20_downsample_raw.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": [
    "## NN, Downsample train, NAICS only embedding\n",
    "Do the embedding model again but downsample training.  This is a baseline for comparing random vs. the custom data generator"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e169c2e8-ba0f-488f-acf6-87d419bd4e99",
   "metadata": {},
   "source": [
    "Use the dataset with no missing values. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d47a7ec9-7d7b-47df-a902-b2f1e54f1135",
   "metadata": {},
   "source": [
    "*This script takes about 15 minutes 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\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "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",
    "import keras\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": "9f1e4f2e-0e45-4e28-8e9c-212e0bb51199",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sba_nn.sba_nn import sbnn_metrics, sbnn_model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cbc00d2-bc83-42d9-83eb-65fcf306dccd",
   "metadata": {},
   "source": [
    "## Input Data, Downsample Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e014cd31-0f8d-42e3-82f9-b529eec8a3cc",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "sba_loans = pd.read_parquet(Path(setup.temp_path).joinpath('01_DATA_transformed_nomiss.parquet'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8cec1356-7868-45ed-a214-7f25ea0fef08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(44684,)\n"
     ]
    }
   ],
   "source": [
    "train_samp, _ = train_test_split(sba_loans[sba_loans['dset'] == 'train'].index,\n",
    "                                train_size=0.10,\n",
    "                                random_state=46)\n",
    "print(train_samp.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "414b4815-1117-48e7-b577-038d07474f02",
   "metadata": {},
   "outputs": [],
   "source": [
    "sba_loans = pd.concat([sba_loans[sba_loans['dset'] != 'train'],\n",
    "                sba_loans.loc[train_samp, :]])      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "52574a1b-ce17-44d0-a87f-43305ee7e14f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(293336, 55)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sba_loans.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "45864963-90d7-43e7-89eb-03e947feec17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dset\n",
       "test     153048\n",
       "val       95604\n",
       "train     44684\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sba_loans['dset'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3027936b-5e0b-4008-a667-15cc4530762a",
   "metadata": {},
   "outputs": [],
   "source": [
    "sba_loans.to_parquet(Path(setup.temp_path).joinpath('20_DATA_downsamp_train.parquet'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8e65cb7c-792c-46ff-a530-0d5c64240d60",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(Path(setup.temp_path).joinpath('01_DATA_features.pkl'), 'rb') as fin:\n",
    "    imputer_features = pickle.load(fin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6f0ce28e-a80c-415b-b068-5c4d49370da8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'NAICS': 1170,\n",
       " 'NAICS_5': 764,\n",
       " 'NAICS_4': 345,\n",
       " 'NAICS_3': 107,\n",
       " 'NAICS_sector': 21}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open(Path(setup.temp_path).joinpath('01_DATA_naics_max_encodings.pkl'), 'rb') as fin:\n",
    "    naics_max_levels= pickle.load(fin)\n",
    "naics_max_levels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d4116da-d689-478a-9c5d-a330f231e615",
   "metadata": {},
   "source": [
    "##### Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f98c6e41-38d0-4d5f-b821-e3837b8c2068",
   "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": [
    "# Numeric features\n",
    "features_numeric = [f for f in imputer_features if 'NAICS' not in f]\n",
    "features_numeric_len = len(features_numeric)\n",
    "print(features_numeric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a74fa386-4011-45ee-bdb5-d671db9ffa91",
   "metadata": {},
   "outputs": [],
   "source": [
    "# NAICS feature - just bas\n",
    "features_naics = ['NAICS']\n",
    "features_naics_max_levels  = [naics_max_levels[n] for n in features_naics]\n",
    "features_naics_emb_dim = [setup.nn_naics_embed_size_dict[n] for n in features_naics]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "867031ee-8b6e-424d-b497-8e19409ba1d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1170]\n",
      "[8]\n"
     ]
    }
   ],
   "source": [
    "print(features_naics_max_levels)\n",
    "print(features_naics_emb_dim)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b979396c-c822-4940-a292-6e07f1b54f58",
   "metadata": {},
   "source": [
    "##### Datasets for train, validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "43bca011-a59d-4496-9f03-87f4e350a253",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = sba_loans[['dset', 'LoanNr_ChkDgt'] + features_numeric].set_index('LoanNr_ChkDgt').sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "aa162d01-ed73-4ae3-832e-4aeea09b70f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training X: (44684, 10), y:(44684,)\n"
     ]
    }
   ],
   "source": [
    "X_train = X[X['dset'] == 'train'].drop(columns='dset')\n",
    "y_train = sba_loans[sba_loans['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": 19,
   "id": "b32b5fd3-6f6e-4aec-8e20-0e2b71a4814c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val X: (95604, 10), y:(95604,)\n"
     ]
    }
   ],
   "source": [
    "X_val = X[X['dset'] == 'val'].drop(columns='dset')\n",
    "y_val = sba_loans[sba_loans['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": 20,
   "id": "01b8ff96-159b-4283-b5fb-c4b5dbd277d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.20295855339718916\n"
     ]
    }
   ],
   "source": [
    "base_thresh = y_train.mean()\n",
    "print(base_thresh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1b8ce4c9-6907-4b70-982a-b92a52b3159d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_naics = [sba_loans[['dset', 'LoanNr_ChkDgt', n]].set_index('LoanNr_ChkDgt').sort_index() \\\n",
    "           for n in features_naics]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a6f129e0-a4d0-4d93-9886-153c9803d403",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_naics_train = [Xn[Xn['dset'] == 'train'].drop(columns='dset') for Xn in X_naics]\n",
    "X_naics_val =  [Xn[Xn['dset'] == 'val'].drop(columns='dset') for Xn in X_naics]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3db4ea1c-77b2-4721-b106-1e66fc502139",
   "metadata": {},
   "source": [
    "## Create, fit model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4d48ddfa-580c-4321-9917-0bd176b67d87",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-20 13:33:22.911595: I metal_plugin/src/device/metal_device.cc:1154] Metal device set to: Apple M1\n",
      "2024-05-20 13:33:22.911633: I metal_plugin/src/device/metal_device.cc:296] systemMemory: 16.00 GB\n",
      "2024-05-20 13:33:22.911652: I metal_plugin/src/device/metal_device.cc:313] maxCacheSize: 5.33 GB\n",
      "2024-05-20 13:33:22.911691: 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",
      "2024-05-20 13:33:22.911708: 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 = sbnn_model.create_emb_model(features_numeric_len,\n",
    "                                         features_naics_max_levels,\n",
    "                                         features_naics_emb_dim,\n",
    "                                         naics_embedding_names = features_naics,\n",
    "                                         hidden_size = setup.nn_layer_sizes,\n",
    "                                         activation='tanh',\n",
    "                                         lr=setup.nn_learning_rate,\n",
    "                                         opt_func = setup.nn_optimizer,\n",
    "                                         dropout = setup.nn_dropout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "47036621-ef13-4957-997b-f8ef86016f7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                Output Shape                 Param #   Connected to                  \n",
      "==================================================================================================\n",
      " input_naics_00 (InputLayer  [(None, 1)]                  0         []                            \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " emb_naics_00 (Embedding)    (None, 1, 8)                 9360      ['input_naics_00[0][0]']      \n",
      "                                                                                                  \n",
      " NAICS (Flatten)             (None, 8)                    0         ['emb_naics_00[0][0]']        \n",
      "                                                                                                  \n",
      " input_numeric (InputLayer)  [(None, 10)]                 0         []                            \n",
      "                                                                                                  \n",
      " input_concat (Concatenate)  (None, 18)                   0         ['NAICS[0][0]',               \n",
      "                                                                     'input_numeric[0][0]']       \n",
      "                                                                                                  \n",
      " layer_00 (Dense)            (None, 128)                  2432      ['input_concat[0][0]']        \n",
      "                                                                                                  \n",
      " dropout_00 (Dropout)        (None, 128)                  0         ['layer_00[0][0]']            \n",
      "                                                                                                  \n",
      " layer_01 (Dense)            (None, 64)                   8256      ['dropout_00[0][0]']          \n",
      "                                                                                                  \n",
      " dropout_01 (Dropout)        (None, 64)                   0         ['layer_01[0][0]']            \n",
      "                                                                                                  \n",
      " output (Dense)              (None, 1)                    65        ['dropout_01[0][0]']          \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 20113 (78.57 KB)\n",
      "Trainable params: 20113 (78.57 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "this_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5b21b5ef-7f75-4708-b8d7-59825d38f601",
   "metadata": {},
   "outputs": [],
   "source": [
    "callback = keras.callbacks.EarlyStopping(patience=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "31772016-3643-468b-8d65-c456168cad6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-20 13:33:23.667974: 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": [
      "1397/1397 [==============================] - ETA: 0s - loss: 0.4823 - auc: 0.2923 - auc_1: 0.6557"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-20 13:33:44.651885: 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": [
      "1397/1397 [==============================] - 59s 41ms/step - loss: 0.4823 - auc: 0.2923 - auc_1: 0.6557 - val_loss: 0.4691 - val_auc: 0.3425 - val_auc_1: 0.7021\n",
      "Epoch 2/20\n",
      "1397/1397 [==============================] - 70s 50ms/step - loss: 0.4644 - auc: 0.3376 - auc_1: 0.6997 - val_loss: 0.4619 - val_auc: 0.3442 - val_auc_1: 0.7043\n",
      "Epoch 3/20\n",
      "1397/1397 [==============================] - 72s 51ms/step - loss: 0.4591 - auc: 0.3494 - auc_1: 0.7102 - val_loss: 0.4625 - val_auc: 0.3509 - val_auc_1: 0.7060\n",
      "Epoch 4/20\n",
      "1397/1397 [==============================] - 73s 52ms/step - loss: 0.4560 - auc: 0.3553 - auc_1: 0.7161 - val_loss: 0.4610 - val_auc: 0.3509 - val_auc_1: 0.7075\n",
      "Epoch 5/20\n",
      "1397/1397 [==============================] - 71s 51ms/step - loss: 0.4544 - auc: 0.3606 - auc_1: 0.7188 - val_loss: 0.4611 - val_auc: 0.3531 - val_auc_1: 0.7079\n",
      "Epoch 6/20\n",
      "1397/1397 [==============================] - 73s 53ms/step - loss: 0.4526 - auc: 0.3633 - auc_1: 0.7225 - val_loss: 0.4604 - val_auc: 0.3520 - val_auc_1: 0.7082\n",
      "Epoch 7/20\n",
      "1397/1397 [==============================] - 72s 52ms/step - loss: 0.4516 - auc: 0.3673 - auc_1: 0.7244 - val_loss: 0.4616 - val_auc: 0.3543 - val_auc_1: 0.7100\n",
      "Epoch 8/20\n",
      "1397/1397 [==============================] - 66s 47ms/step - loss: 0.4504 - auc: 0.3694 - auc_1: 0.7263 - val_loss: 0.4608 - val_auc: 0.3532 - val_auc_1: 0.7086\n",
      "Epoch 9/20\n",
      "1397/1397 [==============================] - 50s 36ms/step - loss: 0.4492 - auc: 0.3721 - auc_1: 0.7285 - val_loss: 0.4625 - val_auc: 0.3466 - val_auc_1: 0.7060\n"
     ]
    }
   ],
   "source": [
    "this_history = this_model.fit([X_train] + X_naics_train, y_train,\n",
    "                              validation_data=([X_val] + X_naics_val, y_val),\n",
    "                              batch_size=setup.nn_batch_size,\n",
    "                              epochs=setup.nn_epochs,\n",
    "                              callbacks=[callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "460c4b43-c673-4ee1-b070-d2d596ac4561",
   "metadata": {},
   "outputs": [],
   "source": [
    "this_history_df = sbnn_model.process_history(this_history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ad4783f2-8d0b-4850-a45a-3a4be8e63318",
   "metadata": {},
   "outputs": [],
   "source": [
    "this_history_df.to_csv(Path(setup.temp_path).joinpath('20_REPORT_fit_history.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "73e89efd-75a9-4b27-81a8-13e12d5482be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 29,
     "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": 30,
   "id": "2342a31f-4891-4834-851a-b90b8b729c3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 30,
     "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": 31,
   "id": "cee80637-cabf-4974-b476-08c5fba165ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 31,
     "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": 32,
   "id": "e184b0ca-1d31-4798-88a0-4e590b67062e",
   "metadata": {},
   "outputs": [],
   "source": [
    "this_model.save(Path(setup.temp_path).joinpath('20_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": 34,
   "id": "cd6910ee-5ffe-4def-9aa8-7dba76446b08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  83/9167 [..............................] - ETA: 16s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-20 13:43:30.771749: 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": [
      "9167/9167 [==============================] - 19s 2ms/step\n"
     ]
    }
   ],
   "source": [
    "all_predictions = this_model.predict([X.drop(columns='dset')] +\n",
    "                                     [Xn.drop(columns='dset') for Xn in X_naics])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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(sba_loans[['target', 'LoanNr_ChkDgt', 'dset', 'dset_naics_holdout', 'NAICS']], \n",
    "           on='LoanNr_ChkDgt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>0.288166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>0.288166</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              predict_prob    target\n",
       "predict_prob      1.000000  0.288166\n",
       "target            0.288166  1.000000"
      ]
     },
     "execution_count": 36,
     "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": 37,
   "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": 38,
   "id": "df36d9e9-f35e-46b3-a5be-08081f998319",
   "metadata": {},
   "outputs": [],
   "source": [
    "thresh_tune_data = sbnn_metrics.get_f1_frame(all_pred_train['target'], \n",
    "                                        all_pred_train['predict_prob'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>11</th>\n",
       "      <td>0.22</td>\n",
       "      <td>0.459205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.20</td>\n",
       "      <td>0.456325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.24</td>\n",
       "      <td>0.453697</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    thresh        f1\n",
       "11    0.22  0.459205\n",
       "10    0.20  0.456325\n",
       "12    0.24  0.453697"
      ]
     },
     "execution_count": 39,
     "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": 40,
   "id": "c746b880-d781-4bc1-9df2-89211127c22a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.22"
      ]
     },
     "execution_count": 40,
     "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": 41,
   "id": "641da72e-1bca-4fe7-96c4-6c029b521bd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_predictions_df['predict_bin'] = sbnn_metrics.get_binary_predictions(all_predictions_df['predict_prob'], best_thresh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "9f6ecdf4-ac01-4c39-8315-e8583daa337e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "predict_bin\n",
       "0    0.54397\n",
       "1    0.45603\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_predictions_df['predict_bin'].value_counts(normalize=True, dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "cbfed830-7ff8-40f6-a691-6b894b26d6bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_predictions_df.to_parquet(Path(setup.temp_path).joinpath('20_DATA_predictions.parquet'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb0ecbd2-abff-46d8-8c70-f7bccd363b69",
   "metadata": {},
   "source": [
    "## Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "be4ceefe-c7a8-45bd-8573-2daefe99a1c2",
   "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>0.601994</td>\n",
       "      <td>0.426119</td>\n",
       "      <td>0.300444</td>\n",
       "      <td>0.732541</td>\n",
       "      <td>0.336807</td>\n",
       "      <td>0.699021</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train</td>\n",
       "      <td>0.665652</td>\n",
       "      <td>0.459205</td>\n",
       "      <td>0.341812</td>\n",
       "      <td>0.699416</td>\n",
       "      <td>0.392223</td>\n",
       "      <td>0.740227</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>val</td>\n",
       "      <td>0.646647</td>\n",
       "      <td>0.431547</td>\n",
       "      <td>0.321049</td>\n",
       "      <td>0.658028</td>\n",
       "      <td>0.346680</td>\n",
       "      <td>0.706018</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test</td>\n",
       "      <td>0.647854</td>\n",
       "      <td>0.430453</td>\n",
       "      <td>0.320569</td>\n",
       "      <td>0.654958</td>\n",
       "      <td>0.345469</td>\n",
       "      <td>0.703519</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>test</td>\n",
       "      <td>0.525769</td>\n",
       "      <td>0.420679</td>\n",
       "      <td>0.278023</td>\n",
       "      <td>0.864013</td>\n",
       "      <td>0.328941</td>\n",
       "      <td>0.706302</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        0.601994  0.426119         0.300444      0.732541   \n",
       "1  train        0.665652  0.459205         0.341812      0.699416   \n",
       "2    val        0.646647  0.431547         0.321049      0.658028   \n",
       "0   test        0.647854  0.430453         0.320569      0.654958   \n",
       "1   test        0.525769  0.420679         0.278023      0.864013   \n",
       "\n",
       "   average_precision_score  roc_auc_score  dset_naics_holdout  \n",
       "0                 0.336807       0.699021                 NaN  \n",
       "1                 0.392223       0.740227                 NaN  \n",
       "2                 0.346680       0.706018                 NaN  \n",
       "0                 0.345469       0.703519                 0.0  \n",
       "1                 0.328941       0.706302                 1.0  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics_dset_df = all_predictions_df.groupby('dset') \\\n",
    "    .apply(lambda x: sbnn_metrics.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: sbnn_metrics.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('20_REPORT_metrics.csv'), index=True)\n",
    "metrics_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52e382b5-e9dc-47a1-9bf4-6d0b6b4a0c61",
   "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
}