monitoring-failing-computer-servers-with-anomaly-detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "34894c86",
   "metadata": {},
   "source": [
    "<h1 align=\"center\">Monitoring Failing Computer Servers with Anomaly Detection</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b106ca88",
   "metadata": {},
   "source": [
    "###### By: [jihad akbar](https://github.com/jihadakbr)\n",
    "###### You can see this notebook clearly using this link [nbviewer.org](https://nbviewer.org/github/jihadakbr/monitoring-failing-computer-servers-with-anomaly-detection/blob/main/monitoring-failing-computer-servers-with-anomaly-detection.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc5444c1",
   "metadata": {},
   "source": [
    "<h1>Credit</h1>\n",
    "\n",
    "<div class=\"alert alert-block alert-success\" style=\"margin-top: 20px\">\n",
    "<p><i><b>This project was completed as a part of the Honors portion of the <a href=\"https://www.coursera.org/learn/unsupervised-learning-recommenders-reinforcement-learning\" target=\"_blank\">Unsupervised Learning, Recommenders, Reinforcement Learning</a> Course on <a href=\"https://www.coursera.org/\" target=\"_blank\">Coursera</a>. </b></i></p>\n",
    "<p><i><b>Credit to DeepLearning.AI, Stanford, and the Coursera platform for providing the course materials and guidance.</b></i></p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44e3fe65",
   "metadata": {},
   "source": [
    "<div id=\"toc\"><h1>Table of Contents</h1></div>\n",
    "\n",
    "- [ 1 - Packages ](#1)\n",
    "- [ 2 - Anomaly detection](#2)\n",
    "  - [ 2.1 Problem Statement](#2.1)\n",
    "  - [ 2.2  Dataset](#2.2)\n",
    "  - [ 2.3 Gaussian distribution](#2.3)\n",
    "    - [ Exercise 1](#ex01)\n",
    "    - [ Exercise 2](#ex02)\n",
    "  - [ 2.4 High dimensional dataset](#2.4)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdad3b6d",
   "metadata": {},
   "source": [
    "_**NOTE:** To prevent errors from the autograder, you are not allowed to edit or delete non-graded cells in this lab. Please also refrain from adding any new cells. \n",
    "**Once you have passed this assignment** and want to experiment with any of the non-graded code, you may follow the instructions at the bottom of this notebook._"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2f9bc17",
   "metadata": {},
   "source": [
    "<a name=\"1\"></a>\n",
    "## 1 - Packages \n",
    "<div style=\"text-align: right;\"><a href=\"#toc\" style=\"text-decoration:none;\">↑ Table of Contents</a></div>\n",
    "\n",
    "First, let's run the cell below to import all the packages that you will need during this assignment.\n",
    "- [numpy](www.numpy.org) is the fundamental package for working with matrices in Python.\n",
    "- [matplotlib](http://matplotlib.org) is a famous library to plot graphs in Python.\n",
    "- ``utils.py`` contains helper functions for this assignment. You do not need to modify code in this file.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "119cf630",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from utils import *\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "893fa200",
   "metadata": {},
   "source": [
    "<a name=\"2\"></a>\n",
    "## 2 - Anomaly detection\n",
    "<div style=\"text-align: right;\"><a href=\"#toc\" style=\"text-decoration:none;\">↑ Table of Contents</a></div>\n",
    "\n",
    "<a name=\"2.1\"></a>\n",
    "### 2.1 Problem Statement\n",
    "<div style=\"text-align: right;\"><a href=\"#toc\" style=\"text-decoration:none;\">↑ Table of Contents</a></div>\n",
    "\n",
    "In this exercise, you will implement an anomaly detection algorithm to\n",
    "detect anomalous behavior in server computers.\n",
    "\n",
    "The dataset contains two features - \n",
    "   * throughput (mb/s) and \n",
    "   * latency (ms) of response of each server.\n",
    "\n",
    "While your servers were operating, you collected $m=307$ examples of how they were behaving, and thus have an unlabeled dataset $\\{x^{(1)}, \\ldots, x^{(m)}\\}$. \n",
    "* You suspect that the vast majority of these examples are “normal” (non-anomalous) examples of the servers operating normally, but there might also be some examples of servers acting anomalously within this dataset.\n",
    "\n",
    "You will use a Gaussian model to detect anomalous examples in your\n",
    "dataset. \n",
    "* You will first start on a 2D dataset that will allow you to visualize what the algorithm is doing.\n",
    "* On that dataset you will fit a Gaussian distribution and then find values that have very low probability and hence can be considered anomalies. \n",
    "* After that, you will apply the anomaly detection algorithm to a larger dataset with many dimensions. \n",
    "\n",
    "<a name=\"2.2\"></a>\n",
    "### 2.2  Dataset\n",
    "<div style=\"text-align: right;\"><a href=\"#toc\" style=\"text-decoration:none;\">↑ Table of Contents</a></div>\n",
    "\n",
    "You will start by loading the dataset for this task. \n",
    "- The `load_data()` function shown below loads the data into the variables `X_train`, `X_val` and `y_val` \n",
    "    - You will use `X_train` to fit a Gaussian distribution \n",
    "    - You will use `X_val` and `y_val` as a cross validation set to select a threshold and determine anomalous vs normal examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "318489d8",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [],
   "source": [
    "# Load the dataset\n",
    "X_train, X_val, y_val = load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17bb7700",
   "metadata": {},
   "source": [
    "#### View the variables\n",
    "Let's get more familiar with your dataset.  \n",
    "- A good place to start is to just print out each variable and see what it contains.\n",
    "\n",
    "The code below prints the first five elements of each of the variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "23237064",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The first 5 elements of X_train are:\n",
      " [[13.04681517 14.74115241]\n",
      " [13.40852019 13.7632696 ]\n",
      " [14.19591481 15.85318113]\n",
      " [14.91470077 16.17425987]\n",
      " [13.57669961 14.04284944]]\n"
     ]
    }
   ],
   "source": [
    "# Display the first five elements of X_train\n",
    "print(\"The first 5 elements of X_train are:\\n\", X_train[:5])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "221bb059",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The first 5 elements of X_val are\n",
      " [[15.79025979 14.9210243 ]\n",
      " [13.63961877 15.32995521]\n",
      " [14.86589943 16.47386514]\n",
      " [13.58467605 13.98930611]\n",
      " [13.46404167 15.63533011]]\n"
     ]
    }
   ],
   "source": [
    "# Display the first five elements of X_val\n",
    "print(\"The first 5 elements of X_val are\\n\", X_val[:5])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d2d0bf1c",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The first 5 elements of y_val are\n",
      " [0 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "# Display the first five elements of y_val\n",
    "print(\"The first 5 elements of y_val are\\n\", y_val[:5])  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7eaa9e15",
   "metadata": {},
   "source": [
    "#### Check the dimensions of your variables\n",
    "\n",
    "Another useful way to get familiar with your data is to view its dimensions.\n",
    "\n",
    "The code below prints the shape of `X_train`, `X_val` and `y_val`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f710a85a",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of X_train is: (307, 2)\n",
      "The shape of X_val is: (307, 2)\n",
      "The shape of y_val is:  (307,)\n"
     ]
    }
   ],
   "source": [
    "print ('The shape of X_train is:', X_train.shape)\n",
    "print ('The shape of X_val is:', X_val.shape)\n",
    "print ('The shape of y_val is: ', y_val.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f04ca6f9",
   "metadata": {},
   "source": [
    "#### Visualize your data\n",
    "\n",
    "Before starting on any task, it is often useful to understand the data by visualizing it. \n",
    "- For this dataset, you can use a scatter plot to visualize the data (`X_train`), since it has only two properties to plot (throughput and latency)\n",
    "\n",
    "- Your plot should look similar to the one below\n",
    "<img src=\"images/figure1.png\" width=\"500\" height=\"500\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "76076aaa",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a scatter plot of the data. To change the markers to blue \"x\",\n",
    "# we used the 'marker' and 'c' parameters\n",
    "plt.scatter(X_train[:, 0], X_train[:, 1], marker='x', c='b') \n",
    "\n",
    "# Set the title\n",
    "plt.title(\"The first dataset\")\n",
    "# Set the y-axis label\n",
    "plt.ylabel('Throughput (mb/s)')\n",
    "# Set the x-axis label\n",
    "plt.xlabel('Latency (ms)')\n",
    "# Set axis range\n",
    "plt.axis([0, 30, 0, 30])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99864e85",
   "metadata": {},
   "source": [
    "<a name=\"2.3\"></a>\n",
    "### 2.3 Gaussian distribution\n",
    "<div style=\"text-align: right;\"><a href=\"#toc\" style=\"text-decoration:none;\">↑ Table of Contents</a></div>\n",
    "\n",
    "To perform anomaly detection, you will first need to fit a model to the data’s distribution.\n",
    "\n",
    "* Given a training set $\\{x^{(1)}, ..., x^{(m)}\\}$ you want to estimate the Gaussian distribution for each\n",
    "of the features $x_i$. \n",
    "\n",
    "* Recall that the Gaussian distribution is given by\n",
    "\n",
    "   $$ p(x ; \\mu,\\sigma ^2) = \\frac{1}{\\sqrt{2 \\pi \\sigma ^2}}\\exp^{ - \\frac{(x - \\mu)^2}{2 \\sigma ^2} }$$\n",
    "\n",
    "   where $\\mu$ is the mean and $\\sigma^2$ is the variance.\n",
    "   \n",
    "* For each feature $i = 1\\ldots n$, you need to find parameters $\\mu_i$ and $\\sigma_i^2$ that fit the data in the $i$-th dimension $\\{x_i^{(1)}, ..., x_i^{(m)}\\}$ (the $i$-th dimension of each example).\n",
    "\n",
    "### 2.3.1 Estimating parameters for a Gaussian distribution\n",
    "<div style=\"text-align: right;\"><a href=\"#toc\" style=\"text-decoration:none;\">↑ Table of Contents</a></div>\n",
    "\n",
    "**Implementation**: \n",
    "\n",
    "Your task is to complete the code in `estimate_gaussian` below."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e1368e5",
   "metadata": {},
   "source": [
    "<a name=\"ex01\"></a>\n",
    "### Exercise 1\n",
    "<div style=\"text-align: right;\"><a href=\"#toc\" style=\"text-decoration:none;\">↑ Table of Contents</a></div>\n",
    "\n",
    "Please complete the `estimate_gaussian` function below to calculate `mu` (mean for each feature in `X`) and `var` (variance for each feature in `X`). \n",
    "\n",
    "You can estimate the parameters, ($\\mu_i$, $\\sigma_i^2$), of the $i$-th\n",
    "feature by using the following equations. To estimate the mean, you will\n",
    "use:\n",
    "\n",
    "$$\\mu_i = \\frac{1}{m} \\sum_{j=1}^m x_i^{(j)}$$\n",
    "\n",
    "and for the variance you will use:\n",
    "$$\\sigma_i^2 = \\frac{1}{m} \\sum_{j=1}^m (x_i^{(j)} - \\mu_i)^2$$\n",
    "\n",
    "If you get stuck, you can check out the hints presented after the cell below to help you with the implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "758d7653",
   "metadata": {
    "deletable": false
   },
   "outputs": [],
   "source": [
    "# UNQ_C1\n",
    "# GRADED FUNCTION: estimate_gaussian\n",
    "\n",
    "def estimate_gaussian(X): \n",
    "    \"\"\"\n",
    "    Calculates mean and variance of all features \n",
    "    in the dataset\n",
    "    \n",
    "    Args:\n",
    "        X (ndarray): (m, n) Data matrix\n",
    "    \n",
    "    Returns:\n",
    "        mu (ndarray): (n,) Mean of all features\n",
    "        var (ndarray): (n,) Variance of all features\n",
    "    \"\"\"\n",
    "\n",
    "    m, n = X.shape\n",
    "    \n",
    "    ### START CODE HERE ### \n",
    "    mu = 1 / m * np.sum(X, axis = 0)\n",
    "    var = 1 / m * np.sum((X - mu) ** 2, axis = 0)\n",
    "    ### END CODE HERE ###\n",
    "        \n",
    "    return mu, var"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf941ff1",
   "metadata": {},
   "source": [
    "<details>\n",
    "  <summary><font size=\"3\" color=\"darkgreen\"><b>Click for hints</b></font></summary>\n",
    "  \n",
    "   * You can implement this function in two ways: \n",
    "      * 1 - by having two nested for loops - one looping over the **columns** of `X` (each feature) and then looping over each data point. \n",
    "      * 2 - in a vectorized manner by using `np.sum()` with `axis = 0` parameter (since we want the sum for each column)\n",
    "\n",
    "    \n",
    "   * Here's how you can structure the overall implementation of this function for the vectorized implementation:\n",
    "     ```python  \n",
    "    def estimate_gaussian(X): \n",
    "        m, n = X.shape\n",
    "    \n",
    "        ### START CODE HERE ### \n",
    "        mu = # Your code here to calculate the mean of every feature\n",
    "        var = # Your code here to calculate the variance of every feature \n",
    "        ### END CODE HERE ### \n",
    "        \n",
    "        return mu, var\n",
    "    ```\n",
    "\n",
    "    If you're still stuck, you can check the hints presented below to figure out how to calculate `mu` and `var`.\n",
    "    \n",
    "    <details>\n",
    "          <summary><font size=\"2\" color=\"darkblue\"><b>Hint to calculate mu</b></font></summary>\n",
    "           &emsp; &emsp; You can use <a href=\"https://numpy.org/doc/stable/reference/generated/numpy.sum.html\">np.sum</a> to with `axis = 0` parameter to get the sum for each column of an array\n",
    "          <details>\n",
    "              <summary><font size=\"2\" color=\"blue\"><b>&emsp; &emsp; More hints to calculate mu</b></font></summary>\n",
    "               &emsp; &emsp; You can compute mu as <code>mu = 1 / m * np.sum(X, axis = 0)</code>\n",
    "           </details>\n",
    "    </details>\n",
    "    \n",
    "    <details>\n",
    "          <summary><font size=\"2\" color=\"darkblue\"><b>Hint to calculate var</b></font></summary>\n",
    "           &emsp; &emsp; You can use <a href=\"https://numpy.org/doc/stable/reference/generated/numpy.sum.html\">np.sum</a> to with `axis = 0` parameter to get the sum for each column of an array and <code>**2</code> to get the square.\n",
    "          <details>\n",
    "              <summary><font size=\"2\" color=\"blue\"><b>&emsp; &emsp; More hints to calculate var</b></font></summary>\n",
    "               &emsp; &emsp; You can compute var as <code> var = 1 / m * np.sum((X - mu) ** 2, axis = 0)</code>\n",
    "           </details>\n",
    "    </details>\n",
    "    \n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "573880e5",
   "metadata": {},
   "source": [
    "You can check if your implementation is correct by running the following test code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d83950ef",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean of each feature: [14.11222578 14.99771051]\n",
      "Variance of each feature: [1.83263141 1.70974533]\n",
      "\u001b[92mAll tests passed!\n"
     ]
    }
   ],
   "source": [
    "# Estimate mean and variance of each feature\n",
    "mu, var = estimate_gaussian(X_train)              \n",
    "\n",
    "print(\"Mean of each feature:\", mu)\n",
    "print(\"Variance of each feature:\", var)\n",
    "    \n",
    "# UNIT TEST\n",
    "from public_tests import *\n",
    "estimate_gaussian_test(estimate_gaussian)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9b51664",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "<table>\n",
    "  <tr>\n",
    "    <td> <b>Mean of each feature: <b>  </td> \n",
    "    <td> [14.11222578 14.99771051]</td> \n",
    "   </tr>    \n",
    "   <tr>\n",
    "    <td> <b>Variance of each feature: <b>  </td>\n",
    "     <td> [1.83263141 1.70974533] </td> \n",
    "  </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfbed0d6",
   "metadata": {},
   "source": [
    "Now that you have completed the code in `estimate_gaussian`, we will visualize the contours of the fitted Gaussian distribution. \n",
    "\n",
    "You should get a plot similar to the figure below. \n",
    "<img src=\"images/figure2.png\" width=\"500\" height=\"500\">\n",
    "\n",
    "\n",
    "From your plot you can see that most of the examples are in the region with the highest probability, while the anomalous examples are in the regions with lower probabilities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7f7d0c30",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Returns the density of the multivariate normal\n",
    "# at each data point (row) of X_train\n",
    "p = multivariate_gaussian(X_train, mu, var)\n",
    "\n",
    "#Plotting code \n",
    "visualize_fit(X_train, mu, var)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "395dda6f",
   "metadata": {},
   "source": [
    "### 2.3.2 Selecting the threshold $\\epsilon$\n",
    "<div style=\"text-align: right;\"><a href=\"#toc\" style=\"text-decoration:none;\">↑ Table of Contents</a></div>\n",
    "\n",
    "Now that you have estimated the Gaussian parameters, you can investigate which examples have a very high probability given this distribution and which examples have a very low probability.  \n",
    "\n",
    "* The low probability examples are more likely to be the anomalies in our dataset. \n",
    "* One way to determine which examples are anomalies is to select a threshold based on a cross validation set. \n",
    "\n",
    "In this section, you will complete the code in `select_threshold` to select the threshold $\\varepsilon$ using the $F_1$ score on a cross validation set.\n",
    "\n",
    "* For this, we will use a cross validation set\n",
    "$\\{(x_{\\rm cv}^{(1)}, y_{\\rm cv}^{(1)}),\\ldots, (x_{\\rm cv}^{(m_{\\rm cv})}, y_{\\rm cv}^{(m_{\\rm cv})})\\}$, where the label $y=1$ corresponds to an anomalous example, and $y=0$ corresponds to a normal example. \n",
    "* For each cross validation example, we will compute $p(x_{\\rm cv}^{(i)})$. The vector of all of these probabilities $p(x_{\\rm cv}^{(1)}), \\ldots, p(x_{\\rm cv}^{(m_{\\rm cv})})$ is passed to `select_threshold` in the vector `p_val`. \n",
    "* The corresponding labels $y_{\\rm cv}^{(1)}, \\ldots, y_{\\rm cv}^{(m_{\\rm cv})}$ are passed to the same function in the vector `y_val`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73888df3",
   "metadata": {},
   "source": [
    "<a name=\"ex02\"></a>\n",
    "### Exercise 2\n",
    "<div style=\"text-align: right;\"><a href=\"#toc\" style=\"text-decoration:none;\">↑ Table of Contents</a></div>\n",
    "\n",
    "Please complete the `select_threshold` function below to find the best threshold to use for selecting outliers based on the results from the validation set (`p_val`) and the ground truth (`y_val`). \n",
    "\n",
    "* In the provided code `select_threshold`, there is already a loop that will try many different values of $\\varepsilon$ and select the best $\\varepsilon$ based on the $F_1$ score. \n",
    "\n",
    "* You need to implement code to calculate the F1 score from choosing `epsilon` as the threshold and place the value in `F1`. \n",
    "\n",
    "  * Recall that if an example $x$ has a low probability $p(x) < \\varepsilon$, then it is classified as an anomaly. \n",
    "        \n",
    "  * Then, you can compute precision and recall by: \n",
    "   $$\\begin{aligned}\n",
    "   prec&=&\\frac{tp}{tp+fp}\\\\\n",
    "   rec&=&\\frac{tp}{tp+fn},\n",
    "   \\end{aligned}$$ where\n",
    "    * $tp$ is the number of true positives: the ground truth label says it’s an anomaly and our algorithm correctly classified it as an anomaly.\n",
    "    * $fp$ is the number of false positives: the ground truth label says it’s not an anomaly, but our algorithm incorrectly classified it as an anomaly.\n",
    "    * $fn$ is the number of false negatives: the ground truth label says it’s an anomaly, but our algorithm incorrectly classified it as not being anomalous.\n",
    "\n",
    "  * The $F_1$ score is computed using precision ($prec$) and recall ($rec$) as follows:\n",
    "    $$F_1 = \\frac{2\\cdot prec \\cdot rec}{prec + rec}$$ \n",
    "\n",
    "**Implementation Note:** \n",
    "In order to compute $tp$, $fp$ and $fn$, you may be able to use a vectorized implementation rather than loop over all the examples.\n",
    "\n",
    "\n",
    "If you get stuck, you can check out the hints presented after the cell below to help you with the implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0ecb687d",
   "metadata": {
    "deletable": false
   },
   "outputs": [],
   "source": [
    "# UNQ_C2\n",
    "# GRADED FUNCTION: select_threshold\n",
    "\n",
    "def select_threshold(y_val, p_val): \n",
    "    \"\"\"\n",
    "    Finds the best threshold to use for selecting outliers \n",
    "    based on the results from a validation set (p_val) \n",
    "    and the ground truth (y_val)\n",
    "    \n",
    "    Args:\n",
    "        y_val (ndarray): Ground truth on validation set\n",
    "        p_val (ndarray): Results on validation set\n",
    "        \n",
    "    Returns:\n",
    "        epsilon (float): Threshold chosen \n",
    "        F1 (float):      F1 score by choosing epsilon as threshold\n",
    "    \"\"\" \n",
    "\n",
    "    best_epsilon = 0\n",
    "    best_F1 = 0\n",
    "    F1 = 0\n",
    "    \n",
    "    step_size = (max(p_val) - min(p_val)) / 1000\n",
    "    \n",
    "    for epsilon in np.arange(min(p_val), max(p_val), step_size):\n",
    "    \n",
    "        ### START CODE HERE ### \n",
    "        predictions = (p_val < epsilon)\n",
    "\n",
    "        tp = np.sum((predictions == 1) & (y_val == 1))\n",
    "        fp = sum((predictions == 1) & (y_val == 0))\n",
    "        fn = np.sum((predictions == 0) & (y_val == 1))\n",
    "\n",
    "        prec = tp / (tp + fp)\n",
    "        rec = tp / (tp + fn)\n",
    "\n",
    "        F1 = 2 * prec * rec / (prec + rec)\n",
    "        ### END CODE HERE ### \n",
    "        \n",
    "        if F1 > best_F1:\n",
    "            best_F1 = F1\n",
    "            best_epsilon = epsilon\n",
    "        \n",
    "    return best_epsilon, best_F1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f698bc2",
   "metadata": {},
   "source": [
    "<details>\n",
    "  <summary><font size=\"3\" color=\"darkgreen\"><b>Click for hints</b></font></summary>\n",
    "\n",
    "   * Here's how you can structure the overall implementation of this function for the vectorized implementation:\n",
    "     ```python  \n",
    "    def select_threshold(y_val, p_val): \n",
    "        best_epsilon = 0\n",
    "        best_F1 = 0\n",
    "        F1 = 0\n",
    "    \n",
    "        step_size = (max(p_val) - min(p_val)) / 1000\n",
    "    \n",
    "        for epsilon in np.arange(min(p_val), max(p_val), step_size):\n",
    "    \n",
    "            ### START CODE HERE ### \n",
    "            predictions = # Your code here to calculate predictions for each example using epsilon as threshold\n",
    "        \n",
    "            tp = # Your code here to calculate number of true positives\n",
    "            fp = # Your code here to calculate number of false positives\n",
    "            fn = # Your code here to calculate number of false negatives\n",
    "        \n",
    "            prec = # Your code here to calculate precision\n",
    "            rec = # Your code here to calculate recall\n",
    "        \n",
    "            F1 = # Your code here to calculate F1\n",
    "            ### END CODE HERE ### \n",
    "        \n",
    "            if F1 > best_F1:\n",
    "                best_F1 = F1\n",
    "                best_epsilon = epsilon\n",
    "        \n",
    "        return best_epsilon, best_F1\n",
    "    ```\n",
    "\n",
    "    If you're still stuck, you can check the hints presented below to figure out how to calculate each variable.\n",
    "    \n",
    "    <details>\n",
    "          <summary><font size=\"2\" color=\"darkblue\"><b>Hint to calculate predictions</b></font></summary>\n",
    "           &emsp; &emsp; If an example  𝑥  has a low probability  $p(x) < \\epsilon$ , then it is classified as an anomaly. To get predictions for each example (0/ False for normal and 1/True for anomaly), you can use <code>predictions = (p_val < epsilon)</code>\n",
    "    </details>\n",
    "    \n",
    "    <details>\n",
    "          <summary><font size=\"2\" color=\"darkblue\"><b>Hint to calculate tp, fp, fn</b></font></summary>\n",
    "           &emsp; &emsp; \n",
    "        <ul>\n",
    "          <li>If you have several binary values in an $n$-dimensional\n",
    "binary vector, you can find out how many values in this vector are 0 by using:  `np.sum(v == 0)`</li>\n",
    "          <li>You can also apply a logical *and* operator to such binary vectors. For instance,  `predictions` is a binary vector of the size of your number of cross validation set, where the $i$-th element is 1 if your algorithm considers $x_{\\rm cv}^{(i)}$ an anomaly, and 0 otherwise. </li>\n",
    "          <li>You can then, for example, compute the number of false positives using:  \n",
    "<code>fp = sum((predictions == 1) & (y_val == 0))</code>.</li>\n",
    "        </ul>\n",
    "         <details>\n",
    "              <summary><font size=\"2\" color=\"blue\"><b>&emsp; &emsp; More hints to calculate tp, fn</b></font></summary>\n",
    "               &emsp; &emsp;\n",
    "             <ul>\n",
    "              <li>You can compute tp as <code> tp = np.sum((predictions == 1) & (y_val == 1))</code></li>\n",
    "              <li>You can compute tn as <code> fn = np.sum((predictions == 0) & (y_val == 1))</code></li>  \n",
    "              </ul>\n",
    "          </details>\n",
    "    </details>\n",
    "        \n",
    "    <details>\n",
    "          <summary><font size=\"2\" color=\"darkblue\"><b>Hint to calculate precision</b></font></summary>\n",
    "           &emsp; &emsp; You can calculate precision as <code>prec = tp / (tp + fp)</code>\n",
    "    </details>\n",
    "        \n",
    "    <details>\n",
    "          <summary><font size=\"2\" color=\"darkblue\"><b>Hint to calculate recall</b></font></summary>\n",
    "           &emsp; &emsp; You can calculate recall as <code>rec = tp / (tp + fn)</code>\n",
    "    </details>\n",
    "        \n",
    "    <details>\n",
    "          <summary><font size=\"2\" color=\"darkblue\"><b>Hint to calculate F1</b></font></summary>\n",
    "           &emsp; &emsp; You can calculate F1 as <code>F1 = 2 * prec * rec / (prec + rec)</code>\n",
    "    </details>\n",
    "    \n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac468df4",
   "metadata": {},
   "source": [
    "You can check your implementation using the code below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a6c2032c",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Jihad\\AppData\\Local\\Temp\\ipykernel_6424\\498098231.py:34: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  prec = tp / (tp + fp)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best epsilon found using cross-validation: 8.990853e-05\n",
      "Best F1 on Cross Validation Set: 0.875000\n",
      "\u001b[92mAll tests passed!\n"
     ]
    }
   ],
   "source": [
    "p_val = multivariate_gaussian(X_val, mu, var)\n",
    "epsilon, F1 = select_threshold(y_val, p_val)\n",
    "\n",
    "print('Best epsilon found using cross-validation: %e' % epsilon)\n",
    "print('Best F1 on Cross Validation Set: %f' % F1)\n",
    "    \n",
    "# UNIT TEST\n",
    "select_threshold_test(select_threshold)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6613543",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "<table>\n",
    "  <tr>\n",
    "    <td> <b>Best epsilon found using cross-validation: <b>  </td> \n",
    "    <td> 8.99e-05</td> \n",
    "   </tr>    \n",
    "   <tr>\n",
    "    <td> <b>Best F1 on Cross Validation Set: <b>  </td>\n",
    "     <td> 0.875 </td> \n",
    "  </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36ff2e10",
   "metadata": {},
   "source": [
    "Now we will run your anomaly detection code and circle the anomalies in the plot (Figure 3 below).\n",
    "\n",
    "<img src=\"images/figure3.png\" width=\"500\" height=\"500\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "940300d1",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x198357c1f30>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Find the outliers in the training set \n",
    "outliers = p < epsilon\n",
    "\n",
    "# Visualize the fit\n",
    "visualize_fit(X_train, mu, var)\n",
    "\n",
    "# Draw a red circle around those outliers\n",
    "plt.plot(X_train[outliers, 0], X_train[outliers, 1], 'ro',\n",
    "         markersize= 10,markerfacecolor='none', markeredgewidth=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d96806cd",
   "metadata": {},
   "source": [
    "<a name=\"2.4\"></a>\n",
    "### 2.4 High dimensional dataset\n",
    "<div style=\"text-align: right;\"><a href=\"#toc\" style=\"text-decoration:none;\">↑ Table of Contents</a></div>\n",
    "\n",
    "Now,  we will run the anomaly detection algorithm that you implemented on a more realistic and much harder dataset.\n",
    "\n",
    "In this dataset, each example is described by 11 features, capturing many more properties of your compute servers.\n",
    "\n",
    "Let's start by loading the dataset.\n",
    "\n",
    "- The `load_data()` function shown below loads the data into variables `X_train_high`, `X_val_high` and `y_val_high`\n",
    "    -  `_high` is meant to distinguish these variables from the ones used in the previous part\n",
    "    - We will use `X_train_high` to fit Gaussian distribution \n",
    "    - We will use `X_val_high` and `y_val_high` as a cross validation set to select a threshold and determine anomalous vs normal examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c5860903",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [],
   "source": [
    "# load the dataset\n",
    "X_train_high, X_val_high, y_val_high = load_data_multi()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a068ed88",
   "metadata": {},
   "source": [
    "#### Check the dimensions of your variables\n",
    "\n",
    "Let's check the dimensions of these new variables to become familiar with the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7922ea37",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of X_train_high is: (1000, 11)\n",
      "The shape of X_val_high is: (100, 11)\n",
      "The shape of y_val_high is:  (100,)\n"
     ]
    }
   ],
   "source": [
    "print ('The shape of X_train_high is:', X_train_high.shape)\n",
    "print ('The shape of X_val_high is:', X_val_high.shape)\n",
    "print ('The shape of y_val_high is: ', y_val_high.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca9ca3d5",
   "metadata": {},
   "source": [
    "#### Anomaly detection \n",
    "\n",
    "Now, let's run the anomaly detection algorithm on this new dataset.\n",
    "\n",
    "The code below will use your code to \n",
    "* Estimate the Gaussian parameters ($\\mu_i$ and $\\sigma_i^2$)\n",
    "* Evaluate the probabilities for both the training data `X_train_high` from which you estimated the Gaussian parameters, as well as for the the cross-validation set `X_val_high`. \n",
    "* Finally, it will use `select_threshold` to find the best threshold $\\varepsilon$. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9b9ceef8",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Jihad\\AppData\\Local\\Temp\\ipykernel_6424\\498098231.py:34: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  prec = tp / (tp + fp)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best epsilon found using cross-validation: 1.377229e-18\n",
      "Best F1 on Cross Validation Set:  0.615385\n",
      "# Anomalies found: 117\n"
     ]
    }
   ],
   "source": [
    "# Apply the same steps to the larger dataset\n",
    "\n",
    "# Estimate the Gaussian parameters\n",
    "mu_high, var_high = estimate_gaussian(X_train_high)\n",
    "\n",
    "# Evaluate the probabilites for the training set\n",
    "p_high = multivariate_gaussian(X_train_high, mu_high, var_high)\n",
    "\n",
    "# Evaluate the probabilites for the cross validation set\n",
    "p_val_high = multivariate_gaussian(X_val_high, mu_high, var_high)\n",
    "\n",
    "# Find the best threshold\n",
    "epsilon_high, F1_high = select_threshold(y_val_high, p_val_high)\n",
    "\n",
    "print('Best epsilon found using cross-validation: %e'% epsilon_high)\n",
    "print('Best F1 on Cross Validation Set:  %f'% F1_high)\n",
    "print('# Anomalies found: %d'% sum(p_high < epsilon_high))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bbf6252",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "<table>\n",
    "  <tr>\n",
    "    <td> <b>Best epsilon found using cross-validation: <b>  </td> \n",
    "    <td> 1.38e-18</td> \n",
    "   </tr>    \n",
    "   <tr>\n",
    "    <td> <b>Best F1 on Cross Validation Set: <b>  </td>\n",
    "     <td> 0.615385 </td> \n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td> <b># anomalies found: <b>  </td>\n",
    "     <td>  117 </td> \n",
    "  </tr>\n",
    "</table>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
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