34_downsample_size_loop_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": [
    "## Loop downsample size - data generator randomization, NAICS base\n",
    "Randomize training during fit (shuffled), loop downsampling training data.  Loop the downsample fraction.  (Fixed random injection at 10%"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e6fba8f-d564-466c-8e50-7773b095157f",
   "metadata": {},
   "source": [
    "Base NAICS only, and data 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\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": "e9d62b12-8bbf-4f4f-90ca-9318dccadab4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.ticker as ticker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5dedd82d-1901-4327-8b7f-f00026a84f18",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.callbacks import EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9f1e4f2e-0e45-4e28-8e9c-212e0bb51199",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sba_nn.sba_nn import sbnn_metrics, sbnn_model, sbnn_plot\n",
    "from sba_nn.sba_nn.sbnn_model import CatInjectGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2a50c5c6-be2c-43a5-9795-d9eabb8a5f5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "sbnn_plot.plot_defaults()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cbc00d2-bc83-42d9-83eb-65fcf306dccd",
   "metadata": {},
   "source": [
    "## Input Data"
   ]
  },
  {
   "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": "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": 11,
   "id": "6f0ce28e-a80c-415b-b068-5c4d49370da8",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(Path(setup.temp_path).joinpath('01_DATA_naics_max_encodings.pkl'), 'rb') as fin:\n",
    "    naics_max_levels= pickle.load(fin)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d4116da-d689-478a-9c5d-a330f231e615",
   "metadata": {},
   "source": [
    "##### Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": 13,
   "id": "ba637a7a-6d62-4e7d-b869-b72be58fd37a",
   "metadata": {},
   "outputs": [],
   "source": [
    "###### NAICS feature\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": 14,
   "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": 15,
   "id": "43bca011-a59d-4496-9f03-87f4e350a253",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = sba_loans[['dset', 'LoanNr_ChkDgt'] + features_numeric + features_naics].set_index('LoanNr_ChkDgt').sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "aa162d01-ed73-4ae3-832e-4aeea09b70f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training X: (446848, 11), 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": 17,
   "id": "b32b5fd3-6f6e-4aec-8e20-0e2b71a4814c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val X: (95604, 11), 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": 18,
   "id": "01b8ff96-159b-4283-b5fb-c4b5dbd277d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.20230592953308507\n"
     ]
    }
   ],
   "source": [
    "base_thresh = y_train.mean()\n",
    "print(base_thresh)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3db4ea1c-77b2-4721-b106-1e66fc502139",
   "metadata": {},
   "source": [
    "## Create, fit model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53be11f2-9c6d-47d7-b5b6-55ca8ed1a4fe",
   "metadata": {},
   "source": [
    "## Loop for different fits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2a9a48fe-9d07-4fec-af0f-3f08e691cff4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def gen_fit_loop(rate=0.1, injection_rate = 0.1):\n",
    "    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)\n",
    "    \n",
    "    # Downsample\n",
    "    train_samp, _ = train_test_split(X_train.index,\n",
    "                                train_size=rate,\n",
    "                                random_state=343)\n",
    "    X_train_ds = X_train.loc[train_samp, :]\n",
    "    y_train_ds = y_train.loc[train_samp]\n",
    "    \n",
    "    \n",
    "    generator = CatInjectGenerator(X_train_ds, y_train_ds,\n",
    "                               categorical_columns=features_naics,\n",
    "                               batch_size = setup.nn_batch_size,\n",
    "                               injection_rate = injection_rate,\n",
    "                               injection_value = 1)\n",
    "    callback = keras.callbacks.EarlyStopping(patience=3)\n",
    "    \n",
    "    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 features_naics], \n",
    "                                               y_val),\n",
    "                              shuffle=True,\n",
    "                             callbacks=[callback])\n",
    "    this_history_df = sbnn_model.process_history(this_history)\n",
    "    this_history_df['rate'] = rate\n",
    "    \n",
    "    # Predictions then metrics\n",
    "    this_predict = this_model.predict([X[features_numeric]]+ [X[f] for f in features_naics])\n",
    "    this_predict_df = pd.DataFrame(this_predict, 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')\n",
    "    \n",
    "    # Threshold tune\n",
    "    this_pred_train = this_predict_df[this_predict_df['dset'] == 'train']\n",
    "    thresh_tune_data = sbnn_metrics.get_f1_frame(this_pred_train['target'], \n",
    "                                        this_pred_train['predict_prob'])\n",
    "    thresh_tune_data.sort_values('f1', ascending=False, inplace=True)\n",
    "    best_thresh = thresh_tune_data['thresh'].iloc[0]\n",
    "    this_predict_df['predict_bin'] = sbnn_metrics.get_binary_predictions(this_predict_df['predict_prob'], \n",
    "                                                                            best_thresh)\n",
    "    \n",
    "    this_metrics_dset = this_predict_df.groupby('dset') \\\n",
    "        .apply(lambda x: sbnn_metrics.dset_metrics(x.target, x.predict_bin, x.predict_prob)) \\\n",
    "        .reset_index()\n",
    "    this_metrics_test = this_predict_df[this_predict_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",
    "    this_metrics = pd.concat([this_metrics_dset, this_metrics_test])\n",
    "    this_metrics['rate'] = rate\n",
    "    this_metrics['thresh'] = best_thresh\n",
    "    this_metrics['train_size'] = len(X_train_ds)\n",
    "    \n",
    "    return this_history_df, this_metrics\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "375ed31d-be98-458c-97ba-dc86cddacc18",
   "metadata": {},
   "source": [
    "## Run Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f2773b95-7041-41cc-ac49-4a5310138fe9",
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_rate_vals = [0.01, 0.05, 0.1, 0.2, 0.4, 0.7, 0.9, 0.95, 0.99]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "74f979e5-8aaa-4d0a-adea-70eac817d042",
   "metadata": {},
   "outputs": [],
   "source": [
    "history = pd.DataFrame()\n",
    "metrics = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9bdc1866-2381-4cde-a0ef-a1cd77133cba",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-25 07:27:09.795150: I metal_plugin/src/device/metal_device.cc:1154] Metal device set to: Apple M1\n",
      "2024-05-25 07:27:09.795180: I metal_plugin/src/device/metal_device.cc:296] systemMemory: 16.00 GB\n",
      "2024-05-25 07:27:09.795187: I metal_plugin/src/device/metal_device.cc:313] maxCacheSize: 5.33 GB\n",
      "2024-05-25 07:27:09.795230: 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",
      "2024-05-25 07:27:09.795251: 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",
      "2024-05-25 07:27:10.679486: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 07:27:14.929113: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 07:30:22.051440: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 07:31:19.060028: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 07:31:27.555123: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 07:33:34.944460: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 07:34:23.570333: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 07:34:39.501177: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 07:40:25.546221: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 07:41:16.548848: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 07:41:51.457149: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 07:48:24.732170: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 07:49:22.920931: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 07:50:30.954954: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 08:01:49.971323: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 08:02:40.663861: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 08:04:42.260570: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 08:33:30.778395: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 08:34:25.088672: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 08:37:07.960031: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 09:01:37.448327: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 09:02:31.221516: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 09:05:18.744342: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 09:39:54.914074: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 09:40:43.898857: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 09:43:04.621117: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 10:07:26.425923: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    }
   ],
   "source": [
    "%%capture\n",
    "for r in sample_rate_vals:\n",
    "    this_hist, this_met = gen_fit_loop(r)\n",
    "    history = pd.concat([history, this_hist])\n",
    "    metrics = pd.concat([metrics, this_met])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ad4783f2-8d0b-4850-a45a-3a4be8e63318",
   "metadata": {},
   "outputs": [],
   "source": [
    "history['epoch'] = history.groupby('rate').cumcount()\n",
    "history.to_csv(Path(setup.temp_path).joinpath('34_REPORT_loop_history.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b1854de9-c6e9-41d4-813a-4c62d43b2bc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics.to_csv(Path(setup.temp_path).joinpath('34_REPORT_loop_metrics.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d934b1b9-a617-4330-8f1e-f3b7c89b1e0f",
   "metadata": {},
   "outputs": [
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       "      <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",
       "      <th>rate</th>\n",
       "      <th>thresh</th>\n",
       "      <th>train_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test</td>\n",
       "      <td>0.573103</td>\n",
       "      <td>0.405601</td>\n",
       "      <td>0.282806</td>\n",
       "      <td>0.716868</td>\n",
       "      <td>0.315488</td>\n",
       "      <td>0.672187</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.18</td>\n",
       "      <td>4468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test</td>\n",
       "      <td>0.591605</td>\n",
       "      <td>0.423279</td>\n",
       "      <td>0.296796</td>\n",
       "      <td>0.737626</td>\n",
       "      <td>0.333983</td>\n",
       "      <td>0.693737</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.18</td>\n",
       "      <td>22342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test</td>\n",
       "      <td>0.616124</td>\n",
       "      <td>0.434722</td>\n",
       "      <td>0.310157</td>\n",
       "      <td>0.726500</td>\n",
       "      <td>0.346791</td>\n",
       "      <td>0.705166</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.20</td>\n",
       "      <td>44684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test</td>\n",
       "      <td>0.645751</td>\n",
       "      <td>0.439228</td>\n",
       "      <td>0.323736</td>\n",
       "      <td>0.682823</td>\n",
       "      <td>0.364643</td>\n",
       "      <td>0.714591</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.26</td>\n",
       "      <td>89369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test</td>\n",
       "      <td>0.649853</td>\n",
       "      <td>0.444765</td>\n",
       "      <td>0.328086</td>\n",
       "      <td>0.690240</td>\n",
       "      <td>0.373609</td>\n",
       "      <td>0.721353</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.22</td>\n",
       "      <td>178739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test</td>\n",
       "      <td>0.654688</td>\n",
       "      <td>0.445382</td>\n",
       "      <td>0.330564</td>\n",
       "      <td>0.682411</td>\n",
       "      <td>0.377877</td>\n",
       "      <td>0.724462</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.22</td>\n",
       "      <td>312793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test</td>\n",
       "      <td>0.648231</td>\n",
       "      <td>0.447084</td>\n",
       "      <td>0.328427</td>\n",
       "      <td>0.699974</td>\n",
       "      <td>0.379639</td>\n",
       "      <td>0.726398</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.26</td>\n",
       "      <td>402163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test</td>\n",
       "      <td>0.661019</td>\n",
       "      <td>0.447273</td>\n",
       "      <td>0.334431</td>\n",
       "      <td>0.675045</td>\n",
       "      <td>0.380609</td>\n",
       "      <td>0.726943</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.26</td>\n",
       "      <td>424505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test</td>\n",
       "      <td>0.657294</td>\n",
       "      <td>0.447776</td>\n",
       "      <td>0.332865</td>\n",
       "      <td>0.683853</td>\n",
       "      <td>0.380559</td>\n",
       "      <td>0.726971</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.22</td>\n",
       "      <td>442379</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   dset  accuracy_score  f1_score  precision_score  recall_score  \\\n",
       "0  test        0.573103  0.405601         0.282806      0.716868   \n",
       "0  test        0.591605  0.423279         0.296796      0.737626   \n",
       "0  test        0.616124  0.434722         0.310157      0.726500   \n",
       "0  test        0.645751  0.439228         0.323736      0.682823   \n",
       "0  test        0.649853  0.444765         0.328086      0.690240   \n",
       "0  test        0.654688  0.445382         0.330564      0.682411   \n",
       "0  test        0.648231  0.447084         0.328427      0.699974   \n",
       "0  test        0.661019  0.447273         0.334431      0.675045   \n",
       "0  test        0.657294  0.447776         0.332865      0.683853   \n",
       "\n",
       "   average_precision_score  roc_auc_score  dset_naics_holdout  rate  thresh  \\\n",
       "0                 0.315488       0.672187                 0.0  0.01    0.18   \n",
       "0                 0.333983       0.693737                 0.0  0.05    0.18   \n",
       "0                 0.346791       0.705166                 0.0  0.10    0.20   \n",
       "0                 0.364643       0.714591                 0.0  0.20    0.26   \n",
       "0                 0.373609       0.721353                 0.0  0.40    0.22   \n",
       "0                 0.377877       0.724462                 0.0  0.70    0.22   \n",
       "0                 0.379639       0.726398                 0.0  0.90    0.26   \n",
       "0                 0.380609       0.726943                 0.0  0.95    0.26   \n",
       "0                 0.380559       0.726971                 0.0  0.99    0.22   \n",
       "\n",
       "   train_size  \n",
       "0        4468  \n",
       "0       22342  \n",
       "0       44684  \n",
       "0       89369  \n",
       "0      178739  \n",
       "0      312793  \n",
       "0      402163  \n",
       "0      424505  \n",
       "0      442379  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics[metrics['dset_naics_holdout'] == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "73e89efd-75a9-4b27-81a8-13e12d5482be",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>dset</th>\n",
       "      <th>accuracy_score</th>\n",
       "      <th>f1_score</th>\n",
       "      <th>precision_score</th>\n",
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       "      <td>0.321821</td>\n",
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       "      <td>0.40</td>\n",
       "      <td>0.22</td>\n",
       "      <td>178739</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>test</td>\n",
       "      <td>0.640622</td>\n",
       "      <td>0.436554</td>\n",
       "      <td>0.317468</td>\n",
       "      <td>0.698612</td>\n",
       "      <td>0.350059</td>\n",
       "      <td>0.717207</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.22</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>test</td>\n",
       "      <td>0.637300</td>\n",
       "      <td>0.437557</td>\n",
       "      <td>0.316626</td>\n",
       "      <td>0.707951</td>\n",
       "      <td>0.351014</td>\n",
       "      <td>0.719131</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.26</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>test</td>\n",
       "      <td>0.653476</td>\n",
       "      <td>0.437581</td>\n",
       "      <td>0.323388</td>\n",
       "      <td>0.676442</td>\n",
       "      <td>0.353664</td>\n",
       "      <td>0.720219</td>\n",
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       "      <td>0.26</td>\n",
       "      <td>424505</td>\n",
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       "      <th>1</th>\n",
       "      <td>test</td>\n",
       "      <td>0.638117</td>\n",
       "      <td>0.437931</td>\n",
       "      <td>0.317122</td>\n",
       "      <td>0.707428</td>\n",
       "      <td>0.351993</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "   dset  accuracy_score  f1_score  precision_score  recall_score  \\\n",
       "1  test        0.519629  0.414037         0.273505      0.851619   \n",
       "1  test        0.628951  0.423740         0.306835      0.684560   \n",
       "1  test        0.587205  0.429574         0.296414      0.779960   \n",
       "1  test        0.620915  0.434539         0.309175      0.730907   \n",
       "1  test        0.651041  0.436524         0.321821      0.678275   \n",
       "1  test        0.640622  0.436554         0.317468      0.698612   \n",
       "1  test        0.637300  0.437557         0.316626      0.707951   \n",
       "1  test        0.653476  0.437581         0.323388      0.676442   \n",
       "1  test        0.638117  0.437931         0.317122      0.707428   \n",
       "\n",
       "   average_precision_score  roc_auc_score  dset_naics_holdout  rate  thresh  \\\n",
       "1                 0.315432       0.698339                 1.0  0.01    0.18   \n",
       "1                 0.315174       0.694951                 1.0  0.05    0.18   \n",
       "1                 0.340633       0.710439                 1.0  0.10    0.20   \n",
       "1                 0.344555       0.715300                 1.0  0.20    0.26   \n",
       "1                 0.346520       0.716550                 1.0  0.40    0.22   \n",
       "1                 0.350059       0.717207                 1.0  0.70    0.22   \n",
       "1                 0.351014       0.719131                 1.0  0.90    0.26   \n",
       "1                 0.353664       0.720219                 1.0  0.95    0.26   \n",
       "1                 0.351993       0.720161                 1.0  0.99    0.22   \n",
       "\n",
       "   train_size  \n",
       "1        4468  \n",
       "1       22342  \n",
       "1       44684  \n",
       "1       89369  \n",
       "1      178739  \n",
       "1      312793  \n",
       "1      402163  \n",
       "1      424505  \n",
       "1      442379  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics[metrics['dset_naics_holdout'] == 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0dae1f5-a6e2-46c3-bba7-f0966f93d3ed",
   "metadata": {},
   "source": [
    "## Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ae6c4e12-c72e-4f71-aaca-62f0704dca6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#metrics = pd.read_csv(Path(setup.temp_path).joinpath('34_REPORT_loop_metrics.csv'))\n",
    "#history = pd.read_csv(Path(setup.temp_path).joinpath('34_REPORT_loop_history.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b14d8c25-2623-40be-a84c-43c752f60fa7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'sba_nn.sba_nn.sbnn_plot' from '/Users/valeriecarey/Documents/projects/2024_05_blog_nn_rand/code/sba_nn/sba_nn/sbnn_plot.py'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importlib.reload(sbnn_plot)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7fba165-3d44-4a97-8227-03057298768b",
   "metadata": {},
   "source": [
    "##### Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5126ef4e-45a3-4a1c-9dba-37c59b8f75ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sbnn_plot.plot_metric_dependence(metrics, xformatter = ticker.PercentFormatter(1),\n",
    "                                      ylabel='PR-AUC',\n",
    "                            xlabel='downsample rate')\n",
    "fig.savefig(Path(setup.temp_path).joinpath('34_PLOT_average_precision.png'),\n",
    "            bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26e38578-60a3-4986-bd7f-fd76b992f135",
   "metadata": {},
   "source": [
    "##### History"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ffc7e367-f428-4f76-a517-4faa77cf7095",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'sba_nn.sba_nn.sbnn_plot' from '/Users/valeriecarey/Documents/projects/2024_05_blog_nn_rand/code/sba_nn/sba_nn/sbnn_plot.py'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importlib.reload(sbnn_plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2342a31f-4891-4834-851a-b90b8b729c3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sbnn_plot.plot_history_group(history,\n",
    "                                  col_format_func = lambda x:f'{float(x):.1%}',\n",
    "                                  ylabel = 'PR-AUC (validation)',\n",
    "                                  leg_title='down rate')\n",
    "fig.savefig(Path(setup.temp_path).joinpath('34_PLOT_hostory_val_auc.png'),\n",
    "            bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9e92ea2f-378d-47a1-b067-2ec715935c9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sbnn_plot.plot_history_group(history,'val_loss',\n",
    "                                  col_format_func = lambda x:f'{float(x):.1%}',\n",
    "                                  ylabel = 'Loss (validation)',\n",
    "                                  leg_title='down rate')\n",
    "fig.savefig(Path(setup.temp_path).joinpath('34_PLOT_hostory_val_loss.png'),\n",
    "            bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0c6acda4-3326-4ece-9ae9-8fc784e9475f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sbnn_plot.plot_epoch_agg(history,\n",
    "                               xformatter = ticker.PercentFormatter(1),\n",
    "                               ylabel='# Epochs',\n",
    "                               xlabel='downsample rate')\n",
    "fig.savefig(Path(setup.temp_path).joinpath('34_PLOT_hostory_epochs.png'),\n",
    "            bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5334471-c99c-44c5-9e2b-4c1b6e602b4a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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