ch14/Ch14-executed.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "&copy; 2024 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;\">"
   ]
  },
  {
   "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;\">"
   ]
  },
  {
   "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;\">"
   ]
  },
  {
   "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;\">"
   ]
  },
  {
   "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: 1797\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",
      "\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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\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/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
      ],
      "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": {
      "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": "iVBORw0KGgoAAAANSUhEUgAAAx0AAALKCAYAAABX1TywAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAABYlAAAWJQFJUiTwAABy7ElEQVR4nO3deZxcZZ3v8c+TrbN0NkJIOgHZQsBAHKADsggGGMBxJ8RtxhnEgONyLyMu44hIN446eEcU0LnjEkCM3tEQEXUkaBQiREFNkCTQDiFhh6QJDUl3ku7O9tw/qipm6c7SXaequp7Pm1e/ipxzus7vWyedrl89zzknxBiRJEmSpKz0K3cBkiRJkqqbTYckSZKkTNl0SJIkScqUTYckSZKkTNl0SJIkScqUTYckSZKkTNl0SJIkScqUTYckSZKkTNl0SJIkScqUTYckSZKkTNl0SJIkScqUTYckSZKkTNl0SJIkScrUgHIX0Bsh/DKWu4ZyWbx4TLlLkCRJ6pH6+vpQ7hq6E0Io+/vLGGPFvj495UiHJEmSpEzZdEiSJEnKVJ+eXiVJkiQV0xCGlbuEquRIhyRJkqRMOdIhSZIk5Q1laLlLqEqOdEiSJEnKlE2HJEmSpEw5vUqSJEnK80TybDjSIUmSJClTjnRIkiRJeY50ZMORDkmSJEmZsumQJEmSlCmnV0mSJEl53qcjG450SJIkScqUTYckSZKkTDm9SpIkScrz6lXZcKRDkiRJUqYc6ZAkSZLyHOnIhiMdkiRJkjJl0yFJkiQpU06vkiRJkvK8T0c2HOmQJEmSlClHOiRJkqQ8TyTPhiMdkiRJkjLlSMd+eO9765gzZyoAl132KDff/PyOdYcfPpinnjq72+/9wQ9W8573LM+8xqy1tLQwb948li5dyoYNGxg1ahTTpk1jxowZ1NbWlru8TKWaPdXckG72VHNDutlTzQ3pZk81t8rPpmMfDj20hq997Tja2rYyfHj3L9fDD7dy551r91j+yCMbsiyvJJqbm2loaKC1tZX6+nomTJjAqlWruPvuu1m6dCmNjY0MHz683GVmItXsqeaGdLOnmhvSzZ5qbkg3e6q5D5TTq7Jh07EPt956Ai0tW7jjjhf55CeP6Ha7hx9u49prV5WusBK65ZZbaG1t5ZJLLuHCCy/csXzOnDnMnz+fuXPnMmvWrDJWmJ1Us6eaG9LNnmpuSDd7qrkh3eyp5lZl8JyOvbjiildx7rkHcemlj7Jx47Zyl1MWzc3NLF++nLFjx3L++efvsm7mzJnU1NSwaNEiOjo6ylRhdlLNnmpuSDd7qrkh3eyp5oZ0s6eauyeGVsB/1aioTUcIYUgI4ZwQwj+GED4VQvjn/P+fE0IYUsx9Ze2444Zx3XXHcOONz3D//a/sc/sJE2r4wAcO5dOfPpIPfOBQpk6tjnmRTU1NAEydOpV+/Xb96zJkyBAmT55MZ2cnK1euLEd5mUo1e6q5Id3sqeaGdLOnmhvSzZ5qblWOojQdIYTRIYT/C7wI/Ar4v8AXgX/L//+vgLUhhP8IIYwuxj6z1L9/YM6cE3jmmQ6uuurx/fqeCy44mG9+cwpf/OIxfPObU1i27AzuuWcahx02OONqs/XCCy8AUFdX1+X68ePHA7B69eqS1VQqqWZPNTekmz3V3JBu9lRzQ7rZU82tytHrczpCCKOA3wLHARuBBcDjwHogACOAY4AzgQ8B54QQzogxruvtvrNyzTVHcdJJI3jd6/5AR8f2vW67adN2Pve5Vdx554s88UQ7AK95zXAaG4/m3HMP4te/rufEEx9k06a+OT1r06ZNAAwd2vVQX2F5Ybtqkmr2VHNDutlTzQ3pZk81N6SbPdXcPeGJ5NkoxonkDeQajq8CDTHGLi/XFEKoBT4HfBS4BvjY/jx5CGFJ92t/cWCV7odTThnBVVcdyfXXP8WDD67f5/Zr126moWHXE8jvv/8VLrhgCYsWncJpp43isssmctNNzxS91koQYwQghFDmSkov1eyp5oZ0s6eaG9LNnmpuSDd7qrlVOsVoOt4O3BNj/PjeNso3Ix8LIZwIXMR+Nh2llJtWNZUVKzbx2c/2bk7jtm2R2bOf57TTRnH22aP7bNOxr08+2ttzoztDhvSpU3b2S6rZU80N6WZPNTekmz3V3JBu9lRz90QNfXtqfKUqRtNRB/zXAWz/IHDG/m4cY6zvbl0Iv4wHsN99qq3tz7HH5obUOjvP73Kb2bOPZ/bs47nhhqe58srH9vp8a9duBmDYsP7FLLOkJkyYAHQ/x3PNmjVA93NE+7JUs6eaG9LNnmpuSDd7qrkh3eyp5lblKEbT0QIcewDbvzr/PRWns3M7s2c/1+W6k08ewcknj+D++1/hscc28sAD6/b5fKedNhJgx7kefdGUKVMAWL58Odu3b9/lihft7e2sWLGCQYMGMWnSpHKVmJlUs6eaG9LNnmpuSDd7qrkh3eyp5lblKMbVq34BvD2E8OF9bRhC+F/AW4G7i7Dfouvo2M7llzd1+fXTn+buNn7bbS9w+eVNzJ3bDMCpp45k4MA95z+ec85BXHnl4QB873svlC5EkY0bN46pU6eydu1aFixYsMu6efPm0dnZyVlnncXgwdU3FJlq9lRzQ7rZU80N6WZPNTekmz3V3D3RrwL+q0ahcOJQj58ghInAQ8DBwFPAL4EV5K5eBTASmAxcABxB7rK602KMz/dqxxR/etXeNDQcTWPj0Vx22aPcfPNfSr/33mkcf3wtCxe+zHPPdQLwmtfUct55YwC4+urH+cIXnix6PYsXjyn6c3anubmZhoYGWltbqa+vZ+LEiaxcuZKmpibq6upobGxk+PDhJaunlFLNnmpuSDd7qrkh3eyp5oZ0s1dS7vr6+oo9Y/2q8O8le3/ZnS/GT1bs69NTvW46AEIIRwH/CRROhNj9SQsv3C+BD8cYn+j1TqmMpuP975/IRRcdwgkn1HLwwQMZOLAfzc2dPPDAer7+9WdYtGhdJvWUsukAaGlp4fbbb2fZsmW0tbUxevRo6uvrufjii6mtrY4bIXYn1eyp5oZ0s6eaG9LNnmpuSDd7peS26dg7m459PVkIRwLnkjvHY2R+8XrgMeDeYjUbf9lf6ZqOSlPqpkOSJKlYKrnp+Ez4ctnfX34hfqJiX5+eKsaJ5DvEGJ8Ebi7mc0qSJEnq24radEiSJEl9WaDqBhkqQnWeHi9JkiQlLITwVAghdvO1ppvvOSOEcFcI4eUQwqYQwrIQwkdDCL2+6ZwjHZIkSVJ1Wg/c0MXyDbsvCCG8DfgR0AH8EHgZeAvwVeBM4B29KcSmQ5IkScqrsvtkrIsxNu5roxDCCODbwDZgeoxxcX75Z4F7gJkhhHfHGH/Q00Kq6lWVJEmSdMBmAmOBHxQaDoAYYwdwdf6PH+rNDhzpkCRJkvKq7ETymhDCe4FXARuBZcB9McZtu213bv7x7i6e4z5gE3BGCKEmxtjZk0JsOiRJkqTqNB6Ys9uyJ0MIl8YYf7PTsmPzjyt2f4IY49YQwpPA8cBRwJ97UohNhyRJklRBQghLulsXY6zfz6e5FbgfeBRoI9cw/C/gA8D8EMLpMcal+W13vql3VwrLR+3nvvdg0yFJkiTlVcv0qhjjtbstegT4YAhhA/BxoBG4aD+frvCi9Phu7TYdkiRJUgU5gNGMnvgGuabj7J2WFUYyRu65OQAjdtvugNl0SJIkSXlVdsncrryYfxy207LHgGnAZGCXqV0hhAHAkcBW4Ime7rTqX1VJkiRJO5yef9y5gbgn//iGLrY/GxgK/K6nV64Cmw5JkiSpqoQQjg8hHNTF8sOBr+f/+L2dVs0DXgLeHUKYttP2g4HP5//4n72pyelVkiRJUl6VnEj+DuBfQgj3Ak+Su3rV0cCbgMHAXcCXCxvHGFtDCJeTaz4WhhB+ALwMvJXc5XTnAT/sTUE2HZIkSVJ1uZdcs3ASuelUw4B1wCJy9+2YE2Pc5UpUMcY7QwivBz4DXEyuOVkJfAy4afftD5RNhyRJkpRXDSMd+Rv//WafG+75fb8F3lj8ijynQ5IkSVLGbDokSZIkZcrpVZIkSVJeAvfpKAtfVUmSJEmZcqRDkiRJyquGE8krkSMdkiRJkjJl0yFJkiQpU06vkiRJkvKcXpUNRzokSZIkZapPj3QsXjym3CWUzbRpLeUuoSxSPuaSJEl9VZ9uOiRJkqRi2upEoEz4qkqSJEnKlCMdkiRJUt5GTyTPhCMdkiRJkjJl0yFJkiQpU06vkiRJkvKcXpUNRzokSZIkZcqRDkmSJClvk5/JZ8JXVZIkSVKmbDokSZIkZcrpVZIkSVKeJ5Jnw5EOSZIkSZlypEOSJEnKc6QjG450SJIkScqUTYckSZKkTDm9SpIkScrzPh3Z8FWVJEmSlClHOiRJkqQ8TyTPhiMdkiRJkjJl0yFJkiQpU06vkiRJkvKcXpUNRzokSZIkZcqRDkmSJCnPS+Zmw6bjALW0tDBv3jyWLl3Khg0bGDVqFNOmTWPGjBnU1taWu7yieu9765gzZyoAl132KDff/PyOdYcfPpinnjq72+/9wQ9W8573LM+8xlJI6ZjvLNXckG72VHNDutlTzQ3pZk81t8rPpuMANDc309DQQGtrK/X19UyYMIFVq1Zx9913s3TpUhobGxk+fHi5yyyKQw+t4WtfO462tq0MH979X5OHH27lzjvX7rH8kUc2ZFleyaR0zHeWam5IN3uquSHd7KnmhnSzp5pblcGm4wDccssttLa2cskll3DhhRfuWD5nzhzmz5/P3LlzmTVrVhkrLJ5bbz2BlpYt3HHHi3zyk0d0u93DD7dx7bWrSldYiaV0zHeWam5IN3uquSHd7KnmhnSzp5r7QHkieTactLafmpubWb58OWPHjuX888/fZd3MmTOpqalh0aJFdHR0lKnC4rniildx7rkHcemlj7Jx47Zyl1M2KR3znaWaG9LNnmpuSDd7qrkh3eyp5lblsOnYT01NTQBMnTqVfv12fdmGDBnC5MmT6ezsZOXKleUor2iOO24Y1113DDfe+Az33//KPrefMKGGD3zgUD796SP5wAcOZerU6pkPmsox312quSHd7KnmhnSzp5ob0s2eam5VDpuO/fTCCy8AUFdX1+X68ePHA7B69eqS1VRs/fsH5sw5gWee6eCqqx7fr++54IKD+eY3p/DFLx7DN785hWXLzuCee6Zx2GGDM642eykc866kmhvSzZ5qbkg3e6q5Id3sqebuiY2Esn9VI5uO/bRp0yYAhg4d2uX6wvLCdn3RNdccxUknjeB973uEjo7te91206btfO5zqzj55AcYNeoeRo26h7PP/iP33PMy55xzEL/+dT1Dh/YvUeXZSOGYdyXV3JBu9lRzQ7rZU80N6WZPNbcqR1lOJA8h/DswI8Z49H5su6S7dYsXLy5qXb0RYwQghL7ZnZ5yygiuuupIrr/+KR58cP0+t1+7djMNDbueQH7//a9wwQVLWLToFE47bRSXXTaRm256JquSy66vH/OeSjU3pJs91dyQbvZUc0O62VPN3RXv05GNcr2qBwNHlGnfPbKvTwDa29uB3LzIviY3rWoqK1Zs4rOf7d1czm3bIrNn5+7ncfbZo4tRXtlU8zHfm1RzQ7rZU80N6WZPNTekmz3V3KocFX/J3BhjfXfrlixZEktVx4QJE4Du5zquWbMG6H6uZCWrre3PsccOA6Cz8/wut5k9+3hmzz6eG254miuvfGyvz7d27WYAhg3r29OrqvmY702quSHd7KnmhnSzp5ob0s2eam5VjqI0HSGE7x7gt5xRjP2W0pQpUwBYvnw527dv3+XKD+3t7axYsYJBgwYxadKkcpXYY52d25k9+7ku15188ghOPnkE99//Co89tpEHHli3z+c77bSRADzxRHsxyyy5aj7me5Nqbkg3e6q5Id3sqeaGdLOnmrsnttG3PzStVMWaXvVe4O/yj/vzdVSR9lsy48aNY+rUqaxdu5YFCxbssm7evHl0dnZy1llnMXhw37tqU0fHdi6/vKnLr5/+NHe38dtue4HLL29i7txmAE49dSQDB+457/Occw7iyisPB+B733uhdCEyUM3HfG9SzQ3pZk81N6SbPdXckG72VHOrcoTCiUO9epIQ1gPPAR/ez2/5F+CCGGOvWslSTq+C3I11GhoaaG1tpb6+nokTJ7Jy5Uqampqoq6ujsbGR4cOHl6SWadNaSrKfhoajaWw8mssue5Sbb35+x/J7753G8cfXsnDhyzz3XCcAr3lNLeedNwaAq69+nC984cmi17N48ZiiP+feVNIxL6VUc0O62VPNDelmTzU3pJu9knLX19dX7BnrIfy0pO8vuxLjWyv29empYjUd9wF/FWMcuZ/b3wr8Q19rOgBaWlq4/fbbWbZsGW1tbYwePZr6+nouvvhiamtLd2O8cjcd73//RC666BBOOKGWgw8eyMCB/Whu7uSBB9bz9a8/w6JF6zKpp9RNB1TOMS+1VHNDutlTzQ3pZk81N6SbvVJy23TsnU1Hd08Swk3AR4DJMcZV+7F9n206KkWpmo5KU46mQ5IkFZdNx95VY9NRrKtX/QY4CzgU2GfTAdwJPFWkfUuSJElFUvEXd+2TivKqxhh/BPzoALb/CfCTYuxbkiRJUmWzlZMkSZJ28O1xFrzPuyRJkqRM2XRIkiRJypTjR5IkSdIOvj3OgiMdkiRJkjJlKydJkiTt4NvjLDjSIUmSJClTNh2SJEmSMuX4kSRJkrSDb4+z4EiHJEmSpEzZykmSJEk7+PY4C450SJIkScqUTYckSZKkTDl+JEmSJO3g2+MsONIhSZIkKVM2HZIkSZIy5fiRJEmStINvj7PgSIckSZKkTNnKSZIkSTv49jgLjnRIkiRJypRNhyRJkqRMOX4kSZIk7eDb4yw40iFJkiQpU7ZykiRJ0g6+Pc6Cr2oftXjxmHKXUBbTpk0rdwlls3jx4nKXIEmS1CNOr5IkSZKUKUc6JEmSpIIh5S6gOjnSIUmSJClTjnRIkiRJBUPLXUB1cqRDkiRJUqZsOiRJkiRlyulVkiRJUoEnkmfCkQ5JkiRJmXKkQ5IkSSpwpCMTjnRIkiRJypRNhyRJkqRMOb1KkiRJKvA+HZlwpEOSJElSpmw6JEmSJGXK6VWSJElSgVevyoQjHZIkSZIy5UiHJEmSVOBIRyYc6ZAkSZKUKZsOSZIkSZlyepUkSZJU4H06MuFIhyRJkqRMOdIhSZIkFXgieSYc6ZAkSZKUKUc6DlBLSwvz5s1j6dKlbNiwgVGjRjFt2jRmzJhBbW1tucvLTDXnfvLJJzniiCO6XLdmzRrq6ur2+v2zZ89m1qxZAEyaNIlVq1YVu8SyqOZjvi+pZk81N6SbPdXckG72VHOr/Gw6DkBzczMNDQ20trZSX1/PhAkTWLVqFXfffTdLly6lsbGR4cOHl7vMoksh97p167jhhhv2WL5hw4a9ft+b3/xmZs2aRVtbW59/DXaWwjHvTqrZU80N6WZPNTekmz3V3AfM6VWZsOk4ALfccgutra1ccsklXHjhhTuWz5kzh/nz5zN37twdn3hXkxRyr1u3jmuvvfaAvufggw/m29/+Nj/4wQ8YP34806dPz6a4MkjhmHcn1eyp5oZ0s6eaG9LNnmpuVQbP6dhPzc3NLF++nLFjx3L++efvsm7mzJnU1NSwaNEiOjo6ylRhNlLNvT++9a1vAfCRj3ykzJUUV8rHPNXsqeaGdLOnmhvSzZ5q7h4ZWgFfVcimYz81NTUBMHXqVPr12/VlGzJkCJMnT6azs5OVK1eWo7zMpJK7pqaGv/u7v+PTn/40V1xxBdOnT98j784uueQSLrroIj74wQ/y8ssvl7DS7KVyzLuSavZUc0O62VPNDelmTzW3KodNx3564YUXALo9qXj8+PEArF69umQ1lUIquevq6vje977HF7/4RW688UbuvfdeHn/8cc4+++w9tn3Vq17FjTfeyJw5c/jJT35Shmqzlcox70qq2VPNDelmTzU3pJs91dyqHEVrOkIIF4UQbgwhXB9COH8v210SQrinWPstlU2bNgEwdGjXY16F5YXtqkUKuW+99VbOPfdcxo0bx9ChQznhhBP4xje+wRFHHMH8+fN5zWtes2PbEAK33XYbGzZs4Iorrihj1dlJ4Zh3J9XsqeaGdLOnmhvSzZ5q7h4ZUgFfVajXJ5KHEALwQ+BiIOQXfzSE8HPgH2KM63b7liOA1x/A8y/pbt3ixYsPqNYsxRiB3JvSlFRD7s997nO7/PnRRx/lQx/6EBs2bOATn/gEjY2NzJgxA4Arr7yS6dOn88Y3vpF169aVodryq4Zj3lOpZk81N6SbPdXckG72VHOrdIox0nEpMBN4DvgM8M9AE/BmYFEI4ZAi7KPs9vUJQHt7O5CbF1lNUs0N8I1vfANgxxSrSZMm8YUvfIFbbrmF+fPnl7O0TKV8zFPNnmpuSDd7qrkh3eyp5u6RwRXwlYEQwt+HEGL+67JutjkjhHBXCOHlEMKmEMKyEMJHQwj9e7v/YjUd64BTYoz/FmP8MnAi8BVgCvCrEMLBPX3yGGN9d19FqH2/TZgwAeh+ruOaNWuA7udK9lWp5gZ48cUXARg2bBgAxx9/PIMHD+b9738/McZdvgqXy125ciUxRt72treVq+xeS/mYp5o91dyQbvZUc0O62VPNrZwQwmHA14Bub0AWQngbcB9wNvBj4D+AQcBXgR/0toZi3KdjKjAvxvhiYUGMcRvwiRDCM8AN5BqPc2KMrxRhf2UxZcoUAJYvX8727dt3ufJDe3s7K1asYNCgQUyaNKlcJWYi1dwAp59+OgBPPPEEAE899RSzZ8/ucts3velN1NXVMXfuXFpbW3nqqadKVWbRpXzMU82eam5IN3uquSHd7Knm1o5TIW4FWoA7gE90sc0I4NvANmB6jHFxfvlngXuAmSGEd8cYe9x8FGOkYxDQ3NWKGONNwP8GXgMsCCGMKsL+ymLcuHFMnTqVtWvXsmDBgl3WzZs3j87OTs466ywGD85oTKxMqj33lClTGD169B7LX/WqV/H1r38dgO9973sALF26lMsvv7zLr8ceewyAq666issvv5ylS5eWLkSRVfsx35tUs6eaG9LNnmpuSDd7qrl7pF8FfBXXFcC55GYnbexmm5nAWOAHhYYDIMbYAVyd/+OHelNEKJw41OMnCOFx4MEY49/vZZuPkptu9Ufgt8A/xRh7PTdsyZIlvSv+ADU3N9PQ0EBrayv19fVMnDiRlStX0tTURF1dHY2NjQwfPryUJZVEJeWeNm1aUZ+voaGBf/mXf+Hee+/lySefpK2tjaOPPpo3velNDBkyhJ///OdcdNFFbNmyZa/Pc++99zJ9+nQmTZrEqlWrilpjQSkvnFBJx7zUUs2eam5IN3uquSHd7JWUu76+vmLPWA+feaKk7y+7Er9wVFFenxDCq4GHgG/EGK8MITQCDcDlMcbZO233PeDvgL+NMf7Xbs8xAFhPbqChNsbY2aNaitB03AGcGmM8dB/bfQr4N2Ar0L8vNh0ALS0t3H777Sxbtoy2tjZGjx5NfX09F198MbW1taUup2QqJXexm46zzz6bD37wg5x00kmMHz+eYcOGsW7dOh5++GHmzJnDnDlz9ut5qq3pgMo55uWQavZUc0O62VPNDelmr5TcFd10XFP+poN/Pfqh7lbt73nN+WbhQWA4cGKMsX0vTccfgWnAtBjjHleODSE8AhwPTIkx/vlAoux4jiI0HZcB3wLeEmP8+T62/SxwLRD7atOh8ip209GXVNIloiVJ6g2bjn0oTtPxOXJXln1djPGB/LJGum46VgDHAMfEGPe4LX0I4bfAGcAZhec6UMU4kfwOoD/dzxHbIcb4r/mTy48own4lSZKkqtPbq7SGEE4FrgKu72mTsPtT5h973JD1uumIMb4MfPMAtr+tt/uUJEmSMtHruTjllZ9WNQdYAXx2P79tff5xZDfrR+y23QEr/vnxkiRJksqlFpgMvBro2OmGgJHc1CqAb+eX3ZD/82P5x8m7P1m+iTmS3HnZT/S0qGJMr5IkSZJUGTqBm7tZdzJwErCIXKNRmHp1D7mrV70B+K/dvudsYChwX0+vXAU2HZIkSdJf9PF5QDHGduCyrtblTyQ/Cbht5xPJgXnAl4B3hxC+ttPNAQcDn89v85+9qcumQ5IkSUpYjLE1hHA5ueZjYQjhB8DLwFuBY/PLf9ibfdh0SJIkSQV9/ETynoox3hlCeD25y+xeDAwGVgIfA26KvbzPhk2HJEmSlIAYYyPQuJf1vwXemMW++/isNUmSJEmVzpEOSZIkqSDR6VVZc6RDkiRJUqYc6ZAkSZIK/Eg+E76skiRJkjJl0yFJkiQpU06vkiRJkgo8kTwTjnRIkiRJypQjHZIkSVKBIx2ZcKRDkiRJUqZsOiRJkiRlyulVkiRJUoEfyWfCl1WSJElSphzpkCRJkgo8kTwTjnRIkiRJypRNhyRJkqRMOb1KkiRJKnB6VSYc6ZAkSZKUKUc61KcsXry43CWUzbRZo8tdQlksvvmVcpdQPgsXlruC8pg+vdwVlI/HXCq/ftvLXUFVcqRDkiRJUqZsOiRJkiRlyulVkiRJUkHYWu4KqpIjHZIkSZIyZdMhSZIkKVNOr5IkSZIKnF6VCUc6JEmSJGXKkQ5JkiSpoJ8jHVlwpEOSJElSpmw6JEmSJGXK6VWSJElSgSeSZ8KRDkmSJEmZcqRDkiRJKnCkIxOOdEiSJEnKlE2HJEmSpEw5vUqSJEkq8D4dmXCkQ5IkSVKmHOmQJEmSCjyRPBOOdEiSJEnKlE2HJEmSpEw5vUqSJEkqcHpVJhzpkCRJkpQpRzokSZKkAi+ZmwmbjgPU0tLCvHnzWLp0KRs2bGDUqFFMmzaNGTNmUFtbW+7yMpNqbkgr+3vfVMucLxwCwGXXruXmH7ftWDfpVQOYce4wLjxjKMe8aiDjxvTnldZtPLiskxu+v56FizvKVXbRpXTMC0YdcwzDDzuMIWPHMnTsWPrX1NDS1MRT8+eXu7SS8Jh7zFM45pBubpWfTccBaG5upqGhgdbWVurr65kwYQKrVq3i7rvvZunSpTQ2NjJ8+PByl1l0qeaGtLIfOq4/X/uXMbRt3M7wYXvOvPzXDx/Eu99Qy6OrNnPXok28vH4bxx4xiLe+fihvO2cYV3zpJb72X61lqLy4UjrmO6s77TSGHnII2zZvZnNbG0NqaspdUsl4zD3mqRzzVHOrMth0HIBbbrmF1tZWLrnkEi688MIdy+fMmcP8+fOZO3cus2bNKmOF2Ug1N6SV/dZrx9Kybjt33LORT14yao/1d/9uE1+6dR0PP7Z5l+Vn1w9mwTfq+Pcrx3D7go2seWlbiSrORkrHfGfPLlzIlrY2Oteto/bQQzn2Xe8qd0kl4zH3mBdU+zFPNfcB80TyTHgi+X5qbm5m+fLljB07lvPPP3+XdTNnzqSmpoZFixbR0VE9U0wg3dyQVvYr/nYE5546hEsb1rKxPXa5zW0/3bBHwwFw35IOFi5up2ZQ4Iy/Gpx1qZlK6ZjvbsOzz9K5bl25yyg5j/m6cpdRcqke81Rzq3LYdOynpqYmAKZOnUq/fru+bEOGDGHy5Ml0dnaycuXKcpSXmVRzQzrZjztyINddcRA3/r9W7n+oZ79stuQ/FNq6reuGpa9I5ZjrLzzm6Un1mKeaW5WjJE1HCOGgEMLIUuwrKy+88AIAdXV1Xa4fP348AKtXry5ZTaWQam5II3v//jDn84fwzJqtXPW1l3v0HK+qG8B5pw5mY/t27lvStz8hS+GYa1ce8/SkesxTzd0jYWv5v6pQUZqOEMLEEMLXQwi/CCH8nxDCmPzyE0MIy4C1wMshhPtCCMcVY5+ltmnTJgCGDh3a5frC8sJ21SLV3JBG9ms+MJqTjhvE+65ZS0fngY9SDBoI3//iIQyu6UfjN15hXdv2DKosnRSOuXblMU9Pqsc81dyqHL0+kTyEcBDwIDAxv+h84K9DCBcCPwfGAEuBCcDrgF+FEE6IMa7r7b4rSYy5N2whhDJXUlqp5oa+n/2UE2q4atYorp+zngeXdR7w9/frB3O+cAivO2kwP7h7A1++bX0GVVaWvn7MdeA85ulJ9ZinmrtL3qcjE8UY6fhf5BqOLwInAtfkH78DtAGTY4wnxxjHA/9Grvn43/v75CGEJd19FaH2/bavTwDa29uB3LzIapJqbqju7LlpVWNZ8fQWPvsfBz6tql8/+N4XDuGdF9Tyw19s4L2feTGDKkuvmo+5uuYxT0+qxzzV3Kocxbhk7tuBP8YYr87/eVkI4QLgDcCMGOMzO217NfAu4M3AvxZh3yUzYcIEoPu5jmvWrAG6nyvZV6WaG6o7e+3Qfhx7xCAAOv94VJfbzG4Yy+yGsdzw/fVc+e8tO5b37w//799yDcf372rjH65ey/a+Patqh2o+5uqaxzw9qR7zVHOrchSj6Tgc+N5uyxYDZwK/23lhjDGGEH4DzNjfJ48x1ne3bsmSJSW7VM6UKVMAWL58Odu3b9/lyg/t7e2sWLGCQYMGMWnSpFKVVBKp5obqzt65OTL7jq5v5Hfyq2s4+dU13P9QO489tYUHlv7l5PCBA2Duv4/j7ecM47aftXHpNWuJffuCVbuo5mOurnnM05PqMU81d8/07ftNVapiTK8aAmzcbdl6gBjj2i62bwaGFWG/JTVu3DimTp3K2rVrWbBgwS7r5s2bR2dnJ2eddRaDB/ft+xTsLtXcUN3ZOzojl3/upS6/fvqb3ND7bT/bwOWfe4m5v8z9eA8aCD/+6njefs4wZt/RWnUNB1T3MVfXPObpSfWYp5pblaMYIx0vAYfstmwj0N0k7zHAuiLst+Te//7309DQwG233cYjjzzCxIkTWblyJU1NTdTV1fHOd76z3CVmItXckHb23X3j6rG86ayhrH15G8+v3cY1/zh6j20WLm7nN4v79mVzUz3mIydNYtTRRwMwcFjuc6HaCRM4PH/X4q0dHTz/m9+Urb4secw95qkc81RzHzhHOrIQYi8/qgwh/AoYGmM84wC2PyjGeHKvdkxpp1cVtLS0cPvtt7Ns2TLa2toYPXo09fX1XHzxxdTW1pa6nJJJNTdUTvZps/Z8k5+Fhg+OpvGDo7ns2rXc/OO2HcvvnV3H9Gl7P8Gw8RuvcO03XilqPYtvLu7z7Y9KOeYsXFiyXdWdfjoTzuj+n/HO9et5ZPbs0hQzfXpp9rMTj/mePObVqVJy19fXV+xlssLt/6/s4/jxHX9bsa9PTxWj6fgS8FFgeIxx8z62HQc8C3wzxrjfV7DqTjmaDqlcStV0VJpyNB0Vo4RvQCtKGd6AVgyPuRJh07F31dh09Pqcjhjjp2KMNftqOPJGAZ8EvtHb/UqSJEnFt70CvqpPMc7p2G8xxseAx0q5T0mSJEnlVdKmQ5IkSapsnkiehWJcMleSJEmSumXTIUmSJClTTq+SJEmSdnB6VRYc6ZAkSZKUKUc6JEmSpB2q85K15eZIhyRJkqRM2XRIkiRJypTTqyRJkqQdPJE8C450SJIkScqUIx2SJEnSDo50ZMGRDkmSJEmZsumQJEmSlCmnV0mSJEk7eJ+OLDjSIUmSJClTNh2SJEmSMuX0KkmSJGkHr16VBUc6JEmSJGXKkQ5JkiRpB0c6suBIhyRJkqRM2XRIkiRJypTTqyRJkqQdvE9HFhzpkCRJkpQpRzokSZKkHTyRPAs2HVIfsfjmV8pdQln8Ytofyl1C2Vy4eHq5S5AkqSicXiVJkiQpU450SJIkSQVxY7krqEqOdEiSJEnKlCMdkiRJUsH2TeWuoCo50iFJkiQpUzYdkiRJkjLl9CpJkiSpwBPJM+FIhyRJkqRMOdIhSZIkFTjSkQlHOiRJkiRlyqZDkiRJUqacXiVJkiQVeJ+OTDjSIUmSJClTjnRIkiRJBZ5InglHOiRJkiRlyqZDkiRJUqacXiVJkiQVOL0qE450SJIkScqUTYckSZKkTDm9SpIkSSrwPh2ZcKRDkiRJUqYc6ZAkSZIKPJE8E450SJIkSVUmhPClEMKvQwjPhhDaQwgvhxD+FEJoCCGM6eZ7zggh3JXfdlMIYVkI4aMhhP69rceRjgPU0tLCvHnzWLp0KRs2bGDUqFFMmzaNGTNmUFtbW+7yMpNqbkg3e2q5T3jvZN465zwAfn7ZQpbe/Odd1g8cNoDTP3USx808mlFHDmdrxzbWLFnL769fyqr5z5Sj5KJL7ZjvLMXso445huGHHcaQsWMZOnYs/WtqaGlq4qn588tdWkmkeMwh3dyJuhJ4CFgAvAgMA04DGoEPhBBOizE+W9g4hPA24EdAB/BD4GXgLcBXgTOBd/SmGJuOA9Dc3ExDQwOtra3U19czYcIEVq1axd13383SpUtpbGxk+PDh5S6z6FLNDelmTy338EOHccHXXkdn22Zqhg/aY33NyEH8/f1v55CpY1j7yMv86ZtNDBw2kGPeegTvuutN/PKKRSz+2vIyVF48qR3znaWave600xh6yCFs27yZzW1tDKmpKXdJJZPqMU819wGrnulVI2KMHbsvDCF8AbgK+DTw4fyyEcC3gW3A9Bjj4vzyzwL3ADNDCO+OMf6gp8XYdByAW265hdbWVi655BIuvPDCHcvnzJnD/PnzmTt3LrNmzSpjhdlINTekmz213G++9VzaWzp47I4nOe2TJ+6x/qzGUzhk6hj+50dP8ON3/ZK4LQIw9NODed8fLua8L5/OqvnP8MrK9SWuvHhSO+Y7SzX7swsXsqWtjc5166g99FCOfde7yl1SyaR6zFPNnaquGo68ueSajmN2WjYTGAt8t9BwFJ4jhHA18GvgQ0CPmw7P6dhPzc3NLF++nLFjx3L++efvsm7mzJnU1NSwaNEiOjq6O759U6q5Id3sqeU+5YqpHHHuRP770nvZsnFLl9scO+NIAO675g87Gg6ATS918Pvrl9J/UH9O/uCUktSbhdSO+c5Szr7h2WfpXLeu3GWUXKrHPNXcPbJ9U/m/svWW/OOynZadm3+8u4vt7wM2AWeEEHo8JJp50xFC+EoIoVdzwCpBU1MTAFOnTqVfv11ftiFDhjB58mQ6OztZuXJlOcrLTKq5Id3sKeUec9wopl93Gn+8cRnP3r+62+1qxw8FYN0TrXusKyw74rxDsymyBFI65rtLOXuqUj3mqebuq0IIS7r76sFzfSKE0BhC+GoI4X7gX8k1HNfttNmx+ccVu39/jHEr8CS5GVJHHXianFKMdHwUOH9fG1W6F154AYC6urou148fPx6A1au7f+PSF6WaG9LNnkru0D/w1jnn0frMBhZe9fu9brvppdwnf6OOHLHHulFH5ZaNOW5U0WsslVSOeVdSzp6qVI95qrkFwCeABnLvyV9HbjTjghjj2p22GZl/7G6ecGH5qJ4W0atzOkII79/PTY/dedsY4y292W85bNqUG+oaOnRol+sLywvbVYtUc0O62VPJfdY10xh30sHMed2dbO3YttdtV/7305z0gSmc1TiNO9/zK+L23BSrIQfVcOrH/gqAAYMHMGBw/30+VyVK5Zh3JeXsqUr1mKeau0cq4ETyGGN9EZ9rPEAIYRxwBrkRjj+FEN4cY3xoP58mFJ6up3X09kTy2fux80iuq3oduYIj0Oeajn2JMfcyhBD2sWV1STU3pJu9GnLXnXIIZ1x1Mr+/finPP9i8z+3vu+YPHHnBobz6nZMY8+rRPPXr5xk4dACT33YEnW1b2LxxC4OGDWT7th7/W1zRquGY91TK2VOV6jFPNXdKYozNwI9DCA+Rm0b1XeCE/OrCSMbIrr4XGLHbdgesGFev2gB8g9wJJrsLwDXkrhH8s548+d7mri1evLi7VUW3r08A2tvbgdy8yGqSam5IN3u15y5Mq3p5xTru++wf9ut7Nja3851TfsSZV9cz6S1HUP/h4+l4pZPH//tpfvuvS/jwE39Hx7pOtm/ZnnH12aj2Y743KWdPVarHPNXcPVPdJ9PHGJ8OITQBJ4YQDo4xvgQ8BkwDJgO7vPcOIQwAjgS2Ak/0dL+9bTouAW4CZgCXxhgX7b5BCOEa4KEY47W93FdZTZgwAeh+ruOaNWuA7udK9lWp5oZ0s1d77kG1Axlz7CgAPtX5j11u86bZ03nT7On84YZl/OrK3wK58zoWfPS3LPjob3fZ9vDpEwj9Aqv/+GKmdWep2o/53qScPVWpHvNUc6tbE/KPhTnB9wB/B7wB+K/dtj0bGArcF2Ps7OkOe9V0xBjnhBAWArcCC0MINwCf6U1BXeyj2zltS5YsKdlchilTcpfDXL58Odu3b9/lyg/t7e2sWLGCQYMGMWnSpFKVVBKp5oZ0s1d77m2d23h49p+7XDf+5IMZf/JYnr1/NS2PreP5B9bs8/lOvDz3ej3y/ceLWmcpVfsx35uUs6cq1WOeau5UhRCOA9bFGNfstrwfuatXHQL8Lsb4Sn7VPOBLwLtDCF/b6eaAg4HP57f5z97U1OurV8UYn40x/jXwcXJ3NfxTCOHU3j5vpRk3bhxTp05l7dq1LFiwYJd18+bNo7Ozk7POOovBgweXqcJspJob0s1e7bm3dmzjrssXdvn1+E+fAmDZbY9x1+UL+fPcVblvCjBw2J6f0fzVrFdz/N8ew5o/reXRPtx0VPsx35uUs6cq1WOeau4e6VcBX733BuDZEMKvQwjfCiH8WwjhFuBxcjcGXANcXtg4xtia/3N/cgMJs0MI/wd4GDidXFPyw94UFAonDhVDCOHVwBzgr4AvkzufoxOYHWP8QNF2lFfKkQ7I3VinoaGB1tZW6uvrmThxIitXrqSpqYm6ujoaGxsZPnx4KUsqiVRzQ7rZKyn3L6bt33kXxXBWwzTOajyFn1+2kKU3/2U0ZOCwAfxT8/t4csFzO+46fthZdUx87TheXrme//rrn7H+6bai13Ph4tJ9flNJx7zUKir7woWl2Q8wctIkRh19NAADhw1j5JFH0rluHW3PPQfA1o4Onv/Nb0pTzPTppdlPXkUd8xKqpNz19fUVe8Z6uDmU/aogcVbs1esTQjiB3B3EzwQOJXep243kTiD/OXBTjPHlLr7vTOAz5BqNwcBKcheAuinG2KvLMxa16QAIIfQndy3gT5M7KeXVwM3V0HQAtLS0cPvtt7Ns2TLa2toYPXo09fX1XHzxxdTW1pa6nJJJNTekm71ScldC09FvQD/e8I2zOex1dQw/dBgA61a18j/zVvH7ryxly8atmdRTyqYDKueYl0PFZC9h01F3+ulMOOOMbtd3rl/PI7Nnl6aYEjcdUEHHvMQqJXdFNx3fqYCm4329azoqUdGbjh1PnJtiNQc4hioZ6ZBUeqVsOipNqZsOVYASNh0VpQxNh8rLpmPvqrHpKMYlc7sUY/xDCGEKUEtuipUkSZKkBGXWdADk5371+CYikiRJUkn1L3cB1ak458dLkiRJUjdsOiRJkiRlKtPpVZIkSVKf4kfymfBllSRJkpQpRzokSZKkAk8kz4QjHZIkSZIyZdMhSZIkKVNOr5IkSZIKnF6VCUc6JEmSJGXKkQ5JkiSpwI/kM+HLKkmSJClTNh2SJEmSMuX0KkmSJKnAE8kz4UiHJEmSpEw50iFJkiQVONKRCUc6JEmSJGXKpkOSJElSppxeJUmSJBX4kXwmfFklSZIkZcqRDkmSJKnAE8kz4UiHJEmSpEzZdEiSJEnKlNOrJEmSpAKnV2XCkQ5JkiRJmXKkQ1JFu3DxqeUuoWym/X50uUsoi8WvfaXcJZTP9OnlrkCSH8lnwpdVkiRJUqZsOiRJkiRlyulVkiRJUoEnkmfCkQ5JkiRJmbLpkCRJkpQpp1dJkiRJBU6vyoQjHZIkSZIy5UiHJEmSVOBH8pnwZZUkSZKUKZsOSZIkSZlyepUkSZJU4InkmXCkQ5IkSVKmHOmQJEmSChzpyIQjHZIkSZIyZdMhSZIkKVNOr5IkSZIK/Eg+E76skiRJkjLlSIckSZJU4InkmXCkQ5IkSVKmbDokSZIkZcrpVZIkSVKB06sy4UiHJEmSpEw50iFJkiQV+JF8JnxZJUmSJGXKkY4D1NLSwrx581i6dCkbNmxg1KhRTJs2jRkzZlBbW1vu8jKTam5IN3uquaG6s1932kFMO6SGyaMGcvDgfrRvjTzdtpU7n9zI15e38nLn9l22H9QPLpsygkuOreWoEQMZPCDw7IatLHi2nesfXs8zG7aWKUlxVfMx35tUc0O62VPNrfILMcZy19BjS5YsKWnxzc3NNDQ00NraSn19PRMmTGDVqlU0NTVRV1dHY2Mjw4cPL2VJJZFqbkg3e6q5obKyT/v96KI/Z+c/HslDaztpemULL7ZvY9iAwGnjazjlkME8v2Erp93xPM9t2AZA/wAL3z6B19UN5s+vbOZXz7bTuT1yytgaXj9xCOs6t3HGHS/w51e2FLXGxa99pajPty+VdMxLKdXckG72SspdX18fSrKjHgj/E8r+5jgeFyv29ekpRzoOwC233EJrayuXXHIJF1544Y7lc+bMYf78+cydO5dZs2aVscJspJob0s2eam6o/uwjZj9F57Y9f59+/rWj+Uz9aD598ig+cl8LABcdNYzX1Q3mV89u4oKfrWHn72o8ZTQNp4zmEyeOYta9a0tUfTaq/Zh3J9XckG72VHOrMnhOx35qbm5m+fLljB07lvPPP3+XdTNnzqSmpoZFixbR0dFRpgqzkWpuSDd7qrkhjexdNRwAc1duBOCYkQN3LDtqRO5zqZ8/3c7u3/WTJ3Pbjx3St3+NpHDMu5Jqbkg3e6q5e6R/BXxVob7926KEmpqaAJg6dSr9+u36sg0ZMoTJkyfT2dnJypUry1FeZlLNDelmTzU3pJ39LUcMBWBZy+Ydyx59OTdt6m8OH8Lu4/xvzm//q+faS1JfVlI95qnmhnSzp5pblcOmYz+98MILANTV1XW5fvz48QCsXr26ZDWVQqq5Id3sqeaGtLJ//MSRNJwymq+cOYb73l7H5197EEtf6uS6h9bt2ObnT2/iR6s2csFhQ1n+7kO54XVj+D+nH8Sv31rH1fWjuWnZer6+vLV8IYogpWO+s1RzQ7rZU82typHZOR0hhKlAI3A2MAx4ApgDXB9j7HOXO9m0aRMAQ4cO7XJ9YXlhu2qRam5IN3uquSGt7J84cSTjh/7lV8D8pzfxvnvW8lLHrlevmvmLZq6ZNorPThvN8QcN2rH8V89u4v89voHtZT/dsndSOuY7SzU3pJs91dw94kfymeh10xFCeAH4cozxKzstOxu4C9j5b/YU4IvAmcBbD+D5l3S3bvHixQdcb1YKVwELoeouNrBXqeaGdLOnmhuqK3vdd54B4JAh/TljfA3XnXYQf3rnRN788zX86aXcFKua/oHvnjeWv3nVUD5y30v85MlNbNq6nTPrBnPT6w7mvrdP4B2/aOanT1Xvm5RqOuYHItXckG72VHOrdIrRy40HdlzYOYTQD7gVGAJcDxwDjAT+htxox5tCCH9XhP2W1L4+AWhvz81rHjJkSMlqKoVUc0O62VPNDWlmf7F9G3c+mbs61ZjB/fnueYfsWPcvJ4/inZNq+czvX+ZbTW00t2+jbUvk7mfamfmLZgb1D9z4ujFlrL73UjzmkG5uSDd7qrlVObKYXnU2cCTwjRjjJ3da/osQwl8Dfwb+Afj+/jxZjLG+u3WlvE/HhAkTgO7nOq5Zswbofq5kX5Vqbkg3e6q5Ie3sz2zYStPLmzlpbA1jBvejpWM7bz489ybl3uf3PFl8WctmWjq2ccSIgRxU02+Pmwr2Fake81RzQ7rZU83dI1V69ahyy2LW2muACHx99xUxxqeBnwMnZrDfTE2ZMgWA5cuXs337rr9c29vbWbFiBYMGDWLSpEnlKC8zqeaGdLOnmhvSzg4wYVjuN23hqro1/XPTLMYO2fM38KB+MGJg7lfI5j58YkeqxzzV3JBu9lRzq3Jk0XQMyz8+0c36VcCoDPabqXHjxjF16lTWrl3LggULdlk3b948Ojs7Oeussxg8eHCZKsxGqrkh3eyp5obqz37sqIGM66KBCORuDjhu6AB+u7qDdflRi/tX50Y4rjp5FIN2+23ReOpoBvYP/KG5gw1b+m7TUe3HvDup5oZ0s6eau0fKfY+OKh1pCYUTh3r8BCFsBxpijP+a//PfAd8FxscY97hNbQjhP4D3xBgP6tWOKe30KsjdWKehoYHW1lbq6+uZOHEiK1eupKmpibq6OhobGxk+fHgpSyqJVHNDutlTzQ2VlX3a70cX9fn+6TUj+PfTx3Df6g5Wrd9CS+c2xg0ZwOsnDObokQNZvXEr5/10NX9+JXd/jgnD+vPgxRM5rHYAT7Zu4e5n2mnPn0j+2nGD2bRlO+f9dDUPNncWtc7Fr32lqM+3L5V0zEsp1dyQbvZKyl1fX1+xZ6yHZ0PZP0mJh8WKfX16qlhNx7r8F0ANuZPLz4sxLuxi+7uAI2KMU3q1Y0rfdAC0tLRw++23s2zZMtra2hg9ejT19fVcfPHF1NbW7vsJ+qhUc0O62VPNDZWTvdhNx/EHDeRDx4/gzLrBHDpsAKNq+rFxS2TF+s38/Ol2blq2nld2Ozfj4MH9+NTJo3jT4UM5cvgA+oXA6k1buee5Dr70p3U8tm5LUWuE0jcdUDnHvNRSzQ3pZq+U3DYde2fT0dUThPAUuXM4dndLYfRjp21HAy8Ad8QYe30Fq3I0HZJUKsVuOvqKcjQdkkqropuO5yug6ZhYfU1Hr69eFWM84gA2rwOuA+7t7X4lSZIk9Q2Z3ZG8KzHGJuDaUu5TkiRJ2m9VeiJ3uXmjd0mSJEmZsumQJEmSlKmSTq+SJEmSKprTqzLhSIckSZKkTDnSIUmSJBX4kXwmfFklSZIkZcqmQ5IkSVKmnF4lSZIkFXgieSYc6ZAkSZKUKUc6JEmSpAJHOjLhSIckSZKkTNl0SJIkScqU06skSZKkAj+Sz4QvqyRJkqRMOdIhSZIkFXgieSYc6ZAkSZKUKZsOSZIkSZlyepUkSZJU4PSqTDjSIUmSJClTNh2SJEmSMuX0KkmSJKnAj+Qz4csqSZIkKVOOdEiSJEkFnkieCUc6JEmSJGXKkQ5JqlCLX/tKuUsoi2mzRpe7hLJZfHOax1xS9bPpkCRJkvJGlruAKuX0KkmSJEmZcqRDkiRJyhtR7gKqlCMdkiRJkjJl0yFJkiQpU06vkiRJkvI8kTwbjnRIkiRJypQjHZIkSVKeIx3ZcKRDkiRJUqZsOiRJkiRlyulVkiRJUp736ciGIx2SJEmSMuVIhyRJkpRXDSeShxDGABcBbwKmAhOBzcBy4Fbg1hjj9i6+7wzgauA0YDCwErgF+FqMcVtvarLpkCRJkqrLO4D/BFYD9wLPAOOAGcBs4G9CCO+IMcbCN4QQ3gb8COgAfgi8DLwF+CpwZv45e8ymQ5IkSaouK4C3Aj/feUQjhHAV8AfgYnINyI/yy0cA3wa2AdNjjIvzyz8L3APMDCG8O8b4g54WZNMhSZIk5VXD9KoY4z3dLF8TQvgG8AVgOvmmA5gJjAW+W2g48tt3hBCuBn4NfAjocdPhieSSJElSOrbkH7futOzc/OPdXWx/H7AJOCOEUNPTnTrSIUmSJFWQEMKS7tbFGOt78bwDgH/I/3HnBuPY/OOKLva3NYTwJHA8cBTw557s26ZDkiRJyqvy+3RcB5wA3BVj/MVOywuzytZ3832F5aN6umObDkmSJKmC9GY0ozshhCuAjwP/A/z9gX57/jHudau9sOmQJEmS8qrhRPLdhRA+AtwINAHnxRhf3m2TwkhGd/FH7LbdAfNEckmSJKlKhRA+CnwdeAQ4J8a4povNHss/Tu7i+wcAR5I78fyJntZh0yFJkiRVoRDCp8jd3O9hcg3Hi91sWrjE7hu6WHc2MBT4XYyxs6e1OL3qALW0tDBv3jyWLl3Khg0bGDVqFNOmTWPGjBnU1taWu7zMpJob0s2eam5IN3tqud/7plrmfOEQAC67di03/7htx7pJrxrAjHOHceEZQznmVQMZN6Y/r7Ru48Flndzw/fUsXNxRrrKLKrVjvrNUs6ea+0BUy/Sq/I39PgcsAS7oYkrVzuYBXwLeHUL42k43BxwMfD6/zX/2qp6d7n7e5yxZsqSkxTc3N9PQ0EBrayv19fVMmDCBVatW0dTURF1dHY2NjQwfPryUJZVEqrkh3eyp5oZ0s1dS7mmzRme+j0PH9Wf5vEPp3y8wfFi/PZqO/7ruEN79hloeXbWZRX/q4OX12zj2iEG89fVDGTAgcMWXXuJr/9Va9LoW3/xK0Z+zO5V0zEst1eyVlLu+vj7se6vy+Dqh7G+O/xexV69PCOES4Dvk7jD+Nbo+F+OpGON3dvqet5NrPjrI3QTwZXJ3NT82v/ydsReNgyMdB+CWW26htbWVSy65hAsvvHDH8jlz5jB//nzmzp3LrFmzylhhNlLNDelmTzU3pJs9tdy3XjuWlnXbueOejXzyklF7rL/7d5v40q3rePixzbssP7t+MAu+Uce/XzmG2xdsZM1L20pUcfGldsx3lmr2VHMfqCq5ZO6R+cf+wEe72eY35BoTAGKMd4YQXg98BrgYGAysBD4G3NSbhgM8p2O/NTc3s3z5csaOHcv555+/y7qZM2dSU1PDokWL6OiojiH3glRzQ7rZU80N6WZPLfcVfzuCc08dwqUNa9nY3vXv0Nt+umGPhgPgviUdLFzcTs2gwBl/NTjrUjOT2jHfWarZU82dqhhjY4wx7ONrehff99sY4xtjjKNjjENijFNjjF+NMfb6Exabjv3U1NQEwNSpU+nXb9eXbciQIUyePJnOzk5WrlxZjvIyk2puSDd7qrkh3ewp5T7uyIFcd8VB3Pj/Wrn/oZ69udqyNfe4dVvZZ2D0WErHfHepZk81typHpk1HCOG1IYT/HUL45xDCO0MIfXbE6oUXXgCgrq6uy/Xjx48HYPXq1SWrqRRSzQ3pZk81N6SbPZXc/fvDnM8fwjNrtnLV1/Z2PmX3XlU3gPNOHczG9u3ct6TvfiKcyjHvSqrZU83dEyMr4Ksa9fqcjhDCG4DpwGcKQy8hhIOBucDrd9t8XQjh8hjjHb3db6lt2rQJgKFDh3a5vrC8sF21SDU3pJs91dyQbvZUcl/zgdGcdNwgXnfpC3R0HvgoxaCB8P0vHsLgmn588qstrGvbnkGVpZHKMe9KqtlTza3KUYwTya8ERu0212seuWv6Pgf8AngFOAG4EPivEMLrYox/3J8nDyEs6W7d4sWLe1x0sRXOrQmhYi/GkIlUc0O62VPNDelmr4bcp5xQw1WzRnH9nPU8uOzALzPfrx/M+cIhvO6kwfzg7g18+bYe35S3T6iGY95TqWZPNXdXvHBwNorRdEwFflr4QwjhNHINx93AjBhjx07rzgfuAj4NzCjCvktmX58AtLe3A7l5kdUk1dyQbvZUc0O62as9d25a1VhWPL2Fz/7HgU+r6tcPvveFQ3jnBbX88BcbeO9nuru3Vt9R7cd8b1LNnmpuVY5iNB0HAS/t9OdTgQh8fOeGAyDGuCCEcCe56Vj7JcZY3926Ut6nY8KECUD3cx3XrMndUb67uZJ9Vaq5Id3sqeaGdLNXe+7aof049ohBAHT+8agut5ndMJbZDWO54fvrufLfW3Ys798f/t+/5RqO79/Vxj9cvZbtfXdW1Q7Vfsz3JtXsqeZW5ShG0/EysPPf0MI1BJ/uZvungD53x50pU6YAsHz5crZv377LlR/a29tZsWIFgwYNYtKkSeUqMROp5oZ0s6eaG9LNXu25OzdHZt/R9Y38Tn51DSe/uob7H2rnsae28MDSv3xWNnAAzP33cbz9nGHc9rM2Lr1mLX34frq7qPZjvjepZk81d08MqIQPFqrw+rLFiPQA8OYQQuHMpEeBQG7EoyunAs8XYb8lNW7cOKZOncratWtZsGDBLuvmzZtHZ2cnZ511FoMH993rtncl1dyQbvZUc0O62as9d0dn5PLPvdTl109/k5tqctvPNnD5515i7i83ArmTxn/81fG8/ZxhzL6jtaoaDqj+Y743qWZPNbcqR+jlzQUJIZxF7o6GvwT+ltxt1h8iN+JxcYzxkfx2A4BryN3l8Ksxxk/0aseUdnoV5G6s09DQQGtrK/X19UycOJGVK1fS1NREXV0djY2NDB/e5wZx9inV3JBu9lRzQ7rZKyn3tFmjS7IfgIYPjqbxg6O57Nq13Pzjth3Lb7l2LJe+bThrX97G/729tcuGY+Hidn6zuLiXzV188ytFfb69qaRjXmqpZq+k3PX19RV7xvpvt4Wyf8RwZv9Ysa9PT/W66QAIIVwNfA7YCPwcWAt8ML/6CWAdcAy5Sw+vBF4bY1zX2/2WuukAaGlp4fbbb2fZsmW0tbUxevRo6uvrufjii6mtrd7rHaSaG9LNnmpuSDd7peSuhKbj3tl1TJ+29xNqG7/xCtd+o7hNQimbDqicY14OqWavlNw2HXtn07G3JwrhHcCXgCPInUi++4u1Ffgh8LEY49pi7LMcTYckKVulbDoqTambDqlcbDr2rhqbjmKcSA5AjPH2EMI84AzgFOAQoD+5e3Q8BtwXY2zZy1NIkiRJZTWg7C1HdSpa0wEQc8Mmv81/SZIkSVJxmw5JkiSpL6uIS+ZWoSq8CrAkSZKkSmLTIUmSJClTTq+SJEmS8jyRPBuOdEiSJEnKlE2HJEmSpEw5vUqSJEnKc3pVNhzpkCRJkpQpRzokSZKkPO/TkQ1HOiRJkiRlyqZDkiRJUqacXiVJkiTleSJ5NhzpkCRJkpQpRzokSZKkPEc6suFIhyRJkqRM2XRIkiRJypTTqyRJkqQ879ORDUc6JEmSJGXKkQ5JkiQpzxPJs+FIhyRJkqRM2XRIkiRJypTTqyRJkqQ8p1dlw5EOSZIkSZlypEOSJEnK6+8lczNh0yFJqiiLb36l3CWUzbRpLeUuoSwWLx5T7hIkZczpVZIkSZIy5UiHJEmSlBe2lbuC6uRIhyRJkqRMOdIhSZIk5TnSkQ1HOiRJkiRlyqZDkiRJUqacXiVJkiTl9fM+HZlwpEOSJElSpmw6JEmSJGXK6VWSJElSnlevyoYjHZIkSZIy5UiHJEmSlOdIRzYc6ZAkSZKUKZsOSZIkSZlyepUkSZKU5306suFIhyRJkqRMOdIhSZIk5XkieTYc6ZAkSZKUKZsOSZIkSZlyepUkSZKU5/SqbDjSIUmSJClTjnRIkiRJeV4yNxuOdEiSJEnKlCMdB6ilpYV58+axdOlSNmzYwKhRo5g2bRozZsygtra23OVlJtXckG72VHNDutlTzQ1pZX/ve+uYM2cqAJdd9ig33/z8jnWHHz6Yp546u9vv/cEPVvOe9yzPvMZSSOmY7yzV3Cq/EGMsdw09tmTJkpIW39zcTENDA62trdTX1zNhwgRWrVpFU1MTdXV1NDY2Mnz48FKWVBKp5oZ0s6eaG9LNnmpuqKzs06a1ZPr8hx5aw/LlZ9C/f2D48AHdNh0PP9zKnXeu3eP7H3lkAz/6UXPR61q8eEzRn3NvKumYl1Il5a6vrw8l2VEPbFseyv7muP/UWLGvT0850nEAbrnlFlpbW7nkkku48MILdyyfM2cO8+fPZ+7cucyaNauMFWYj1dyQbvZUc0O62VPNDWllv/XWE2hp2cIdd7zIJz95RLfbPfxwG9deu6p0hZVYSsd8Z6nmVmXwnI791NzczPLlyxk7diznn3/+LutmzpxJTU0NixYtoqOjo0wVZiPV3JBu9lRzQ7rZU80NaWW/4opXce65B3HppY+ycWO61wRN6ZjvLNXcPRG2lf+rGtl07KempiYApk6dSr9+u75sQ4YMYfLkyXR2drJy5cpylJeZVHNDutlTzQ3pZk81N6ST/bjjhnHddcdw443PcP/9r+xz+wkTavjABw7l058+kg984FCmTq2euf6pHPPdpZpblcOmYz+98MILANTV1XW5fvz48QCsXr26ZDWVQqq5Id3sqeaGdLOnmhvSyN6/f2DOnBN45pkOrrrq8f36ngsuOJhvfnMKX/ziMXzzm1NYtuwM7rlnGocdNjjjarOXwjHvSqq5VTl63XSEELaEEH4SQnhTCKHqTnop2LRpEwBDhw7tcn1heWG7apFqbkg3e6q5Id3sqeaGNLJfc81RnHTSCN73vkfo6Nj7DQg2bdrO5z63ipNPfoBRo+5h1Kh7OPvsP3LPPS9zzjkH8etf1zN0aP8SVZ6NFI55V1LN3RP9tpf/qxoVY6SjP/Bm4KfA0yGEa0IIE4vwvH1K4SpgVdx3dSnV3JBu9lRzQ7rZU80NfT/7KaeM4KqrjuT665/iwQfX73P7tWs309Cwij/9qY3167eyfv1W7r//FS64YAkPPriOY44ZxmWXVfev+L5+zHsq1dwqnWJNr7oX+D1wKNAAPJUf/Xhjb0c/QghLuvsqRuH7a1+fALS3twO5eZHVJNXckG72VHNDutlTzQ3VnT03rWoqK1Zs4rOf7d08/W3bIrNn5y6te/bZo4tRXtlU8zHfm1Rzq3IU65K598UYPxdCOB74IPB3wFvIjYA8F0KYDdwSY3x+b09SySZMmAB0P9dxzZo1QPdzJfuqVHNDutlTzQ3pZk81N1R39tra/hx77DAAOjvP73Kb2bOPZ/bs47nhhqe58srH9vp8a9duBmDYsL49vaqaj/nepJq7R7aWu4DqVNT7dMQYHwX+dwjhk8C7gA8ApwONwDUhhJ8D3wLmx/28K2GMsb67daW8OeCUKVMAWL58Odu3b9/lyg/t7e2sWLGCQYMGMWnSpFKVVBKp5oZ0s6eaG9LNnmpuqO7snZ3bmT37uS7XnXzyCE4+eQT33/8Kjz22kQceWLfP5zvttJEAPPFEezHLLLlqPuZ7k2puVY5Mrl4VY+yIMd4WYzwTOB74OtAKvBX4GfBkFvvN0rhx45g6dSpr165lwYIFu6ybN28enZ2dnHXWWQwe3Pev7LGzVHNDutlTzQ3pZk81N1R39o6O7Vx+eVOXXz/9ae5u47fd9gKXX97E3Lm5u4yfeupIBg7cc1b0OeccxJVXHg7A9773QulCZKCaj/nepJq7R7ZWwFcVCvs54ND9E4SwHWiMMX5uH9vVAO8kN/pxZoyx1w1PKUc6IHdjnYaGBlpbW6mvr2fixImsXLmSpqYm6urqaGxsZPjw4aUsqSRSzQ3pZk81N6SbPdXcUFnZp01rKcl+GhqOprHxaC677FFuvvkvM5/vvXcaxx9fy8KFL/Pcc50AvOY1tZx33hgArr76cb7wheJ/brh48ZiiP+feVNIxL6VKyl1fX1+5Z6z/LpT0/WWXzoiV+/r0UMmajt2+57gY4//0aseUvukAaGlp4fbbb2fZsmW0tbUxevRo6uvrufjii6mtrZ6bJ+0u1dyQbvZUc0O62VPNDZWTvdxNx/vfP5GLLjqEE06o5eCDBzJwYD+amzt54IH1fP3rz7Bo0bpM6il10wGVc8xLrVJy23Tsg01HF0/Qg6ajWMrRdEiSlJVSNR2VphxNh8qropuO+yqg6Ti7+pqOYpxIfinwcBGeR5IkSVIV6nXTEWO8rRiFSJIkSWVXpSdyl1smV6+SJEmSpAKbDkmSJEmZKurNASVJkqQ+zelVmXCkQ5IkSVKmHOmQJEmSChzpyIQjHZIkSZIyZdMhSZIkKVNOr5IkSZIKnF6VCUc6JEmSJGXKkQ5JkiSpwJGOTDjSIUmSJClTNh2SJEmSMuX0KkmSJKnA6VWZcKRDkiRJUqYc6ZAkSZIKHOnIhCMdkiRJkjJl0yFJkiQpU06vkiRJkgqcXpUJRzokSZIkZcqmQ5IkSVKmnF4lSZIkFTi9KhOOdEiSJEnKlCMdkiRJUoEjHZlwpEOSJElSphzp6KsWLix3BSq1O+8sdwXlccMN5a5AKpnFi8eUu4SymHbP6HKXUDaL+/243CWUR319uStQidl0SJIkSQUbyl1AdXJ6lSRJkqRMOdIhSZIkFVTBSEcIYSbweuBE4K+A4cD3Y4zv3cv3nAFcDZwGDAZWArcAX4sxbuttTTYdkiRJUnW5mlyzsQF4DjhubxuHEN4G/AjoAH4IvAy8BfgqcCbwjt4W5PQqSZIkqbpcCUwGRgAf2tuGIYQRwLeBbcD0GOOsGOMnyY2SPADMDCG8u7cFOdIhSZIkFVTB9KoY472F/w8h7GvzmcBY4LsxxsU7PUdHCOFq4NfkGpcf9KYmRzokSZKkdJ2bf7y7i3X3AZuAM0IINb3ZiSMdkiRJUkEVjHQcoGPzjyt2XxFj3BpCeBI4HjgK+HNPd2LTIUmSJFWQEMKS7tbFGIt9Z8WR+cf13awvLB/Vm504vUqSJElSdwonhcTePIkjHZIkSVJBBUyvymA0Y28KIxkju1k/YrftesSRDkmSJCldj+UfJ+++IoQwADgS2Ao80ZudONIhSZIkFVTASEeJ3QP8HfAG4L92W3c2MBS4L8bY2ZudONIhSZIkpWse8BLw7hDCtMLCEMJg4PP5P/5nb3fiSIckSZJURUIIbwfenv/j+Pzj6SGE7+T//6UY4ycAYoytIYTLyTUfC0MIPwBeBt5K7nK684Af9rYmmw5JkiSpoDqmV50IXLLbsqPyXwBPA58orIgx3hlCeD3wGeBiYDCwEvgYcFOMsVdXrgKbDkmSJKmqxBgbgcYD/J7fAm/Moh6w6ZAkSZL+ojpGOiqOJ5JLkiRJypRNhyRJkqRMOb1KkiRJKnB6VSYc6ZAkSZKUKZsOSZIkSZlyetUBamlpYd68eSxdupQNGzYwatQopk2bxowZM6itrS13eZkYdcwxDD/sMIaMHcvQsWPpX1NDS1MTT82fX+7SMpVq7v4jRjDq7LMZefrpDDnqKAaNHUvcsoX2J57gpbvuouWuu6D3l+uuaCn+nEO6uSHd7NWc+7qzD2La+Bomjx7IwUP60b418nTrVu5cuZGvP9TKyx3bd9l+2MDAp04dxcxjh3HkyAF0bI0sad7M9X9cx/wn28uUorhS/b12wJxelQmbjgPQ3NxMQ0MDra2t1NfXM2HCBFatWsXdd9/N0qVLaWxsZPjw4eUus+jqTjuNoYccwrbNm9nc1saQmppyl1QSqeYefc45HP6JT7D5pZdoe+ghXnnxRQaOHs2os8/miH/5F0aedhpPfPaz5S4zM6n+nKeaG9LNXu25r5w2koeaO1nwdDsvbtrGsIGB0+pquPbMg/jAa0Zw2vef57m2bQCMrOnH/e+ZwNSxg3hk7Wa+ubSNYQMDbz16GHfNrOOKX7/E1x5qLXOi3kv195oqg03HAbjllltobW3lkksu4cILL9yxfM6cOcyfP5+5c+cya9asMlaYjWcXLmRLWxud69ZRe+ihHPuud5W7pJJINXfns8+y8lOfYv0DD+wyovH8t77Fq7/1LUZPn86o17+edb/5TRmrzE6qP+ep5oZ0s1d77hE3PkXntj1HZT//utF85vTRfPq1o/jIr1oAaDxjNFPHDuJHKzbyrp82U/i2Tw95mT+8dyJfnj6G+U9sYuW6raWMUHSp/l47YI50ZMJzOvZTc3Mzy5cvZ+zYsZx//vm7rJs5cyY1NTUsWrSIjo6OMlWYnQ3PPkvnunXlLqPkUs3d9tBDrP/d7/aYQrX15ZdZ+5OfADD8pJPKUVrmUv05TzU3pJs9hdxdNRwAcx/bCMAxowfuWDZj8jAArln0Mjt/20vt27l+8XoG9Q988MQR2RVbIqn+XlNlsOnYT01NTQBMnTqVfv12fdmGDBnC5MmT6ezsZOXKleUoTyqJuDX3KV/ctq3MlWQj1Z/zVHNDutlTzQ3wlqOHArBs7eYdy8YP6w/AE+v3HMl4Yt0WAM47fEgJqpOqV+ZNRwjhiBDCRSGEN4UQRme9v6y88MILANTV1XW5fvz48QCsXr26ZDVJJdW/P2PyUzBaf//7MheTjVR/zlPNDelmTyn3x08ZScMZo/nKOWO47z11fP6sg1j6YifX/X7djm1eas99kHLkyD1nnR81KjcictxBA/dYpyq1oQK+qlBRmo4Qwt+GEFaGENaHEOaFEA7OL78OeByYB/wUeC6E8L+Ksc9S27RpEwBDhw7tcn1heWE7qdpM/Md/ZMjRR7P+gQdo/cMfyl1OJlL9OU81N6SbPaXcnzhlJI1njubKaSM569AhzH9iExfcvoaX2v9y9ar/XpXL2XjGaPqFv3zvQYP78bFpIwEYPKAfgwcEJPVMr08kDyGcBswBAtAGzACGhRBuA/4ZeB74AzAWOBO4MYTw5xjjr3u770oS8/PfQ/AfJFWfsRdfzPj3vIf2p57iyX/913KXUzap/pynmhvSzV5Nuev+7zMAHDK0P2dMrOG6sw/iT5dM5M0/WsOfXsxNsbrmt69wwRFDeOdxtbx6zCB+/XQ7QwcG3jZpKG2bIxs3b2fYoH5s217dlwtXTqyAKyT3/Z+8PRVjpONjwBbgnBjjSGB6/uta4JfA5BjjxTHGs4E357/niv198hDCku6+ilD7ftvXpz7t7bm/oUOGOOdT1WXsRRfxqo9+lPYnn2TFP/0T29rayl1SZlL9OU81N6SbPcXcL27axp2P50Y5xgzuz3ffeMiOdc0bt3HKnOe5ccl6hg0MfPikEbxt0lD+e9Um/nruaoYMDKzr2MaW7XvZgaS9KsYlc+uBn8YYfwMQY7wvhPAz4GLgXTH+pV+MMc4PIfwceG0R9ltSEyZMALqf37pmzRqg+/mxUl90yDvewWFXXEH7qlWs+OhH2VrlVz1J9ec81dyQbvZUcwM807qVppbNnDSuhjFD+tGSn2b1Uvt2PnpPCx+9p2WX7acfNph+IfDHNZ3lKFeqGsVoOuqA3S9vUfjz/3SxfRPwhv198hhjfXfrlixZUrJxzilTpgCwfPlytm/fvsvVPtrb21mxYgWDBg1i0qRJpSpJytS4v/1bDv3Qh9i0YgUrPvYxtq1fX+6SMpfqz3mquSHd7KnmLphQm7ta1bb9GLm4/DW5S+V+v6lKz+7VHiphQKt/uQvIQDGmV7UAB++2bEz+cXwX248H+tyZaePGjWPq1KmsXbuWBQsW7LJu3rx5dHZ2ctZZZzF48OAyVSgVz/hLLuHQD32Ijf/zP6z46EeTaDgg3Z/zVHNDutmrPfexBw1k3LA937YFcjcHHDdsAL99voN1ndt3LB82cM9Z9LOmDudvp9Typ+ZOvv9nmw6pN0KMvRssCCH8EjgReHWMsSWEMIbcaMYA4JYY4yd32nYC8Gfg0RjjGb3aMaUd6YDczZQaGhpobW2lvr6eiRMnsnLlSpqamqirq6OxsZHhw4eXppiFC0uzH2DkpEmMOvpoAAYOG8bII4+kc9062p57DoCtHR08X4V3p6643HfeWZLdHPSGN3DkZz5D3LqVF++4g20b9vxFu3nNGlrmzy9JPdxwQ2n2k1dRP+cllGpuSDd7JeWedk9xr6j/T/Uj+PfXj+G+5zpYtW4LLe3bGDdsAK8/bDBHjxrI6g1bOW/uav7ckrsHx7CBgeYPH86Cp9tZmb8vx1kTB/PaCYNZ+coW/nruap5uzeZu5Iv7/TiT5+1KJf1eq//4xyv2XOnNIZT9igGDYqzY16enitF0vAn4GbAG+D1wKjCO3Dkd84A7gYXkrl71gfy6K2OMN/Vqx5S+6QBoaWnh9ttvZ9myZbS1tTF69Gjq6+u5+OKLqa2tLV0hJWw66k4/nQlndN8jdq5fzyOzZ5esnlKpuNwlajrqLr2UCe9//163afvTn1hxxX5fD6J3Stx0QAX9nJdYqrkh3eyVkrvYTcfxBw/kQyeO4MyJgzm0dgCjBvdj45bIipc38/Mn2rnpofW80vGXSTQD+sE3zj+Y1x2a2x5g1fotzHtsI19ZvJ6NW7J7u1HKpqOSfq/ZdOydTUd3TxLCl4CPk5uutRn45xjjTSGEfwauAwo7CcAC4I0xxl7f0rgcTUfFKGHToQpRoqaj4pSh6ZBUWsVuOvqSUjYdlcSmY++qsekoxonkxBg/FUL4CnA48HiM8ZX88v8TQvg9uUvl1gD3A/NiMTodSZIkqch6/am4ulSUpgMgxtgMNHex/DdA9U34lyRJkrRfitZ0SJIkSX1dJVwytxoV45K5kiRJktQtmw5JkiRJmXJ6lSRJkpTnieTZcKRDkiRJUqZsOiRJkiRlyulVkiRJUp7Tq7LhSIckSZKkTDnSIUmSJOV5n45sONIhSZIkKVM2HZIkSZIy5fQqSZIkKc8TybPhSIckSZKkTDnSIUmSJOU50pENRzokSZIkZcqmQ5IkSVKmnF4lSZIk5Xmfjmw40iFJkiQpU450SJIkSXmeSJ4NRzokSZIkZcqmQ5IkSVKmnF4lSZIk5Tm9KhuOdEiSJEnKlCMdkiRJUp6XzM2GTUdfNX16uStQqaV6zBcuLHcF5ZPqMVdyFvf7cblLKJtpSy8qdwllEctdgErO6VWSJEmSMuVIhyRJkpTnKEw2HOmQJEmSlClHOiRJkqQ8Rzqy4UiHJEmSpEzZdEiSJEnKlNOrJEmSpDzv05ENRzokSZIkZcqmQ5IkSVKmnF4lSZIk5Xn1qmw40iFJkiQpU450SJIkSXmOdGTDkQ5JkiRJmbLpkCRJkpQpp1dJkiRJed6nIxuOdEiSJEnKlCMdkiRJUp4nkmfDkQ5JkiRJmbLpkCRJkpQpp1dJkiRJeU6vyoYjHZIkSZIy5UiHJEmSlOclc7PhSIckSZKkTNl0SJIkScqU06sOUEtLC/PmzWPp0qVs2LCBUaNGMW3aNGbMmEFtbW25y8tMqrkh3ewp5h51zDEMP+wwhowdy9CxY+lfU0NLUxNPzZ9f7tJKIsVjXpBq9hRzp/hz/t4zapnzwUMAuOzmtdz8m7Yd6wb0hw+fN4ITX1XDSYcPYsrEQQwaEPbYLiWeSJ4Nm44D0NzcTENDA62trdTX1zNhwgRWrVrF3XffzdKlS2lsbGT48OHlLrPoUs0N6WZPNXfdaacx9JBD2LZ5M5vb2hhSU1Pukkom1WMO6WZPNXdqP+eHHtSfr/39GNratzN8yJ4TXIbV9OPG9x4MwJp1W1mzfhuvGuPbQxWff6sOwC233EJrayuXXHIJF1544Y7lc+bMYf78+cydO5dZs2aVscJspJob0s2eau5nFy5kS1sbnevWUXvooRz7rneVu6SSSfWYQ7rZU82d2s/5rZePpWXDdu5YspFPvnHUHus3dW7nb768moef3sya9dtouGg0jReNLn2hFcSRjmx4Tsd+am5uZvny5YwdO5bzzz9/l3UzZ86kpqaGRYsW0dHRUaYKs5Fqbkg3e6q5ATY8+yyd69aVu4ySS/mYp5o91dyQ1s/5FReM4NxXD+HS2WvZ2Nn1W+kt2+DuZe2sWb+txNUpNUVvOkIII0IIp4YQLgwh/HUI4TUhhP7F3k+pNTU1ATB16lT69dv1ZRsyZAiTJ0+ms7OTlStXlqO8zKSaG9LNnmrulKV8zFPNnmrulBw3YSDXvfMgbvxlK/c/Vn3No/qeojUdIYR/CCH8EXgFeAC4C/gF8CdgfQjh5hDCEcXaX6m98MILANTV1XW5fvz48QCsXr26ZDWVQqq5Id3sqeZOWcrHPNXsqeZORf9+MOcfD+GZlq1cdfvL5S6nz9leAV/VqNdNRwihXwhhLnArUA+Enb4eB36f38+lwNIQwl/3dp/lsGnTJgCGDh3a5frC8sJ21SLV3JBu9lRzpyzlY55q9lRzp+Kat4/mpMMH8b5vr6Vji2coqDIU40TyjwAzgTuBzwJPAEcBnwPeALwH+DPwD8C/AXeEEE6IMT6zP08eQljS3brFixf3qvBiijH3Qx1CKHMlpZVqbkg3e6q5U5byMU81e6q5q8EpR9Vw1VtGcf389Ty4srPc5fRNVXq56HIrxvSqS4FHgXfEGB+NMbbHGB8F3kmuAfl8jLEjxvgtck3IEOBTRdhvSe3rU5/29nYgNxe2mqSaG9LNnmrulKV8zFPNnmruapebVjWWFWu28NkfOa1KlaUYIx3HAt+KMe5y2YMY47YQwq/INSWFZX8MIdxFrvnYLzHG+u7WLVmypGRjhhMmTAC6n9+6Zs0aoPv5sX1Vqrkh3eyp5k5Zysc81eyp5q52tYP7cWzdIAA6bzmqy21mzxrL7FljueEX67ny+y2lLE+JK0bTsQ0Y1s26ocDA3ZY9ClxQhP2W1JQpUwBYvnw527dv3+VqH+3t7axYsYJBgwYxadKkcpWYiVRzQ7rZU82dspSPearZU81d7Tq3RGYvbO1y3clH1HDyETXc/1g7j63ewgMrvaJVt0aNKncFVakY06seBd4SQtjlTjL5P78VeGy37YcD7UXYb0mNGzeOqVOnsnbtWhYsWLDLunnz5tHZ2clZZ53F4MGDy1RhNlLNDelmTzV3ylI+5qlmTzV3tevYErn8lpe6/Prpn3JT6W5btIHLb3mJub/fWOZqlZpQOFmsx08QwvuB2cAq4HrgSeBI4GPA0cAnYoxf3Wn7e4HaGOMpvdoxpZ1eBbmbKTU0NNDa2kp9fT0TJ05k5cqVNDU1UVdXR2NjI8OHDy9lSSWRam5IN3tF5V64sDT7AUZOmsSoo48GYOCwYYw88kg6162j7bnnANja0cHzv/lNyeph+vSS7aqijnmJpZq9onIn/HM+belFJdlP4U7jl928lpt/07bLuk+9eSTH5adlnfiqQZx4eA2/XdHB481bAFi0omOP7+mt+N2jKvYqBUsOO6zsl/yqf/bZin19eqrXTQdACOG7wHvZ9c7xgdy9Ot4aY9ye3244sAC4PcZ4fW/3W+qmA6ClpYXbb7+dZcuW0dbWxujRo6mvr+fiiy+mtoqvdpBqbkg3e8XkLuGbkbrTT2fCGWd0u75z/XoemT27ZPWUsumACjrmZZBq9orJnfDPeSU0Hfd+uo7pr+7+ogHfub+NS7+9tqj1VHTTMXVq+ZuO5csr9vXpqaI0HQAhhLcDFwHjgZeA/wZ+EIu1gy6Uo+mQVGIlfDNScUrcdEhlk/DPeamajkpj07F31dh0FONEcgBijHeSu1eHJEmS1Dd5InkminEiuSRJkiR1y6ZDkiRJUqaKNr1KkiRJ6vOcXpUJRzokSZIkZcqRDkmSJKnAkY5MONIhSZIkKVM2HZIkSZIy5fQqSZIkqcDpVZlwpEOSJElSphzpkCRJkgoc6ciEIx2SJEmSMmXTIUmSJClTTq+SJEmSCpxelQlHOiRJkiRlypEOSZIkqcCRjkw40iFJkiQpUzYdkiRJkjLl9CpJkiSpwOlVmXCkQ5IkSaoyIYRDQwi3hBBeCCF0hhCeCiHcEEIYXY56HOmQJEmSqkgI4Wjgd8AhwE+A/wFOBf4JeEMI4cwYY0spa7LpkCRJkgqqY3rV/yXXcFwRY/xaYWEI4SvAlcAXgA+WsiCnV0mSJElVIoRwFHAB8BTwH7utbgA2An8fQhhWyroc6ZAkSZIK+v5Ix7n5x1/GGLfvvCLG2BZC+C25puQ04NelKsqRDkmSJKl6HJt/XNHN+sfzj5NLUMsOjnRIkiRJFSSEsKS7dTHG+n18+8j84/pu1heWjzrAsnqlTzcd9fX1oVz7Lvxl2I8DX1VSzQ3pZi977vryvdxlz14mqeaGdLOXPXfCP+exHDul/LkrWTnfX+6k26ajCAr5SvrXr083HZIkSVK16WUzWBjJGNnN+hG7bVcSntMhSZIkVY/H8o/dnbNxTP6xu3M+MmHTIUmSJFWPe/OPF4QQdnmvH0IYDpwJtAMPlrIomw5JkiSpSsQYVwG/BI4APrLb6muBYcB3Y4wbS1mX53RIkiRJ1eXDwO+Am0II5wF/Bl4LnENuWtVnSl2QIx2SJElSFcmPdkwDvkOu2fg4cDRwE3B6jLGl1DWFGMt1sTZJkiRJKXCkQ5IkSVKmbDokSZIkZcqmQ5IkSVKmbDokSZIkZcqmQ5IkSVKmbDokSZIkZcqmQ5IkSVKmbDoOUAjh0BDCLSGEF0IInSGEp0IIN4QQRpe7tqyEEGaGEL4WQrg/hNAaQoghhO+Vu66shRDGhBAuCyH8OISwMoTQHkJYH0JYFEKYFUKo2p+fEMKXQgi/DiE8m8/9cgjhTyGEhhDCmHLXV0ohhL/P/52PIYTLyl1PVvL/lsVuvtaUu75SCCGcFUL4UQhhdf7f99UhhF+GEN5Y7tqKLYTwvr0c78LXtnLXmaUQwpvyx/e5/L9zT4QQbg8hnF7u2rISct4fQngwhNAWQtiU/7f9ihBC/3LXp+rmzQEPQAjhaHK3lD8E+AnwP8Cp5G4p/xhwZjnu8Ji1EMLDwF8BG4DngOOA78cY31vOurIWQvgg8J/AauBe4BlgHDADGAn8CHhHrMIfohDCZuAhoAl4ERgGnEbu7qYvAKfFGJ8tX4WlEUI4DFgO9AdqgctjjLPLW1U2QghPAaOAG7pYvSHG+OVS1lNqIYSrgX8FXgL+m9zP/cHAScC9McZ/LmN5RRdCOBF4ezerzwLOBX4eY3xzqWoqpRDCl4B/BlqAO8kd90nAW4EBwD/EGKvuw7UQwneBvyf37/rPgI3AXwNTqOLfaaoMNh0HIITwC+AC4IoY49d2Wv4V4ErgmzHGD5arvqyEEM4h12ysBF5P7g14Ck3HueTebP88xrh9p+XjgT8AhwEzY4w/KlOJmQkhDI4xdnSx/AvAVcB/xhg/XPrKSieEEIAFwJHAHcAnqP6mgxjjEeWtpPRCCO8A5gK/AmbEGNt2Wz8wxrilLMWVQQjhAXIfMrwtxvjTctdTbPl/w58H1gKviTG+uNO6c4B7gCdjjEeVqcRMhBDeDvwYeBI4Ncb4Un75QHJ//98OXBpj/E6ZSlSVq9rpIcUWQjiKXMPxFPAfu61uIPdpwd+HEIaVuLTMxRjvjTE+ntqnHzHGe2KMP9u54cgvXwN8I//H6SUvrAS6ajjy5uYfjylVLWV0BblPey8l9/OtKpSfJvklYBPwt7s3HACJNRwnkGs4ngd+XuZysnI4ufc/v9+54YDc7zugDRhbjsIyNiP/eH2h4YAdf78/m//j/y55VUrGgHIX0Iecm3/8ZRdvQttCCL8l15ScBvy61MWp5ApvQraWtYrSe0v+cVlZq8hYCOHVwHXAjTHG+/KjXimoCSG8F3gVuUZrGXBfjLGa5/afQW40ax7wSgjhTcAJQAfwhxjjA+Usrgz+Mf94cxUf98eBzcCpIYSDd34DHkI4GxhObspVtRmff3yii3WFZSeHEEbFGNeVpiSlxKZj/x2bf1zRzfrHyTUdk7HpqGohhAHAP+T/eHc5a8laCOET5M5lGEnufI7XkXsjel0568pS/vjOIXcOz1VlLqfUxpPLvrMnQwiXxhh/U46CSuCU/GMzufOYpu68MoRwH7lplGtLXViphRCGAO8FtgNVOY0QIMb4cgjhU8BXgKYQwp3kzu04mtw5HQv4S/NVTQrN1ZFdrNt5KtlxwIPZl6PUOL1q/43MP67vZn1h+ajsS1GZXUfuk9C7Yoy/KHcxGfsEuemDHyXXcNwNXFDlb8CuIXfy8PtijO3lLqaEbgXOI9d4DCP35vubwBHA/BDCX5WvtEwdkn/8IDCE3Em1w8n9jP8COBu4vTylldw7yf0Om1/tF4qIMd5AbrrRAOBy4F+AdwDPAt/ZfdpVlfjv/OPHQggHFRbmP2i5dqftqvZqnCovm47iCfnHpM57SE0I4Qrg4+SuXPb3ZS4nczHG8THGQO6N6Axyn4b9KYRwcnkry0YI4VRyoxvXpzatJsZ4bf48puYY46YY4yP5C2N8hdyb8cbyVpiZwmVCA7kRjV/HGDfEGB8FLiJ3EY3XV/NlVHfygfzjN8taRQmEEP6Z3JS675Ab4RgG1JObZvT9EML/KV91mfkBMJ9c3qYQwrdCCDcADwNvJDdjA6Bap9WpzGw69l9hJGNkN+tH7LadqkwI4SPAjeQuI3tOjPHlMpdUMvk3oj8mN4VwDPDdMpdUdDtNq1rBX06q1F8umnB2WavIziv5xydijEt3XpEf6SqMZp5a0qpKLIQwhdz5Lc8Bd5W5nEyFEKaTu3jAT2OMH4sxPpFvtB8i12g+D3w8fwGZqpE/H/Wt5Eaw15D74Oz95I7568hNMYPc5XSlorPp2H+P5R8nd7O+cDWf7s75UB8WQvgo8HXgEXINRxI3S9tdjPFpck3X8SGEg8tdT5HVkvv5fjXQsfNN0shNMQP4dn7ZDeUqsgwKb0Cq7sp8eYV/29d1s77QlAzJvpSySuEE8oLCvUfu3X1FjHETuUui9yM3zbKqxBi3xhivjzGeGGMcEmMcEWN8A7l/108E2oFHy1qkqpYnku+/wj9OF4QQ+u1234bhwJnkflg9+arK5E84vI7cEPT5O1/pJFET8o/V9sakE7i5m3Unk3sDsojcm9SUpl4VphV1dcWbanAfuavQHRNCGBRj3Lzb+hPyj0+VtKoSCiEMJvep93a6/xmoJjX5x+4ui1tYvvvfhWr298Bg4LaULhGt0nKkYz/FGFcBvyR3UuVHdlt9LblPAb8bY/R6/lUkhPBZcg3HEuC8FBqOEMJx+Ztn7b68X/7mgIcAv4sxvrLnd/ddMcb2GONlXX0BhRuk3ZZf9sNy1lpsIYTjdz6xdKflh5Mb4QOourszA+R/pn9IbursNTuvCyGcD1xIbtpsNV+p7h3kTh6+q9pPIM+7P//4gRDCxJ1XhBD+htyHiB3A70pdWNZCCCO6WHYKud9zG4DPlbwoJcORjgPzYXL/CN0UQjgP+DPwWuAcctOqPlPG2jKTv4vp2/N/LLwZPT2E8J38/78UY/xEicvKXAjhEnL/AG8j90vqitxNqnfxVBXevfUNwL/nLxW6itw833Hk7kZ/FLm5wJeXrzxl4B3Av4QQ7iV3t+I2ciebvoncp593AV8uX3mZ+xi5f8s/k79Pwx/I3UDuInI//5dX+X0LCieQf6usVZTOPHJ3n/9r4M8hhB+T+3ft1eSmXgXgX2KMLd0/RZ+1IITQTm6qcBtwPLmTyDuBGTHGah3RVAUIid1kutdCCIeReyP6BnIn1K4mdxOha6v1xOIQQiN/mdPeladjjEeUpprS2Y/cAL+JMU7PvprSyd+R+EPkPu07lNwlNDeSa6x/DtxUrX/Xu7PT34XLY4xVd/+CEMLryV0y9iT+csncdeSmFM4B5sQq/2WRH+m5mlyjMZHcG7JFwL/FGKt22mz+RphN5E4mPiKB8zkACCEMJDdr4d3AFGAo8DK5hvOmGOMvy1heZkIInySX+Why5ym9QO5iCdfFGJ8qY2lKgE2HJEmSpEx5TockSZKkTNl0SJIkScqUTYckSZKkTNl0SJIkScqUTYckSZKkTNl0SJIkScqUTYckSZKkTNl0SJIkScqUTYckSZKkTNl0SJIkScqUTYckSZKkTNl0SJIkScqUTYckSZKkTNl0SJIkScqUTYckSZKkTNl0SJIkScqUTYckSZKkTP1/Dp5w3jywFlMAAAAASUVORK5CYII=\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** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimators = {\n",
    "    'KNeighborsClassifier': knn, \n",
    "    'SVC': SVC(),\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=98.83%; standard deviation=0.72%\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=98.33%; standard deviation=0.90%\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",
    "\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",
    "\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\n"
   ]
  },
  {
   "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, learning_rate='auto', init='pca', random_state=11) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**New: `learning_rate='auto'` and `init='pca'` added because they will soon be new defaults for TSNE.**"
   ]
  },
  {
   "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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/pauldeitel/anaconda3/envs/pydsft0522/lib/python3.10/site-packages/sklearn/manifold/_t_sne.py:991: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "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": {
    "tags": []
   },
   "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('display.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": 64,
   "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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</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",
       "5                5.4               3.9                1.7               0.4   \n",
       "6                4.6               3.4                1.4               0.3   \n",
       "7                5.0               3.4                1.5               0.2   \n",
       "8                4.4               2.9                1.4               0.2   \n",
       "9                4.9               3.1                1.5               0.1   \n",
       "\n",
       "  species  \n",
       "0  setosa  \n",
       "1  setosa  \n",
       "2  setosa  \n",
       "3  setosa  \n",
       "4  setosa  \n",
       "5  setosa  \n",
       "6  setosa  \n",
       "7  setosa  \n",
       "8  setosa  \n",
       "9  setosa  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Calling `describe` on the `'species'` column confirms that it contains three unique values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count        150\n",
       "unique         3\n",
       "top       setosa\n",
       "freq          50\n",
       "Name: species, dtype: object"
      ]
     },
     "execution_count": 66,
     "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": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x396 with 20 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 412,
       "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": 68,
   "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": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=3, random_state=11)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=3, random_state=11)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KMeans(n_clusters=3, random_state=11)"
      ]
     },
     "execution_count": 69,
     "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": 70,
   "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": 71,
   "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": 72,
   "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": "code",
   "execution_count": 73,
   "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": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.cluster_centers_"
   ]
  },
  {
   "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": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PCA(n_components=2, random_state=11)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PCA</label><div class=\"sk-toggleable__content\"><pre>PCA(n_components=2, random_state=11)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "PCA(n_components=2, random_state=11)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.fit(iris.data)  # trains estimator once"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 2)"
      ]
     },
     "execution_count": 78,
     "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": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_pca_df = pd.DataFrame(iris_pca, \n",
    "                           columns=['Component1', 'Component2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.68</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.71</td>\n",
       "      <td>-0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.89</td>\n",
       "      <td>-0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.75</td>\n",
       "      <td>-0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.73</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>1.94</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>1.53</td>\n",
       "      <td>-0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>1.76</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>1.90</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>1.39</td>\n",
       "      <td>-0.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Component1  Component2\n",
       "0         -2.68        0.32\n",
       "1         -2.71       -0.18\n",
       "2         -2.89       -0.14\n",
       "3         -2.75       -0.32\n",
       "4         -2.73        0.33\n",
       "..          ...         ...\n",
       "145        1.94        0.19\n",
       "146        1.53       -0.38\n",
       "147        1.76        0.08\n",
       "148        1.90        0.12\n",
       "149        1.39       -0.28\n",
       "\n",
       "[150 rows x 2 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_pca_df"
   ]
  },
  {
   "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": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 265,
       "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": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import DBSCAN, MeanShift,\\\n",
    "    SpectralClustering, AgglomerativeClustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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=1, count=50\n",
      "50-99:\n",
      "   label=2, count=50\n",
      "100-149:\n",
      "   label=0, 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; 1992-2024 by Pearson Education, Inc. All Rights Reserved. The content in this notebook is based on the book [**Python for Programmers**](https://amzn.to/2VvdnxE)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}