05_xgb_one_hot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4c65f557-651f-4958-a567-a1b6b5a82b2b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Modify the file 00_setup.py to define input/output file paths on your system\n",
    "# The information in 00_setup.py will be used across notebooks\n",
    "from importlib.machinery import SourceFileLoader\n",
    "setup = SourceFileLoader(\"setup\", \"./00_setup.py\").load_module()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b01d0fdb-d4bf-465a-8541-997addc50b0c",
   "metadata": {},
   "source": [
    "# XGBoost with one-hot NAICS \n",
    "XGBoost model, one hot encoded NAICS sector"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56161f89-1f54-47f9-860d-3f945a941c1f",
   "metadata": {},
   "source": [
    "*This script takes about 5 minutes on my MacBook Air*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ec6b1778-7abc-4129-ab5f-ac2992bd8ca6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import xgboost as xgb\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "785af927-a634-4e34-84c4-7a532236f2ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fd09527b-32dd-47fa-b62f-f9307f89f553",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn as sk\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.inspection import permutation_importance, partial_dependence\n",
    "from scikitplot.metrics import plot_lift_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0732ba40-8318-457b-b29f-f9b6de3b70aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sba_gnn.sba_gnn import sg_plot "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d14a5947-6599-4253-acf3-91ddeffdedd3",
   "metadata": {},
   "source": [
    "## Import Processed Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f6bc5cc5-b635-420f-852c-6cc9bba68a52",
   "metadata": {},
   "outputs": [],
   "source": [
    "sba_loans = pd.read_parquet(Path(setup.temp_path).joinpath('01_DATA_transformed.parquet'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a5f6c362-c1ab-48d8-812b-2b970b06586b",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = sba_loans[sba_loans['dset'] == 'train'].copy()\n",
    "test_df = sba_loans[sba_loans['dset'] == 'test']\n",
    "val_df = sba_loans[sba_loans['dset'] == 'val']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7b1db53b-181c-450d-9f7e-be350bc3111f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(436120, 55)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a6d13f46-cfc4-4351-a2a9-89fa42d34999",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(436120, 55)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.drop_duplicates('LoanNr_ChkDgt').shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2160b50-1e22-44f0-915b-0241ebcd45d4",
   "metadata": {},
   "source": [
    "#### Set the predictor features.  Include one-hot NAICS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c07ddd4e-6987-454a-926a-3b59abf6caa6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['NoEmp', 'CreateJob', 'LowDoc', 'DisbursementGross', 'new_business', 'urban_flag', 'franchise_flag', 'NS___Accommodation and Food Services', 'NS___Administrative and Support and Waste Management and Remediation Services', 'NS___Construction', 'NS___Health Care and Social Assistance', 'NS___Manufacturing', 'NS___Other Services (except Public Administration)', 'NS___Professional, Scientific, and Technical Services', 'NS___Retail Trade', 'NS___Wholesale Trade', 'NS___infrequent_sklearn']\n"
     ]
    }
   ],
   "source": [
    "predictor_features = setup.predictor_features + [c for c in train_df.columns if c.startswith('NS__')]\n",
    "print(predictor_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "51a66d4e-7977-4c8d-847e-e8fc0b5d8923",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NoEmp</th>\n",
       "      <th>CreateJob</th>\n",
       "      <th>LowDoc</th>\n",
       "      <th>DisbursementGross</th>\n",
       "      <th>new_business</th>\n",
       "      <th>urban_flag</th>\n",
       "      <th>franchise_flag</th>\n",
       "      <th>NS___Accommodation and Food Services</th>\n",
       "      <th>NS___Administrative and Support and Waste Management and Remediation Services</th>\n",
       "      <th>NS___Construction</th>\n",
       "      <th>NS___Health Care and Social Assistance</th>\n",
       "      <th>NS___Manufacturing</th>\n",
       "      <th>NS___Other Services (except Public Administration)</th>\n",
       "      <th>NS___Professional, Scientific, and Technical Services</th>\n",
       "      <th>NS___Retail Trade</th>\n",
       "      <th>NS___Wholesale Trade</th>\n",
       "      <th>NS___infrequent_sklearn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>436120.000000</td>\n",
       "      <td>436120.000000</td>\n",
       "      <td>432893.000000</td>\n",
       "      <td>4.361200e+05</td>\n",
       "      <td>435584.000000</td>\n",
       "      <td>351624.000000</td>\n",
       "      <td>436120.000000</td>\n",
       "      <td>436120.000000</td>\n",
       "      <td>436120.000000</td>\n",
       "      <td>436120.000000</td>\n",
       "      <td>436120.000000</td>\n",
       "      <td>436120.000000</td>\n",
       "      <td>436120.000000</td>\n",
       "      <td>436120.000000</td>\n",
       "      <td>436120.000000</td>\n",
       "      <td>436120.000000</td>\n",
       "      <td>436120.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.723145</td>\n",
       "      <td>2.164835</td>\n",
       "      <td>0.097777</td>\n",
       "      <td>1.944230e+05</td>\n",
       "      <td>0.281016</td>\n",
       "      <td>0.815075</td>\n",
       "      <td>0.052733</td>\n",
       "      <td>0.104930</td>\n",
       "      <td>0.051410</td>\n",
       "      <td>0.096409</td>\n",
       "      <td>0.084023</td>\n",
       "      <td>0.089833</td>\n",
       "      <td>0.104813</td>\n",
       "      <td>0.080063</td>\n",
       "      <td>0.190216</td>\n",
       "      <td>0.068965</td>\n",
       "      <td>0.129338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>61.690596</td>\n",
       "      <td>11.315009</td>\n",
       "      <td>0.297013</td>\n",
       "      <td>2.989452e+05</td>\n",
       "      <td>0.449496</td>\n",
       "      <td>0.388237</td>\n",
       "      <td>0.223501</td>\n",
       "      <td>0.306463</td>\n",
       "      <td>0.220833</td>\n",
       "      <td>0.295152</td>\n",
       "      <td>0.277422</td>\n",
       "      <td>0.285943</td>\n",
       "      <td>0.306313</td>\n",
       "      <td>0.271391</td>\n",
       "      <td>0.392472</td>\n",
       "      <td>0.253395</td>\n",
       "      <td>0.335574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000e+03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.572600e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.148528e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9999.000000</td>\n",
       "      <td>3000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.144632e+07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "               NoEmp      CreateJob         LowDoc  DisbursementGross  \\\n",
       "count  436120.000000  436120.000000  432893.000000       4.361200e+05   \n",
       "mean        9.723145       2.164835       0.097777       1.944230e+05   \n",
       "std        61.690596      11.315009       0.297013       2.989452e+05   \n",
       "min         0.000000       0.000000       0.000000       4.000000e+03   \n",
       "25%         2.000000       0.000000       0.000000       3.572600e+04   \n",
       "50%         4.000000       0.000000       0.000000       9.000000e+04   \n",
       "75%         9.000000       2.000000       0.000000       2.148528e+05   \n",
       "max      9999.000000    3000.000000       1.000000       1.144632e+07   \n",
       "\n",
       "        new_business     urban_flag  franchise_flag  \\\n",
       "count  435584.000000  351624.000000   436120.000000   \n",
       "mean        0.281016       0.815075        0.052733   \n",
       "std         0.449496       0.388237        0.223501   \n",
       "min         0.000000       0.000000        0.000000   \n",
       "25%         0.000000       1.000000        0.000000   \n",
       "50%         0.000000       1.000000        0.000000   \n",
       "75%         1.000000       1.000000        0.000000   \n",
       "max         1.000000       1.000000        1.000000   \n",
       "\n",
       "       NS___Accommodation and Food Services  \\\n",
       "count                         436120.000000   \n",
       "mean                               0.104930   \n",
       "std                                0.306463   \n",
       "min                                0.000000   \n",
       "25%                                0.000000   \n",
       "50%                                0.000000   \n",
       "75%                                0.000000   \n",
       "max                                1.000000   \n",
       "\n",
       "       NS___Administrative and Support and Waste Management and Remediation Services  \\\n",
       "count                                      436120.000000                               \n",
       "mean                                            0.051410                               \n",
       "std                                             0.220833                               \n",
       "min                                             0.000000                               \n",
       "25%                                             0.000000                               \n",
       "50%                                             0.000000                               \n",
       "75%                                             0.000000                               \n",
       "max                                             1.000000                               \n",
       "\n",
       "       NS___Construction  NS___Health Care and Social Assistance  \\\n",
       "count      436120.000000                           436120.000000   \n",
       "mean            0.096409                                0.084023   \n",
       "std             0.295152                                0.277422   \n",
       "min             0.000000                                0.000000   \n",
       "25%             0.000000                                0.000000   \n",
       "50%             0.000000                                0.000000   \n",
       "75%             0.000000                                0.000000   \n",
       "max             1.000000                                1.000000   \n",
       "\n",
       "       NS___Manufacturing  NS___Other Services (except Public Administration)  \\\n",
       "count       436120.000000                                      436120.000000    \n",
       "mean             0.089833                                           0.104813    \n",
       "std              0.285943                                           0.306313    \n",
       "min              0.000000                                           0.000000    \n",
       "25%              0.000000                                           0.000000    \n",
       "50%              0.000000                                           0.000000    \n",
       "75%              0.000000                                           0.000000    \n",
       "max              1.000000                                           1.000000    \n",
       "\n",
       "       NS___Professional, Scientific, and Technical Services  \\\n",
       "count                                      436120.000000       \n",
       "mean                                            0.080063       \n",
       "std                                             0.271391       \n",
       "min                                             0.000000       \n",
       "25%                                             0.000000       \n",
       "50%                                             0.000000       \n",
       "75%                                             0.000000       \n",
       "max                                             1.000000       \n",
       "\n",
       "       NS___Retail Trade  NS___Wholesale Trade  NS___infrequent_sklearn  \n",
       "count      436120.000000         436120.000000            436120.000000  \n",
       "mean            0.190216              0.068965                 0.129338  \n",
       "std             0.392472              0.253395                 0.335574  \n",
       "min             0.000000              0.000000                 0.000000  \n",
       "25%             0.000000              0.000000                 0.000000  \n",
       "50%             0.000000              0.000000                 0.000000  \n",
       "75%             0.000000              0.000000                 0.000000  \n",
       "max             1.000000              1.000000                 1.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[predictor_features].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2a4f4a9e-f7e5-4143-b1e4-b5fbb5f7280d",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NoEmp</th>\n",
       "      <th>CreateJob</th>\n",
       "      <th>LowDoc</th>\n",
       "      <th>DisbursementGross</th>\n",
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       "      <th>NoEmp</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.062158</td>\n",
       "      <td>-0.013926</td>\n",
       "      <td>0.095658</td>\n",
       "      <td>-0.041427</td>\n",
       "      <td>0.001610</td>\n",
       "      <td>0.006409</td>\n",
       "      <td>0.014462</td>\n",
       "      <td>0.005095</td>\n",
       "      <td>0.006727</td>\n",
       "      <td>-0.004596</td>\n",
       "      <td>0.044005</td>\n",
       "      <td>-0.015206</td>\n",
       "      <td>-0.010840</td>\n",
       "      <td>-0.021256</td>\n",
       "      <td>0.003478</td>\n",
       "      <td>-0.011293</td>\n",
       "      <td>-0.027403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CreateJob</th>\n",
       "      <td>0.062158</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.062740</td>\n",
       "      <td>0.134686</td>\n",
       "      <td>0.039302</td>\n",
       "      <td>0.009062</td>\n",
       "      <td>0.050517</td>\n",
       "      <td>0.078203</td>\n",
       "      <td>0.001869</td>\n",
       "      <td>-0.009232</td>\n",
       "      <td>-0.004157</td>\n",
       "      <td>0.020165</td>\n",
       "      <td>-0.017586</td>\n",
       "      <td>-0.012573</td>\n",
       "      <td>-0.032883</td>\n",
       "      <td>-0.008557</td>\n",
       "      <td>-0.007133</td>\n",
       "      <td>-0.010002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowDoc</th>\n",
       "      <td>-0.013926</td>\n",
       "      <td>-0.062740</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.133141</td>\n",
       "      <td>0.153034</td>\n",
       "      <td>-0.120001</td>\n",
       "      <td>0.040841</td>\n",
       "      <td>0.009976</td>\n",
       "      <td>0.001201</td>\n",
       "      <td>-0.028548</td>\n",
       "      <td>0.024883</td>\n",
       "      <td>-0.013266</td>\n",
       "      <td>0.019457</td>\n",
       "      <td>-0.023428</td>\n",
       "      <td>0.043125</td>\n",
       "      <td>-0.026793</td>\n",
       "      <td>-0.023125</td>\n",
       "      <td>-0.079523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DisbursementGross</th>\n",
       "      <td>0.095658</td>\n",
       "      <td>0.134686</td>\n",
       "      <td>-0.133141</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.065443</td>\n",
       "      <td>-0.007016</td>\n",
       "      <td>0.085712</td>\n",
       "      <td>0.072568</td>\n",
       "      <td>-0.059327</td>\n",
       "      <td>-0.042251</td>\n",
       "      <td>0.018609</td>\n",
       "      <td>0.093683</td>\n",
       "      <td>-0.039053</td>\n",
       "      <td>-0.049970</td>\n",
       "      <td>-0.028533</td>\n",
       "      <td>0.059058</td>\n",
       "      <td>-0.020446</td>\n",
       "      <td>-0.120788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_business</th>\n",
       "      <td>-0.041427</td>\n",
       "      <td>0.039302</td>\n",
       "      <td>0.153034</td>\n",
       "      <td>-0.065443</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.047592</td>\n",
       "      <td>0.137325</td>\n",
       "      <td>0.109894</td>\n",
       "      <td>-0.019128</td>\n",
       "      <td>-0.077699</td>\n",
       "      <td>0.050413</td>\n",
       "      <td>-0.056183</td>\n",
       "      <td>0.027980</td>\n",
       "      <td>-0.034445</td>\n",
       "      <td>0.023308</td>\n",
       "      <td>-0.062550</td>\n",
       "      <td>0.009077</td>\n",
       "      <td>0.013878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>urban_flag</th>\n",
       "      <td>0.001610</td>\n",
       "      <td>0.009062</td>\n",
       "      <td>-0.120001</td>\n",
       "      <td>-0.007016</td>\n",
       "      <td>-0.047592</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.006588</td>\n",
       "      <td>-0.024158</td>\n",
       "      <td>0.011619</td>\n",
       "      <td>-0.028348</td>\n",
       "      <td>0.014454</td>\n",
       "      <td>-0.013415</td>\n",
       "      <td>0.020886</td>\n",
       "      <td>0.052913</td>\n",
       "      <td>-0.010011</td>\n",
       "      <td>0.034271</td>\n",
       "      <td>-0.034351</td>\n",
       "      <td>0.053785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>franchise_flag</th>\n",
       "      <td>0.006409</td>\n",
       "      <td>0.050517</td>\n",
       "      <td>0.040841</td>\n",
       "      <td>0.085712</td>\n",
       "      <td>0.137325</td>\n",
       "      <td>-0.006588</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.273763</td>\n",
       "      <td>0.003841</td>\n",
       "      <td>-0.065668</td>\n",
       "      <td>-0.047940</td>\n",
       "      <td>-0.051414</td>\n",
       "      <td>0.010032</td>\n",
       "      <td>-0.054333</td>\n",
       "      <td>-0.000643</td>\n",
       "      <td>-0.055429</td>\n",
       "      <td>-0.033951</td>\n",
       "      <td>-0.012731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NS___Accommodation and Food Services</th>\n",
       "      <td>0.014462</td>\n",
       "      <td>0.078203</td>\n",
       "      <td>0.009976</td>\n",
       "      <td>0.072568</td>\n",
       "      <td>0.109894</td>\n",
       "      <td>-0.024158</td>\n",
       "      <td>0.273763</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.079709</td>\n",
       "      <td>-0.111839</td>\n",
       "      <td>-0.103700</td>\n",
       "      <td>-0.107567</td>\n",
       "      <td>-0.117158</td>\n",
       "      <td>-0.101008</td>\n",
       "      <td>-0.165943</td>\n",
       "      <td>-0.093186</td>\n",
       "      <td>-0.131965</td>\n",
       "      <td>0.011713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NS___Administrative and Support and Waste Management and Remediation Services</th>\n",
       "      <td>0.005095</td>\n",
       "      <td>0.001869</td>\n",
       "      <td>0.001201</td>\n",
       "      <td>-0.059327</td>\n",
       "      <td>-0.019128</td>\n",
       "      <td>0.011619</td>\n",
       "      <td>0.003841</td>\n",
       "      <td>-0.079709</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.076043</td>\n",
       "      <td>-0.070508</td>\n",
       "      <td>-0.073138</td>\n",
       "      <td>-0.079659</td>\n",
       "      <td>-0.068679</td>\n",
       "      <td>-0.112830</td>\n",
       "      <td>-0.063360</td>\n",
       "      <td>-0.089727</td>\n",
       "      <td>0.016817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NS___Construction</th>\n",
       "      <td>0.006727</td>\n",
       "      <td>-0.009232</td>\n",
       "      <td>-0.028548</td>\n",
       "      <td>-0.042251</td>\n",
       "      <td>-0.077699</td>\n",
       "      <td>-0.028348</td>\n",
       "      <td>-0.065668</td>\n",
       "      <td>-0.111839</td>\n",
       "      <td>-0.076043</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.098930</td>\n",
       "      <td>-0.102620</td>\n",
       "      <td>-0.111770</td>\n",
       "      <td>-0.096363</td>\n",
       "      <td>-0.158311</td>\n",
       "      <td>-0.088901</td>\n",
       "      <td>-0.125896</td>\n",
       "      <td>0.033661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NS___Health Care and Social Assistance</th>\n",
       "      <td>-0.004596</td>\n",
       "      <td>-0.004157</td>\n",
       "      <td>0.024883</td>\n",
       "      <td>0.018609</td>\n",
       "      <td>0.050413</td>\n",
       "      <td>0.014454</td>\n",
       "      <td>-0.047940</td>\n",
       "      <td>-0.103700</td>\n",
       "      <td>-0.070508</td>\n",
       "      <td>-0.098930</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.095151</td>\n",
       "      <td>-0.103635</td>\n",
       "      <td>-0.089350</td>\n",
       "      <td>-0.146790</td>\n",
       "      <td>-0.082430</td>\n",
       "      <td>-0.116733</td>\n",
       "      <td>-0.077876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NS___Manufacturing</th>\n",
       "      <td>0.044005</td>\n",
       "      <td>0.020165</td>\n",
       "      <td>-0.013266</td>\n",
       "      <td>0.093683</td>\n",
       "      <td>-0.056183</td>\n",
       "      <td>-0.013415</td>\n",
       "      <td>-0.051414</td>\n",
       "      <td>-0.107567</td>\n",
       "      <td>-0.073138</td>\n",
       "      <td>-0.102620</td>\n",
       "      <td>-0.095151</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.107500</td>\n",
       "      <td>-0.092682</td>\n",
       "      <td>-0.152264</td>\n",
       "      <td>-0.085504</td>\n",
       "      <td>-0.121087</td>\n",
       "      <td>-0.038002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NS___Other Services (except Public Administration)</th>\n",
       "      <td>-0.015206</td>\n",
       "      <td>-0.017586</td>\n",
       "      <td>0.019457</td>\n",
       "      <td>-0.039053</td>\n",
       "      <td>0.027980</td>\n",
       "      <td>0.020886</td>\n",
       "      <td>0.010032</td>\n",
       "      <td>-0.117158</td>\n",
       "      <td>-0.079659</td>\n",
       "      <td>-0.111770</td>\n",
       "      <td>-0.103635</td>\n",
       "      <td>-0.107500</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.100945</td>\n",
       "      <td>-0.165840</td>\n",
       "      <td>-0.093128</td>\n",
       "      <td>-0.131883</td>\n",
       "      <td>-0.002690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NS___Professional, Scientific, and Technical Services</th>\n",
       "      <td>-0.010840</td>\n",
       "      <td>-0.012573</td>\n",
       "      <td>-0.023428</td>\n",
       "      <td>-0.049970</td>\n",
       "      <td>-0.034445</td>\n",
       "      <td>0.052913</td>\n",
       "      <td>-0.054333</td>\n",
       "      <td>-0.101008</td>\n",
       "      <td>-0.068679</td>\n",
       "      <td>-0.096363</td>\n",
       "      <td>-0.089350</td>\n",
       "      <td>-0.092682</td>\n",
       "      <td>-0.100945</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.142980</td>\n",
       "      <td>-0.080291</td>\n",
       "      <td>-0.113704</td>\n",
       "      <td>-0.002407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NS___Retail Trade</th>\n",
       "      <td>-0.021256</td>\n",
       "      <td>-0.032883</td>\n",
       "      <td>0.043125</td>\n",
       "      <td>-0.028533</td>\n",
       "      <td>0.023308</td>\n",
       "      <td>-0.010011</td>\n",
       "      <td>-0.000643</td>\n",
       "      <td>-0.165943</td>\n",
       "      <td>-0.112830</td>\n",
       "      <td>-0.158311</td>\n",
       "      <td>-0.146790</td>\n",
       "      <td>-0.152264</td>\n",
       "      <td>-0.165840</td>\n",
       "      <td>-0.142980</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.131908</td>\n",
       "      <td>-0.186800</td>\n",
       "      <td>0.022554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NS___Wholesale Trade</th>\n",
       "      <td>0.003478</td>\n",
       "      <td>-0.008557</td>\n",
       "      <td>-0.026793</td>\n",
       "      <td>0.059058</td>\n",
       "      <td>-0.062550</td>\n",
       "      <td>0.034271</td>\n",
       "      <td>-0.055429</td>\n",
       "      <td>-0.093186</td>\n",
       "      <td>-0.063360</td>\n",
       "      <td>-0.088901</td>\n",
       "      <td>-0.082430</td>\n",
       "      <td>-0.085504</td>\n",
       "      <td>-0.093128</td>\n",
       "      <td>-0.080291</td>\n",
       "      <td>-0.131908</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.104899</td>\n",
       "      <td>-0.009766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NS___infrequent_sklearn</th>\n",
       "      <td>-0.011293</td>\n",
       "      <td>-0.007133</td>\n",
       "      <td>-0.023125</td>\n",
       "      <td>-0.020446</td>\n",
       "      <td>0.009077</td>\n",
       "      <td>-0.034351</td>\n",
       "      <td>-0.033951</td>\n",
       "      <td>-0.131965</td>\n",
       "      <td>-0.089727</td>\n",
       "      <td>-0.125896</td>\n",
       "      <td>-0.116733</td>\n",
       "      <td>-0.121087</td>\n",
       "      <td>-0.131883</td>\n",
       "      <td>-0.113704</td>\n",
       "      <td>-0.186800</td>\n",
       "      <td>-0.104899</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.030791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>-0.027403</td>\n",
       "      <td>-0.010002</td>\n",
       "      <td>-0.079523</td>\n",
       "      <td>-0.120788</td>\n",
       "      <td>0.013878</td>\n",
       "      <td>0.053785</td>\n",
       "      <td>-0.012731</td>\n",
       "      <td>0.011713</td>\n",
       "      <td>0.016817</td>\n",
       "      <td>0.033661</td>\n",
       "      <td>-0.077876</td>\n",
       "      <td>-0.038002</td>\n",
       "      <td>-0.002690</td>\n",
       "      <td>-0.002407</td>\n",
       "      <td>0.022554</td>\n",
       "      <td>-0.009766</td>\n",
       "      <td>0.030791</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                       NoEmp  CreateJob  \\\n",
       "NoEmp                                               1.000000   0.062158   \n",
       "CreateJob                                           0.062158   1.000000   \n",
       "LowDoc                                             -0.013926  -0.062740   \n",
       "DisbursementGross                                   0.095658   0.134686   \n",
       "new_business                                       -0.041427   0.039302   \n",
       "urban_flag                                          0.001610   0.009062   \n",
       "franchise_flag                                      0.006409   0.050517   \n",
       "NS___Accommodation and Food Services                0.014462   0.078203   \n",
       "NS___Administrative and Support and Waste Manag...  0.005095   0.001869   \n",
       "NS___Construction                                   0.006727  -0.009232   \n",
       "NS___Health Care and Social Assistance             -0.004596  -0.004157   \n",
       "NS___Manufacturing                                  0.044005   0.020165   \n",
       "NS___Other Services (except Public Administration) -0.015206  -0.017586   \n",
       "NS___Professional, Scientific, and Technical Se... -0.010840  -0.012573   \n",
       "NS___Retail Trade                                  -0.021256  -0.032883   \n",
       "NS___Wholesale Trade                                0.003478  -0.008557   \n",
       "NS___infrequent_sklearn                            -0.011293  -0.007133   \n",
       "target                                             -0.027403  -0.010002   \n",
       "\n",
       "                                                      LowDoc  \\\n",
       "NoEmp                                              -0.013926   \n",
       "CreateJob                                          -0.062740   \n",
       "LowDoc                                              1.000000   \n",
       "DisbursementGross                                  -0.133141   \n",
       "new_business                                        0.153034   \n",
       "urban_flag                                         -0.120001   \n",
       "franchise_flag                                      0.040841   \n",
       "NS___Accommodation and Food Services                0.009976   \n",
       "NS___Administrative and Support and Waste Manag...  0.001201   \n",
       "NS___Construction                                  -0.028548   \n",
       "NS___Health Care and Social Assistance              0.024883   \n",
       "NS___Manufacturing                                 -0.013266   \n",
       "NS___Other Services (except Public Administration)  0.019457   \n",
       "NS___Professional, Scientific, and Technical Se... -0.023428   \n",
       "NS___Retail Trade                                   0.043125   \n",
       "NS___Wholesale Trade                               -0.026793   \n",
       "NS___infrequent_sklearn                            -0.023125   \n",
       "target                                             -0.079523   \n",
       "\n",
       "                                                    DisbursementGross  \\\n",
       "NoEmp                                                        0.095658   \n",
       "CreateJob                                                    0.134686   \n",
       "LowDoc                                                      -0.133141   \n",
       "DisbursementGross                                            1.000000   \n",
       "new_business                                                -0.065443   \n",
       "urban_flag                                                  -0.007016   \n",
       "franchise_flag                                               0.085712   \n",
       "NS___Accommodation and Food Services                         0.072568   \n",
       "NS___Administrative and Support and Waste Manag...          -0.059327   \n",
       "NS___Construction                                           -0.042251   \n",
       "NS___Health Care and Social Assistance                       0.018609   \n",
       "NS___Manufacturing                                           0.093683   \n",
       "NS___Other Services (except Public Administration)          -0.039053   \n",
       "NS___Professional, Scientific, and Technical Se...          -0.049970   \n",
       "NS___Retail Trade                                           -0.028533   \n",
       "NS___Wholesale Trade                                         0.059058   \n",
       "NS___infrequent_sklearn                                     -0.020446   \n",
       "target                                                      -0.120788   \n",
       "\n",
       "                                                    new_business  urban_flag  \\\n",
       "NoEmp                                                  -0.041427    0.001610   \n",
       "CreateJob                                               0.039302    0.009062   \n",
       "LowDoc                                                  0.153034   -0.120001   \n",
       "DisbursementGross                                      -0.065443   -0.007016   \n",
       "new_business                                            1.000000   -0.047592   \n",
       "urban_flag                                             -0.047592    1.000000   \n",
       "franchise_flag                                          0.137325   -0.006588   \n",
       "NS___Accommodation and Food Services                    0.109894   -0.024158   \n",
       "NS___Administrative and Support and Waste Manag...     -0.019128    0.011619   \n",
       "NS___Construction                                      -0.077699   -0.028348   \n",
       "NS___Health Care and Social Assistance                  0.050413    0.014454   \n",
       "NS___Manufacturing                                     -0.056183   -0.013415   \n",
       "NS___Other Services (except Public Administration)      0.027980    0.020886   \n",
       "NS___Professional, Scientific, and Technical Se...     -0.034445    0.052913   \n",
       "NS___Retail Trade                                       0.023308   -0.010011   \n",
       "NS___Wholesale Trade                                   -0.062550    0.034271   \n",
       "NS___infrequent_sklearn                                 0.009077   -0.034351   \n",
       "target                                                  0.013878    0.053785   \n",
       "\n",
       "                                                    franchise_flag  \\\n",
       "NoEmp                                                     0.006409   \n",
       "CreateJob                                                 0.050517   \n",
       "LowDoc                                                    0.040841   \n",
       "DisbursementGross                                         0.085712   \n",
       "new_business                                              0.137325   \n",
       "urban_flag                                               -0.006588   \n",
       "franchise_flag                                            1.000000   \n",
       "NS___Accommodation and Food Services                      0.273763   \n",
       "NS___Administrative and Support and Waste Manag...        0.003841   \n",
       "NS___Construction                                        -0.065668   \n",
       "NS___Health Care and Social Assistance                   -0.047940   \n",
       "NS___Manufacturing                                       -0.051414   \n",
       "NS___Other Services (except Public Administration)        0.010032   \n",
       "NS___Professional, Scientific, and Technical Se...       -0.054333   \n",
       "NS___Retail Trade                                        -0.000643   \n",
       "NS___Wholesale Trade                                     -0.055429   \n",
       "NS___infrequent_sklearn                                  -0.033951   \n",
       "target                                                   -0.012731   \n",
       "\n",
       "                                                    NS___Accommodation and Food Services  \\\n",
       "NoEmp                                                                           0.014462   \n",
       "CreateJob                                                                       0.078203   \n",
       "LowDoc                                                                          0.009976   \n",
       "DisbursementGross                                                               0.072568   \n",
       "new_business                                                                    0.109894   \n",
       "urban_flag                                                                     -0.024158   \n",
       "franchise_flag                                                                  0.273763   \n",
       "NS___Accommodation and Food Services                                            1.000000   \n",
       "NS___Administrative and Support and Waste Manag...                             -0.079709   \n",
       "NS___Construction                                                              -0.111839   \n",
       "NS___Health Care and Social Assistance                                         -0.103700   \n",
       "NS___Manufacturing                                                             -0.107567   \n",
       "NS___Other Services (except Public Administration)                             -0.117158   \n",
       "NS___Professional, Scientific, and Technical Se...                             -0.101008   \n",
       "NS___Retail Trade                                                              -0.165943   \n",
       "NS___Wholesale Trade                                                           -0.093186   \n",
       "NS___infrequent_sklearn                                                        -0.131965   \n",
       "target                                                                          0.011713   \n",
       "\n",
       "                                                    NS___Administrative and Support and Waste Management and Remediation Services  \\\n",
       "NoEmp                                                                                        0.005095                               \n",
       "CreateJob                                                                                    0.001869                               \n",
       "LowDoc                                                                                       0.001201                               \n",
       "DisbursementGross                                                                           -0.059327                               \n",
       "new_business                                                                                -0.019128                               \n",
       "urban_flag                                                                                   0.011619                               \n",
       "franchise_flag                                                                               0.003841                               \n",
       "NS___Accommodation and Food Services                                                        -0.079709                               \n",
       "NS___Administrative and Support and Waste Manag...                                           1.000000                               \n",
       "NS___Construction                                                                           -0.076043                               \n",
       "NS___Health Care and Social Assistance                                                      -0.070508                               \n",
       "NS___Manufacturing                                                                          -0.073138                               \n",
       "NS___Other Services (except Public Administration)                                          -0.079659                               \n",
       "NS___Professional, Scientific, and Technical Se...                                          -0.068679                               \n",
       "NS___Retail Trade                                                                           -0.112830                               \n",
       "NS___Wholesale Trade                                                                        -0.063360                               \n",
       "NS___infrequent_sklearn                                                                     -0.089727                               \n",
       "target                                                                                       0.016817                               \n",
       "\n",
       "                                                    NS___Construction  \\\n",
       "NoEmp                                                        0.006727   \n",
       "CreateJob                                                   -0.009232   \n",
       "LowDoc                                                      -0.028548   \n",
       "DisbursementGross                                           -0.042251   \n",
       "new_business                                                -0.077699   \n",
       "urban_flag                                                  -0.028348   \n",
       "franchise_flag                                              -0.065668   \n",
       "NS___Accommodation and Food Services                        -0.111839   \n",
       "NS___Administrative and Support and Waste Manag...          -0.076043   \n",
       "NS___Construction                                            1.000000   \n",
       "NS___Health Care and Social Assistance                      -0.098930   \n",
       "NS___Manufacturing                                          -0.102620   \n",
       "NS___Other Services (except Public Administration)          -0.111770   \n",
       "NS___Professional, Scientific, and Technical Se...          -0.096363   \n",
       "NS___Retail Trade                                           -0.158311   \n",
       "NS___Wholesale Trade                                        -0.088901   \n",
       "NS___infrequent_sklearn                                     -0.125896   \n",
       "target                                                       0.033661   \n",
       "\n",
       "                                                    NS___Health Care and Social Assistance  \\\n",
       "NoEmp                                                                            -0.004596   \n",
       "CreateJob                                                                        -0.004157   \n",
       "LowDoc                                                                            0.024883   \n",
       "DisbursementGross                                                                 0.018609   \n",
       "new_business                                                                      0.050413   \n",
       "urban_flag                                                                        0.014454   \n",
       "franchise_flag                                                                   -0.047940   \n",
       "NS___Accommodation and Food Services                                             -0.103700   \n",
       "NS___Administrative and Support and Waste Manag...                               -0.070508   \n",
       "NS___Construction                                                                -0.098930   \n",
       "NS___Health Care and Social Assistance                                            1.000000   \n",
       "NS___Manufacturing                                                               -0.095151   \n",
       "NS___Other Services (except Public Administration)                               -0.103635   \n",
       "NS___Professional, Scientific, and Technical Se...                               -0.089350   \n",
       "NS___Retail Trade                                                                -0.146790   \n",
       "NS___Wholesale Trade                                                             -0.082430   \n",
       "NS___infrequent_sklearn                                                          -0.116733   \n",
       "target                                                                           -0.077876   \n",
       "\n",
       "                                                    NS___Manufacturing  \\\n",
       "NoEmp                                                         0.044005   \n",
       "CreateJob                                                     0.020165   \n",
       "LowDoc                                                       -0.013266   \n",
       "DisbursementGross                                             0.093683   \n",
       "new_business                                                 -0.056183   \n",
       "urban_flag                                                   -0.013415   \n",
       "franchise_flag                                               -0.051414   \n",
       "NS___Accommodation and Food Services                         -0.107567   \n",
       "NS___Administrative and Support and Waste Manag...           -0.073138   \n",
       "NS___Construction                                            -0.102620   \n",
       "NS___Health Care and Social Assistance                       -0.095151   \n",
       "NS___Manufacturing                                            1.000000   \n",
       "NS___Other Services (except Public Administration)           -0.107500   \n",
       "NS___Professional, Scientific, and Technical Se...           -0.092682   \n",
       "NS___Retail Trade                                            -0.152264   \n",
       "NS___Wholesale Trade                                         -0.085504   \n",
       "NS___infrequent_sklearn                                      -0.121087   \n",
       "target                                                       -0.038002   \n",
       "\n",
       "                                                    NS___Other Services (except Public Administration)  \\\n",
       "NoEmp                                                                                       -0.015206    \n",
       "CreateJob                                                                                   -0.017586    \n",
       "LowDoc                                                                                       0.019457    \n",
       "DisbursementGross                                                                           -0.039053    \n",
       "new_business                                                                                 0.027980    \n",
       "urban_flag                                                                                   0.020886    \n",
       "franchise_flag                                                                               0.010032    \n",
       "NS___Accommodation and Food Services                                                        -0.117158    \n",
       "NS___Administrative and Support and Waste Manag...                                          -0.079659    \n",
       "NS___Construction                                                                           -0.111770    \n",
       "NS___Health Care and Social Assistance                                                      -0.103635    \n",
       "NS___Manufacturing                                                                          -0.107500    \n",
       "NS___Other Services (except Public Administration)                                           1.000000    \n",
       "NS___Professional, Scientific, and Technical Se...                                          -0.100945    \n",
       "NS___Retail Trade                                                                           -0.165840    \n",
       "NS___Wholesale Trade                                                                        -0.093128    \n",
       "NS___infrequent_sklearn                                                                     -0.131883    \n",
       "target                                                                                      -0.002690    \n",
       "\n",
       "                                                    NS___Professional, Scientific, and Technical Services  \\\n",
       "NoEmp                                                                                       -0.010840       \n",
       "CreateJob                                                                                   -0.012573       \n",
       "LowDoc                                                                                      -0.023428       \n",
       "DisbursementGross                                                                           -0.049970       \n",
       "new_business                                                                                -0.034445       \n",
       "urban_flag                                                                                   0.052913       \n",
       "franchise_flag                                                                              -0.054333       \n",
       "NS___Accommodation and Food Services                                                        -0.101008       \n",
       "NS___Administrative and Support and Waste Manag...                                          -0.068679       \n",
       "NS___Construction                                                                           -0.096363       \n",
       "NS___Health Care and Social Assistance                                                      -0.089350       \n",
       "NS___Manufacturing                                                                          -0.092682       \n",
       "NS___Other Services (except Public Administration)                                          -0.100945       \n",
       "NS___Professional, Scientific, and Technical Se...                                           1.000000       \n",
       "NS___Retail Trade                                                                           -0.142980       \n",
       "NS___Wholesale Trade                                                                        -0.080291       \n",
       "NS___infrequent_sklearn                                                                     -0.113704       \n",
       "target                                                                                      -0.002407       \n",
       "\n",
       "                                                    NS___Retail Trade  \\\n",
       "NoEmp                                                       -0.021256   \n",
       "CreateJob                                                   -0.032883   \n",
       "LowDoc                                                       0.043125   \n",
       "DisbursementGross                                           -0.028533   \n",
       "new_business                                                 0.023308   \n",
       "urban_flag                                                  -0.010011   \n",
       "franchise_flag                                              -0.000643   \n",
       "NS___Accommodation and Food Services                        -0.165943   \n",
       "NS___Administrative and Support and Waste Manag...          -0.112830   \n",
       "NS___Construction                                           -0.158311   \n",
       "NS___Health Care and Social Assistance                      -0.146790   \n",
       "NS___Manufacturing                                          -0.152264   \n",
       "NS___Other Services (except Public Administration)          -0.165840   \n",
       "NS___Professional, Scientific, and Technical Se...          -0.142980   \n",
       "NS___Retail Trade                                            1.000000   \n",
       "NS___Wholesale Trade                                        -0.131908   \n",
       "NS___infrequent_sklearn                                     -0.186800   \n",
       "target                                                       0.022554   \n",
       "\n",
       "                                                    NS___Wholesale Trade  \\\n",
       "NoEmp                                                           0.003478   \n",
       "CreateJob                                                      -0.008557   \n",
       "LowDoc                                                         -0.026793   \n",
       "DisbursementGross                                               0.059058   \n",
       "new_business                                                   -0.062550   \n",
       "urban_flag                                                      0.034271   \n",
       "franchise_flag                                                 -0.055429   \n",
       "NS___Accommodation and Food Services                           -0.093186   \n",
       "NS___Administrative and Support and Waste Manag...             -0.063360   \n",
       "NS___Construction                                              -0.088901   \n",
       "NS___Health Care and Social Assistance                         -0.082430   \n",
       "NS___Manufacturing                                             -0.085504   \n",
       "NS___Other Services (except Public Administration)             -0.093128   \n",
       "NS___Professional, Scientific, and Technical Se...             -0.080291   \n",
       "NS___Retail Trade                                              -0.131908   \n",
       "NS___Wholesale Trade                                            1.000000   \n",
       "NS___infrequent_sklearn                                        -0.104899   \n",
       "target                                                         -0.009766   \n",
       "\n",
       "                                                    NS___infrequent_sklearn  \\\n",
       "NoEmp                                                             -0.011293   \n",
       "CreateJob                                                         -0.007133   \n",
       "LowDoc                                                            -0.023125   \n",
       "DisbursementGross                                                 -0.020446   \n",
       "new_business                                                       0.009077   \n",
       "urban_flag                                                        -0.034351   \n",
       "franchise_flag                                                    -0.033951   \n",
       "NS___Accommodation and Food Services                              -0.131965   \n",
       "NS___Administrative and Support and Waste Manag...                -0.089727   \n",
       "NS___Construction                                                 -0.125896   \n",
       "NS___Health Care and Social Assistance                            -0.116733   \n",
       "NS___Manufacturing                                                -0.121087   \n",
       "NS___Other Services (except Public Administration)                -0.131883   \n",
       "NS___Professional, Scientific, and Technical Se...                -0.113704   \n",
       "NS___Retail Trade                                                 -0.186800   \n",
       "NS___Wholesale Trade                                              -0.104899   \n",
       "NS___infrequent_sklearn                                            1.000000   \n",
       "target                                                             0.030791   \n",
       "\n",
       "                                                      target  \n",
       "NoEmp                                              -0.027403  \n",
       "CreateJob                                          -0.010002  \n",
       "LowDoc                                             -0.079523  \n",
       "DisbursementGross                                  -0.120788  \n",
       "new_business                                        0.013878  \n",
       "urban_flag                                          0.053785  \n",
       "franchise_flag                                     -0.012731  \n",
       "NS___Accommodation and Food Services                0.011713  \n",
       "NS___Administrative and Support and Waste Manag...  0.016817  \n",
       "NS___Construction                                   0.033661  \n",
       "NS___Health Care and Social Assistance             -0.077876  \n",
       "NS___Manufacturing                                 -0.038002  \n",
       "NS___Other Services (except Public Administration) -0.002690  \n",
       "NS___Professional, Scientific, and Technical Se... -0.002407  \n",
       "NS___Retail Trade                                   0.022554  \n",
       "NS___Wholesale Trade                               -0.009766  \n",
       "NS___infrequent_sklearn                             0.030791  \n",
       "target                                              1.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[predictor_features + ['target']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1f8c475d-7ead-4f92-a19b-326343528f59",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(Path(setup.temp_path).joinpath('05_DATA_features_predict.pkl'), 'wb') as fout:\n",
    "    pickle.dump(predictor_features, fout)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b204a92-d109-4f6d-87a5-3d5dc05d2e5f",
   "metadata": {},
   "source": [
    "## Model Fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c07828e0-5f5e-433f-9d96-fe78e9d668ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'subsample': 0.8,\n",
       " 'scale_pos_weight': 1.0,\n",
       " 'reg_lambda': 0.01,\n",
       " 'reg_alpha': 0.001,\n",
       " 'min_child_weight': 50,\n",
       " 'max_depth': 8,\n",
       " 'learning_rate': 0.1,\n",
       " 'gamma': 2}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_params_df = pd.read_csv(Path(setup.temp_path).joinpath('03_REPORT_fit_parameter_selected.csv'))\n",
    "best_params = best_params_df.to_dict(orient='records')[0]\n",
    "best_params"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09bb1219-1d1e-432e-a2ca-be94168cf7ba",
   "metadata": {},
   "source": [
    "##### Fit using pre-determined parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3eeec4bc-9ca2-4d11-87b8-a81a7fa93383",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_params = dict(best_params, \n",
    "                   **{'objective':\"binary:logistic\", 'n_estimators':setup.xgb_n_estimators})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7cb431ab-94aa-4922-8107-e97f0e50314f",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_model = xgb.XGBClassifier(objective=\"binary:logistic\",\n",
    "                             n_estimators = setup.xgb_n_estimators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1e17a637-89fd-4ee7-8e4f-a219133d2d75",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_model = xgb_model.set_params(**xgb_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c3d7133b-075a-48a2-b9c8-c004dab7be61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-logloss:0.50188\tvalidation_1-logloss:0.50530\n",
      "[1]\tvalidation_0-logloss:0.49556\tvalidation_1-logloss:0.49903\n",
      "[2]\tvalidation_0-logloss:0.49030\tvalidation_1-logloss:0.49385\n",
      "[3]\tvalidation_0-logloss:0.48568\tvalidation_1-logloss:0.48930\n",
      "[4]\tvalidation_0-logloss:0.48186\tvalidation_1-logloss:0.48556\n",
      "[5]\tvalidation_0-logloss:0.47847\tvalidation_1-logloss:0.48222\n",
      "[6]\tvalidation_0-logloss:0.47561\tvalidation_1-logloss:0.47942\n",
      "[7]\tvalidation_0-logloss:0.47321\tvalidation_1-logloss:0.47708\n",
      "[8]\tvalidation_0-logloss:0.47109\tvalidation_1-logloss:0.47503\n",
      "[9]\tvalidation_0-logloss:0.46932\tvalidation_1-logloss:0.47333\n",
      "[10]\tvalidation_0-logloss:0.46773\tvalidation_1-logloss:0.47182\n",
      "[11]\tvalidation_0-logloss:0.46631\tvalidation_1-logloss:0.47047\n",
      "[12]\tvalidation_0-logloss:0.46512\tvalidation_1-logloss:0.46934\n",
      "[13]\tvalidation_0-logloss:0.46410\tvalidation_1-logloss:0.46837\n",
      "[14]\tvalidation_0-logloss:0.46319\tvalidation_1-logloss:0.46756\n",
      "[15]\tvalidation_0-logloss:0.46242\tvalidation_1-logloss:0.46684\n",
      "[16]\tvalidation_0-logloss:0.46165\tvalidation_1-logloss:0.46616\n",
      "[17]\tvalidation_0-logloss:0.46101\tvalidation_1-logloss:0.46557\n",
      "[18]\tvalidation_0-logloss:0.46050\tvalidation_1-logloss:0.46510\n",
      "[19]\tvalidation_0-logloss:0.46005\tvalidation_1-logloss:0.46470\n",
      "[20]\tvalidation_0-logloss:0.45962\tvalidation_1-logloss:0.46431\n",
      "[21]\tvalidation_0-logloss:0.45918\tvalidation_1-logloss:0.46394\n",
      "[22]\tvalidation_0-logloss:0.45883\tvalidation_1-logloss:0.46363\n",
      "[23]\tvalidation_0-logloss:0.45852\tvalidation_1-logloss:0.46337\n",
      "[24]\tvalidation_0-logloss:0.45827\tvalidation_1-logloss:0.46317\n",
      "[25]\tvalidation_0-logloss:0.45795\tvalidation_1-logloss:0.46291\n",
      "[26]\tvalidation_0-logloss:0.45766\tvalidation_1-logloss:0.46268\n",
      "[27]\tvalidation_0-logloss:0.45748\tvalidation_1-logloss:0.46252\n",
      "[28]\tvalidation_0-logloss:0.45727\tvalidation_1-logloss:0.46233\n",
      "[29]\tvalidation_0-logloss:0.45708\tvalidation_1-logloss:0.46220\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=2, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=8, max_leaves=None,\n",
       "              min_child_weight=50, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=30, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=2, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=8, max_leaves=None,\n",
       "              min_child_weight=50, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=30, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=2, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=8, max_leaves=None,\n",
       "              min_child_weight=50, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=30, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_model.fit(train_df[predictor_features], train_df['target'],\n",
    "              eval_set = [(train_df[predictor_features], train_df['target']),\n",
    "                         (val_df[predictor_features], val_df['target'])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "63408512-21ae-4b5b-91e4-63ed4076bdbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target\n",
       "0    346866\n",
       "1     89254\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['target'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0913a102-a730-4b94-9977-e584df764d64",
   "metadata": {},
   "source": [
    "##### Save the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "af21dd7c-09d4-407f-9960-0748ba8f2247",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_model.save_model(Path(setup.temp_path).joinpath('05_MODEL_xgboost.json'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8e5d3e7d-0bd5-4252-b287-c9cc65f7a132",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(Path(setup.temp_path).joinpath('05_MODEL_xgboost.pkl'), 'wb') as po:\n",
    "    pickle.dump(xgb_model, po)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f34ad059-70dc-4672-adff-0d961fc74b07",
   "metadata": {},
   "source": [
    "## Probability Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6a7c8509-0109-4f8e-a466-d6e21164012c",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_df = pd.concat([sba_loans[['LoanNr_ChkDgt', 'dset', 'target', 'dset_naics_holdout']].reset_index(),\n",
    "                     pd.DataFrame({'predict_prob':\n",
    "                                   xgb_model.predict_proba(sba_loans[predictor_features])[:,1]})],\n",
    "                    axis=1) \\\n",
    "    .set_index('index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7084012d-b68a-4ca4-b714-160f8d564500",
   "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>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.187792</td>\n",
       "      <td>0.108479</td>\n",
       "      <td>0.01637</td>\n",
       "      <td>0.663749</td>\n",
       "      <td>0.182601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.271903</td>\n",
       "      <td>0.093979</td>\n",
       "      <td>0.01637</td>\n",
       "      <td>0.683506</td>\n",
       "      <td>0.290298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            mean       std      min       max    median\n",
       "target                                                 \n",
       "0       0.187792  0.108479  0.01637  0.663749  0.182601\n",
       "1       0.271903  0.093979  0.01637  0.683506  0.290298"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_df.groupby('target')['predict_prob'].agg(['mean', 'std', 'min', 'max', 'median'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d33cb4dc-6c89-4a37-98c7-d3a666abc4a5",
   "metadata": {},
   "source": [
    "## Decision Threshold\n",
    "Max f1, on training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d7727498-825f-4f53-882f-cfcce730b1fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_train_df = pred_df[pred_df['dset'] == 'train']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7b553798-eead-48f8-b14b-5f036d9183d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "thresh_tune_data = sg_plot.get_f1_frame(pred_train_df['target'], \n",
    "                                        pred_train_df['predict_prob'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7a1cde01-a30b-475f-8fd7-409b13946c6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "thresh_tune_data.to_csv(Path(setup.temp_path).joinpath('05_REPORT_thresh_tune.csv'), index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "947a487c-e71d-433c-bbf0-2ed7e57a908a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.22\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>thresh</th>\n",
       "      <th>f1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.22</td>\n",
       "      <td>0.440351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.24</td>\n",
       "      <td>0.439662</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    thresh        f1\n",
       "11    0.22  0.440351\n",
       "12    0.24  0.439662"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thresh_head = thresh_tune_data.sort_values('f1', ascending=False).head(2)\n",
    "best_thresh = thresh_head['thresh'].iloc[0]\n",
    "print(best_thresh)\n",
    "thresh_head"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ee5330e-c1a7-4310-afbe-be9bc0d26f43",
   "metadata": {},
   "source": [
    "##### Apply decision threshold\n",
    "All datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f0d4c1cf-6d5b-49e9-9123-a067697a9e39",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_df['predict_bin'] = np.where(pred_df['predict_prob'] >= best_thresh, 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8253c32c-0438-47b9-8570-7cde6f7f5590",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "predict_bin\n",
       "0    357566\n",
       "1    330515\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_df['predict_bin'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "dbb46f4a-a5ef-4fd6-9da4-30de4a00334d",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_df.to_parquet(Path(setup.temp_path).joinpath('05_DATA_combined_predictions.parquet'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ab860d0-eddc-4f63-b853-88da15d451f4",
   "metadata": {},
   "source": [
    "## Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f2801964-124e-425f-b4cb-9bcf1cf3f04a",
   "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>dset</th>\n",
       "      <th>accuracy_score</th>\n",
       "      <th>f1_score</th>\n",
       "      <th>precision_score</th>\n",
       "      <th>recall_score</th>\n",
       "      <th>average_precision_score</th>\n",
       "      <th>roc_auc_score</th>\n",
       "      <th>dset_naics_holdout</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test</td>\n",
       "      <td>0.624881</td>\n",
       "      <td>0.432973</td>\n",
       "      <td>0.307531</td>\n",
       "      <td>0.731252</td>\n",
       "      <td>0.355820</td>\n",
       "      <td>0.722092</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train</td>\n",
       "      <td>0.614377</td>\n",
       "      <td>0.440351</td>\n",
       "      <td>0.313199</td>\n",
       "      <td>0.741300</td>\n",
       "      <td>0.363523</td>\n",
       "      <td>0.716353</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>val</td>\n",
       "      <td>0.610932</td>\n",
       "      <td>0.439200</td>\n",
       "      <td>0.313295</td>\n",
       "      <td>0.734296</td>\n",
       "      <td>0.364549</td>\n",
       "      <td>0.712160</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test</td>\n",
       "      <td>0.611086</td>\n",
       "      <td>0.439001</td>\n",
       "      <td>0.312104</td>\n",
       "      <td>0.739791</td>\n",
       "      <td>0.358867</td>\n",
       "      <td>0.713595</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>test</td>\n",
       "      <td>0.644700</td>\n",
       "      <td>0.423228</td>\n",
       "      <td>0.300156</td>\n",
       "      <td>0.717367</td>\n",
       "      <td>0.350732</td>\n",
       "      <td>0.733087</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    dset  accuracy_score  f1_score  precision_score  recall_score  \\\n",
       "0   test        0.624881  0.432973         0.307531      0.731252   \n",
       "1  train        0.614377  0.440351         0.313199      0.741300   \n",
       "2    val        0.610932  0.439200         0.313295      0.734296   \n",
       "0   test        0.611086  0.439001         0.312104      0.739791   \n",
       "1   test        0.644700  0.423228         0.300156      0.717367   \n",
       "\n",
       "   average_precision_score  roc_auc_score  dset_naics_holdout  \n",
       "0                 0.355820       0.722092                 NaN  \n",
       "1                 0.363523       0.716353                 NaN  \n",
       "2                 0.364549       0.712160                 NaN  \n",
       "0                 0.358867       0.713595                 0.0  \n",
       "1                 0.350732       0.733087                 1.0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics_dset_df = pred_df.groupby('dset') \\\n",
    "    .apply(lambda x: sg_plot.dset_metrics(x.target, x.predict_bin, x.predict_prob)) \\\n",
    "    .reset_index()\n",
    "metrics_test_df = pred_df[pred_df['dset'] == 'test'] \\\n",
    "    .groupby(['dset', 'dset_naics_holdout']) \\\n",
    "    .apply(lambda x: sg_plot.dset_metrics(x.target, x.predict_bin, x.predict_prob)) \\\n",
    "    .reset_index()\n",
    "metrics_df = pd.concat([metrics_dset_df, metrics_test_df])\n",
    "metrics_df.to_csv(Path(setup.temp_path).joinpath('05_REPORT_metrics.csv'), index=True)\n",
    "metrics_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d906270-4264-4b0b-9f50-dcef8f75a5fd",
   "metadata": {},
   "source": [
    "## Lift\n",
    "Test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "82d9c0ab-8989-450d-a2b1-7997ca636052",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "pred_test_df = pred_df[pred_df['dset'] == 'test']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c01f5279-1151-4a8d-9b21-e12d4092f3c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_lift_curve(pred_test_df['target'], \n",
    "                pd.concat([1-pred_test_df['predict_prob'], pred_test_df['predict_prob']], axis=1), \n",
    "                title='Lift Curve', ax=None, figsize=(4,3), \n",
    "                title_fontsize='large', text_fontsize='medium')\n",
    "plt.gca().legend(loc='upper right')\n",
    "plt.gcf().savefig(Path(setup.temp_path).joinpath('05_PLOT_lift_curve.png'),bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04994f41-3c98-412a-bb2f-11e5ae22b432",
   "metadata": {},
   "source": [
    "## Feature Importance "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "926af7df-e407-484b-bd86-ae7d76a46392",
   "metadata": {},
   "source": [
    "##### XGBoost gain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ec3f6e1f-2b29-4b46-ac12-7a8451a3a45b",
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_impgain = pd.DataFrame(xgb_model.get_booster().get_score(importance_type='gain'),\n",
    "                                     index=['importance']).T \\\n",
    "    .sort_values('importance', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9586c637-7add-4401-b61c-37abd55b11b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_impgain.to_csv(Path(setup.temp_path).joinpath('05_REPORT_importance_gain.csv'), index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "311e111f-d8e5-4a52-8adc-6a7c5f9daa8f",
   "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>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>urban_flag</th>\n",
       "      <td>696.576416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowDoc</th>\n",
       "      <td>153.650986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DisbursementGross</th>\n",
       "      <td>74.793709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NS___Health Care and Social Assistance</th>\n",
       "      <td>63.256439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_business</th>\n",
       "      <td>25.414274</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        importance\n",
       "urban_flag                              696.576416\n",
       "LowDoc                                  153.650986\n",
       "DisbursementGross                        74.793709\n",
       "NS___Health Care and Social Assistance   63.256439\n",
       "new_business                             25.414274"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_impgain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9c88564a-7289-4c49-b2cd-b97338d1b042",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_impgain.plot(kind='barh', legend=None)\n",
    "plt.gca().invert_yaxis()\n",
    "plt.xlabel('gain importance')\n",
    "plt.gcf().savefig(Path(setup.temp_path).joinpath('05_PLOT_importance_gain.png'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27e5fb90-f305-40ff-acaf-285d7d056c15",
   "metadata": {},
   "source": [
    "##### Permutation Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e2c1c123-20d0-423d-b684-585c0a5d6d2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "perm_importance = permutation_importance(xgb_model, \n",
    "                                         train_df[predictor_features],  train_df['target'],\n",
    "                                         n_repeats=30, scoring = 'average_precision')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ab0df42e-af4e-46b1-b0cf-630877137678",
   "metadata": {},
   "outputs": [],
   "source": [
    "perm_importance_df = pd.DataFrame({'features':predictor_features,\n",
    "                                   'importance': perm_importance['importances_mean']}) \n",
    "perm_importance_df['abs_importance'] = np.abs(perm_importance_df['importance'])\n",
    "perm_importance_df = perm_importance_df.sort_values('abs_importance', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0cbc5d84-6775-404f-a685-44f5be5cd481",
   "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>features</th>\n",
       "      <th>importance</th>\n",
       "      <th>abs_importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DisbursementGross</td>\n",
       "      <td>0.088143</td>\n",
       "      <td>0.088143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>urban_flag</td>\n",
       "      <td>0.048297</td>\n",
       "      <td>0.048297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CreateJob</td>\n",
       "      <td>0.030702</td>\n",
       "      <td>0.030702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LowDoc</td>\n",
       "      <td>0.015892</td>\n",
       "      <td>0.015892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NoEmp</td>\n",
       "      <td>0.013535</td>\n",
       "      <td>0.013535</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            features  importance  abs_importance\n",
       "3  DisbursementGross    0.088143        0.088143\n",
       "5         urban_flag    0.048297        0.048297\n",
       "1          CreateJob    0.030702        0.030702\n",
       "2             LowDoc    0.015892        0.015892\n",
       "0              NoEmp    0.013535        0.013535"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perm_importance_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "38059608-2f72-491f-86b3-0c1418b83531",
   "metadata": {},
   "outputs": [],
   "source": [
    "perm_importance_df \\\n",
    "    .to_csv(Path(setup.temp_path).joinpath('05_DATA_importance_perturb.csv'),\n",
    "            index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c27499c8-065b-4143-a7dc-bce301bf3edf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "perm_importance_df.head(10).plot(kind='barh', x='features', y='importance', legend=None)\n",
    "plt.gca().invert_yaxis()\n",
    "plt.xlabel('permutation importance')\n",
    "plt.ylabel(None)\n",
    "plt.gcf().savefig(Path(setup.temp_path).joinpath('05_PLOT_importance_perturb.png'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fd44a7c-bcf5-4284-8b9f-b575fa523ff3",
   "metadata": {},
   "source": [
    "## PDP Plot - NAICS one hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0f5a4774-bf8b-4c7f-9003-e1bcb4c101f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "naics_features = list(train_df.filter(like='NS__').columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "730411c5-81d3-4911-ad8f-8743dc1617e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "85ab4bf4-0f82-4121-842d-fd22164982cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x900 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(int(np.ceil(len(naics_features)/2)), 2, sharex=True, sharey=True, figsize=(6, 9))\n",
    "for i in range(len(naics_features)):\n",
    "    col = int(i % 2)\n",
    "    row = int(np.floor(i / 2))\n",
    "    results = partial_dependence(xgb_model, train_df[predictor_features], \n",
    "                                 [naics_features[i]])\n",
    "    pdp_df = pd.DataFrame({'grid_values':results[\"grid_values\"][0],\n",
    "                       'pdp':results[\"average\"][0]})\n",
    "    pdp_df.plot(x='grid_values', y='pdp', legend=None, ax=ax[row, col])\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig(Path(setup.temp_path).joinpath('05_PLOT_pdp_naics_onehot.png'),\n",
    "               bbox_inches='tight')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tf_p39",
   "language": "python",
   "name": "tf_p39"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}