LiL-CrimeModel.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "21dd2fbb-05f4-4858-8d5d-e560d07a01e8",
   "metadata": {},
   "source": [
    "# Predict Crime in the UK Using SageMaker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "59a7bb13-041e-4231-bd52-15c94f5e8eca",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sagemaker\n",
    "bucket=sagemaker.Session().default_bucket()\n",
    "prefix = 'sagemaker/DEMO-linlearn-dm'\n",
    " \n",
    "# Define IAM role\n",
    "import boto3\n",
    "import re\n",
    "from sagemaker import get_execution_role\n",
    "\n",
    "role = get_execution_role()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7af82f3a-4ef7-49b7-b587-19ca62b8fef1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np                                \n",
    "import pandas as pd                               \n",
    "import matplotlib.pyplot as plt                   \n",
    "from IPython.display import Image                 # For displaying images in the notebook\n",
    "from IPython.display import display               # For displaying outputs in the notebook\n",
    "from time import gmtime, strftime                 # For labeling SageMaker models, endpoints, etc.\n",
    "import sys                                        # For writing outputs to notebook\n",
    "import math                                       # For ceiling function\n",
    "import json                                       # For parsing hosting outputs\n",
    "import os                                         # For manipulating filepath names\n",
    "import sagemaker \n",
    "import zipfile     # Amazon SageMaker's Python SDK provides many helper functions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "159870a1-0844-49c1-a02b-9a4bc94787f6",
   "metadata": {},
   "source": [
    "# Get the data from SageMaker Feature Store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1e755829-79bc-4c76-ae5a-fbe692dfb7c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sagemaker.session import Session\n",
    "from sagemaker.feature_store.feature_group import FeatureGroup\n",
    "\n",
    "region = boto3.Session().region_name\n",
    "boto_session = boto3.Session(region_name=region)\n",
    "\n",
    "sagemaker_client = boto_session.client(service_name='sagemaker', region_name=region)\n",
    "featurestore_runtime = boto_session.client(service_name='sagemaker-featurestore-runtime', region_name=region)\n",
    "\n",
    "feature_store_session = Session(\n",
    "    boto_session=boto_session,\n",
    "    sagemaker_client=sagemaker_client,\n",
    "    sagemaker_featurestore_runtime_client=featurestore_runtime\n",
    ")\n",
    "\n",
    "feature_group_name = \"FG-ndcCrime-7dee9362\" # replace with your feature group name \n",
    "feature_group = FeatureGroup(name=feature_group_name, sagemaker_session=feature_store_session)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "716788af-2c5a-490d-a03a-520e1d2d88b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running SELECT * FROM \"fg-ndccrime-7dee9362-1673879553\"\n"
     ]
    }
   ],
   "source": [
    "# Build SQL query to features group\n",
    "fs_query = feature_group.athena_query()\n",
    "fs_table = fs_query.table_name\n",
    "query_string = 'SELECT * FROM \"'+fs_table+'\"'\n",
    "print('Running ' + query_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2b2d0d24-1352-405e-b149-b4cdbdd0be3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run Athena query. The output is loaded to a Pandas dataframe.\n",
    "fs_query.run(query_string=query_string, output_location='s3://'+bucket+'/'+prefix+'/fs_query_results/')\n",
    "fs_query.wait()\n",
    "model_data = fs_query.as_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "67ca8218-5fee-47a1-972c-bed738371fe6",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>outcome</th>\n",
       "      <th>gender_male</th>\n",
       "      <th>gender_female</th>\n",
       "      <th>gender_other</th>\n",
       "      <th>age_range_18-24</th>\n",
       "      <th>age_range_25-34</th>\n",
       "      <th>age_range_over_34</th>\n",
       "      <th>age_range_10-17</th>\n",
       "      <th>age_range_under_10</th>\n",
       "      <th>date_month_1</th>\n",
       "      <th>...</th>\n",
       "      <th>date_quarter_2</th>\n",
       "      <th>ethnicity_white</th>\n",
       "      <th>ethnicity_black</th>\n",
       "      <th>ethnicity_asian</th>\n",
       "      <th>ethnicity_other</th>\n",
       "      <th>ethnicity_mixed</th>\n",
       "      <th>coords</th>\n",
       "      <th>write_time</th>\n",
       "      <th>api_invocation_time</th>\n",
       "      <th>is_deleted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>1.192488e-08</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001633</td>\n",
       "      <td>2023-01-16 14:52:15.097</td>\n",
       "      <td>2023-01-16 14:52:15.097</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>1.192488e-08</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001633</td>\n",
       "      <td>2023-01-16 14:52:15.097</td>\n",
       "      <td>2023-01-16 14:52:15.097</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>1.192488e-08</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000373</td>\n",
       "      <td>2023-01-16 14:52:15.097</td>\n",
       "      <td>2023-01-16 14:52:15.097</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>1.192488e-08</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.002686</td>\n",
       "      <td>2023-01-16 14:52:15.097</td>\n",
       "      <td>2023-01-16 14:52:15.097</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>1.192488e-08</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.002693</td>\n",
       "      <td>2023-01-16 14:52:15.097</td>\n",
       "      <td>2023-01-16 14:52:15.097</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   outcome  gender_male  gender_female  gender_other  age_range_18-24  \\\n",
       "0        0          1.0            0.0           0.0              0.0   \n",
       "1        0          1.0            0.0           0.0              1.0   \n",
       "2        1          0.0            1.0           0.0              0.0   \n",
       "3        0          1.0            0.0           0.0              0.0   \n",
       "4        0          1.0            0.0           0.0              0.0   \n",
       "\n",
       "   age_range_25-34  age_range_over_34  age_range_10-17  age_range_under_10  \\\n",
       "0              1.0                0.0              0.0                 0.0   \n",
       "1              0.0                0.0              0.0                 0.0   \n",
       "2              0.0                1.0              0.0                 0.0   \n",
       "3              0.0                1.0              0.0                 0.0   \n",
       "4              0.0                1.0              0.0                 0.0   \n",
       "\n",
       "   date_month_1  ...  date_quarter_2  ethnicity_white  ethnicity_black  \\\n",
       "0          -0.5  ...    1.192488e-08              1.0              0.0   \n",
       "1          -0.5  ...    1.192488e-08              1.0              0.0   \n",
       "2          -0.5  ...    1.192488e-08              1.0              0.0   \n",
       "3          -0.5  ...    1.192488e-08              1.0              0.0   \n",
       "4          -0.5  ...    1.192488e-08              1.0              0.0   \n",
       "\n",
       "   ethnicity_asian  ethnicity_other  ethnicity_mixed    coords  \\\n",
       "0              0.0              0.0              0.0  0.001633   \n",
       "1              0.0              0.0              0.0  0.001633   \n",
       "2              0.0              0.0              0.0 -0.000373   \n",
       "3              0.0              0.0              0.0 -0.002686   \n",
       "4              0.0              0.0              0.0 -0.002693   \n",
       "\n",
       "                write_time      api_invocation_time  is_deleted  \n",
       "0  2023-01-16 14:52:15.097  2023-01-16 14:52:15.097       False  \n",
       "1  2023-01-16 14:52:15.097  2023-01-16 14:52:15.097       False  \n",
       "2  2023-01-16 14:52:15.097  2023-01-16 14:52:15.097       False  \n",
       "3  2023-01-16 14:52:15.097  2023-01-16 14:52:15.097       False  \n",
       "4  2023-01-16 14:52:15.097  2023-01-16 14:52:15.097       False  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "503ebecb-ee5c-4349-85d1-c96bff08219f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove unwanted columns\n",
    "model_data = model_data.drop(['write_time', 'api_invocation_time', 'is_deleted'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b6a1e59f-15b7-425f-a00d-9d66a5d811e7",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>outcome</th>\n",
       "      <th>gender_male</th>\n",
       "      <th>gender_female</th>\n",
       "      <th>gender_other</th>\n",
       "      <th>age_range_18-24</th>\n",
       "      <th>age_range_25-34</th>\n",
       "      <th>age_range_over_34</th>\n",
       "      <th>age_range_10-17</th>\n",
       "      <th>age_range_under_10</th>\n",
       "      <th>date_month_1</th>\n",
       "      <th>...</th>\n",
       "      <th>date_day_of_year_1</th>\n",
       "      <th>date_day_of_year_2</th>\n",
       "      <th>date_quarter_1</th>\n",
       "      <th>date_quarter_2</th>\n",
       "      <th>ethnicity_white</th>\n",
       "      <th>ethnicity_black</th>\n",
       "      <th>ethnicity_asian</th>\n",
       "      <th>ethnicity_other</th>\n",
       "      <th>ethnicity_mixed</th>\n",
       "      <th>coords</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.187718</td>\n",
       "      <td>0.982223</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.192488e-08</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.187718</td>\n",
       "      <td>0.982223</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.192488e-08</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.187718</td>\n",
       "      <td>0.982223</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.192488e-08</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.187718</td>\n",
       "      <td>0.982223</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.192488e-08</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.002686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.187718</td>\n",
       "      <td>0.982223</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.192488e-08</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.002693</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   outcome  gender_male  gender_female  gender_other  age_range_18-24  \\\n",
       "0        0          1.0            0.0           0.0              0.0   \n",
       "1        0          1.0            0.0           0.0              1.0   \n",
       "2        1          0.0            1.0           0.0              0.0   \n",
       "3        0          1.0            0.0           0.0              0.0   \n",
       "4        0          1.0            0.0           0.0              0.0   \n",
       "\n",
       "   age_range_25-34  age_range_over_34  age_range_10-17  age_range_under_10  \\\n",
       "0              1.0                0.0              0.0                 0.0   \n",
       "1              0.0                0.0              0.0                 0.0   \n",
       "2              0.0                1.0              0.0                 0.0   \n",
       "3              0.0                1.0              0.0                 0.0   \n",
       "4              0.0                1.0              0.0                 0.0   \n",
       "\n",
       "   date_month_1  ...  date_day_of_year_1  date_day_of_year_2  date_quarter_1  \\\n",
       "0          -0.5  ...           -0.187718            0.982223            -1.0   \n",
       "1          -0.5  ...           -0.187718            0.982223            -1.0   \n",
       "2          -0.5  ...           -0.187718            0.982223            -1.0   \n",
       "3          -0.5  ...           -0.187718            0.982223            -1.0   \n",
       "4          -0.5  ...           -0.187718            0.982223            -1.0   \n",
       "\n",
       "   date_quarter_2  ethnicity_white  ethnicity_black  ethnicity_asian  \\\n",
       "0    1.192488e-08              1.0              0.0              0.0   \n",
       "1    1.192488e-08              1.0              0.0              0.0   \n",
       "2    1.192488e-08              1.0              0.0              0.0   \n",
       "3    1.192488e-08              1.0              0.0              0.0   \n",
       "4    1.192488e-08              1.0              0.0              0.0   \n",
       "\n",
       "   ethnicity_other  ethnicity_mixed    coords  \n",
       "0              0.0              0.0  0.001633  \n",
       "1              0.0              0.0  0.001633  \n",
       "2              0.0              0.0 -0.000373  \n",
       "3              0.0              0.0 -0.002686  \n",
       "4              0.0              0.0 -0.002693  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c0de5de7-46e8-40a6-96c6-5f701de4f34e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare data SageMaker's Linear Learner algorithm\n",
    "# Amazon SageMaker's Linear Learner container expects data in CSV data format. \n",
    "# Note that the first column must be the target variable and the CSV should not include headers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "70aea7dd-972e-49c6-a85b-29d0f3e7327c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.187718</td>\n",
       "      <td>0.982223</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.192488e-08</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.187718</td>\n",
       "      <td>0.982223</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.192488e-08</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.187718</td>\n",
       "      <td>0.982223</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.192488e-08</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.187718</td>\n",
       "      <td>0.982223</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.192488e-08</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.002686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.187718</td>\n",
       "      <td>0.982223</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.192488e-08</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.002693</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   0    1    2    3    4    5    6    7    8    9   ...        17        18  \\\n",
       "0   0  1.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0 -0.5  ... -0.187718  0.982223   \n",
       "1   0  1.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0 -0.5  ... -0.187718  0.982223   \n",
       "2   1  0.0  1.0  0.0  0.0  0.0  1.0  0.0  0.0 -0.5  ... -0.187718  0.982223   \n",
       "3   0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0 -0.5  ... -0.187718  0.982223   \n",
       "4   0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0 -0.5  ... -0.187718  0.982223   \n",
       "\n",
       "    19            20   21   22   23   24   25        26  \n",
       "0 -1.0  1.192488e-08  1.0  0.0  0.0  0.0  0.0  0.001633  \n",
       "1 -1.0  1.192488e-08  1.0  0.0  0.0  0.0  0.0  0.001633  \n",
       "2 -1.0  1.192488e-08  1.0  0.0  0.0  0.0  0.0 -0.000373  \n",
       "3 -1.0  1.192488e-08  1.0  0.0  0.0  0.0  0.0 -0.002686  \n",
       "4 -1.0  1.192488e-08  1.0  0.0  0.0  0.0  0.0 -0.002693  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove the headers\n",
    "model_data.columns = range(model_data.shape[1])   # Delete headers\n",
    "\n",
    "model_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ad8d8dba-f3e4-46dd-8db5-f23ac0f8230c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# separate data into train/test data split\n",
    "\n",
    "#The model will be trained on 70% of data, it will then be evaluated on 20% of data to give us an estimate of the accuracy \n",
    "#we hope to have on \"new\" data, and 10% will be held back as a final testing dataset which will be used later on.\n",
    "\n",
    "# Randomly sort the data then split out first 70%, second 20%, and last 10%\n",
    "train_data, validation_data, test_data = np.split(model_data.sample(frac=1, random_state=1729), \n",
    "                                                  [int(0.7 * len(model_data)), int(0.9 * len(model_data))]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ea59fbfc-39b5-4c41-ab9b-73b7148a9023",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Convert data to CSV\n",
    "train_data.to_csv('train.csv', index=False, header=False)\n",
    "validation_data.to_csv('validation.csv', index=False, header=False)\n",
    "test_data.to_csv('test.csv', index=False, header=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "26cca799-cc86-4e66-8a61-2cd347439735",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#copy data to S3 for SageMaker to access\n",
    "boto3.Session().resource('s3').Bucket(bucket).Object(os.path.join(prefix, 'train/train.csv')).upload_file('train.csv')\n",
    "boto3.Session().resource('s3').Bucket(bucket).Object(os.path.join(prefix, 'validation/validation.csv')).upload_file('validation.csv')\n",
    "boto3.Session().resource('s3').Bucket(bucket).Object(os.path.join(prefix, 'test/test.csv')).upload_file('test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99727a83-3e43-4eb3-976c-69f58adba851",
   "metadata": {},
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0c6626fa-e6ba-42e8-bd1d-ede28d2ac263",
   "metadata": {},
   "outputs": [],
   "source": [
    "#specify the ECR container location for Amazon SageMaker's implementation of XGBoost\n",
    "\n",
    "container = sagemaker.image_uris.retrieve(region=boto3.Session().region_name, framework='xgboost', version='latest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "97e4b18f-776b-4768-994c-5f70bd69ccbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Then, because we're training with the CSV file format, we'll create s3_inputs that our training function can use \n",
    "# as a pointer to the files in S3, which also specify that the content type is CSV.\n",
    "s3_input_train = sagemaker.inputs.TrainingInput(s3_data='s3://{}/{}/train'.format(bucket, prefix), content_type='csv')\n",
    "s3_input_validation = sagemaker.inputs.TrainingInput(s3_data='s3://{}/{}/validation/'.format(bucket, prefix), content_type='csv')\n",
    "s3_input_test = sagemaker.inputs.TrainingInput(s3_data='s3://{}/{}/test/'.format(bucket, prefix), content_type='csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bea6b62-72a5-440c-a7bb-0fe4a1f3330a",
   "metadata": {},
   "source": [
    "First we'll need to specify training parameters to the estimator. This includes:\n",
    "\n",
    "The ll algorithm container\n",
    "The IAM role to use\n",
    "Training instance type and count\n",
    "S3 location for output data\n",
    "Algorithm hyperparameters\n",
    "And then a .fit() function which specifies:\n",
    "\n",
    "S3 location for output data. In this case we have both a training and validation set which are passed in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e5d5341f-afff-476a-8217-94cf77e49049",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-02-12 02:36:42 Starting - Starting the training job...ProfilerReport-1676169401: InProgress\n",
      "...\n",
      "2023-02-12 02:37:26 Starting - Preparing the instances for training......\n",
      "2023-02-12 02:38:32 Downloading - Downloading input data......\n",
      "2023-02-12 02:39:38 Training - Training image download completed. Training in progress...\u001b[34mArguments: train\u001b[0m\n",
      "\u001b[34m[2023-02-12:02:39:54:INFO] Running standalone xgboost training.\u001b[0m\n",
      "\u001b[34m[2023-02-12:02:39:54:INFO] File size need to be processed in the node: 601.88mb. Available memory size in the node: 8603.77mb\u001b[0m\n",
      "\u001b[34m[2023-02-12:02:39:54:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[34m[02:39:54] S3DistributionType set as FullyReplicated\u001b[0m\n",
      "\u001b[34m[02:39:55] 1762710x26 matrix with 45830460 entries loaded from /opt/ml/input/data/train?format=csv&label_column=0&delimiter=,\u001b[0m\n",
      "\u001b[34m[2023-02-12:02:39:55:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[34m[02:39:55] S3DistributionType set as FullyReplicated\u001b[0m\n",
      "\u001b[34m[02:39:56] 503632x26 matrix with 13094432 entries loaded from /opt/ml/input/data/validation?format=csv&label_column=0&delimiter=,\u001b[0m\n",
      "\u001b[34m[02:39:59] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 60 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[0]#011train-auc:0.600208#011validation-auc:0.601623\u001b[0m\n",
      "\u001b[34m[02:40:01] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[1]#011train-auc:0.60577#011validation-auc:0.60679\u001b[0m\n",
      "\u001b[34m[02:40:04] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 60 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[2]#011train-auc:0.608416#011validation-auc:0.609347\u001b[0m\n",
      "\u001b[34m[02:40:06] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[3]#011train-auc:0.610234#011validation-auc:0.611144\u001b[0m\n",
      "\u001b[34m[02:40:08] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[4]#011train-auc:0.614091#011validation-auc:0.614833\u001b[0m\n",
      "\u001b[34m[02:40:09] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[5]#011train-auc:0.614793#011validation-auc:0.615475\u001b[0m\n",
      "\u001b[34m[02:40:11] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[6]#011train-auc:0.617233#011validation-auc:0.617867\u001b[0m\n",
      "\u001b[34m[02:40:13] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[7]#011train-auc:0.61795#011validation-auc:0.61841\u001b[0m\n",
      "\u001b[34m[02:40:15] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[8]#011train-auc:0.620247#011validation-auc:0.620787\u001b[0m\n",
      "\u001b[34m[02:40:17] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[9]#011train-auc:0.621168#011validation-auc:0.621523\u001b[0m\n",
      "\u001b[34m[02:40:18] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[10]#011train-auc:0.622234#011validation-auc:0.622563\u001b[0m\n",
      "\u001b[34m[02:40:20] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[11]#011train-auc:0.625366#011validation-auc:0.62561\u001b[0m\n",
      "\u001b[34m[02:40:22] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[12]#011train-auc:0.625935#011validation-auc:0.626141\u001b[0m\n",
      "\u001b[34m[02:40:24] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[13]#011train-auc:0.626263#011validation-auc:0.626475\u001b[0m\n",
      "\u001b[34m[02:40:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[14]#011train-auc:0.626657#011validation-auc:0.626776\u001b[0m\n",
      "\u001b[34m[02:40:27] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[15]#011train-auc:0.627684#011validation-auc:0.627691\u001b[0m\n",
      "\u001b[34m[02:40:29] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 58 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[16]#011train-auc:0.628587#011validation-auc:0.628477\u001b[0m\n",
      "\u001b[34m[02:40:31] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[17]#011train-auc:0.629846#011validation-auc:0.629643\u001b[0m\n",
      "\u001b[34m[02:40:32] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 60 extra nodes, 2 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[18]#011train-auc:0.630263#011validation-auc:0.630002\u001b[0m\n",
      "\u001b[34m[02:40:34] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 60 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[19]#011train-auc:0.630643#011validation-auc:0.630341\u001b[0m\n",
      "\u001b[34m[02:40:36] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[20]#011train-auc:0.631979#011validation-auc:0.631601\u001b[0m\n",
      "\u001b[34m[02:40:38] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 56 extra nodes, 4 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[21]#011train-auc:0.63246#011validation-auc:0.632058\u001b[0m\n",
      "\u001b[34m[02:40:39] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 48 extra nodes, 4 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[22]#011train-auc:0.632842#011validation-auc:0.632425\u001b[0m\n",
      "\u001b[34m[02:40:41] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[23]#011train-auc:0.633425#011validation-auc:0.632943\u001b[0m\n",
      "\u001b[34m[02:40:43] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[24]#011train-auc:0.633928#011validation-auc:0.633434\u001b[0m\n",
      "\u001b[34m[02:40:45] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[25]#011train-auc:0.634529#011validation-auc:0.633991\u001b[0m\n",
      "\u001b[34m[02:40:47] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 56 extra nodes, 2 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[26]#011train-auc:0.634837#011validation-auc:0.634287\u001b[0m\n",
      "\u001b[34m[02:40:48] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[27]#011train-auc:0.635198#011validation-auc:0.634654\u001b[0m\n",
      "\u001b[34m[02:40:50] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[28]#011train-auc:0.635552#011validation-auc:0.635\u001b[0m\n",
      "\u001b[34m[02:40:52] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[29]#011train-auc:0.636162#011validation-auc:0.635509\u001b[0m\n",
      "\u001b[34m[02:40:54] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[30]#011train-auc:0.636636#011validation-auc:0.635952\u001b[0m\n",
      "\u001b[34m[02:40:56] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 60 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[31]#011train-auc:0.63706#011validation-auc:0.636349\u001b[0m\n",
      "\u001b[34m[02:40:57] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[32]#011train-auc:0.637381#011validation-auc:0.636673\u001b[0m\n",
      "\u001b[34m[02:40:59] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[33]#011train-auc:0.637607#011validation-auc:0.636853\u001b[0m\n",
      "\u001b[34m[02:41:01] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 60 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[34]#011train-auc:0.638003#011validation-auc:0.637179\u001b[0m\n",
      "\u001b[34m[02:41:03] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 36 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[35]#011train-auc:0.638268#011validation-auc:0.637408\u001b[0m\n",
      "\u001b[34m[02:41:05] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 60 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[36]#011train-auc:0.638587#011validation-auc:0.637708\u001b[0m\n",
      "\u001b[34m[02:41:07] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 50 extra nodes, 2 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[37]#011train-auc:0.638862#011validation-auc:0.63796\u001b[0m\n",
      "\u001b[34m[02:41:09] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 58 extra nodes, 2 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[38]#011train-auc:0.639056#011validation-auc:0.638096\u001b[0m\n",
      "\u001b[34m[02:41:11] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 48 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[39]#011train-auc:0.639165#011validation-auc:0.638163\u001b[0m\n",
      "\u001b[34m[02:41:12] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 60 extra nodes, 2 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[40]#011train-auc:0.639611#011validation-auc:0.638576\u001b[0m\n",
      "\u001b[34m[02:41:14] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[41]#011train-auc:0.640008#011validation-auc:0.638927\u001b[0m\n",
      "\u001b[34m[02:41:16] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[42]#011train-auc:0.640255#011validation-auc:0.639153\u001b[0m\n",
      "\u001b[34m[02:41:18] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 60 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[43]#011train-auc:0.640511#011validation-auc:0.639365\u001b[0m\n",
      "\u001b[34m[02:41:19] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 56 extra nodes, 2 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[44]#011train-auc:0.64073#011validation-auc:0.639524\u001b[0m\n",
      "\u001b[34m[02:41:21] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[45]#011train-auc:0.64104#011validation-auc:0.639809\u001b[0m\n",
      "\u001b[34m[02:41:23] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[46]#011train-auc:0.641312#011validation-auc:0.640032\u001b[0m\n",
      "\u001b[34m[02:41:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[47]#011train-auc:0.641717#011validation-auc:0.640369\u001b[0m\n",
      "\u001b[34m[02:41:26] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 54 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[48]#011train-auc:0.641899#011validation-auc:0.640514\u001b[0m\n",
      "\u001b[34m[02:41:28] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 60 extra nodes, 2 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[49]#011train-auc:0.642242#011validation-auc:0.640773\u001b[0m\n",
      "\u001b[34m[02:41:30] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[50]#011train-auc:0.642559#011validation-auc:0.641014\u001b[0m\n",
      "\u001b[34m[02:41:32] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 58 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[51]#011train-auc:0.642819#011validation-auc:0.641251\u001b[0m\n",
      "\u001b[34m[02:41:34] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[52]#011train-auc:0.64309#011validation-auc:0.641456\u001b[0m\n",
      "\u001b[34m[02:41:35] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 56 extra nodes, 4 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[53]#011train-auc:0.643368#011validation-auc:0.641677\u001b[0m\n",
      "\u001b[34m[02:41:37] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 46 extra nodes, 12 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[54]#011train-auc:0.643484#011validation-auc:0.641767\u001b[0m\n",
      "\u001b[34m[02:41:39] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 60 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[55]#011train-auc:0.643754#011validation-auc:0.642005\u001b[0m\n",
      "\u001b[34m[02:41:41] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 56 extra nodes, 2 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[56]#011train-auc:0.643938#011validation-auc:0.64217\u001b[0m\n",
      "\u001b[34m[02:41:43] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 58 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[57]#011train-auc:0.644136#011validation-auc:0.642344\u001b[0m\n",
      "\u001b[34m[02:41:44] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 42 extra nodes, 4 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[58]#011train-auc:0.644327#011validation-auc:0.642477\u001b[0m\n",
      "\u001b[34m[02:41:46] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 30 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[59]#011train-auc:0.644397#011validation-auc:0.642525\u001b[0m\n",
      "\u001b[34m[02:41:48] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[60]#011train-auc:0.644647#011validation-auc:0.642686\u001b[0m\n",
      "\u001b[34m[02:41:50] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[61]#011train-auc:0.644848#011validation-auc:0.64285\u001b[0m\n",
      "\u001b[34m[02:41:51] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[62]#011train-auc:0.645067#011validation-auc:0.643068\u001b[0m\n",
      "\u001b[34m[02:41:53] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[63]#011train-auc:0.645364#011validation-auc:0.643301\u001b[0m\n",
      "\u001b[34m[02:41:55] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 52 extra nodes, 4 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[64]#011train-auc:0.645476#011validation-auc:0.643409\u001b[0m\n",
      "\u001b[34m[02:41:57] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[65]#011train-auc:0.645748#011validation-auc:0.643644\u001b[0m\n",
      "\u001b[34m[02:41:58] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 40 extra nodes, 2 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[66]#011train-auc:0.645822#011validation-auc:0.643705\u001b[0m\n",
      "\u001b[34m[02:42:00] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[67]#011train-auc:0.646113#011validation-auc:0.643936\u001b[0m\n",
      "\u001b[34m[02:42:02] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 58 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[68]#011train-auc:0.646266#011validation-auc:0.644071\u001b[0m\n",
      "\u001b[34m[02:42:04] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 54 extra nodes, 2 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[69]#011train-auc:0.646462#011validation-auc:0.644236\u001b[0m\n",
      "\u001b[34m[02:42:06] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[70]#011train-auc:0.646656#011validation-auc:0.644367\u001b[0m\n",
      "\u001b[34m[02:42:08] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[71]#011train-auc:0.646833#011validation-auc:0.64452\u001b[0m\n",
      "\u001b[34m[02:42:10] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 50 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[72]#011train-auc:0.646933#011validation-auc:0.644581\u001b[0m\n",
      "\u001b[34m[02:42:12] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 60 extra nodes, 2 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[73]#011train-auc:0.647095#011validation-auc:0.644709\u001b[0m\n",
      "\u001b[34m[02:42:15] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 56 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[74]#011train-auc:0.64722#011validation-auc:0.644795\u001b[0m\n",
      "\u001b[34m[02:42:17] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 58 extra nodes, 4 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[75]#011train-auc:0.647333#011validation-auc:0.644855\u001b[0m\n",
      "\u001b[34m[02:42:20] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 56 extra nodes, 4 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[76]#011train-auc:0.647543#011validation-auc:0.644995\u001b[0m\n",
      "\u001b[34m[02:42:22] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 60 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[77]#011train-auc:0.647695#011validation-auc:0.645141\u001b[0m\n",
      "\u001b[34m[02:42:23] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[78]#011train-auc:0.647955#011validation-auc:0.64536\u001b[0m\n",
      "\u001b[34m[02:42:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 60 extra nodes, 2 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[79]#011train-auc:0.648081#011validation-auc:0.645456\u001b[0m\n",
      "\u001b[34m[02:42:27] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 56 extra nodes, 2 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[80]#011train-auc:0.648275#011validation-auc:0.645597\u001b[0m\n",
      "\u001b[34m[02:42:29] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 46 extra nodes, 2 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[81]#011train-auc:0.648337#011validation-auc:0.645632\u001b[0m\n",
      "\u001b[34m[02:42:30] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[82]#011train-auc:0.64852#011validation-auc:0.645766\u001b[0m\n",
      "\u001b[34m[02:42:32] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 50 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[83]#011train-auc:0.648636#011validation-auc:0.645857\u001b[0m\n",
      "\u001b[34m[02:42:34] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 48 extra nodes, 4 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[84]#011train-auc:0.648743#011validation-auc:0.64594\u001b[0m\n",
      "\u001b[34m[02:42:36] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 56 extra nodes, 4 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[85]#011train-auc:0.648926#011validation-auc:0.646101\u001b[0m\n",
      "\u001b[34m[02:42:38] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 34 extra nodes, 2 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[86]#011train-auc:0.64896#011validation-auc:0.646136\u001b[0m\n",
      "\u001b[34m[02:42:39] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 58 extra nodes, 4 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[87]#011train-auc:0.649171#011validation-auc:0.646338\u001b[0m\n",
      "\u001b[34m[02:42:41] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 54 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[88]#011train-auc:0.649327#011validation-auc:0.646443\u001b[0m\n",
      "\u001b[34m[02:42:43] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 56 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[89]#011train-auc:0.649484#011validation-auc:0.646558\u001b[0m\n",
      "\u001b[34m[02:42:45] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 60 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[90]#011train-auc:0.649678#011validation-auc:0.646711\u001b[0m\n",
      "\u001b[34m[02:42:46] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 60 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[91]#011train-auc:0.649827#011validation-auc:0.646819\u001b[0m\n",
      "\u001b[34m[02:42:48] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 56 extra nodes, 2 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[92]#011train-auc:0.649974#011validation-auc:0.646931\u001b[0m\n",
      "\u001b[34m[02:42:50] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 56 extra nodes, 2 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[93]#011train-auc:0.650184#011validation-auc:0.647102\u001b[0m\n",
      "\u001b[34m[02:42:52] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 54 extra nodes, 2 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[94]#011train-auc:0.650317#011validation-auc:0.647208\u001b[0m\n",
      "\u001b[34m[02:42:54] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 60 extra nodes, 2 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[95]#011train-auc:0.650454#011validation-auc:0.647323\u001b[0m\n",
      "\u001b[34m[02:42:55] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 26 extra nodes, 6 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[96]#011train-auc:0.650535#011validation-auc:0.647374\u001b[0m\n",
      "\u001b[34m[02:42:57] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 46 extra nodes, 2 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[97]#011train-auc:0.650638#011validation-auc:0.647453\u001b[0m\n",
      "\u001b[34m[02:42:59] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 56 extra nodes, 2 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[98]#011train-auc:0.650805#011validation-auc:0.647564\u001b[0m\n",
      "\u001b[34m[02:43:01] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 56 extra nodes, 2 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[34m[99]#011train-auc:0.65092#011validation-auc:0.647657\u001b[0m\n",
      "\n",
      "2023-02-12 02:43:15 Uploading - Uploading generated training model\n",
      "2023-02-12 02:43:15 Completed - Training job completed\n",
      "Training seconds: 283\n",
      "Billable seconds: 283\n"
     ]
    }
   ],
   "source": [
    "sess = sagemaker.Session()\n",
    "\n",
    "xgb = sagemaker.estimator.Estimator(container,\n",
    "                                    role, \n",
    "                                    instance_count=1, \n",
    "                                    instance_type='ml.m4.xlarge',\n",
    "                                    output_path='s3://{}/{}/output'.format(bucket, prefix),\n",
    "                                    sagemaker_session=sess)\n",
    "\n",
    "xgb.set_hyperparameters(max_depth=5,eta=0.2,gamma=4,min_child_weight=6,subsample=0.8,silent=0,objective='binary:logistic',\n",
    "                        eval_metric='auc', num_round=100)\n",
    "\n",
    "xgb.fit({'train': s3_input_train, 'validation': s3_input_validation})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e6ff449-c640-4897-b852-b032579d9843",
   "metadata": {},
   "source": [
    "# Model Hosting w/ Endpoint Creation & Model Evaluation\n",
    "Now that we have successfully trained our model, let’s deploy it and see how it does on the test data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7d099e5b-7838-49b5-8c80-c5be7431850d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------!"
     ]
    }
   ],
   "source": [
    "xgb_predictor = xgb.deploy(initial_instance_count=1,\n",
    "                           instance_type='ml.m4.xlarge')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ad4d5603-21d6-4bec-8dc0-7e48531ccff6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#need to make sure data is in correct format for deployed model\n",
    "from sagemaker.predictor import csv_serializer, json_deserializer\n",
    "\n",
    "xgb_predictor.serializer = sagemaker.serializers.CSVSerializer()\n",
    "xgb_predictor.deserializer = sagemaker.deserializers.JSONDeserializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "db5b1ac9-1b10-41cf-af01-07af25a5996d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>880546</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.002090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2085095</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.960150</td>\n",
       "      <td>0.279486</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>1.192488e-08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.028585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1734339</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.742278e-08</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.119881</td>\n",
       "      <td>-0.992788</td>\n",
       "      <td>-8.742278e-08</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.056046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1494162</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.204552</td>\n",
       "      <td>0.978856</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220295</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.983798</td>\n",
       "      <td>0.179281</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.004871</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         0    1    2    3    4    5    6    7    8             9   ...  \\\n",
       "880546    1  1.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  8.660254e-01  ...   \n",
       "2085095   1  1.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0 -1.000000e+00  ...   \n",
       "1734339   1  1.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0 -8.742278e-08  ...   \n",
       "1494162   1  0.0  1.0  0.0  0.0  0.0  1.0  0.0  0.0  0.000000e+00  ...   \n",
       "220295    0  1.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  8.660254e-01  ...   \n",
       "\n",
       "               17        18            19            20   21   22   23   24  \\\n",
       "880546   0.866025  0.500000  0.000000e+00  1.000000e+00  1.0  0.0  0.0  0.0   \n",
       "2085095 -0.960150  0.279486 -1.000000e+00  1.192488e-08  0.0  1.0  0.0  0.0   \n",
       "1734339 -0.119881 -0.992788 -8.742278e-08 -1.000000e+00  1.0  0.0  0.0  0.0   \n",
       "1494162  0.204552  0.978856  0.000000e+00  1.000000e+00  0.0  1.0  0.0  0.0   \n",
       "220295   0.983798  0.179281  0.000000e+00  1.000000e+00  0.0  1.0  0.0  0.0   \n",
       "\n",
       "          25        26  \n",
       "880546   0.0 -0.002090  \n",
       "2085095  0.0 -0.028585  \n",
       "1734339  0.0 -0.056046  \n",
       "1494162  0.0 -0.000628  \n",
       "220295   0.0 -0.004871  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77b975bc-7be2-4792-85df-56f1c2f0b28b",
   "metadata": {},
   "source": [
    "# Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "95d5d5ec-42fe-42c7-a7b2-0abf05282412",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7110561728477478"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run the prediction on a single observation\n",
    "prediction = xgb_predictor.predict(np.array([1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,-0.8660252094268799,0.5000002980232239,0.2993628978729248,\n",
    "                                             -0.9541393518447876,0.7071067690849304,0.7071067690849304,-0.8124867677688599,0.582979679107666,\n",
    "                                             -0.7221164703369141,0.6917715072631836,-1.0,1.1924880638503056e-08,1.0,0.0,0.0,0.0,0.0,\n",
    "                                             -0.0260128165460627]))\n",
    "       \n",
    "#The response is the probability that the person will commit a crime\n",
    "#Target value is 1\n",
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e93424e6-492e-4cce-b75d-3b3a42e08380",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6541838049888611"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run the prediction on a single observation\n",
    "prediction = xgb_predictor.predict(np.array([1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.866025447845459,0.4999999701976776,0.7247928380966187,\n",
    "                                             0.6889668703079224,-0.4999997615814209,0.8660255670547485,0.8124868869781494,0.5829794406890869,\n",
    "                                             0.8826788663864136,0.4699766635894775,0.0,1.0,1.0,0.0,0.0,0.0,0.0,-0.0005222935888651238]))\n",
    "       \n",
    "#The response is the probability that the person will commit a crime\n",
    "#Target value is 0\n",
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2ace302d-dd63-4e7b-9acb-0bb7452561cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "880546     1\n",
       "2085095    1\n",
       "1734339    1\n",
       "1494162    1\n",
       "220295     0\n",
       "Name: 0, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split target variable and feature variables\n",
    "target_values = test_data[0]\n",
    "\n",
    "target_values.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5c8b0cf6-5534-4859-955f-674b9e8f8764",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop first column (ie target) of test data, the first column shouldn't be the target\n",
    "test_data.drop(columns=test_data.columns[0], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "468ee6ce-2bda-4865-931c-b3da9b0d04b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>...</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>880546</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.002090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2085095</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>1.192488e-08</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.960150</td>\n",
       "      <td>0.279486</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>1.192488e-08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.028585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1734339</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.742278e-08</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.119881</td>\n",
       "      <td>-0.992788</td>\n",
       "      <td>-8.742278e-08</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.056046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1494162</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.204552</td>\n",
       "      <td>0.978856</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220295</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.983798</td>\n",
       "      <td>0.179281</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.004871</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          1    2    3    4    5    6    7    8             9             10  \\\n",
       "880546   1.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  8.660254e-01  5.000000e-01   \n",
       "2085095  1.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0 -1.000000e+00  1.192488e-08   \n",
       "1734339  1.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0 -8.742278e-08 -1.000000e+00   \n",
       "1494162  0.0  1.0  0.0  0.0  0.0  1.0  0.0  0.0  0.000000e+00  1.000000e+00   \n",
       "220295   1.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  8.660254e-01  5.000000e-01   \n",
       "\n",
       "         ...        17        18            19            20   21   22   23  \\\n",
       "880546   ...  0.866025  0.500000  0.000000e+00  1.000000e+00  1.0  0.0  0.0   \n",
       "2085095  ... -0.960150  0.279486 -1.000000e+00  1.192488e-08  0.0  1.0  0.0   \n",
       "1734339  ... -0.119881 -0.992788 -8.742278e-08 -1.000000e+00  1.0  0.0  0.0   \n",
       "1494162  ...  0.204552  0.978856  0.000000e+00  1.000000e+00  0.0  1.0  0.0   \n",
       "220295   ...  0.983798  0.179281  0.000000e+00  1.000000e+00  0.0  1.0  0.0   \n",
       "\n",
       "          24   25        26  \n",
       "880546   0.0  0.0 -0.002090  \n",
       "2085095  0.0  0.0 -0.028585  \n",
       "1734339  0.0  0.0 -0.056046  \n",
       "1494162  0.0  0.0 -0.000628  \n",
       "220295   0.0  0.0 -0.004871  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "efce9247-ff6f-4742-b1a5-d01ca8234a2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.63504213 0.72230083 0.49093464 ... 0.53990823 0.52268291 0.58905762]\n"
     ]
    }
   ],
   "source": [
    "#run predictions on the entire test data set\n",
    "#It takes about 30 minutes for this code to execute\n",
    "\n",
    "def predict(data, predictor, rows=500 ):\n",
    "    #splits test dataset into 5 arrays of observations\n",
    "    split_array = np.array_split(data, int(data.shape[0] / float(rows) + 1))\n",
    "    predictions = ''\n",
    "    \n",
    "    for array in split_array:\n",
    "        for observation in array:\n",
    "            prediction = predictor.predict(observation)\n",
    "            predictions = ','.join([predictions, str(prediction)])\n",
    "            \n",
    "    return np.fromstring(predictions[1:], sep=',')\n",
    "\n",
    "#numpy array of predictions\n",
    "predictions = predict(test_data.to_numpy(), xgb_predictor)\n",
    "print(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c6319b81-ebdf-45f8-b537-54b549085e55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.635042130947113\n"
     ]
    }
   ],
   "source": [
    "print(predictions[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3b3c8d46-a9ba-46ee-9d58-564a6bb89229",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7223008275032043\n"
     ]
    }
   ],
   "source": [
    "print(predictions[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "97df20e3-562c-4444-8bfd-ad4e22a38268",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4909346401691437\n"
     ]
    }
   ],
   "source": [
    "print(predictions[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed9e1ff4-bfd9-48c2-be9e-8c63c328e159",
   "metadata": {},
   "source": [
    "# Evaluate the model with a Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0f3fd9c0-47e5-437b-9e53-4575f5a00a35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.635042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.722301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.490935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.581013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.573738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251811</th>\n",
       "      <td>1</td>\n",
       "      <td>0.385045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251812</th>\n",
       "      <td>1</td>\n",
       "      <td>0.461111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251813</th>\n",
       "      <td>1</td>\n",
       "      <td>0.539908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251814</th>\n",
       "      <td>1</td>\n",
       "      <td>0.522683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251815</th>\n",
       "      <td>0</td>\n",
       "      <td>0.589058</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>251816 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Actual  Predicted\n",
       "0            1   0.635042\n",
       "1            1   0.722301\n",
       "2            1   0.490935\n",
       "3            1   0.581013\n",
       "4            0   0.573738\n",
       "...        ...        ...\n",
       "251811       1   0.385045\n",
       "251812       1   0.461111\n",
       "251813       1   0.539908\n",
       "251814       1   0.522683\n",
       "251815       0   0.589058\n",
       "\n",
       "[251816 rows x 2 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#compare the actual values (ie, target) with the values predicted by the model\n",
    "pred_test_df = pd.DataFrame({'Actual': target_values.to_numpy(), 'Predicted': predictions})\n",
    "\n",
    "pred_test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b5ca39b3-3099-44f5-8d3e-1b0f78bdf01d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert to binary value for predictions w/ threshold of .5\n",
    "rounded_predictions = [round(value) for value in predictions]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6e63d6aa-c6af-4231-8960-e2a6aa358174",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251811</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251812</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251813</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251814</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251815</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>251816 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Actual  Predicted\n",
       "0            1          1\n",
       "1            1          1\n",
       "2            1          0\n",
       "3            1          1\n",
       "4            0          1\n",
       "...        ...        ...\n",
       "251811       1          0\n",
       "251812       1          0\n",
       "251813       1          1\n",
       "251814       1          1\n",
       "251815       0          1\n",
       "\n",
       "[251816 rows x 2 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_test_df = pd.DataFrame({'Actual': target_values.to_numpy(), 'Predicted': rounded_predictions})\n",
    "\n",
    "pred_test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "c4b5dc12-6345-40be-9d7a-c022ee42bed3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 51492  64021]\n",
      " [ 33720 102583]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "results = confusion_matrix(target_values.to_numpy(),rounded_predictions)\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5d65b516-4c6a-4aea-9dba-5e481262ceaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the confusion matrix\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=results, \n",
    "                              display_labels=['0','1'])\n",
    "disp.plot(values_format='')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c1c6b62e-743f-4333-9cf5-fa9fac2984a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.45      0.60      0.51     85212\n",
      "           1       0.75      0.62      0.68    166604\n",
      "\n",
      "    accuracy                           0.61    251816\n",
      "   macro avg       0.60      0.61      0.60    251816\n",
      "weighted avg       0.65      0.61      0.62    251816\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# run classification report\n",
    "from sklearn.metrics import classification_report\n",
    " \n",
    "print(classification_report(rounded_predictions, target_values.to_numpy()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d38b5604-b6d8-4998-a0b7-a113bdb0ecac",
   "metadata": {},
   "source": [
    "# Clean-up\n",
    "If you’re ready to be done with this notebook, please run the cell below. This will remove the hosted endpoint you created and avoid any charges from a stray instance being left on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "b41bbec2-1e9f-4134-b4b5-6ac51379a87f",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_predictor.delete_endpoint()"
   ]
  }
 ],
 "metadata": {
  "instance_type": "ml.m5.xlarge",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.9"
  },
  "vscode": {
   "interpreter": {
    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}