01_introduction.ipynb

{
 "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 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. What article X should I recommend to my customers 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\n",
    "- We have a vague understanding of the problem domeani, 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",
    "\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",
    " \n",
    "    1812: Bayes Theorem\n",
    "    1913: Markov Chains\n",
    "    1951: First neural network\n",
    "    1959: first use or term \"machine learning\" AI pioneer Arthur Samuel\n",
    "    1969: Book \"Perceptrons\": Limitations of Neural Networks\n",
    "    1986: Backpropagation to learn neural networks\n",
    "    1995: Randomized Forests and Support Vector Machines\n",
    "    1998: Public appearance of ML: naive Bayes Classifier for Spam detection\n",
    "    2000+: 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 terms: 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": [
    "\n",
    "**Definitions**:\n",
    "- every row of such a matrix is called a **sample** or **feature vector**. \n",
    "\n",
    "- the cells in a row are **feature values**.\n",
    "- every column name is called a **feature name** or **attribute**."
   ]
  },
  {
   "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`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Almost) all machine learning algorithms require that your data is numerical and/or categorial. In some applications it is not obvious how to transform data to a numerical presentation.\n",
    "\n",
    "**Definition**:\n",
    "\n",
    "*Categorical data*: data which has only a limited set of allowed values. A `taste` feature could only allow values `sour`, `bitter`, `sweet`, `salty`."
   ]
  },
  {
   "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",
    "In this case we would call the features `alcohol_content`, `bitterness`, `darkness`, `fruitiness` our **input features** and `is_yummy` our **target value**."
   ]
  },
  {
   "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": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "dd = load_digits()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we plot the first nine digits from this data set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1296x360 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 9\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)\n",
    "    plt.imshow(image, cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And this is the first image from the data set, it is a 8 x 8 matrix 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": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8, 8)\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(dd.images[0].shape)\n",
    "print(dd.images[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To transform such an image to a feature vector we just have to concatenate the rows to one single vector of size 64:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(64,)\n",
      "[ 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": [
    "vector = dd.images[0].flatten()\n",
    "print(vector.shape)\n",
    "print(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 and fix an enumerated dictionary or words for this project. The final representation of texts as feature vectors 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 position `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 according 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, this results then 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": 77,
   "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 = {\"like\": 0, \"dislike\": 1, \"american\": 2, \"italian\": 3, \"beer\": 4, \"pizza\": 5}\n",
    "\n",
    "vectorizer = CountVectorizer(vocabulary=vocabulary)\n",
    "\n",
    "# create count vector for a pice of text:\n",
    "vector = vectorizer.fit_transform([\"I dislike american pizza. But american beer is nice\"]).toarray().flatten()\n",
    "print(vector)"
   ]
  },
  {
   "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`. (two class problem).\n",
    "\n",
    "- Classification: predict the digit-shown based on a 8 x 8 pixel image (this is a multi-class problem).\n",
    "\n",
    "- Regression: Predict the length of a salmon based on its age and weight."
   ]
  },
  {
   "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: 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=60%></td>\n",
    "    <td><img src=\"./nonlin-pca.png/\" width=60%></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": [
    "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": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
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       "      <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": 126,
     "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": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x11fb0c470>"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 774.8x720 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",
    "for_plot[\"is_yummy\"] = for_plot[\"is_yummy\"].apply(lambda s: [\":-(\", \":-)\"][s])\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": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   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",
      "0    0\n",
      "1    0\n",
      "2    1\n",
      "3    1\n",
      "4    0\n",
      "Name: is_yummy, dtype: int64\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.head(5))\n",
    "print()\n",
    "print(labels.head(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We experiment now the so called `LogisticRegression` classifier. The name is misleading: logistic regression internally uses a kind of regression algorithm for probabilities with the final goal to classify data. So even if the name contains \"regression\" it still is a classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = LogisticRegression(C=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In `scikit-learn` all classifiers have a `fit` method to learn from data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1, 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": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.fit(input_features, labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also `scikit-learn` classifiers have a `predict` method for predicting classes for input features. Here we just re-classify our learning data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_labels = classifier.predict(input_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets check our result with a few examples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "225 examples\n",
      "191 labeled correctly\n"
     ]
    }
   ],
   "source": [
    "print(len(labels), \"examples\")\n",
    "print(sum(predicted_labels == labels), \"labeled correctly\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comment: `predicted_labels == labels` evaluates as a vector of values `True` or `False`. Python handles `True` as `1` and `False` as `0` when used as numbers. So the `sum(...)` just counts the correct results.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What happened ?\n",
    "\n",
    "Why were not not all labels where predicted correctly ?\n",
    "\n",
    "Neither `Python` nor `scikit-learn` is broken. What we observed above is very typical for machine-learning applications.\n",
    "\n",
    "The reason here is that we have incomplete information: other features of beer which also contribute to the rating (like \"maltiness\") where not measured or can not be measured. So even the best algorithm can not predict the target values reliably.\n",
    "\n",
    "Another reason might be mistakes in the input data, e.g. some labels are assigned incorrectly.\n",
    "\n",
    "* Finding good features is crucial for the performance of ML algorithms !\n",
    "\n",
    "\n",
    "* Another important issue is 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": [
    "Now we play with a different ML algorithm, the so called `Support Vector Classifier` (which belongs to a class of algorithms named `SVM`s (`Support Vector Machines`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "classifier = SVC(C=1)\n",
    "classifier.fit(features, labels)\n",
    "\n",
    "predicted_labels = classifier.predict(features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets evaluate the performance again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(225,)\n",
      "(225,)\n",
      "205\n"
     ]
    }
   ],
   "source": [
    "print(predicted_labels.shape)\n",
    "print(labels.shape)\n",
    "print(sum(predicted_labels == labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a better result ! **But this does not indicate that `SVC` is always superior to `LogisticRegression`.**\n",
    "\n",
    "Here `SVC` just seems to fit better to our current machine learning task.\n",
    "\n",
    "### Instructions:\n",
    "\n",
    "- Play with parameter `C` for `LogisticRegresseion` and `SVC`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "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",
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