21_nn_baseline.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8816b366-5be4-4748-b769-4dc6f7d1735f",
   "metadata": {},
   "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": "a945de0c-f042-46b2-9d06-ab064af6c05b",
   "metadata": {},
   "source": [
    "# Neural Network Baseline\n",
    "Traditional, simple neural network model with no NAICS input"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d47a7ec9-7d7b-47df-a902-b2f1e54f1135",
   "metadata": {},
   "source": [
    "*This script takes about 2 hours on my MacBook Air*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fb42f9b4-99a8-4753-aacf-8722469be4c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bf51170a-4487-4d10-b63d-c88f41b529f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import importlib, pickle\n",
    "from sklearn import model_selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "09dd973b-60a0-434b-9193-8c5fcec27dd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import re\n",
    "\n",
    "from tensorflow.keras import layers, optimizers, losses, metrics, Model\n",
    "from sklearn import preprocessing, feature_extraction, model_selection\n",
    "from IPython.display import display, HTML\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "22b466e7-a419-45aa-83f7-5dafc7a3998b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout\n",
    "import tensorflow.keras.metrics as km"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9f1e4f2e-0e45-4e28-8e9c-212e0bb51199",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sba_gnn.sba_gnn import sg_plot "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cbc00d2-bc83-42d9-83eb-65fcf306dccd",
   "metadata": {},
   "source": [
    "## Input Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5f0183dc-7ee1-49eb-8723-a9f4cea14b88",
   "metadata": {},
   "outputs": [],
   "source": [
    "business_data = pd.read_parquet(Path(setup.temp_path).joinpath('20_DATA_combined_scaled_all.parquet'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "feaedcfe-7722-41c1-be4c-4921f0d11db9",
   "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>target</th>\n",
       "      <th>dset_naics_holdout</th>\n",
       "      <th>mhier_NAICS</th>\n",
       "      <th>menc_NAICS</th>\n",
       "      <th>cenc_NAICS</th>\n",
       "      <th>NS___Accommodation and Food Services</th>\n",
       "      <th>NS___Administrative and Support and Waste Management and Remediation Services</th>\n",
       "      <th>NS___Construction</th>\n",
       "      <th>NS___Health Care and Social Assistance</th>\n",
       "      <th>NS___Manufacturing</th>\n",
       "      <th>...</th>\n",
       "      <th>new_business</th>\n",
       "      <th>urban_flag</th>\n",
       "      <th>franchise_flag</th>\n",
       "      <th>missingindicator_LowDoc</th>\n",
       "      <th>missingindicator_new_business</th>\n",
       "      <th>missingindicator_urban_flag</th>\n",
       "      <th>NAICS</th>\n",
       "      <th>NAICS_alt</th>\n",
       "      <th>NAICS_alt3</th>\n",
       "      <th>NAICS_alt4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>688081.000000</td>\n",
       "      <td>688081.000000</td>\n",
       "      <td>688081.000000</td>\n",
       "      <td>688081.000000</td>\n",
       "      <td>688081.000000</td>\n",
       "      <td>688081.000000</td>\n",
       "      <td>688081.000000</td>\n",
       "      <td>688081.000000</td>\n",
       "      <td>688081.000000</td>\n",
       "      <td>688081.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>688081.000000</td>\n",
       "      <td>688081.000000</td>\n",
       "      <td>688081.000000</td>\n",
       "      <td>688081.000000</td>\n",
       "      <td>688081.000000</td>\n",
       "      <td>688081.000000</td>\n",
       "      <td>688081.000000</td>\n",
       "      <td>688081.000000</td>\n",
       "      <td>688081.000000</td>\n",
       "      <td>688081.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.203011</td>\n",
       "      <td>0.094541</td>\n",
       "      <td>0.203906</td>\n",
       "      <td>0.205152</td>\n",
       "      <td>0.007265</td>\n",
       "      <td>0.097958</td>\n",
       "      <td>0.047133</td>\n",
       "      <td>0.095606</td>\n",
       "      <td>0.079398</td>\n",
       "      <td>0.096843</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.444129</td>\n",
       "      <td>0.705279</td>\n",
       "      <td>-0.899352</td>\n",
       "      <td>-0.985316</td>\n",
       "      <td>-0.997529</td>\n",
       "      <td>-0.602849</td>\n",
       "      <td>701.045842</td>\n",
       "      <td>767.921227</td>\n",
       "      <td>769.528095</td>\n",
       "      <td>769.233577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.402241</td>\n",
       "      <td>0.292580</td>\n",
       "      <td>0.095690</td>\n",
       "      <td>0.091362</td>\n",
       "      <td>0.010443</td>\n",
       "      <td>0.297258</td>\n",
       "      <td>0.211922</td>\n",
       "      <td>0.294051</td>\n",
       "      <td>0.270359</td>\n",
       "      <td>0.295744</td>\n",
       "      <td>...</td>\n",
       "      <td>0.895963</td>\n",
       "      <td>0.708930</td>\n",
       "      <td>0.437226</td>\n",
       "      <td>0.170743</td>\n",
       "      <td>0.070251</td>\n",
       "      <td>0.797856</td>\n",
       "      <td>383.284385</td>\n",
       "      <td>326.131581</td>\n",
       "      <td>324.361889</td>\n",
       "      <td>326.453771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012987</td>\n",
       "      <td>0.020495</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.126003</td>\n",
       "      <td>0.137475</td>\n",
       "      <td>0.000718</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>436.000000</td>\n",
       "      <td>651.000000</td>\n",
       "      <td>644.000000</td>\n",
       "      <td>638.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.205592</td>\n",
       "      <td>0.204655</td>\n",
       "      <td>0.003056</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>756.000000</td>\n",
       "      <td>844.000000</td>\n",
       "      <td>825.000000</td>\n",
       "      <td>825.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.264286</td>\n",
       "      <td>0.254574</td>\n",
       "      <td>0.009309</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1031.000000</td>\n",
       "      <td>1034.000000</td>\n",
       "      <td>1034.000000</td>\n",
       "      <td>1034.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.572148</td>\n",
       "      <td>0.551324</td>\n",
       "      <td>0.044873</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              target  dset_naics_holdout    mhier_NAICS     menc_NAICS  \\\n",
       "count  688081.000000       688081.000000  688081.000000  688081.000000   \n",
       "mean        0.203011            0.094541       0.203906       0.205152   \n",
       "std         0.402241            0.292580       0.095690       0.091362   \n",
       "min         0.000000            0.000000       0.012987       0.020495   \n",
       "25%         0.000000            0.000000       0.126003       0.137475   \n",
       "50%         0.000000            0.000000       0.205592       0.204655   \n",
       "75%         0.000000            0.000000       0.264286       0.254574   \n",
       "max         1.000000            1.000000       0.572148       0.551324   \n",
       "\n",
       "          cenc_NAICS  NS___Accommodation and Food Services  \\\n",
       "count  688081.000000                         688081.000000   \n",
       "mean        0.007265                              0.097958   \n",
       "std         0.010443                              0.297258   \n",
       "min         0.000000                              0.000000   \n",
       "25%         0.000718                              0.000000   \n",
       "50%         0.003056                              0.000000   \n",
       "75%         0.009309                              0.000000   \n",
       "max         0.044873                              1.000000   \n",
       "\n",
       "       NS___Administrative and Support and Waste Management and Remediation Services  \\\n",
       "count                                      688081.000000                               \n",
       "mean                                            0.047133                               \n",
       "std                                             0.211922                               \n",
       "min                                             0.000000                               \n",
       "25%                                             0.000000                               \n",
       "50%                                             0.000000                               \n",
       "75%                                             0.000000                               \n",
       "max                                             1.000000                               \n",
       "\n",
       "       NS___Construction  NS___Health Care and Social Assistance  \\\n",
       "count      688081.000000                           688081.000000   \n",
       "mean            0.095606                                0.079398   \n",
       "std             0.294051                                0.270359   \n",
       "min             0.000000                                0.000000   \n",
       "25%             0.000000                                0.000000   \n",
       "50%             0.000000                                0.000000   \n",
       "75%             0.000000                                0.000000   \n",
       "max             1.000000                                1.000000   \n",
       "\n",
       "       NS___Manufacturing  ...   new_business     urban_flag  franchise_flag  \\\n",
       "count       688081.000000  ...  688081.000000  688081.000000   688081.000000   \n",
       "mean             0.096843  ...      -0.444129       0.705279       -0.899352   \n",
       "std              0.295744  ...       0.895963       0.708930        0.437226   \n",
       "min              0.000000  ...      -1.000000      -1.000000       -1.000000   \n",
       "25%              0.000000  ...      -1.000000       1.000000       -1.000000   \n",
       "50%              0.000000  ...      -1.000000       1.000000       -1.000000   \n",
       "75%              0.000000  ...       1.000000       1.000000       -1.000000   \n",
       "max              1.000000  ...       1.000000       1.000000        1.000000   \n",
       "\n",
       "       missingindicator_LowDoc  missingindicator_new_business  \\\n",
       "count            688081.000000                  688081.000000   \n",
       "mean                 -0.985316                      -0.997529   \n",
       "std                   0.170743                       0.070251   \n",
       "min                  -1.000000                      -1.000000   \n",
       "25%                  -1.000000                      -1.000000   \n",
       "50%                  -1.000000                      -1.000000   \n",
       "75%                  -1.000000                      -1.000000   \n",
       "max                   1.000000                       1.000000   \n",
       "\n",
       "       missingindicator_urban_flag          NAICS      NAICS_alt  \\\n",
       "count                688081.000000  688081.000000  688081.000000   \n",
       "mean                     -0.602849     701.045842     767.921227   \n",
       "std                       0.797856     383.284385     326.131581   \n",
       "min                      -1.000000       0.000000       1.000000   \n",
       "25%                      -1.000000     436.000000     651.000000   \n",
       "50%                      -1.000000     756.000000     844.000000   \n",
       "75%                      -1.000000    1031.000000    1034.000000   \n",
       "max                       1.000000    1166.000000    1166.000000   \n",
       "\n",
       "          NAICS_alt3     NAICS_alt4  \n",
       "count  688081.000000  688081.000000  \n",
       "mean      769.528095     769.233577  \n",
       "std       324.361889     326.453771  \n",
       "min         0.000000       0.000000  \n",
       "25%       644.000000     638.000000  \n",
       "50%       825.000000     825.000000  \n",
       "75%      1034.000000    1034.000000  \n",
       "max      1166.000000    1166.000000  \n",
       "\n",
       "[8 rows x 29 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "business_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "555f4031-e66d-4476-a37e-50e2d382ecf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(Path(setup.temp_path).joinpath('20_DATA_features.pkl'), 'rb') as fin:\n",
    "    imputer_features = pickle.load(fin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3a916e26-2419-4bd0-8fd4-58a959dc4bac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['NoEmp', 'CreateJob', 'LowDoc', 'DisbursementGross', 'new_business', 'urban_flag', 'franchise_flag', 'missingindicator_LowDoc', 'missingindicator_new_business', 'missingindicator_urban_flag']\n"
     ]
    }
   ],
   "source": [
    "features = [f for f in imputer_features if 'NAICS' not in f]\n",
    "print(features)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d94006a0-f010-4874-a7f1-0fd80c1e9a35",
   "metadata": {},
   "source": [
    "##### Datasets for train, validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a02a909d-834a-46a8-9cdd-ccfa36b07d69",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = business_data[['dset', 'LoanNr_ChkDgt'] + features].set_index('LoanNr_ChkDgt').sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "42dc5c92-f5a2-457c-be3a-60cb0f6c2ef8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training X: (436120, 10), y:(436120,)\n"
     ]
    }
   ],
   "source": [
    "X_train = X[X['dset'] == 'train'].drop(columns='dset')\n",
    "y_train = business_data[business_data['dset'] == 'train'].set_index('LoanNr_ChkDgt').sort_index()['target']\n",
    "print(f'training X: {X_train.shape}, y:{y_train.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "038b0e4f-a84a-4c8b-863e-4c594c1c9f32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val X: (93454, 10), y:(93454,)\n"
     ]
    }
   ],
   "source": [
    "X_val = X[X['dset'] == 'val'].drop(columns='dset')\n",
    "y_val = business_data[business_data['dset'] == 'val'].set_index('LoanNr_ChkDgt').sort_index()['target']\n",
    "print(f'val X: {X_val.shape}, y:{y_val.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bcb86909-e8a7-4f22-b089-6ca61dd86c73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2046546821975603\n"
     ]
    }
   ],
   "source": [
    "base_thresh = y_train.mean()\n",
    "print(base_thresh)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5c68623-4599-4472-a13a-9400d101633e",
   "metadata": {},
   "source": [
    "## Function to create model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9f4deffd-2b85-43d0-9cd1-1d9cf707b9e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_model(n_feat = len(features), \n",
    "                 hidden_size = setup.nn_layer_sizes,\n",
    "                 activation='tanh', lr=setup.nn_learning_rate,\n",
    "                 opt_func = setup.nn_optimizer, dropout = setup.nn_dropout):\n",
    "    \n",
    "    n_layers = len(hidden_size)\n",
    "    model = Sequential()\n",
    "    model.add(Dense(hidden_size[0], input_shape=(n_feat,), activation=activation))\n",
    "    for i in range(1, n_layers):\n",
    "        model.add(Dropout(dropout))\n",
    "        model.add(Dense(hidden_size[i], input_shape=(hidden_size[i-1],), activation=activation))\n",
    "    model.add(Dropout(dropout))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    # Compile model\n",
    "    optimizer = opt_func(learning_rate=lr)\n",
    "    model.compile(loss='binary_crossentropy', optimizer=optimizer, \n",
    "                  metrics=[km.AUC(curve='PR'), km.AUC(curve='ROC')])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3db4ea1c-77b2-4721-b106-1e66fc502139",
   "metadata": {},
   "source": [
    "## Create, fit model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4d48ddfa-580c-4321-9917-0bd176b67d87",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-03-29 06:30:53.635191: I metal_plugin/src/device/metal_device.cc:1154] Metal device set to: Apple M1\n",
      "2024-03-29 06:30:53.635217: I metal_plugin/src/device/metal_device.cc:296] systemMemory: 16.00 GB\n",
      "2024-03-29 06:30:53.635223: I metal_plugin/src/device/metal_device.cc:313] maxCacheSize: 5.33 GB\n",
      "2024-03-29 06:30:53.635269: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:303] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
      "2024-03-29 06:30:53.635287: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:269] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n"
     ]
    }
   ],
   "source": [
    "this_model = create_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bb765db3-1646-44f2-84ec-f8f4a7b236ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 128)               1408      \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 128)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 64)                8256      \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 64)                0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 1)                 65        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 9729 (38.00 KB)\n",
      "Trainable params: 9729 (38.00 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "this_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "31772016-3643-468b-8d65-c456168cad6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-03-29 06:30:54.236973: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13629/13629 [==============================] - ETA: 0s - loss: 0.4750 - auc: 0.3068 - auc_1: 0.6717"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-03-29 06:34:14.184024: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13629/13629 [==============================] - 223s 16ms/step - loss: 0.4750 - auc: 0.3068 - auc_1: 0.6717 - val_loss: 0.4716 - val_auc: 0.3300 - val_auc_1: 0.6878\n",
      "Epoch 2/20\n",
      "13629/13629 [==============================] - 190s 14ms/step - loss: 0.4689 - auc: 0.3226 - auc_1: 0.6854 - val_loss: 0.4704 - val_auc: 0.3370 - val_auc_1: 0.6938\n",
      "Epoch 3/20\n",
      "13629/13629 [==============================] - 188s 14ms/step - loss: 0.4675 - auc: 0.3272 - auc_1: 0.6890 - val_loss: 0.4710 - val_auc: 0.3366 - val_auc_1: 0.6944\n",
      "Epoch 4/20\n",
      "13629/13629 [==============================] - 238s 17ms/step - loss: 0.4669 - auc: 0.3295 - auc_1: 0.6906 - val_loss: 0.4718 - val_auc: 0.3407 - val_auc_1: 0.6962\n",
      "Epoch 5/20\n",
      "13629/13629 [==============================] - 1083s 79ms/step - loss: 0.4664 - auc: 0.3311 - auc_1: 0.6918 - val_loss: 0.4686 - val_auc: 0.3418 - val_auc_1: 0.6971\n",
      "Epoch 6/20\n",
      "13629/13629 [==============================] - 742s 54ms/step - loss: 0.4662 - auc: 0.3322 - auc_1: 0.6924 - val_loss: 0.4719 - val_auc: 0.3416 - val_auc_1: 0.6972\n",
      "Epoch 7/20\n",
      "13629/13629 [==============================] - 1130s 83ms/step - loss: 0.4661 - auc: 0.3321 - auc_1: 0.6926 - val_loss: 0.4691 - val_auc: 0.3415 - val_auc_1: 0.6971\n",
      "Epoch 8/20\n",
      "13629/13629 [==============================] - 1079s 79ms/step - loss: 0.4659 - auc: 0.3322 - auc_1: 0.6933 - val_loss: 0.4689 - val_auc: 0.3438 - val_auc_1: 0.6977\n",
      "Epoch 9/20\n",
      "13629/13629 [==============================] - 1079s 79ms/step - loss: 0.4658 - auc: 0.3325 - auc_1: 0.6933 - val_loss: 0.4693 - val_auc: 0.3422 - val_auc_1: 0.6963\n",
      "Epoch 10/20\n",
      "13629/13629 [==============================] - 179s 13ms/step - loss: 0.4658 - auc: 0.3324 - auc_1: 0.6932 - val_loss: 0.4684 - val_auc: 0.3457 - val_auc_1: 0.6979\n",
      "Epoch 11/20\n",
      "13629/13629 [==============================] - 181s 13ms/step - loss: 0.4656 - auc: 0.3329 - auc_1: 0.6938 - val_loss: 0.4688 - val_auc: 0.3429 - val_auc_1: 0.6973\n",
      "Epoch 12/20\n",
      "13629/13629 [==============================] - 1080s 79ms/step - loss: 0.4656 - auc: 0.3330 - auc_1: 0.6937 - val_loss: 0.4698 - val_auc: 0.3437 - val_auc_1: 0.6984\n",
      "Epoch 13/20\n",
      "13629/13629 [==============================] - 1079s 79ms/step - loss: 0.4656 - auc: 0.3337 - auc_1: 0.6939 - val_loss: 0.4677 - val_auc: 0.3445 - val_auc_1: 0.6981\n",
      "Epoch 14/20\n",
      "13629/13629 [==============================] - 1080s 79ms/step - loss: 0.4655 - auc: 0.3335 - auc_1: 0.6942 - val_loss: 0.4678 - val_auc: 0.3443 - val_auc_1: 0.6978\n",
      "Epoch 15/20\n",
      "13629/13629 [==============================] - 426s 31ms/step - loss: 0.4655 - auc: 0.3334 - auc_1: 0.6940 - val_loss: 0.4677 - val_auc: 0.3460 - val_auc_1: 0.6983\n",
      "Epoch 16/20\n",
      "13629/13629 [==============================] - 181s 13ms/step - loss: 0.4653 - auc: 0.3342 - auc_1: 0.6945 - val_loss: 0.4675 - val_auc: 0.3446 - val_auc_1: 0.6988\n",
      "Epoch 17/20\n",
      "13629/13629 [==============================] - 1080s 79ms/step - loss: 0.4653 - auc: 0.3341 - auc_1: 0.6946 - val_loss: 0.4678 - val_auc: 0.3442 - val_auc_1: 0.6983\n",
      "Epoch 18/20\n",
      "13629/13629 [==============================] - 182s 13ms/step - loss: 0.4654 - auc: 0.3337 - auc_1: 0.6942 - val_loss: 0.4677 - val_auc: 0.3454 - val_auc_1: 0.6984\n",
      "Epoch 19/20\n",
      "13629/13629 [==============================] - 186s 14ms/step - loss: 0.4652 - auc: 0.3347 - auc_1: 0.6947 - val_loss: 0.4683 - val_auc: 0.3459 - val_auc_1: 0.6989\n",
      "Epoch 20/20\n",
      "13629/13629 [==============================] - 190s 14ms/step - loss: 0.4653 - auc: 0.3337 - auc_1: 0.6943 - val_loss: 0.4699 - val_auc: 0.3428 - val_auc_1: 0.6983\n"
     ]
    }
   ],
   "source": [
    "this_history = this_model.fit(X_train, y_train,\n",
    "                              validation_data=(X_val, y_val),\n",
    "                              batch_size=setup.nn_batch_size,\n",
    "                              epochs=setup.nn_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "460c4b43-c673-4ee1-b070-d2d596ac4561",
   "metadata": {},
   "outputs": [],
   "source": [
    "this_history_df = pd.DataFrame(this_history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c85bd09a-2fa9-4255-958b-4d48b17085ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename columns\n",
    "try:\n",
    "    this_history_df.columns = ['_'.join(c.split('_')[0:-1])  \\\n",
    "                               if re.search(r'_\\d+$', c) else c for c in this_history_df.columns]\n",
    "except:\n",
    "    pass\n",
    "try:\n",
    "    cur_col = list(this_history_df.columns)\n",
    "    this_history_df.columns = [cur_col[0]] + \\\n",
    "        [f'{cur_col[i]}_roc'  if (cur_col[i] == cur_col[i-1]) and 'auc'in cur_col[i] \\\n",
    "         else cur_col[i] for i in range(1, len(cur_col))]\n",
    "except:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8ea25384-9fc0-4916-a577-4bd9bc9b203f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['loss', 'auc', 'auc_roc', 'val_loss', 'val_auc', 'val_auc_roc'], dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "this_history_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ad4783f2-8d0b-4850-a45a-3a4be8e63318",
   "metadata": {},
   "outputs": [],
   "source": [
    "this_history_df.to_csv(Path(setup.temp_path).joinpath('21_REPORT_fit_history.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "73e89efd-75a9-4b27-81a8-13e12d5482be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "this_history_df[['loss', 'val_loss']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2342a31f-4891-4834-851a-b90b8b729c3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "this_history_df[['auc', 'val_auc']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cee80637-cabf-4974-b476-08c5fba165ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "this_history_df[['auc_roc', 'val_auc_roc']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e184b0ca-1d31-4798-88a0-4e590b67062e",
   "metadata": {},
   "outputs": [],
   "source": [
    "this_model.save(Path(setup.temp_path).joinpath('21_DATA_model.keras'),save_format='tf')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d326c9ea-4759-4418-a64d-56c151049c25",
   "metadata": {},
   "source": [
    "## Predictions on all data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cd6910ee-5ffe-4def-9aa8-7dba76446b08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   93/21503 [..............................] - ETA: 35s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-03-29 09:47:32.244179: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21503/21503 [==============================] - 30s 1ms/step\n"
     ]
    }
   ],
   "source": [
    "all_predictions = this_model.predict(X.drop(columns='dset'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "98a2cce1-b7d3-4ebc-8869-80705297e145",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_predictions_df = pd.DataFrame(all_predictions, index=X.index) \\\n",
    "    .set_axis(['predict_prob'], axis=1) \\\n",
    "    .reset_index() \\\n",
    "    .merge(business_data[['target', 'LoanNr_ChkDgt', 'dset', 'dset_naics_holdout']], on='LoanNr_ChkDgt')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3fc7da9c-6bd2-4d1b-b77f-b62679825d5d",
   "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>predict_prob</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>predict_prob</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.280619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>0.280619</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              predict_prob    target\n",
       "predict_prob      1.000000  0.280619\n",
       "target            0.280619  1.000000"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_predictions_df[['predict_prob', 'target']].corr(method='spearman')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e79afc4e-cb67-4064-9b19-dc52500baef2",
   "metadata": {},
   "source": [
    "##### Threshold Tune & Binary Predictions\n",
    "Using training probability predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ccd0bc23-fbc0-40d0-8d8b-f2302405a875",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_pred_train = all_predictions_df[all_predictions_df['dset'] == 'train']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "df36d9e9-f35e-46b3-a5be-08081f998319",
   "metadata": {},
   "outputs": [],
   "source": [
    "thresh_tune_data = sg_plot.get_f1_frame(all_pred_train['target'], \n",
    "                                        all_pred_train['predict_prob'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "de634156-caf6-4a52-b5d7-44b8e986aa1d",
   "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>thresh</th>\n",
       "      <th>f1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.20</td>\n",
       "      <td>0.428662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.18</td>\n",
       "      <td>0.427022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.22</td>\n",
       "      <td>0.426960</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    thresh        f1\n",
       "10    0.20  0.428662\n",
       "9     0.18  0.427022\n",
       "11    0.22  0.426960"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thresh_tune_data.sort_values('f1', ascending=False, inplace=True)\n",
    "thresh_tune_data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c746b880-d781-4bc1-9df2-89211127c22a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_thresh = thresh_tune_data['thresh'].iloc[0]\n",
    "best_thresh"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c0ac5e2-be0d-42f0-960d-1f81b4a5d4e2",
   "metadata": {},
   "source": [
    "##### Append binary predictions to probability predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "641da72e-1bca-4fe7-96c4-6c029b521bd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_predictions_df['predict_bin'] = sg_plot.get_binary_predictions(all_predictions_df['predict_prob'], best_thresh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9f6ecdf4-ac01-4c39-8315-e8583daa337e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "predict_bin\n",
       "1    0.507763\n",
       "0    0.492237\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_predictions_df['predict_bin'].value_counts(normalize=True, dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "cbfed830-7ff8-40f6-a691-6b894b26d6bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_predictions_df.to_parquet(Path(setup.temp_path).joinpath('21_DATA_predictions.parquet'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69903966-6baa-4579-8e84-ce38e8ebdd77",
   "metadata": {},
   "source": [
    "## Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "09b7437b-4b51-42c4-a267-fb244663df49",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_predictions_df = pd.read_parquet(Path(setup.temp_path).joinpath('21_DATA_predictions.parquet'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0c748e1c-ddb3-4d92-8f84-485736633e10",
   "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>0.603317</td>\n",
       "      <td>0.427501</td>\n",
       "      <td>0.297976</td>\n",
       "      <td>0.756217</td>\n",
       "      <td>0.340404</td>\n",
       "      <td>0.711034</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train</td>\n",
       "      <td>0.591229</td>\n",
       "      <td>0.428662</td>\n",
       "      <td>0.300202</td>\n",
       "      <td>0.749289</td>\n",
       "      <td>0.339337</td>\n",
       "      <td>0.698480</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>val</td>\n",
       "      <td>0.591393</td>\n",
       "      <td>0.431874</td>\n",
       "      <td>0.303488</td>\n",
       "      <td>0.748530</td>\n",
       "      <td>0.342958</td>\n",
       "      <td>0.698365</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test</td>\n",
       "      <td>0.591911</td>\n",
       "      <td>0.432538</td>\n",
       "      <td>0.302907</td>\n",
       "      <td>0.756125</td>\n",
       "      <td>0.340217</td>\n",
       "      <td>0.699359</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>test</td>\n",
       "      <td>0.619704</td>\n",
       "      <td>0.419558</td>\n",
       "      <td>0.290292</td>\n",
       "      <td>0.756366</td>\n",
       "      <td>0.341215</td>\n",
       "      <td>0.727186</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        0.603317  0.427501         0.297976      0.756217   \n",
       "1  train        0.591229  0.428662         0.300202      0.749289   \n",
       "2    val        0.591393  0.431874         0.303488      0.748530   \n",
       "0   test        0.591911  0.432538         0.302907      0.756125   \n",
       "1   test        0.619704  0.419558         0.290292      0.756366   \n",
       "\n",
       "   average_precision_score  roc_auc_score  dset_naics_holdout  \n",
       "0                 0.340404       0.711034                 NaN  \n",
       "1                 0.339337       0.698480                 NaN  \n",
       "2                 0.342958       0.698365                 NaN  \n",
       "0                 0.340217       0.699359                 0.0  \n",
       "1                 0.341215       0.727186                 1.0  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics_dset_df = all_predictions_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 = all_predictions_df[all_predictions_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('21_REPORT_metrics.csv'), index=True)\n",
    "metrics_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bf6b2f6-992d-4c4b-88cb-02eb418ae751",
   "metadata": {},
   "source": [
    "## Embeddings\n",
    "Save the hidden layer weight prior to sigmoid output, for possible later use in unsupervised GNN "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f8bf0603-5235-4344-a73d-a5d632befd0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "embed_model = Model(inputs=this_model.inputs, outputs=this_model.layers[-2].output) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0ee68a3c-a630-4fde-b765-f441d2bb3895",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  161/21503 [..............................] - ETA: 20s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-03-29 09:48:25.779876: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21503/21503 [==============================] - 20s 923us/step\n"
     ]
    }
   ],
   "source": [
    "embed_out = embed_model.predict(X.drop(columns='dset'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f622987d-4206-494f-8482-08ac99fff2f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(688081, 64)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embed_out.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2193800a-3e35-460c-8ee0-576d460f53bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "embed_df = pd.DataFrame(embed_out, index=X.index) \n",
    "embed_df.columns = [f'emb_{i:03d}' for i in range(len(embed_df.columns))]\n",
    "embed_df.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "17f1e84b-2aec-4b6b-bd27-04a79008b2de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>LoanNr_ChkDgt</th>\n",
       "      <th>emb_000</th>\n",
       "      <th>emb_001</th>\n",
       "      <th>emb_002</th>\n",
       "      <th>emb_003</th>\n",
       "      <th>emb_004</th>\n",
       "      <th>emb_005</th>\n",
       "      <th>emb_006</th>\n",
       "      <th>emb_007</th>\n",
       "      <th>emb_008</th>\n",
       "      <th>...</th>\n",
       "      <th>emb_054</th>\n",
       "      <th>emb_055</th>\n",
       "      <th>emb_056</th>\n",
       "      <th>emb_057</th>\n",
       "      <th>emb_058</th>\n",
       "      <th>emb_059</th>\n",
       "      <th>emb_060</th>\n",
       "      <th>emb_061</th>\n",
       "      <th>emb_062</th>\n",
       "      <th>emb_063</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000014003</td>\n",
       "      <td>0.952909</td>\n",
       "      <td>-0.999519</td>\n",
       "      <td>0.998237</td>\n",
       "      <td>-0.999588</td>\n",
       "      <td>-0.999004</td>\n",
       "      <td>0.828328</td>\n",
       "      <td>0.777259</td>\n",
       "      <td>-0.831848</td>\n",
       "      <td>-0.999618</td>\n",
       "      <td>...</td>\n",
       "      <td>0.999905</td>\n",
       "      <td>-0.999998</td>\n",
       "      <td>0.989482</td>\n",
       "      <td>-0.544452</td>\n",
       "      <td>-0.999999</td>\n",
       "      <td>-0.974307</td>\n",
       "      <td>-0.999991</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.995986</td>\n",
       "      <td>-0.999999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000024006</td>\n",
       "      <td>0.973122</td>\n",
       "      <td>-0.989506</td>\n",
       "      <td>0.999934</td>\n",
       "      <td>-0.999596</td>\n",
       "      <td>-0.999897</td>\n",
       "      <td>-0.676184</td>\n",
       "      <td>0.968064</td>\n",
       "      <td>-0.926758</td>\n",
       "      <td>-0.997828</td>\n",
       "      <td>...</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>-0.999975</td>\n",
       "      <td>0.963025</td>\n",
       "      <td>0.439916</td>\n",
       "      <td>-0.999989</td>\n",
       "      <td>-0.974976</td>\n",
       "      <td>-0.999987</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.999646</td>\n",
       "      <td>-0.999998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000034009</td>\n",
       "      <td>0.998728</td>\n",
       "      <td>-0.999950</td>\n",
       "      <td>-0.966181</td>\n",
       "      <td>-0.085618</td>\n",
       "      <td>0.990105</td>\n",
       "      <td>0.105785</td>\n",
       "      <td>0.739314</td>\n",
       "      <td>0.986810</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.893906</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.999992</td>\n",
       "      <td>-0.995424</td>\n",
       "      <td>-0.999986</td>\n",
       "      <td>-0.940575</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.999954</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000084002</td>\n",
       "      <td>0.997700</td>\n",
       "      <td>-0.999995</td>\n",
       "      <td>-0.995849</td>\n",
       "      <td>0.257922</td>\n",
       "      <td>0.998237</td>\n",
       "      <td>0.890733</td>\n",
       "      <td>0.371696</td>\n",
       "      <td>0.992805</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.231656</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.999997</td>\n",
       "      <td>-0.998140</td>\n",
       "      <td>-0.999997</td>\n",
       "      <td>-0.945281</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.999990</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000094005</td>\n",
       "      <td>0.994474</td>\n",
       "      <td>-0.962092</td>\n",
       "      <td>0.999992</td>\n",
       "      <td>-0.998879</td>\n",
       "      <td>-0.999873</td>\n",
       "      <td>-0.822873</td>\n",
       "      <td>0.991674</td>\n",
       "      <td>-0.788198</td>\n",
       "      <td>-0.997908</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.999976</td>\n",
       "      <td>0.987424</td>\n",
       "      <td>0.898494</td>\n",
       "      <td>-0.999985</td>\n",
       "      <td>-0.981905</td>\n",
       "      <td>-0.999993</td>\n",
       "      <td>-0.999999</td>\n",
       "      <td>0.999505</td>\n",
       "      <td>-0.999998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 65 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  LoanNr_ChkDgt   emb_000   emb_001   emb_002   emb_003   emb_004   emb_005  \\\n",
       "0    1000014003  0.952909 -0.999519  0.998237 -0.999588 -0.999004  0.828328   \n",
       "1    1000024006  0.973122 -0.989506  0.999934 -0.999596 -0.999897 -0.676184   \n",
       "2    1000034009  0.998728 -0.999950 -0.966181 -0.085618  0.990105  0.105785   \n",
       "3    1000084002  0.997700 -0.999995 -0.995849  0.257922  0.998237  0.890733   \n",
       "4    1000094005  0.994474 -0.962092  0.999992 -0.998879 -0.999873 -0.822873   \n",
       "\n",
       "    emb_006   emb_007   emb_008  ...   emb_054   emb_055   emb_056   emb_057  \\\n",
       "0  0.777259 -0.831848 -0.999618  ...  0.999905 -0.999998  0.989482 -0.544452   \n",
       "1  0.968064 -0.926758 -0.997828  ...  0.999999 -0.999975  0.963025  0.439916   \n",
       "2  0.739314  0.986810 -1.000000  ...  0.893906 -1.000000  0.999992 -0.995424   \n",
       "3  0.371696  0.992805 -1.000000  ... -0.231656 -1.000000  0.999997 -0.998140   \n",
       "4  0.991674 -0.788198 -0.997908  ...  1.000000 -0.999976  0.987424  0.898494   \n",
       "\n",
       "    emb_058   emb_059   emb_060   emb_061   emb_062   emb_063  \n",
       "0 -0.999999 -0.974307 -0.999991 -1.000000  0.995986 -0.999999  \n",
       "1 -0.999989 -0.974976 -0.999987 -1.000000  0.999646 -0.999998  \n",
       "2 -0.999986 -0.940575 -1.000000 -1.000000 -0.999954 -1.000000  \n",
       "3 -0.999997 -0.945281 -1.000000 -1.000000 -0.999990 -1.000000  \n",
       "4 -0.999985 -0.981905 -0.999993 -0.999999  0.999505 -0.999998  \n",
       "\n",
       "[5 rows x 65 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embed_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6a8d3c99-24d1-404c-a867-b67f6dd6a4a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "embed_df.to_parquet(Path(setup.temp_path).joinpath('21_DATA_embeddings.parquet'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61477278-45ce-4742-b352-05fdde43506f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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