06_xgb_num_naics.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 numeric NAICS code\n",
    "XGBoost model, numeric NAICS"
   ]
  },
  {
   "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": "d56e9533-8071-494a-a7da-4917a03fb701",
   "metadata": {},
   "source": [
    "#### Set the predictor features.  Inlcude the numeric 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', 'NAICS_num']\n"
     ]
    }
   ],
   "source": [
    "predictor_features = setup.predictor_features + ['NAICS_num']\n",
    "print(predictor_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "51a66d4e-7977-4c8d-847e-e8fc0b5d8923",
   "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",
       "      <th>new_business</th>\n",
       "      <th>urban_flag</th>\n",
       "      <th>franchise_flag</th>\n",
       "      <th>NAICS_num</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",
       "    </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>517419.546595</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>174222.153995</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>111110.000000</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>423910.000000</td>\n",
       "    </tr>\n",
       "    <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>514191.000000</td>\n",
       "    </tr>\n",
       "    <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>624190.000000</td>\n",
       "    </tr>\n",
       "    <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>928120.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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      NAICS_num  \n",
       "count  435584.000000  351624.000000   436120.000000  436120.000000  \n",
       "mean        0.281016       0.815075        0.052733  517419.546595  \n",
       "std         0.449496       0.388237        0.223501  174222.153995  \n",
       "min         0.000000       0.000000        0.000000  111110.000000  \n",
       "25%         0.000000       1.000000        0.000000  423910.000000  \n",
       "50%         0.000000       1.000000        0.000000  514191.000000  \n",
       "75%         1.000000       1.000000        0.000000  624190.000000  \n",
       "max         1.000000       1.000000        1.000000  928120.000000  "
      ]
     },
     "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",
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       "      <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>NAICS_num</th>\n",
       "      <th>target</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <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>-0.015311</td>\n",
       "      <td>-0.027403</td>\n",
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       "    <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>0.029364</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>0.031339</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>-0.015379</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>0.124843</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>0.049435</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>0.161479</td>\n",
       "      <td>-0.012731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAICS_num</th>\n",
       "      <td>-0.015311</td>\n",
       "      <td>0.029364</td>\n",
       "      <td>0.031339</td>\n",
       "      <td>-0.015379</td>\n",
       "      <td>0.124843</td>\n",
       "      <td>0.049435</td>\n",
       "      <td>0.161479</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005640</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>-0.005640</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                      NoEmp  CreateJob    LowDoc  DisbursementGross  \\\n",
       "NoEmp              1.000000   0.062158 -0.013926           0.095658   \n",
       "CreateJob          0.062158   1.000000 -0.062740           0.134686   \n",
       "LowDoc            -0.013926  -0.062740  1.000000          -0.133141   \n",
       "DisbursementGross  0.095658   0.134686 -0.133141           1.000000   \n",
       "new_business      -0.041427   0.039302  0.153034          -0.065443   \n",
       "urban_flag         0.001610   0.009062 -0.120001          -0.007016   \n",
       "franchise_flag     0.006409   0.050517  0.040841           0.085712   \n",
       "NAICS_num         -0.015311   0.029364  0.031339          -0.015379   \n",
       "target            -0.027403  -0.010002 -0.079523          -0.120788   \n",
       "\n",
       "                   new_business  urban_flag  franchise_flag  NAICS_num  \\\n",
       "NoEmp                 -0.041427    0.001610        0.006409  -0.015311   \n",
       "CreateJob              0.039302    0.009062        0.050517   0.029364   \n",
       "LowDoc                 0.153034   -0.120001        0.040841   0.031339   \n",
       "DisbursementGross     -0.065443   -0.007016        0.085712  -0.015379   \n",
       "new_business           1.000000   -0.047592        0.137325   0.124843   \n",
       "urban_flag            -0.047592    1.000000       -0.006588   0.049435   \n",
       "franchise_flag         0.137325   -0.006588        1.000000   0.161479   \n",
       "NAICS_num              0.124843    0.049435        0.161479   1.000000   \n",
       "target                 0.013878    0.053785       -0.012731  -0.005640   \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",
       "NAICS_num         -0.005640  \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('06_DATA_features_predict.pkl'), 'wb') as fout:\n",
    "    pickle.dump(predictor_features, fout)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d2878b1-7185-4304-af03-ea14d13f379e",
   "metadata": {},
   "source": [
    "## Model Fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "18b3be2f-3e08-4df3-8539-703ca5ca31f2",
   "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": "03e5c457-246c-4126-9542-93ab42fb3c00",
   "metadata": {},
   "source": [
    "##### Fit using pre-determined parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7f6c337c-9a06-44c2-a036-4c86f8acbbd7",
   "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": "e2905f3e-60e6-456a-beaa-179bea24ce12",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_model = xgb.XGBClassifier(objective=\"binary:logistic\",\n",
    "                             n_estimators = setup.xgb_n_estimators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "58b3c7ce-8514-44d5-a352-eac4b18dad90",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_model = xgb_model.set_params(**xgb_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "db05c703-7ddb-45d6-b27a-70f604d42dca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-logloss:0.50172\tvalidation_1-logloss:0.50520\n",
      "[1]\tvalidation_0-logloss:0.49516\tvalidation_1-logloss:0.49874\n",
      "[2]\tvalidation_0-logloss:0.48974\tvalidation_1-logloss:0.49340\n",
      "[3]\tvalidation_0-logloss:0.48514\tvalidation_1-logloss:0.48894\n",
      "[4]\tvalidation_0-logloss:0.48103\tvalidation_1-logloss:0.48493\n",
      "[5]\tvalidation_0-logloss:0.47751\tvalidation_1-logloss:0.48150\n",
      "[6]\tvalidation_0-logloss:0.47447\tvalidation_1-logloss:0.47852\n",
      "[7]\tvalidation_0-logloss:0.47190\tvalidation_1-logloss:0.47603\n",
      "[8]\tvalidation_0-logloss:0.46961\tvalidation_1-logloss:0.47382\n",
      "[9]\tvalidation_0-logloss:0.46758\tvalidation_1-logloss:0.47188\n",
      "[10]\tvalidation_0-logloss:0.46583\tvalidation_1-logloss:0.47023\n",
      "[11]\tvalidation_0-logloss:0.46443\tvalidation_1-logloss:0.46889\n",
      "[12]\tvalidation_0-logloss:0.46306\tvalidation_1-logloss:0.46758\n",
      "[13]\tvalidation_0-logloss:0.46184\tvalidation_1-logloss:0.46642\n",
      "[14]\tvalidation_0-logloss:0.46081\tvalidation_1-logloss:0.46545\n",
      "[15]\tvalidation_0-logloss:0.45996\tvalidation_1-logloss:0.46467\n",
      "[16]\tvalidation_0-logloss:0.45905\tvalidation_1-logloss:0.46387\n",
      "[17]\tvalidation_0-logloss:0.45824\tvalidation_1-logloss:0.46311\n",
      "[18]\tvalidation_0-logloss:0.45758\tvalidation_1-logloss:0.46252\n",
      "[19]\tvalidation_0-logloss:0.45700\tvalidation_1-logloss:0.46204\n",
      "[20]\tvalidation_0-logloss:0.45643\tvalidation_1-logloss:0.46153\n",
      "[21]\tvalidation_0-logloss:0.45590\tvalidation_1-logloss:0.46108\n",
      "[22]\tvalidation_0-logloss:0.45538\tvalidation_1-logloss:0.46064\n",
      "[23]\tvalidation_0-logloss:0.45495\tvalidation_1-logloss:0.46020\n",
      "[24]\tvalidation_0-logloss:0.45457\tvalidation_1-logloss:0.45988\n",
      "[25]\tvalidation_0-logloss:0.45426\tvalidation_1-logloss:0.45961\n",
      "[26]\tvalidation_0-logloss:0.45396\tvalidation_1-logloss:0.45934\n",
      "[27]\tvalidation_0-logloss:0.45354\tvalidation_1-logloss:0.45898\n",
      "[28]\tvalidation_0-logloss:0.45321\tvalidation_1-logloss:0.45869\n",
      "[29]\tvalidation_0-logloss:0.45293\tvalidation_1-logloss:0.45849\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": "95c426a5-0de0-401a-8f3b-cf6a552fd0c6",
   "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('06_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('06_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>0.186380</td>\n",
       "      <td>0.108796</td>\n",
       "      <td>0.01613</td>\n",
       "      <td>0.635792</td>\n",
       "      <td>0.176589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.275932</td>\n",
       "      <td>0.097565</td>\n",
       "      <td>0.01613</td>\n",
       "      <td>0.659318</td>\n",
       "      <td>0.288841</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            mean       std      min       max    median\n",
       "target                                                 \n",
       "0       0.186380  0.108796  0.01613  0.635792  0.176589\n",
       "1       0.275932  0.097565  0.01613  0.659318  0.288841"
      ]
     },
     "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('06_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>0.448622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.24</td>\n",
       "      <td>0.448543</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    thresh        f1\n",
       "11    0.22  0.448622\n",
       "12    0.24  0.448543"
      ]
     },
     "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    362068\n",
       "1    326013\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('06_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>0.636855</td>\n",
       "      <td>0.440019</td>\n",
       "      <td>0.315205</td>\n",
       "      <td>0.728482</td>\n",
       "      <td>0.369338</td>\n",
       "      <td>0.730181</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train</td>\n",
       "      <td>0.622475</td>\n",
       "      <td>0.448622</td>\n",
       "      <td>0.319941</td>\n",
       "      <td>0.750454</td>\n",
       "      <td>0.380662</td>\n",
       "      <td>0.727154</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>val</td>\n",
       "      <td>0.619364</td>\n",
       "      <td>0.446798</td>\n",
       "      <td>0.319848</td>\n",
       "      <td>0.740846</td>\n",
       "      <td>0.377932</td>\n",
       "      <td>0.721673</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test</td>\n",
       "      <td>0.619935</td>\n",
       "      <td>0.446564</td>\n",
       "      <td>0.318756</td>\n",
       "      <td>0.745461</td>\n",
       "      <td>0.373796</td>\n",
       "      <td>0.723166</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>test</td>\n",
       "      <td>0.661163</td>\n",
       "      <td>0.429141</td>\n",
       "      <td>0.309246</td>\n",
       "      <td>0.700871</td>\n",
       "      <td>0.362137</td>\n",
       "      <td>0.738718</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        0.636855  0.440019         0.315205      0.728482   \n",
       "1  train        0.622475  0.448622         0.319941      0.750454   \n",
       "2    val        0.619364  0.446798         0.319848      0.740846   \n",
       "0   test        0.619935  0.446564         0.318756      0.745461   \n",
       "1   test        0.661163  0.429141         0.309246      0.700871   \n",
       "\n",
       "   average_precision_score  roc_auc_score  dset_naics_holdout  \n",
       "0                 0.369338       0.730181                 NaN  \n",
       "1                 0.380662       0.727154                 NaN  \n",
       "2                 0.377932       0.721673                 NaN  \n",
       "0                 0.373796       0.723166                 0.0  \n",
       "1                 0.362137       0.738718                 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('06_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('06_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('06_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>694.555054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowDoc</th>\n",
       "      <td>158.772552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DisbursementGross</th>\n",
       "      <td>87.067230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_business</th>\n",
       "      <td>30.828922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CreateJob</th>\n",
       "      <td>25.082647</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   importance\n",
       "urban_flag         694.555054\n",
       "LowDoc             158.772552\n",
       "DisbursementGross   87.067230\n",
       "new_business        30.828922\n",
       "CreateJob           25.082647"
      ]
     },
     "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('06_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>0.089375</td>\n",
       "      <td>0.089375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NAICS_num</td>\n",
       "      <td>0.048442</td>\n",
       "      <td>0.048442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>urban_flag</td>\n",
       "      <td>0.048002</td>\n",
       "      <td>0.048002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CreateJob</td>\n",
       "      <td>0.027486</td>\n",
       "      <td>0.027486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LowDoc</td>\n",
       "      <td>0.015673</td>\n",
       "      <td>0.015673</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            features  importance  abs_importance\n",
       "3  DisbursementGross    0.089375        0.089375\n",
       "7          NAICS_num    0.048442        0.048442\n",
       "5         urban_flag    0.048002        0.048002\n",
       "1          CreateJob    0.027486        0.027486\n",
       "2             LowDoc    0.015673        0.015673"
      ]
     },
     "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('06_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('06_PLOT_importance_perturb.png'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fd44a7c-bcf5-4284-8b9f-b575fa523ff3",
   "metadata": {},
   "source": [
    "## PDP Plot - NAICS raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0e9ee010-d1ce-4b1a-86ef-27d691583fc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = partial_dependence(xgb_model, train_df[predictor_features], ['NAICS_num'])\n",
    "pdp_df = pd.DataFrame({'grid_values':results[\"grid_values\"][0],\n",
    "                       'pdp':results[\"average\"][0]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "730411c5-81d3-4911-ad8f-8743dc1617e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3267866a-3453-443f-9015-5dbcd90e7594",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (8, 3))\n",
    "pdp_df.plot(x='grid_values', y='pdp', legend=None, ax=ax)\n",
    "ax.set_ylabel('effect')\n",
    "fig.savefig(Path(setup.temp_path).joinpath('06_PLOT_pdp_naics_meanenc.png'),\n",
    "               bbox_inches='tight')"
   ]
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