{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Multi Objective Optimization with Credit Risk Analysis\n",
    "Here we will optimize our AI model for multiple objectives - Performance with Area Under Curve (AUC), and Fairness with Statistical Disparity (SD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "#Cell magic to hide outputs \n",
    "\n",
    "#Install dependencies\n",
    "from io import StringIO\n",
    "import os\n",
    "import time\n",
    "import sys\n",
    "import IPython\n",
    "from time import gmtime, strftime\n",
    "\n",
    "import boto3\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import urllib\n",
    "\n",
    "import sagemaker\n",
    "from sagemaker.s3 import S3Uploader\n",
    "from sagemaker.processing import ProcessingInput, ProcessingOutput\n",
    "from sagemaker.sklearn.processing import SKLearnProcessor\n",
    "from sagemaker.inputs import TrainingInput\n",
    "from sagemaker.xgboost import XGBoost\n",
    "from sagemaker.s3 import S3Downloader\n",
    "from sagemaker.s3 import S3Uploader\n",
    "from sagemaker import Session\n",
    "from sagemaker import get_execution_role\n",
    "from sagemaker.xgboost import XGBoostModel\n",
    "from sagemaker.sklearn import SKLearnModel\n",
    "from sagemaker.pipeline import PipelineModel\n",
    "from sagemaker.inputs import TrainingInput\n",
    "\n",
    "\n",
    "session = Session()\n",
    "bucket = session.default_bucket()\n",
    "prefix = \"sagemaker/sagemaker-amt-credit-risk-model\"\n",
    "region = session.boto_region_name\n",
    "\n",
    "# Define IAM role\n",
    "role = get_execution_role()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pygmentize processing/preprocessor.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Train XGBoost Model\n",
    "In this step, we will train an XGBoost model on the preprocessed data. We will use our own training script with the built-in XGBoost container provided by SageMaker.\n",
    "\n",
    "Alternatively, for your own use case, you can also bring your own model (trained elsewhere) to SageMaker for processing with SageMaker Clarify\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set up XGBoost Estimator\n",
    "\n",
    "Next, let us set up:    \n",
    " 1. Pre-defined values for Hyperparameters for XGBoost algorithm\n",
    " 1. XGBoost Estimator for SageMaker\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "hyperparameters = {\n",
    "    \"max_depth\": \"5\",\n",
    "    \"eta\": \"0.1\",\n",
    "    \"gamma\": \"4\",\n",
    "    \"min_child_weight\": \"6\",\n",
    "    \"silent\": \"1\",\n",
    "    \"objective\": \"binary:logistic\",\n",
    "    \"num_round\": \"100\",\n",
    "    \"subsample\": \"0.8\",\n",
    "    \"eval_metric\": \"auc\",\n",
    "    \"early_stopping_rounds\": \"20\",\n",
    "    \"output_data_dir\": \"/opt/ml/output/data/\",\n",
    "}\n",
    "\n",
    "entry_point = \"train_xg_amt_multi.py\"\n",
    "source_dir = \"training/\"\n",
    "output_path = \"s3://{0}/{1}/{2}\".format(bucket, prefix, \"xgb_model\")\n",
    "code_location = \"s3://{0}/{1}/code\".format(bucket, prefix)\n",
    "\n",
    "estimator = XGBoost(\n",
    "    entry_point=entry_point,\n",
    "    source_dir=source_dir,\n",
    "    output_path=output_path,\n",
    "    code_location=code_location,\n",
    "    hyperparameters=hyperparameters,\n",
    "    instance_type=\"ml.c5.xlarge\",\n",
    "    instance_count=1,\n",
    "    framework_version=\"0.90-2\",\n",
    "    py_version=\"py3\",\n",
    "    role=role,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### SageMaker AMT (HPO)\n",
    "\n",
    "Now it's time to run the HPO job to train and find the best model \n",
    "\n",
    "#### NOTE: THIS CELL WILL RUN FOR APPROX. 5-8 MINUTES! PLEASE BE PATIENT."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%store -r train_data_path\n",
    "%store -r val_data_path\n",
    "%store -r model_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No finished training job found associated with this estimator. Please make sure this estimator is only used for building workflow config\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...................................................................................................................................................!\n",
      "!\n"
     ]
    }
   ],
   "source": [
    "# Automatic Model Tuning (HPO)\n",
    "\n",
    "# output_data_dir = 's3://sagemaker-us-east-2-921553072635/sagemaker/sagemaker-amt-credit-risk-model/data/output/'\n",
    "\n",
    "from sagemaker.tuner import IntegerParameter, CategoricalParameter, ContinuousParameter, HyperparameterTuner\n",
    "\n",
    "hyperparameter_ranges = {'eta': ContinuousParameter(1e-3, 1, 'Logarithmic'),\n",
    "                        'min_child_weight': ContinuousParameter(1e-6, 10, 'Logarithmic'),\n",
    "                        'gamma': ContinuousParameter(1e-6, 10, 'Logarithmic'),\n",
    "                        'max_depth': IntegerParameter(2, 10, 'Linear')}\n",
    "\n",
    "objective_metric_name = 'validation:auc'\n",
    "\n",
    "tuner = HyperparameterTuner(estimator,\n",
    "                            objective_metric_name,\n",
    "                            hyperparameter_ranges,\n",
    "                            max_jobs=100, # 100\n",
    "                            max_parallel_jobs=10, #16\n",
    "                           )\n",
    "\n",
    "tuning_job_name = \"xgb-tuner-{}\".format(strftime(\"%d-%H-%M-%S\", gmtime()))\n",
    "\n",
    "# inputs = {'train': train_data_path, 'validation': val_data_path, 'output_data_dir': output_data_dir}\n",
    "\n",
    "inputs = {'train': train_data_path, 'validation': val_data_path}\n",
    "\n",
    "tuner.fit(inputs, job_name=tuning_job_name)\n",
    "\n",
    "tuner.wait()\n",
    "\n",
    "tuner_metrics = sagemaker.HyperparameterTuningJobAnalytics(tuning_job_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'xgb-tuner-27-00-32-14-052-be514283'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuner.best_training_job()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgboost_model_data = (\n",
    "    \"s3://{}/{}/{}/{}\".format(bucket, prefix, \"xgb_model\", tuner.best_training_job()) + \"/output/model.tar.gz\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgboost_inference_code_location = \"s3://{}/{}/{}/code\".format(bucket, prefix, \"xgb_model\")\n",
    "\n",
    "xgboost_model = XGBoostModel(\n",
    "    name=\"xgb-model-{0}\".format(str(int(time.time()))),\n",
    "    model_data=xgboost_model_data,\n",
    "    entry_point=\"inference.py\",\n",
    "    source_dir=\"inference/xgboost/\",\n",
    "    code_location=xgboost_inference_code_location,\n",
    "    framework_version=\"0.90-2\",\n",
    "    py_version=\"py3\",\n",
    "    role=role,\n",
    "    sagemaker_session=session,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EndpointName = MULTI-2023-01-27-0044\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "endpoint_name = f\"MULTI-{datetime.utcnow():%Y-%m-%d-%H%M}\"\n",
    "print(\"EndpointName =\", endpoint_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----!"
     ]
    }
   ],
   "source": [
    "#Create Endpoint Instance\n",
    "\n",
    "#Instance Count\n",
    "initial_instance_count=1 # Example\n",
    "#instance Type\n",
    "instance_type='ml.m5.large' # Example\n",
    "\n",
    "FinalModel = xgboost_model.deploy(\n",
    "    initial_instance_count=initial_instance_count,\n",
    "    instance_type=instance_type,\n",
    "    endpoint_name=endpoint_name\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'s3://sagemaker-us-east-2-921553072635/sagemaker/sagemaker-amt-credit-risk-model/data/preprocessed/val/val_features.csv'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Validation\n",
    "n = val_data_path+\"val_features.csv\"\n",
    "test_dataset = S3Downloader.read_file(n)\n",
    "n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44157\n",
      "221\n"
     ]
    }
   ],
   "source": [
    "print(len(test_dataset))\n",
    "test_dataset = test_dataset.split(\"\\n\")\n",
    "print(len(test_dataset))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(test_dataset)-1):\n",
    "    test_dataset[i] = np.fromstring(test_dataset[i], dtype=float, sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Set up predictor \n",
    "from sagemaker.serializers import CSVSerializer\n",
    "predictor = sagemaker.predictor.Predictor(\n",
    "    endpoint_name,\n",
    "    session,\n",
    "    serializer=sagemaker.serializers.CSVSerializer(),\n",
    "    deserializer=sagemaker.deserializers.CSVDeserializer(),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Make Predictions on the test dataset\n",
    "Multi_Optimized_Predictions = []\n",
    "for i in range(len(test_dataset)-1):\n",
    "    Multi_Optimized_Predictions.append(predictor.predict(test_dataset[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['0.9327612']]\n",
      "[['0.86414945']]\n",
      "[['0.9440027']]\n"
     ]
    }
   ],
   "source": [
    "#See first 3 predictions and how they look. We have the double brackets meaning each element is a 3 dimensional element, we will make them a single dimension in the next cell \n",
    "print(Multi_Optimized_Predictions[0])\n",
    "print(Multi_Optimized_Predictions[1])\n",
    "print(Multi_Optimized_Predictions[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Turn each item into a single dimension element in the list  \n",
    "Predictions = []\n",
    "for i in range(len(Multi_Optimized_Predictions)):\n",
    "    Predictions.append(Multi_Optimized_Predictions[i][0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9327612\n",
      "0.86414945\n",
      "0.9440027\n"
     ]
    }
   ],
   "source": [
    "#Look at first 3 processed predictions. They are now i nthe format we want\n",
    "print(Predictions[0])\n",
    "print(Predictions[1])\n",
    "print(Predictions[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Each prediction value is between 0 - 1. But the target variable \"Credit Risk\" is either bad (0) or good (1) there for we will round up/down to ensure every value is either 0 or 1\n",
    "for i in range(len(Predictions)):\n",
    "    if (float(Predictions[i]) >= float(0.5)):\n",
    "        Predictions[i] = 1 \n",
    "    elif (float(Predictions[i]) < float(0.5)):\n",
    "        Predictions[i] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "1\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "#Look at first 3 predictions now to confirm we will be able to see they are either 0 or 1 \n",
    "print(Predictions[0])\n",
    "print(Predictions[1])\n",
    "print(Predictions[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "71\n",
      "149\n"
     ]
    }
   ],
   "source": [
    "#Check how many predictions were 0 (Bad Credit) and 1(Good Credit)\n",
    "print(Predictions.count(0))\n",
    "print(Predictions.count(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Graph the predictions as a pie graph\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "slices = [Predictions.count(0),Predictions.count(1)]\n",
    "labels = ['Bad Credit', 'Good Credit']\n",
    "colors = ['red', 'green']\n",
    "explode = [0, 0.1]\n",
    "plt.pie(slices, labels=labels, autopct='%1.1f%%', explode=explode, colors=colors, wedgeprops={'edgecolor': 'black'})\n",
    "plt.title(\"Credit Risk Ratio For a Multi Optimized Model - All\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now let us take a look at Foreign worker predictions only. the last element in the list represents Foreign workers (1 means foreign 2 means non-foreign)\n",
    "filtered_dataset=[]\n",
    "for i in range(len(test_dataset)-1):\n",
    "    if test_dataset[i][-1] == 1:\n",
    "        filtered_dataset.append(test_dataset[i])\n",
    "len(filtered_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(filtered_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Make Predictions on the test dataset\n",
    "Multi_F_Predictions = []\n",
    "for i in range(len(filtered_dataset)):\n",
    "    Multi_F_Predictions.append(predictor.predict(filtered_dataset[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Turn each item into a single dimension element in the list  \n",
    "for i in range(len(Multi_F_Predictions)):\n",
    "    Multi_F_Predictions[i]=(Multi_F_Predictions[i][0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['0.24157475',\n",
       " '4.7756188e-05',\n",
       " '0.06342731',\n",
       " '0.93585235',\n",
       " '6.523058e-05',\n",
       " '0.5261048',\n",
       " '0.7142568',\n",
       " '0.014334942',\n",
       " '0.017389935',\n",
       " '9.352473e-05',\n",
       " '0.6630546',\n",
       " '0.4402347',\n",
       " '0.044109173',\n",
       " '0.13703965',\n",
       " '0.13513479',\n",
       " '0.09022986',\n",
       " '0.27975675',\n",
       " '0.7313284',\n",
       " '0.8185003',\n",
       " '0.123835415',\n",
       " '0.042360604',\n",
       " '0.000822239',\n",
       " '0.0046404405',\n",
       " '0.03956926',\n",
       " '0.932917',\n",
       " '0.76213884',\n",
       " '0.00038311264',\n",
       " '0.12971416',\n",
       " '0.90214473',\n",
       " '0.0013121088',\n",
       " '0.0013635819',\n",
       " '0.8794973',\n",
       " '0.026009575',\n",
       " '0.08722933',\n",
       " '0.85275924',\n",
       " '0.0009555685']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Multi_F_Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Each prediction value is between 0 - 1. But the target variable \"Credit Risk\" is either bad (0) or good (1) there for we will round up/down to ensure every value is either 0 or 1\n",
    "for i in range(len(Multi_F_Predictions)):\n",
    "    if (float(Multi_F_Predictions[i]) >= float(0.5)):\n",
    "        Multi_F_Predictions[i] = 1 \n",
    "    elif (float(Multi_F_Predictions[i]) < float(0.5)):\n",
    "        Multi_F_Predictions[i] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25\n",
      "11\n"
     ]
    }
   ],
   "source": [
    "#Check how many predictions were 0 (Bad Credit) and 1(Good Credit)\n",
    "print(Multi_F_Predictions.count(0))\n",
    "print(Multi_F_Predictions.count(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kTZf0KLBmmnn9AviJmX0CYGbNZnZF9PonJZ0n6Sngh5I2kfSUpEmS/idpZDTdJpKmSJoInJa0/B0k3R+1Zk8GzpQ0WdK22fgQJG00WLqlEj46DH7+HAx9EQaOB2qzsYAiUguMB56DgS/D4PHwk0r4cLB0i6RxMYfnCpWZ9fkboStgMmFM5CJgk+jxcmBQdH8YYYSPgE0I3QjVhGQ8g5BEu853PjA4zTKfBP4Z3e9PaHUOj/4/jNB9AOGYy/bR/QuB16P7OxC6CADOSbX8JfwsNhoMTywDDX+ExOdg5rdv3OaB/RESy0DD4PDdbRX3euy3wrp5yzVoMrNxZrYWsDtwvaTOPtLzJE0ldMGtCIwAtgXuMrNGM1sM3LuEy701+rsmsB7wiKTJwNnAKEmDgSFm9lQ03YQlXE6PJK03WHpoCDx3Lmz3MVT/EvoNy9UCi9yywC+h3xyovhC2WA4eGSI9I2mTuGNzhcGTaxdmNpHQSh1O2BscTmjJjgPmApWdk2YwuzcIrdx0OsdUCngjSvDjzGx9M9s1ejynRxwlrTpYumsQvHQ27PIxVJ0OZZU9v9QRdjlOAs2C6vNg6yHwzGDpPklrxB2bi5cn1y4krUWoZf8CGAx8ZmZtknYEVo4mexo4QFKVpIHAPmlmdz5wgaTlo3kPkHR6iummA8MlbRlN11/Suma2EFgkaZtouvFpllMHDOzl+6ysks6thjd+DHvPhqqzoF91b2bivlQBnAqKPsfda2BKtXRBVFjh+iBPrkFVdDBoMmFX/RgzawduBDaV9Aohsb0NYGavRtNNBu4Ankk1UzN7ELgUeFTSG8AkUozQMLNW4GDgz5KmRPPdKnr6OODS6IBWU5r47yMk+4wOaEnaoxbe3xl+9BZU/QbKe5WZXVo1wNlQPh2qdoPTauADSXvHHZfLPx/n2odIGj0IrqyFra+Cmt3jDqgPeBg4Hhrr4NnFcJKZfRR3TC4/vOXaB0hSuXRcFbx1Juz0nifWvNkVmAHVZ8KO1fBmuXR8dLDUlThvuZY4ScMHwY3LwVa3Q82GcQfUh00DDoCGz+CpOjjKzObHHZPLHW+5ljBJu1TD9BNg+9c9scZufeB1qDkGdq6GdyTtHHdMLne85VqCJJVVwx8q4Ye3QvW34w7IfcPDwOHQ1AqXNcBZ0QFUV0I8uZYYSYMGwp1jYYsHoGZE3AG5tOYB+0HjNJhUB/uZ2YK4Y3LZ490CJUTS2FqYdhhs/bwn1oI3DHgKqo+CzWpgWjTG2pUIT64lQtKe1TDprzDqCqgsxdMAlqJy4FIY8A8YWQ0vS9oz7phcdni3QAnoL51YAxc9AFVbxx2MW2LPA3tBUyP8rMXs4rjjcUvHk2uRq5J+Pgh+/QxUj407GLfUPgC2gcYF8Ocm+L35Blq0PLkWKYWi9QuHwynPQvWouANyWfMpsC00zIErG+BMT7DFyZNrEZLUrxauXgUOegJq/LSApWc+sCM0vA931MPxPlSr+HhyLTKSVAsT1oMDHoZqP+FK6aoHdg+XAP9vHRxqZh1xx+Qy56MFiogk1cDFY2D/Rzyxlrxa4BGoWRt2r4V/+zkJiosn1yJSBb9fEY59HGr62nWs+qoqQoIdDYdXh/MDuyLhybVIVEo/HgZnPg01Q+MOxuXVIOCp0Lf+gyrprLjjcZnxPtciUC4duSxc/jJUrxR3MC42s4BNoXE+fL/N7Jq443Hd8+Ra4CRtVgtPTITq9eIOxsVuOrApNNXDrmb2bNzxuPS8W6CASVqhGh660ROri6wJ/AeqquE+SaPjjsel58m1QEmqGggP/xwG7ht3MK6g7A78BmoHwsOSquKOx6XmybUARWNZr/82rHZ2uHqzc1/zUyjfDVYeCDf4EK3C5Mm1AJXDSSvAHjdAlW81LhUB10PVKNitAn4Ydzzum/yAVoGRtGY1vPoKVK8ddzCu4M0AxkFjA2xuZq/HHY/7irdcC4ikioFw94VQ6YnVZWIM8HeoqoW7JQ2IOx73FU+uBaQa/rQFrHSKfy+uF04AbQcja+DCuGNxX/FugQIhaYeh8MB0qB4edzCu6MwDxkLTAtjHzB6LOx7nLaSCIKmyFm683hOrW0LDgJuhqgZuCYNNXNw8uRaAKvj19jBk7zwvdyFwMLAWsDYwEZgCbAmsD+wDLO7m9e3ARkBy3D8DNgCOTnpsAvCPbAXt0toN2BtqavwELwXBk2vMJK0hOPNfUJ3vZf+QMCD9bUJSXRs4EfgTMA04gO478f4RvabTIsJ1oKYSEu80oAm4Fjg1u6G7NC6GqjI4QdK4uGPp6zy5xkiSBsG1v4OKfNcxLgaeBk6I/q8AhhBq17eLHtsFuCPN62cDDxCScacyoBUwQlLtT0jOp+OVEPkyHPgLVA6Ca724IF6eXON12HKw4RnQL98Lfp+wIR5H2LU/EWgA1gPujab5D+FMTKmcAVzA11eggcBB0fxWBQYDLwP7ZTd014MTQCvCGMGRccfSl3lyjYmkATVw0dVQUx7D8hPAq8ApwGtADaE74GrgUmAToI7Qou3qfmC5aJqufgpMBv4K/Bo4F7gSOBT4QzbfgEurH3A11FTBP8J1LF0cPLnGpBy+uxlUbRvT8kdFt82j/w8mJNu1gIeBScB3gNVTvPY5Qut2FeBw4HG+2UR6Lfo7FrgeuA14HXg3W2/AdWsLYEeo6A/fjzuWvsqTawwk1VTA7/8aLpMUi+WB0YQ+VoDHgHWAz6L/OwgtzZNTvPZ8Qp/rh8AtwE7ADV2m6Wy1thEObkFY2RqzEr3LxJ/DXtHZkvxyazHw5BqDCjhjF+i/UcxxXAyMJwydmgz8EriZ0NpcC1iB0CcL8AmwZ4bzvRv4VvT6IXw1tEvAhtkI3GVkXWAv6FcJP447lr7IK7TyTNKQKpg9GWrGxh2MK3kzgA2goQlGm9mCuOPpS7zlmmcD4EcHQpknVpcPY4BDoKwq1He4PPKWax5JqqqCuZNgoJ/1yuVL1Hqtb4IRZubd3nniLdc8Ehy1FcgTq8unMcBWgEIXu8sTb7nmSVSN9eFdsNJOcQfj+pxHgQNhZh2sar7R54W3XPNnp2VhmR3jjsL1STsDy8KyhJFzLg88uebJYPjZT6HGi71dHAT8HGoGhxF3Lg+8WyAPJA2rhNmfwQAfze3i0ggMh+ZGGGNmH8cdT6nzlmseCA7bExKeWF2cqoEDwMpC1bLLMU+ueTAYTjkhnBvFuVgdC1UD4Xtxx9EXeLdAjkkaMwimfgFVcZz9yrlk7cAwaFoIG5vZ23HHU8q85ZpjFXDMkVDmidUVgn7AUdBvwNevxONywFuuOTZY+uh/MHqLuANxLjIJ2AHm1sNIH/OaO95yzSFJqxgM3yzuQJxLsjEwIFw4wk9xkUOeXHNrjz2gwz9kV0gE7AVSuD6lyxHf7nNoKBy6fwxXdXWuJ/tA1dBw9R2XI97nmiOSBgyAhR9D5bJxB+NcFwuBEdDSCkPNrCnueEqRt1xzZ9s1oNUTqytEQ4C1oQWI6zJuJc+Ta470hx338cIBV8AOgNqqzK/e43rJk2uODISdtwzDCp0rSFuGKxTsEHccpcr7XHNAUtkAqJ8JVSPiDsa5NOYDI0O/a42Ztff4Atcr3nLNjTUGQYcnVlfIlgGWDVc/XyvuWEqRJ9fc2GwL8F0CV/CiykGvc8kBT645UAPbbucHs1wR2BZqa2GbuOMoRZ5cc6ASNtogFMI4V9A2Biq+bMC6bPLkmgMtsOoacQfhXAbGAE0wOu44SpGPFsgySZXl4Rrx/fw0g67QdQCVkGiDZcysLu54Som3XLNvtRHQ6InVFYMyYIVwea3V4o6l1Hhyzb411vCRAq6IjAl/Vo83itLjyTX7Vl8HKuMOwrlMrQdVfJljXbZ4cs2yATBqNFTEHYdzmVoV+td4cs06T65ZVgUj/UxYrpgsAwyA5eKOo9R4cs2yfjDCk6srJkMBwbC44yg1nlyzb1lPrq6YDAUsNGBdFnlyzbIELOPJ1RWTZYB2GBx3HKXGk2uWtcHAoXEH4VwvDAVaYVDccZQaT65ZZtDPhwq4YlILJHyES9Z5cs0ygzI/Y4srJuVAh+eCrPMqzRzwtTQ3bgfmxB1ECWonNArijqPUeHLNMgN5yzX7pgFHQ1053BZ3LKWoEt6JO4ZS48k1ywzkTYDsuxbaDC5fbHZW3LE4lwnPAzngZ23JrnbgGmhrhmvijsW5THlyzbL+0OQnxcyuJ4AO+NjM3ow7Fucy5ck1y/pD/cK4gygxV0BjHfwz7jic6w1PrllWBosWxh1ECWkA7oV+HXBT3LE41xt+QCvLBAsXxh1ECbkLqIQXm8w+S/W8pM2B4fmNqs+YbWaT4w6iWHlyzTKDLxbGHUQJuRzqFsK/Uj0naQ3KeYpRNOc5rNLXgZhNBeFE2m4JeHLNshaY80XcQZSIT4GXwzp6T8oJ+nEMGyH28pOOZF0L8Gda4g6jmHmfa5Y1wHsfQlvccZSCG6FjANxjZk1dn5Mk+nESG3lNfE60A6I97jCKmSfX7Jv5DnwjGbjeuwwaFsPlaZ7emiqqGZnXkPoOT65LzZNr9s18L+4ISsBUYE7YOX065QQVnMSmVOO1xrnRAYhE3GEUM0+u2TdjFlR6ldbSuRba2uEaM+vo+pykSjo4mA18/c2ZNqCM1rjDKGa+cmbf/A5onxd3FEWsnZBcuyl33YsRtPthrBxaBJTxSdxhFDNPrllmZlYNM9+KO5Ai9jjQEcZYpv4YKzmFTRmY36j6mEWA4T1cS8GTaw60wXOvxh1EEYvKXdONbR1Ggm1YO99R9TELMVrwNsJS8OSaA/Xw3LOhctP1Uj1wX/flroexBgkq8xlVHzSfRowP4w6jmHlyzY1JL4bjra6XonLXFyxNuSuVnMrG1OQ3qj5oPglgZtxhFDNPrrnx5lwYsDjuKIpQD+WuYzFWZbU8B9UXLaIf8FHcYRQzT645YGaJgTDjtbgDKTKfAJOgH3BvygnKOZYN6Ue/vIbV93QAjVQBs+IOpZh5cs2RZnjiGe8a6JUboaM/3J2m3LWMMk5knJe75lwdUE69mfkJcZaCJ9ccaYQH7gnHZ1yGLoOGOvh3mqe3pppKL3fNg0VAOZ/GHUax8+SaO09PhUq/5EtmpgBzQ7nrMyknqOAkNqHGy13zYCEAH8QbRPHz5JojZtZQC1OfjDuQInEttLbD1d2Uux7k5a558hEtNPNU3GEUO19Zc2gR3PEgfk7MnkTlru3dlLvuzfJe7po379GCeXJdWp5cc6gd/ncvtPlJXLr3WPjzkZm9nXKCSk5hEy93zYsmYDEDgElxh1LsPLnm1pRF0DIt7igK3JXQuCjN1V2jctetWSffUfVRHwEVTDYzP+H7UvLkmkNm1tEB19/gVyZIq7Pc1eCWNJMczhokGJDPqPqwD2ijhQfjDqMUeHLNsSa4/lrvGkjrTqASJnq5a4GYQRMdPBF3GKXAk2vuTWmB+RPjjqJA9VDuuiawipe75kkLMJ9K4OW4QykFnlxzzEKZy1UTfNTAN3wMvBrKXe9LOYGXu+bXR8AA3vDKrOzw5JoHrXDDTdDh2fXronLXu7opdz3By13z6EMStPBA3GGUCk+ueWBmM8pgym1xB1JAjB7LXbehmiqWz2dUfdy7NNLBk3GHUSo8uebJQjjvfKjzA1vBFOCz0FXybMoJ/Oqu+bUQmE8/4LmYIykZnlzz58FZ0PRi3FEUiGtCuetV3ZS7Hsj6vn7mzWQSlHGz97dmj6+8eWJm7U1wwV+gMe5Y4pYAruu+3HUfL3fNIwNeoYVWLo87lFLiyTWP2uGqB0B9/XrFjwGCmWY2PeUEfnXX/JoJtDEPL3nNKk+ueWRmC/vBdef18WFZV0JDN+Wuw0mwlV/dNY8m0UQrl5qZHxLIIk+uedYAv7saOmbHHUhM6oD7obyHctd2L3fNkxbgLcowJsQdSqnx5JpnZjanDK44t4+2XqNy1+fN7POUE1RyKptQnd+o+rC3gHImmtmcuEMpNZ5cY9AAf7gB2vvidYszKHddmVXzG1Of9hJ1NHNJ3GGUIk+uMTCzzwX//C30qWEvs4HXui93Pc7LXfNoAfAZIt334ZaKJ9eYNMKfboNEXzrXa1TuemeqsZRe7hqDSSQo4yYza407lFLkyTUmZvZFG/zseGjoC4doo3LXxh7KXQd4uWueNAEvkqCVC+IOpVR5co1RAi5/B2bfGHJPSZsMzAubdOryygq+y6Z+dde8eYEEZdxrZu/FHUqp8uQaIzNrXwzHnQ5Ni+MOJseugdY2uDJNuWsVHRzgV3fNk2ZgIm20cHbcoZSy8rgD6OvMbOIg6e6z4aCLKM3RnQngemhvgWvTTLIPI2lnUB6DgnDxnWsIl5/tANYBdiQUKN9OOJnJEOAQoCrF65uAe4HPAAH7AaOBR4B3geWBA6Npp0TTb5GLN9JLL5JAPGhm78YdSinzlkIBqIMzroKWUj2py6OA4EMzeyflBHFd3bUcOAY4BTgZmAHMIpyna1Xg9Ohv6vN2wX+BMcAPotcPI7QKZwGnEjp75hKS+GTgW7l5G70SOmbaaOFXcYdS6jy5FgAz+7wRjj8YGhviDiYHMih33TKWclfx1b5Ce3QTMB0YFz0+Dkh1we9mQk3+xtH/5YTWraL5GCGplhF6mTeHghhi9jStwH/SntfBZY0n1wJhZncsgvt/WGJjX+uAB7ordxXfYWyM5a4dhJKGC4HVgVGES9J2tqMHAql+8RYA1cDdwGXAPUArIVmvHT02FKgEPgHWytUb6IWFwMu008rP4w6lL/DkWkDq4Lu3QF0pXdf4DqASnjOzeSknGMCpbBxjuWsZoVvgR4SLes3N8HUdwKeEXf2TgQq+6j7YJprnbsDjhH7cScBtwFPZCnwJPEIj8Hcz+zTGKPoMT64FxMwWNcAh46EpdeF98bms+3LXtYCVCuLqrlXAKoR+11pCk5vob6oLew+KbqOi/9chJNtknf8vSzigdSjh4NcX2Qq6Fz4BppMgwfkxLL1P8uRaYMzsqVa47GBoaI87mKU0G5gSehrvTzlBOccxjn6xrYUNhAM8EPpH3ycclFqTcACK6O+aKV47EBgMdLbH3weGd5mms9Xa2QcLoU+2bakj750EcDsNtHOGmdX1OL3LCh+KVYAa4aevwVZnw8bnQ/+441lSN4Ry1zua0pW79uf4WMtd6wh9ph2E5LcuIZGOBv4DvEZIoIdE0y8mDL06Mvp/D0K/Rzuhf3X/pHm/BawIXw4vG0U4pDcC8l6F9gSt1DMRSzsUzuWA/Py4hUnSctXwxg0w7IC4g1kCBqwK9TNhTzN7puvzkrZnKPfxQ7/iQE59BExgMW2MNbNMe5RdFni3QIEys88aYfejoHFq3MEsgdeAL8Jw/NTlrgP4Lpuk7M102dIK/IdG2jjeE2v+eXItYGY2qRFO2BUai+1MxtdAS7flru3s7+WuOfY/mmnhITO7I+5Q+iJfuQtch9kti+HC7aBhYdzBZCgBTICObspd942l3LUveQ+YSgOtnBh3KH2VJ9ci0AS/+wQmfBsaiuG63I8Agg/S1q5Xcqpf3TWHmoA7aKSNI8xsYdzh9FWeXIuAmVkDnDYdHtoPGvM9kqe3eih3XY4EmxdExVKpeoAmEtxiZg/HHUpf5sm1SJhZRz0c8SK8eCQ0faMjs0AsBh4M5a63ppxAfIc1/equOTMZYzoLaOX0uEPp6zy5FhEza6uDvR+CaYdDUyLugFKIyl2f7bbcdSO/umtOvA88QD1t7BJ2dlycPLkWGTNrrIMd/wsv7QuNhXZ97h7KXdcGRhVEuWupmQvcQhNt7GNmb8YdjvPkWpSiBLvrs/DELgV0msJZwNSey137+1qXZYuB62iilRPMLM5Tw7gkvpoXKTNrrYP9X4N7C2WYVlTueruZfaNBLakMcTzjirectyA1A9fSQCt/NLOb4w7HfcWTaxEzs0Q9jJ8OEzaChhlxxgJcHq7uekWaSbajlgq/umsWtQM30Ug9t5LgvLjDcV/nybXImVlHvdkpH8PPNoamx2OK41UyKHfd1Mtds8aAu2lmDi/QyvfMTxJScDy5lohWs0vrYK99YPHF0J7vLS26uusVqTZySdUk2J/1fX3LmidpYzrv08q+ZlaIA0f6PF/ZS4iZPdEIG/8SZp0Azfm6XkxU7treAtelmWRfViTh5a5ZYITE+jzzaGUnH3JVuDy5lhgze68eNrwdHlsfGt7IwzIfBsrg/W7KXeO5umup6QDuo5nn+YA2NvYzXRU2T64lyMwW18E+H8APN4PGS6Ajl90EPZS7jiDB5rFc3bWUtAE308jrvEYr3zKzYjtRWp/jybVEmZklzK5qhI1+Ce/sBo25uC7XYuChTMpd47veQPFrBK6mgZk8TCs7mNniuENyPfPkWuLM7J062PA5+Pca0DQBsnpY+XagEp4xs9SX3RvAKbFe3bXYLQL+TQPzuIZWDjKz1rhDcpnx5NoHmFlrg9mZi2CH02DGVtAwPUvz7qHcdR1gFKtmaWF9zVzgchqp41xrtR+kOvG4K1yeXPsQM3upDtZ5FX6zETSeDW1LM6LgI2BaWIceSDlBOcexkZe7LpEPgatoopGTLGEXxB2O6z1f7fsYM0u0mP2tCda8GB5dFRpvJByI7q0J0F7efbnrcV7u2ksdwPO0cwP1tLKfmd0Ud0huyXhy7aPMbPYisz3nwN6nwhtjoeF+wjDKjF4P/Bua6tOXu27PQCoYkZ14+4R64HoaeZI3SbCBmT0Sd0huyXly7ePM7InFsP57MP4I+HBjqH86g9dNAuZDA/B8ygm83LV33gcuoYmP+RetbGJmH8Qdkls6nlwdFtxTB2OmwKl7wdwNof4/hOqrVKJy1yu7KXfdz8tdM9AKPEgLN7OAZva1VvuJmRX6lXxcBnzld18ys/YOswn1sOJUOPYkmLICNP4NOpIHVrYBN4Ry12vTzGo/ViThNVk9+Ai4hEYm8xBtrGFmj8Ydksse+cl0XHckbTYIfpWAXceDvgsD5gJHwusLzNZP+ZoqPcOebMMGeQ62WLQBj9HKJJpo43gzuzPukFz2ecvVdcvMXlpktl8jjL0e/rQjzD0Q2nsod/2WX901hXbgVYz/o5HX+G/UWu02sUoaIekmSe9LmiRpoqQDshGOpCclbZri8f6S/iTpXUmvS3pJ0h5LsZxjJV0S3T9Z0tFJj6+w5O+gsJXHHYArDmY2CzhH0rnAukDq6zSJI1jLy12/xoC3gf/SQDPTaeEHZpb6QGASSQLuBq4zsyOix1YG9s1luMDvgZHAembWImkEsH2K+PqZWXtvZmxmlyX9eyzwOvDJUsRasDy5ul6JqoSmpZ1gAKf41V2TfAg8RD0LmBtd7vqhXpzYeiegNTkhmdlM4GIASZWE6rhNCccef2RmT3TzeBVwDbAO8BZQ1XWBkqqBk4BVO8cvR2ffui16vh74G7Ab8GNJqwCnAxXAi8CpZtYu6TjgF8CnwDtAS/T6cwiDzj6M4rtRUhOwpZk1Zfi5FAXvFnBZI2ldxIpe7grMIVzb6iY+Yy4n08pYM3uwl1cMWJdwkYd0TgOw0Pf9HeC6KLGme/wUoNHMNgD+CGySYp5jgI+6OTlMDfC6mW0OfAEcBmxtZuMIHR/jJY0EfgdsDexCSOZfY2a3A68A481sXKklVvCWq8um8ugChH35J/sL4DEaeZcECX6NcVm2TrYi6VJgG0Jr9lvR/YsBzOxtSTOBsd08vh1wUfT4VElTlyCMduCO6P7OhAT9cujBoAr4DNgceNLMPo/ivjVafp/iydVlhaQy+nNsnyx3bSP0qb5IHXMQ8HcSXGBmdUs55zeAgzr/MbPTJA0jtPgAlOZ16R6HnovwZgArSRqYJv7mpH5WEfqDf/G1hUv7Z7CckteX2xguu3ZgIP37VLnrHOB+WriQZh7gBWZzEgmGWZv9OguJFeBxoFLSKUmPJfdnPw2MB5A0FlgJmJ7h4+vBNwfLmVkjcBVwkaSKaNqRko5MEd9jwMGSloumWyY64PYisIOkZSX1Bw5J8/7qoHRHQ3vL1WXHAI5lFLW0QkmPFGgGpmG8SD2LaMO4nARX5KJc1cwsagX+n6SfAp8TSo5/Fk3yT+AySdMIB66OjY7up3v8X8A1UXfAZOClNIs+G/gD8Kak5miZv0kR35uSzgYellRGaMOfZmYvRAeuJhIOaL0K9EuxnGujOEvygJYXEbiskLQtlfyJBJuwOgnGUcMYKIlOgmZgJjCNJt6mjHKeoJl/AI/0diiS6zs8ubqskjQcOJBKTiTB+oyihdUZyEqIFSiOZNtKKE19nwTv0Mh8KhnAFJq5FWOCmX0Wd4iu8HlydTkT9cVtS392pB/fppXVGEYTq1PFyvRnNBTEebPagNnAB7TzDg18ThUVvEUL99LBY8ALZpavK5W7EuHJ1eWNpBpgM8rYjgHsTivjqKGNVShnJFUMgi9vtaTupVsabcB8wnCpeRhzaeQzEsyniv68T4IHSPAw8JyZNWR56a6P8eTqYiOpH7A+sA39WZdyVgdWIsEIEgxiAM0MpI3BiKFUMoQKqgiDfIxw1v509zuAxTSzgFYWAfVU0EZ/KphLGe/QxmskeINwFH2amS3K+wfgSponV1eQoiE8ywOjvryVsyrljATaMdqBBEbiy79Ge/S3LbrNIezwz4r+fu4X+XP54snVOedywIsInHMuBzy5OudcDnhydc65HPDk6pxzOeDJ1TnncsCTq3PO5YAnV+ecywFPrs45lwOeXJ1zLgc8uTrnXA54cnXOuRzw5OqcczngydU553LAk6tzzuWAJ1fnnMsBT67OOZcDnlydcy4HPLk651wOeHJ1zrkc8OTqnHM58P/M/SyW40BPlwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Pie Graph\n",
    "slices = [Multi_F_Predictions.count(0),Multi_F_Predictions.count(1)]\n",
    "labels = ['Bad Credit', 'Good Credit']\n",
    "colors = ['red', 'green']\n",
    "explode = [0, 0.1]\n",
    "plt.pie(slices, labels=labels, autopct='%1.1f%%', explode=explode, colors=colors, wedgeprops={'edgecolor': 'black'})\n",
    "plt.title(\"Credit Risk Ratio For a Multi Optimized Model - Foreign\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "46\n",
      "138\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#TESTING - Non Foreign Workers\n",
    "filtered_dataset=[]\n",
    "for i in range(len(test_dataset)-1):\n",
    "    if test_dataset[i][-1] == 2:\n",
    "        filtered_dataset.append(test_dataset[i])\n",
    "len(filtered_dataset)\n",
    "\n",
    "Single_F_Predictions = []\n",
    "for i in range(len(filtered_dataset)):\n",
    "    Single_F_Predictions.append(predictor.predict(filtered_dataset[i]))\n",
    "    \n",
    "for i in range(len(Single_F_Predictions)):\n",
    "    Single_F_Predictions[i]=(Single_F_Predictions[i][0][0])\n",
    "    \n",
    "for i in range(len(Single_F_Predictions)):\n",
    "    if (float(Single_F_Predictions[i]) >= float(0.5)):\n",
    "        Single_F_Predictions[i] = 1 \n",
    "    elif (float(Single_F_Predictions[i]) < float(0.5)):\n",
    "        Single_F_Predictions[i] = 0\n",
    "\n",
    "print(Single_F_Predictions.count(0))\n",
    "print(Single_F_Predictions.count(1))\n",
    "\n",
    "#Pie Graph\n",
    "slices = [Single_F_Predictions.count(0),Single_F_Predictions.count(1)]\n",
    "labels = ['Bad Credit', 'Good Credit']\n",
    "colors = ['red', 'green']\n",
    "explode = [0, 0.1]\n",
    "plt.pie(slices, labels=labels, autopct='%1.1f%%', explode=explode, colors=colors, wedgeprops={'edgecolor': 'black'})\n",
    "plt.title(\"Credit Risk Ratio For a Single Optimized Model - Non-Foreign\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate Pareto Frontier \n",
    "def get_pareto_frontier(Xs, Ys, maxX=False, maxY=True):\n",
    "    \"\"\"plot Pareto front\n",
    "\n",
    "    in AMT this is impossible, since there is only one merged metric, But I\n",
    "    show here just from a scientific viewpoint\n",
    "\n",
    "    \"\"\"\n",
    "    sorted_list = sorted([[Xs[i], Ys[i]] for i in range(len(Xs))], reverse=maxX)\n",
    "    pareto_front = [sorted_list[0]]\n",
    "    for pair in sorted_list[1:]:\n",
    "        if maxY:\n",
    "            if pair[1] >= pareto_front[-1][1]:\n",
    "                pareto_front.append(pair)\n",
    "        else:\n",
    "            if pair[1] <= pareto_front[-1][1]:\n",
    "                pareto_front.append(pair)\n",
    "\n",
    "    pf_X = [pair[0] for pair in pareto_front]\n",
    "    pf_Y = [pair[1] for pair in pareto_front]\n",
    "    return pf_X, pf_Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "s3.ObjectSummary(bucket_name='sagemaker-us-east-2-921553072635', key='sagemaker/sagemaker-amt-credit-risk-model/data/output/tmp/02397abf-output.txt')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# S3 bucket's tmp folder is populated by results from training jobs run by HPO (Automatic Model Tuning)\n",
    "import boto3\n",
    "import pandas as pd\n",
    "import io\n",
    "\n",
    "s3 = boto3.resource('s3')\n",
    "bucket = s3.Bucket(session.default_bucket())\n",
    "prefix_objs = bucket.objects.filter(Prefix=\"sagemaker/sagemaker-amt-credit-risk-model/data/output/tmp/\")\n",
    "objects = iter(prefix_objs)\n",
    "next(objects)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "prefix_df = []\n",
    "\n",
    "from io import BytesIO\n",
    "\n",
    "for obj in objects:\n",
    "    key = obj.key\n",
    "    body = obj.get()['Body'].read()\n",
    "    tmp = str(body, 'UTF-8').strip()\n",
    "    prefix_df.append(tmp)\n",
    "    \n",
    "# prefix_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(prefix_df)\n",
    "df['models_list'] = df[0].str.split(',')\n",
    "df_models = pd.DataFrame(df['models_list'].tolist()).fillna('').add_prefix('model_')\n",
    "df_models.rename(columns={'model_0': 'sd', 'model_1': 'auc'}, inplace=True)\n",
    "df_models.to_csv('sd_auc_pareto_frontier.csv', index=False, header=True)\n",
    "sd_str_list = df_models.iloc[:, 0].tolist()\n",
    "sd_float_list = [float(x) for x in sd_str_list]\n",
    "auc_str_list = df_models.iloc[:, -1].tolist()\n",
    "auc_float_list = [float(x) for x in auc_str_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "pf_X, pf_Y = get_pareto_frontier(Xs=sd_float_list,Ys=auc_float_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Eliminate all 0's in pf_X and corresponding points in pf_Y \n",
    "i = 0 \n",
    "while i < len(pf_X):\n",
    "    if pf_X[i] != 0:\n",
    "        i += 1\n",
    "    else:\n",
    "        del(pf_X[i])\n",
    "        del(pf_Y[i])\n",
    "#Eliminate all 0's in sd_float_list and corresponding points in auc_float_listY \n",
    "i = 0 \n",
    "while i < len(sd_float_list):\n",
    "    if sd_float_list[i] != 0:\n",
    "        i += 1\n",
    "    else:\n",
    "        del(sd_float_list[i])\n",
    "        del(auc_float_list[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline Statistical Disparity:  0.75\n",
      "Baseline Area Under Curve:  0.74 \n",
      "\n",
      "Optimized Statistical Disparity:  0.43\n",
      "Optimized Area Under Curve:  0.72 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "#Worst one wihtout optimization - You can get thess baseline value from the values you found in Notebook 2\n",
    "baseline_sd = 0.75 \n",
    "baseline_auc = 0.74\n",
    "\n",
    "#REAL ONE After optimization - follow steps from Notebook 2 for the training job created in this notebook to find these values. \n",
    "optimized_sd = 0.43\n",
    "optimized_auc = 0.72\n",
    "#combined = 0.75\n",
    "ax = plt.subplots()\n",
    "#Add grid \n",
    "plt.grid()\n",
    "\n",
    "#Graph pareto front chart \n",
    "plt.title(\"Pareto Chart\")\n",
    "plt.xlabel(\"Difference in Positive Proportions in Predicted Labels (DPPL)\")\n",
    "plt.ylabel(\"Area Under Curve (AUC)\")\n",
    "\n",
    "#Plot co ordinates \n",
    "plt.plot(pf_X, pf_Y, linestyle='--', linewidth=3, marker='o', c='#8B8000', mfc='b')\n",
    "#Plot additional points\n",
    "plt.plot(sd_float_list, auc_float_list, linestyle='None', marker='o', markersize=3, color='#86C5DA')\n",
    "#Show baseline SD & AUC \n",
    "plt.plot(baseline_sd, baseline_auc, \"ro\", markersize='10', label=(\"Baseline (\"+str(baseline_sd)+\", \"+str(baseline_auc)+\")\"))\n",
    "#Show optimized SD & AUC  \n",
    "plt.plot(optimized_sd, optimized_auc, \"gv\", markersize='10', label=(\"Optimized (\"+str(optimized_sd)+\", \"+str(optimized_auc)+\")\"))\n",
    "#Legend\n",
    "plt.legend(loc=\"lower left\")\n",
    "\n",
    "#Print Baseline Values \n",
    "print(\"Baseline Statistical Disparity: \",baseline_sd)\n",
    "print(\"Baseline Area Under Curve: \",baseline_auc,\"\\n\")\n",
    "\n",
    "#Print Optimized Values\n",
    "print(\"Optimized Statistical Disparity: \",optimized_sd)\n",
    "print(\"Optimized Area Under Curve: \",optimized_auc,\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The cell below provides a range of visualization for the tuning job above. This visualization and its libraries were provided by the following repo:\n",
    "\n",
    "https://github.com/aws-samples/amazon-sagemaker-amt-visualize/blob/main/1_tuning_of_builtin_xgboost.ipynb "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting altair renderer to default.\n",
      "Tuning job xgb-tuner-27-00-32-14     status: Completed\n",
      "\n",
      "Number of training jobs with valid objective: 100\n",
      "Lowest: 0.5139889717102051 Highest 0.6838629841804504\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>eta</th>\n",
       "      <th>gamma</th>\n",
       "      <th>max_depth</th>\n",
       "      <th>min_child_weight</th>\n",
       "      <th>TrainingJobName</th>\n",
       "      <th>TrainingJobStatus</th>\n",
       "      <th>TrainingStartTime</th>\n",
       "      <th>TrainingEndTime</th>\n",
       "      <th>TrainingElapsedTimeSeconds</th>\n",
       "      <th>TuningJobName</th>\n",
       "      <th>validation:auc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0.548612</td>\n",
       "      <td>0.012421</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000066</td>\n",
       "      <td>xgb-tuner-27-00-32-14-052-be514283</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2023-01-27 00:39:38+00:00</td>\n",
       "      <td>2023-01-27 00:40:47+00:00</td>\n",
       "      <td>69.0</td>\n",
       "      <td>xgb-tuner-27-00-32-14</td>\n",
       "      <td>0.683863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>0.544451</td>\n",
       "      <td>0.000904</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000078</td>\n",
       "      <td>xgb-tuner-27-00-32-14-032-c6499eb4</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2023-01-27 00:37:02+00:00</td>\n",
       "      <td>2023-01-27 00:37:40+00:00</td>\n",
       "      <td>38.0</td>\n",
       "      <td>xgb-tuner-27-00-32-14</td>\n",
       "      <td>0.678610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>0.643851</td>\n",
       "      <td>0.000088</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>xgb-tuner-27-00-32-14-029-43d2f514</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2023-01-27 00:36:36+00:00</td>\n",
       "      <td>2023-01-27 00:37:08+00:00</td>\n",
       "      <td>32.0</td>\n",
       "      <td>xgb-tuner-27-00-32-14</td>\n",
       "      <td>0.677474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>0.648377</td>\n",
       "      <td>0.000098</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>xgb-tuner-27-00-32-14-014-7c090c65</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2023-01-27 00:35:18+00:00</td>\n",
       "      <td>2023-01-27 00:35:50+00:00</td>\n",
       "      <td>32.0</td>\n",
       "      <td>xgb-tuner-27-00-32-14</td>\n",
       "      <td>0.669266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>0.718554</td>\n",
       "      <td>0.054962</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>xgb-tuner-27-00-32-14-019-b6885f1d</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2023-01-27 00:35:35+00:00</td>\n",
       "      <td>2023-01-27 00:36:12+00:00</td>\n",
       "      <td>37.0</td>\n",
       "      <td>xgb-tuner-27-00-32-14</td>\n",
       "      <td>0.667242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.524844</td>\n",
       "      <td>0.066738</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>xgb-tuner-27-00-32-14-065-f1f06d11</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2023-01-27 00:40:21+00:00</td>\n",
       "      <td>2023-01-27 00:40:58+00:00</td>\n",
       "      <td>37.0</td>\n",
       "      <td>xgb-tuner-27-00-32-14</td>\n",
       "      <td>0.665081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>0.611708</td>\n",
       "      <td>0.000091</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.004199</td>\n",
       "      <td>xgb-tuner-27-00-32-14-013-e0d8f46a</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2023-01-27 00:35:16+00:00</td>\n",
       "      <td>2023-01-27 00:35:53+00:00</td>\n",
       "      <td>37.0</td>\n",
       "      <td>xgb-tuner-27-00-32-14</td>\n",
       "      <td>0.663159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0.730583</td>\n",
       "      <td>0.002627</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000487</td>\n",
       "      <td>xgb-tuner-27-00-32-14-056-250a354c</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2023-01-27 00:39:24+00:00</td>\n",
       "      <td>2023-01-27 00:40:01+00:00</td>\n",
       "      <td>37.0</td>\n",
       "      <td>xgb-tuner-27-00-32-14</td>\n",
       "      <td>0.659589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>0.474216</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000453</td>\n",
       "      <td>xgb-tuner-27-00-32-14-012-33b1a582</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2023-01-27 00:35:14+00:00</td>\n",
       "      <td>2023-01-27 00:35:51+00:00</td>\n",
       "      <td>37.0</td>\n",
       "      <td>xgb-tuner-27-00-32-14</td>\n",
       "      <td>0.658692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.498717</td>\n",
       "      <td>0.000230</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>xgb-tuner-27-00-32-14-060-c903e078</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2023-01-27 00:39:45+00:00</td>\n",
       "      <td>2023-01-27 00:40:22+00:00</td>\n",
       "      <td>37.0</td>\n",
       "      <td>xgb-tuner-27-00-32-14</td>\n",
       "      <td>0.657385</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "         eta     gamma  max_depth  min_child_weight                     TrainingJobName TrainingJobStatus         TrainingStartTime           TrainingEndTime  TrainingElapsedTimeSeconds          TuningJobName  validation:auc\n",
       "48  0.548612  0.012421        4.0          0.000066  xgb-tuner-27-00-32-14-052-be514283         Completed 2023-01-27 00:39:38+00:00 2023-01-27 00:40:47+00:00                        69.0  xgb-tuner-27-00-32-14        0.683863\n",
       "68  0.544451  0.000904        4.0          0.000078  xgb-tuner-27-00-32-14-032-c6499eb4         Completed 2023-01-27 00:37:02+00:00 2023-01-27 00:37:40+00:00                        38.0  xgb-tuner-27-00-32-14        0.678610\n",
       "71  0.643851  0.000088        4.0          0.000003  xgb-tuner-27-00-32-14-029-43d2f514         Completed 2023-01-27 00:36:36+00:00 2023-01-27 00:37:08+00:00                        32.0  xgb-tuner-27-00-32-14        0.677474\n",
       "86  0.648377  0.000098        4.0          0.000018  xgb-tuner-27-00-32-14-014-7c090c65         Completed 2023-01-27 00:35:18+00:00 2023-01-27 00:35:50+00:00                        32.0  xgb-tuner-27-00-32-14        0.669266\n",
       "81  0.718554  0.054962        4.0          0.000013  xgb-tuner-27-00-32-14-019-b6885f1d         Completed 2023-01-27 00:35:35+00:00 2023-01-27 00:36:12+00:00                        37.0  xgb-tuner-27-00-32-14        0.667242\n",
       "35  0.524844  0.066738        3.0          0.000008  xgb-tuner-27-00-32-14-065-f1f06d11         Completed 2023-01-27 00:40:21+00:00 2023-01-27 00:40:58+00:00                        37.0  xgb-tuner-27-00-32-14        0.665081\n",
       "87  0.611708  0.000091        4.0          0.004199  xgb-tuner-27-00-32-14-013-e0d8f46a         Completed 2023-01-27 00:35:16+00:00 2023-01-27 00:35:53+00:00                        37.0  xgb-tuner-27-00-32-14        0.663159\n",
       "44  0.730583  0.002627        4.0          0.000487  xgb-tuner-27-00-32-14-056-250a354c         Completed 2023-01-27 00:39:24+00:00 2023-01-27 00:40:01+00:00                        37.0  xgb-tuner-27-00-32-14        0.659589\n",
       "88  0.474216  0.000016        3.0          0.000453  xgb-tuner-27-00-32-14-012-33b1a582         Completed 2023-01-27 00:35:14+00:00 2023-01-27 00:35:51+00:00                        37.0  xgb-tuner-27-00-32-14        0.658692\n",
       "40  0.498717  0.000230        5.0          0.000013  xgb-tuner-27-00-32-14-060-c903e078         Completed 2023-01-27 00:39:45+00:00 2023-01-27 00:40:22+00:00                        37.0  xgb-tuner-27-00-32-14        0.657385"
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0.6117077699446535, \"gamma\": 9.091706117443728e-05, \"max_depth\": 4.0, \"min_child_weight\": 0.004199212213659081, \"TrainingJobName\": \"xgb-tuner-27-00-32-14-013-e0d8f46a\", \"TrainingJobStatus\": \"Completed\", \"TrainingStartTime\": \"2023-01-27T00:35:16+00:00\", \"TrainingEndTime\": \"2023-01-27T00:35:53+00:00\", \"TrainingElapsedTimeSeconds\": 37.0, \"TuningJobName\": \"xgb-tuner-27-00-32-14\", \"validation:auc\": 0.6631590127944946}, {\"eta\": 0.5248442554876624, \"gamma\": 0.06673827004382778, \"max_depth\": 3.0, \"min_child_weight\": 8.449355147035803e-06, \"TrainingJobName\": \"xgb-tuner-27-00-32-14-065-f1f06d11\", \"TrainingJobStatus\": \"Completed\", \"TrainingStartTime\": \"2023-01-27T00:40:21+00:00\", \"TrainingEndTime\": \"2023-01-27T00:40:58+00:00\", \"TrainingElapsedTimeSeconds\": 37.0, \"TuningJobName\": \"xgb-tuner-27-00-32-14\", \"validation:auc\": 0.6650810241699219}, {\"eta\": 0.7185536475898795, \"gamma\": 0.054961803514725814, \"max_depth\": 4.0, \"min_child_weight\": 1.2877573217460173e-05, \"TrainingJobName\": \"xgb-tuner-27-00-32-14-019-b6885f1d\", \"TrainingJobStatus\": \"Completed\", \"TrainingStartTime\": \"2023-01-27T00:35:35+00:00\", \"TrainingEndTime\": \"2023-01-27T00:36:12+00:00\", \"TrainingElapsedTimeSeconds\": 37.0, \"TuningJobName\": \"xgb-tuner-27-00-32-14\", \"validation:auc\": 0.6672419905662537}, {\"eta\": 0.6483771027602893, \"gamma\": 9.798457390479842e-05, \"max_depth\": 4.0, \"min_child_weight\": 1.762853305772171e-05, \"TrainingJobName\": \"xgb-tuner-27-00-32-14-014-7c090c65\", \"TrainingJobStatus\": \"Completed\", \"TrainingStartTime\": \"2023-01-27T00:35:18+00:00\", \"TrainingEndTime\": \"2023-01-27T00:35:50+00:00\", \"TrainingElapsedTimeSeconds\": 32.0, \"TuningJobName\": \"xgb-tuner-27-00-32-14\", \"validation:auc\": 0.6692659854888916}, {\"eta\": 0.6438506797337316, \"gamma\": 8.847851158810656e-05, \"max_depth\": 4.0, \"min_child_weight\": 3.294385609871094e-06, \"TrainingJobName\": \"xgb-tuner-27-00-32-14-029-43d2f514\", \"TrainingJobStatus\": \"Completed\", \"TrainingStartTime\": \"2023-01-27T00:36:36+00:00\", \"TrainingEndTime\": \"2023-01-27T00:37:08+00:00\", \"TrainingElapsedTimeSeconds\": 32.0, \"TuningJobName\": \"xgb-tuner-27-00-32-14\", \"validation:auc\": 0.6774740219116211}, {\"eta\": 0.5444508070897833, \"gamma\": 0.000904360464974121, \"max_depth\": 4.0, \"min_child_weight\": 7.76497369277073e-05, \"TrainingJobName\": \"xgb-tuner-27-00-32-14-032-c6499eb4\", \"TrainingJobStatus\": \"Completed\", \"TrainingStartTime\": \"2023-01-27T00:37:02+00:00\", \"TrainingEndTime\": \"2023-01-27T00:37:40+00:00\", \"TrainingElapsedTimeSeconds\": 38.0, \"TuningJobName\": \"xgb-tuner-27-00-32-14\", \"validation:auc\": 0.6786100268363953}, {\"eta\": 0.5486115853265142, \"gamma\": 0.012420693362461344, \"max_depth\": 4.0, \"min_child_weight\": 6.600165759874769e-05, \"TrainingJobName\": \"xgb-tuner-27-00-32-14-052-be514283\", \"TrainingJobStatus\": \"Completed\", \"TrainingStartTime\": \"2023-01-27T00:39:38+00:00\", \"TrainingEndTime\": \"2023-01-27T00:40:47+00:00\", \"TrainingElapsedTimeSeconds\": 69.0, \"TuningJobName\": \"xgb-tuner-27-00-32-14\", \"validation:auc\": 0.6838629841804504}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.VConcatChart(...)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#!pip install altair\n",
    "from amtviz import visualize_tuning_job\n",
    "visualize_tuning_job(tuner, trials_only=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clean-up\n",
    "\n",
    "Remove any resources to save on AWS cost."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#CLEAN UP - Uncomment code in this cell to Spin Down Endpoint\n",
    "\n",
    "# Create a low-level SageMaker service client.\n",
    "sagemaker_client = boto3.client('sagemaker', region_name=\"us-east-2\")\n",
    "\n",
    "# Delete endpoint\n",
    "sagemaker_client.delete_endpoint(EndpointName=endpoint_name)\n",
    "\n",
    "#Clean-up temporary files in s3 bucket.Removing these temp files will impact re-creating the 'pareto frontier' chart\n",
    "\n",
    "import boto3\n",
    "s3_client = boto3.client('s3')\n",
    "session = Session()\n",
    "s3_bucket = session.default_bucket()\n",
    "s3_bucket_prefix = \"sagemaker/sagemaker-amt-credit-risk-model/data/output/tmp/\"\n",
    "response = s3_client.list_objects_v2(Bucket=s3_bucket, Prefix=s3_bucket_prefix)\n",
    "for object in response['Contents']:\n",
    "    s3_client.delete_object(Bucket=s3_bucket, Key=object['Key'])"
   ]
  }
 ],
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   "codemirror_mode": {
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