Skip to content
Snippets Groups Projects
07_regression.ipynb 1.51 MiB
Newer Older
  • Learn to ignore specific revisions
  • {
     "cells": [
      {
       "cell_type": "code",
    
    schmittu's avatar
    schmittu committed
       "execution_count": 18,
    
       "metadata": {},
       "outputs": [
        {
         "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",
           "    \n",
           "    div#maintoolbar {display: none !important;}\n",
    
    schmittu's avatar
    schmittu committed
           "    /*\n",
           "\n",
           "    div#site { \n",
           "        border-top: 20px solid #1F407A; \n",
           "        border-right: 20px solid #1F407A; \n",
           "        margin-bottom: 0;\n",
           "        padding-bottom: 0;\n",
           "    }\n",
           "    div#toc-wrapper { \n",
           "        border-left: 20px solid #1F407A; \n",
           "        border-top: 20px solid #1F407A; \n",
           "\n",
           "    }\n",
           "\n",
           "    body {\n",
           "        margin-botton:10px;\n",
           "    }\n",
           "    */\n",
           "\n",
    
    schmittu's avatar
    schmittu committed
           "</style>\n",
           "    <script>\n",
           "IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
           "        return false;\n",
           "}\n",
    
    schmittu's avatar
    schmittu committed
           "    </script>\n",
           "\n",
           "\n",
           "<footer id=\"attribution\" style=\"float:left; color:#1F407A; background:#fff; font-family: helvetica;\">\n",
           "    Copyright (C) 2019 Scientific IT Services of ETH Zurich,\n",
           "    <p>\n",
           "    Contributing Authors:\n",
           "    Dr. Tarun Chadha,\n",
           "    Dr. Franziska Oschmann,\n",
           "    Dr. Mikolaj Rybinski,\n",
           "    Dr. Uwe Schmitt.\n",
           "    </p<\n",
           "</footer>\n"
    
          ],
          "text/plain": [
           "<IPython.core.display.HTML object>"
          ]
         },
    
    schmittu's avatar
    schmittu committed
         "execution_count": 18,
    
         "metadata": {},
         "output_type": "execute_result"
        }
       ],
       "source": [
    
    schmittu's avatar
    schmittu committed
        "# IGNORE THIS CELL WHICH CUSTOMIZES LAYOUT AND STYLING OF THE NOTEBOOK !\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "%config InlineBackend.figure_format = 'retina'\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore', category=FutureWarning)\n",
    
    schmittu's avatar
    schmittu committed
        "warnings.filterwarnings('ignore', category=DeprecationWarning)\n",
    
    schmittu's avatar
    schmittu committed
        "warnings.filterwarnings = lambda *a, **kw: None\n",
    
        "from IPython.core.display import HTML; HTML(open(\"custom.html\", \"r\").read())"
       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
        "# Chapter 7: Regression\n",
        "\n",
    
    schmittu's avatar
    schmittu committed
        "Regression belongs like classification to the field of supervised learning. \n",
        "\n",
    
        "<div class=\"alert alert-block alert-warning\">\n",
    
    schmittu's avatar
    schmittu committed
        "<i class=\"fa fa-info-circle\"></i>&nbsp; \n",
        "<strong>Regression predicts numerical values</strong> \n",
        "in contrast to classification which predicts categories.\n",
        "</div>\n",
        "\n",
    
        "<img src=\"./images/30416v.jpg\" title=\"made at imgflip.com\" width=35%/>\n",
    
    schmittu's avatar
    schmittu committed
        "\n",
    
    schmittu's avatar
    schmittu committed
        "<div class=\"alert alert-block alert-warning\">\n",
        "<i class=\"fa fa-info-circle\"></i>&nbsp; \n",
    
    schmittu's avatar
    schmittu committed
        "    Other main differences are:\n",
    
    schmittu's avatar
    schmittu committed
        "\n",
    
    schmittu's avatar
    schmittu committed
        "* Different quality measures\n",
    
    schmittu's avatar
    schmittu committed
        "* Other algorithms\n",
        "</div>"
    
       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
        "## Example: Salmon weight\n",
        "\n",
    
        "The dataset `data/salmon.csv` holds measurements of `circumference`, `length` and `weight` for  `atlantic` and `sockeye` salmons.\n",
    
        "\n",
        "Our goal is to predict `weight` based on the other three features."
       ]
      },
      {
       "cell_type": "code",
    
    schmittu's avatar
    schmittu committed
       "execution_count": 19,
    
       "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>circumference</th>\n",
           "      <th>length</th>\n",
           "      <th>kind</th>\n",
           "      <th>weight</th>\n",
           "    </tr>\n",
           "  </thead>\n",
           "  <tbody>\n",
           "    <tr>\n",
           "      <th>0</th>\n",
    
           "      <td>19.0</td>\n",
           "      <td>59.5</td>\n",
           "      <td>sockeye</td>\n",
           "      <td>5.1</td>\n",
    
           "    </tr>\n",
           "    <tr>\n",
           "      <th>1</th>\n",
    
           "      <td>18.0</td>\n",
           "      <td>53.0</td>\n",
           "      <td>sockeye</td>\n",
           "      <td>4.1</td>\n",
    
           "    </tr>\n",
           "    <tr>\n",
           "      <th>2</th>\n",
    
           "      <td>28.0</td>\n",
           "      <td>75.5</td>\n",
    
           "      <td>atlantic</td>\n",
    
           "      <td>9.1</td>\n",
    
           "    </tr>\n",
           "    <tr>\n",
           "      <th>3</th>\n",
    
           "      <td>33.5</td>\n",
           "      <td>89.0</td>\n",
    
           "      <td>atlantic</td>\n",
    
           "      <td>15.6</td>\n",
    
           "    </tr>\n",
           "    <tr>\n",
           "      <th>4</th>\n",
    
           "      <td>23.5</td>\n",
           "      <td>63.0</td>\n",
    
           "      <td>atlantic</td>\n",
    
           "      <td>5.2</td>\n",
    
           "    </tr>\n",
           "  </tbody>\n",
           "</table>\n",
           "</div>"
          ],
          "text/plain": [
           "   circumference  length      kind  weight\n",
    
           "0           19.0    59.5   sockeye     5.1\n",
           "1           18.0    53.0   sockeye     4.1\n",
           "2           28.0    75.5  atlantic     9.1\n",
           "3           33.5    89.0  atlantic    15.6\n",
           "4           23.5    63.0  atlantic     5.2"
    
    schmittu's avatar
    schmittu committed
         "execution_count": 19,
    
         "metadata": {},
         "output_type": "execute_result"
        }
       ],
       "source": [
        "import pandas as pd\n",
        "\n",
    
        "df = pd.read_csv(\"data/salmon.csv\")\n",
    
        "df.head()"
       ]
      },
      {
       "cell_type": "code",
    
    schmittu's avatar
    schmittu committed
       "execution_count": 20,
    
       "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>circumference</th>\n",
           "      <th>length</th>\n",
           "      <th>kind</th>\n",
           "      <th>weight</th>\n",
           "    </tr>\n",
           "  </thead>\n",
           "  <tbody>\n",
           "    <tr>\n",
           "      <th>95</th>\n",
    
           "      <td>24.0</td>\n",
           "      <td>76.0</td>\n",
           "      <td>atlantic</td>\n",
           "      <td>6.7</td>\n",
    
           "    </tr>\n",
           "    <tr>\n",
           "      <th>96</th>\n",
           "      <td>18.5</td>\n",
           "      <td>67.0</td>\n",
           "      <td>sockeye</td>\n",
    
           "      <td>5.0</td>\n",
    
           "    </tr>\n",
           "    <tr>\n",
           "      <th>97</th>\n",
    
           "      <td>18.0</td>\n",
           "      <td>59.5</td>\n",
    
           "      <td>sockeye</td>\n",
    
           "      <td>4.7</td>\n",
    
           "    </tr>\n",
           "    <tr>\n",
           "      <th>98</th>\n",
    
           "      <td>20.0</td>\n",
           "      <td>64.5</td>\n",
           "      <td>atlantic</td>\n",
           "      <td>4.1</td>\n",
    
           "    </tr>\n",
           "    <tr>\n",
           "      <th>99</th>\n",
    
           "      <td>23.0</td>\n",
           "      <td>75.0</td>\n",
    
           "      <td>sockeye</td>\n",
    
           "      <td>7.2</td>\n",
    
           "    </tr>\n",
           "  </tbody>\n",
           "</table>\n",
           "</div>"
          ],
          "text/plain": [
    
           "    circumference  length      kind  weight\n",
           "95           24.0    76.0  atlantic     6.7\n",
           "96           18.5    67.0   sockeye     5.0\n",
           "97           18.0    59.5   sockeye     4.7\n",
           "98           20.0    64.5  atlantic     4.1\n",
           "99           23.0    75.0   sockeye     7.2"
    
    schmittu's avatar
    schmittu committed
         "execution_count": 20,
    
         "metadata": {},
         "output_type": "execute_result"
        }
       ],
       "source": [
        "df.tail()"
       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
        "Let us inspect the features and their distributions:"
       ]
      },
      {
       "cell_type": "code",
    
    schmittu's avatar
    schmittu committed
       "execution_count": 21,
    
       "metadata": {},
       "outputs": [
        {
         "data": {
    
    schmittu's avatar
    schmittu committed
          "image/png": "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\n",
    
          "text/plain": [
           "<Figure size 619.85x540 with 12 Axes>"
          ]
         },
         "metadata": {
          "image/png": {
    
    schmittu's avatar
    schmittu committed
           "height": 532,
           "width": 617
    
          }
         },
         "output_type": "display_data"
        }
       ],
       "source": [
        "import seaborn as sns\n",
        "sns.set(style=\"ticks\")\n",
        "\n",
        "sns.pairplot(df, hue=\"kind\", diag_kind=\"hist\");"
       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
    
    schmittu's avatar
    schmittu committed
        "In contrast to our previous examples, our data set contains a non-numerical text column `kind`.\n",
        "\n",
    
    schmittu's avatar
    schmittu committed
        "As discussed before a common method to encode categorical features is **one-hot-encoding**, <code>sklearn.preprocessing.OneHotEncoder</code> is a preprocessor which transforms a categorical feature to this encoding:\n"
    
       ]
      },
      {
       "cell_type": "code",
    
    schmittu's avatar
    schmittu committed
       "execution_count": 22,
    
       "metadata": {},
       "outputs": [
        {
         "data": {
          "text/plain": [
    
           "array([[0., 1.],\n",
           "       [0., 1.],\n",
           "       [1., 0.],\n",
           "       [1., 0.],\n",
           "       [1., 0.]])"
    
    schmittu's avatar
    schmittu committed
         "execution_count": 22,
    
         "metadata": {},
         "output_type": "execute_result"
        }
       ],
       "source": [
    
        "from sklearn.preprocessing import OneHotEncoder\n",
    
        "features = df.iloc[:, :-1]\n",
        "values = df.iloc[:, -1]\n",
        "\n",
    
        "# needs 2d data structure, features.iloc[2] has dimension 1\n",
        "encoder = OneHotEncoder(sparse=False)\n",
        "one_hot = encoder.fit_transform(features.iloc[:, 2: 3]) \n",
    
        "one_hot[:5, :]"
       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
        "So the one-hot encoder computes two columns with exclusive flags 0 and 1."
    
       ]
      },
      {
       "cell_type": "code",
    
    schmittu's avatar
    schmittu committed
       "execution_count": 23,
    
       "metadata": {},
    
    schmittu's avatar
    schmittu committed
       "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>circumference</th>\n",
           "      <th>length</th>\n",
    
           "      <th>kind</th>\n",
    
           "      <th>is_atlantic</th>\n",
           "      <th>is_sockeye</th>\n",
    
    schmittu's avatar
    schmittu committed
           "    </tr>\n",
           "  </thead>\n",
           "  <tbody>\n",
           "    <tr>\n",
    
           "      <th>0</th>\n",
    
    schmittu's avatar
    schmittu committed
           "      <td>19.0</td>\n",
    
           "      <td>59.5</td>\n",
           "      <td>sockeye</td>\n",
    
           "      <td>0.0</td>\n",
           "      <td>1.0</td>\n",
    
    schmittu's avatar
    schmittu committed
           "    </tr>\n",
           "    <tr>\n",
    
           "      <th>1</th>\n",
           "      <td>18.0</td>\n",
           "      <td>53.0</td>\n",
           "      <td>sockeye</td>\n",
    
           "      <td>0.0</td>\n",
           "      <td>1.0</td>\n",
    
    schmittu's avatar
    schmittu committed
           "    </tr>\n",
           "    <tr>\n",
    
           "      <th>2</th>\n",
           "      <td>28.0</td>\n",
           "      <td>75.5</td>\n",
           "      <td>atlantic</td>\n",
    
           "      <td>0.0</td>\n",
    
    schmittu's avatar
    schmittu committed
           "    </tr>\n",
           "    <tr>\n",
    
           "      <th>3</th>\n",
           "      <td>33.5</td>\n",
           "      <td>89.0</td>\n",
           "      <td>atlantic</td>\n",
    
           "      <td>0.0</td>\n",
    
    schmittu's avatar
    schmittu committed
           "    </tr>\n",
           "    <tr>\n",
    
           "      <th>4</th>\n",
           "      <td>23.5</td>\n",
           "      <td>63.0</td>\n",
           "      <td>atlantic</td>\n",
    
           "      <td>0.0</td>\n",
    
    schmittu's avatar
    schmittu committed
           "    </tr>\n",
           "  </tbody>\n",
           "</table>\n",
           "</div>"
          ],
          "text/plain": [
    
           "   circumference  length      kind  is_atlantic  is_sockeye\n",
           "0           19.0    59.5   sockeye          0.0         1.0\n",
           "1           18.0    53.0   sockeye          0.0         1.0\n",
           "2           28.0    75.5  atlantic          1.0         0.0\n",
           "3           33.5    89.0  atlantic          1.0         0.0\n",
           "4           23.5    63.0  atlantic          1.0         0.0"
    
    schmittu's avatar
    schmittu committed
          ]
         },
    
    schmittu's avatar
    schmittu committed
         "execution_count": 23,
    
    schmittu's avatar
    schmittu committed
         "metadata": {},
         "output_type": "execute_result"
        }
       ],
    
        "features[\"is_atlantic\"] = one_hot[:, 0]\n",
        "features[\"is_sockeye\"] = one_hot[:, 1]\n",
        "\n",
        "features.head()"
       ]
      },
      {
       "cell_type": "code",
    
    schmittu's avatar
    schmittu committed
       "execution_count": 24,
    
       "metadata": {},
       "outputs": [],
       "source": [
        "# we remove the categorical column now:\n",
        "del features[\"kind\"]"
    
       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
        "Now we prepare the data for training and testing:"
       ]
      },
      {
       "cell_type": "code",
    
    schmittu's avatar
    schmittu committed
       "execution_count": 25,
    
       "metadata": {},
       "outputs": [],
       "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "(features_train, features_test, \n",
        " values_train, \n",
        " values_test) = train_test_split(features, values, random_state=42)"
       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
        "Without further explanation we pick a regression algorithm, more about regrssion algorithms will be discussed later:"
       ]
      },
      {
       "cell_type": "code",
    
    schmittu's avatar
    schmittu committed
       "execution_count": 26,
    
       "metadata": {},
       "outputs": [],
       "source": [
        "from sklearn.kernel_ridge import KernelRidge\n",
    
        "kr = KernelRidge(alpha=.001, kernel=\"rbf\", gamma=.05)"
    
       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
    
    schmittu's avatar
    schmittu committed
        "<div class=\"alert alert-block alert-warning\">\n",
    
    schmittu's avatar
    schmittu committed
        "    <i class=\"fa fa-info-circle\"></i>&nbsp; Regression methods in <code>scikit-learn</code> also have <code>fit</code> and <code>predict</code> methods. Thus cross validation, pipelines and hyperparameter-optimization will be available.\n",
        "    \n",
        "</div>"
    
       ]
      },
      {
       "cell_type": "code",
    
    schmittu's avatar
    schmittu committed
       "execution_count": 27,
    
       "metadata": {},
       "outputs": [],
       "source": [
        "kr.fit(features_train, values_train)\n",
        "predicted = kr.predict(features_test)"
       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
        "Let us plot how good given and predicted values match on the training data set (sic !)."
       ]
      },
      {
       "cell_type": "code",
    
    schmittu's avatar
    schmittu committed
       "execution_count": 28,
    
       "metadata": {},
       "outputs": [
        {
         "data": {
    
    schmittu's avatar
    schmittu committed
          "image/png": "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\n",
    
          "text/plain": [
    
           "<Figure size 864x288 with 2 Axes>"
    
          ]
         },
         "metadata": {
          "image/png": {
    
          }
         },
         "output_type": "display_data"
        }
       ],
       "source": [
    
        "import numpy as np\n",
    
        "def plot_fit_quality(values_test, predicted):\n",
        "    \n",
        "    plt.figure(figsize=(12, 4))\n",
        "    plt.subplot(1, 2, 1)\n",
    
        "    x = np.arange(len(predicted))\n",
    
        "    plt.scatter(x, predicted - values_test, color='steelblue', marker='o') \n",
    
        "    plt.plot([0, len(predicted)], [0, 0], \"k:\")\n",
    
        "    max_diff = np.max(np.abs(predicted - values_test))\n",
        "    plt.ylim([-max_diff, max_diff])\n",
    
        "    plt.ylabel(\"error\")\n",
        "    plt.xlabel(\"sample id\")\n",
        "\n",
        "    plt.subplot(1, 2, 2)\n",
        "\n",
    
        "    plt.scatter(x, (predicted - values_test) / values_test, color='steelblue', marker='o') \n",
    
        "    plt.plot([0, len(predicted)], [0, 0], \"k:\")\n",
        "    plt.ylim([-.5, .5])\n",
        "      \n",
        "    plt.ylabel(\"relative error\")\n",
        "    plt.xlabel(\"sample id\")\n",
        "\n",
    
        "    \n",
        "plot_fit_quality(values_test, predicted)"
       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
        "For assessing the quality of the predictions of a regression method, we can use multiple methods which we will discuss later in this script.\n",
        "\n",
    
    schmittu's avatar
    schmittu committed
        "For our current example we compute the average absolute difference between given values $y_i$ and predicted values  $\\hat{y}_i$:\n",
    
    schmittu's avatar
    schmittu committed
        "\\frac{1}{n} \\left(\\, |y_1 - \\hat{y}_1| \\, + \\, |y_2 - \\hat{y}_2| \\, + \\, \\ldots \\,+ \\,|y_n - \\hat{y}_n| \\,\\right)\n",
    
    schmittu's avatar
    schmittu committed
        "$$\n"
    
       ]
      },
      {
       "cell_type": "code",
    
    schmittu's avatar
    schmittu committed
       "execution_count": 29,
    
       "metadata": {},
    
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": [
    
    schmittu's avatar
    schmittu committed
          "0.7122581321318665\n"
    
       "source": [
        "import numpy as np\n",
        "\n",
    
    schmittu's avatar
    schmittu committed
        "error = np.sum(np.abs(predicted - values_test)) / len(values_test)\n",
        "print(error)"
    
       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
        "## Metrics / error measures"
       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
        "When we used classification metrics (like accuracy, precision, recall, F1) high values indicated good classification performance. \n",
        "\n",
        "Most regression metrics turn this upside down. E.g. smaller values indicate a better regression model.\n",
        "\n",
        "The hyperparameter optimization functions from `scikit-learn` select configurations which yield a large score. To make regression functions work in this framework, we have to flip the sign of the error value to achieva a usable score.\n",
        "\n",
        "E.g.\n",
        "\n",
        "- an average absolute error of 0.1 is scored as -0.1\n",
        "- an average absolute error of 0.2 is scored as -0.2\n",
        "\n",
        "In this situation the first case would be prefered: higher score indicates lower error.\n",
        "   \n",
        "\n",
        "`scikit-learn` offers the following metrics for measuring regression quality:\n",
        "\n",
        "### 1. Mean absolute error\n",
        "\n",
    
    schmittu's avatar
    schmittu committed
        "This is the metric we used before. Taking absolute values before adding up the deviatons assures that deviations with different signs can not cancel out.\n",
        "\n",
    
        "<div class=\"alert alert-block alert-warning\">\n",
    
    schmittu's avatar
    schmittu committed
        "    <i class=\"fa fa-info-circle\"></i>&nbsp; <strong>mean absolute error</strong> is defined as \n",
    
    schmittu's avatar
    schmittu committed
        "\n",
    
    schmittu's avatar
    schmittu committed
        "\\frac{1}{n} \\left(\\, |y_1 - \\hat{y}_1| \\, + \\, |y_2 - \\hat{y}_2| \\, + \\, \\ldots \\,+ \\,|y_n - \\hat{y}_n| \\,\\right)\n",
    
    schmittu's avatar
    schmittu committed
        "\n",
        "</div>\n",
        "\n",
        "\n",
    
        "The name of the corresponding score in `scikit-learn` is `neg_mean_absolute_error`.\n",
        "\n",
        "\n",
        "### 2. Mean squared error\n",
        "\n",
    
    schmittu's avatar
    schmittu committed
        "Here we replace the absolute difference by its squared difference. Squaring also insures positive differeces.\n",
    
    schmittu's avatar
    schmittu committed
        "\n",
    
        "<div class=\"alert alert-block alert-warning\">\n",
    
    schmittu's avatar
    schmittu committed
        "    <i class=\"fa fa-info-circle\"></i>&nbsp; <strong>mean squared error</strong> is defined as \n",
        "\n",
        "\n",
    
    schmittu's avatar
    schmittu committed
        "\n",
    
    schmittu's avatar
    schmittu committed
        "\\frac{1}{n} \\left(\\, (y_1 - \\hat{y}_1)^2 \\, + \\, (y_2 - \\hat{y}_2)^2 \\, \\, \\ldots \\,+ \\,(y_n - \\hat{y}_n)^2 \\,\\right)\n",
    
    schmittu's avatar
    schmittu committed
        "\n",
        "</div>\n",
        "\n",
        "\n",
        "\n",
    
        "This measure is more sensitive to outliers: A few larger differences contribute more significantly to a larger mean squared error. The name of the corresponding score in `scikit-learn` is `neg_mean_squared_error`.\n",
        "\n",
        "\n",
        "### 3. Median absolute error\n",
        "\n",
    
    schmittu's avatar
    schmittu committed
        "Here we replace mean calculation by median. \n",
    
        "<div class=\"alert alert-block alert-warning\">\n",
    
    schmittu's avatar
    schmittu committed
        "    <i class=\"fa fa-info-circle\"></i>&nbsp; <strong>median absolute error</strong> is defined as \n",
        "\n",
        "\n",
        "\n",
    
    schmittu's avatar
    schmittu committed
        "\\text{median}\\left(\\,|y_1 - \\hat{y}_1|, \\,|y_2 - \\hat{y}_2|, \\,\\ldots, \\,|y_n - \\hat{y}_n| \\, \\right)\n",
    
    schmittu's avatar
    schmittu committed
        "\n",
        "</div>\n",
        "\n",
        "\n",
    
        "This measure is less sensitive to outliers than the metrics we discussed before: A few larger differences will not contribute significantly to a larger error value. The name of the corresponding score in `scikit-learn` is `neg_median_absolute_error`.\n",
        "\n",
        "### 4. Mean squared log error\n",
        "\n",
        "The formula for this metric can be found [here](https://scikit-learn.org/stable/modules/model_evaluation.html#mean-squared-log-error). \n",
        "\n",
        "This metric is recommended when your target values are distributed over a huge range of values, like popoluation numbers. \n",
        "The previous error metrics would put a larger weight on large target values. One could consider relative deviations to compensate such effects but relative deviations come with other problems like division by zero.\n",
        "\n",
        "\n",
        "The name is `neg_mean_squared_log_error`\n",
        "\n",
        "\n",
        "### 5. Explained variance and $r^2$-score\n",
        "\n",
        "Two other scores to mention are *explained variance* and $r^2$-score. For both larger values indicate better regression results.\n",
        "\n",
        "The formula for [r2 can be found here](https://scikit-learn.org/stable/modules/model_evaluation.html#r2-score), the score takes values in the range $0 .. 1$. The name within `scikit-learn` is `r2`.\n",
        "\n",
        "The formula for [explained variance](https://scikit-learn.org/stable/modules/model_evaluation.html#explained-variance-score), the score takes values up to $1$. The name within `scikit-learn` is `explained_variance`.\n"
       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
        "## Some algorithms from sklearn\n",
        "\n",
        "- `sklearn.linear_model.LinearRegression` is a linear regression method, which only works well for target values which can be described as a linear combination of feature values.\n",
        "\n",
        "\n",
        "- `sklearn.kernel_ridge.KernelRidge` is [documented here](https://scikit-learn.org/stable/modules/kernel_ridge.html#kernel-ridge). It combines the kernel trick from SVMs with classical least squares regression.\n",
        "\n",
        "\n",
        "- `sklearn.svm.SVR` is an extension of support vector classification concept to regression, [you find examples here](https://scikit-learn.org/stable/modules/svm.html#svm-regression)\n",
        "\n",
        "\n",
        "- `sklearn.neighbors.KNeighborsRegressor` extends the idea of nearest neighbour classification to regression: Search for similar data points in the learning data set and compute the predicted value from the values from the neighbourhood, e.g. by averaging or by linear interpolation. [Documentation is available here](https://scikit-learn.org/stable/modules/neighbors.html#regression)\n",
        "\n",
        "\n",
        "- `sklearn.tree.DecisionTreeRegressor` expands the concept of decision trees to regression [is documented here](https://scikit-learn.org/stable/modules/tree.html#regression).\n",
        "\n",
        "\n"
       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
        "## A full pipeline\n",
        "\n",
        "Let us now try to find a good regressor using `scikit-learn`s hyper-parameter tuning:"
       ]
      },
      {
       "cell_type": "code",
    
    schmittu's avatar
    schmittu committed
       "execution_count": 30,
    
       "metadata": {},
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": [
    
    schmittu's avatar
    schmittu committed
          "cross val score: -0.7568859642342642\n"
    
    schmittu's avatar
    schmittu committed
          "image/png": "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\n",
    
          "text/plain": [
    
           "<Figure size 864x288 with 2 Axes>"
    
          ]
         },
         "metadata": {
          "image/png": {
    
         },
         "output_type": "display_data"
        }
       ],
       "source": [
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n",
        "from sklearn.kernel_ridge import KernelRidge\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.decomposition import PCA\n",
        "\n",
        "\n",
    
        "def eval_regression(p, features, values):\n",
    
    schmittu's avatar
    schmittu committed
        "    score = cross_val_score(p, features, values, scoring=\"neg_median_absolute_error\", cv=4).mean()\n",
    
        "    print(\"cross val score:\", score)\n",
        "  \n",
        "    predicted = p.fit(features_train, values_train).predict(features_test)\n",
        "    plot_fit_quality(values_test, predicted)\n",
        "\n",
        "    \n",
        "p = make_pipeline(PolynomialFeatures(2), PCA(2), LinearRegression())\n",
    
        "eval_regression(p, features, values)"
    
       ]
      },
      {
       "cell_type": "code",
    
    schmittu's avatar
    schmittu committed
       "execution_count": 31,
    
       "metadata": {},
       "outputs": [],
       "source": [
        "p = make_pipeline(PolynomialFeatures(), PCA(), LinearRegression())\n",
        "\n",
        "param_grid = {'polynomialfeatures__degree': range(3, 6),\n",
    
        "              'pca__n_components': range(3, 11),\n",
    
        "             }"
       ]
      },
      {
       "cell_type": "code",
    
    schmittu's avatar
    schmittu committed
       "execution_count": 32,
    
       "metadata": {},
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": [
    
          "{'pca__n_components': 10, 'polynomialfeatures__degree': 3}\n",
    
    schmittu's avatar
    schmittu committed
          "cross val score: -0.22752607270361858\n"
    
    schmittu's avatar
    schmittu committed
          "image/png": "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\n",
    
          "text/plain": [
    
           "<Figure size 864x288 with 2 Axes>"
    
          ]
         },
         "metadata": {
          "image/png": {
    
         },
         "output_type": "display_data"
        }
       ],
       "source": [
    
    schmittu's avatar
    schmittu committed
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "search = GridSearchCV(p, param_grid, scoring=\"neg_median_absolute_error\", cv=4, n_jobs=4)\n",
        "\n",
        "search.fit(features, values)\n",
        "\n",
        "print(search.best_params_)\n",
    
        "eval_regression(search, features, values)"
    
       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
    
        "## Exercise section\n",
    
    schmittu's avatar
    schmittu committed
        "- Play with the examples above and try different algorithms, metrics and pipelines.\n",
    
        "### Optional exercise: Timeseries prediction\n",
    
        "The file  `data/sales.csv` holds sales data of a swiss sports shop selling skiing equipment. The time axis is in units of months, starting with January.\n",
    
        "* Load the data and plot sales value over months"
    
       ]
      },
      {
       "cell_type": "code",
    
    schmittu's avatar
    schmittu committed
       "execution_count": 33,
    
    schmittu's avatar
    schmittu committed
       "metadata": {
    
    schmittu's avatar
    schmittu committed
        "tags": [
         "solution"
        ]
    
    schmittu's avatar
    schmittu committed
       },
       "outputs": [
        {
    
         "data": {
          "text/html": [
           "<div>\n",
           "<style scoped>\n",
           "    .dataframe tbody tr th:only-of-type {\n",
           "        vertical-align: middle;\n",
           "    }\n",
           "\n",