notebooks/get_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.datasets import fetch_openml"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### UCI Iris Dataset - Binary Classification\n",
    "\n",
    "The objective is to predict the flower species, encoded as {0,1,2}.\n",
    "It is a small dataset containing only 4 features that is well-known in the ML community."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\"\n",
    "columns = [\n",
    "    \"Sepal Length\", \n",
    "    \"Sepal Width\", \n",
    "    \"Petal Length\", \n",
    "    \"Petal Width\", \n",
    "    \"Species\" # Target\n",
    "]\n",
    "df = pd.read_csv(url, header=None, names=columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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         "name": "Sepal Length",
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         "type": "float"
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        {
         "name": "Sepal Width",
         "rawType": "float64",
         "type": "float"
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         "name": "Petal Length",
         "rawType": "float64",
         "type": "float"
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         "name": "Petal Width",
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         "0",
         "5.1",
         "3.5",
         "1.4",
         "0.2",
         "Iris-setosa"
        ],
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         "1",
         "4.9",
         "3.0",
         "1.4",
         "0.2",
         "Iris-setosa"
        ],
        [
         "2",
         "4.7",
         "3.2",
         "1.3",
         "0.2",
         "Iris-setosa"
        ],
        [
         "3",
         "4.6",
         "3.1",
         "1.5",
         "0.2",
         "Iris-setosa"
        ],
        [
         "4",
         "5.0",
         "3.6",
         "1.4",
         "0.2",
         "Iris-setosa"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sepal Length</th>\n",
       "      <th>Sepal Width</th>\n",
       "      <th>Petal Length</th>\n",
       "      <th>Petal Width</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "   Sepal Length  Sepal Width  Petal Length  Petal Width      Species\n",
       "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
       "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
       "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
       "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
       "4           5.0          3.6           1.4          0.2  Iris-setosa"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"../data/iris.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### UCI Wine Dataset - Multiclass Classification\n",
    "\n",
    "The objective is to predict the wine type from different regions in Italy, encoded as {0,1,2}.  \n",
    "It is a small, well-structured dataset with 13 features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\"\n",
    "columns = [\n",
    "    \"Class\", # Target\n",
    "    \"Alcohol\", \n",
    "    \"Malic Acid\", \n",
    "    \"Ash\", \n",
    "    \"Alcalinity of Ash\",\n",
    "    \"Magnesium\", \n",
    "    \"Total Phenols\",\n",
    "    \"Flavanoids\",\n",
    "    \"Nonflavanoid Phenols\",\n",
    "    \"Proanthocyanins\",    \n",
    "    \"Color Intensity\", \n",
    "    \"Hue\", \n",
    "    \"OD280/OD315\", \n",
    "    \"Proline\"\n",
    "]\n",
    "\n",
    "df = pd.read_csv(url, header=None, names=columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>Class</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Malic Acid</th>\n",
       "      <th>Ash</th>\n",
       "      <th>Alcalinity of Ash</th>\n",
       "      <th>Magnesium</th>\n",
       "      <th>Total Phenols</th>\n",
       "      <th>Flavanoids</th>\n",
       "      <th>Nonflavanoid Phenols</th>\n",
       "      <th>Proanthocyanins</th>\n",
       "      <th>Color Intensity</th>\n",
       "      <th>Hue</th>\n",
       "      <th>OD280/OD315</th>\n",
       "      <th>Proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735</td>\n",
       "    </tr>\n",
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       "   Class  Alcohol  Malic Acid   Ash  Alcalinity of Ash  Magnesium  \\\n",
       "0      1    14.23        1.71  2.43               15.6        127   \n",
       "1      1    13.20        1.78  2.14               11.2        100   \n",
       "2      1    13.16        2.36  2.67               18.6        101   \n",
       "3      1    14.37        1.95  2.50               16.8        113   \n",
       "4      1    13.24        2.59  2.87               21.0        118   \n",
       "\n",
       "   Total Phenols  Flavanoids  Nonflavanoid Phenols  Proanthocyanins  \\\n",
       "0           2.80        3.06                  0.28             2.29   \n",
       "1           2.65        2.76                  0.26             1.28   \n",
       "2           2.80        3.24                  0.30             2.81   \n",
       "3           3.85        3.49                  0.24             2.18   \n",
       "4           2.80        2.69                  0.39             1.82   \n",
       "\n",
       "   Color Intensity   Hue  OD280/OD315  Proline  \n",
       "0             5.64  1.04         3.92     1065  \n",
       "1             4.38  1.05         3.40     1050  \n",
       "2             5.68  1.03         3.17     1185  \n",
       "3             7.80  0.86         3.45     1480  \n",
       "4             4.32  1.04         2.93      735  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"../data/wine.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sklearn Boston Housing Dataset \n",
    "\n",
    "The objective is to predict the house price, using the median value of owner-occupied homes in $1000s, ```MEDV``` as the target.  \n",
    "It is one of the most widely used regression datasets in ML research with 13 features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = fetch_openml(name=\"boston\", version=1, as_frame=True).frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
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       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
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       "      <td>7.07</td>\n",
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       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
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       "      <td>45.8</td>\n",
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       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
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       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
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       "      CRIM    ZN  INDUS CHAS    NOX     RM   AGE     DIS RAD    TAX  PTRATIO  \\\n",
       "0  0.00632  18.0   2.31    0  0.538  6.575  65.2  4.0900   1  296.0     15.3   \n",
       "1  0.02731   0.0   7.07    0  0.469  6.421  78.9  4.9671   2  242.0     17.8   \n",
       "2  0.02729   0.0   7.07    0  0.469  7.185  61.1  4.9671   2  242.0     17.8   \n",
       "3  0.03237   0.0   2.18    0  0.458  6.998  45.8  6.0622   3  222.0     18.7   \n",
       "4  0.06905   0.0   2.18    0  0.458  7.147  54.2  6.0622   3  222.0     18.7   \n",
       "\n",
       "        B  LSTAT  MEDV  \n",
       "0  396.90   4.98  24.0  \n",
       "1  396.90   9.14  21.6  \n",
       "2  392.83   4.03  34.7  \n",
       "3  394.63   2.94  33.4  \n",
       "4  396.90   5.33  36.2  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"../data/boston_housing.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### UCI Energy Efficiency Dataset - Multi Regression\n",
    "\n",
    "The objective is to predict energy efficiency of buildings through two targets: (i) ```Heating Load```, the amount of energy required for heating, and (ii) ```Cooling Load```, the amount of energy required for cooling.\n",
    "It is composed of 8 features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/00242/ENB2012_data.xlsx\"\n",
    "df = pd.read_excel(url)\n",
    "df.columns = [\n",
    "    \"Relative Compactness\", \n",
    "    \"Surface Area\", \n",
    "    \"Wall Area\", \n",
    "    \"Roof Area\", \n",
    "    \"Overall Height\", \n",
    "    \"Orientation\", \n",
    "    \"Glazing Area\", \n",
    "    \"Glazing Area Distribution\", \n",
    "    \"Heating Load\", \n",
    "    \"Cooling Load\"\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "   Relative Compactness  Surface Area  Wall Area  Roof Area  Overall Height  \\\n",
       "0                  0.98         514.5      294.0     110.25             7.0   \n",
       "1                  0.98         514.5      294.0     110.25             7.0   \n",
       "2                  0.98         514.5      294.0     110.25             7.0   \n",
       "3                  0.98         514.5      294.0     110.25             7.0   \n",
       "4                  0.90         563.5      318.5     122.50             7.0   \n",
       "\n",
       "   Orientation  Glazing Area  Glazing Area Distribution  Heating Load  \\\n",
       "0            2           0.0                          0         15.55   \n",
       "1            3           0.0                          0         15.55   \n",
       "2            4           0.0                          0         15.55   \n",
       "3            5           0.0                          0         15.55   \n",
       "4            2           0.0                          0         20.84   \n",
       "\n",
       "   Cooling Load  \n",
       "0         21.33  \n",
       "1         21.33  \n",
       "2         21.33  \n",
       "3         21.33  \n",
       "4         28.28  "
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     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"../data/energy_efficiency.csv\", index=False)"
   ]
  }
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