ch14/Ch14-executed.ipynb

{
 "cells": [
  {
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   "metadata": {},
   "source": [
    "&copy; 2019 by Pearson Education, Inc. All Rights Reserved. The content in this notebook is based on the book [**Python for Programmers**](https://amzn.to/2VvdnxE)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# enable high-res images in notebook \n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 14. Machine Learning: Classification, Regression and Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 14.1 Introduction to Machine Learning\n",
    "* **Can we make computers learn?** \n",
    "* **machine learning**—one of the most exciting and promising subfields of **artificial intelligence**\n",
    "* **Rather than programming expertise into our applications, we program them to learn from data**\n",
    "* We build machine-learning **models** that make **remarkably accurate predictions**\n",
    "* The **“secret sauce”** is **data**&mdash;and **lots of it** \n",
    "* **Quickly solve problems** that novices and experienced programmers alike probably would not have attempted just a few years ago \n",
    "* Friendly, hands-on introduction to **simpler machine-learning techniques**\n",
    "* Popular machine-learning applications (there are thousands) \n",
    "    * **Chatbots, computer vision, fraud detection, handwriting recognition, language translation, medical diagnosis, predicting loan defaults, recommender systems, self-driving cars, sentiment analysis, spam filtering, stock price forecasting and voice recognition**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.1.2 Two Main Types of Machine Learning "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Types of machine learning diagram](./ch14images/TypesOfMachineLearning.png \"Types of machine learning diagram\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Big Data and Big Computer Processing Power\n",
    "* **Exploding, low-cost computing power, memory and secondary storage** enable us to think differently about solution approaches \n",
    "* **Before machine learning**: “**I’m drowning in data** and I don’t know what to do with it” \n",
    "* **With machine learning**: “**Flood me with big data** so I can use machine learning to extract insights and make valuable predictions from the data”"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 14.2 Case Study: Classification with k-Nearest Neighbors and the Digits Dataset, Part 1\n",
    "* To process mail efficiently and route each letter to the correct destination, postal service computers must be able to **scan handwritten names, addresses and zip codes** and **recognize the letters and digits**\n",
    "* **Scikit-learn** enables even novice programmers to make such machine-learning problems manageable\n",
    "\n",
    "### Supervised Machine Learning: Classification \n",
    "* Attempt to **predict the distinct class** (category) to which a **sample** belongs\n",
    "    * **Binary classification**&mdash;**two** classes (e.g., “dog” or “cat”)\n",
    "* [**Digits dataset**](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) bundled with scikit-learn\n",
    "    * 8-by-8 pixel images representing 1797 hand-written digits (0 through 9) \n",
    "* Goal: **Predict** which digit an image represents\n",
    "    * **Multi-classification**&mdash;**10 possible digits** (the classes)\n",
    "* Train a classification model using **labeled data**—know in advance each digit’s class\n",
    "* We’ll use one of the simplest machine-learning classification algorithms, **k-nearest neighbors (k-NN)**, to **recognize handwritten digits** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.2.1 k-Nearest Neighbors Algorithm (k-NN) \n",
    "* Predict a sample’s class by looking at the **_k_ training samples** **nearest in \"distance\"** to the **sample** \n",
    "* Filled dots represent four distinct classes—A (blue), B (green), C (red) and D (purple) \n",
    "* **Class with the most “votes” wins**\n",
    "    * **Odd _k_ value** **avoids ties** &mdash; there’s never an equal number of votes\n",
    "    \n",
    "<img src=\"./ch14images/nearest.png\" alt=\"Diagram for the discussion of the k-nearest neighbors algorithm\" width=300/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.2.2 Loading the Dataset with the **`load_digits` Function** \n",
    "* Returns a **`Bunch`** object containing **digit samples** and **metadata**\n",
    "* A **`Bunch`** is a dictionary with additional **dataset-specific attributes**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_digits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits = load_digits()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Displaying Digits Dataset's Description\n",
    "* **Digits dataset** is a subset of the [**UCI (University of California Irvine) ML hand-written digits dataset**](http://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits)\n",
    "    * Original dataset: **5620 samples**—3823 for **training** and 1797 for **testing** \n",
    "    * **Digits dataset**: Only the **1797 testing samples**\n",
    "* A Bunch’s **`DESCR` attribute** contains dataset's description \n",
    "    * Each sample has **`64` features** (**`Number of Attributes`**) that represent an **8-by-8 image** with **pixel values `0`–`16`** (**`Attribute Information`**)\n",
    "    * **No missing values** (**`Missing Attribute Values`**) \n",
    "* **64 features** may seem like a lot\n",
    "    * Datasets can have **hundreds**, **thousands** or even **millions of features**\n",
    "    * Processing datasets like these can require enormous computing capabilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _digits_dataset:\n",
      "\n",
      "Optical recognition of handwritten digits dataset\n",
      "--------------------------------------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 5620\n",
      "    :Number of Attributes: 64\n",
      "    :Attribute Information: 8x8 image of integer pixels in the range 0..16.\n",
      "    :Missing Attribute Values: None\n",
      "    :Creator: E. Alpaydin (alpaydin '@' boun.edu.tr)\n",
      "    :Date: July; 1998\n",
      "\n",
      "This is a copy of the test set of the UCI ML hand-written digits datasets\n",
      "https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits\n",
      "\n",
      "The data set contains images of hand-written digits: 10 classes where\n",
      "each class refers to a digit.\n",
      "\n",
      "Preprocessing programs made available by NIST were used to extract\n",
      "normalized bitmaps of handwritten digits from a preprinted form. From a\n",
      "total of 43 people, 30 contributed to the training set and different 13\n",
      "to the test set. 32x32 bitmaps are divided into nonoverlapping blocks of\n",
      "4x4 and the number of on pixels are counted in each block. This generates\n",
      "an input matrix of 8x8 where each element is an integer in the range\n",
      "0..16. This reduces dimensionality and gives invariance to small\n",
      "distortions.\n",
      "\n",
      "For info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G.\n",
      "T. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C.\n",
      "L. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469,\n",
      "1994.\n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "  - C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their\n",
      "    Applications to Handwritten Digit Recognition, MSc Thesis, Institute of\n",
      "    Graduate Studies in Science and Engineering, Bogazici University.\n",
      "  - E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.\n",
      "  - Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin.\n",
      "    Linear dimensionalityreduction using relevance weighted LDA. School of\n",
      "    Electrical and Electronic Engineering Nanyang Technological University.\n",
      "    2005.\n",
      "  - Claudio Gentile. A New Approximate Maximal Margin Classification\n",
      "    Algorithm. NIPS. 2000.\n"
     ]
    }
   ],
   "source": [
    "print(digits.DESCR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking the Sample and Target Sizes (1 of 2)\n",
    "* `Bunch` object’s **`data`** and **`target`** attributes are **NumPy arrays**:\n",
    "    * **`data` array**: The **1797 samples** (digit images), each with **64 features** with values**&nbsp;0** (white) to **16** (black), representing **pixel intensities**\n",
    "    ![Pixel intensities in grayscale shades from white (0) to black (16)](./ch14images/grays.png \"Pixel intensities in grayscale shades from white (0) to black (16)\")\n",
    "\n",
    "    * **`target` array**: The **images’ labels**, (classes) indicating **which digit** each image represents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.target[:20]  # first twenty target values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking the Sample and Target Sizes (2 of 2)\n",
    "* Confirm number of **samples** and **features** (per sample) via `data` array’s **`shape`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 64)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Confirm that **number of target values matches number of samples** via `target` array’s `shape`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797,)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.target.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A Sample Digit Image \n",
    "* Images are **two-dimensional**—width and a height in pixels \n",
    "* Digits dataset's `Bunch` object has an **`images` attribute**\n",
    "    * Each element is an **8-by-8 array** representing a **digit image’s pixel intensities**\n",
    "* Scikit-learn stores the intensity values as **NumPy type `float64`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  2.,  9., 15., 14.,  9.,  3.,  0.],\n",
       "       [ 0.,  4., 13.,  8.,  9., 16.,  8.,  0.],\n",
       "       [ 0.,  0.,  0.,  6., 14., 15.,  3.,  0.],\n",
       "       [ 0.,  0.,  0., 11., 14.,  2.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.,  2., 15., 11.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.,  0.,  2., 15.,  4.,  0.],\n",
       "       [ 0.,  1.,  5.,  6., 13., 16.,  6.,  0.],\n",
       "       [ 0.,  2., 12., 12., 13., 11.,  0.,  0.]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.images[13]  # show array for sample image at index 13"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Visualization of `digits.images[13]`\n",
    "\n",
    "    <img src=\"./ch14images/sampledigit3.png\" alt=\"Image of a handwritten digit 3\" width=\"200px\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparing the Data for Use with Scikit-Learn (1 of 2)\n",
    "* Scikit-learn estimators require samples to be stored in a **two-dimensional array of floating-point values** (or **list of lists** or **pandas `DataFrame`**): \n",
    "\t* Each **row** represents one **sample** \n",
    "\t* Each **column** in a given row represents one **feature** for that sample\n",
    "* Multi-dimensional data samples must be **flattened** into a **one-dimensional array** \n",
    "* For **categorical features** (e.g., **strings** like `'spam'` or `'not-spam'`), you’d have to **preprocess** those features into **numerical values**—known as **one-hot encoding** (discussed later in deep learning)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparing the Data for Use with Scikit-Learn (2 of 2)\n",
    "* **`load_digits`** returns the **preprocessed data** ready for machine learning \n",
    "* **8-by-8 array `digits.images[13]`** corresponds to **1-by-64 array `digits.data[13]`**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  2.,  9., 15., 14.,  9.,  3.,  0.,  0.,  4., 13.,  8.,  9.,\n",
       "       16.,  8.,  0.,  0.,  0.,  0.,  6., 14., 15.,  3.,  0.,  0.,  0.,\n",
       "        0., 11., 14.,  2.,  0.,  0.,  0.,  0.,  0.,  2., 15., 11.,  0.,\n",
       "        0.,  0.,  0.,  0.,  0.,  2., 15.,  4.,  0.,  0.,  1.,  5.,  6.,\n",
       "       13., 16.,  6.,  0.,  0.,  2., 12., 12., 13., 11.,  0.,  0.])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.data[13]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.2.3 Visualizing the Data\n",
    "* **Data exploration**—familiarize yourself with the data\n",
    "* **Visualizing** helps you get a sense of your data\n",
    "\n",
    "### Creating the Diagram\n",
    "* **Color map `plt.cm.gray_r`** is for **grayscale** with **0 for white**\n",
    "* [**Matplotlib’s color map names**](https://matplotlib.org/examples/color/colormaps_reference.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LsmLFipZ+XsWE58z4+HhowA0bNoTiNm3a1DAmOhcGBwdDcUNDQ6G4/v7+Kd/v4DlT+DomYnh4OBQXnXtbtmxpGFPkemjFihXavn37eGEDpiM8XyLbmsh2Rop/6bZjx45QXMTNN98ciovsgHTwfJFYx0xLB8+ZQudLdN0RnQfRuNe97nUNYyL7TFEzmS9c8wIAAAAgCRQvAAAAAJJA8QIAAAAgCRQvAAAAAJJA8QIAAAAgCRQvAAAAAJJA8QIAAAAgCc3o81K4NWvWhOIi9yuP3qN67dq1oTi0VrTPS+T+9lLs9xy95/rGjRtDcdH76jfq84JiRH+/0XVHkU28orm1ui9Vu7rhhhsaxmzdujU0Vk9PTyjummuuCcUNDAw0jCly7qH1otut6N8764XqGR0dbRgT7QUX3R+KzoPo/KsCjrwAAAAASALFCwAAAIAkULwAAAAASALFCwAAAIAkULwAAAAASALFCwAAAIAkULwAAAAASALFCwAAAIAkULwAAAAASMLsshOIiHYNjnQHHRwcDI0V6byO1ot0mZZiXWylWNf04eHh0FjRjtrROYjWiP6tR7vdj4yMhOIi67XofI9+JqbW39/fMCa6bomMJcXnH93S0xWdM1u3bg3FrV+/fibpoES7d+9uGBNdd0Tn1dDQUChufHw8FFcFHHkBAAAAkASKFwAAAABJoHgBAAAAkASKFwAAAABJoHgBAAAAkASKFwAAAABJoHgBAAAAkASKFwAAAABJoHgBAAAAkITZZX54tJtntNtopANxSh1E0XxFdiaPdruNdFZHMTZs2NAw5sYbbwyNFe1qHf397t+/v2FMdN2H1ol0yH46cdHfMduudEW3DVGDg4OFjofWWb16dcOYxYsXh8bavHlzKC66nxOZV9H1ULP3czjyAgAAACAJFC8AAAAAkkDxAgAAACAJFC8AAAAAkkDxAgAAACAJFC8AAAAAkkDxAgAAACAJFC8AAAAAklBqk8poE5vh4eHCPjPaOGzfvn2huEhjTFRXpIlhtInc2rVrQ3FFNsbE1Ips7Ldp06ZQXGRORZ133nmFjYXGIn/DRTdfu+SSSwodD9UT3Z+IWrJkSShu+fLlDWPWrVsXGivSXBHFKGu9H2nYHN2mbtmyZWbJNMCRFwAAAABJoHgBAAAAkASKFwAAAABJoHgBAAAAkASKFwAAAABJoHgBAAAAkASKFwAAAABJoHgBAAAAkASKFwAAAABJmF12AhHRjsaRDtg9PT2hsXp7e0NxSFtkbo2OjobG6u/vD8VFO88ODAyE4jC54eHhhjHR7tcjIyOhuP3794fiFi9e3DCGrtatFVnvDw0NhcbavHnzDLM5VmQ9FF0HobUi66Gn4/LLL2/5WKyLihHZ3lxzzTWhsaL7EuPj46G4yLptcHAwNFazceQFAAAAQBIoXgAAAAAkgeIFAAAAQBIoXgAAAAAkgeIFAAAAQBIoXgAAAAAkgeIFAAAAQBIoXgAAAAAkgeIFAAAAQBJml51AxNq1a0NxGzdubBjT09NT6GdGOjJLjTuXHj58ODROp4t2Q9+6dWsobu/evQ1jNmzYEBor2lk92u0WMxf5+9y0aVNorOjcW7BgQShuYGAgFIfWifyOo+uWNWvWhOKWL18eiuvv7w/FoXqindCL7l4e2Y+J7DdJse0W+zGNRbZJw8PDobEa7VdOiG67op9bBRx5AQAAAJAEihcAAAAASaB4AQAAAJAEihcAAAAASaB4AQAAAJAEihcAAAAASaB4AQAAAJAEihcAAAAASaB4AQAAAJCE2WUnEBHtIhrpABvtUjwyMhKKi3RLlRp30/71r38dGqfTRTvFrl+/vsmZ/K7Vq1eH4qLzGdUS6VYtST09PaE45kH1jI6ONoxZs2ZNaKz9+/eH4qLbGqQrut8RXcdEO6Fv3LixYUx0u1Wr1RrGzJkzJzQWihHdH2q0/5kijrwAAAAASALFCwAAAIAkULwAAAAASALFCwAAAIAkULwAAAAASALFCwAAAIAkULwAAAAASALFCwAAAIAkmLsXO6DZo11dXQv7+voKG/PQoUOhuAceeKBhzLx580JjRZv/zJo1KxR3+umnT/n+vffeqzlz5ujAgQMWGrCNPJ05c/jw4dCYkYalUUeOHAnFRRt0LVu2bCbp/NbY2Jjmzp2rxx57rKPmTDPWMRHRORVdd0TmQXd3d2isiLGxMR06dOgxd19U2KAJeDrz5eDBgw1jdu7cGfrcp556KhR37rnnhuJa3QCwU+eLVN465uGHHw7FRfZ1pNgcjDbVjayvOnXOlDVfouuik08+ORR35plnziSdp20m86UZxcsuSfMljRc6cPurSTrg7kvKTqTVmDPTVlMHzhnmy7TVxHxBXE0dOF8k5swM1NSBc4b5Mm01TXO+FF68AAAAAEAzcM0LAAAAgCRQvAAAAABIAsULAAAAgCRQvAAAAABIAsULAAAAgCRQvAAAAABIAsULAAAAgCRQvAAAAABIAsULAAAAgCRQvAAAAABIAsULAAAAgCRQvAAAAABIAsULAAAAgCRQvAAAAABIAsULAAAAgCRQvAAAAABIAsULAAAAgCRQvAAAAABIAsULAAAAgCRQvAAAAABIAsULAAAAgCRQvAAAAABIAsULAAAAgCRQvAAAAABIAsULAAAAgCRQvAAAAABIAsULAAAAgCRQvAAAAABIAsULAAAAgCS0ffFiZieZ2SVm9i0z+4WZ/cbM9pnZD8zsL8ysu+wcUS2WeauZfdvMHjWzJ8xsl5l9zMyWlJ0fqsfMTjOzjWa2M58ve8zsZjN7Rdm5oTrMrNvMXm9m7zOzr5nZI2bm+c85ZeeH6jGzs81sbb4+udfMfm1mB81sh5ldZ2anl50jqsPMXpKvX/7BzO42s/35nPm5mW02s8GycyyCuXvZOTSNmc2TdLOki+pePiCpW5Lly7slXeTu97Q4PVSQmT1D0hckrc5felLSQUkL8uXHJa129++UkB4qyMxeLOk7khblLx2QdIqyL4dc0tXufl1J6aFC8h2HmyZ5u8/d72hlPqg2MztL2T6K1b18QNIzJc3Kl/dKeqO739ri9FBBZvYxSZfVvfS4pNmS5ta99kVJb3H337QytyK1+5GX/6ascHFJV0vqdfceZb/Et0jaJ2mxpL8rLUNUzfXKCpcnJV0hqcfdF0o6S1lRc4qkL/FtFyTJzLokfVlZ4XKbpHPzdcwCSX+tbKfjWjN7ZXlZomIelvRVSeskXVpyLqi2iQLlFklvkrQwX7/Mk/SHknYpW9eMmNlp5aSIivmesn2XFZK63b3b3bsknS3pr/KYN0q6qqT8CtHuR152K/uFfdLd33GC94ck3ZAvLnT3vS1MDxVjZqdKuk/SHEkfcPcrj3t/lqT/J6lP0kfd/d2tzxJVYmZrJa1X9u3WOe7+8+Pev0nSoKTt7r6ihBRRIWY2y92P1C3XlO2AShx5wXHMrEdSzd13TPL+Ocq+NJkradjd17UyP6THzD4t6W2S7nH3ZWXnM13tfuTlX+SPt03y/ra65/OanAuq7yJlhYuU7ZAeI9/p+Jt88S35KWbobG/NHz93fOGSm/im63yuaUB94QI04u77Jytc8vfvkPT9fJEvRxDxo/zxjFKzmKF2L17G88fzJnl/4o99j6QHmp4Nqm5x/rjf3R+aJGbim9EFks5vfkqoqvxmHxPrkK9PEvZ9Sfvz5xdNEgMA0/Vo/jhryigg83v5464poyqu3YuX/5E/XmJmV+WHYGVmc8zszcq+XXdJ7/F2Pn8OURNzYKq/i9l1z1/YxFxQfX06eiHtT04U4O5PSbozX3xBK5IC0BnMbLak388Xby8zF1SXmZ1iZi82s49IenP+8ofLzGmmZjcOSdoGSUskvVvStcounN2v7G5jJyn7VvQv3f0r5aWICtmdP3ab2Vnuft8JYup3QJM+7IoZq79pw1RHbife4yYPAIr0bkmnSXpK0qdKzgUVYmZnKruG93hPSHq/u3+0xSkVqq2PvOTnF6+V9F+U3T1Kknp09N/dLenZJaSGarpV0uH8+ZXHv2lmc5TNpwn0COpsz6x7fmiKuF/lj6c0MRcAHSS/Rfv788UPu/sJj/6iYx1RdknEHh3dr3lS2Rf5SR91kdq8eMlvHfhdZbcs/ayk5cp2IJ4r6c8lLZX0STO7trQkURnu/rCkj+WL/9HM/tLMzjSzZ5jZecpub7pE0sS90Z8qI09UhjUOAYBi5bfqH1F2o6FtOsGXbehs7v6gu5/m7qdJ6pL0fGVH59ZJGjWzpE97b+viRdkv6mWS/t7dh9z9n939l+5+d940bqKRz5+Z2bnlpYkK+TNljU1NWW+g+5R9a7Fd0iskfUTSREPTfWUkiMp4vO551xRxE3cyfHyKGABoyMwWSvqGsi/SfibpNe7+RLlZocrc/Sl3vytvGfJBZS1EPmNmydYAySbeiJm9QNKqfPF3bnsrSe7+aWV36jhJ0mtblBoqzN1/raxJ5cWSNku6W9ldOb4m6Y+UnTZ2dh7+szJyRGXUX+cy1fVPE+892MRcALS5/KZDX5d0rqR7Ja109z3lZoXEfCh/7Nfkd+KtvHa+YL+v7vlUt4S7R1l37FpTs0Ey8jvPfSH/OYaZvUxHv2X//vHvo6PcoewOdabsznN3Hh+Qf7P1/Hzxp61LDUA7MbNnKjt1+SWSHlJWuNxbblZIUH0/smU6tt9hMtr2yIuOvR7h7Emjjvb2ONjEXNA+Lskft7g7vYE6mLsflPTjfHHVJGEvV3aTEEn6dtOTAtB2zKxL2enMv6fsbJGV7s6Rf0zHkrrnyZ7K3M7Fy2jd83edKMDMXifp1HzxB03PCEkzs38t6Z35Ijd5gCR9Ln98a34R7fHekz9uc/ffOTIDAFPJ73L5JUkXKrvO8pXcWQwnYmazzKzRjWT+NH98UtL3mpxS07Rt8eLuu5Rd1CZJa83sWjM7Vfptw54hSZvy98clfbnVOaJ6zOxCM7vCzJaa2az8tQVm9ifKzjWeLekT7v6NKQdCp/i4sv5A3ZK+kl9rJzPrNrMPKLtOSspu/gDIzJ418SNpQd1bvfXvpXwxLYqRb4M+J+lVys4OebW7by83K1TYWZJ+bGZvz/u8SMpOXzazfjP7rI5+Afshd99bSpYFsHZuLJ9/E/ptHXv9y0Ed259jj7IVwm2tzA3VlBe1N+SLTyo7rNqjo7fF/TtJf5z3EAJkZsuVrWcW5S8dUHZL9pOUXRNzdX53Q0BmFt3oLnH38Wbmgmozsz+QtDVffELS/inC73P3lzY/K1SVmdV07DXeTyjbh+mWdHLd65skvcvdn1Si2vmCfbn7g2a2QtKlyr4BPVfZjugBZXeRukVZ9fmL8rJExfyjpI2S/kDZtVLdku5X1i/oE+5+a4m5oYLcfUd+q/U/V3bXwucoOy/9h5LWuzvXugCYjvqjb3Pzn8lwu2Q8IOnNyto6vEzS6cq+VHtC0k5lp4nd4O7fLS3DgrT1kRcAAAAA7YNzagEAAAAkgeIFAAAAQBIoXgAAAAAkgeIFAAAAQBIoXgAAAAAkgeIFAAAAQBIoXgAAAAAkgeIFAAAAQBIoXgAAAAAkgeIFAAAAQBJmFz2gme2SNF/SeNFjt7mapAPuvqTsRFqNOTNtNXXgnGG+TFtNzBfE1dSB80VizsxATR04Z5gv01bTNOdL4cWLpPldXV0L+/r6FhY14Pj4eChu3rx5DWMeffTR0Fjd3d2huDPPPDMU18jY2Jjmzp1b2P9ZYgqfM9Hf84MPPtgwZvHixaGxonOmKB08ZwqdL4cOHQrFRddDc+bMCcVF5supp54aGitibGws/G9tM4WvXyIOHz4cirv99tsL+8xzzz03FBeZox08X6SC50xkO/N04pYuXRqK6+3tDcUVpYPnTHi+HDlypOFgDz30UOhDDxw4EIqL/k5mzZrVMCY69yLbt5nMl2YUL+N9fX0Lt23bVtiAQ0NDobj+/v6GMZs2bQqNNTAwEIrbsGFDKK6RFStWFDJOogqfM9Hf8/DwcMOYT3ziE6GxonOmKB08ZwqdL6Ojo6G46HqoVquF4iLzZe3ataGxIlasWKHt27ePFzZgOgpfv4Q+NFjsLllS3JfUN998cyguMkc7eL5IBc+ZyHZGktatWxeK++AHPxiKW716dSiuKB08Z8LzZe/evQ1jrr/++tCHfvOb3wzFbd++PRQXKTg++tGPhsZauXJlw5iZzBeueQEAAACQBIoXAAAAAEmgeAEAAACQBIoXAAAAAEmgeAEAAACQBIoXAAAAAEmgeAEAAACQhGb0eSlctNFSpF9DdKxon5BoH4Zo7wcUY2RkJBS3e/fuhjFF9wZCtUR7MOzYsaPQuM2bNzeMGRwcDI3F+qV6on1ekLZ9+/Y1jIluj6J9WaLrBXcPxaF17rnnnoYx0f5Cq1atKjQu0jfmyiuvDI3V7L5aHHkBAAAAkASKFwAAAABJoHgBAAAAkASKFwAAAABJoHgBAAAAkASKFwAAAABJoHgBAAAAkASKFwAAAABJoHgBAAAAkITZZScQEe0mu2HDhoYx0U7Uvb29oTg6W1dTf39/KG50dLRhzI033hgaK9qpnTnTOlu2bGkYE+l0L0mXX355KC46D6JzFK0T6ZYeWWdI8XkQdcEFFzSMYd3SepF9hejf+qZNm0JxRW7fWA+11ooVKxrGRDrdPx333HNPKO7zn/98w5jLLrtspukUgiMvAAAAAJJA8QIAAAAgCRQvAAAAAJJA8QIAAAAgCRQvAAAAAJJA8QIAAAAgCRQvAAAAAJJA8QIAAAAgCRQvAAAAAJIwu+wEIqIdYCOdstesWRMaK9rpFtW0du3aUFykA3u0a3X0M0dGRkJxqJYNGzYUOt7u3bsLHQ8zF1nvX3HFFc1PBG0lum3Yt29fKG7Hjh2huOi2C2m65557QnHLli0LxZ1//vkNYy699NLQWM3GkRcAAAAASaB4AQAAAJAEihcAAAAASaB4AQAAAJAEihcAAAAASaB4AQAAAJAEihcAAAAASaB4AQAAAJCEJJpUDg4OhuKWL1/eMGZoaCg0Vm9vbygO1RT9YtCr5gAABGVJREFU/UWaVEZF5+no6GgoLtqcFZMbGBgobKxoA7no3LvgggsaxkSb5Q4PD4fiMLVIM8HonIo2Nb3xxhtDcePj46E4VE90XR5tZhlZd0jsx7S7pUuXhuKWLFkSirvqqqsaxixYsCA0VrNx5AUAAABAEiheAAAAACSB4gUAAABAEiheAAAAACSB4gUAAABAEiheAADA/2/vjlEayqIwAF9RjCkcZaYVySAIVroDLbTWxl53oJVb0NLSLrZWcQe6AmMlCGIKK2GMTKEQkMwOzME8o2fyffXPvRdy8p4/QS5ACsoLAACQgvICAACkoLwAAAApTP30AaoUuS09egt6lTevMx729/dDueht6K1Wa4jTEDU3NxfKRT+36M3qr6+vAzONRiO0FqMTvS296s/OLOTVbrdDuWazGcqdn58PcRrGzdbWVih3dHQ0MLO7uzvscSrhlxcAACAF5QUAAEhBeQEAAFJQXgAAgBSUFwAAIAXlBQAASEF5AQAAUlBeAACAFJQXAAAghamf3Dxyw3Qp8ZuFI+t1Op3QWuQWna3ozccRj4+Podzl5WUoN2hWe71eaB0+t7e3F8q1Wq1K943M6M7OTqV7MjrR91bU9fX1wEz0/Vb12fhc1d/jm5ubynLRs62trYVyDO/k5CSU63a7odzFxUUoF/276TfwywsAAJCC8gIAAKSgvAAAACkoLwAAQArKCwAAkILyAgAApKC8AAAAKSgvAABACsoLAACQwtRPbj4/Px/KRW/93NjYGJg5PT0NrUVuzWYzlDs8PKxsz9XV1VBue3s7lBv0/ZicnAytw+cODg5CuXa7HcpdXV2FcpEZjT4j+X0i76NSSllfXw/lIvPX6XRCazUajVCOakSfMa1WK5SLPmNGvefHx8eQp6GUUo6Pj0O56N/Gm5ubodzZ2Vko9xv45QUAAEhBeQEAAFJQXgAAgBSUFwAAIAXlBQAASEF5AQAAUlBeAACAFJQXAAAghYl+v1/tghMT/9Tr9T9XVlYqW/Ph4SGUe39/H5hZWFgIrTXqy+Hu7u7KzMxMeXl5mRjpxr/Ad8zM8/NzKPf09FTZnvV6PZSbnp4O5QZdJHd/f19qtVrpdrtjNTNVz0uv1wvlohcARi9qizyLZmdnQ2tF3N3dlff395d+v/9XZYsm8B3Pl4iq5+rt7W1gZmlpKbRWZK7GdV5KqX5mou+j6KWDVYo+r5aXlwdm7u/vx3Jmqp6X29vbUC762UXfI4uLiwMztVottFbEMM+Y7ygvj6WUP0opnUoX/v9rlFL+7ff7f//0QUbNzHxZo4zhzJiXL2sU80Jco4zhvJRiZobQKGM4M+blyxrli/NSeXkBAAD4Dv7nBQAASEF5AQAAUlBeAACAFJQXAAAgBeUFAABIQXkBAABSUF4AAIAUlBcAACAF5QUAAEhBeQEAAFJQXgAAgBSUFwAAIAXlBQAASEF5AQAAUlBeAACAFJQXAAAgBeUFAABIQXkBAABSUF4AAIAU/gNXPIHRvy0JCAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 24 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 280,
       "width": 407
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure, axes = plt.subplots(nrows=4, ncols=6, figsize=(6, 4))\n",
    "\n",
    "for item in zip(axes.ravel(), digits.images, digits.target):\n",
    "    axes, image, target = item \n",
    "    axes.imshow(image, cmap=plt.cm.gray_r)\n",
    "    axes.set_xticks([])  # remove x-axis tick marks\n",
    "    axes.set_yticks([])  # remove y-axis tick marks\n",
    "    axes.set_title(target)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.2.4 Splitting the Data for Training and Testing\n",
    "* Typically **train** a model with a **subset of a dataset**\n",
    "* **Save a portion for testing**, so you can evaluate a model’s performance using **unseen data**\n",
    "* Function **`train_test_split`** **shuffles** the data to **randomize** it, then **splits** the **samples** in the `data` array and the **target values** in the `target` array into **training** and **testing sets**\n",
    "    * Shuffling helps ensure that the **training and testing sets** have **similar characteristics**\n",
    "* Convention: \n",
    "    * **Uppercase `X`** represents **samples**\n",
    "    * **Lowercase `y`** represents **target values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    digits.data, digits.target, random_state=11)  # random_state for reproducibility"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Scikit-learn bundled classification datasets have **balanced classes**\n",
    "    * Samples are **divided evenly** among the classes\n",
    "    * **Unbalanced classes** could lead to incorrect results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training and Testing Set Sizes \n",
    "* **By default**, `train_test_split` reserves **75%** of the data for **training** and **25%** for **testing**\n",
    "    * Customizable: See how in my [**Python Fundamentals LiveLessons** videos](https://learning.oreilly.com/videos/python-fundamentals/9780135917411/9780135917411-PFLL_Lesson14_11) or in [**Python for Programmers**, Section 14.2.4](https://learning.oreilly.com/library/view/python-for-programmers/9780135231364/ch14.xhtml#ch14lev2sec8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1347, 64)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(450, 64)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.2.5 Creating the Model \n",
    "* In **scikit-learn**, **models** are called **estimators** \n",
    "* **`KNeighborsClassifier`** estimator implements the **k-nearest neighbors algorithm**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.2.6 Training the Model with the `KNeighborsClassifier` Object’s **`fit` method** (1 of 2)\n",
    "* Load **sample training set (`X_train`)** and **target training set (`y_train`)** into the estimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(X=X_train, y=y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **`n_neighbors`** corresponds to **_k_ in the k-nearest neighbors algorithm** \n",
    "* [`KNeighborsClassifier` default settings](http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.2.6 Training the Model with the `KNeighborsClassifier` Object’s **`fit` method** (2 of 2)\n",
    "* **`fit` normally loads data** into an **estimator** then performs complex calculations **behind the scenes** that **learn** from the data to train a model\n",
    "* **`KNeighborsClassifier`’s `fit` method** **just loads the data** \n",
    "    * **No initial learning process** \n",
    "    * The **estimator** is **lazy** &mdash; work is performed only when you use it to make predictions\n",
    "* **Lots of models** have **significant training phases** that can take minutes, hours, days or more \n",
    "    * High-performance **GPUs** and **TPUs** can significantly **reduce model training time**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.2.7 Predicting Digit Classes with the `KNeighborsClassifier`’s  **`predict` method** (1 of 2)\n",
    "* Returns an array containing the **predicted class of each test image**: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted = knn.predict(X=X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "expected = y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **`predicted` digits** vs. **`expected` digits** for the first 20 test samples&mdash;see **index 18**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 4, 9, 9, 3, 1, 4, 1, 5, 0, 4, 9, 4, 1, 5, 3, 3, 8, 5, 6])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 4, 9, 9, 3, 1, 4, 1, 5, 0, 4, 9, 4, 1, 5, 3, 3, 8, 3, 6])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expected[:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.2.7 Predicting Digit Classes with the `KNeighborsClassifier`’s **`predict` method** (2 of 2)\n",
    "* Locate **all incorrect predictions** for the **entire test set**: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "wrong = [(p, e) for (p, e) in zip(predicted, expected) if p != e]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(5, 3),\n",
       " (8, 9),\n",
       " (4, 9),\n",
       " (7, 3),\n",
       " (7, 4),\n",
       " (2, 8),\n",
       " (9, 8),\n",
       " (3, 8),\n",
       " (3, 8),\n",
       " (1, 8)]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wrong"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Incorrectly predicted only 10 of the 450 test samples**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 14.3 Case Study: Classification with k-Nearest Neighbors and the Digits Dataset, Part 2\n",
    "## 14.3.1 Metrics for Measuring Model Accuracy \n",
    "\n",
    "### Estimator Method `score`\n",
    "* Returns an **indication of how well the estimator performs** on **test data** \n",
    "* For **classification estimators**, returns the **prediction accuracy** for the test data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "97.78%\n"
     ]
    }
   ],
   "source": [
    "print(f'{knn.score(X_test, y_test):.2%}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* `kNeighborsClassifier` with default **_k_** of 5 achieved **97.78% prediction accuracy** using only the estimator’s **default parameters**\n",
    "* Can use **hyperparameter tuning** to try to determine the **optimal value for _k_**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix (1 of 2)\n",
    "* Shows correct and incorrect predicted values (the **hits** and **misses**) for a given class "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "confusion = confusion_matrix(y_true=expected, y_pred=predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[45,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0, 45,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0, 54,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0, 42,  0,  1,  0,  1,  0,  0],\n",
       "       [ 0,  0,  0,  0, 49,  0,  0,  1,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0, 38,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0, 42,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0, 45,  0,  0],\n",
       "       [ 0,  1,  1,  2,  0,  0,  0,  0, 39,  1],\n",
       "       [ 0,  0,  0,  0,  1,  0,  0,  0,  1, 41]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix (2 of 2)\n",
    "* **Correct predictions** shown on **principal diagonal** from top-left to bottom-right\n",
    "* **Nonzero values** not on **principal diagonal** indicate **incorrect predictions** \n",
    "* Each **row** represents **one distinct class** (0–9) \n",
    "* **Columns** specify how many **test samples** were classified into classes 0–9 \n",
    "* **Row 0** shows digit class **`0`**&mdash;**all 0s were predicted correctly**\n",
    ">`[45,  0,  0,  0,  0,  0,  0,  0,  0,  0]`\n",
    "* **Row 8** shows digit class **`8`**&mdash;**five 8s were predicted incorrectly**\n",
    ">`[ 0,  1,  1,  2,  0,  0,  0,  0, 39,  1]`\n",
    "\n",
    "    * **Correctly predicted 88.63%** (39 of 44) of `8`s\n",
    "    * 8s harder to recognize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the Confusion Matrix \n",
    "* A **heat map** displays **values** as **colors**\n",
    "* Convert the **confusion matrix** into a **`DataFrame`**, then graph it\n",
    "* **Principal diagonal** and **incorrect predictions** stand out nicely in **heat map**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "confusion_df = pd.DataFrame(confusion, index=range(10), columns=range(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "    0   1   2   3   4   5   6   7   8   9\n",
       "0  45   0   0   0   0   0   0   0   0   0\n",
       "1   0  45   0   0   0   0   0   0   0   0\n",
       "2   0   0  54   0   0   0   0   0   0   0\n",
       "3   0   0   0  42   0   1   0   1   0   0\n",
       "4   0   0   0   0  49   0   0   1   0   0\n",
       "5   0   0   0   0   0  38   0   0   0   0\n",
       "6   0   0   0   0   0   0  42   0   0   0\n",
       "7   0   0   0   0   0   0   0  45   0   0\n",
       "8   0   1   1   2   0   0   0   0  39   1\n",
       "9   0   0   0   0   1   0   0   0   1  41"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 357,
       "width": 398
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure = plt.figure(figsize=(7, 6))\n",
    "axes = sns.heatmap(confusion_df, annot=True, \n",
    "                   cmap=plt.cm.nipy_spectral_r) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- ### Visualizing the Confusion Matrix (3 of 4)\n",
    "![Confusion matrix displayed as a heat map](./ch14images/confusion_nipy_spectral_r.png \"Confusion matrix displayed as a heat map\") -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.3.2 K-Fold Cross-Validation\n",
    "* Uses **all of your data** for **training and testing**\n",
    "* Gives a better sense of how well your model will make predictions\n",
    "* **Splits the dataset** into **_k_ equal-size folds** (unrelated to**&nbsp;k** in the k-nearest neighbors algorithm)\n",
    "* **Repeatedly trains** your model with **_k_ – 1 folds** and **test the model** with the **remaining fold**\n",
    "* Consider using **_k_ = 10** with **folds numbered 1 through 10**\n",
    "\t* **train** with **folds 1–9**, then **test** with **fold 10**\n",
    "\t* **train** with **folds 1–8 and 10**, then **test** with **fold 9**\n",
    "\t* **train** with **folds 1–7** and **9–10**, then **test** with **fold 8**\n",
    "    * ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `KFold` Class\n",
    "* **`KFold`** class and function **`cross_val_score`** perform **k-fold cross validation** \n",
    "* **`n_splits=10`** specifies the **number of folds**\n",
    "* **`shuffle=True`** **randomizes** the data before **splitting it into folds** \n",
    "\t* Particularly **important** if the **samples** might be **ordered** or **grouped** (as in **Iris dataset** we'll see later)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "kfold = KFold(n_splits=10, random_state=11, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calling Function `cross_val_score` to Train and Test Your Model (1 of 2)\n",
    "* **`estimator=knn`** &mdash; **estimator** to validate\n",
    "* **`X=digits.data`** &mdash; **samples** to use for training and testing\n",
    "* **`y=digits.target`** &mdash; **target predictions** for the samples\n",
    "* **`cv=kfold`** &mdash; **cross-validation generator** that defines how to **split** the **samples** and **targets** for training and testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = cross_val_score(estimator=knn, X=digits.data, y=digits.target, cv=kfold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calling Function `cross_val_score` to Train and Test Your Model (2 of 2)\n",
    "* Lowest accuracy was **97.78%** &mdash; one was **100%**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.97777778, 0.99444444, 0.98888889, 0.97777778, 0.98888889,\n",
       "       0.99444444, 0.97777778, 0.98882682, 1.        , 0.98324022])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores  # array of accuracy scores for each fold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean accuracy: 98.72%\n"
     ]
    }
   ],
   "source": [
    "print(f'Mean accuracy: {scores.mean():.2%}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Mean accuracy even better than the **97.78% we achieved** when we **trained** the model with **75%** of the data and **tested** the model with **25%** earlier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.3.3 Running Multiple Models to Find the Best One (1 of 3)\n",
    "* **Difficult to know in advance** which machine learning model(s) will **perform best for a given dataset**\n",
    "    * Especially when they hide the details of how they operate\n",
    "* Even though the **`KNeighborsClassifier`** predicts digit images with a high degree of accuracy, it’s **possible** that other estimators are **even more accurate**\n",
    "* Let’s **compare** **`KNeighborsClassifier`**, **`SVC`** and **`GaussianNB`**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.3.3 Running Multiple Models to Find the Best One (2 of 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Create the estimators** \n",
    "* To avoid a scikit-learn warning, we supplied a keyword argument when creating the **`SVC`** estimator\n",
    "    * This argument’s value will become the default in scikit-learn version 0.22"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimators = {\n",
    "    'KNeighborsClassifier': knn, \n",
    "    'SVC': SVC(gamma='scale'),\n",
    "    'GaussianNB': GaussianNB()}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.3.3 Running Multiple Models to Find the Best One (3 of 3)\n",
    "* **Execute the models**: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNeighborsClassifier: mean accuracy=98.72%; standard deviation=0.75%\n",
      "                 SVC: mean accuracy=98.72%; standard deviation=0.79%\n",
      "          GaussianNB: mean accuracy=84.48%; standard deviation=3.47%\n"
     ]
    }
   ],
   "source": [
    "for estimator_name, estimator_object in estimators.items():\n",
    "    kfold = KFold(n_splits=10, random_state=11, shuffle=True)\n",
    "    scores = cross_val_score(estimator=estimator_object, \n",
    "        X=digits.data, y=digits.target, cv=kfold)\n",
    "    print(f'{estimator_name:>20}: ' + \n",
    "          f'mean accuracy={scores.mean():.2%}; ' +\n",
    "          f'standard deviation={scores.std():.2%}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **`KNeighborsClassifier`** and **`SVC`** estimators’ accuracies are identical so we might want to **perform hyperparameter tuning** on each to determine the best"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.3.4 Hyperparameter Tuning (1 of 3)\n",
    "* In real-world machine learning studies, you’ll want to **tune hyperparameters** to choose values that produce the **best possible predictions**\n",
    "* To **determine** the **best value** for **_k_** in the **kNN algorithm**, **try different values** and **compare performance**  \n",
    "* Scikit-learn also has **automated hyperparameter tuning** capabilities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.3.4 Hyperparameter Tuning (2 of 3)\n",
    "* Create `KNeighborsClassifiers` with odd **k** values from 1 through 19\n",
    "* Perform **k-fold cross-validation** on each"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k=1 ; mean accuracy=98.83%; standard deviation=0.58%\n",
      "k=3 ; mean accuracy=98.78%; standard deviation=0.78%\n",
      "k=5 ; mean accuracy=98.72%; standard deviation=0.75%\n",
      "k=7 ; mean accuracy=98.44%; standard deviation=0.96%\n",
      "k=9 ; mean accuracy=98.39%; standard deviation=0.80%\n",
      "k=11; mean accuracy=98.39%; standard deviation=0.80%\n",
      "k=13; mean accuracy=97.89%; standard deviation=0.89%\n",
      "k=15; mean accuracy=97.89%; standard deviation=1.02%\n",
      "k=17; mean accuracy=97.50%; standard deviation=1.00%\n",
      "k=19; mean accuracy=97.66%; standard deviation=0.96%\n"
     ]
    }
   ],
   "source": [
    "for k in range(1, 20, 2):  # k is an odd value 1-19; odds prevent ties\n",
    "    kfold = KFold(n_splits=10, random_state=11, shuffle=True)\n",
    "    knn = KNeighborsClassifier(n_neighbors=k)\n",
    "    scores = cross_val_score(estimator=knn, \n",
    "        X=digits.data, y=digits.target, cv=kfold)\n",
    "    print(f'k={k:<2}; mean accuracy={scores.mean():.2%}; ' +\n",
    "          f'standard deviation={scores.std():.2%}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.3.4 Hyperparameter Tuning (3 of 3)\n",
    "* **Machine learning** is not without its **costs**, especially in **big data** and **deep learning**\n",
    "* **Compute time grows with _k_**, because **k-NN** needs to perform **more calculations** to find the **nearest neighbors**\n",
    "* Can use function **`cross_validate`** to perform cross-validation **and** time the results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 14.4 Case Study: Time Series and Simple Linear Regression \n",
    "\\[**Note:** I no longer cover this case study in this webinar due to lack of time. See the full presentation in my [**Python Fundamentals** videos (8 videos in this case study)](https://learning.oreilly.com/videos/python-fundamentals/9780135917411/9780135917411-PFLL_Lesson14_23)\\]\n",
    "* **Simple linear regression** is the **simplest** regression algorithm\n",
    "* Given a collection of numeric values representing an **independent variable** and a **dependent variable**, simple linear regression **describes the relationship between these variables with a straight line**, known as the **regression line**\n",
    "* Using a **time series** of average New York City January high-temperature data for 1895 through 2018, we'll\n",
    "    * Perform **simple linear regression**\n",
    "    * Display a **scatter plot** with a **regression line** \n",
    "    * Use the **coefficient** and **intercept values** calculated by the estimator to **make predictions**\n",
    "* Temperature data stored in **`ave_hi_nyc_jan_1895-2018.csv`**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 14.5 Case Study: Multiple Linear Regression with the California Housing Dataset\n",
    "\\[**Note:** I no longer cover this case study in this webinar due to lack of time. See the full presentation in my [**Python Fundamentals** videos (10 videos in this case study)](https://learning.oreilly.com/videos/python-fundamentals/9780135917411/9780135917411-PFLL_Lesson14_31)\\]\n",
    "* [**California Housing dataset**](http://lib.stat.cmu.edu/datasets) bundled with scikit-learn \n",
    "* **Larger real-world dataset** \n",
    "    **20,640 samples**, each with **eight numerical features**\n",
    "\t* Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions, Statistics and Probability Letters, 33 (1997) 291-297. Submitted to the StatLib Datasets Archive by Kelley Pace (kpace@unix1.sncc.lsu.edu). [9/Nov/99]. \n",
    "* Perform **multiple linear regression** using **all eight numerical features** \n",
    "    * Make **more sophisticated housing price predictions** than if we were to use only a **single feature** or a **subset of the features**\n",
    "* **`LinearRegression`** estimator performs **multiple linear regression** by default"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 14.6 Case Study: Unsupervised Machine Learning, Part 1—Dimensionality Reduction (1 of 3)\n",
    "* **Unsupervised machine learning** and **visualization** can help you do this by **finding patterns and relationships among unlabeled samples**\n",
    "* Visualizing data with **two variables** is easy\n",
    "    * Plot data in **2D** with **one variable along each axis**\n",
    "    * Visualization libraries also can plot datasets with **three variables in 3D** \n",
    "* But how do you visualize data with **more than three dimensions**?\n",
    "    * **Digits dataset** samples each have **64 features (dimensions) and a target value** \n",
    "    * **Big data** samples can have **hundreds**, **thousands** or even **millions of features (dimensions)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 14.6 Case Study: Unsupervised Machine Learning, Part 1—Dimensionality Reduction (2 of 3)\n",
    "* Must **reduce** the data to **two** or **three dimensions**\n",
    "* **Unsupervised machine learning** technique called **dimensionality reduction** \n",
    "    * There are also **supervised dimensionality-reduction** techniques\n",
    "* **Patterns in the data** might help you **choose the most appropriate machine learning algorithms** to use\n",
    "* See **clusters** of points? Might indicate **distinct classes** of information within the dataset\n",
    "\t* So a **classification algorithm** might be appropriate. \n",
    "\t* You’d still need to **determine the class** of the samples in each cluster\n",
    "\t* This might require **consulting with a domain expert** and **studying samples in a cluster** to see **what they have in common** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 14.6 Case Study: Unsupervised Machine Learning, Part 1—Dimensionality Reduction (3 of 3)\n",
    "* **Dimensionality reduction** also serves other purposes\n",
    "    * **Training estimators on big data** with **significant numbers of dimensions** can take **hours, days, weeks or longer**. \n",
    "    * **Difficult for humans to think about highly dimensional data**\n",
    "    * Could eliminate or combine **closely correlated features** to **improve training performance** \n",
    "        * Might **reduce the accuracy** of the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the Digits Dataset\n",
    "* Let’s **ignore Digits dataset labels** and use **dimensionality reduction** to help visualize the data in two dimensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_digits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits = load_digits()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a `TSNE` Estimator for Dimensionality Reduction (1 of 2)\n",
    "* Uses an algorithm called **t-distributed Stochastic Neighbor Embedding (t-SNE)** to analyze a dataset’s features and reduce them to the specified number of dimensions \n",
    "\t* [Algorithm’s details](https://scikit-learn.org/stable/modules/manifold.html#t-sne) are **beyond scope**\n",
    "\t* We first tried the popular **`PCA`** (principal components analysis) estimator but did not like the results, so we switched to **`TSNE`**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a `TSNE` Estimator for Dimensionality Reduction (2 of 2)\n",
    "* Create a `TSNE` object that **reduces a dataset’s features to two dimensions** \n",
    "* `random_state` for **reproducibility of the “render sequence”** when we display the digit clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "tsne = TSNE(n_components=2, random_state=11)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transforming the Digits Dataset’s Features into Two Dimensions\n",
    "* **Lecture note: Takes about 15-20 seconds, so run code first**\n",
    "* Two steps\n",
    "\t* **Train the estimator** with the dataset\n",
    "\t* **Use the estimator** to **transform** the data into the **specified number of dimensions**\n",
    "* Can **perform separately** with `TSNE` methods **`fit`** and **`transform`**\n",
    "* Perform in **one statement** using **`fit_transform`**\n",
    "    * Returns array with **same number of rows** as `digits.data` and **two columns** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "reduced_data = tsne.fit_transform(digits.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 2)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reduced_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the Reduced Data (1 of 2)\n",
    "* Rather than Seaborn’s `scatterplot` function, use Matplotlib’s **`scatter` function**\n",
    "    * Returns collection of plotted items, which we’ll use in a second scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 302,
       "width": 321
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "figure = plt.figure(figsize=(5, 5))\n",
    "dots = plt.scatter(reduced_data[:, 0], reduced_data[:, 1], c='black')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- ![Black and white Digits dataset clustering scatterplot after TSNE dimensionality reduction to two dimensions](./ch14images/digits_black.png \"Black and white Digits dataset clustering scatterplot after TSNE dimensionality reduction to two dimensions\") -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the Reduced Data (2 of 2)\n",
    "* **Did not label axes** &mdash; they **do not correspond to specific features** of the original dataset\n",
    "* **New features** produced by **`TSNE`** could be quite different from **dataset’s original features**\n",
    "* Clear **clusters** of related data points\n",
    "* Appear to be **11 main clusters, rather than 10** \n",
    "* Some **\"loose\" data points**  \n",
    "    * Makes sense because, as you saw, **some digits were difficult to classify**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the Reduced Data with Different Colors for Each Digit\n",
    "* **Don’t know** whether all the **items in each cluster** represent the **same digit** \n",
    "    * If not, then the clusters are not helpful \n",
    "* Use **`target`s** in **Digits dataset** to **color the dots** to see whether clusters indeed represent specific digits\n",
    "* **`c=digits.target`** &mdash; use `target` values determine dot colors\n",
    "* **`cmap=plt.cm.get_cmap('nipy_spectral_r', 10)`** &mdash; **color map** to use \n",
    "    * Specifically use **10 distinct colors** for the 10 digits \n",
    "* Last statement adds color bar key "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 306,
       "width": 354
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure = plt.figure(figsize=(6, 5))\n",
    "\n",
    "dots = plt.scatter(reduced_data[:, 0], reduced_data[:, 1],\n",
    "    c=digits.target, cmap=plt.cm.get_cmap('nipy_spectral_r', 10))\n",
    " \n",
    "colorbar = plt.colorbar(dots)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3D Plot\n",
    "* **Lecture Note: Run `digits3d.py` first**\n",
    "* Can use Matplotlib’s **`Axes3D`** for plotting in three-dimensional graphs\n",
    "* Run provided **`digits3d.py`** file from the command line\n",
    "    * Diagram in JupyterLab is not interactive without additional tools installed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 14.7 Case Study: Unsupervised Machine Learning, Part 2—k-Means Clustering (1 of 2)\n",
    "* **Simplest** unsupervised machine learning algorithm \n",
    "* Analyze **unlabeled samples** and **attempt to place them in clusters**\n",
    "* **_k_** hyperparameter represents **number of clusters** to impose on the data\n",
    "* Organizes clusters using **distance calculations** similar to the **k-NN classification** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 14.7 Case Study: Unsupervised Machine Learning, Part 2—k-Means Clustering (2 of 2)\n",
    "* Each **cluster** is grouped around a **centroid** (cluster’s **center point**)\n",
    "* Initially, the algorithm **chooses _k_ centroids at random** from **dataset’s samples**\n",
    "* **Remaining samples** placed in the cluster whose **centroid is the closest** \n",
    "* **Centroids are iteratively recalculated** and **samples re-assigned** to clusters until, for all clusters, **distances** from a given centroid to the samples in its cluster are **minimized**\n",
    "Results are:\n",
    "\t* **one-dimensional array of labels** indicating **cluster** to which **each sample belongs** \n",
    "\t* **two-dimensional array of clusters' centroids** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Iris Dataset \n",
    "* **Iris dataset** &mdash; commonly analyzed with **classification and clustering**\n",
    "\t* Fisher, R.A., “The use of multiple measurements in taxonomic problems,” Annual Eugenics, 7, Part II, 179-188 (1936); also in “Contributions to Mathematical Statistics” (John Wiley, NY, 1950).\n",
    "* Dataset is **labeled** &mdash; we’ll **ignore labels** to demonstrate clustering\n",
    "    * Use labels later to determine **how well k-means algorithm clustered samples**\n",
    "* **\"Toy dataset\"** &mdash; has only **150 samples** and **four features**\n",
    "    * **50 samples** for each of **three _Iris_ flower species** (balanced classes)\n",
    "    * **Iris setosa**, **Iris versicolor** and **Iris virginica**\n",
    "    * Features: **sepal length**, **sepal width**, **petal length** and **petal width**, all measured in centimeters. \n",
    "    * **Sepals** are **larger outer parts** of each flower that protect smaller inside **petals** before buds bloom"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Iris setosa**: https://commons.wikimedia.org/wiki/File:Wild_iris_KEFJ_(9025144383).jpg.\n",
    "Credit: Courtesy of Nation Park services.\n",
    "\n",
    "<img src=\"./ch14images/Wild_iris_KEFJ_(9025144383).png\" alt=\"https://commons.wikimedia.org/wiki/File:Wild_iris_KEFJ_(9025144383).jpg. Credit: Courtesy of Nation Park services.\" width=300/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Iris versicolor**: https://commons.wikimedia.org/wiki/Iris_versicolor#/media/File:IrisVersicolor-FoxRoost-Newfoundland.jpg. \n",
    "Credit: Courtesy of Jefficus, https://commons.wikimedia.org/w/index.php?title=User:Jefficus&action=edit&redlink=1\n",
    "\n",
    "<img src=\"./ch14images/IrisVersicolor-FoxRoost-Newfoundland.png\" alt=\"Iris versicolor: https://commons.wikimedia.org/wiki/Iris_versicolor#/media/File:IrisVersicolor-FoxRoost-Newfoundland.jpg. Credit: Courtesy of Jefficus, https://commons.wikimedia.org/w/index.php?title=User:Jefficus&action=edit&redlink=1.\" width=300/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Iris virginica**: https://commons.wikimedia.org/wiki/File:IMG_7911-Iris_virginica.jpg. Credit: Christer T Johansson.\n",
    "\n",
    "<img src=\"./ch14images/IMG_7911-Iris_virginica.png\" alt=\"Iris virginica: https://commons.wikimedia.org/wiki/File:IMG_7911-Iris_virginica.jpg. Credit: Christer T Johansson.\" width=300/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.7.1 Loading the Iris Dataset\n",
    "* **Classifies samples** by **labeling** them with the integers **0, 1 and 2**, representing **Iris setosa**, **Iris versicolor** and **Iris virginica**, respectively "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _iris_dataset:\n",
      "\n",
      "Iris plants dataset\n",
      "--------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 150 (50 in each of three classes)\n",
      "    :Number of Attributes: 4 numeric, predictive attributes and the class\n",
      "    :Attribute Information:\n",
      "        - sepal length in cm\n",
      "        - sepal width in cm\n",
      "        - petal length in cm\n",
      "        - petal width in cm\n",
      "        - class:\n",
      "                - Iris-Setosa\n",
      "                - Iris-Versicolour\n",
      "                - Iris-Virginica\n",
      "                \n",
      "    :Summary Statistics:\n",
      "\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "                    Min  Max   Mean    SD   Class Correlation\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "    sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
      "    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
      "    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
      "    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "    :Class Distribution: 33.3% for each of 3 classes.\n",
      "    :Creator: R.A. Fisher\n",
      "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
      "    :Date: July, 1988\n",
      "\n",
      "The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\n",
      "from Fisher's paper. Note that it's the same as in R, but not as in the UCI\n",
      "Machine Learning Repository, which has two wrong data points.\n",
      "\n",
      "This is perhaps the best known database to be found in the\n",
      "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
      "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
      "data set contains 3 classes of 50 instances each, where each class refers to a\n",
      "type of iris plant.  One class is linearly separable from the other 2; the\n",
      "latter are NOT linearly separable from each other.\n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\n",
      "     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
      "     Mathematical Statistics\" (John Wiley, NY, 1950).\n",
      "   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n",
      "     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
      "   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
      "     Structure and Classification Rule for Recognition in Partially Exposed\n",
      "     Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
      "     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
      "   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
      "     on Information Theory, May 1972, 431-433.\n",
      "   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
      "     conceptual clustering system finds 3 classes in the data.\n",
      "   - Many, many more ...\n"
     ]
    }
   ],
   "source": [
    "print(iris.DESCR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking the Numbers of Samples, Features and Targets "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150,)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Array `target_names` Contains Names for the `target` Array’s Numeric Labels\n",
    "* **`dtype='<U10'`** &mdash; elements are **strings with a max of 10 characters**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Array `feature_names` Contains Names for Each Column in the `data` array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.feature_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.7.2 Exploring the Iris Dataset: Descriptive Statistics with a Pandas `DataFrame` \n",
    "### Create a `DataFrame` containing the `data` array’s contents\n",
    "* Use **`feature_names`** as the **column names**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "pd.set_option('precision', 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_df = pd.DataFrame(iris.data, columns=iris.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                  5.1               3.5                1.4               0.2\n",
       "1                  4.9               3.0                1.4               0.2\n",
       "2                  4.7               3.2                1.3               0.2\n",
       "3                  4.6               3.1                1.5               0.2\n",
       "4                  5.0               3.6                1.4               0.2\n",
       "..                 ...               ...                ...               ...\n",
       "145                6.7               3.0                5.2               2.3\n",
       "146                6.3               2.5                5.0               1.9\n",
       "147                6.5               3.0                5.2               2.0\n",
       "148                6.2               3.4                5.4               2.3\n",
       "149                5.9               3.0                5.1               1.8\n",
       "\n",
       "[150 rows x 4 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add a column containing each sample’s species name\n",
    "* **List comprehension** uses each value in **`target` array** to look up the corresponding **species name** in **`target_names` array**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_df['species'] = [iris.target_names[i] for i in iris.target]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Look at a few samples  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "  species  \n",
       "0  setosa  \n",
       "1  setosa  \n",
       "2  setosa  \n",
       "3  setosa  \n",
       "4  setosa  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Calling `describe` on the `'species'` column confirms that it contains three unique values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.00</td>\n",
       "      <td>150.00</td>\n",
       "      <td>150.00</td>\n",
       "      <td>150.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.84</td>\n",
       "      <td>3.06</td>\n",
       "      <td>3.76</td>\n",
       "      <td>1.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.83</td>\n",
       "      <td>0.44</td>\n",
       "      <td>1.77</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.30</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.10</td>\n",
       "      <td>2.80</td>\n",
       "      <td>1.60</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.80</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.35</td>\n",
       "      <td>1.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.40</td>\n",
       "      <td>3.30</td>\n",
       "      <td>5.10</td>\n",
       "      <td>1.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.90</td>\n",
       "      <td>4.40</td>\n",
       "      <td>6.90</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal length (cm)  sepal width (cm)  petal length (cm)  \\\n",
       "count             150.00            150.00             150.00   \n",
       "mean                5.84              3.06               3.76   \n",
       "std                 0.83              0.44               1.77   \n",
       "min                 4.30              2.00               1.00   \n",
       "25%                 5.10              2.80               1.60   \n",
       "50%                 5.80              3.00               4.35   \n",
       "75%                 6.40              3.30               5.10   \n",
       "max                 7.90              4.40               6.90   \n",
       "\n",
       "       petal width (cm)  \n",
       "count            150.00  \n",
       "mean               1.20  \n",
       "std                0.76  \n",
       "min                0.10  \n",
       "25%                0.30  \n",
       "50%                1.30  \n",
       "75%                1.80  \n",
       "max                2.50  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count        150\n",
       "unique         3\n",
       "top       setosa\n",
       "freq          50\n",
       "Name: species, dtype: object"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df['species'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We **know in advance** that there are **three classes** to which the samples belong\n",
    "    * This is **not** typically the case in **unsupervised machine learning**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.7.3 Visualizing the Dataset with a Seaborn pairplot (1 of 3)\n",
    "* To **learn more about your data**, **visualize** how the features relate to one another\n",
    "* Four features &mdash; cannot graph one against other three in a single graph\n",
    "* Can **plot pairs of features** against one another \n",
    "* **Seaborn function `pairplot`** creates a **grid of graphs**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x396 with 20 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 411,
       "width": 645
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set_style('whitegrid')\n",
    "grid = sns.pairplot(data=iris_df, vars=iris_df.columns[0:4], hue='species')\n",
    "grid.fig.set_size_inches(9, 5.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.7.4 Using a `KMeans` Estimator\n",
    "* Use k-means clustering via **`KMeans` estimator** to place each sample in the Iris dataset into a cluster\n",
    "\n",
    "### Creating the `KMeans` Estimator \n",
    "* [`KMeans` default arguments](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html)\n",
    "* When you **train a `KMeans` estimator**, it calculates for each cluster a **centroid** representing the **cluster’s center data point** \n",
    "\t* Often, you’ll rely on **domain experts** to help **choose an appropriate _k_** (`n_clusters`). \n",
    "* Can also use **hyperparameter tuning** to estimate the appropriate **k**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "kmeans = KMeans(n_clusters=3, random_state=11)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitting the Model Via the `KMeans` object’s `fit` Method\n",
    "* When the training completes, the `KMeans` object contains: \n",
    "\t* **`labels_` array** with values from **`0` to `n_clusters - 1`**, indicating the clusters to which the samples belong\n",
    "\t* **`cluster_centers_` array** in which **each row represents a centroid**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=3, random_state=11)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.fit(iris.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing the Cluster Labels to the Iris Dataset’s Target Values (1 of 2)\n",
    "* **Iris dataset** is **labeled**, so we can look at **`target` array values** to get a sense of **how well the k-means algorithm clustered the samples** \n",
    "    * With **unlabeled data**, we’d depend on a **domain expert** to help **evaluate whether the predicted classes make sense**\n",
    "* First 50 samples are **Iris setosa**, next 50 are **Iris versicolor**, last 50 are **Iris virginica**\n",
    "    * **`target` array** represents these with values **0–2** \n",
    "* If **`KMeans` chose clusters perfectly**, then **each group of 50 elements in the estimator’s `labels_` array should have a distinct label**. \n",
    "    * **`KMeans` labels** are **not related** to dataset’s **`target` array** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing the Cluster Labels to the Iris Dataset’s Target Values (2 of 2)\n",
    "* First 50 samples should be **one cluster** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "print(kmeans.labels_[0:50])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Next 50 samples should be a **second cluster** (two are not)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "print(kmeans.labels_[50:100])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Last 50 samples should be a **third cluster** (14 are not)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 0 2 2 2 2 0 2 2 2 2 2 2 0 0 2 2 2 2 0 2 0 2 0 2 2 0 0 2 2 2 2 2 0 2 2 2\n",
      " 2 0 2 2 2 0 2 2 2 0 2 2 0]\n"
     ]
    }
   ],
   "source": [
    "print(kmeans.labels_[100:150])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Results confirm what we saw in **`pairplot` diagrams**\n",
    "    * **Iris setosa** is “in a class by itself” \n",
    "    * There is confusion between **Iris versicolor** and **Iris virginica**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.7.5 Dimensionality Reduction with Principal Component Analysis\n",
    "* Use **`PCA` estimator** to perform dimensionality reduction from **4 to 2 dimensions**\n",
    "\t* [Algorithm’s details](https://scikit-learn.org/stable/modules/decomposition.html#pca) beyond scope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=2, random_state=11)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transforming the Iris Dataset’s Features into Two Dimensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(n_components=2, random_state=11)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.fit(iris.data)  # trains estimator once"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_pca = pca.transform(iris.data)  # can be called many times to reduce data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We'll call **`transform`** again to **reduce the cluster centroids from four dimensions to two** for plotting \n",
    "* **`transform`** returns an array with same number of rows as `iris.data`, but only two columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 2)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_pca.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the Reduced Data in a Scatter Plot\n",
    "* Place reduced data in a **`DataFrame`** and **add a species column** that we’ll use to **determine dot colors**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_pca_df = pd.DataFrame(iris_pca, \n",
    "                           columns=['Component1', 'Component2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_pca_df['species'] = iris_df.species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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>Component1</th>\n",
       "      <th>Component2</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.68</td>\n",
       "      <td>0.32</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.71</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.89</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.75</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.73</td>\n",
       "      <td>0.33</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Component1  Component2 species\n",
       "0       -2.68        0.32  setosa\n",
       "1       -2.71       -0.18  setosa\n",
       "2       -2.89       -0.14  setosa\n",
       "3       -2.75       -0.32  setosa\n",
       "4       -2.73        0.33  setosa"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_pca_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatterplot the Data with Seaborn\n",
    "* Each **centroid** in **`cluster_centers_`** array has **same number of features** (four) as dataset's samples\n",
    "* **To plot centroids**, we must **reduce their dimensions**\n",
    "* Think of a **centroid** as the **“average” sample in its cluster**\n",
    "\t* So each centroid should be **transformed** using **same `PCA` estimator** as **other samples**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.9016129 , 2.7483871 , 4.39354839, 1.43387097],\n",
       "       [5.006     , 3.428     , 1.462     , 0.246     ],\n",
       "       [6.85      , 3.07368421, 5.74210526, 2.07105263]])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 389
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = sns.scatterplot(data=iris_pca_df, x='Component1', \n",
    "    y='Component2', hue='species', legend='brief') \n",
    "\n",
    "# reduce centroids to 2 dimensions\n",
    "iris_centers = pca.transform(kmeans.cluster_centers_)\n",
    "\n",
    "# plot centroids as larger black dots\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "dots = plt.scatter(iris_centers[:,0], iris_centers[:,1], s=100, c='k')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.7.6 Choosing the Best Clustering Estimator (1 of 4)\n",
    "* **Run multiple clustering algorithms** and see **how well they cluster Iris species** \n",
    "\t* We’re running `KMeans` here on the **small** Iris dataset\n",
    "    * If you experience **performance problems with `KMeans`** on larger datasets, consider **`MiniBatchKMeans`**\n",
    "    * Documentation indicates **`MiniBatchKMeans` is faster on large datasets** and the results are almost as good"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.7.6 Choosing the Best Clustering Estimator (2 of 4)\n",
    "* For the `DBSCAN` and `MeanShift` estimators, we do **not** specify number of clusters in advance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import DBSCAN, MeanShift,\\\n",
    "    SpectralClustering, AgglomerativeClustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimators = {\n",
    "    'KMeans': kmeans,\n",
    "    'DBSCAN': DBSCAN(),\n",
    "    'MeanShift': MeanShift(),\n",
    "    'SpectralClustering': SpectralClustering(n_clusters=3),\n",
    "    'AgglomerativeClustering': \n",
    "        AgglomerativeClustering(n_clusters=3)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.7.6 Choosing the Best Clustering Estimator (3 of 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "KMeans:\n",
      "0-49:\n",
      "   label=1, count=50\n",
      "50-99:\n",
      "   label=0, count=48\n",
      "   label=2, count=2\n",
      "100-149:\n",
      "   label=0, count=14\n",
      "   label=2, count=36\n",
      "\n",
      "DBSCAN:\n",
      "0-49:\n",
      "   label=-1, count=1\n",
      "   label=0, count=49\n",
      "50-99:\n",
      "   label=-1, count=6\n",
      "   label=1, count=44\n",
      "100-149:\n",
      "   label=-1, count=10\n",
      "   label=1, count=40\n",
      "\n",
      "MeanShift:\n",
      "0-49:\n",
      "   label=1, count=50\n",
      "50-99:\n",
      "   label=0, count=49\n",
      "   label=1, count=1\n",
      "100-149:\n",
      "   label=0, count=50\n",
      "\n",
      "SpectralClustering:\n",
      "0-49:\n",
      "   label=0, count=50\n",
      "50-99:\n",
      "   label=2, count=50\n",
      "100-149:\n",
      "   label=1, count=35\n",
      "   label=2, count=15\n",
      "\n",
      "AgglomerativeClustering:\n",
      "0-49:\n",
      "   label=1, count=50\n",
      "50-99:\n",
      "   label=0, count=49\n",
      "   label=2, count=1\n",
      "100-149:\n",
      "   label=0, count=15\n",
      "   label=2, count=35\n"
     ]
    }
   ],
   "source": [
    "for name, estimator in estimators.items():\n",
    "    estimator.fit(iris.data)\n",
    "    print(f'\\n{name}:')\n",
    "    for i in range(0, 101, 50):\n",
    "        labels, counts = np.unique(\n",
    "            estimator.labels_[i:i+50], return_counts=True)\n",
    "        print(f'{i}-{i+49}:')\n",
    "        for label, count in zip(labels, counts):\n",
    "            print(f'   label={label}, count={count}')          "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.7.6 Choosing the Best Clustering Estimator (4 of 4)\n",
    "* **`DBSCAN` correctly predicted three clusters** (labeled `-1`, `0` and `1`)\n",
    "    * Placed 84 of the 100 **Iris virginica** and **Iris versicolor** in the same cluster\n",
    "* **`MeanShift` predicted only two clusters** (labeled as `0` and `1`)\n",
    "    * Placed 99 of 100 **Iris virginica** and **Iris versicolor** samples in same cluster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# More Info \n",
    "* See Lesson 14 in [**Python Fundamentals LiveLessons** here on O'Reilly Online Learning](https://learning.oreilly.com/videos/python-fundamentals/9780135917411)\n",
    "* See Chapter 14 in [**Python for Programmers** on O'Reilly Online Learning](https://learning.oreilly.com/library/view/python-for-programmers/9780135231364/)\n",
    "* See Chapter 15 in [**Intro Python for Computer Science and Data Science** on O'Reilly Online Learning](https://learning.oreilly.com/library/view/intro-to-python/9780135404799/)\n",
    "* Interested in a print book? Check out:\n",
    "\n",
    "| Python for Programmers<br>(640-page professional book) | Intro to Python for Computer<br>Science and Data Science<br>(880-page college textbook)\n",
    "| :------ | :------\n",
    "| <a href=\"https://amzn.to/2VvdnxE\"><img alt=\"Python for Programmers cover\" src=\"../images/PyFPCover.png\" width=\"150\" border=\"1\"/></a> | <a href=\"https://amzn.to/2LiDCmt\"><img alt=\"Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and the Cloud\" src=\"../images/IntroToPythonCover.png\" width=\"159\" border=\"1\"></a>\n",
    "\n",
    ">Please **do not** purchase both books&mdash;_Python for Programmers_ is a subset of _Intro to Python for Computer Science and Data Science_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "&copy; 2019 by Pearson Education, Inc. All Rights Reserved. The content in this notebook is based on the book [**Python for Programmers**](https://amzn.to/2VvdnxE)."
   ]
  }
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