=False)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-11-28T17:42:18.129775Z",
     "end_time": "2023-11-28T17:42:18.160301Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## AIF360\n",
    "Quantifying biases in data and models\n",
    "### Data Biases\n",
    "In this section, we assess biases in the ground truth labels (NOT predicted labels) in training, validation and test data.\n",
    "#### Train-Validation-Test Set Predictions: Bias Assessment"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 825,
   "outputs": [
    {
     "data": {
      "text/plain": "(8122, 7)"
     },
     "execution_count": 825,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-11-28T17:42:18.139771Z",
     "end_time": "2023-11-28T17:42:18.160301Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 826,
   "outputs": [
    {
     "data": {
      "text/plain": "8122"
     },
     "execution_count": 826,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_true = np.hstack([np_train[1][:,1], np_val[1][:,1], np_test[1][:,1]])\n",
    "len(y_true)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-11-28T17:42:18.158295Z",
     "end_time": "2023-11-28T17:42:18.171308Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 827,
   "outputs": [
    {
     "data": {
      "text/plain": "        PID gender   age   race  generation disability  year  y_true\n0       1.0   Male  18.0  Asian         2.0         No     1     0.0\n1       1.0   Male  18.0  Asian         2.0         No     1     0.0\n2       1.0   Male  18.0  Asian         2.0         No     1     0.0\n3       1.0   Male  18.0  Asian         2.0         No     1     0.0\n4       1.0   Male  18.0  Asian         2.0         No     1     0.0\n...     ...    ...   ...    ...         ...        ...   ...     ...\n1078  999.0   Male  21.0  Asian         1.0         No     4     0.0\n1079  999.0   Male  21.0  Asian         1.0         No     4     0.0\n1080  999.0   Male  21.0  Asian         1.0         No     4     0.0\n1081  999.0   Male  21.0  Asian         1.0         No     4     0.0\n1082  999.0   Male  21.0  Asian         1.0         No     4     0.0\n\n[8122 rows x 8 columns]",
      "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>PID</th>\n      <th>gender</th>\n      <th>age</th>\n      <th>race</th>\n      <th>generation</th>\n      <th>disability</th>\n      <th>year</th>\n      <th>y_true</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.0</td>\n      <td>Male</td>\n      <td>18.0</td>\n      <td>Asian</td>\n      <td>2.0</td>\n      <td>No</td>\n      <td>1</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1.0</td>\n      <td>Male</td>\n      <td>18.0</td>\n      <td>Asian</td>\n      <td>2.0</td>\n      <td>No</td>\n      <td>1</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1.0</td>\n      <td>Male</td>\n      <td>18.0</td>\n      <td>Asian</td>\n      <td>2.0</td>\n      <td>No</td>\n      <td>1</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1.0</td>\n      <td>Male</td>\n      <td>18.0</td>\n      <td>Asian</td>\n      <td>2.0</td>\n      <td>No</td>\n      <td>1</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1.0</td>\n      <td>Male</td>\n      <td>18.0</td>\n      <td>Asian</td>\n      <td>2.0</td>\n      <td>No</td>\n      <td>1</td>\n      <td>0.0</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    </tr>\n    <tr>\n      <th>1078</th>\n      <td>999.0</td>\n      <td>Male</td>\n      <td>21.0</td>\n      <td>Asian</td>\n      <td>1.0</td>\n      <td>No</td>\n      <td>4</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1079</th>\n      <td>999.0</td>\n      <td>Male</td>\n      <td>21.0</td>\n      <td>Asian</td>\n      <td>1.0</td>\n      <td>No</td>\n      <td>4</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1080</th>\n      <td>999.0</td>\n      <td>Male</td>\n      <td>21.0</td>\n      <td>Asian</td>\n      <td>1.0</td>\n      <td>No</td>\n      <td>4</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1081</th>\n      <td>999.0</td>\n      <td>Male</td>\n      <td>21.0</td>\n      <td>Asian</td>\n      <td>1.0</td>\n      <td>No</td>\n      <td>4</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1082</th>\n      <td>999.0</td>\n      <td>Male</td>\n      <td>21.0</td>\n      <td>Asian</td>\n      <td>1.0</td>\n      <td>No</td>\n      <td>4</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>8122 rows × 8 columns</p>\n</div>"
     },
     "execution_count": 827,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:, 'y_true'] = y_true\n",
    "df"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-11-28T17:42:18.170306Z",
     "end_time": "2023-11-28T17:42:18.272008Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 828,
   "outputs": [],
   "source": [
    "protected_attributes = ['gender', 'race', 'disability']\n",
    "privileged_classes = [['Male'], ['White'], ['No']]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-11-28T17:42:18.201301Z",
     "end_time": "2023-11-28T17:42:18.272008Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 829,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Missing Data: 39 rows removed from StandardDataset.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluating Outcome Bias for gender\n",
      "Group Fairness - Mean Difference: 0.09766973734141249\n",
      "Group Fairness - Disparate Impact Ratio: 1.242453459179544\n",
      "\n",
      "Evaluating Outcome Bias for race\n",
      "Group Fairness - Mean Difference: 0.08433062057355284\n",
      "Group Fairness - Disparate Impact Ratio: 1.2089234030257527\n",
      "\n",
      "Evaluating Outcome Bias for disability\n",
      "Group Fairness - Mean Difference: 0.10543596883167844\n",
      "Group Fairness - Disparate Impact Ratio: 1.23180498527643\n"
     ]
    },
    {
     "data": {
      "text/plain": "  protected_attribute  mean_difference  disparate_impact_ratio\n0              gender         0.097670                1.242453\n1                race         0.084331                1.208923\n2          disability         0.105436                1.231805",
      "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>protected_attribute</th>\n      <th>mean_difference</th>\n      <th>disparate_impact_ratio</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>gender</td>\n      <td>0.097670</td>\n      <td>1.242453</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>race</td>\n      <td>0.084331</td>\n      <td>1.208923</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>disability</td>\n      <td>0.105436</td>\n      <td>1.231805</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 829,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = StandardDataset(df,\n",
    "                          label_name='y_true',\n",
    "                          favorable_classes=[1],  # not a favorable outcome, but perhaps the most important\n",
    "                          protected_attribute_names=protected_attributes,\n",
    "                          privileged_classes=privileged_classes)\n",
    "\n",
    "bias_columns_metric = pd.DataFrame(columns=['protected_attribute', 'mean_difference', 'disparate_impact_ratio'])\n",
    "for attr in dataset.protected_attribute_names:\n",
    "    print(\"\\nEvaluating Outcome Bias for {}\".format(attr))\n",
    "    idx = dataset.protected_attribute_names.index(attr)\n",
    "    privileged_groups = [{attr: dataset.privileged_protected_attributes[idx][0]}]\n",
    "    unprivileged_groups = [{attr: dataset.unprivileged_protected_attributes[idx][0]}]\n",
    "\n",
    "    metric_data = BinaryLabelDatasetMetric(dataset, unprivileged_groups=unprivileged_groups,\n",
    "                                           privileged_groups=privileged_groups)\n",
    "    bias_columns_metric.loc[len(bias_columns_metric.index)] = [attr, metric_data.mean_difference(), metric_data.disparate_impact()]\n",
    "    print(\"Group Fairness - Mean Difference: {}\".format(metric_data.mean_difference()))\n",
    "    print(\"Group Fairness - Disparate Impact Ratio: {}\".format(metric_data.disparate_impact()))\n",
    "bias_columns_metric.head(10)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-11-28T17:42:18.222300Z",
     "end_time": "2023-11-28T17:42:18.279919Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 830,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.rcParams.update({'font.size': 15})\n",
    "# plt.rcParams[\"font.weight\"] = \"bold\"\n",
    "plt.rcParams[\"axes.labelweight\"] = \"bold\"\n",
    "ax = bias_columns_metric[['protected_attribute', 'disparate_impact_ratio']].plot(kind='barh', colormap='Dark2')\n",
    "ax.set_ylabel('Protected Attribute')\n",
    "ax.set_xlabel('Disparate Impact Ratio')\n",
    "ax.get_legend().remove()\n",
    "ax.set_yticklabels(bias_columns_metric['protected_attribute'])\n",
    "\n",
    "plt.axvline(x=1.0, color='#e72a8a', linestyle='--')\n",
    "plt.axvspan(0.8, 1.25, facecolor='#e72a8a', alpha=0.1)\n",
    "plt.text(1.01,0,'Optimal Value', rotation=270)\n",
    "plt.title(\"Disparate Impact Ratio for Protected Attributes\")\n",
    "plt.savefig(os.path.join(image_folder, 'representationDIR.pdf'), format='pdf', bbox_inches=\"tight\")\n",
    "plt.savefig(os.path.join(image_folder, 'representationDIR.png'), bbox_inches=\"tight\")\n",
    "# plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-11-28T17:42:18.282915Z",
     "end_time": "2023-11-28T17:42:18.764736Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Test Set Predictions: Bias Assessment"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 831,
   "outputs": [
    {
     "data": {
      "text/plain": "     gender   race disability  y_pred  y_true\n0      Male  Asian        Yes       0     0.0\n1      Male  Asian        Yes       1     0.0\n2      Male  Asian        Yes       1     0.0\n3      Male  Asian        Yes       0     0.0\n4      Male  Asian        Yes       0     0.0\n...     ...    ...        ...     ...     ...\n1078   Male  Asian         No       0     0.0\n1079   Male  Asian         No       0     0.0\n1080   Male  Asian         No       0     0.0\n1081   Male  Asian         No       0     0.0\n1082   Male  Asian         No       0     0.0\n\n[1083 rows x 5 columns]",
      "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>gender</th>\n      <th>race</th>\n      <th>disability</th>\n      <th>y_pred</th>\n      <th>y_true</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Male</td>\n      <td>Asian</td>\n      <td>Yes</td>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Male</td>\n      <td>Asian</td>\n      <td>Yes</td>\n      <td>1</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Male</td>\n      <td>Asian</td>\n      <td>Yes</td>\n      <td>1</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Male</td>\n      <td>Asian</td>\n      <td>Yes</td>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Male</td>\n      <td>Asian</td>\n      <td>Yes</td>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1078</th>\n      <td>Male</td>\n      <td>Asian</td>\n      <td>No</td>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1079</th>\n      <td>Male</td>\n      <td>Asian</td>\n      <td>No</td>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1080</th>\n      <td>Male</td>\n      <td>Asian</td>\n      <td>No</td>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1081</th>\n      <td>Male</td>\n      <td>Asian</td>\n      <td>No</td>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1082</th>\n      <td>Male</td>\n      <td>Asian</td>\n      <td>No</td>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>1083 rows × 5 columns</p>\n</div>"
     },
     "execution_count": 831,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test_listfile.rename({'wake':'y_true'}, axis=1, inplace=True)\n",
    "# test_listfile = test_listfile[['y_true', 'nsrr_age_gt89', 'nsrr_age_gt65', 'nsrr_sex', 'nsrr_race']]\n",
    "test_listfile.loc[:, 'y_true'] = np_test[1][:,1]\n",
    "test_listfile"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-11-28T17:42:18.773777Z",
     "end_time": "2023-11-28T17:42:18.780752Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 832,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Missing Data: 11 rows removed from StandardDataset.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluating Outcome Bias for gender\n",
      "Group Fairness - Mean Difference: 0.16593630383093966\n",
      "Group Fairness - Disparate Impact Ratio: 1.5904245229333434\n",
      "\n",
      "Evaluating Outcome Bias for race\n",
      "Group Fairness - Mean Difference: 0.06265714961367136\n",
      "Group Fairness - Disparate Impact Ratio: 1.1909910705091429\n",
      "\n",
      "Evaluating Outcome Bias for disability\n",
      "Group Fairness - Mean Difference: -0.10091079621217258\n",
      "Group Fairness - Disparate Impact Ratio: 0.7344316897638732\n"
     ]
    },
    {
     "data": {
      "text/plain": "  protected_attribute  mean_difference  disparate_impact_ratio\n0              gender         0.165936                1.590425\n1                race         0.062657                1.190991\n2          disability        -0.100911                0.734432",
      "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>protected_attribute</th>\n      <th>mean_difference</th>\n      <th>disparate_impact_ratio</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>gender</td>\n      <td>0.165936</td>\n      <td>1.590425</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>race</td>\n      <td>0.062657</td>\n      <td>1.190991</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>disability</td>\n      <td>-0.100911</td>\n      <td>0.734432</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 832,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = StandardDataset(test_listfile,\n",
    "                          label_name='y_true',\n",
    "                          favorable_classes=[1],  # not a favorable outcome, but perhaps the most important\n",
    "                          protected_attribute_names=protected_attributes,\n",
    "                          privileged_classes=privileged_classes)\n",
    "\n",
    "bias_columns_metric = pd.DataFrame(columns=['protected_attribute', 'mean_difference', 'disparate_impact_ratio'])\n",
    "for attr in dataset.protected_attribute_names:\n",
    "    print(\"\\nEvaluating Outcome Bias for {}\".format(attr))\n",
    "    idx = dataset.protected_attribute_names.index(attr)\n",
    "    privileged_groups = [{attr: dataset.privileged_protected_attributes[idx][0]}]\n",
    "    unprivileged_groups = [{attr: dataset.unprivileged_protected_attributes[idx][0]}]\n",
    "\n",
    "    metric_data = BinaryLabelDatasetMetric(dataset, unprivileged_groups=unprivileged_groups,\n",
    "                                           privileged_groups=privileged_groups)\n",
    "    bias_columns_metric.loc[len(bias_columns_metric.index)] = [attr, metric_data.mean_difference(), metric_data.disparate_impact()]\n",
    "    print(\"Group Fairness - Mean Difference: {}\".format(metric_data.mean_difference()))\n",
    "    print(\"Group Fairness - Disparate Impact Ratio: {}\".format(metric_data.disparate_impact()))\n",
    "bias_columns_metric.head(10)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-11-28T17:42:18.783751Z",
     "end_time": "2023-11-28T17:42:18.834056Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 833,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.rcParams.update({'font.size': 15})\n",
    "# plt.rcParams[\"font.weight\"] = \"bold\"\n",
    "plt.rcParams[\"axes.labelweight\"] = \"bold\"\n",
    "ax = bias_columns_metric[['protected_attribute', 'disparate_impact_ratio']].plot(kind='barh', colormap='Dark2')\n",
    "ax.set_ylabel('Protected Attribute')\n",
    "ax.set_xlabel('Disparate Impact Ratio')\n",
    "ax.get_legend().remove()\n",
    "ax.set_yticklabels(bias_columns_metric['protected_attribute'])\n",
    "\n",
    "plt.axvline(x=1.0, color='#e72a8a', linestyle='--')\n",
    "plt.axvspan(0.8, 1.25, facecolor='#e72a8a', alpha=0.1)\n",
    "plt.text(1.01,0,'Optimal Value', rotation=270)\n",
    "plt.title(\"Disparate Impact Ratio for Protected Attributes\")\n",
    "plt.savefig(os.path.join(image_folder, 'representationDIR.pdf'), format='pdf', bbox_inches=\"tight\")\n",
    "plt.savefig(os.path.join(image_folder, 'representationDIR.png'), bbox_inches=\"tight\")\n",
    "# plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-11-28T17:42:18.815057Z",
     "end_time": "2023-11-28T17:42:19.327185Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Model Biases\n",
    "In this section, we assess biases in the predicted labels in the training, validation, and test data, or in the test data only.\n",
    "**Interpretations:**\n",
    "* Disparate Impact Ratio (Same with Statistical Parity Difference): How many users receive mortality predictions in the unprivileged group compared to the privileged group?\n",
    "* False Positive Rate Ratio: From all the patients who did not eventually die, how many wrongfully received a mortality prediction?\n",
    "* False Discovery Rate Ratio: TBA\n",
    "* **False Negative Rate Ratio:** From all the patients who did die, how many had wrongfully received a non-mortality prediction?\n",
    "* **False Omission Rate Ratio:** From all the patients who received a non-mortality prediction (rightfully or not), how many actually died?\n",
    "* Error Rate Ratio: How many times was the mortality prediction model wrong?\n",
    "* Equal Opportunity Difference: From all the patients who did eventually die, how many received a mortality prediction?\n",
    "* Average Absolute Odds Difference: TBD\n",
    "\n",
    "![image](metrictree.png)\n",
    "#### Test Set Predictions: Bias Assessment"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 834,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "------------- ATTRIBUTE: gender -------------\n",
      "\n",
      "statistical_parity_difference: -0.027599715099715094\n",
      "disparate_impact: 0.9322552447552448\n",
      "equal_opportunity_difference: -0.07687803245775476\n",
      "average_absolute_odds_difference: 0.042126176852454206\n",
      "error_rate_difference: 0.06767429193899777\n",
      "error_rate_ratio: 1.1493389423076923\n",
      "false_discovery_rate_ratio: 0.8424225119761429\n",
      "false_negative_rate_ratio: 1.132230215827338\n",
      "false_omission_rate_ratio: 1.7149354005167958\n",
      "false_positive_rate_ratio: 1.0182971880568474\n",
      "true_positive_rate_difference: -0.07687803245775476\n",
      "Privileged Group: {'TP': 54.0, 'FP': 133.0, 'TN': 197.0, 'FN': 75.0}\n",
      "Unprivileged Group: {'TP': 95.0, 'FP': 142.0, 'TN': 204.0, 'FN': 183.0}\n",
      "\n",
      "Overall Accuracy: 0.5078485687903971\n",
      "Privileged Group: 0.5468409586056645\n",
      "Unprivileged Group: 0.4791666666666667\n",
      "\n",
      "Overall Error Rate: 0.4921514312096029\n",
      "Privileged Group: 0.4531590413943355\n",
      "Unprivileged Group: 0.5208333333333333\n",
      "\n",
      "------------- ATTRIBUTE: race -------------\n",
      "\n",
      "statistical_parity_difference: -0.044414522675392254\n",
      "disparate_impact: 0.8949824837675304\n",
      "equal_opportunity_difference: -0.026167168674698815\n",
      "average_absolute_odds_difference: 0.038281038068340795\n",
      "error_rate_difference: -0.009623227014531333\n",
      "error_rate_ratio: 0.9806771711533617\n",
      "false_discovery_rate_ratio: 0.8974193548387097\n",
      "false_negative_rate_ratio: 1.0425857843137256\n",
      "false_omission_rate_ratio: 1.1529719942987018\n",
      "false_positive_rate_ratio: 0.8857715430861723\n",
      "true_positive_rate_difference: -0.026167168674698815\n",
      "Privileged Group: {'TP': 32.0, 'FP': 75.0, 'TN': 95.0, 'FN': 51.0}\n",
      "Unprivileged Group: {'TP': 115.0, 'FP': 195.0, 'TN': 304.0, 'FN': 205.0}\n",
      "\n",
      "Overall Accuracy: 0.5093283582089553\n",
      "Privileged Group: 0.5019762845849802\n",
      "Unprivileged Group: 0.5115995115995116\n",
      "\n",
      "Overall Error Rate: 0.4906716417910447\n",
      "Privileged Group: 0.4980237154150198\n",
      "Unprivileged Group: 0.48840048840048844\n",
      "\n",
      "------------- ATTRIBUTE: disability -------------\n",
      "\n",
      "statistical_parity_difference: -0.04443202146690517\n",
      "disparate_impact: 0.8870188207198499\n",
      "equal_opportunity_difference: -0.11962025316455699\n",
      "average_absolute_odds_difference: 0.07013770847775236\n",
      "error_rate_difference: -0.003935599284436542\n",
      "error_rate_ratio: 0.9920058139534883\n",
      "false_discovery_rate_ratio: 1.244106463878327\n",
      "false_negative_rate_ratio: 1.1897590361445782\n",
      "false_omission_rate_ratio: 0.8145438898450947\n",
      "false_positive_rate_ratio: 0.9493437998282841\n",
      "true_positive_rate_difference: -0.11962025316455699\n",
      "Privileged Group: {'TP': 146.0, 'FP': 263.0, 'TN': 382.0, 'FN': 249.0}\n",
      "Unprivileged Group: {'TP': 3.0, 'FP': 12.0, 'TN': 19.0, 'FN': 9.0}\n",
      "\n",
      "Overall Accuracy: 0.5078485687903971\n",
      "Privileged Group: 0.5076923076923077\n",
      "Unprivileged Group: 0.5116279069767442\n",
      "\n",
      "Overall Error Rate: 0.4921514312096029\n",
      "Privileged Group: 0.49230769230769234\n",
      "Unprivileged Group: 0.4883720930232558\n"
     ]
    }
   ],
   "source": [
    "# Dataset containing predictions\n",
    "df_protected = []\n",
    "df_metric = []\n",
    "df_value = []\n",
    "\n",
    "for protected_attribute in protected_attributes:\n",
    "    print(\"\\n------------- ATTRIBUTE: {} -------------\\n\".format(protected_attribute))\n",
    "    true_values, predictions = prepare_dataset_aif360(protected_attribute, test_listfile)\n",
    "    metric_pred, classified_metric = aif360_model(true_values, predictions, protected_attribute, privileged_classes[protected_attributes.index(protected_attribute)], favorable_class=1)\n",
    "    fairness_metrics = print_aif360_result(metric_pred, classified_metric)\n",
    "    df_protected = df_protected + [protected_attribute for x in range(len(fairness_metrics))]\n",
    "    df_metric = df_metric + list(fairness_metrics.keys())\n",
    "    df_value = df_value + list(fairness_metrics.values())\n",
    "    # evaluating in terms of accuracy\n",
    "    print_aif360_accuracy_metrics(classified_metric)\n",
    "df_fairness_metrics = pd.DataFrame({'protected_attribute': df_protected, 'fairness_metric': df_metric, 'value': df_value, 'parameters': trainable_count, 'model_name': model_name, 'subfolder': subfolder, 'added_layers': added_layers, 'frozen_layers': frozen_layers, 'tag': tag})"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-11-28T17:42:19.331180Z",
     "end_time": "2023-11-28T17:42:19.375920Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 835,
   "outputs": [
    {
     "data": {
      "text/plain": "   protected_attribute                   fairness_metric     value  \\\n0               gender     statistical_parity_difference -0.027600   \n1               gender                  disparate_impact  0.932255   \n2               gender      equal_opportunity_difference -0.076878   \n3               gender  average_absolute_odds_difference  0.042126   \n4               gender             error_rate_difference  0.067674   \n5               gender                  error_rate_ratio  1.149339   \n6               gender        false_discovery_rate_ratio  0.842423   \n7               gender         false_negative_rate_ratio  1.132230   \n8               gender         false_omission_rate_ratio  1.714935   \n9               gender         false_positive_rate_ratio  1.018297   \n10              gender     true_positive_rate_difference -0.076878   \n11                race     statistical_parity_difference -0.044415   \n12                race                  disparate_impact  0.894982   \n13                race      equal_opportunity_difference -0.026167   \n14                race  average_absolute_odds_difference  0.038281   \n15                race             error_rate_difference -0.009623   \n16                race                  error_rate_ratio  0.980677   \n17                race        false_discovery_rate_ratio  0.897419   \n18                race         false_negative_rate_ratio  1.042586   \n19                race         false_omission_rate_ratio  1.152972   \n20                race         false_positive_rate_ratio  0.885772   \n21                race     true_positive_rate_difference -0.026167   \n22          disability     statistical_parity_difference -0.044432   \n23          disability                  disparate_impact  0.887019   \n24          disability      equal_opportunity_difference -0.119620   \n25          disability  average_absolute_odds_difference  0.070138   \n26          disability             error_rate_difference -0.003936   \n27          disability                  error_rate_ratio  0.992006   \n28          disability        false_discovery_rate_ratio  1.244106   \n29          disability         false_negative_rate_ratio  1.189759   \n30          disability         false_omission_rate_ratio  0.814544   \n31          disability         false_positive_rate_ratio  0.949344   \n32          disability     true_positive_rate_difference -0.119620   \n\n    parameters               model_name  \\\n0        94754  simclr.frozen.1.08.hdf5   \n1        94754  simclr.frozen.1.08.hdf5   \n2        94754  simclr.frozen.1.08.hdf5   \n3        94754  simclr.frozen.1.08.hdf5   \n4        94754  simclr.frozen.1.08.hdf5   \n5        94754  simclr.frozen.1.08.hdf5   \n6        94754  simclr.frozen.1.08.hdf5   \n7        94754  simclr.frozen.1.08.hdf5   \n8        94754  simclr.frozen.1.08.hdf5   \n9        94754  simclr.frozen.1.08.hdf5   \n10       94754  simclr.frozen.1.08.hdf5   \n11       94754  simclr.frozen.1.08.hdf5   \n12       94754  simclr.frozen.1.08.hdf5   \n13       94754  simclr.frozen.1.08.hdf5   \n14       94754  simclr.frozen.1.08.hdf5   \n15       94754  simclr.frozen.1.08.hdf5   \n16       94754  simclr.frozen.1.08.hdf5   \n17       94754  simclr.frozen.1.08.hdf5   \n18       94754  simclr.frozen.1.08.hdf5   \n19       94754  simclr.frozen.1.08.hdf5   \n20       94754  simclr.frozen.1.08.hdf5   \n21       94754  simclr.frozen.1.08.hdf5   \n22       94754  simclr.frozen.1.08.hdf5   \n23       94754  simclr.frozen.1.08.hdf5   \n24       94754  simclr.frozen.1.08.hdf5   \n25       94754  simclr.frozen.1.08.hdf5   \n26       94754  simclr.frozen.1.08.hdf5   \n27       94754  simclr.frozen.1.08.hdf5   \n28       94754  simclr.frozen.1.08.hdf5   \n29       94754  simclr.frozen.1.08.hdf5   \n30       94754  simclr.frozen.1.08.hdf5   \n31       94754  simclr.frozen.1.08.hdf5   \n32       94754  simclr.frozen.1.08.hdf5   \n\n                                     subfolder  added_layers  frozen_layers  \\\n0   20231128-152601_e200_esTrue_bs128_wTrue_f1             2              1   \n1   20231128-152601_e200_esTrue_bs128_wTrue_f1             2              1   \n2   20231128-152601_e200_esTrue_bs128_wTrue_f1             2              1   \n3   20231128-152601_e200_esTrue_bs128_wTrue_f1             2              1   \n4   20231128-152601_e200_esTrue_bs128_wTrue_f1             2              1   \n5   20231128-152601_e200_esTrue_bs128_wTrue_f1             2              1   \n6   20231128-152601_e200_esTrue_bs128_wTrue_f1             2              1   \n7   20231128-152601_e200_esTrue_bs128_wTrue_f1             2              1   \n8   20231128-152601_e200_esTrue_bs128_wTrue_f1             2              1   \n9   20231128-152601_e200_esTrue_bs128_wTrue_f1             2              1   \n10  20231128-152601_e200_esTrue_bs128_wTrue_f1             2              1   \n11  20231128-152601_e200_esTrue_bs128_wTrue_f1             2              1   \n12  20231128-152601_e200_esTrue_bs128_wTrue_f1             2              1   \n13  20231128-152601_e200_esTrue_bs128_wTrue_f1             2              1   \n14  20231128-152601_e200_esTrue_bs128_wTrue_f1             2              1   \n15  20231128-152601_e200_esTrue_bs128_wTrue_f1             2              1   \n16  20231128-152601_e200_esTrue_bs128_wTrue_f1             2              1   \n17  20231128-152601_e200_esTrue_bs128_wTrue_f1             2              1   \n18  20231128-152601_e200_esTrue_bs128_wTrue_f1             2              1   \n19  20231128-152601_e200_esTrue_bs128_wTrue_f1             2              1   \n20  20231128-152601_e200_esTrue_bs128_wTrue_f1             2              1   \n21  20231128-152601_e200_esTrue_bs128_wTrue_f1             2              1   \n22  20231128-152601_e200_esTrue_bs128_wTrue_f1             2              1   \n23  20231128-152601_e200_esTrue_bs128_wTrue_f1             2              1   \n24  20231128-152601_e200_esTrue_bs128_wTrue_f1             2              1   \n25  20231128-152601_e200_esTrue_bs128_wTrue_f1             2              1   \n26  20231128-152601_e200_esTrue_bs128_wTrue_f1             2              1   \n27  20231128-152601_e200_esTrue_bs128_wTrue_f1             2              1   \n28  20231128-152601_e200_esTrue_bs128_wTrue_f1             2              1   \n29  20231128-152601_e200_esTrue_bs128_wTrue_f1             2              1   \n30  20231128-152601_e200_esTrue_bs128_wTrue_f1             2              1   \n31  20231128-152601_e200_esTrue_bs128_wTrue_f1             2              1   \n32  20231128-152601_e200_esTrue_bs128_wTrue_f1             2              1   \n\n        tag  \n0   frozen1  \n1   frozen1  \n2   frozen1  \n3   frozen1  \n4   frozen1  \n5   frozen1  \n6   frozen1  \n7   frozen1  \n8   frozen1  \n9   frozen1  \n10  frozen1  \n11  frozen1  \n12  frozen1  \n13  frozen1  \n14  frozen1  \n15  frozen1  \n16  frozen1  \n17  frozen1  \n18  frozen1  \n19  frozen1  \n20  frozen1  \n21  frozen1  \n22  frozen1  \n23  frozen1  \n24  frozen1  \n25  frozen1  \n26  frozen1  \n27  frozen1  \n28  frozen1  \n29  frozen1  \n30  frozen1  \n31  frozen1  \n32  frozen1  ",
      "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>protected_attribute</th>\n      <th>fairness_metric</th>\n      <th>value</th>\n      <th>parameters</th>\n      <th>model_name</th>\n      <th>subfolder</th>\n      <th>added_layers</th>\n      <th>frozen_layers</th>\n      <th>tag</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>gender</td>\n      <td>statistical_parity_difference</td>\n      <td>-0.027600</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>gender</td>\n      <td>disparate_impact</td>\n      <td>0.932255</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>gender</td>\n      <td>equal_opportunity_difference</td>\n      <td>-0.076878</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>gender</td>\n      <td>average_absolute_odds_difference</td>\n      <td>0.042126</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>gender</td>\n      <td>error_rate_difference</td>\n      <td>0.067674</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>gender</td>\n      <td>error_rate_ratio</td>\n      <td>1.149339</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>gender</td>\n      <td>false_discovery_rate_ratio</td>\n      <td>0.842423</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>gender</td>\n      <td>false_negative_rate_ratio</td>\n      <td>1.132230</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>gender</td>\n      <td>false_omission_rate_ratio</td>\n      <td>1.714935</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>gender</td>\n      <td>false_positive_rate_ratio</td>\n      <td>1.018297</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>gender</td>\n      <td>true_positive_rate_difference</td>\n      <td>-0.076878</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>race</td>\n      <td>statistical_parity_difference</td>\n      <td>-0.044415</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>race</td>\n      <td>disparate_impact</td>\n      <td>0.894982</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>race</td>\n      <td>equal_opportunity_difference</td>\n      <td>-0.026167</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>race</td>\n      <td>average_absolute_odds_difference</td>\n      <td>0.038281</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>race</td>\n      <td>error_rate_difference</td>\n      <td>-0.009623</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>race</td>\n      <td>error_rate_ratio</td>\n      <td>0.980677</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>race</td>\n      <td>false_discovery_rate_ratio</td>\n      <td>0.897419</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>race</td>\n      <td>false_negative_rate_ratio</td>\n      <td>1.042586</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>race</td>\n      <td>false_omission_rate_ratio</td>\n      <td>1.152972</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>race</td>\n      <td>false_positive_rate_ratio</td>\n      <td>0.885772</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>race</td>\n      <td>true_positive_rate_difference</td>\n      <td>-0.026167</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>disability</td>\n      <td>statistical_parity_difference</td>\n      <td>-0.044432</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>disability</td>\n      <td>disparate_impact</td>\n      <td>0.887019</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>disability</td>\n      <td>equal_opportunity_difference</td>\n      <td>-0.119620</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>disability</td>\n      <td>average_absolute_odds_difference</td>\n      <td>0.070138</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>disability</td>\n      <td>error_rate_difference</td>\n      <td>-0.003936</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>disability</td>\n      <td>error_rate_ratio</td>\n      <td>0.992006</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>disability</td>\n      <td>false_discovery_rate_ratio</td>\n      <td>1.244106</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>disability</td>\n      <td>false_negative_rate_ratio</td>\n      <td>1.189759</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>disability</td>\n      <td>false_omission_rate_ratio</td>\n      <td>0.814544</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>disability</td>\n      <td>false_positive_rate_ratio</td>\n      <td>0.949344</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>disability</td>\n      <td>true_positive_rate_difference</td>\n      <td>-0.119620</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 835,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fairness_metrics"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-11-28T17:42:19.380916Z",
     "end_time": "2023-11-28T17:42:19.448473Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 836,
   "outputs": [
    {
     "data": {
      "text/plain": "  protected_attribute                   fairness_metric     value  parameters  \\\n0              gender     statistical_parity_difference -0.027600       94754   \n1              gender                  disparate_impact  0.932255       94754   \n2              gender      equal_opportunity_difference -0.076878       94754   \n3              gender  average_absolute_odds_difference  0.042126       94754   \n4              gender             error_rate_difference  0.067674       94754   \n\n                model_name                                   subfolder  \\\n0  simclr.frozen.1.08.hdf5  20231128-152601_e200_esTrue_bs128_wTrue_f1   \n1  simclr.frozen.1.08.hdf5  20231128-152601_e200_esTrue_bs128_wTrue_f1   \n2  simclr.frozen.1.08.hdf5  20231128-152601_e200_esTrue_bs128_wTrue_f1   \n3  simclr.frozen.1.08.hdf5  20231128-152601_e200_esTrue_bs128_wTrue_f1   \n4  simclr.frozen.1.08.hdf5  20231128-152601_e200_esTrue_bs128_wTrue_f1   \n\n   added_layers  frozen_layers      tag  \n0             2              1  frozen1  \n1             2              1  frozen1  \n2             2              1  frozen1  \n3             2              1  frozen1  \n4             2              1  frozen1  ",
      "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>protected_attribute</th>\n      <th>fairness_metric</th>\n      <th>value</th>\n      <th>parameters</th>\n      <th>model_name</th>\n      <th>subfolder</th>\n      <th>added_layers</th>\n      <th>frozen_layers</th>\n      <th>tag</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>gender</td>\n      <td>statistical_parity_difference</td>\n      <td>-0.027600</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>gender</td>\n      <td>disparate_impact</td>\n      <td>0.932255</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>gender</td>\n      <td>equal_opportunity_difference</td>\n      <td>-0.076878</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>gender</td>\n      <td>average_absolute_odds_difference</td>\n      <td>0.042126</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>gender</td>\n      <td>error_rate_difference</td>\n      <td>0.067674</td>\n      <td>94754</td>\n      <td>simclr.frozen.1.08.hdf5</td>\n      <td>20231128-152601_e200_esTrue_bs128_wTrue_f1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>frozen1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 836,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fairness_metrics.to_csv(os.path.join(working_directory, subfolder, model_name.replace('.hdf5', '_fairness_metrics.csv')), index=False)\n",
    "df_fairness_metrics.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-11-28T17:42:19.408732Z",
     "end_time": "2023-11-28T17:42:19.479977Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 837,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- Test Performance Overall ---\n",
      "{'Accuracy': 0.5078485687903971, 'Balanced Accuracy': 0.4796443161827777, 'AUROC': 0.4823175784714246, 'AUPRC Macro': 0.3686948688724948, 'AUPRC Micro': 0.3686948688724948, 'Confusion Matrix': array([[401, 275],\n",
      "       [258, 149]], dtype=int64), 'F1 Macro': 0.47967657756324455, 'F1 Micro': 0.5078485687903971, 'F1 Weighted': 0.50974906026207, 'Precision': 0.47995640908179915, 'Recall': 0.4796443161827777, 'Kappa': -0.04037582208828905}\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n--- Test Performance Overall ---\")\n",
    "test_predictions = pretrained_model.predict(np_test[0])\n",
    "test_performance = simclr_utitlities.evaluate_model_simple(test_predictions, np_test[1], return_dict=True)\n",
    "print(test_performance)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-11-28T17:42:19.422295Z",
     "end_time": "2023-11-28T17:42:20.014205Z"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}