01_introduction.ipynb

{
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  {
   "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 when I break now ?\n",
    "   2. Where on the night sky will I see the moon tonight ?\n",
    "   2. 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 spamming 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 for spam emails, other words are specific for my personal and job emails.\n",
    "- My mailbox is full with examples for spam vs non-spam.\n",
    "\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 <b>data mining</b> 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 first must \"learn\" such a model on given data. \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",
    "    1984: Book \"Classification And Regression Trees\"\n",
    "    1974-86: Neural networks learning breakthrough: backpropagation method\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 releases of frameworks such as `TensorFlow` or `PyTorch` were released with`Python` support early.\n",
    "\n",
    "The prevalent packages in the Python eco-system used for ML include:\n",
    "\n",
    "- `pandas` for handling tabualar 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 for machine-learning on the previos 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": [
    "### How to represent images as feature vectors?\n",
    "\n",
    "To simplify our explanations we consider gray images only here. 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",
    "As we said, most machine learning algorithms require that every sample is represented as a  vector containing numbers. \n",
    "\n",
    "So how can we represent images as vectors then ?\n",
    "\n",
    "`scikit-learn`  includes some example data sets which we load now:"
   ]
  },
  {
   "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": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the first ten digits from this data set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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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. 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": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "images.ndim: 3\n",
      "images[0].shape: (8, 8)\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",
      "images.shape: (1797, 8, 8)\n",
      "images.size: 115008\n",
      "images.dtype: float64\n",
      "images.itemsize: 8\n",
      "target.size: 1797\n",
      "target_names: [0 1 2 3 4 5 6 7 8 9]\n",
      "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(\"images.ndim:\", dd.images.ndim) # number of dimensions of the array\n",
    "print(\"images[0].shape:\", dd.images[0].shape) # dimensions of a first sample array\n",
    "print(\"images[0]:\\n\", dd.images[0]) # first sample array\n",
    "print(\"images.shape:\", dd.images.shape) # dimensions of the array of all samples\n",
    "print(\"images.size:\", dd.images.size) # total number of elements of the array\n",
    "print(\"images.dtype:\", dd.images.dtype) # type of the elements in the array\n",
    "print(\"images.itemsize:\", dd.images.itemsize) # size in bytes of each element of the array\n",
    "print(\"target.size:\", dd.target.size) # size of the target feature vector (labels of samples)\n",
    "print(\"target_names:\", dd.target_names) # classes vector\n",
    "print(\"DESCR:\\n\", dd.DESCR[:500], \"\\n[...]\") # description of the dataset"
   ]
  },
  {
   "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": 6,
   "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": [
    "### 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 - set of words for this project. 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": [
    "And this is how we can compute such a word vector using Python:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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",
    "# create count vector for a pice 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-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 the data comes with an additional target 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 for 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, in which the training data consists of samples without any corresponding target values, one tries 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 for 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": [
    "# Exercise section 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-danger\">\n",
    "<strong>TODO:</strong> prepare set of actual small exercises out of it (currently it's just more of a tutorial/example).\n",
    "</div>"
   ]
  },
  {
   "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": "code",
   "execution_count": 8,
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# read some data\n",
    "beer_data = pd.read_csv(\"beers.csv\")\n",
    "beer_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 813.6x720 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",
    "    return \"not yummy\" if value == 0 else \"yummy\"\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": [
    "Now we split our data frame into the input features and target values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "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": [
    "Let's start learning with the so called `LogisticRegression` classifier.\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<i class=\"fa fa-info-circle\"></i>\n",
    "In logistic regression the linear regression is used internally and then transformed (using logistic function) to probability of belonging to one of the two classes. Even so the name contains \"regression\", it is still a classifier.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "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": 11,
     "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>`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": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train first..\n",
      "(225,)\n"
     ]
    }
   ],
   "source": [
    "# Sanity check: can't predict if not fitted (trained)\n",
    "from sklearn.exceptions import NotFittedError\n",
    "try:\n",
    "    classifier.predict(input_features)\n",
    "except NotFittedError:\n",
    "    print(\"train first..\")\n",
    "\n",
    "# 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": 13,
   "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": 14,
   "metadata": {},
   "outputs": [