{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 1: General Introduction to machine learning (ML)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ML = \"learning models from data\"\n",
    "\n",
    "\n",
    "### About models\n",
    "\n",
    "A \"model\" allows us to explain observations and to answer questions. For example:\n",
    "\n",
    "   1. Where will my car at given velocity stop if I apply break now?\n",
    "   2. Where on the night sky will I see the moon tonight?\n",
    "   3. Is the email I received spam?\n",
    "   4. Which article \"X\" should I recommend to a customer \"Y\"?\n",
    "   \n",
    "- The first two questions can be answered based on existing physical models (formulas). \n",
    "\n",
    "- For the  questions 3 and 4 it is difficult to develop explicitly formulated models. \n",
    "\n",
    "### What is needed to apply ML ?\n",
    "\n",
    "Problems 3 and 4 have the following in common:\n",
    "\n",
    "- No exact model known or implementable because we have a vague understanding of the problem domain.\n",
    "- But enough data with sufficient and implicit information is available.\n",
    "\n",
    "\n",
    "\n",
    "E.g. for the spam email example:\n",
    "\n",
    "- We have no explicit formula for such a task (and devising one would boil down to lots of trial with different statistics or scores and possibly weighting of them).\n",
    "- We have a vague understanding of the problem domain because we know that some words are specific to spam emails and others are specific to my personal and work-related emails.\n",
    "- My mailbox is full with examples of both spam and non-spam emails.\n",
    "\n",
    "**In such cases machine learning offers approaches to build models based on example data.**\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<i class=\"fa fa-info-circle\"></i>\n",
    "The closely-related concept of <strong>data mining</strong> usually means use of predictive machine learning models to explicitly discover previously unknown knowledge from a specific data set, such as, for instance, association rules between customer and article types in the Problem 4 above.\n",
    "</div>\n",
    "\n",
    "\n",
    "\n",
    "## ML: what is \"learning\" ?\n",
    "\n",
    "To create a predictive model, we must first **train** such a model on given data. \n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<i class=\"fa fa-info-circle\"></i>\n",
    "Alternative names for \"to train\" a model are \"to <strong>fit</strong>\" or \"to <strong>learn</strong>\" a model.\n",
    "</div>\n",
    "\n",
    "\n",
    "All ML algorithms have in common that they rely on internal data structures and/or parameters. Learning then builds up such data structures or adjusts parameters based on the given data. After that such models can be used to explain observations or to answer questions.\n",
    "\n",
    "The important difference between explicit models and models learned from data:\n",
    "\n",
    "- Explicit models usually offer exact answers to questions\n",
    "- Models we learn from data usually come with inherent uncertainty."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Some history\n",
    "\n",
    "Some parts of ML are older than you might think. This is a rough time line with a few selected achievements from this field:\n",
    "\n",
    "    1805: Least squares regression\n",
    "    1812: Bayes' rule\n",
    "    1913: Markov Chains\n",
    "\n",
    "    1951: First neural network\n",
    "    1957-65: \"k-means\" clustering algorithm\n",
    "    1959: Term \"machine learning\" is coined by Arthur Samuel, an AI pioneer\n",
    "    1969: Book \"Perceptrons\": Limitations of Neural Networks\n",
    "    1974-86: Neural networks learning breakthrough: backpropagation method\n",
    "    1984: Book \"Classification And Regression Trees\"\n",
    "    1995: Randomized Forests and Support Vector Machines methods\n",
    "    1998: Public appearance: first ML implementations of spam filtering methods; naive Bayes Classifier method\n",
    "    2006-12: Neural networks learning breakthrough: deep learning\n",
    "    \n",
    "So the field is not as new as one might think, but due to \n",
    "\n",
    "- more available data\n",
    "- more processing power \n",
    "- development of better algorithms \n",
    "\n",
    "more applications of machine learning appeared during the last 15 years."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Machine learning with Python\n",
    "\n",
    "Currently (2018) `Python` is the  dominant programming language for ML. Especially the advent of deep-learning pushed this forward. First versions of frameworks such as `TensorFlow` or `PyTorch` got early `Python` releases.\n",
    "\n",
    "The prevalent packages in the Python eco-system used for ML include:\n",
    "\n",
    "- `pandas` for handling tabular data\n",
    "- `matplotlib` and `seaborn` for plotting\n",
    "- `scikit-learn` for classical (non-deep-learning) ML\n",
    "- `TensorFlow`, `PyTorch` and `Keras` for deep-learning.\n",
    "\n",
    "`scikit-learn` is very comprehensive and the online-documentation itself provides a good introducion into ML."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ML lingo: What are \"features\" ?\n",
    "\n",
    "A typical and very common situation is that our data is presented as a table, as in the following example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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>alcohol_content</th>\n",
       "      <th>bitterness</th>\n",
       "      <th>darkness</th>\n",
       "      <th>fruitiness</th>\n",
       "      <th>is_yummy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.739295</td>\n",
       "      <td>0.422503</td>\n",
       "      <td>0.989463</td>\n",
       "      <td>0.215791</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.207849</td>\n",
       "      <td>0.841668</td>\n",
       "      <td>0.928626</td>\n",
       "      <td>0.380420</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.709494</td>\n",
       "      <td>0.322037</td>\n",
       "      <td>5.374682</td>\n",
       "      <td>0.145231</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.684743</td>\n",
       "      <td>0.434315</td>\n",
       "      <td>4.072805</td>\n",
       "      <td>0.191321</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.148710</td>\n",
       "      <td>0.570586</td>\n",
       "      <td>1.461568</td>\n",
       "      <td>0.260218</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alcohol_content  bitterness  darkness  fruitiness  is_yummy\n",
       "0         3.739295    0.422503  0.989463    0.215791         0\n",
       "1         4.207849    0.841668  0.928626    0.380420         0\n",
       "2         4.709494    0.322037  5.374682    0.145231         1\n",
       "3         4.684743    0.434315  4.072805    0.191321         1\n",
       "4         4.148710    0.570586  1.461568    0.260218         0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "features = pd.read_csv(\"beers.csv\")\n",
    "features.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<i class=\"fa fa-warning\"></i>&nbsp;<strong>Definitions</strong>\n",
    "<ul>\n",
    "    <li>every row of such a matrix is called a <strong>sample</strong> or <strong>feature vector</strong>;</li>\n",
    "    <li>the cells in a row are <strong>feature values</strong>;</li>\n",
    "    <li>every column name is called a <strong>feature name</strong> or <strong>attribute</strong>.</li>\n",
    "</ul>\n",
    "\n",
    "Features are also commonly called <strong>variables</strong>.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This table shown holds five samples.\n",
    "\n",
    "The feature names are `alcohol_content`, `bitterness`, `darkness`, `fruitiness` and `is_yummy`.\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "<i class=\"fa fa-warning\"></i>&nbsp;<strong>More definitions</strong>\n",
    "<ul>\n",
    "    <li>The first four features have continuous numerical values within some ranges - these are called <strong>numerical features</strong>,</li>\n",
    "    <li>the <code>is_yummy</code> feature has only a finite set of values (\"categories\"): <code>0</code> (\"no\") and <code>1</code> (\"yes\") - this is called a <strong>categorical feature</strong>.</li>\n",
    "</ul>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A straight-forward application of machine-learning on the previous beer dataset is: **\"can we predict `is_yummy` from the other features\"** ?\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "<i class=\"fa fa-warning\"></i>&nbsp;<strong>Even more definitions</strong>\n",
    "\n",
    "In context of the question above we call:\n",
    "<ul>\n",
    "    <li>the <code>alcohol_content</code>, <code>bitterness</code>, <code>darkness</code>, <code>fruitiness</code> features our <strong>input features</strong>, and</li>\n",
    "    <li>the <code>is_yummy</code> feature our <strong>target/output feature</strong> or a <strong>label</strong> of our data samples.\n",
    "        <ul>\n",
    "            <li>Values of categorical labels, such as <code>0</code> (\"no\") and <code>1</code> (\"yes\") here, are often called <strong>classes</strong>.</li>\n",
    "        </ul>\n",
    "    </li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the machine learning algorithms require that every sample is represented as a vector containing numbers. Let's look now at two examples of how one can create feature vectors from data which is not naturally given as vectors:\n",
    "\n",
    "1. Feature vectors from images\n",
    "2. Feature vectors from text.\n",
    "\n",
    "### 1st Example: How to represent images as feature vectors ?\n",
    "\n",
    "In order to simplify our explanations we only consider grayscale images in this section. \n",
    "Computers represent images as matrices. Every cell in the matrix represents one pixel, and the numerical value in the matrix cell its gray value.\n",
    "\n",
    "So how can we represent images as vectors?\n",
    "\n",
    "To demonstrate this we will now load a sample dataset that is included in `scikit-learn`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['DESCR', 'data', 'images', 'target', 'target_names']\n"
     ]
    }
   ],
   "source": [
    "dd = load_digits()\n",
    "print(dir(dd))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DESCR:\n",
      " Optical Recognition of Handwritten Digits Data Set\n",
      "===================================================\n",
      "\n",
      "Notes\n",
      "-----\n",
      "Data Set Characteristics:\n",
      "    :Number of Instances: 5620\n",
      "    :Number of Attributes: 64\n",
      "    :Attribute Information: 8x8 image of integer pixels in the range 0..16.\n",
      "    :Missing Attribute Values: None\n",
      "    :Creator: E. Alpaydin (alpaydin '@' boun.edu.tr)\n",
      "    :Date: July; 1998\n",
      "\n",
      "This is a copy of the test set of the UCI ML hand-written digits datasets\n",
      "http://archive.ics.uci.edu/ml/datas \n",
      "[...]\n"
     ]
    }
   ],
   "source": [
    "print(\"DESCR:\\n\", dd.DESCR[:500], \"\\n[...]\") # description of the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the first ten digits from this data set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x360 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 10\n",
    "\n",
    "plt.figure(figsize=(2 * N, 5))\n",
    "\n",
    "for i, image in enumerate(dd.images[:N]):\n",
    "    plt.subplot(1, N, i + 1).set_title(dd.target[i])\n",
    "    plt.imshow(image, cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data is a set of 8 x 8 matrices with values 0 to 15 (black to white). The range 0 to 15 is fixed for this specific data set. Other formats allow e.g. values 0..255 or floating point values in the range 0 to 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "images[0].shape: (8, 8)\n",
      "\n",
      "images[0]:\n",
      " [[ 0.  0.  5. 13.  9.  1.  0.  0.]\n",
      " [ 0.  0. 13. 15. 10. 15.  5.  0.]\n",
      " [ 0.  3. 15.  2.  0. 11.  8.  0.]\n",
      " [ 0.  4. 12.  0.  0.  8.  8.  0.]\n",
      " [ 0.  5.  8.  0.  0.  9.  8.  0.]\n",
      " [ 0.  4. 11.  0.  1. 12.  7.  0.]\n",
      " [ 0.  2. 14.  5. 10. 12.  0.  0.]\n",
      " [ 0.  0.  6. 13. 10.  0.  0.  0.]]\n"
     ]
    }
   ],
   "source": [
    "print(\"images[0].shape:\", dd.images[0].shape) # dimensions of a first sample array\n",
    "print()\n",
    "print(\"images[0]:\\n\", dd.images[0]) # first sample array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To transform such an image to a feature vector we just have to flatten the matrix by concatenating the rows to one single vector of size 64:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image_vector.shape: (64,)\n",
      "image_vector: [ 0.  0.  5. 13.  9.  1.  0.  0.  0.  0. 13. 15. 10. 15.  5.  0.  0.  3.\n",
      " 15.  2.  0. 11.  8.  0.  0.  4. 12.  0.  0.  8.  8.  0.  0.  5.  8.  0.\n",
      "  0.  9.  8.  0.  0.  4. 11.  0.  1. 12.  7.  0.  0.  2. 14.  5. 10. 12.\n",
      "  0.  0.  0.  0.  6. 13. 10.  0.  0.  0.]\n"
     ]
    }
   ],
   "source": [
    "image_vector = dd.images[0].flatten()\n",
    "print(\"image_vector.shape:\", image_vector.shape)\n",
    "print(\"image_vector:\", image_vector)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2nd Example: How to present textual data as feature vectors?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we start a machine learning project for texts, we first have to choose a dictionary (a set of words) for this project. The words in the dictionary are enumerated. The final representation of a text as a feature vector depends on this dictionary.\n",
    "\n",
    "Such a dictionary can be very large, but for the sake of simplicity we use a very small enumerated dictionary to explain the overall procedure:\n",
    "\n",
    "\n",
    "| Word     | Index |\n",
    "|----------|-------|\n",
    "| like     | 0     |\n",
    "| dislike  | 1     |\n",
    "| american | 2     |\n",
    "| italian  | 3     |\n",
    "| beer     | 4     |\n",
    "| pizza    | 5     |\n",
    "\n",
    "To \"vectorize\" a given text we count the words in the text which also exist in the vocabulary and put the counts at the given `Index`.\n",
    "\n",
    "E.g. `\"I dislike american pizza, but american beer is nice\"`:\n",
    "\n",
    "| Word     | Index | Count |\n",
    "|----------|-------|-------|\n",
    "| like     | 0     | 0     |\n",
    "| dislike  | 1     | 1     |\n",
    "| american | 2     | 2     |\n",
    "| italian  | 3     | 0     |\n",
    "| beer     | 4     | 1     |\n",
    "| pizza    | 5     | 1     |\n",
    "\n",
    "The respective feature vector is the `Count` column, which is:\n",
    "\n",
    "`[0, 1, 2, 0, 1, 1]`\n",
    "\n",
    "In real case scenarios the dictionary is much bigger, which often results in vectors with only few non-zero entries (so called **sparse vectors**)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below you find is a short code example to demonstrate how text feature vectors can be created with `scikit-learn`.\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<i class=\"fa fa-info-circle\"></i>\n",
    "Such vectorization is usually not done manually. Actually there are improved but more complicated procedures which compute multiplicative weights for the vector entries to emphasize informative words such as, e.g., <a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html\">\"term frequency-inverse document frequency\" vectorizer</a>.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 0 1 1]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from itertools import count\n",
    "\n",
    "vocabulary = {\n",
    "    \"like\": 0,\n",
    "    \"dislike\": 1,\n",
    "    \"american\": 2,\n",
    "    \"italian\": 3,\n",
    "    \"beer\": 4,\n",
    "    \"pizza\": 5,\n",
    "}\n",
    "\n",
    "vectorizer = CountVectorizer(vocabulary=vocabulary)\n",
    "\n",
    "# this how one can create a count vector for a given piece of text:\n",
    "vector = vectorizer.fit_transform([\n",
    "    \"I dislike american pizza. But american beer is nice\"\n",
    "]).toarray().flatten()\n",
    "print(vector)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ML lingo: What are the different types of datasets?\n",
    "\n",
    "<div class=\"alert alert-block alert-danger\">\n",
    "<strong>TODO:</strong> move to later section about cross validation.</div>\n",
    "\n",
    "\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "<i class=\"fa fa-warning\"></i>&nbsp;<strong>Definitions</strong>\n",
    "\n",
    "Subset of data used for:\n",
    "<ul>\n",
    "    <li>learning (training) a model is called a <strong>training set</strong>;</li>\n",
    "    <li>improving ML method performance by adjusting its parameters is called <strong>validation set</strong>;</li>\n",
    "    <li>assesing final performance is called <strong>test set</strong>.</li>\n",
    "</ul>\n",
    "</div>\n",
    "\n",
    "<table>\n",
    "    <tr>\n",
    "        <td><img src=\"./data_split.png\" width=300px></td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td style=\"font-size:75%\"><center>Img source: https://dziganto.github.io</center></td>\n",
    "    </tr>\n",
    "</table>\n",
    "\n",
    "\n",
    "You will learn more on how to select wisely subsets of your data and about related issues later in the course. For now just remember that:\n",
    "1. the training and validation datasets must be disjunct during each iteration of the method improvement, and\n",
    "1. the test dataset must be independent from the model (hence, from the other datasets), i.e. it is indeed used only for the final assesment of the method's performance (think: locked in the safe until you're done with model tweaking).\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Taxonomy of machine learning\n",
    "\n",
    "Most applications of ML belong to two categories: **supervised** and **unsupervised** learning.\n",
    "\n",
    "### Supervised learning \n",
    "\n",
    "In supervised learning the data comes with an additional target/label value that we want to predict. Such a problem can be either \n",
    "\n",
    "- **classification**: we want to predict a categorical value.\n",
    "    \n",
    "- **regression**: we want to predict numbers in a given range.\n",
    "    \n",
    "  \n",
    "\n",
    "Examples of supervised learning:\n",
    "\n",
    "- Classification: predict the class `is_yummy`  based on the attributes `alcohol_content`,\t`bitterness`, \t`darkness` and `fruitiness` (a standard two class problem).\n",
    "\n",
    "- Classification: predict the digit-shown based on a 8 x 8 pixel image (a multi-class problem).\n",
    "\n",
    "- Regression: predict temperature based on how long sun was shining in the last 10 minutes.\n",
    "\n",
    "\n",
    "\n",
    "<table>\n",
    "    <tr>\n",
    "    <td><img src=\"./classification-svc-2d-poly.png\" width=400px></td>\n",
    "    <td><img src=\"./regression-lin-1d.png\" width=400px></td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td><center>Classification</center></td>\n",
    "        <td><center>Linear regression</center></td>\n",
    "    </tr>\n",
    "</table>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unsupervised learning \n",
    "\n",
    "In unsupervised learning the training data consists of samples without any corresponding target/label values and the aim is to find structure in data. Some common applications are:\n",
    "\n",
    "- Clustering: find groups in data.\n",
    "- Density estimation, novelty detection: find a probability distribution in your data.\n",
    "- Dimension reduction (e.g. PCA): find latent structures in your data.\n",
    "\n",
    "Examples of unsupervised learning:\n",
    "\n",
    "- Can we split up our beer data set into sub-groups of similar beers?\n",
    "- Can we reduce our data set because groups of features are somehow correlated?\n",
    "\n",
    "<table>\n",
    "    <tr>\n",
    "    <td><img src=\"./cluster-image.png/\" width=400px></td>\n",
    "    <td><img src=\"./nonlin-pca.png/\" width=400px></td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td><center>Clustering</center></td>\n",
    "        <td><center>Dimension reduction: detecting 2D structure in 3D data</center></td>\n",
    "    </tr>\n",
    "</table>\n",
    "\n",
    "\n",
    "\n",
    "This course will only introduce concepts and methods from **supervised learning**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to apply machine learning in practice?\n",
    "\n",
    "Application of machine learning in practice consists of several phases:\n",
    "\n",
    "1. Understand and clean your data.\n",
    "1. Learn / train a model \n",
    "2. Analyze model for its quality / performance\n",
    "2. Apply this model to new incoming data\n",
    "\n",
    "In practice steps 1. and 2. are iterated for different machine learning algorithms with different configurations until performance is optimal or sufficient. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hands-on section"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-danger\">\n",
    "<strong>TODO:</strong> transform to a set of small exercises (instead of a tutorial/example as it is now).\n",
    "</div>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our example beer data set reflects the very personal opinion of one of the tutors which beer he likes and which not. To learn a predictive model and to understand influential factors all beers went through some lab analysis to measure alcohol content, bitterness, darkness and fruitiness."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Load the data and show the overall structure using `pandas`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(225, 5)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# read some data\n",
    "beer_data = pd.read_csv(\"beers.csv\")\n",
    "print(beer_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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>alcohol_content</th>\n",
       "      <th>bitterness</th>\n",
       "      <th>darkness</th>\n",
       "      <th>fruitiness</th>\n",
       "      <th>is_yummy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.739295</td>\n",
       "      <td>0.422503</td>\n",
       "      <td>0.989463</td>\n",
       "      <td>0.215791</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.207849</td>\n",
       "      <td>0.841668</td>\n",
       "      <td>0.928626</td>\n",
       "      <td>0.380420</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.709494</td>\n",
       "      <td>0.322037</td>\n",
       "      <td>5.374682</td>\n",
       "      <td>0.145231</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.684743</td>\n",
       "      <td>0.434315</td>\n",
       "      <td>4.072805</td>\n",
       "      <td>0.191321</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.148710</td>\n",
       "      <td>0.570586</td>\n",
       "      <td>1.461568</td>\n",
       "      <td>0.260218</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alcohol_content  bitterness  darkness  fruitiness  is_yummy\n",
       "0         3.739295    0.422503  0.989463    0.215791         0\n",
       "1         4.207849    0.841668  0.928626    0.380420         0\n",
       "2         4.709494    0.322037  5.374682    0.145231         1\n",
       "3         4.684743    0.434315  4.072805    0.191321         1\n",
       "4         4.148710    0.570586  1.461568    0.260218         0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show first 5 rows\n",
    "beer_data.head(5)\n",
    "\n",
    "# there is alos beer_data.tail(5) !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>alcohol_content</th>\n",
       "      <th>bitterness</th>\n",
       "      <th>darkness</th>\n",
       "      <th>fruitiness</th>\n",
       "      <th>is_yummy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>225.000000</td>\n",
       "      <td>225.000000</td>\n",
       "      <td>225.000000</td>\n",
       "      <td>225.000000</td>\n",
       "      <td>225.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.711873</td>\n",
       "      <td>0.463945</td>\n",
       "      <td>2.574963</td>\n",
       "      <td>0.223111</td>\n",
       "      <td>0.528889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.437040</td>\n",
       "      <td>0.227366</td>\n",
       "      <td>1.725916</td>\n",
       "      <td>0.117272</td>\n",
       "      <td>0.500278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.073993</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.429183</td>\n",
       "      <td>0.281291</td>\n",
       "      <td>1.197640</td>\n",
       "      <td>0.135783</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.740846</td>\n",
       "      <td>0.488249</td>\n",
       "      <td>2.026548</td>\n",
       "      <td>0.242396</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.005170</td>\n",
       "      <td>0.631056</td>\n",
       "      <td>4.043995</td>\n",
       "      <td>0.311874</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.955272</td>\n",
       "      <td>1.080170</td>\n",
       "      <td>7.221285</td>\n",
       "      <td>0.535315</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       alcohol_content  bitterness    darkness  fruitiness    is_yummy\n",
       "count       225.000000  225.000000  225.000000  225.000000  225.000000\n",
       "mean          4.711873    0.463945    2.574963    0.223111    0.528889\n",
       "std           0.437040    0.227366    1.725916    0.117272    0.500278\n",
       "min           3.073993    0.000000    0.000000    0.000000    0.000000\n",
       "25%           4.429183    0.281291    1.197640    0.135783    0.000000\n",
       "50%           4.740846    0.488249    2.026548    0.242396    1.000000\n",
       "75%           5.005170    0.631056    4.043995    0.311874    1.000000\n",
       "max           5.955272    1.080170    7.221285    0.535315    1.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show basic statistics of the data\n",
    "beer_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Visualy inspect data using `seaborn`\n",
    "\n",
    "Such checks are very useful before you start throwning ML on your data. Some vague understanding how features are distributed and correlate can later be very helpfull to optimize performance of ML procedures.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 776.6x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set(style=\"ticks\")\n",
    "\n",
    "for_plot = beer_data.copy()\n",
    "\n",
    "def translate_label(value):\n",
    "    # seaborn has issues if labes are numbers or strings which represent numbers,\n",
    "    # for whatever reason \"real\" text labels work\n",
    "    return \"no\" if value == 0 else \"yes\"\n",
    "\n",
    "for_plot[\"is_yummy\"] = for_plot[\"is_yummy\"].apply(translate_label)\n",
    "\n",
    "sns.pairplot(for_plot, hue=\"is_yummy\", diag_kind=\"hist\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What do we see?\n",
    "\n",
    "- Points and colors don't look randomly distributed.\n",
    "- We can see that some pairs like `darkness` vs `bitterness` seem to carry information which could support building a classifier.\n",
    "- We also see that `bitterness` and `fruitiness` show correlation.\n",
    "\n",
    "Features which show no structure can also decrease performance of ML and often it makes sense to discard them.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Prepare data: split features and labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# INPUT FEATURES\n",
      "   alcohol_content  bitterness  darkness  fruitiness\n",
      "0         3.739295    0.422503  0.989463    0.215791\n",
      "1         4.207849    0.841668  0.928626    0.380420\n",
      "2         4.709494    0.322037  5.374682    0.145231\n",
      "3         4.684743    0.434315  4.072805    0.191321\n",
      "4         4.148710    0.570586  1.461568    0.260218\n",
      "...\n",
      "(225, 4)\n",
      "\n",
      "# LABELS\n",
      "0    0\n",
      "1    0\n",
      "2    1\n",
      "3    1\n",
      "4    0\n",
      "Name: is_yummy, dtype: int64\n",
      "...\n",
      "(225,)\n"
     ]
    }
   ],
   "source": [
    "# all columns up to the last one:\n",
    "input_features = beer_data.iloc[:, :-1]\n",
    "\n",
    "# only the last column:\n",
    "labels = beer_data.iloc[:, -1]\n",
    "\n",
    "print('# INPUT FEATURES')\n",
    "print(input_features.head(5))\n",
    "print('...')\n",
    "print(input_features.shape)\n",
    "print()\n",
    "print('# LABELS')\n",
    "print(labels.head(5))\n",
    "print('...')\n",
    "print(labels.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Start machine learning using `scikit-learn`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's finally do some machine learning starting with the so called `LogisticRegression` classifier from `scikit-learn` package. The intention here is to experiment first. Details of this and further ML algorithms are not necessary at this point, but do not worry, they will come later during the course.\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<i class=\"fa fa-info-circle\"></i>\n",
    "<code>LogisticRegression</code> is a classification method, even so the name contains \"regression\"-as the other group of unsupervised learning methods. In fact, in logistic regression method the (linear) regression is used internally and the result is then transformed (using logistic function) to probability of belonging to one of the two classes.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "classifier = LogisticRegression()\n",
    "classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<i class=\"fa fa-warning\"></i>&nbsp;<strong>Built-in documentation</strong>\n",
    "\n",
    "If you want to learn more about <code>LogisticRegression</code> you can use <code>help(LogisticRegression)</code> or <code>?LogisticRegression</code> to see the related documenation. The latter version works only in Jupyter Notebooks (or in IPython shell).\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<i class=\"fa fa-warning\"></i>&nbsp;<strong>`scikit-learn` API</strong>\n",
    "\n",
    "In <code>scikit-learn</code> all classifiers have:\n",
    "<ul>\n",
    "    <li>a <strong><code>fit()</code></strong> method to learn from data, and</li>\n",
    "    <li>and a subsequent <strong><code>predict()</code></strong> method for predicting classes from input features.</li>\n",
    "</ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "ename": "NotFittedError",
     "evalue": "This LogisticRegression instance is not fitted yet",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNotFittedError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-15-9e1ed3d39774>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Sanity check: can't predict if not fitted (trained)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mclassifier\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_features\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/Projects/machinelearning-introduction-workshop/venv3.6/lib/python3.6/site-packages/sklearn/linear_model/base.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    322\u001b[0m             \u001b[0mPredicted\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0mper\u001b[0m \u001b[0msample\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    323\u001b[0m         \"\"\"\n\u001b[0;32m--> 324\u001b[0;31m         \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecision_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    325\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscores\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    326\u001b[0m             \u001b[0mindices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mscores\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Projects/machinelearning-introduction-workshop/venv3.6/lib/python3.6/site-packages/sklearn/linear_model/base.py\u001b[0m in \u001b[0;36mdecision_function\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    296\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'coef_'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoef_\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    297\u001b[0m             raise NotFittedError(\"This %(name)s instance is not fitted \"\n\u001b[0;32m--> 298\u001b[0;31m                                  \"yet\" % {'name': type(self).__name__})\n\u001b[0m\u001b[1;32m    299\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    300\u001b[0m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'csr'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNotFittedError\u001b[0m: This LogisticRegression instance is not fitted yet"
     ]
    }
   ],
   "source": [
    "# Sanity check: can't predict if not fitted (trained)\n",
    "classifier.predict(input_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(225,)\n"
     ]
    }
   ],
   "source": [
    "# Fit\n",
    "classifier.fit(input_features, labels)\n",
    "\n",
    "# Predict\n",
    "predicted_labels = classifier.predict(input_features)\n",
    "print(predicted_labels.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we've just re-classified our training data. Lets check our result with a few examples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0\n",
      "0 1\n",
      "1 1\n",
      "1 1\n",
      "0 0\n"
     ]
    }
   ],
   "source": [
    "for i in range(5):\n",
    "    print(labels[i], predicted_labels[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks suspicious !\n",
    "\n",
    "Lets investigate this further:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "225 examples\n",
      "187 labeled correctly\n"
     ]
    }
   ],
   "source": [
    "print(len(labels), \"examples\")\n",
    "print(sum(predicted_labels == labels), \"labeled correctly\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<i class=\"fa fa-info-circle\"></i>\n",
    "<code>predicted_labels == labels</code> evaluates to a vector of <code>True</code> or <code>False</code> Boolean values. When used as numbers, Python handles <code>True</code> as <code>1</code> and <code>False</code> as <code>0</code>. So, <code>sum(...)</code> simply counts the correctly predicted labels.\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What happened?\n",
    "\n",
    "Why were not  all labels  predicted correctly?\n",
    "\n",
    "Neither `Python` nor `scikit-learn` is broken. What we observed above is very typical for machine-learning applications.\n",
    "\n",
    "Reasons could be:\n",
    "\n",
    "- we have incomplete information: other features of beer which also contribute to the rating (like \"maltiness\") were not measured or can not be measured. \n",
    "\n",
    "- the used classifiers might have been not suitable for the given problem.\n",
    "\n",
    "- noise in the data as incorrectly assigned labels also affect results.\n",
    "\n",
    "\n",
    "**Finding good features is crucial for the performance of ML algorithms!**\n",
    "\n",
    "\n",
    "Another important requirement is to make sure that you have clean data: input-features might be corrupted by flawed entries, feeding such data into a ML algorithm will usually lead to reduced performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise section 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-danger\">\n",
    "<strong>TODO:</strong> I propose to start separate excercise session 2 w/ SVC here (so if someone is stuck on previous, he/she can skip).\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Compare with alternative machine learning method from `scikit-learn`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, using previously loaded and prepared beer data, train a different `scikit-learn` classifier - the so called **Support Vector Classifier** `SVC`, and evaluate its \"re-classification\" performance again.\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<i class=\"fa fa-info-circle\"></i>\n",
    "<code>SVC</code>  belongs to a class of algorithms named \"Support Vector Machines\" (SVMs). Again, it will be discussed in more detail in the following scripts.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "225 examples\n",
      "205 labeled correctly\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "# ...\n",
    "# REMOVE or HIDE the following lines in the target script\n",
    "classifier = SVC()\n",
    "classifier.fit(input_features, labels)\n",
    "\n",
    "predicted_labels = classifier.predict(input_features)\n",
    "\n",
    "assert(predicted_labels.shape == labels.shape)\n",
    "print(len(labels), \"examples\")\n",
    "print(sum(predicted_labels == labels), \"labeled correctly\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Better?\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<i class=\"fa fa-info-circle\"></i>\n",
    "Better re-classification in our example does not indicate here that <code>SVC</code> is better than <code>LogisticRegression</code> in all cases. The performance of a classifier strongly depends on the data set.\n",
    "</div>\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Experiment with (hyper)parameters of ML methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both `LogisticRegression` and `SVC` classifiers have a parameter `C` which allows to enforce a \"simplification\" (often called **regularization**) of the resulting model. Test the beers data \"re-classification\" with different values of this parameter.\n",
    "\n",
    "\n",
    "**TOBE discussed**: is \"regularization\" to technical here ? decision surfaces and details of classifers come later. Original purpose (Uwe) was to demonstrate that classifiers can be tuned to the data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Recall: ?LogisticRegression\n",
    "# ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-danger\">\n",
    "<strong>TODO:</strong> prepare a solution.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise section 2 (optional)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-danger\">\n",
    "<strong>TODO:</strong> finish solution - missing classification and \"re-classification\" assesment.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load and inspect the cannonical Fisher's \"Iris\" data set, which is included in `scikit-learn`: see [docs for `sklearn.datasets.load_iris`](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html). What's conceptually diffferent?\n",
    "\n",
    "Apply `LogisticRegression` or `SVC` classifiers. Is it easier or more difficult than classification of the beers data?\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['setosa' 'versicolor' 'virginica']\n",
      "(150, 4)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "\n",
    "data = load_iris()\n",
    "\n",
    "# labels as text\n",
    "print(data.target_names) \n",
    "\n",
    "# (rows, columns) of the feature matrix:\n",
    "print(data.data.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<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>class</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>0</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>0</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>0</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>0</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>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "   class  \n",
       "0      0  \n",
       "1      0  \n",
       "2      0  \n",
       "3      0  \n",
       "4      0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# transform the scikit-learn data structure into a data frame:\n",
    "df = pd.DataFrame(data.data, columns=data.feature_names)\n",
    "df[\"class\"] = data.target\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "      <td>0.819232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal length (cm)  sepal width (cm)  petal length (cm)  \\\n",
       "count         150.000000        150.000000         150.000000   \n",
       "mean            5.843333          3.054000           3.758667   \n",
       "std             0.828066          0.433594           1.764420   \n",
       "min             4.300000          2.000000           1.000000   \n",
       "25%             5.100000          2.800000           1.600000   \n",
       "50%             5.800000          3.000000           4.350000   \n",
       "75%             6.400000          3.300000           5.100000   \n",
       "max             7.900000          4.400000           6.900000   \n",
       "\n",
       "       petal width (cm)       class  \n",
       "count        150.000000  150.000000  \n",
       "mean           1.198667    1.000000  \n",
       "std            0.763161    0.819232  \n",
       "min            0.100000    0.000000  \n",
       "25%            0.300000    0.000000  \n",
       "50%            1.300000    1.000000  \n",
       "75%            1.800000    2.000000  \n",
       "max            2.500000    2.000000  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 806.85x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set(style=\"ticks\")\n",
    "\n",
    "for_plot = df.copy()\n",
    "\n",
    "def transform_label(class_):\n",
    "    return data.target_names[class_]\n",
    "\n",
    "# seaborn does not work here if we use numeric values in the class\n",
    "# column, or strings which represent numbers. To fix this we\n",
    "# create textual class labels\n",
    "for_plot[\"class\"] = for_plot[\"class\"].apply(transform_label)\n",
    "sns.pairplot(for_plot, hue=\"class\", diag_kind=\"hist\") ;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-danger\">\n",
    "<strong>TODO:</strong> hide tech stuff below.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/uweschmitt/Projects/machinelearning-introduction-workshop/venv3.6/lib/python3.6/site-packages/ipykernel_launcher.py:9: UserWarning: get_ipython_dir has moved to the IPython.paths module since IPython 4.0.\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       "    \n",
       "    @import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
       "    \n",
       "    @import url('http://fonts.googleapis.com/css?family=Kameron');\n",
       "    @import url('http://fonts.googleapis.com/css?family=Crimson+Text');\n",
       "    \n",
       "    @import url('http://fonts.googleapis.com/css?family=Lato');\n",
       "    @import url('http://fonts.googleapis.com/css?family=Source+Sans+Pro');\n",
       "    \n",
       "    @import url('http://fonts.googleapis.com/css?family=Lora'); \n",
       "\n",
       "    \n",
       "    body {\n",
       "        font-family: 'Lora', Consolas, sans-serif;\n",
       "       \n",
       "        -webkit-print-color-adjust: exact important !;\n",
       "        \n",
       "      \n",
       "       \n",
       "    }\n",
       "    \n",
       "    .alert-block {\n",
       "        width: 95%;\n",
       "        margin: auto;\n",
       "    }\n",
       "    \n",
       "    .rendered_html code\n",
       "    {\n",
       "        color: black;\n",
       "        background: #eaf0ff;\n",
       "        background: #f5f5f5; \n",
       "        padding: 1pt;\n",
       "        font-family:  'Source Code Pro', Consolas, monocco, monospace;\n",
       "    }\n",
       "    \n",
       "    p {\n",
       "      line-height: 140%;\n",
       "    }\n",
       "    \n",
       "    strong code {\n",
       "        background: red;\n",
       "    }\n",
       "    \n",
       "    .rendered_html strong code\n",
       "    {\n",
       "        background: #f5f5f5;\n",
       "    }\n",
       "    \n",
       "    .CodeMirror pre {\n",
       "    font-family: 'Source Code Pro', monocco, Consolas, monocco, monospace;\n",
       "    }\n",
       "    \n",
       "    .cm-s-ipython span.cm-keyword {\n",
       "        font-weight: normal;\n",
       "     }\n",
       "     \n",
       "     strong {\n",
       "         background: #f5f5f5;\n",
       "         margin-top: 4pt;\n",
       "         margin-bottom: 4pt;\n",
       "         padding: 2pt;\n",
       "         border: 0.5px solid #a0a0a0;\n",
       "         font-weight: bold;\n",
       "         color: darkred;\n",
       "     }\n",
       "     \n",
       "    \n",
       "    div #notebook {\n",
       "        # font-size: 10pt; \n",
       "        line-height: 145%;\n",
       "        }\n",
       "        \n",
       "    li {\n",
       "        line-height: 145%;\n",
       "    }\n",
       "\n",
       "    div.output_area pre {\n",
       "        background: #fff9d8 !important;\n",
       "        padding: 5pt;\n",
       "       \n",
       "       -webkit-print-color-adjust: exact; \n",
       "        \n",
       "    }\n",
       " \n",
       "    \n",
       " \n",
       "    h1, h2, h3, h4 {\n",
       "        font-family: Kameron, arial;\n",
       "    }\n",
       "    \n",
       "    div#maintoolbar {display: none !important;}\n",
       "    </style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#REMOVEBEGIN\n",
    "# THE LINES BELOW ARE JUST FOR STYLING THE CONTENT ABOVE !\n",
    "\n",
    "from IPython import utils\n",
    "from IPython.core.display import HTML\n",
    "import os\n",
    "def css_styling():\n",
    "    \"\"\"Load default custom.css file from ipython profile\"\"\"\n",
    "    base = utils.path.get_ipython_dir()\n",
    "    styles = \"\"\"<style>\n",
    "    \n",
    "    @import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
    "    \n",
    "    @import url('http://fonts.googleapis.com/css?family=Kameron');\n",
    "    @import url('http://fonts.googleapis.com/css?family=Crimson+Text');\n",
    "    \n",
    "    @import url('http://fonts.googleapis.com/css?family=Lato');\n",
    "    @import url('http://fonts.googleapis.com/css?family=Source+Sans+Pro');\n",
    "    \n",
    "    @import url('http://fonts.googleapis.com/css?family=Lora'); \n",
    "\n",
    "    \n",
    "    body {\n",
    "        font-family: 'Lora', Consolas, sans-serif;\n",
    "       \n",
    "        -webkit-print-color-adjust: exact important !;\n",
    "        \n",
    "      \n",
    "       \n",
    "    }\n",
    "    \n",
    "    .alert-block {\n",
    "        width: 95%;\n",
    "        margin: auto;\n",
    "    }\n",
    "    \n",
    "    .rendered_html code\n",
    "    {\n",
    "        color: black;\n",
    "        background: #eaf0ff;\n",
    "        background: #f5f5f5; \n",
    "        padding: 1pt;\n",
    "        font-family:  'Source Code Pro', Consolas, monocco, monospace;\n",
    "    }\n",
    "    \n",
    "    p {\n",
    "      line-height: 140%;\n",
    "    }\n",
    "    \n",
    "    strong code {\n",
    "        background: red;\n",
    "    }\n",
    "    \n",
    "    .rendered_html strong code\n",
    "    {\n",
    "        background: #f5f5f5;\n",
    "    }\n",
    "    \n",
    "    .CodeMirror pre {\n",
    "    font-family: 'Source Code Pro', monocco, Consolas, monocco, monospace;\n",
    "    }\n",
    "    \n",
    "    .cm-s-ipython span.cm-keyword {\n",
    "        font-weight: normal;\n",
    "     }\n",
    "     \n",
    "     strong {\n",
    "         background: #f5f5f5;\n",
    "         margin-top: 4pt;\n",
    "         margin-bottom: 4pt;\n",
    "         padding: 2pt;\n",
    "         border: 0.5px solid #a0a0a0;\n",
    "         font-weight: bold;\n",
    "         color: darkred;\n",
    "     }\n",
    "     \n",
    "    \n",
    "    div #notebook {\n",
    "        # font-size: 10pt; \n",
    "        line-height: 145%;\n",
    "        }\n",
    "        \n",
    "    li {\n",
    "        line-height: 145%;\n",
    "    }\n",
    "\n",
    "    div.output_area pre {\n",
    "        background: #fff9d8 !important;\n",
    "        padding: 5pt;\n",
    "       \n",
    "       -webkit-print-color-adjust: exact; \n",
    "        \n",
    "    }\n",
    " \n",
    "    \n",
    " \n",
    "    h1, h2, h3, h4 {\n",
    "        font-family: Kameron, arial;\n",
    "    }\n",
    "    \n",
    "    div#maintoolbar {display: none !important;}\n",
    "    </style>\"\"\"\n",
    "    return HTML(styles)\n",
    "css_styling()\n",
    "#REMOVEEND"
   ]
  }
 ],
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