07_xgb_menc_allfeat.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 Target Encoded NAICS (All level features)\n",
    "Same as the baseline but include the target (mean) encoded NAICS, for all levels.  Not blended but individual features"
   ]
  },
  {
   "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": "code",
   "execution_count": 9,
   "id": "eac2faef-f286-4bb6-8895-fb0f2c067479",
   "metadata": {},
   "outputs": [],
   "source": [
    "menc_cols = list(sba_loans.filter(like='menc_').columns)"
   ]
  },
  {
   "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": 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', 'menc_NAICS', 'menc_NAICS_5', 'menc_NAICS_4', 'menc_NAICS_3', 'menc_NAICS_sector']\n"
     ]
    }
   ],
   "source": [
    "predictor_features = setup.predictor_features + menc_cols\n",
    "print(predictor_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1f8c475d-7ead-4f92-a19b-326343528f59",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(Path(setup.temp_path).joinpath('07_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": 12,
   "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": 12,
     "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": 13,
   "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": 14,
   "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": 15,
   "id": "9eebd329-7e39-4e52-be94-d839ad559b5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_model = xgb_model.set_params(**xgb_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a7f80910-9719-486b-8eac-93889064708a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-logloss:0.50022\tvalidation_1-logloss:0.50377\n",
      "[1]\tvalidation_0-logloss:0.49261\tvalidation_1-logloss:0.49640\n",
      "[2]\tvalidation_0-logloss:0.48618\tvalidation_1-logloss:0.49023\n",
      "[3]\tvalidation_0-logloss:0.48075\tvalidation_1-logloss:0.48499\n",
      "[4]\tvalidation_0-logloss:0.47619\tvalidation_1-logloss:0.48064\n",
      "[5]\tvalidation_0-logloss:0.47227\tvalidation_1-logloss:0.47690\n",
      "[6]\tvalidation_0-logloss:0.46890\tvalidation_1-logloss:0.47366\n",
      "[7]\tvalidation_0-logloss:0.46602\tvalidation_1-logloss:0.47093\n",
      "[8]\tvalidation_0-logloss:0.46350\tvalidation_1-logloss:0.46859\n",
      "[9]\tvalidation_0-logloss:0.46132\tvalidation_1-logloss:0.46656\n",
      "[10]\tvalidation_0-logloss:0.45937\tvalidation_1-logloss:0.46476\n",
      "[11]\tvalidation_0-logloss:0.45776\tvalidation_1-logloss:0.46328\n",
      "[12]\tvalidation_0-logloss:0.45636\tvalidation_1-logloss:0.46201\n",
      "[13]\tvalidation_0-logloss:0.45517\tvalidation_1-logloss:0.46098\n",
      "[14]\tvalidation_0-logloss:0.45409\tvalidation_1-logloss:0.46000\n",
      "[15]\tvalidation_0-logloss:0.45308\tvalidation_1-logloss:0.45910\n",
      "[16]\tvalidation_0-logloss:0.45225\tvalidation_1-logloss:0.45840\n",
      "[17]\tvalidation_0-logloss:0.45152\tvalidation_1-logloss:0.45778\n",
      "[18]\tvalidation_0-logloss:0.45088\tvalidation_1-logloss:0.45727\n",
      "[19]\tvalidation_0-logloss:0.45027\tvalidation_1-logloss:0.45675\n",
      "[20]\tvalidation_0-logloss:0.44975\tvalidation_1-logloss:0.45633\n",
      "[21]\tvalidation_0-logloss:0.44927\tvalidation_1-logloss:0.45598\n",
      "[22]\tvalidation_0-logloss:0.44878\tvalidation_1-logloss:0.45559\n",
      "[23]\tvalidation_0-logloss:0.44838\tvalidation_1-logloss:0.45529\n",
      "[24]\tvalidation_0-logloss:0.44804\tvalidation_1-logloss:0.45507\n",
      "[25]\tvalidation_0-logloss:0.44778\tvalidation_1-logloss:0.45489\n",
      "[26]\tvalidation_0-logloss:0.44746\tvalidation_1-logloss:0.45466\n",
      "[27]\tvalidation_0-logloss:0.44717\tvalidation_1-logloss:0.45444\n",
      "[28]\tvalidation_0-logloss:0.44691\tvalidation_1-logloss:0.45430\n",
      "[29]\tvalidation_0-logloss:0.44662\tvalidation_1-logloss:0.45410\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": 16,
     "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": 17,
   "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": 17,
     "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": 18,
   "id": "af21dd7c-09d4-407f-9960-0748ba8f2247",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_model.save_model(Path(setup.temp_path).joinpath('07_MODEL_xgboost.json'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8e5d3e7d-0bd5-4252-b287-c9cc65f7a132",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(Path(setup.temp_path).joinpath('07_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": 20,
   "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": 21,
   "id": "7084012d-b68a-4ca4-b714-160f8d564500",
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "      <th>0</th>\n",
       "      <td>0.184700</td>\n",
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       "      <td>0.015744</td>\n",
       "      <td>0.680819</td>\n",
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       "      <td>0.681501</td>\n",
       "      <td>0.286764</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            mean       std       min       max    median\n",
       "target                                                  \n",
       "0       0.184700  0.114491  0.015744  0.680819  0.170971\n",
       "1       0.284712  0.110051  0.016498  0.681501  0.286764"
      ]
     },
     "execution_count": 21,
     "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": 22,
   "id": "d7727498-825f-4f53-882f-cfcce730b1fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_train_df = pred_df[pred_df['dset'] == 'train']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": 24,
   "id": "7a1cde01-a30b-475f-8fd7-409b13946c6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "thresh_tune_data.to_csv(Path(setup.temp_path).joinpath('07_REPORT_thresh_tune.csv'), index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>0.457966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.22</td>\n",
       "      <td>0.456154</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    thresh        f1\n",
       "12    0.24  0.457966\n",
       "11    0.22  0.456154"
      ]
     },
     "execution_count": 25,
     "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": 26,
   "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": 27,
   "id": "8253c32c-0438-47b9-8570-7cde6f7f5590",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "predict_bin\n",
       "0    403585\n",
       "1    284496\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_df['predict_bin'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "dbb46f4a-a5ef-4fd6-9da4-30de4a00334d",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_df.to_parquet(Path(setup.temp_path).joinpath('07_DATA_combined_predictions.parquet'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ab860d0-eddc-4f63-b853-88da15d451f4",
   "metadata": {},
   "source": [
    "## Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>0.659214</td>\n",
       "      <td>0.440302</td>\n",
       "      <td>0.324545</td>\n",
       "      <td>0.684416</td>\n",
       "      <td>0.371854</td>\n",
       "      <td>0.730723</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train</td>\n",
       "      <td>0.664849</td>\n",
       "      <td>0.457966</td>\n",
       "      <td>0.342269</td>\n",
       "      <td>0.691823</td>\n",
       "      <td>0.396499</td>\n",
       "      <td>0.738269</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>val</td>\n",
       "      <td>0.659801</td>\n",
       "      <td>0.451949</td>\n",
       "      <td>0.339427</td>\n",
       "      <td>0.676070</td>\n",
       "      <td>0.388941</td>\n",
       "      <td>0.728978</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test</td>\n",
       "      <td>0.659119</td>\n",
       "      <td>0.451451</td>\n",
       "      <td>0.337409</td>\n",
       "      <td>0.681944</td>\n",
       "      <td>0.386990</td>\n",
       "      <td>0.730515</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>test</td>\n",
       "      <td>0.659349</td>\n",
       "      <td>0.423457</td>\n",
       "      <td>0.305767</td>\n",
       "      <td>0.688436</td>\n",
       "      <td>0.354598</td>\n",
       "      <td>0.732690</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        0.659214  0.440302         0.324545      0.684416   \n",
       "1  train        0.664849  0.457966         0.342269      0.691823   \n",
       "2    val        0.659801  0.451949         0.339427      0.676070   \n",
       "0   test        0.659119  0.451451         0.337409      0.681944   \n",
       "1   test        0.659349  0.423457         0.305767      0.688436   \n",
       "\n",
       "   average_precision_score  roc_auc_score  dset_naics_holdout  \n",
       "0                 0.371854       0.730723                 NaN  \n",
       "1                 0.396499       0.738269                 NaN  \n",
       "2                 0.388941       0.728978                 NaN  \n",
       "0                 0.386990       0.730515                 0.0  \n",
       "1                 0.354598       0.732690                 1.0  "
      ]
     },
     "execution_count": 29,
     "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('07_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": 30,
   "id": "82d9c0ab-8989-450d-a2b1-7997ca636052",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "pred_test_df = pred_df[pred_df['dset'] == 'test']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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('07_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": 32,
   "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": 33,
   "id": "9586c637-7add-4401-b61c-37abd55b11b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_impgain.to_csv(Path(setup.temp_path).joinpath('07_REPORT_importance_gain.csv'), index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>341.641968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>menc_NAICS</th>\n",
       "      <td>194.098557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowDoc</th>\n",
       "      <td>128.352005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DisbursementGross</th>\n",
       "      <td>53.441845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>franchise_flag</th>\n",
       "      <td>23.818733</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   importance\n",
       "urban_flag         341.641968\n",
       "menc_NAICS         194.098557\n",
       "LowDoc             128.352005\n",
       "DisbursementGross   53.441845\n",
       "franchise_flag      23.818733"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_impgain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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('07_PLOT_importance_gain.png'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27e5fb90-f305-40ff-acaf-285d7d056c15",
   "metadata": {},
   "source": [
    "##### Permutation Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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": 37,
   "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": 38,
   "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>menc_NAICS</td>\n",
       "      <td>0.080827</td>\n",
       "      <td>0.080827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DisbursementGross</td>\n",
       "      <td>0.067245</td>\n",
       "      <td>0.067245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>urban_flag</td>\n",
       "      <td>0.036094</td>\n",
       "      <td>0.036094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CreateJob</td>\n",
       "      <td>0.018519</td>\n",
       "      <td>0.018519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LowDoc</td>\n",
       "      <td>0.014574</td>\n",
       "      <td>0.014574</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            features  importance  abs_importance\n",
       "7         menc_NAICS    0.080827        0.080827\n",
       "3  DisbursementGross    0.067245        0.067245\n",
       "5         urban_flag    0.036094        0.036094\n",
       "1          CreateJob    0.018519        0.018519\n",
       "2             LowDoc    0.014574        0.014574"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perm_importance_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "38059608-2f72-491f-86b3-0c1418b83531",
   "metadata": {},
   "outputs": [],
   "source": [
    "perm_importance_df \\\n",
    "    .to_csv(Path(setup.temp_path).joinpath('07_DATA_importance_perturb.csv'),\n",
    "            index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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('07_PLOT_importance_perturb.png'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fd44a7c-bcf5-4284-8b9f-b575fa523ff3",
   "metadata": {},
   "source": [
    "## PDP Plot - NAICS menc features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7688932c-86f7-486f-b578-6ef251183a8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x900 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(int(np.ceil(len(menc_cols)/2)), 2, sharex=True, sharey=True, figsize=(6, 9))\n",
    "for i in range(len(menc_cols)):\n",
    "    col = int(i % 2)\n",
    "    row = int(np.floor(i / 2))\n",
    "    results = partial_dependence(xgb_model, train_df[predictor_features], \n",
    "                                 [menc_cols[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', label=menc_cols[i], ax=ax[row, col])\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig(Path(setup.temp_path).joinpath('07_PLOT_pdp_naics_menc.png'),\n",
    "               bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "730411c5-81d3-4911-ad8f-8743dc1617e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close()"
   ]
  },
  {
   "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
}