43_random_hier_cust.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": [
    "## Random Hierarchy for NAICS (data generator randomization)\n",
    "Neural Network With NAICS Embedding Layer + dummy hieararchy. Add NAICS embedding to model.  Add additional dummy levels to the hieararchy.   Modify training data to inject 10% 1 values for all levels.  Use a data generator to shuffle randomiztion"
   ]
  },
  {
   "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 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",
    "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": "22b466e7-a419-45aa-83f7-5dafc7a3998b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import Dense, Dropout, Input, Embedding, \\\n",
    "    Concatenate, Reshape, concatenate, Flatten\n",
    "import tensorflow.keras.metrics as km"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9f1e4f2e-0e45-4e28-8e9c-212e0bb51199",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sba_nn.sba_nn import sbnn_metrics, sbnn_model \n",
    "from sba_nn.sba_nn.sbnn_model import CatInjectGenerator"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cbc00d2-bc83-42d9-83eb-65fcf306dccd",
   "metadata": {},
   "source": [
    "## Input Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6f0ce28e-a80c-415b-b068-5c4d49370da8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'NAICS_orig': 1170, 'rgrp_345': 337, 'rgrp_106': 107, 'rgrp_020': 21}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open(Path(setup.temp_path).joinpath('41_DATA_naics_max_encodings.pkl'), 'rb') as fin:\n",
    "    naics_max_levels= pickle.load(fin)\n",
    "naics_max_levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "958fb977-c8de-4e6b-8f1b-c8e54acaddb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "grp_features = list(naics_max_levels.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 10,
   "id": "fca4391e-98cd-44c4-b6fd-313f57e431cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "naics_new = pd.read_parquet(Path(setup.temp_path).joinpath('41_DATA_naics_groups_loans.parquet'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7066d473-6c62-4407-8862-6e544e61077b",
   "metadata": {},
   "outputs": [],
   "source": [
    "sba_loans = sba_loans.drop(columns=grp_features, errors='ignore') \\\n",
    "    .merge(naics_new[['LoanNr_ChkDgt'] + grp_features], on='LoanNr_ChkDgt')"
   ]
  },
  {
   "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": "markdown",
   "id": "4d4116da-d689-478a-9c5d-a330f231e615",
   "metadata": {},
   "source": [
    "# Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 14,
   "id": "a74fa386-4011-45ee-bdb5-d671db9ffa91",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use analagous embedding dimensions\n",
    "features_naics_hier = ['NAICS_4', 'NAICS_3', 'NAICS_sector']\n",
    "features_grp_all = ['NAICS'] + features_naics_hier\n",
    "grp_emb_dim = [setup.nn_naics_embed_size_dict[n] for n in features_grp_all]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "867031ee-8b6e-424d-b497-8e19409ba1d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'NAICS_orig': 1170, 'rgrp_345': 337, 'rgrp_106': 107, 'rgrp_020': 21}\n",
      "[8, 4, 4, 2]\n"
     ]
    }
   ],
   "source": [
    "print(naics_max_levels)\n",
    "print(grp_emb_dim)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "323e2d5f-3391-4844-a0ac-815fca94a854",
   "metadata": {},
   "source": [
    "##### Datasets for train, validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "43bca011-a59d-4496-9f03-87f4e350a253",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = sba_loans[['dset', 'LoanNr_ChkDgt'] + features_numeric + grp_features].set_index('LoanNr_ChkDgt').sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "aa162d01-ed73-4ae3-832e-4aeea09b70f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training X: (446848, 14), y:(446848,)\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": 18,
   "id": "b32b5fd3-6f6e-4aec-8e20-0e2b71a4814c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val X: (95604, 14), 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": "markdown",
   "id": "3db4ea1c-77b2-4721-b106-1e66fc502139",
   "metadata": {},
   "source": [
    "## Create, fit model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4d48ddfa-580c-4321-9917-0bd176b67d87",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-29 17:30:42.164476: I metal_plugin/src/device/metal_device.cc:1154] Metal device set to: Apple M1\n",
      "2024-05-29 17:30:42.164517: I metal_plugin/src/device/metal_device.cc:296] systemMemory: 16.00 GB\n",
      "2024-05-29 17:30:42.164594: I metal_plugin/src/device/metal_device.cc:313] maxCacheSize: 5.33 GB\n",
      "2024-05-29 17:30:42.164708: 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",
      "2024-05-29 17:30:42.164740: 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",
    "                                         list(naics_max_levels.values()),\n",
    "                                         grp_emb_dim,\n",
    "                                         naics_embedding_names = grp_features,\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": 20,
   "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",
      " input_naics_01 (InputLayer  [(None, 1)]                  0         []                            \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " input_naics_02 (InputLayer  [(None, 1)]                  0         []                            \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " input_naics_03 (InputLayer  [(None, 1)]                  0         []                            \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " emb_naics_00 (Embedding)    (None, 1, 8)                 9360      ['input_naics_00[0][0]']      \n",
      "                                                                                                  \n",
      " emb_naics_01 (Embedding)    (None, 1, 4)                 1348      ['input_naics_01[0][0]']      \n",
      "                                                                                                  \n",
      " emb_naics_02 (Embedding)    (None, 1, 4)                 428       ['input_naics_02[0][0]']      \n",
      "                                                                                                  \n",
      " emb_naics_03 (Embedding)    (None, 1, 2)                 42        ['input_naics_03[0][0]']      \n",
      "                                                                                                  \n",
      " NAICS_orig (Flatten)        (None, 8)                    0         ['emb_naics_00[0][0]']        \n",
      "                                                                                                  \n",
      " rgrp_345 (Flatten)          (None, 4)                    0         ['emb_naics_01[0][0]']        \n",
      "                                                                                                  \n",
      " rgrp_106 (Flatten)          (None, 4)                    0         ['emb_naics_02[0][0]']        \n",
      "                                                                                                  \n",
      " rgrp_020 (Flatten)          (None, 2)                    0         ['emb_naics_03[0][0]']        \n",
      "                                                                                                  \n",
      " input_numeric (InputLayer)  [(None, 10)]                 0         []                            \n",
      "                                                                                                  \n",
      " input_concat (Concatenate)  (None, 28)                   0         ['NAICS_orig[0][0]',          \n",
      "                                                                     'rgrp_345[0][0]',            \n",
      "                                                                     'rgrp_106[0][0]',            \n",
      "                                                                     'rgrp_020[0][0]',            \n",
      "                                                                     'input_numeric[0][0]']       \n",
      "                                                                                                  \n",
      " layer_00 (Dense)            (None, 128)                  3712      ['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: 23211 (90.67 KB)\n",
      "Trainable params: 23211 (90.67 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "this_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7c48897f-b8d2-48d5-b15b-dc04ee954c90",
   "metadata": {},
   "outputs": [],
   "source": [
    "generator = CatInjectGenerator(X_train, y_train,\n",
    "                               categorical_columns=grp_features,\n",
    "                               batch_size = setup.nn_batch_size,\n",
    "                               injection_rate = 0.1,\n",
    "                               injection_value = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cbb85f7c-127b-4a78-bcd0-bc00cc6bed14",
   "metadata": {},
   "outputs": [],
   "source": [
    "callback = keras.callbacks.EarlyStopping(patience=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "31772016-3643-468b-8d65-c456168cad6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-29 17:30:43.753466: 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": [
      "13962/13964 [============================>.] - ETA: 0s - loss: 0.4631 - auc: 0.3350 - auc_1: 0.6976"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-29 17:34:53.619603: 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": [
      "13964/13964 [==============================] - 275s 20ms/step - loss: 0.4631 - auc: 0.3350 - auc_1: 0.6976 - val_loss: 0.4544 - val_auc: 0.3697 - val_auc_1: 0.7226\n",
      "Epoch 2/20\n",
      "13964/13964 [==============================] - 279s 20ms/step - loss: 0.4554 - auc: 0.3564 - auc_1: 0.7144 - val_loss: 0.4522 - val_auc: 0.3778 - val_auc_1: 0.7273\n",
      "Epoch 3/20\n",
      "13964/13964 [==============================] - 285s 20ms/step - loss: 0.4534 - auc: 0.3626 - auc_1: 0.7190 - val_loss: 0.4517 - val_auc: 0.3801 - val_auc_1: 0.7283\n",
      "Epoch 4/20\n",
      "13964/13964 [==============================] - 291s 21ms/step - loss: 0.4521 - auc: 0.3670 - auc_1: 0.7219 - val_loss: 0.4502 - val_auc: 0.3825 - val_auc_1: 0.7299\n",
      "Epoch 5/20\n",
      "13964/13964 [==============================] - 273s 20ms/step - loss: 0.4512 - auc: 0.3696 - auc_1: 0.7234 - val_loss: 0.4503 - val_auc: 0.3846 - val_auc_1: 0.7301\n",
      "Epoch 6/20\n",
      "13964/13964 [==============================] - 270s 19ms/step - loss: 0.4506 - auc: 0.3719 - auc_1: 0.7243 - val_loss: 0.4507 - val_auc: 0.3834 - val_auc_1: 0.7306\n",
      "Epoch 7/20\n",
      "13964/13964 [==============================] - 254s 18ms/step - loss: 0.4502 - auc: 0.3746 - auc_1: 0.7253 - val_loss: 0.4512 - val_auc: 0.3816 - val_auc_1: 0.7300\n"
     ]
    }
   ],
   "source": [
    "this_history = this_model.fit(generator,\n",
    "                              batch_size=setup.nn_batch_size,\n",
    "                              epochs = setup.nn_epochs,\n",
    "                              validation_data=([X_val[features_numeric]] + [X_val[f] for f in grp_features], \n",
    "                                               y_val),\n",
    "                              shuffle=True,\n",
    "                              callbacks=[callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "460c4b43-c673-4ee1-b070-d2d596ac4561",
   "metadata": {},
   "outputs": [],
   "source": [
    "this_history_df = sbnn_model.process_history(this_history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ad4783f2-8d0b-4850-a45a-3a4be8e63318",
   "metadata": {},
   "outputs": [],
   "source": [
    "this_history_df.to_csv(Path(setup.temp_path).joinpath('43_REPORT_fit_history.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "73e89efd-75a9-4b27-81a8-13e12d5482be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 26,
     "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": 27,
   "id": "2342a31f-4891-4834-851a-b90b8b729c3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 27,
     "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": 28,
   "id": "cee80637-cabf-4974-b476-08c5fba165ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 28,
     "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": 29,
   "id": "e184b0ca-1d31-4798-88a0-4e590b67062e",
   "metadata": {},
   "outputs": [],
   "source": [
    "this_model.save(Path(setup.temp_path).joinpath('43_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": 30,
   "id": "b78b9d61-2546-40a3-8217-51079af5a1cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   42/21735 [..............................] - ETA: 54s "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-29 18:02:49.727261: 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": [
      "21735/21735 [==============================] - 44s 2ms/step\n"
     ]
    }
   ],
   "source": [
    "all_predictions = this_model.predict([X[features_numeric]]+ [X[f] for f in grp_features])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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": 32,
   "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>0.320519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>0.320519</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              predict_prob    target\n",
       "predict_prob      1.000000  0.320519\n",
       "target            0.320519  1.000000"
      ]
     },
     "execution_count": 32,
     "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": 33,
   "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": 34,
   "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": 35,
   "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",
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       "\n",
       "    .dataframe tbody tr th {\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>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>0.452726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.26</td>\n",
       "      <td>0.452699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.22</td>\n",
       "      <td>0.450457</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    thresh        f1\n",
       "12    0.24  0.452726\n",
       "13    0.26  0.452699\n",
       "11    0.22  0.450457"
      ]
     },
     "execution_count": 35,
     "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": 36,
   "id": "c746b880-d781-4bc1-9df2-89211127c22a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.24"
      ]
     },
     "execution_count": 36,
     "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": 37,
   "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": 38,
   "id": "9f6ecdf4-ac01-4c39-8315-e8583daa337e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "predict_bin\n",
       "0    0.555037\n",
       "1    0.444963\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_predictions_df['predict_bin'].value_counts(normalize=True, dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "cbfed830-7ff8-40f6-a691-6b894b26d6bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_predictions_df.to_parquet(Path(setup.temp_path).joinpath('43_DATA_predictions.parquet'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb0ecbd2-abff-46d8-8c70-f7bccd363b69",
   "metadata": {},
   "source": [
    "## Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "be4ceefe-c7a8-45bd-8573-2daefe99a1c2",
   "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>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>0.635539</td>\n",
       "      <td>0.439588</td>\n",
       "      <td>0.318619</td>\n",
       "      <td>0.708636</td>\n",
       "      <td>0.363508</td>\n",
       "      <td>0.719030</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train</td>\n",
       "      <td>0.646276</td>\n",
       "      <td>0.452726</td>\n",
       "      <td>0.329496</td>\n",
       "      <td>0.723197</td>\n",
       "      <td>0.386168</td>\n",
       "      <td>0.734076</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>val</td>\n",
       "      <td>0.645182</td>\n",
       "      <td>0.451828</td>\n",
       "      <td>0.329756</td>\n",
       "      <td>0.717401</td>\n",
       "      <td>0.381759</td>\n",
       "      <td>0.730068</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test</td>\n",
       "      <td>0.641962</td>\n",
       "      <td>0.446329</td>\n",
       "      <td>0.325405</td>\n",
       "      <td>0.710276</td>\n",
       "      <td>0.378498</td>\n",
       "      <td>0.725597</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>test</td>\n",
       "      <td>0.624863</td>\n",
       "      <td>0.428553</td>\n",
       "      <td>0.307678</td>\n",
       "      <td>0.705857</td>\n",
       "      <td>0.332465</td>\n",
       "      <td>0.707481</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        0.635539  0.439588         0.318619      0.708636   \n",
       "1  train        0.646276  0.452726         0.329496      0.723197   \n",
       "2    val        0.645182  0.451828         0.329756      0.717401   \n",
       "0   test        0.641962  0.446329         0.325405      0.710276   \n",
       "1   test        0.624863  0.428553         0.307678      0.705857   \n",
       "\n",
       "   average_precision_score  roc_auc_score  dset_naics_holdout  \n",
       "0                 0.363508       0.719030                 NaN  \n",
       "1                 0.386168       0.734076                 NaN  \n",
       "2                 0.381759       0.730068                 NaN  \n",
       "0                 0.378498       0.725597                 0.0  \n",
       "1                 0.332465       0.707481                 1.0  "
      ]
     },
     "execution_count": 40,
     "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('43_REPORT_metrics.csv'), index=True)\n",
    "metrics_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52e382b5-e9dc-47a1-9bf4-6d0b6b4a0c61",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f69cf93-02e2-413b-8f2b-fcdb908057d3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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