{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 2: A first Example - Classifying Flowers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The purpose of this notebook is to look at a first example of a typical data science application, namely **statistical learning**, which is often referred to by its more well-known name **machine learning**. To do so, we look at a very popular example involving the classification of flowers. Albeit simplistic and almost boring in its kind, the example is a rather good one to look at from a beginner's point of view as it does not involve too many decision variables. That makes understanding technicalities and visualizing the data set a lot easier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's at first review a couple of generic definitions to get started.\n",
    "\n",
    "Machine learning is the process of **extracting knowledge from data** in an automated fashion.\n",
    "\n",
    "Typical use cases regard making predictions on new and unseen data or simply understanding a given dataset better by finding patterns.\n",
    "\n",
    "Central to machine learning is the idea of **automating** the **decision making** from data **without** the user specifying **explicit rules** how these decisions should be made.\n",
    "\n",
    "That is in direct opposition to what we learned in the \"Expressing Logic\" section in Chapter 0, where we learned how to implement decision criterions \"by hand\" with the `if` statement."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"./static/what_is_machine_learning.png\" width=\"60%\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example Applications"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"static/examples.png\" width=\"60%\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Types of Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Concete machine learning algorithms are commonly classified into three broad categories that may overlap as well:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"static/3_types_of_machine_learning.png\" width=\"60%\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Supervised** (focus of the example in this notebook): Each entry in the dataset comes with a **label**. Examples are a list of emails where spam mail is already marked as such or a sample of handwritten digits. The goal is to use the historic data to make predictions.\n",
    "\n",
    "- **Unsupervised**: There is no desired output associated with a data entry. In a sense, one can think of unsupervised learning as a means of discovering labels from the data itself. A popular example is the clustering of customer data.\n",
    "\n",
    "- **Reinforcement**: Conceptually, this can be seen as \"learning by doing\". Some kind of **reward function** tells how good a predicted outcome is. A rather recent and extremely popular example for his approach is the Alpha Go machine."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Types of Supervised Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Algorithms from the supervised learning category are often broken down further into classification and regression:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"static/classification_vs_regression.png\" width=\"60%\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- In **classification** tasks, the labels are *discrete*, such as \"spam\" or \"no spam\" for emails. Often, labels are nominal (e.g., colors of something), or ordinal (e.g., T-shirt sizes in S, M, or L).\n",
    "- In **regression**, the labels are *continuous*. For example, given a person's age, education, and position, infer his/her salary."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example: Iris Flower Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the example, we are given measurments regarding the size of various parts of the so-called Iris flower kind. A concrete flower always belongs to one of three distinct special Iris classes. This example application is about classifying a given flower into one of the three classes by only looking at the measurements."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"static/iris_data.png\" width=\"60%\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `sklearn` library provides several sample datasets, among which is also the Iris dataset.\n",
    "\n",
    "In a tabular visualization, the dataset could be portrayed somewhat like this:\n",
    "\n",
    "<img src=\"static/iris.png\" width=\"50%\">\n",
    "\n",
    "However, the data object imported from `sklearn` is organized slightly different. In particular, the so-called **features** are separated from the **labels**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using Python's `dir()` function we can inspect the data object, i.e. find out what **attributes** it has."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DESCR',\n",
       " 'data',\n",
       " 'data_module',\n",
       " 'feature_names',\n",
       " 'filename',\n",
       " 'frame',\n",
       " 'target',\n",
       " 'target_names']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(iris)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`iris.data` provides us with a `numpy.ndarray`, where the first dimension equals the number of observed flowers (i.e., the **instances**) and the second dimension lists the various features of a flower."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.1, 3.5, 1.4, 0.2],\n",
       "       [4.9, 3. , 1.4, 0.2],\n",
       "       [4.7, 3.2, 1.3, 0.2],\n",
       "       [4.6, 3.1, 1.5, 0.2],\n",
       "       [5. , 3.6, 1.4, 0.2],\n",
       "       [5.4, 3.9, 1.7, 0.4],\n",
       "       [4.6, 3.4, 1.4, 0.3],\n",
       "       [5. , 3.4, 1.5, 0.2],\n",
       "       [4.4, 2.9, 1.4, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.1],\n",
       "       [5.4, 3.7, 1.5, 0.2],\n",
       "       [4.8, 3.4, 1.6, 0.2],\n",
       "       [4.8, 3. , 1.4, 0.1],\n",
       "       [4.3, 3. , 1.1, 0.1],\n",
       "       [5.8, 4. , 1.2, 0.2],\n",
       "       [5.7, 4.4, 1.5, 0.4],\n",
       "       [5.4, 3.9, 1.3, 0.4],\n",
       "       [5.1, 3.5, 1.4, 0.3],\n",
       "       [5.7, 3.8, 1.7, 0.3],\n",
       "       [5.1, 3.8, 1.5, 0.3],\n",
       "       [5.4, 3.4, 1.7, 0.2],\n",
       "       [5.1, 3.7, 1.5, 0.4],\n",
       "       [4.6, 3.6, 1. , 0.2],\n",
       "       [5.1, 3.3, 1.7, 0.5],\n",
       "       [4.8, 3.4, 1.9, 0.2],\n",
       "       [5. , 3. , 1.6, 0.2],\n",
       "       [5. , 3.4, 1.6, 0.4],\n",
       "       [5.2, 3.5, 1.5, 0.2],\n",
       "       [5.2, 3.4, 1.4, 0.2],\n",
       "       [4.7, 3.2, 1.6, 0.2],\n",
       "       [4.8, 3.1, 1.6, 0.2],\n",
       "       [5.4, 3.4, 1.5, 0.4],\n",
       "       [5.2, 4.1, 1.5, 0.1],\n",
       "       [5.5, 4.2, 1.4, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.2],\n",
       "       [5. , 3.2, 1.2, 0.2],\n",
       "       [5.5, 3.5, 1.3, 0.2],\n",
       "       [4.9, 3.6, 1.4, 0.1],\n",
       "       [4.4, 3. , 1.3, 0.2],\n",
       "       [5.1, 3.4, 1.5, 0.2],\n",
       "       [5. , 3.5, 1.3, 0.3],\n",
       "       [4.5, 2.3, 1.3, 0.3],\n",
       "       [4.4, 3.2, 1.3, 0.2],\n",
       "       [5. , 3.5, 1.6, 0.6],\n",
       "       [5.1, 3.8, 1.9, 0.4],\n",
       "       [4.8, 3. , 1.4, 0.3],\n",
       "       [5.1, 3.8, 1.6, 0.2],\n",
       "       [4.6, 3.2, 1.4, 0.2],\n",
       "       [5.3, 3.7, 1.5, 0.2],\n",
       "       [5. , 3.3, 1.4, 0.2],\n",
       "       [7. , 3.2, 4.7, 1.4],\n",
       "       [6.4, 3.2, 4.5, 1.5],\n",
       "       [6.9, 3.1, 4.9, 1.5],\n",
       "       [5.5, 2.3, 4. , 1.3],\n",
       "       [6.5, 2.8, 4.6, 1.5],\n",
       "       [5.7, 2.8, 4.5, 1.3],\n",
       "       [6.3, 3.3, 4.7, 1.6],\n",
       "       [4.9, 2.4, 3.3, 1. ],\n",
       "       [6.6, 2.9, 4.6, 1.3],\n",
       "       [5.2, 2.7, 3.9, 1.4],\n",
       "       [5. , 2. , 3.5, 1. ],\n",
       "       [5.9, 3. , 4.2, 1.5],\n",
       "       [6. , 2.2, 4. , 1. ],\n",
       "       [6.1, 2.9, 4.7, 1.4],\n",
       "       [5.6, 2.9, 3.6, 1.3],\n",
       "       [6.7, 3.1, 4.4, 1.4],\n",
       "       [5.6, 3. , 4.5, 1.5],\n",
       "       [5.8, 2.7, 4.1, 1. ],\n",
       "       [6.2, 2.2, 4.5, 1.5],\n",
       "       [5.6, 2.5, 3.9, 1.1],\n",
       "       [5.9, 3.2, 4.8, 1.8],\n",
       "       [6.1, 2.8, 4. , 1.3],\n",
       "       [6.3, 2.5, 4.9, 1.5],\n",
       "       [6.1, 2.8, 4.7, 1.2],\n",
       "       [6.4, 2.9, 4.3, 1.3],\n",
       "       [6.6, 3. , 4.4, 1.4],\n",
       "       [6.8, 2.8, 4.8, 1.4],\n",
       "       [6.7, 3. , 5. , 1.7],\n",
       "       [6. , 2.9, 4.5, 1.5],\n",
       "       [5.7, 2.6, 3.5, 1. ],\n",
       "       [5.5, 2.4, 3.8, 1.1],\n",
       "       [5.5, 2.4, 3.7, 1. ],\n",
       "       [5.8, 2.7, 3.9, 1.2],\n",
       "       [6. , 2.7, 5.1, 1.6],\n",
       "       [5.4, 3. , 4.5, 1.5],\n",
       "       [6. , 3.4, 4.5, 1.6],\n",
       "       [6.7, 3.1, 4.7, 1.5],\n",
       "       [6.3, 2.3, 4.4, 1.3],\n",
       "       [5.6, 3. , 4.1, 1.3],\n",
       "       [5.5, 2.5, 4. , 1.3],\n",
       "       [5.5, 2.6, 4.4, 1.2],\n",
       "       [6.1, 3. , 4.6, 1.4],\n",
       "       [5.8, 2.6, 4. , 1.2],\n",
       "       [5. , 2.3, 3.3, 1. ],\n",
       "       [5.6, 2.7, 4.2, 1.3],\n",
       "       [5.7, 3. , 4.2, 1.2],\n",
       "       [5.7, 2.9, 4.2, 1.3],\n",
       "       [6.2, 2.9, 4.3, 1.3],\n",
       "       [5.1, 2.5, 3. , 1.1],\n",
       "       [5.7, 2.8, 4.1, 1.3],\n",
       "       [6.3, 3.3, 6. , 2.5],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [7.1, 3. , 5.9, 2.1],\n",
       "       [6.3, 2.9, 5.6, 1.8],\n",
       "       [6.5, 3. , 5.8, 2.2],\n",
       "       [7.6, 3. , 6.6, 2.1],\n",
       "       [4.9, 2.5, 4.5, 1.7],\n",
       "       [7.3, 2.9, 6.3, 1.8],\n",
       "       [6.7, 2.5, 5.8, 1.8],\n",
       "       [7.2, 3.6, 6.1, 2.5],\n",
       "       [6.5, 3.2, 5.1, 2. ],\n",
       "       [6.4, 2.7, 5.3, 1.9],\n",
       "       [6.8, 3. , 5.5, 2.1],\n",
       "       [5.7, 2.5, 5. , 2. ],\n",
       "       [5.8, 2.8, 5.1, 2.4],\n",
       "       [6.4, 3.2, 5.3, 2.3],\n",
       "       [6.5, 3. , 5.5, 1.8],\n",
       "       [7.7, 3.8, 6.7, 2.2],\n",
       "       [7.7, 2.6, 6.9, 2.3],\n",
       "       [6. , 2.2, 5. , 1.5],\n",
       "       [6.9, 3.2, 5.7, 2.3],\n",
       "       [5.6, 2.8, 4.9, 2. ],\n",
       "       [7.7, 2.8, 6.7, 2. ],\n",
       "       [6.3, 2.7, 4.9, 1.8],\n",
       "       [6.7, 3.3, 5.7, 2.1],\n",
       "       [7.2, 3.2, 6. , 1.8],\n",
       "       [6.2, 2.8, 4.8, 1.8],\n",
       "       [6.1, 3. , 4.9, 1.8],\n",
       "       [6.4, 2.8, 5.6, 2.1],\n",
       "       [7.2, 3. , 5.8, 1.6],\n",
       "       [7.4, 2.8, 6.1, 1.9],\n",
       "       [7.9, 3.8, 6.4, 2. ],\n",
       "       [6.4, 2.8, 5.6, 2.2],\n",
       "       [6.3, 2.8, 5.1, 1.5],\n",
       "       [6.1, 2.6, 5.6, 1.4],\n",
       "       [7.7, 3. , 6.1, 2.3],\n",
       "       [6.3, 3.4, 5.6, 2.4],\n",
       "       [6.4, 3.1, 5.5, 1.8],\n",
       "       [6. , 3. , 4.8, 1.8],\n",
       "       [6.9, 3.1, 5.4, 2.1],\n",
       "       [6.7, 3.1, 5.6, 2.4],\n",
       "       [6.9, 3.1, 5.1, 2.3],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [6.8, 3.2, 5.9, 2.3],\n",
       "       [6.7, 3.3, 5.7, 2.5],\n",
       "       [6.7, 3. , 5.2, 2.3],\n",
       "       [6.3, 2.5, 5. , 1.9],\n",
       "       [6.5, 3. , 5.2, 2. ],\n",
       "       [6.2, 3.4, 5.4, 2.3],\n",
       "       [5.9, 3. , 5.1, 1.8]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To find out what the four features are, we can list them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.feature_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, we can also print the flowers' labels (a.k.a. targets):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The three flower classes are encoded with integers. Let's show the corresponding names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple Visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the data is four-dimensional, we cannot visualize all features together. Instead, we can plot the distribution of the flower classes by a single feature using histograms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_index = 2\n",
    "colors = [\"blue\", \"red\", \"green\"]\n",
    "\n",
    "for label, color in zip(range(len(iris.target_names)), colors):\n",
    "    plt.hist(\n",
    "        iris.data[iris.target == label, feature_index],\n",
    "        label=iris.target_names[label],\n",
    "        color=color,\n",
    "    )\n",
    "\n",
    "plt.xlabel(iris.feature_names[feature_index])\n",
    "plt.legend(loc=\"upper right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also, we can draw scatter plots of two features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "first_feature_index = 1\n",
    "second_feature_index = 0\n",
    "\n",
    "colors = [\"blue\", \"red\", \"green\"]\n",
    "\n",
    "for label, color in zip(range(len(iris.target_names)), colors):\n",
    "    plt.scatter(\n",
    "        iris.data[iris.target == label, first_feature_index],\n",
    "        iris.data[iris.target == label, second_feature_index],\n",
    "        label=iris.target_names[label],\n",
    "        c=color,\n",
    "    )\n",
    "\n",
    "plt.xlabel(iris.feature_names[first_feature_index])\n",
    "plt.ylabel(iris.feature_names[second_feature_index])\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the higher level library `pandas`, one can easily create a so-called **scatterplot matrix**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris_df = pd.DataFrame(iris.data, columns=iris.feature_names)\n",
    "\n",
    "pd.plotting.scatter_matrix(iris_df, figsize=(8, 8));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Concept of Generalization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The goal of a supervised machine learning model is to make predictions on *new* (i.e., previously unseen) data.\n",
    "\n",
    "In a real-world application, we are not interested in marking an already labeled email as spam or not. Instead, we want to make the user's life easier by automatically classifying new incoming mail.\n",
    "\n",
    "In order to get an idea of how good a model **generalizes**, a best practice is to *split* the available data into a **training** and a **test** set. Only the former is used to train the model. Then, predictions are made on the test data and the predictions can be compared with the actual labels.\n",
    "\n",
    "Common splits are 75/25 or 60/40."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"./static/generalization.png\" width=\"60%\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train/Test Split for the Iris data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is common practice to refer to the feature matrix as `X` and the vector of labels as `y`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = iris.data, iris.target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A naive splitting approach could be to use array slicing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = X[0:100, :], X[100:150, :], y[0:100], y[100:150]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, this would lead to unbalanced label distributions. For example, the test set would only be made up of flowers of the same type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  0, 50])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.bincount(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`sklearn` provides a function that not only randomizes the split but also ensures that the resulting label distribution is proportionate to the overall distribution, a concept called **stratification**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 1, 2, 1, 2, 2, 1, 1, 0, 2, 0, 0, 2, 2, 0, 2, 1, 0, 0, 0, 1, 0,\n",
       "       1, 2, 2, 1, 1, 1, 1, 0, 2, 2, 1, 0, 2, 0, 0, 0, 0, 1, 1, 0, 2, 2,\n",
       "       1])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, train_size=0.7, test_size=0.3, random_state=42, stratify=y\n",
    ")\n",
    "\n",
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([15, 15, 15])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.bincount(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A simple Classification Model: k-Nearest Neighbors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To predict the label for any observation, just determine the k \"nearest\" observations in the training set (e.g., by Euclidean distance) and use a simple majority vote."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"./static/knn.png\" width=\"60%\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training and Predicting with the Iris data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`sklearn` provides a uniform interface for all its classification models. They all have a `.fit()` and a `.predict()` method that abstract away the actual machine learning algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=5)\n",
    "\n",
    "knn.fit(X_train, y_train)\n",
    "\n",
    "y_pred = knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us list the labels predicted for the test set ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 1, 2, 1, 2, 2, 1, 1, 0, 2, 0, 0, 2, 2, 0, 2, 1, 0, 0, 0, 1, 0,\n",
       "       1, 2, 2, 1, 1, 1, 1, 0, 2, 2, 1, 0, 2, 0, 0, 0, 0, 1, 1, 0, 1, 2,\n",
       "       1])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... and compare them with the actual labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 1, 2, 1, 2, 2, 1, 1, 0, 2, 0, 0, 2, 2, 0, 2, 1, 0, 0, 0, 1, 0,\n",
       "       1, 2, 2, 1, 1, 1, 1, 0, 2, 2, 1, 0, 2, 0, 0, 0, 0, 1, 1, 0, 2, 2,\n",
       "       1])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`numpy` shows us the indices where the predictions are wrong."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([42]),)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(y_pred != y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, we can calculate the fraction of correctly predicted flowers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.9777777777777777)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(y_pred == y_test) / len(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is important to mention that we can also \"predict\" the training set. Somehow surprisingly, the model does not get the training set 100% correct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.9714285714285714)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_pred = knn.predict(X_train)\n",
    "\n",
    "np.sum(y_train_pred == y_train) / len(y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A visualization reveals that the misclassified flowers are right \"at the borderline\" between two neighboring clusters of flower classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "first_feature_index = 3\n",
    "second_feature_index = 0\n",
    "\n",
    "correct_idx = np.where(y_pred == y_test)[0]\n",
    "incorrect_idx = np.where(y_pred != y_test)[0]\n",
    "\n",
    "colors = [\"darkblue\", \"darkgreen\", \"gray\"]\n",
    "\n",
    "for n, color in enumerate(colors):\n",
    "    idx = np.where(y_test == n)[0]\n",
    "    plt.scatter(\n",
    "        X_test[idx, first_feature_index],\n",
    "        X_test[idx, second_feature_index],\n",
    "        color=color,\n",
    "        label=iris.target_names[n],\n",
    "    )\n",
    "\n",
    "plt.scatter(\n",
    "    X_test[incorrect_idx, first_feature_index],\n",
    "    X_test[incorrect_idx, second_feature_index],\n",
    "    color=\"darkred\",\n",
    "    label=\"misclassified\",\n",
    ")\n",
    "\n",
    "plt.xlabel(\"sepal width [cm]\")\n",
    "plt.ylabel(\"petal length [cm]\")\n",
    "plt.legend(loc=\"best\")\n",
    "plt.title(\"Iris Classification results\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In practice, the number of neighbors must be chosen before the model is trained. Therefore, it is possible to \"optimize\" it. This process is referred to as **hyper-parameter tuning**. For the Iris dataset this does not make much of a difference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 0.9333333333333333\n",
      "2 0.9111111111111111\n",
      "3 0.9555555555555556\n",
      "4 0.9555555555555556\n",
      "5 0.9777777777777777\n",
      "6 0.9333333333333333\n",
      "7 0.9555555555555556\n",
      "8 0.9333333333333333\n",
      "9 0.9555555555555556\n",
      "10 0.9555555555555556\n",
      "11 0.9333333333333333\n",
      "12 0.9333333333333333\n",
      "13 0.9333333333333333\n",
      "14 0.9333333333333333\n",
      "15 0.9555555555555556\n",
      "16 0.9555555555555556\n",
      "17 0.9555555555555556\n",
      "18 0.9555555555555556\n",
      "19 0.9555555555555556\n",
      "20 0.9333333333333333\n",
      "21 0.9555555555555556\n",
      "22 0.9333333333333333\n",
      "23 0.9555555555555556\n",
      "24 0.9333333333333333\n",
      "25 0.9333333333333333\n",
      "26 0.9555555555555556\n",
      "27 0.9333333333333333\n",
      "28 0.9111111111111111\n",
      "29 0.9111111111111111\n",
      "30 0.9111111111111111\n"
     ]
    }
   ],
   "source": [
    "for i in range(1, 31):\n",
    "    knn = KNeighborsClassifier(n_neighbors=i)\n",
    "    knn.fit(X_train, y_train)\n",
    "    y_pred = knn.predict(X_test)\n",
    "    correct = np.sum(y_pred == y_test) / len(y_test)\n",
    "    print(i, correct)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Further Resources on Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Depending on the programming language one chooses, the following books are recommended:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [Python Machine Learning](https://www.amazon.de/Python-Machine-Learning-scikit-learn-TensorFlow/dp/1787125939/ref=sr_1_1?__mk_de_DE=%C3%85M%C3%85%C5%BD%C3%95%C3%91&keywords=python+machine+learning&qid=1575545025&sr=8-1) by Sebastian Raschka\n",
    "\n",
    "<img src=\"static/python_ml_book.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [An Introduction to Statistical Learning](http://faculty.marshall.usc.edu/gareth-james/ISL/)\n",
    "\n",
    "<img src=\"static/r_ml_book.png\">"
   ]
  }
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