32_downsample_size_loop_rand.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 - fixed randomization, NAICS base\n",
    "Randomize training before model, 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 4 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": "edd1164e-3ceb-4e3a-ad25-79bb9cf19a2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 6,
   "id": "e9d62b12-8bbf-4f4f-90ca-9318dccadab4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.ticker as ticker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5dedd82d-1901-4327-8b7f-f00026a84f18",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.callbacks import EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 9,
   "id": "2a50c5c6-be2c-43a5-9795-d9eabb8a5f5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "sbnn_plot.plot_defaults()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d333dac-3cc2-4d62-8e70-c4663a79199d",
   "metadata": {},
   "source": [
    "## Input Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 11,
   "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": 12,
   "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": 12,
     "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": "f7e5fd3f-ee27-482b-a0ae-eb90d023d62b",
   "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": [
    "# NAICS feature - just base\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": 15,
   "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": "8b208e70-4f1f-4397-9fd9-f409a8b35bfd",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Data Adjustment\n",
    "Inject \"1\" values into train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9813c1ba-0668-40f0-82fa-f04d3b514e3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, n in enumerate(features_naics):\n",
    "    adj_index, _ = train_test_split(sba_loans[sba_loans['dset'] == 'train'].index, \n",
    "                                    train_size=0.10, random_state=645 + i)\n",
    "    sba_loans.loc[adj_index, n] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "61e7b587-cdf0-4074-810b-8462bb12d822",
   "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>NAICS</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>test</th>\n",
       "      <td>95552</td>\n",
       "      <td>57496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train</th>\n",
       "      <td>402164</td>\n",
       "      <td>44684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>val</th>\n",
       "      <td>95593</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "NAICS   False  True \n",
       "dset                \n",
       "test    95552  57496\n",
       "train  402164  44684\n",
       "val     95593     11"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show unseen counts - expect some in train now!\n",
    "pd.crosstab(sba_loans['dset'], sba_loans['NAICS'] == 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8f892e5-64c1-4ce4-a6d5-09136eb19bfd",
   "metadata": {},
   "source": [
    "##### Datasets for train, validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 19,
   "id": "aa162d01-ed73-4ae3-832e-4aeea09b70f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training X: (446848, 10), 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": 20,
   "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": 21,
   "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": "code",
   "execution_count": 22,
   "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": 23,
   "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": "53be11f2-9c6d-47d7-b5b6-55ca8ed1a4fe",
   "metadata": {},
   "source": [
    "## Loop for different fits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2a9a48fe-9d07-4fec-af0f-3f08e691cff4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def gen_fit_loop(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",
    "    X_naics_train_ds = [xn.loc[train_samp, :] for xn in X_naics_train]\n",
    "    y_train_ds = y_train.loc[train_samp]\n",
    "    \n",
    "    callback = keras.callbacks.EarlyStopping(patience=3)\n",
    "    \n",
    "    # Fit\n",
    "    this_history = this_model.fit([X_train_ds] + X_naics_train_ds, y_train_ds,\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],\n",
    "                                 shuffle=True)\n",
    "    \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.drop(columns='dset')] +\n",
    "                                     [Xn.drop(columns='dset') for Xn in X_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",
    "    \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": 25,
   "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": 26,
   "id": "74f979e5-8aaa-4d0a-adea-70eac817d042",
   "metadata": {},
   "outputs": [],
   "source": [
    "history = pd.DataFrame()\n",
    "metrics = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9bdc1866-2381-4cde-a0ef-a1cd77133cba",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-25 23:43:16.035114: I metal_plugin/src/device/metal_device.cc:1154] Metal device set to: Apple M1\n",
      "2024-05-25 23:43:16.035173: I metal_plugin/src/device/metal_device.cc:296] systemMemory: 16.00 GB\n",
      "2024-05-25 23:43:16.035184: I metal_plugin/src/device/metal_device.cc:313] maxCacheSize: 5.33 GB\n",
      "2024-05-25 23:43:16.035555: 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 23:43:16.035616: 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 23:43:17.826534: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 23:43:24.899515: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 23:48:40.084235: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 23:50:51.140451: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 23:51:10.150851: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 23:56:13.483518: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 23:57:34.736946: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 23:57:55.919027: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-26 00:03:58.702431: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-26 00:05:23.210288: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-26 00:06:06.219795: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-26 00:18:59.625733: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-26 00:20:28.929015: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-26 00:22:17.578223: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-26 00:33:38.908491: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-26 00:34:57.046125: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-26 00:37:49.785371: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-26 01:01:28.645295: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-26 01:02:22.883404: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-26 01:05:00.109393: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-26 01:36:16.193637: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-26 01:36:58.881295: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-26 01:39:11.129159: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-26 02:01:39.452001: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-26 02:02:20.846776: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-26 02:04:37.812439: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-26 02:43:11.606002: 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": 28,
   "id": "ad4783f2-8d0b-4850-a45a-3a4be8e63318",
   "metadata": {},
   "outputs": [],
   "source": [
    "history['epoch'] = history.groupby('rate').cumcount()\n",
    "history.to_csv(Path(setup.temp_path).joinpath('32_REPORT_loop_history.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b1854de9-c6e9-41d4-813a-4c62d43b2bc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics.to_csv(Path(setup.temp_path).joinpath('32_REPORT_loop_metrics.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d934b1b9-a617-4330-8f1e-f3b7c89b1e0f",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.328741</td>\n",
       "      <td>0.693124</td>\n",
       "      <td>0.375028</td>\n",
       "      <td>0.723840</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.22</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test</td>\n",
       "      <td>0.664525</td>\n",
       "      <td>0.446157</td>\n",
       "      <td>0.335673</td>\n",
       "      <td>0.665053</td>\n",
       "      <td>0.378378</td>\n",
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       "      <td>0.446424</td>\n",
       "      <td>0.330118</td>\n",
       "      <td>0.689261</td>\n",
       "      <td>0.379508</td>\n",
       "      <td>0.726332</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   dset  accuracy_score  f1_score  precision_score  recall_score  \\\n",
       "0  test        0.574066  0.400671         0.280539      0.700747   \n",
       "0  test        0.609395  0.420068         0.300761      0.696266   \n",
       "0  test        0.637787  0.435054         0.318439      0.686428   \n",
       "0  test        0.644338  0.437541         0.322344      0.680865   \n",
       "0  test        0.627301  0.442416         0.317813      0.727736   \n",
       "0  test        0.650094  0.445965         0.328741      0.693124   \n",
       "0  test        0.664525  0.446157         0.335673      0.665053   \n",
       "0  test        0.660004  0.446270         0.333486      0.674324   \n",
       "0  test        0.652689  0.446424         0.330118      0.689261   \n",
       "\n",
       "   average_precision_score  roc_auc_score  dset_naics_holdout  rate  thresh  \n",
       "0                 0.310532       0.664645                 0.0  0.01    0.18  \n",
       "0                 0.329213       0.689677                 0.0  0.05    0.20  \n",
       "0                 0.349222       0.706472                 0.0  0.10    0.20  \n",
       "0                 0.364151       0.713179                 0.0  0.20    0.20  \n",
       "0                 0.372027       0.720738                 0.0  0.40    0.18  \n",
       "0                 0.375028       0.723840                 0.0  0.70    0.22  \n",
       "0                 0.378378       0.725302                 0.0  0.90    0.22  \n",
       "0                 0.378691       0.725897                 0.0  0.95    0.22  \n",
       "0                 0.379508       0.726332                 0.0  0.99    0.24  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics[metrics['dset_naics_holdout'] == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "73e89efd-75a9-4b27-81a8-13e12d5482be",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <td>0.698874</td>\n",
       "      <td>0.346668</td>\n",
       "      <td>0.715158</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>test</td>\n",
       "      <td>0.653563</td>\n",
       "      <td>0.436178</td>\n",
       "      <td>0.322775</td>\n",
       "      <td>0.672427</td>\n",
       "      <td>0.350150</td>\n",
       "      <td>0.718330</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>test</td>\n",
       "      <td>0.649249</td>\n",
       "      <td>0.437031</td>\n",
       "      <td>0.321279</td>\n",
       "      <td>0.683163</td>\n",
       "      <td>0.352665</td>\n",
       "      <td>0.719609</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>test</td>\n",
       "      <td>0.656451</td>\n",
       "      <td>0.436153</td>\n",
       "      <td>0.324071</td>\n",
       "      <td>0.666754</td>\n",
       "      <td>0.348434</td>\n",
       "      <td>0.718413</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>test</td>\n",
       "      <td>0.642570</td>\n",
       "      <td>0.434301</td>\n",
       "      <td>0.317195</td>\n",
       "      <td>0.688487</td>\n",
       "      <td>0.351545</td>\n",
       "      <td>0.717933</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   dset  accuracy_score  f1_score  precision_score  recall_score  \\\n",
       "1  test        0.547842  0.422166         0.283209      0.828838   \n",
       "1  test        0.587936  0.428026         0.295851      0.773675   \n",
       "1  test        0.638500  0.427639         0.312384      0.677664   \n",
       "1  test        0.637700  0.434655         0.315410      0.698874   \n",
       "1  test        0.620810  0.434119         0.308937      0.729859   \n",
       "1  test        0.653563  0.436178         0.322775      0.672427   \n",
       "1  test        0.649249  0.437031         0.321279      0.683163   \n",
       "1  test        0.656451  0.436153         0.324071      0.666754   \n",
       "1  test        0.642570  0.434301         0.317195      0.688487   \n",
       "\n",
       "   average_precision_score  roc_auc_score  dset_naics_holdout  rate  thresh  \n",
       "1                 0.300960       0.692946                 1.0  0.01    0.18  \n",
       "1                 0.318160       0.699795                 1.0  0.05    0.20  \n",
       "1                 0.335934       0.707253                 1.0  0.10    0.20  \n",
       "1                 0.346668       0.715158                 1.0  0.20    0.20  \n",
       "1                 0.345497       0.715704                 1.0  0.40    0.18  \n",
       "1                 0.350150       0.718330                 1.0  0.70    0.22  \n",
       "1                 0.352665       0.719609                 1.0  0.90    0.22  \n",
       "1                 0.348434       0.718413                 1.0  0.95    0.22  \n",
       "1                 0.351545       0.717933                 1.0  0.99    0.24  "
      ]
     },
     "execution_count": 31,
     "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": 32,
   "id": "ae6c4e12-c72e-4f71-aaca-62f0704dca6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#metrics = pd.read_csv(Path(setup.temp_path).joinpath('32_REPORT_loop_metrics.csv'))\n",
    "#history = pd.read_csv(Path(setup.temp_path).joinpath('32_REPORT_loop_history.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": 33,
     "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": 34,
   "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('32_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": 35,
   "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": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importlib.reload(sbnn_plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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('32_PLOT_hostory_val_auc.png'),\n",
    "            bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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('32_PLOT_hostory_val_loss.png'),\n",
    "            bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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('32_PLOT_hostory_epochs.png'),\n",
    "            bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5334471-c99c-44c5-9e2b-4c1b6e602b4a",
   "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
}