Metal Machining/1.C_anomaly-results.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Anomaly Detection Results\n",
    "> We've trained the variational autoencoders, and in this post, we see how the models perform in anomaly detection. We check both the input and latent space for anomaly detection effectiveness. (notebook originally featured at [tvhahn.com](https://www.tvhahn.com/), official GitHub repo: https://github.com/tvhahn/ml-tool-wear)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the [last post](https://www.tvhahn.com/posts/building-vae/) we built and trained a bunch of variational autoencoders to reconstruct milling machine signals. This is shown by steps 1 and 2 in the figure below. In this post, we'll be demonstrating the last step in the random search loop by checking a trained VAE model for its anomaly detection performance (step 3).\n",
    "\n",
    "The anomaly detection is done using both the reconstruction error (input space anomaly detection) and measuring the difference in KL-divergence between samples (latent space anomaly detection). We'll see how this is done, and also dive into the results (plus pretty charts). Finally, I'll suggest some potential areas for further exploration. \n",
    "\n",
    "<div style=\"text-align: center; \">\n",
    "<figure>\n",
    "  <img src=\"images/vae_training_step3.jpg\" alt=\"random search training process\" style=\"background:none; border:none; box-shadow:none; text-align:center\" width=\"800px\"/>\n",
    "  <div style=\"text-align: left; \">\n",
    "  <figcaption style=\"color:grey; font-size:smaller\">The random search training process has three steps. First, randomly select the hyperparameters. Second, train the VAE with these parameters. Third, check the anomaly detection performance of the trained VAE. In this post, we'll be looking at the third step. (Image from author)</figcaption>\n",
    "  </div>\n",
    "</figure>\n",
    "</div>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup Notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The notebook can be run with google colab. Alternatively, clone the repo and run on your local machine. You'll need python 3.6+ with the following packages in your local environment:\n",
    "\n",
    "* Numpy\n",
    "* SciPy\n",
    "* Pandas\n",
    "* Matplotlib\n",
    "* Seaborn\n",
    "* h5py\n",
    "* scikit-learn\n",
    "\n",
    "First, we will load all the neccessary packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.io as sio # for reading matlab files\n",
    "import pathlib\n",
    "from pathlib import Path\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os\n",
    "import zipfile\n",
    "import h5py\n",
    "\n",
    "from sklearn.metrics import (\n",
    "    confusion_matrix,\n",
    "    roc_auc_score,\n",
    "    roc_curve,\n",
    "    precision_recall_curve,\n",
    "    auc,\n",
    ")\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.utils import shuffle\n",
    "\n",
    "from IPython.display import clear_output, display # clear the output if needed\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the appropriate working folders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# move into the root directory of 'Manufacturing-Data-Science-with-Python'\n",
    "cwd = Path.cwd() # current working directory\n",
    "os.chdir('../')\n",
    "root_dir = Path.cwd() # set the root directory as a Pathlib path\n",
    "\n",
    "folder_raw_data = root_dir / 'Data Sets/milling_uc_berkeley/raw/' # raw data folder that holds the .zip .mat files for milling data\n",
    "folder_processed_data = root_dir / 'Data Sets/milling_uc_berkeley/processed/' # processed data folder\n",
    "working_folder = root_dir / 'Metal Machining' # working folder\n",
    "\n",
    "folder_models = Path('models/') # folder for saved models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now need to prepare the notebook by downloading the milling data file and other important files. This needs to be done if you are running in google colab. If the repository has been cloned from github, then there is no need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# if the the raw data folder does not exist, then you are likely\n",
    "# in a google colab environment. In that case, we will create the \n",
    "# raw data and processed data folders and download the appropriate\n",
    "# files\n",
    "if folder_raw_data.exists() == False:\n",
    "\n",
    "    pathlib.Path(folder_raw_data).mkdir(parents=True, exist_ok=True)\n",
    "    os.chdir(folder_raw_data)\n",
    "    !wget 'https://ti.arc.nasa.gov/m/project/prognostic-repository/mill.zip'\n",
    "    \n",
    "os.chdir(cwd) # change back to root directory\n",
    "\n",
    "if folder_processed_data.exists() == False:\n",
    "\n",
    "    pathlib.Path(folder_processed_data).mkdir(parents=True, exist_ok=True)\n",
    "    os.chdir(folder_processed_data)\n",
    "    !wget 'https://raw.githubusercontent.com/tvhahn/Manufacturing-Data-Science-with-Python/master/Data%20Sets/milling_uc_berkeley/processed/labels_with_tool_class.csv'\n",
    "    !wget 'https://raw.githubusercontent.com/tvhahn/Manufacturing-Data-Science-with-Python/master/Data%20Sets/milling_uc_berkeley/processed/data_processed.zip'\n",
    "    \n",
    "    \n",
    "    os.chdir(cwd)\n",
    "    \n",
    "    # download important python files into the main directory\n",
    "    !wget 'https://raw.githubusercontent.com/tvhahn/ml-tool-wear/master/data_prep.py'\n",
    "    !wget 'https://raw.githubusercontent.com/tvhahn/ml-tool-wear/master/tcn.py'\n",
    "    !wget 'https://raw.githubusercontent.com/tvhahn/ml-tool-wear/master/threshold.py'\n",
    "\n",
    "if folder_models.exists() == False:\n",
    "\n",
    "    pathlib.Path(folder_models / 'best_models').mkdir(parents=True, exist_ok=True)\n",
    "    pathlib.Path(folder_models / 'saved_models').mkdir(parents=True, exist_ok=True)\n",
    "    os.chdir(folder_models / 'best_models')\n",
    "    !wget 'https://github.com/tvhahn/ml-tool-wear/raw/master/models/best_models/best_models.zip'\n",
    "    \n",
    "    os.chdir(cwd)    \n",
    "\n",
    "# extract mill.mat from the zip file\n",
    "with zipfile.ZipFile(folder_raw_data / 'mill.zip', 'r') as zip_ref:\n",
    "    zip_ref.extractall(folder_raw_data)\n",
    "    \n",
    "# extract best models from the zip file\n",
    "with zipfile.ZipFile(folder_models / 'best_models' / 'best_models.zip', 'r') as zip_ref:\n",
    "    zip_ref.extractall(folder_models / 'best_models')\n",
    "    \n",
    "# extract the processed hdf5 files from the zip file\n",
    "with zipfile.ZipFile(folder_processed_data / 'data_processed.zip', 'r') as zip_ref:\n",
    "    zip_ref.extractall(folder_processed_data)\n",
    "    \n",
    "# import those packages that we just downloaded\n",
    "import data_prep\n",
    "import threshold\n",
    "from tcn import TCN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Background\n",
    "\n",
    "## Input Space Anomaly Detection\n",
    "Our variational autoencoders have been trained on \"healthy\" tool wear data. As such, if we feed the trained VAEs data that is unhealthy, or just abnormal, we should generate a large reconstruction error (see my [previous post](https://www.tvhahn.com/posts/anomaly-detection-with-autoencoders/) for more details). A threshold can be set on this reconstruction error, whereby data producing a reconstruction error above the threshold is considered an anomaly. This is input space anomaly detection.\n",
    "\n",
    "We'll measure the reconstruction error using mean-squared-error (MSE). Because the reconstruction is of all six signals, we can calculate the MSE for each individual signal (`mse` function), and for all six signals combined (`mse_total` function). Here is what these two functions look like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mse(X, recon):\n",
    "    \"\"\"Calculate MSE for images in X_val and recon_val\"\"\"\n",
    "\n",
    "    # need to calculate mean across the rows, and then across the columns\n",
    "    return np.mean(\n",
    "        np.square(X.astype(\"float32\") - recon.astype(\"float32\")), axis=1\n",
    "    )\n",
    "\n",
    "\n",
    "def mse_total(X, recon):\n",
    "    \"\"\"Calculate MSE for images in X_val and recon_val\"\"\"\n",
    "\n",
    "    # need to calculate mean across the rows, and then across the columns\n",
    "    return np.mean(\n",
    "        np.mean(\n",
    "            np.square(X.astype(\"float32\") - recon.astype(\"float32\")), axis=1\n",
    "        ),\n",
    "        axis=1,\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The reconstruction values (`recon`) are produced by feeding the windowed cut-signals into the trained VAE, like this: `recon = model.predict(X, batch_size=64)`.\n",
    "\n",
    "Reconstruction probabilities is another method of input space anomaly detection (sort of?). An and Cho introduced the method in their 2015 paper.\n",
    "\n",
    "I’m not as familiar with the reconstruction probability method, but James McCaffrey has a good explanation (and implementation in PyTorch) on his blog. He says: “The idea of reconstruction probability anomaly detection is to compute a second probability distribution and then use it to calculate the likelihood that an input item came from the distribution. Data items with a low reconstruction probability are not likely to have come from the distribution, and so are anomalous in some way.”\n",
    "\n",
    "We will not be using reconstruction probabilities for anomaly detection, but it would be interesting to implement. Maybe you can give it a try?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Latent Space Anomaly Detection\n",
    "Anomaly detection can also be performed using mean and standard deviation codings in the latents space. Here are two methods:\n",
    "\n",
    "\n",
    "1. Most naively, you can measure the difference in mean or standard deviation encodings, through an average, and set some threshold on these values. This is very similar to the input space anomaly detection, except instead of reconstruction error, you’re measuring the “error” in the codings that are produced by the encoder. This method doesn’t take advantage of the expressiveness of the VAE, which is why it’s not used often.\n",
    "2. You can use KL-divergence to measure the relative difference in entropy between data samples. A threshold can be set on this relative difference indicating when a data sample is anomalous. This is the method that we’ll be using.\n",
    "    \n",
    "Adam Lineberry has a good example of the KL-divergence anomaly detection, implemented in PyTorch, on [his blog](http://adamlineberry.ai/vae-series/vae-code-experiments). Here is the KL-divergence function (implemented with Keras and TensorFlow) that we will be using:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def kl_divergence(mu, log_var):\n",
    "    return -0.5 * K.sum(1 + log_var - K.exp(log_var) - K.square(mu), axis=-1,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "where `mu` is the mean ($\\boldsymbol{\\mu}$) and the `log_var` is the logarithm of the variance ($\\log{\\boldsymbol{\\sigma}^2}$). The log of the variance is used for the training of the VAE as it is more stable than just the variance.\n",
    "\n",
    "To generate the KL-divergence scores we use the following function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_kls_scores(encoder, X,):\n",
    "    \"\"\"Get the KL-divergence scores across from a trained VAE encoder.\n",
    " \n",
    "    Parameters\n",
    "    ===========\n",
    "    encoder : TenorFlow model\n",
    "        Encoder of the VAE\n",
    "    \n",
    "    X : tensor\n",
    "        data that KL-div. scores will be calculated from\n",
    "\n",
    "    Returns\n",
    "    ===========\n",
    "    kls : numpy array\n",
    "        Returns the KL-divergence scores as a numpy array\n",
    "    \"\"\"\n",
    "    codings_mean, codings_log_var, codings = encoder.predict(X, batch_size=64)\n",
    "    kls = np.array(kl_divergence(codings_mean, codings_log_var))\n",
    "    return kls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation Metrics\n",
    "After you've calculated your reconstruction errors or KL-divergence scores, you are ready to set a decision-threshold. Any values above the threshold will be anomalous (likely a worn tool) and any values below will be normal (a healthy tool).\n",
    "\n",
    "\n",
    "To fully evaluate a model's performance you have to look at a range of potential decision-thresholds. Two common approaches are the [reciever operating characteristic](https://en.wikipedia.org/wiki/Receiver_operating_characteristic) (ROC) and the [precision-recall](https://en.wikipedia.org/wiki/Precision_and_recall) curve. The ROC curve plots the true positive rate versus the false positive rate. The precision-recall curve, like the name implies, plots the precision versus the recall. Measuring the area under the curve then provides a good method for comparing different models.\n",
    "\n",
    "We'll be using the precision-recall area-under-curve (PR-AUC) to evaulate model performance as it peforms well on imbalanced data. Below is a figure explaining what precision and recall is and how the precision-recall curve is built.\n",
    "\n",
    "<div style=\"text-align: center; \">\n",
    "<figure>\n",
    "  <img src=\"images/thresholds.png\" alt=\"setting thresholds\" style=\"background:none; border:none; box-shadow:none; text-align:center\" width=\"1000px\"/>\n",
    "  <div style=\"text-align: center; \">\n",
    "  <figcaption style=\"color:grey; font-size:smaller\">The precision-recall curve is created by varying the decision-threshold accross the anomaly detection model. (Image from author)</figcaption>\n",
    "  </div>\n",
    "</figure>\n",
    "</div>\n",
    "\n",
    "\n",
    "Ultimately, the evaluation of a model's performance and the setting of its decision threshold is application specific. For example, a manufacturer may prioritize the prevention of tool failures over frequent tool changes. Thus, they may set a low threshold to detect more tool failures (higher recall), but at the cost of having more false-positives (lower precision)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Nitty-Gritty Details\n",
    "There are a number of functions that are used to calculate the ROC-AUC and the PR-AUC scores. We'll cover them here, at a high-level.\n",
    "\n",
    "First, we have the `pr_auc_kl` function. It takes the encoder, the data (along with the labels), and calculates the precision and recall scores based on latent space anomaly detection. The function also calculates a rough example threshold (done using a [single data-point to calculate the ROC score](https://stats.stackexchange.com/a/372977https://stats.stackexchange.com/a/372977))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pr_auc_kl(\n",
    "    encoder,\n",
    "    X,\n",
    "    y,\n",
    "    grid_iterations=10,\n",
    "    date_model_ran=\"date\",\n",
    "    model_name=\"encoder\",\n",
    "    class_to_remove=[2],\n",
    "):\n",
    "\n",
    "    \"\"\"\n",
    "    Function that gets the precision and recall scores for the encoder \n",
    "    \"\"\"\n",
    "\n",
    "    codings_mean, codings_log_var, codings = encoder.predict(X, batch_size=64)\n",
    "\n",
    "    kls = np.array(kl_divergence(codings_mean, codings_log_var))\n",
    "    kls = np.reshape(kls, (-1, 1))\n",
    "\n",
    "    lower_bound = np.min(kls)\n",
    "    upper_bound = np.max(kls)\n",
    "\n",
    "    recon_check = threshold.SelectThreshold(\n",
    "        encoder,\n",
    "        X,\n",
    "        y,\n",
    "        X,\n",
    "        X,\n",
    "        y,\n",
    "        X,\n",
    "        class_to_remove,\n",
    "        class_names=[\"0\", \"1\", \"2\"],\n",
    "        model_name=model_name,\n",
    "        date_time=date_model_ran,\n",
    "    )\n",
    "\n",
    "    (\n",
    "        example_threshold,\n",
    "        best_roc_score,\n",
    "        precisions,\n",
    "        recalls,\n",
    "        tprs,\n",
    "        fprs,\n",
    "    ) = recon_check.threshold_grid_search(\n",
    "        y, lower_bound, upper_bound, kls, grid_iterations,\n",
    "    )\n",
    "\n",
    "    pr_auc_score_train = auc(recalls, precisions)\n",
    "    roc_auc_score_train = auc(fprs, tprs)\n",
    "    \n",
    "    return (\n",
    "        pr_auc_score_train,\n",
    "        roc_auc_score_train,\n",
    "        recalls,\n",
    "        precisions,\n",
    "        tprs,\n",
    "        fprs,\n",
    "        example_threshold,\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One thing to note: the above `pr_auc_kl` function creates a `SelectThreshold` class. The `threshold_grid_search` method can then be used to peform a grid search over the all the KL-divergence scores, outputting both the recalls and precisions. You'll have to see the accompanying `threshold.py` to fully understand what is going on.\n",
    "\n",
    "The next function I want to highlight is the `get_latent_input_anomaly_scores` function. As the name implies, this function calculates the input and latent space anomaly detection scores (ROC/PR-AUC). The function relies heavily on the `SelectThreshold` class for the input space anomaly detection .\n",
    "\n",
    "Here's the `get_latent_input_anomaly_scores` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_latent_input_anomaly_scores(\n",
    "    model_name,\n",
    "    saved_model_dir,\n",
    "    class_to_remove,\n",
    "    X_train,\n",
    "    y_train,\n",
    "    X_val,\n",
    "    y_val,\n",
    "    grid_iterations,\n",
    "    search_iterations,\n",
    "    X_train_slim,\n",
    "    X_val_slim\n",
    "):\n",
    "    \"\"\"\n",
    "    Function that gets does an iterative grid search to get the precision and recall \n",
    "    scores for the anomaly detection model in the input and latent space\n",
    "    \n",
    "    Note, because the output from the encoder is partially stochastic, we run a number\n",
    "    of 'search iterations' and take the mean afterwards.\n",
    "    \"\"\"\n",
    "    # model date and name\n",
    "    date_model_ran = model_name.split(\"_\")[0]\n",
    "\n",
    "    #!#!# INPUT SPACE ANOMALY DETECTION #!#!# \n",
    "    # load model\n",
    "    loaded_json = open(\n",
    "        r\"{}/{}/model.json\".format(saved_model_dir, model_name), \"r\"\n",
    "    ).read()\n",
    "    \n",
    "    # need to load custom TCN layer function\n",
    "    beta_vae_model = model_from_json(\n",
    "        loaded_json, custom_objects={\"TCN\": TCN, \"Sampling\": Sampling}\n",
    "    )\n",
    "\n",
    "    # restore weights\n",
    "    beta_vae_model.load_weights(r\"{}/{}/weights.h5\".format(saved_model_dir, model_name))\n",
    "\n",
    "    # instantiate class\n",
    "    recon_check = threshold.SelectThreshold(\n",
    "        beta_vae_model,\n",
    "        X_train,\n",
    "        y_train,\n",
    "        X_train_slim,\n",
    "        X_val,\n",
    "        y_val,\n",
    "        X_val_slim,\n",
    "        class_to_remove,\n",
    "        class_names=[\"0\", \"1\", \"2\"],\n",
    "        model_name=model_name,\n",
    "        date_time=date_model_ran,\n",
    "    )\n",
    "\n",
    "    # peform the grid search, and put the results in\n",
    "    # a pandas dataframe\n",
    "    df = recon_check.compare_error_method(\n",
    "        show_results=False,\n",
    "        grid_iterations=grid_iterations,\n",
    "        search_iterations=search_iterations,\n",
    "    )\n",
    "\n",
    "    #!#!# LATENT SPACE ANOMALY DETECTION #!#!# \n",
    "    # load encoder\n",
    "    loaded_json = open(\n",
    "        r\"{}/{}/model.json\".format(saved_model_dir, date_model_ran + \"_encoder\"), \"r\"\n",
    "    ).read()\n",
    "    \n",
    "    encoder = model_from_json(\n",
    "        loaded_json, custom_objects={\"TCN\": TCN, \"Sampling\": Sampling}\n",
    "    )\n",
    "\n",
    "    # restore weights\n",
    "    encoder.load_weights(\n",
    "        r\"{}/{}/weights.h5\".format(saved_model_dir, date_model_ran + \"_encoder\")\n",
    "    )\n",
    "\n",
    "    # create empty lists to store results\n",
    "    prauc_train_kls = []\n",
    "    prauc_val_kls = []\n",
    "    roc_train_kls = []\n",
    "    roc_val_kls = []\n",
    "    recalls_array = []\n",
    "    precisions_array = []\n",
    "    tprs_array = []\n",
    "    fprs_array = []\n",
    "\n",
    "    for i in range(search_iterations):\n",
    "        print(\"search_iter:\", i)\n",
    "        # look through train data\n",
    "        (\n",
    "            pr_auc_score_train,\n",
    "            roc_auc_score_train,\n",
    "            recalls,\n",
    "            precisions,\n",
    "            tprs,\n",
    "            fprs,\n",
    "            example_threshold_kl,\n",
    "        ) = pr_auc_kl(\n",
    "            encoder,\n",
    "            X_train,\n",
    "            y_train,\n",
    "            grid_iterations=grid_iterations,\n",
    "            date_model_ran=\"date\",\n",
    "            model_name=\"encoder\",\n",
    "            class_to_remove=class_to_remove,\n",
    "        )\n",
    "\n",
    "        prauc_train_kls.append(pr_auc_score_train)\n",
    "        roc_train_kls.append(roc_auc_score_train)\n",
    "\n",
    "        # look through val data\n",
    "        (\n",
    "            pr_auc_score_val,\n",
    "            roc_auc_score_val,\n",
    "            recalls,\n",
    "            precisions,\n",
    "            tprs,\n",
    "            fprs,\n",
    "            example_threshold,\n",
    "        ) = pr_auc_kl(\n",
    "            encoder,\n",
    "            X_val,\n",
    "            y_val,\n",
    "            grid_iterations=grid_iterations,\n",
    "            date_model_ran=\"date\",\n",
    "            model_name=\"encoder\",\n",
    "            class_to_remove=class_to_remove,\n",
    "        )\n",
    "\n",
    "        prauc_val_kls.append(pr_auc_score_val)\n",
    "        roc_val_kls.append(roc_auc_score_val)\n",
    "        recalls_array.append(recalls)\n",
    "        precisions_array.append(precisions)\n",
    "        tprs_array.append(tprs)\n",
    "        fprs_array.append(fprs)\n",
    "        \n",
    "    # take the mean of the values across all search_iterations\n",
    "    df[\"pr_auc_train_score_kl\"] = np.mean(np.array(prauc_train_kls))\n",
    "    df[\"pr_auc_val_score_kl\"] = np.mean(np.array(prauc_val_kls))\n",
    "    df[\"roc_train_score_kl\"] = np.mean(np.array(roc_train_kls))\n",
    "    df[\"roc_val_score_kl\"] = np.mean(np.array(roc_val_kls))\n",
    "    df[\"example_threshold_kl\"] = example_threshold_kl\n",
    "\n",
    "    recalls_array = np.array(recalls_array)\n",
    "    precisions_array = np.array(precisions_array)\n",
    "    tprs_array = np.array(tprs_array)\n",
    "    fprs_array = np.array(fprs_array)\n",
    "\n",
    "    return df, recalls_array, precisions_array, tprs_array, fprs_array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we need some simple functions that we'll use later in recreating the training/validation/testing data sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scaler(x, min_val_array, max_val_array):\n",
    "    '''\n",
    "    Function to scale the data with min-max values\n",
    "    '''\n",
    "\n",
    "    # get the shape of the array\n",
    "    s, _, sub_s = np.shape(x)\n",
    "\n",
    "    for i in range(s):\n",
    "        for j in range(sub_s):\n",
    "            x[i, :, j] = np.divide(\n",
    "                (x[i, :, j] - min_val_array[j]),\n",
    "                np.abs(max_val_array[j] - min_val_array[j]),\n",
    "            )\n",
    "\n",
    "    return x\n",
    "\n",
    "\n",
    "# min-max function\n",
    "def get_min_max(x):\n",
    "    '''\n",
    "    Function to get the min-max values\n",
    "    '''\n",
    "\n",
    "    # flatten the input array http://bit.ly/2MQuXZd\n",
    "    flat_vector = np.concatenate(x)\n",
    "\n",
    "    min_vals = np.min(flat_vector, axis=0)\n",
    "    max_vals = np.max(flat_vector, axis=0)\n",
    "\n",
    "    return min_vals, max_vals\n",
    "\n",
    "\n",
    "def load_train_test(directory):\n",
    "    '''\n",
    "    Function to quickly load the train/val/test data hdf5 files\n",
    "    '''\n",
    "\n",
    "    path = directory\n",
    "\n",
    "    with h5py.File(path / \"X_train.hdf5\", \"r\") as f:\n",
    "        X_train = f[\"X_train\"][:]\n",
    "    with h5py.File(path / \"y_train.hdf5\", \"r\") as f:\n",
    "        y_train = f[\"y_train\"][:]\n",
    "\n",
    "    with h5py.File(path / \"X_train_slim.hdf5\", \"r\") as f:\n",
    "        X_train_slim = f[\"X_train_slim\"][:]\n",
    "    with h5py.File(path / \"y_train_slim.hdf5\", \"r\") as f:\n",
    "        y_train_slim = f[\"y_train_slim\"][:]\n",
    "\n",
    "    with h5py.File(path / \"X_val.hdf5\", \"r\") as f:\n",
    "        X_val = f[\"X_val\"][:]\n",
    "    with h5py.File(path / \"y_val.hdf5\", \"r\") as f:\n",
    "        y_val = f[\"y_val\"][:]\n",
    "\n",
    "    with h5py.File(path / \"X_val_slim.hdf5\", \"r\") as f:\n",
    "        X_val_slim = f[\"X_val_slim\"][:]\n",
    "    with h5py.File(path / \"y_val_slim.hdf5\", \"r\") as f:\n",
    "        y_val_slim = f[\"y_val_slim\"][:]\n",
    "\n",
    "    with h5py.File(path / \"X_test.hdf5\", \"r\") as f:\n",
    "        X_test = f[\"X_test\"][:]\n",
    "    with h5py.File(path / \"y_test.hdf5\", \"r\") as f:\n",
    "        y_test = f[\"y_test\"][:]\n",
    "\n",
    "    return (\n",
    "        X_train,\n",
    "        y_train,\n",
    "        X_train_slim,\n",
    "        y_train_slim,\n",
    "        X_val,\n",
    "        y_val,\n",
    "        X_val_slim,\n",
    "        y_val_slim,\n",
    "        X_test,\n",
    "        y_test,\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyze the Best Model\n",
    "Now that some of the \"background\" information is covered, we can begin analyzing the trained VAE models. You would calculated peformance metrics against each model -- the PR-AUC score -- and see which one is the best. I've already trained a bunch of models and selected top one. Here are the parameters of the model:\n",
    "\n",
    "| Parameter                | Value              |\n",
    "| ------------------------ | ------------------ |\n",
    "| Disentanglement, $\\beta$ | 3.92              |\n",
    "| Latent coding size       | 21                 |\n",
    "| Filter size              | 16                 |\n",
    "| Kernel size              | 3                  |\n",
    "| Dilations                | [1, 2, 4, 8]       |\n",
    "| Layers                   | 2                  |\n",
    "| Final activation         | SeLU               |\n",
    "| Trainable parameters     | 4.63 x 10^4 |\n",
    "| Epochs trained           | 118                |\n",
    "\n",
    "\n",
    "## Calculate PR-AUC Scores\n",
    "Let's see what the PR-AUC scores are for the different training/validation/testing sets, and plot the precision-recall and ROC curves. But first, we need to load the data and packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load approriate modules\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import tensorboard\n",
    "from tensorflow.keras.models import model_from_json\n",
    "\n",
    "# functions needed for model inference\n",
    "K = keras.backend\n",
    "\n",
    "class Sampling(keras.layers.Layer):\n",
    "    def call(self, inputs):\n",
    "        mean, log_var = inputs\n",
    "        return K.random_normal(tf.shape(log_var)) * K.exp(log_var / 2) + mean\n",
    "    \n",
    "\n",
    "# reload the data sets\n",
    "(X_train, y_train, \n",
    "X_train_slim, y_train_slim,\n",
    "X_val, y_val,\n",
    "X_val_slim, y_val_slim,\n",
    "X_test,y_test) = load_train_test(folder_processed_data) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `get_results` function takes a model and spits out the peformance of the model across the training, validation, and testing sets. It also retures the precisions, recall, true positives, and false positives for a given number of iterations (called `grid_iterations`). Because the outputs from a VAE are partially stochastic (random), you can also run a number of searches (`search_iterations`), and then take an average across all the searches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_results(\n",
    "    model_name,\n",
    "    model_folder,\n",
    "    grid_iterations,\n",
    "    search_iterations,\n",
    "    X_train,\n",
    "    y_train,\n",
    "    X_val,\n",
    "    y_val,\n",
    "    X_test,\n",
    "    y_test,\n",
    "):\n",
    "\n",
    "    # get results for train and validation sets\n",
    "    dfr_val, _, _, _, _ = get_latent_input_anomaly_scores(\n",
    "        model_name,\n",
    "        model_folder,\n",
    "        [2],\n",
    "        X_train,\n",
    "        y_train,\n",
    "        X_val,\n",
    "        y_val,\n",
    "        grid_iterations=grid_iterations,\n",
    "        search_iterations=search_iterations,\n",
    "        X_train_slim=X_train_slim,\n",
    "        X_val_slim=X_val_slim,\n",
    "    )\n",
    "\n",
    "    date_time = dfr_val[\"date_time\"][0]\n",
    "    example_threshold_val = dfr_val[\"best_threshold\"][0]\n",
    "    example_threshold_kl_val = dfr_val[\"example_threshold_kl\"][0]\n",
    "    pr_auc_train_score = dfr_val[\"pr_auc_train_score\"][0]\n",
    "    pr_auc_val_score = dfr_val[\"pr_auc_val_score\"][0]\n",
    "    pr_auc_train_score_kl = dfr_val[\"pr_auc_train_score_kl\"][0]\n",
    "    pr_auc_val_score_kl = dfr_val[\"pr_auc_val_score_kl\"][0]\n",
    "\n",
    "    # get results for test set\n",
    "    # df, recalls_array, precisions_array, tprs_array, fprs_array\n",
    "    dfr_test, recalls_test, precisions_test, tprs_test, fprs_test = get_latent_input_anomaly_scores(\n",
    "        model_name,\n",
    "        model_folder,\n",
    "        [2],\n",
    "        X_train,\n",
    "        y_train,\n",
    "        X_test,\n",
    "        y_test,\n",
    "        grid_iterations=grid_iterations,\n",
    "        search_iterations=search_iterations,\n",
    "        X_train_slim=X_train_slim,\n",
    "        X_val_slim=X_val_slim,\n",
    "    )\n",
    "\n",
    "    example_threshold_test = dfr_test[\"best_threshold\"][0]\n",
    "    example_threshold_kl_test = dfr_test[\"example_threshold_kl\"][0]\n",
    "    pr_auc_test_score = dfr_test[\"pr_auc_val_score\"][0]\n",
    "    pr_auc_test_score_kl = dfr_test[\"pr_auc_val_score_kl\"][0]\n",
    "\n",
    "    # collate the results into one dataframe\n",
    "    df_result = pd.DataFrame()\n",
    "\n",
    "    df_result[\"Data Set\"] = [\"train\", \"validation\", \"test\"]\n",
    "    df_result[\"PR-AUC Input Space\"] = [\n",
    "        pr_auc_train_score,\n",
    "        pr_auc_val_score,\n",
    "        pr_auc_test_score,\n",
    "    ]\n",
    "    \n",
    "    df_result[\"PR-AUC Latent Space\"] = [\n",
    "        pr_auc_train_score_kl,\n",
    "        pr_auc_val_score_kl,\n",
    "        pr_auc_test_score_kl,\n",
    "    ]\n",
    "    \n",
    "    return df_result, recalls_test, precisions_test, tprs_test, fprs_test, example_threshold_test, example_threshold_kl_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is how we generate the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Data Set</th>\n",
       "      <th>PR-AUC Input Space</th>\n",
       "      <th>PR-AUC Latent Space</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train</td>\n",
       "      <td>0.376927</td>\n",
       "      <td>0.391694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>validation</td>\n",
       "      <td>0.433502</td>\n",
       "      <td>0.493395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>test</td>\n",
       "      <td>0.418776</td>\n",
       "      <td>0.449931</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Data Set  PR-AUC Input Space  PR-AUC Latent Space\n",
       "0       train            0.376927             0.391694\n",
       "1  validation            0.433502             0.493395\n",
       "2        test            0.418776             0.449931"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set model folder\n",
    "model_folder = \"./models/best_models\"\n",
    "\n",
    "# the best model from the original grid search\n",
    "model_name = \"20200620-053315_bvae\"\n",
    "\n",
    "\n",
    "grid_iterations = 250\n",
    "search_iterations = 1\n",
    "\n",
    "(df_result, \n",
    " recalls, \n",
    " precisions, \n",
    " tprs, \n",
    " fprs, \n",
    " example_threshold_test, \n",
    " example_threshold_kl_test) = get_results(model_name, model_folder,\n",
    "                                          grid_iterations, search_iterations,\n",
    "                                          X_train, y_train,\n",
    "                                          X_val, y_val, \n",
    "                                          X_test,y_test,)\n",
    "\n",
    "clear_output(wait=True)\n",
    "df_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The latent space anomaly detection outpeforms the input space anomaly detection. This is not unsuprising. The information contained in the latent space is more expressive and thus more likely to identify differences between cuts."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Precision-Recall Curve\n",
    "We want to visualize the peformance of the model. Let's plot the precision-recall curve and the ROC curve for the anomaly detection model in the latent space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x900 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "roc_auc_val = auc(fprs[0, :], tprs[0, :])\n",
    "pr_auc_val = auc(recalls[0, :], precisions[0, :])\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 6), sharex=True, sharey=True, dpi=150)\n",
    "fig.tight_layout(pad=5.0)\n",
    "\n",
    "pal = sns.cubehelix_palette(6, rot=-0.25, light=0.7)\n",
    "\n",
    "axes[0].plot(\n",
    "    recalls[0, :],\n",
    "    precisions[0, :],\n",
    "    marker=\"\",\n",
    "    label=\"Best model\",\n",
    "    color=pal[5],\n",
    "    linewidth=2,\n",
    ")\n",
    "\n",
    "axes[0].plot(\n",
    "    np.array([0, 1]),\n",
    "    np.array([0.073, 0.073]),\n",
    "    marker=\"\",\n",
    "    linestyle=\"--\",\n",
    "    label=\"No skill model\",\n",
    "    color=\"orange\",\n",
    "    linewidth=2,\n",
    ")\n",
    "\n",
    "axes[0].legend()\n",
    "axes[0].title.set_text(\"Precision-Recall Curve\")\n",
    "axes[0].set_xlabel(\"Recall\")\n",
    "axes[0].set_ylabel(\"Precision\")\n",
    "axes[0].text(\n",
    "    x=-0.05,\n",
    "    y=-0.3,\n",
    "    s=\"Precision-Recall Area-Under-Curve = {:.3f}\".format(pr_auc_val),\n",
    "    horizontalalignment=\"left\",\n",
    "    verticalalignment=\"center\",\n",
    "    rotation=\"horizontal\",\n",
    "    alpha=1,\n",
    ")\n",
    "\n",
    "axes[1].plot(\n",
    "    fprs[0, :], tprs[0, :], marker=\"\", label=\"Best model\", color=pal[5], linewidth=2,\n",
    ")\n",
    "\n",
    "axes[1].plot(\n",
    "    np.array([0, 1]),\n",
    "    np.array([0, 1]),\n",
    "    marker=\"\",\n",
    "    linestyle=\"--\",\n",
    "    label=\"No skill\",\n",
    "    color=\"orange\",\n",
    "    linewidth=2,\n",
    ")\n",
    "\n",
    "axes[1].title.set_text(\"ROC Curve\")\n",
    "axes[1].set_xlabel(\"False Positive Rate\")\n",
    "axes[1].set_ylabel(\"True Positive Rate\")\n",
    "axes[1].text(\n",
    "    x=-0.05,\n",
    "    y=-0.3,\n",
    "    s=\"ROC Area-Under-Curve = {:.3f}\".format(roc_auc_val),\n",
    "    horizontalalignment=\"left\",\n",
    "    verticalalignment=\"center\",\n",
    "    rotation=\"horizontal\",\n",
    "    alpha=1,\n",
    ")\n",
    "\n",
    "for ax in axes.flatten():\n",
    "    ax.yaxis.set_tick_params(labelleft=True, which=\"major\")\n",
    "    ax.grid(False)\n",
    "    \n",
    "\n",
    "# plt.savefig('prauc_results.svg',dpi=150, bbox_inches = \"tight\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dashed lines in the above plot represent what a \"no skilled model\" would obtain if it was doing the anomaly detection -- that is, if a model randomly assigned a class (normal or abnorml) to each sub-cut in the data set. This random model is represented by a diagonal line in the ROC plot, and a horizontal line, set at a precision 0.073 (the percentage of failed sub-cuts in the testint set), on the PR-AUC plot. \n",
    "\n",
    "Compare the precision-recall curve and the ROC curve. The ROC curve gives a more optimistic view of the peformance of the model; that is an area-under-curve of 0.883. However, the precision-recall area-under-curve is not nearly as good, with a value of 0.450. Why the difference? It's because of the severe imbalance in our data set. This is the exact reason why you would want to use the PR-AUC instead of ROC-AUC metric. The PR-AUC will provide a more realistic view on the model's peformance.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Violin Plot for the Latent Space\n",
    "A violin plot is an effective method of visualizing the decision boundary and seeing where samples are misclassified. \n",
    "\n",
    "Here's the `violin_plot` function that will will use to create the plot. It takes the trained encoder, the sub-cuts (`X`), the labels (`y`), and an example threshold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def violin_plot(\n",
    "    model,\n",
    "    X,\n",
    "    y,\n",
    "    example_threshold=0.034,\n",
    "    caption=\"Distribution of Latent Space Anomaly Predictions\",\n",
    "    save_fig=False\n",
    "):\n",
    "\n",
    "    # generate the KL-divergence scores\n",
    "    scores = build_kls_scores(model, X)\n",
    "\n",
    "    colors = [\"#e31a1c\", \"black\"]\n",
    "\n",
    "    # set your custom color palette\n",
    "    customPalette = sns.set_palette(sns.color_palette(colors))\n",
    "\n",
    "    min_x = scores.min()\n",
    "    max_x = scores.max() + scores.max() * 0.05\n",
    "\n",
    "    min_y = -0.5\n",
    "    max_y = 2.7\n",
    "\n",
    "    fig, ax = plt.subplots(1, 1, figsize=(8, 10),)\n",
    "\n",
    "    # violin plot\n",
    "    ax = sns.violinplot(\n",
    "        x=scores,\n",
    "        y=y,\n",
    "        scale=\"count\",\n",
    "        inner=None,\n",
    "        linewidth=2,\n",
    "        color=\"white\",\n",
    "        saturation=1,\n",
    "        cut=0,\n",
    "        orient=\"h\",\n",
    "        zorder=0,\n",
    "        width=1,\n",
    "    )\n",
    "\n",
    "    sns.despine(left=True)\n",
    "\n",
    "    # strip plot\n",
    "    ax = sns.stripplot(\n",
    "        x=scores,\n",
    "        y=y,\n",
    "        size=6,\n",
    "        jitter=0.15,\n",
    "        color=\"black\",\n",
    "        linewidth=0.5,\n",
    "        marker=\"o\",\n",
    "        edgecolor=None,\n",
    "        alpha=0.1,\n",
    "        palette=customPalette,\n",
    "        zorder=4,\n",
    "        orient=\"h\",\n",
    "    )\n",
    "\n",
    "    # vertical line\n",
    "    ax.plot(\n",
    "        [example_threshold, example_threshold],\n",
    "        [min_y, max_y],\n",
    "        linestyle=\"--\",\n",
    "        label=\"\",\n",
    "        color=\"#d62728\",\n",
    "    )\n",
    "\n",
    "    # add the fill areas for the predicted Failed and Healthy regions\n",
    "    plt.fill_between(\n",
    "        x=[0, example_threshold],\n",
    "        y1=min_y,\n",
    "        y2=max_y,\n",
    "        color=\"#b2df8a\",\n",
    "        alpha=0.4,\n",
    "        linewidth=0,\n",
    "        zorder=0,\n",
    "    )\n",
    "\n",
    "    plt.fill_between(\n",
    "        x=[example_threshold, max_x + 0.001],\n",
    "        y1=min_y,\n",
    "        y2=max_y,\n",
    "        color=\"#e31a1c\",\n",
    "        alpha=0.1,\n",
    "        linewidth=0,\n",
    "        zorder=0,\n",
    "    )\n",
    "\n",
    "    # add text for the predicted Failed and Healthy regions\n",
    "    ax.text(\n",
    "        x=0 + (example_threshold) / 2,\n",
    "        y=max_y - 0.2,\n",
    "        s=\"Normal\\nPrediction\",\n",
    "        horizontalalignment=\"center\",\n",
    "        verticalalignment=\"center\",\n",
    "        size=14,\n",
    "        color=\"#33a02c\",\n",
    "        rotation=\"horizontal\",\n",
    "        weight=\"normal\",\n",
    "    )\n",
    "\n",
    "    ax.text(\n",
    "        x=example_threshold + (max_x - example_threshold) / 2,\n",
    "        y=max_y - 0.2,\n",
    "        s=\"Abnormal (failed)\\nPrediction\",\n",
    "        horizontalalignment=\"center\",\n",
    "        verticalalignment=\"center\",\n",
    "        size=14,\n",
    "        color=\"#d62728\",\n",
    "        rotation=\"horizontal\",\n",
    "        weight=\"normal\",\n",
    "    )\n",
    "\n",
    "    # Set text labels and properties.\n",
    "    plt.yticks([0, 1, 2], [\"Healthy\", \"Degraded\", \"Failed\"], weight=\"normal\", size=14)\n",
    "    plt.xlabel(\"\")  # remove x-label\n",
    "    plt.ylabel(\"\")  # remove y-label\n",
    "\n",
    "    plt.tick_params(\n",
    "        axis=\"both\",  # changes apply to the x-axis\n",
    "        which=\"both\",  # both major and minor ticks are affected\n",
    "        bottom=False,\n",
    "        left=False,\n",
    "    )\n",
    "\n",
    "    ax.axes.get_xaxis().set_visible(False)  # hide x-axis\n",
    "    ax.spines[\"bottom\"].set_visible(False)\n",
    "    ax.spines[\"left\"].set_visible(False)\n",
    "    plt.title(caption, x=0.05, y=0.97, loc=\"left\", weight=\"normal\", size=14)\n",
    "    if save_fig:\n",
    "        plt.savefig('violin_plot.png',dpi=150, bbox_inches = \"tight\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to load the encoder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load best encoder for latent space anomaly detection\n",
    "folder_name_encoder = '20200620-053315_encoder'\n",
    "\n",
    "loaded_json = open(r'models/best_models/{}/model.json'.format(folder_name_encoder), 'r').read()\n",
    "encoder = model_from_json(loaded_json, custom_objects={'TCN': TCN, 'Sampling': Sampling})\n",
    "\n",
    "# restore weights\n",
    "encoder.load_weights(r'models/best_models/{}/weights.h5'.format(folder_name_encoder))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... and plot!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "violin_plot(\n",
    "    encoder,\n",
    "    X_test,\n",
    "    y_test,\n",
    "    example_threshold=example_threshold_kl_test,\n",
    "    caption=\"Distribution of Latent Space Anomaly Predictions\",\n",
    "    save_fig=False    \n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see in the above violin plot how different thresholds would misclassify varying numbers of data points (imagine the red dashed line moving left or right on the plot). This is the inherent struggle with anomaly detection -- seperating the noise from the anomalies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare Results for Different Cutting Parameters\n",
    "If you remember from the previous posts, there are six cutting parameters in total:\n",
    "\n",
    "* the metal type (either cast iron or steel)\n",
    "* the depth of cut (either 0.75 mm or 1.5 mm)\n",
    "* the feed rate (either 0.25 mm/rev or 0.5 mm/rev)\n",
    "\n",
    "We can see if our selected anomaly detection model is better at detecting failed tools on one set of parameters over another. We'll do this by feeding only one type of parameter into the model at a time. For example, we'll feed the cuts that were made with cast-iron and see the results. Then we'll move to steel. Etc. etc.\n",
    "\n",
    "### Code to Compare Parameters\n",
    "There is a whole bunch of code needed to compare the different cutting parameters... feel free to skip to the bottom to see the results.\n",
    "\n",
    "First, there are a number of functions needed to \"filter\" out the parameters we are not interested in. The input to these functions are the `X` data, a dataframe that has additional label data, `dfy`, and the parameter we are concerned with.\n",
    "\n",
    "(side note: we'll have to generate the `dfy` later. In the original experiment, I did not think I would need the additional label information, like case, cut number, and cutting parameters. So I had to tack it on at the end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_x_material(X, dfy, material=\"cast_iron\"):\n",
    "\n",
    "    cast_iron_cases = [1, 2, 3, 4, 9, 10, 11, 12]\n",
    "    steel_cases = list(list(set(range(1, 17)) - set(cast_iron_cases)))\n",
    "\n",
    "    if material == \"cast_iron\":\n",
    "        case_list = cast_iron_cases\n",
    "    else:\n",
    "        # material is 'steel'\n",
    "        case_list = steel_cases\n",
    "\n",
    "    index_keep = dfy[dfy[\"case\"].isin(case_list)].copy().index\n",
    "    X_sorted = X[index_keep]\n",
    "    y_sorted = np.array(dfy[dfy[\"case\"].isin(case_list)][\"class\"].copy(), dtype=\"int\")\n",
    "    return X_sorted, y_sorted\n",
    "\n",
    "\n",
    "def filter_x_feed(X, dfy, feed):\n",
    "    fast_feed_cases = [1, 2, 5, 8, 9, 12, 14, 16]\n",
    "    slow_feed_cases = list(list(set(range(1, 17)) - set(fast_feed_cases)))\n",
    "\n",
    "    if feed == 0.5:\n",
    "        case_list = fast_feed_cases\n",
    "    else:\n",
    "        # feed is 0.25\n",
    "        case_list = slow_feed_cases\n",
    "\n",
    "    index_keep = dfy[dfy[\"case\"].isin(case_list)].copy().index\n",
    "    X_sorted = X[index_keep]\n",
    "    y_sorted = np.array(dfy[dfy[\"case\"].isin(case_list)][\"class\"].copy(), dtype=\"int\")\n",
    "    return X_sorted, y_sorted\n",
    "\n",
    "\n",
    "def filter_x_depth(X, dfy, feed):\n",
    "    deep_cases = [1, 4, 5, 6, 9, 10, 15, 16]\n",
    "    shallow_cases = list(list(set(range(1, 17)) - set(deep_cases)))\n",
    "\n",
    "    if feed == 1.5:\n",
    "        case_list = deep_cases\n",
    "    else:\n",
    "        # depth is 0.75\n",
    "        case_list = shallow_cases\n",
    "\n",
    "    index_keep = dfy[dfy[\"case\"].isin(case_list)].copy().index\n",
    "    X_sorted = X[index_keep]\n",
    "    y_sorted = np.array(dfy[dfy[\"case\"].isin(case_list)][\"class\"].copy(), dtype=\"int\")\n",
    "    return X_sorted, y_sorted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we'll generate the `dfy` dataframes that include additional label information. These dataframes include the class, case, and the sequential count of each sub-cut.\n",
    "\n",
    "To generate the `dfy`s we recreate the data prep pipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# raw data location\n",
    "data_file = folder_raw_data / \"mill.mat\"\n",
    "\n",
    "prep = data_prep.DataPrep(data_file)\n",
    "\n",
    "# load the labeled CSV (NaNs filled in by hand)\n",
    "df_labels = pd.read_csv(\n",
    "    folder_processed_data / 'labels_with_tool_class.csv'\n",
    ")\n",
    "\n",
    "\n",
    "# discard certain cuts as they are strange\n",
    "cuts_remove = [17, 94]\n",
    "df_labels.drop(cuts_remove, inplace=True)\n",
    "\n",
    "# return the X and y data, along with additional dfy datafram\n",
    "X, y, dfy = prep.return_xy(df_labels, prep.data,prep.field_names[7:],window_size=64, stride=64, track_y=True)\n",
    "\n",
    "# execute same splits -- IMPORTANT that the same random_state be used\n",
    "X_train, X_test, dfy_train, dfy_test = train_test_split(X, dfy, test_size=0.33, random_state=15, \n",
    "                                                    stratify=dfy['class'])\n",
    "X_val, X_test, dfy_val, dfy_test = train_test_split(X_test, dfy_test, test_size=0.50, random_state=10, \n",
    "                                                stratify=dfy_test['class'])\n",
    "\n",
    "# we need the entire \"X\" data later\n",
    "# so we need to make sure it is scaled appropriately\n",
    "min_vals, max_vals = get_min_max(X_train)\n",
    "X = scaler(X, min_vals, max_vals)\n",
    "\n",
    "\n",
    "# reload the scaled data since we overwrote some of it above with\n",
    "# unscaled data\n",
    "# reload the data sets\n",
    "(X_train, y_train, \n",
    "X_train_slim, y_train_slim,\n",
    "X_val, y_val,\n",
    "X_val_slim, y_val_slim,\n",
    "X_test,y_test) = load_train_test(folder_processed_data) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is what the `dfy_val` looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>class</th>\n",
       "      <th>counter</th>\n",
       "      <th>case</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3804</th>\n",
       "      <td>0.0</td>\n",
       "      <td>54.0062</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3643</th>\n",
       "      <td>0.0</td>\n",
       "      <td>52.0045</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1351</th>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0021</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1167</th>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0047</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7552</th>\n",
       "      <td>0.0</td>\n",
       "      <td>109.0046</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      class   counter  case\n",
       "3804    0.0   54.0062   9.0\n",
       "3643    0.0   52.0045   9.0\n",
       "1351    0.0   20.0021   2.0\n",
       "1167    1.0   16.0047   1.0\n",
       "7552    0.0  109.0046   5.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfy_val.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can combine all the \"filter\" functions to see how the model peforms when only looking at one parameter at a time. This will take a bit more time to run since we have to iterate through the six different parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_folder = './models/best_models'\n",
    "model_name = '20200620-053315_bvae'\n",
    "\n",
    "grid_iterations = 250\n",
    "search_iterations = 1 # <---- CHANGE THIS TO 4 TO GET SAME RESULTS AS IN PAPER (but takes loooong to run)\n",
    "\n",
    "# look at different material types\n",
    "# STEEL\n",
    "X_train_mat1, y_train_mat1 =  filter_x_material(X, dfy_train, 'steel')\n",
    "X_val_mat1, y_val_mat1 =  filter_x_material(X, dfy_val, 'steel')\n",
    "X_test_mat1, y_test_mat1 =  filter_x_material(X, dfy_test, 'steel')\n",
    "\n",
    "dfr_steel, _, _, _, _, _, _ = get_results(model_name, model_folder, grid_iterations, search_iterations, \n",
    "                X_train_mat1, y_train_mat1, X_val_mat1, y_val_mat1, X_test_mat1, y_test_mat1)\n",
    "\n",
    "# CAST-IRON\n",
    "X_train_mat2, y_train_mat2 =  filter_x_material(X, dfy_train, 'cast_iron')\n",
    "X_val_mat2, y_val_mat2 =  filter_x_material(X, dfy_val, 'cast_iron')\n",
    "X_test_mat2, y_test_mat2 =  filter_x_material(X, dfy_test, 'cast_iron')\n",
    "\n",
    "dfr_iron, _, _, _, _, _, _  = get_results(model_name, model_folder, grid_iterations, search_iterations, \n",
    "                X_train_mat2, y_train_mat2, X_val_mat2, y_val_mat2, X_test_mat2, y_test_mat2)\n",
    "\n",
    "# look at different feed rates\n",
    "# 0.5 mm/rev\n",
    "X_train_f1, y_train_f1 =  filter_x_feed(X, dfy_train, 0.5)\n",
    "X_val_f1, y_val_f1 =  filter_x_feed(X, dfy_val, 0.5)\n",
    "X_test_f1, y_test_f1 =  filter_x_feed(X, dfy_test, 0.5)\n",
    "\n",
    "dfr_fast, _, _, _, _, _, _  = get_results(model_name, model_folder, grid_iterations, search_iterations, \n",
    "                X_train_f1, y_train_f1, X_val_f1, y_val_f1, X_test_f1, y_test_f1)\n",
    "\n",
    "# 0.25 mm/rev\n",
    "X_train_f2, y_train_f2 =  filter_x_feed(X, dfy_train, 0.25)\n",
    "X_val_f2, y_val_f2 =  filter_x_feed(X, dfy_val, 0.25)\n",
    "X_test_f2, y_test_f2 =  filter_x_feed(X, dfy_test, 0.25)\n",
    "\n",
    "dfr_slow, _, _, _, _, _, _  = get_results(model_name, model_folder, grid_iterations, search_iterations, \n",
    "                X_train_f2, y_train_f2, X_val_f2, y_val_f2, X_test_f2, y_test_f2)\n",
    "\n",
    "# look at different depths of cut\n",
    "# 1.5 mm\n",
    "X_train_d1, y_train_d1 =  filter_x_depth(X, dfy_train, 1.5)\n",
    "X_val_d1, y_val_d1 =  filter_x_depth(X, dfy_val, 1.5)\n",
    "X_test_d1, y_test_d1 =  filter_x_depth(X, dfy_test, 1.5)\n",
    "\n",
    "dfr_deep, _, _, _, _, _, _  = get_results(model_name, model_folder, grid_iterations, search_iterations, \n",
    "                X_train_d1, y_train_d1, X_val_d1, y_val_d1, X_test_d1, y_test_d1)\n",
    "\n",
    "# 0.75 mm\n",
    "X_train_d2, y_train_d2 =  filter_x_depth(X, dfy_train, 0.75)\n",
    "X_val_d2, y_val_d2 =  filter_x_depth(X, dfy_val, 0.75)\n",
    "X_test_d2, y_test_d2 =  filter_x_depth(X, dfy_test, 0.75)\n",
    "\n",
    "dfr_shallow, _, _, _, _, _, _  = get_results(model_name, model_folder, grid_iterations, search_iterations, \n",
    "                X_train_d2, y_train_d2, X_val_d2, y_val_d2, X_test_d2, y_test_d2)\n",
    "\n",
    "clear_output(wait=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now see the results for each of the six parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Data Set</th>\n",
       "      <th>PR-AUC Input Space</th>\n",
       "      <th>PR-AUC Latent Space</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train</td>\n",
       "      <td>0.430570</td>\n",
       "      <td>0.477521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>validation</td>\n",
       "      <td>0.519147</td>\n",
       "      <td>0.579512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>test</td>\n",
       "      <td>0.524665</td>\n",
       "      <td>0.522996</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Data Set  PR-AUC Input Space  PR-AUC Latent Space\n",
       "0       train            0.430570             0.477521\n",
       "1  validation            0.519147             0.579512\n",
       "2        test            0.524665             0.522996"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# steel material\n",
    "dfr_steel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Data Set</th>\n",
       "      <th>PR-AUC Input Space</th>\n",
       "      <th>PR-AUC Latent Space</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train</td>\n",
       "      <td>0.044626</td>\n",
       "      <td>0.041689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>validation</td>\n",
       "      <td>0.038812</td>\n",
       "      <td>0.037067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>test</td>\n",
       "      <td>0.026930</td>\n",
       "      <td>0.029680</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Data Set  PR-AUC Input Space  PR-AUC Latent Space\n",
       "0       train            0.044626             0.041689\n",
       "1  validation            0.038812             0.037067\n",
       "2        test            0.026930             0.029680"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cast-iron material\n",
    "dfr_iron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Data Set</th>\n",
       "      <th>PR-AUC Input Space</th>\n",
       "      <th>PR-AUC Latent Space</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train</td>\n",
       "      <td>0.135335</td>\n",
       "      <td>0.144827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>validation</td>\n",
       "      <td>0.272640</td>\n",
       "      <td>0.231796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>test</td>\n",
       "      <td>0.232898</td>\n",
       "      <td>0.209018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Data Set  PR-AUC Input Space  PR-AUC Latent Space\n",
       "0       train            0.135335             0.144827\n",
       "1  validation            0.272640             0.231796\n",
       "2        test            0.232898             0.209018"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fast feed rate, 0.5 mm/rev\n",
    "dfr_fast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Data Set</th>\n",
       "      <th>PR-AUC Input Space</th>\n",
       "      <th>PR-AUC Latent Space</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train</td>\n",
       "      <td>0.582924</td>\n",
       "      <td>0.599790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>validation</td>\n",
       "      <td>0.606066</td>\n",
       "      <td>0.672304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>test</td>\n",
       "      <td>0.590973</td>\n",
       "      <td>0.645599</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Data Set  PR-AUC Input Space  PR-AUC Latent Space\n",
       "0       train            0.582924             0.599790\n",
       "1  validation            0.606066             0.672304\n",
       "2        test            0.590973             0.645599"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# slow feed rate, 0.25 mm/rev\n",
    "dfr_slow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Data Set</th>\n",
       "      <th>PR-AUC Input Space</th>\n",
       "      <th>PR-AUC Latent Space</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train</td>\n",
       "      <td>0.122281</td>\n",
       "      <td>0.118633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>validation</td>\n",
       "      <td>0.118372</td>\n",
       "      <td>0.124065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>test</td>\n",
       "      <td>0.162790</td>\n",
       "      <td>0.158938</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Data Set  PR-AUC Input Space  PR-AUC Latent Space\n",
       "0       train            0.122281             0.118633\n",
       "1  validation            0.118372             0.124065\n",
       "2        test            0.162790             0.158938"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# deep cuts, 1.5 mm in depth\n",
    "dfr_deep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Data Set</th>\n",
       "      <th>PR-AUC Input Space</th>\n",
       "      <th>PR-AUC Latent Space</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train</td>\n",
       "      <td>0.721970</td>\n",
       "      <td>0.749864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>validation</td>\n",
       "      <td>0.827365</td>\n",
       "      <td>0.829849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>test</td>\n",
       "      <td>0.736809</td>\n",
       "      <td>0.804361</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Data Set  PR-AUC Input Space  PR-AUC Latent Space\n",
       "0       train            0.721970             0.749864\n",
       "1  validation            0.827365             0.829849\n",
       "2        test            0.736809             0.804361"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shallow cuts, 0.75 mm in depth\n",
    "dfr_shallow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make the Plot and Discuss\n",
    "Let's combine all the results into one table and plot the results on a bar chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Parameter</th>\n",
       "      <th>PR-AUC Latent Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.75 Depth</td>\n",
       "      <td>0.804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.25 Feed\\nRate</td>\n",
       "      <td>0.646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Steel</td>\n",
       "      <td>0.523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.5 Feed\\nRate</td>\n",
       "      <td>0.209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.5 Depth</td>\n",
       "      <td>0.159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Iron</td>\n",
       "      <td>0.030</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Parameter  PR-AUC Latent Score\n",
       "5       0.75 Depth                0.804\n",
       "2  0.25 Feed\\nRate                0.646\n",
       "0            Steel                0.523\n",
       "3   0.5 Feed\\nRate                0.209\n",
       "4        1.5 Depth                0.159\n",
       "1             Iron                0.030"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# parameter names\n",
    "cutting_parameters = [\n",
    "    \"Steel\",\n",
    "    \"Iron\",\n",
    "    \"0.25 Feed\\nRate\",\n",
    "    \"0.5 Feed\\nRate\",\n",
    "    \"1.5 Depth\",\n",
    "    \"0.75 Depth\",\n",
    "]\n",
    "\n",
    "# pr-auc latent scores\n",
    "pr_auc_latent = [\n",
    "    dfr_steel[dfr_steel['Data Set']=='test']['PR-AUC Latent Space'].iloc[0],\n",
    "    dfr_iron[dfr_iron['Data Set']=='test']['PR-AUC Latent Space'].iloc[0],\n",
    "    dfr_slow[dfr_slow['Data Set']=='test']['PR-AUC Latent Space'].iloc[0],\n",
    "    dfr_fast[dfr_fast['Data Set']=='test']['PR-AUC Latent Space'].iloc[0],\n",
    "    dfr_deep[dfr_deep['Data Set']=='test']['PR-AUC Latent Space'].iloc[0],\n",
    "    dfr_shallow[dfr_shallow['Data Set']=='test']['PR-AUC Latent Space'].iloc[0],\n",
    "]\n",
    "\n",
    "# make the dataframe and sort values from largest to smallest\n",
    "dfr_param = (\n",
    "    pd.DataFrame(\n",
    "        {\"Parameter\": cutting_parameters, \"PR-AUC Latent Score\": pr_auc_latent}\n",
    "    )\n",
    "    .round(3)\n",
    "    .sort_values(\"PR-AUC Latent Score\", ascending=False)\n",
    ")\n",
    "\n",
    "dfr_param"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... and make a pretty plot!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.1, style=\"whitegrid\") # set format\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(10, 5),)\n",
    "\n",
    "ax = sns.barplot(\n",
    "    \"PR-AUC Latent Score\", y=\"Parameter\", data=dfr_param, palette=\"Blues_d\",\n",
    ")\n",
    "\n",
    "for p in ax.patches:\n",
    "    # help from https://stackoverflow.com/a/56780852/9214620\n",
    "    space = 0.015\n",
    "    _x = p.get_x() + p.get_width() + float(space)\n",
    "    _y = p.get_y() + p.get_height() / 2\n",
    "    value = p.get_width()\n",
    "    ax.text(_x, _y, value, ha=\"left\", va=\"center\", weight=\"semibold\", size=12)\n",
    "\n",
    "ax.spines[\"bottom\"].set_visible(True)\n",
    "ax.set_ylabel(\"\")\n",
    "ax.set_xlabel(\"\")\n",
    "ax.grid(alpha=0.7, linewidth=1, axis=\"x\")\n",
    "ax.set_xticks([0])\n",
    "ax.set_xticklabels([])\n",
    "plt.title(\"PR-AUC Score on Latent Variables for Different Parameters\", loc=\"left\")\n",
    "\n",
    "sns.despine(left=True, bottom=True)\n",
    "plt.savefig('prauc_params.svg',dpi=150,bbox_inches = \"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clearly, this \"best\" model finds some cutting parameters more useful than others. Certain cutting parameters may produce signals carrying more information and/or have a higher signal-to-noise ratio.\n",
    "\n",
    "The model may also develop a preference, during training, for some parameters over others. The preference may be a function of the way the model was constructed (e.g. the $\\beta$ parameter or the coding size), along with the way the model was trained. I suspect that there may be other model configurations that prefer other parameters, such as cast-iron over steel. An [ensemble of models](https://en.wikipedia.org/wiki/Ensemble_learning) may thus produce significantly better results. This would be an interesting area of further research!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Trend the KL-Divergence Scores\n",
    "The KL-divergence scores can be trended sequentially to see how our anomaly detection model works. This is my favourite chart -- it's pretty, and gives good insight. Note: you can also trend the input space reconstruction errors, but we won't do that here (check out the [other github repository](https://github.com/tvhahn/ml-tool-wear) to see it being done).\n",
    "\n",
    "First, let's do some quick exploration to see how these trends will look. \n",
    "\n",
    "We need a function to sort the sub-cuts sequentially:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sorted_x(X, dfy, case):\n",
    "    \"\"\"Function that sorts the sub-cuts based on case no. and a dfy dataframe\"\"\"\n",
    "    \n",
    "    index_keep = dfy[dfy[\"case\"] == case].sort_values(by=[\"counter\"].copy()).index\n",
    "    X_sorted = X[index_keep]\n",
    "    y_sorted = np.array(\n",
    "        dfy[dfy[\"case\"] == case].sort_values(by=[\"counter\"])[\"class\"].copy()\n",
    "    )\n",
    "    return X_sorted, y_sorted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now do a quick plot of the trend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff3d1de4290>]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# try the same, as above, but in the latent space\n",
    "X_sort, y_sort = sorted_x(X, dfy_val, 13)\n",
    "\n",
    "kls = build_kls_scores(encoder, X_sort)\n",
    "plt.plot(kls)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have all we need to create a plot that trends the KL-divergence score over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_one_signal_sequentially(\n",
    "    scores,\n",
    "    y_sort,\n",
    "    case_no,\n",
    "    avg_window_size=10,\n",
    "    dpi=150,\n",
    "    opacity_color=0.10,\n",
    "    opacity_grid=0.10,\n",
    "    caption=\"Latent Space KL-Divergence Trend\",\n",
    "    y_label=\"KL-Divergence Score\",\n",
    "    legend_label=\"KL-Divergence\",\n",
    "    save_fig=False\n",
    "):\n",
    "    \"\"\"\n",
    "    Make a trend of the reconstruction or KL-divergence score.\n",
    "    \"\"\"\n",
    "\n",
    "    # plot parameters\n",
    "    colors = [\"#33a02c\", \"#fdbf6f\", \"#e31a1c\"]  # green, orange, red\n",
    "    failed_reg = [\"Healthy\", \"Degraded\", \"Failed\"]\n",
    "    pad_size = 0\n",
    "    x_min = -pad_size\n",
    "\n",
    "    # create pallette for color of trend lines\n",
    "    pal = sns.cubehelix_palette(6, rot=-0.25, light=0.7)\n",
    "\n",
    "    # get the parameters based on the case number\n",
    "    # and append to the caption\n",
    "    if case_no in [1, 2, 3, 4, 9, 10, 11, 12]:\n",
    "        material = 'cast iron'\n",
    "    else:\n",
    "        material = 'steel'\n",
    "    if case_no in [1, 2, 5, 8, 9, 12, 14, 16]:\n",
    "        feed_rate = 'fast speed'\n",
    "    else:\n",
    "        feed_rate = 'slow speed'\n",
    "    if case_no in [1, 4, 5, 6, 9, 10, 15, 16]:\n",
    "        cut_depth = 'deep cut'\n",
    "    else:\n",
    "        cut_depth = 'shallow cut'\n",
    "    \n",
    "    # set the title of the plot\n",
    "    caption = f'{caption} for Case {case_no} ({material}, {feed_rate}, {cut_depth})'\n",
    "    \n",
    "    # identify where the tool class changes (from healthy, to degraded, to failed)\n",
    "    tool_class_change_index = np.where(y_sort[:-1] != y_sort[1:])[0] - avg_window_size\n",
    "\n",
    "    # need the index to start at zero. Concatenate on a zero\n",
    "    tool_class_change_index = np.concatenate(\n",
    "        ([0], tool_class_change_index, [np.shape(y_sort)[0] - avg_window_size + 1])\n",
    "    )\n",
    "\n",
    "    indexer = (\n",
    "        np.arange(2)[None, :]\n",
    "        + np.arange(np.shape(tool_class_change_index)[0] - 1)[:, None]\n",
    "    )\n",
    "\n",
    "    # establish shaded region\n",
    "    shade_reg = tool_class_change_index[indexer]\n",
    "\n",
    "    x_max = len(y_sort) - avg_window_size + pad_size\n",
    "\n",
    "    # define colour palette and seaborn style for plot\n",
    "    sns.set(style=\"white\", context=\"notebook\")\n",
    "\n",
    "    fig, axes = plt.subplots(\n",
    "        1, 1, dpi=150, figsize=(7, 2.5), sharex=True, constrained_layout=True,\n",
    "    )\n",
    "\n",
    "    # moving average function\n",
    "    def moving_average(a, n=3):\n",
    "        # from https://stackoverflow.com/a/14314054\n",
    "        ret = np.cumsum(a, dtype=float)\n",
    "        ret[n:] = ret[n:] - ret[:-n]\n",
    "        return ret[n - 1 :] / n\n",
    "    \n",
    "    x = moving_average(scores, n=1)\n",
    "    x2 = moving_average(scores, n=avg_window_size)\n",
    "    y_avg = np.array([i for i in range(len(x2))]) + avg_window_size\n",
    "    axes.plot(y_avg, x2, linewidth=1.5, alpha=1, color=pal[5], label=\"Rolling average\")\n",
    "    axes.plot(x, linewidth=0.5, alpha=0.5, color=\"grey\", label=legend_label)\n",
    "    y_min = np.min(x)\n",
    "    y_max = np.max(x)\n",
    "    y_pad = (np.abs(y_min) + np.abs(y_max)) * 0.02\n",
    "\n",
    "    # remove all spines except bottom\n",
    "    axes.spines[\"top\"].set_visible(False)\n",
    "    axes.spines[\"right\"].set_visible(False)\n",
    "    axes.spines[\"left\"].set_visible(False)  \n",
    "    axes.spines[\"bottom\"].set_visible(True)\n",
    "    \n",
    "    # set bottom spine width\n",
    "    axes.spines[\"bottom\"].set_linewidth(0.5)\n",
    "    \n",
    "    # very light gridlines\n",
    "    axes.grid(alpha=opacity_grid, linewidth=0.5)\n",
    "    \n",
    "    # set label size for ticks\n",
    "    axes.tick_params(axis=\"x\", labelsize=7.5)\n",
    "    axes.tick_params(axis=\"y\", labelsize=7.5)\n",
    "\n",
    "\n",
    "    # colors\n",
    "    axes.set_ylim(y_min - y_pad, y_max + y_pad)\n",
    "    axes.set_xlim(x_min, x_max)\n",
    "\n",
    "    for region in range(len(shade_reg)):\n",
    "\n",
    "        f = failed_reg[region % 3]\n",
    "        c = colors[region % 3]\n",
    "        axes.fill_between(\n",
    "            x=shade_reg[region],\n",
    "            y1=y_min - y_pad,\n",
    "            y2=y_max + y_pad,\n",
    "            color=c,\n",
    "            alpha=opacity_color,\n",
    "            linewidth=0,\n",
    "            zorder=1,\n",
    "        )\n",
    "\n",
    "        # for text\n",
    "        axes.text(\n",
    "            x=(\n",
    "                shade_reg[region][0] + (shade_reg[region][1] - shade_reg[region][0]) / 2\n",
    "            ),\n",
    "            y=y_max + y_pad - y_max * 0.1,\n",
    "            s=f,\n",
    "            horizontalalignment=\"center\",\n",
    "            verticalalignment=\"center\",\n",
    "            size=8.5,\n",
    "            color=c,\n",
    "            rotation=\"horizontal\",\n",
    "            weight=\"semibold\",\n",
    "            alpha=1,\n",
    "        )\n",
    "\n",
    "    # axis label\n",
    "    axes.set_xlabel(\"Sample Number\", fontsize=7.5)\n",
    "    axes.set_ylabel(y_label, fontsize=7.5)\n",
    "\n",
    "    fig.suptitle(caption, fontsize=8.5)\n",
    "#     plt.legend(\n",
    "#         loc=\"center left\", bbox_to_anchor=(0.02, 0.6), fontsize=7.5,\n",
    "#     )\n",
    "    \n",
    "    if save_fig:\n",
    "        plt.savefig(f'{caption}.png',dpi=250, bbox_inches=\"tight\")\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll trend case 13, which is peformed on steel, at slow speed, and is a shallow cut."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1050x375 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "case = 13\n",
    "\n",
    "X_sort, y_sort = sorted_x(X, dfy, case)\n",
    "kls = build_kls_scores(encoder, X_sort)\n",
    "\n",
    "plot_one_signal_sequentially(kls, y_sort, case, save_fig=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks pretty good! The model produces a nice clear trend. However, as we've seen in the previous section, our anomaly detection model does have some difficulty in discerning when a tool is abnormal (failed or unhealthy) under certain cutting condititions. Let's look at another example -- case 11."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1050x375 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "case = 11\n",
    "\n",
    "X_sort, y_sort = sorted_x(X, dfy, case)\n",
    "kls = build_kls_scores(encoder, X_sort)\n",
    "\n",
    "plot_one_signal_sequentially(kls, y_sort, case, save_fig=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see how the trend increases through the \"degraded\" area, but then promptly drops off when it reaches the red \"failed\" area. Why? Well, I don't know exactly. It could be that the samples at the end of the trend are more similar to healthy samples. There is much more analysis that could be done... which I'll leave up to you. Change the case number and see what you get."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Further Ideas\n",
    "What we've gone through here is a method to do anomaly detection, on an industrial data set, using a VAE. I have no doubt that these methods can be improved upon, and that other interesting areas can be explored. I hope that some industrious researcher or student can use this as a spring-board to do some really interesting things! Here are some things I'd be interested in doing further:\n",
    "\n",
    "* As I mentioned above, an ensemble of models may produce significantly better results.\n",
    "* The $\\beta$ in the VAE makes this a disentangled-variational-autoencoder. It would be interesting to see how the codings change with different cutting parameters, and if the codings do represent certain cutting parameters.\n",
    "* I used the TCN in the VAE, but I think a regular convolutional neural network, with dilations, would work well too (that's my hunch). This would make the model training simpler.\n",
    "* If I were to start over again, I would integrate in more model tests. These model tests (like unit tests) would check the model's peformance against the different cutting parameters. This would make it easier to find which models generalize well across a cutting parameters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "In this post we've explored the peformance of our trained VAE model. We found that using the latent space for anomaly detection, using KL-divergence, was more effective than the input space anomaly detection. The principals demonstrated here can be used across many domains where anomaly detection is used.\n",
    "\n",
    "I hope you've enjoyed this series, and perhaps, have learned something new. If you found this useful, and are an academic researcher, feel free to cite the work ([preprint of the IJHM paper here](https://www.researchgate.net/publication/350842309_Self-supervised_learning_for_tool_wear_monitoring_with_a_disentangled-variational-autoencoder)). And give me a follow on Twitter!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# References \n",
    "\n",
    "An, Jinwon, and Sungzoon Cho. \"[Variational autoencoder based anomaly detection using reconstruction probability](http://dm.snu.ac.kr/static/docs/TR/SNUDM-TR-2015-03.pdf).\" Special Lecture on IE 2.1 (2015): 1-18.\n",
    "\n",
    "Davis, Jesse, and Mark Goadrich. \"[The relationship between Precision-Recall and ROC curves](https://dl.acm.org/doi/pdf/10.1145/1143844.1143874?casa_token=CSQzAhypHQ0AAAAA:WqAGJXokpttfPIStrcXb_2tXdufgXdDu085FVIBhtQA1hLgXZrJGVHThaTBx4tzGUky8KTRuMJqidg).\" Proceedings of the 23rd international conference on Machine learning. 2006.\n",
    "\n",
    "Saito, Takaya, and Marc Rehmsmeier. \"[The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0118432).\" PloS one 10.3 (2015): e0118432."
   ]
  },
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