10_xgb_mhier.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 Hierarchical Target Encoded NAICS\n",
    "Same as the baseline but include the hiearchical encoded 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": "31bf325d-4e9b-4a98-9927-83f6ad19fc11",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = sba_loans[sba_loans['dset'] == 'train']\n",
    "test_df = sba_loans[sba_loans['dset'] == 'test']\n",
    "val_df = sba_loans[sba_loans['dset'] == 'val']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d56e9533-8071-494a-a7da-4917a03fb701",
   "metadata": {},
   "source": [
    "#### Set the predictor features.  Inlcude the mean-encoded NAICS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c07ddd4e-6987-454a-926a-3b59abf6caa6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['NoEmp', 'CreateJob', 'LowDoc', 'DisbursementGross', 'new_business', 'urban_flag', 'franchise_flag', 'mhier_NAICS']\n"
     ]
    }
   ],
   "source": [
    "predictor_features = setup.predictor_features + ['mhier_NAICS']\n",
    "print(predictor_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1f8c475d-7ead-4f92-a19b-326343528f59",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(Path(setup.temp_path).joinpath('10_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": 10,
   "id": "af48634b-fc39-4c29-9792-bcf1cdf241c0",
   "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": 10,
     "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": "bc698909-3663-440f-98ce-fcfe312faa82",
   "metadata": {},
   "source": [
    "##### Fit using pre-determined parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e8eca4a6-66e8-4eb2-b68a-1271f59cdbec",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_params = dict(best_params, \n",
    "                   **{'objective':\"binary:logistic\", 'n_estimators':setup.xgb_n_estimators})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6d124de6-1d5d-4ff9-8071-4d05b0717f13",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_model = xgb.XGBClassifier(objective=\"binary:logistic\",\n",
    "                             n_estimators =  setup.xgb_n_estimators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9eebd329-7e39-4e52-be94-d839ad559b5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_model = xgb_model.set_params(**xgb_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a7f80910-9719-486b-8eac-93889064708a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-logloss:0.50032\tvalidation_1-logloss:0.50388\n",
      "[1]\tvalidation_0-logloss:0.49278\tvalidation_1-logloss:0.49664\n",
      "[2]\tvalidation_0-logloss:0.48637\tvalidation_1-logloss:0.49047\n",
      "[3]\tvalidation_0-logloss:0.48099\tvalidation_1-logloss:0.48532\n",
      "[4]\tvalidation_0-logloss:0.47645\tvalidation_1-logloss:0.48100\n",
      "[5]\tvalidation_0-logloss:0.47248\tvalidation_1-logloss:0.47722\n",
      "[6]\tvalidation_0-logloss:0.46919\tvalidation_1-logloss:0.47408\n",
      "[7]\tvalidation_0-logloss:0.46635\tvalidation_1-logloss:0.47138\n",
      "[8]\tvalidation_0-logloss:0.46381\tvalidation_1-logloss:0.46902\n",
      "[9]\tvalidation_0-logloss:0.46166\tvalidation_1-logloss:0.46705\n",
      "[10]\tvalidation_0-logloss:0.45982\tvalidation_1-logloss:0.46533\n",
      "[11]\tvalidation_0-logloss:0.45826\tvalidation_1-logloss:0.46392\n",
      "[12]\tvalidation_0-logloss:0.45689\tvalidation_1-logloss:0.46270\n",
      "[13]\tvalidation_0-logloss:0.45575\tvalidation_1-logloss:0.46172\n",
      "[14]\tvalidation_0-logloss:0.45468\tvalidation_1-logloss:0.46076\n",
      "[15]\tvalidation_0-logloss:0.45370\tvalidation_1-logloss:0.45989\n",
      "[16]\tvalidation_0-logloss:0.45283\tvalidation_1-logloss:0.45914\n",
      "[17]\tvalidation_0-logloss:0.45217\tvalidation_1-logloss:0.45858\n",
      "[18]\tvalidation_0-logloss:0.45155\tvalidation_1-logloss:0.45812\n",
      "[19]\tvalidation_0-logloss:0.45097\tvalidation_1-logloss:0.45763\n",
      "[20]\tvalidation_0-logloss:0.45051\tvalidation_1-logloss:0.45729\n",
      "[21]\tvalidation_0-logloss:0.45004\tvalidation_1-logloss:0.45693\n",
      "[22]\tvalidation_0-logloss:0.44960\tvalidation_1-logloss:0.45656\n",
      "[23]\tvalidation_0-logloss:0.44925\tvalidation_1-logloss:0.45632\n",
      "[24]\tvalidation_0-logloss:0.44899\tvalidation_1-logloss:0.45615\n",
      "[25]\tvalidation_0-logloss:0.44869\tvalidation_1-logloss:0.45591\n",
      "[26]\tvalidation_0-logloss:0.44845\tvalidation_1-logloss:0.45574\n",
      "[27]\tvalidation_0-logloss:0.44821\tvalidation_1-logloss:0.45558\n",
      "[28]\tvalidation_0-logloss:0.44799\tvalidation_1-logloss:0.45546\n",
      "[29]\tvalidation_0-logloss:0.44781\tvalidation_1-logloss:0.45535\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "              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": 14,
     "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": 15,
   "id": "9bbd8636-a5f1-4cbc-9498-329a78165472",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target\n",
       "0    346866\n",
       "1     89254\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 15,
     "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": 16,
   "id": "af21dd7c-09d4-407f-9960-0748ba8f2247",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_model.save_model(Path(setup.temp_path).joinpath('10_MODEL_xgboost.json'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8e5d3e7d-0bd5-4252-b287-c9cc65f7a132",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(Path(setup.temp_path).joinpath('10_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": 18,
   "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": 19,
   "id": "7084012d-b68a-4ca4-b714-160f8d564500",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>0.184297</td>\n",
       "      <td>0.115828</td>\n",
       "      <td>0.015962</td>\n",
       "      <td>0.650073</td>\n",
       "      <td>0.167606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.284831</td>\n",
       "      <td>0.112114</td>\n",
       "      <td>0.016041</td>\n",
       "      <td>0.673507</td>\n",
       "      <td>0.291550</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            mean       std       min       max    median\n",
       "target                                                  \n",
       "0       0.184297  0.115828  0.015962  0.650073  0.167606\n",
       "1       0.284831  0.112114  0.016041  0.673507  0.291550"
      ]
     },
     "execution_count": 19,
     "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": 20,
   "id": "d7727498-825f-4f53-882f-cfcce730b1fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_train_df = pred_df[pred_df['dset'] == 'train']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": 22,
   "id": "7a1cde01-a30b-475f-8fd7-409b13946c6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "thresh_tune_data.to_csv(Path(setup.temp_path).joinpath('10_REPORT_thresh_tune.csv'), index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "947a487c-e71d-433c-bbf0-2ed7e57a908a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.24\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>12</th>\n",
       "      <td>0.24</td>\n",
       "      <td>0.454880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.22</td>\n",
       "      <td>0.454286</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    thresh        f1\n",
       "12    0.24  0.454880\n",
       "11    0.22  0.454286"
      ]
     },
     "execution_count": 23,
     "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": 24,
   "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": 25,
   "id": "8253c32c-0438-47b9-8570-7cde6f7f5590",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "predict_bin\n",
       "0    409286\n",
       "1    278795\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_df['predict_bin'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "dbb46f4a-a5ef-4fd6-9da4-30de4a00334d",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_df.to_parquet(Path(setup.temp_path).joinpath('10_DATA_combined_predictions.parquet'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ab860d0-eddc-4f63-b853-88da15d451f4",
   "metadata": {},
   "source": [
    "## Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>0.674488</td>\n",
       "      <td>0.442278</td>\n",
       "      <td>0.332824</td>\n",
       "      <td>0.659000</td>\n",
       "      <td>0.376333</td>\n",
       "      <td>0.731086</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train</td>\n",
       "      <td>0.664677</td>\n",
       "      <td>0.454880</td>\n",
       "      <td>0.340835</td>\n",
       "      <td>0.683622</td>\n",
       "      <td>0.392977</td>\n",
       "      <td>0.735772</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>val</td>\n",
       "      <td>0.661267</td>\n",
       "      <td>0.451350</td>\n",
       "      <td>0.339903</td>\n",
       "      <td>0.671532</td>\n",
       "      <td>0.385388</td>\n",
       "      <td>0.726702</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test</td>\n",
       "      <td>0.660671</td>\n",
       "      <td>0.450608</td>\n",
       "      <td>0.337801</td>\n",
       "      <td>0.676533</td>\n",
       "      <td>0.382942</td>\n",
       "      <td>0.728478</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>test</td>\n",
       "      <td>0.694337</td>\n",
       "      <td>0.428456</td>\n",
       "      <td>0.324481</td>\n",
       "      <td>0.630488</td>\n",
       "      <td>0.367300</td>\n",
       "      <td>0.732314</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        0.674488  0.442278         0.332824      0.659000   \n",
       "1  train        0.664677  0.454880         0.340835      0.683622   \n",
       "2    val        0.661267  0.451350         0.339903      0.671532   \n",
       "0   test        0.660671  0.450608         0.337801      0.676533   \n",
       "1   test        0.694337  0.428456         0.324481      0.630488   \n",
       "\n",
       "   average_precision_score  roc_auc_score  dset_naics_holdout  \n",
       "0                 0.376333       0.731086                 NaN  \n",
       "1                 0.392977       0.735772                 NaN  \n",
       "2                 0.385388       0.726702                 NaN  \n",
       "0                 0.382942       0.728478                 0.0  \n",
       "1                 0.367300       0.732314                 1.0  "
      ]
     },
     "execution_count": 27,
     "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('10_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": 28,
   "id": "82d9c0ab-8989-450d-a2b1-7997ca636052",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "pred_test_df = pred_df[pred_df['dset'] == 'test']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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('10_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": 30,
   "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": 31,
   "id": "9586c637-7add-4401-b61c-37abd55b11b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_impgain.to_csv(Path(setup.temp_path).joinpath('10_REPORT_importance_gain.csv'), index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>286.014801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowDoc</th>\n",
       "      <td>117.089386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mhier_NAICS</th>\n",
       "      <td>97.880165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DisbursementGross</th>\n",
       "      <td>45.795738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>franchise_flag</th>\n",
       "      <td>20.987465</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   importance\n",
       "urban_flag         286.014801\n",
       "LowDoc             117.089386\n",
       "mhier_NAICS         97.880165\n",
       "DisbursementGross   45.795738\n",
       "franchise_flag      20.987465"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_impgain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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('10_PLOT_importance_gain.png'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27e5fb90-f305-40ff-acaf-285d7d056c15",
   "metadata": {},
   "source": [
    "##### Permutation Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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": 35,
   "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": 36,
   "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>7</th>\n",
       "      <td>mhier_NAICS</td>\n",
       "      <td>0.086862</td>\n",
       "      <td>0.086862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DisbursementGross</td>\n",
       "      <td>0.064575</td>\n",
       "      <td>0.064575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>urban_flag</td>\n",
       "      <td>0.033907</td>\n",
       "      <td>0.033907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CreateJob</td>\n",
       "      <td>0.019238</td>\n",
       "      <td>0.019238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LowDoc</td>\n",
       "      <td>0.014229</td>\n",
       "      <td>0.014229</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            features  importance  abs_importance\n",
       "7        mhier_NAICS    0.086862        0.086862\n",
       "3  DisbursementGross    0.064575        0.064575\n",
       "5         urban_flag    0.033907        0.033907\n",
       "1          CreateJob    0.019238        0.019238\n",
       "2             LowDoc    0.014229        0.014229"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perm_importance_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "38059608-2f72-491f-86b3-0c1418b83531",
   "metadata": {},
   "outputs": [],
   "source": [
    "perm_importance_df \\\n",
    "    .to_csv(Path(setup.temp_path).joinpath('10_DATA_importance_perturb.csv'),\n",
    "            index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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('10_PLOT_importance_perturb.png'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fd44a7c-bcf5-4284-8b9f-b575fa523ff3",
   "metadata": {},
   "source": [
    "## PDP Plot - NAICS mean encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0e9ee010-d1ce-4b1a-86ef-27d691583fc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = partial_dependence(xgb_model, train_df[predictor_features], ['mhier_NAICS'])\n",
    "pdp_df = pd.DataFrame({'grid_values':results[\"grid_values\"][0],\n",
    "                       'pdp':results[\"average\"][0]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "730411c5-81d3-4911-ad8f-8743dc1617e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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('10_PLOT_pdp_naics_meanenc.png'),\n",
    "               bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5488c384-feb9-45b4-8d95-e2ea4954719b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83c4bf72-5710-489a-888f-bd2b7590e93b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2b6ef2b-9fe4-4595-9ba9-7fc205a864f6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}