load_and_augment_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Diabetes data\n",
    "\n",
    "Sklearn diabetes dataset (`sklearn.datasets.load_diabetes`) is a normalised version of this [raw data](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.tab.txt).\n",
    "\n",
    "See [sklearn](https://scikit-learn.org/stable/datasets/index.html#diabetes-dataset) for details of the data.\n",
    "\n",
    "### Target\n",
    "\n",
    "* Column 11 is a quantitative measure of disease progression one year after baseline\n",
    "\n",
    "### Attribute Information\n",
    "\n",
    "* **age** age in years\n",
    "* **sex**\n",
    "* **bmi** body mass index\n",
    "* **bp** average blood pressure\n",
    "* **s1** tc, T-Cells (a type of white blood cells)\n",
    "* **s2** ldl, low-density lipoproteins\n",
    "* **s3** hdl, high-density lipoproteins\n",
    "* **s4** tch, thyroid stimulating hormone\n",
    "* **s5** ltg, lamotrigine\n",
    "* **s6** glu, blood sugar level\n",
    "\n",
    "## Notes on edits to data\n",
    "\n",
    "- should we only allow matplotlib?\n",
    "- i've included the nulls below, but we can remove that in favour of normalisation if we want\n",
    "- BMI and blood pressure would possibly reward log normalisation as they are, but i've made s5 into a feature that definitely needs it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AGE</th>\n",
       "      <th>SEX</th>\n",
       "      <th>BMI</th>\n",
       "      <th>BP</th>\n",
       "      <th>S1</th>\n",
       "      <th>S2</th>\n",
       "      <th>S3</th>\n",
       "      <th>S4</th>\n",
       "      <th>S5</th>\n",
       "      <th>S6</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59</td>\n",
       "      <td>2</td>\n",
       "      <td>32.1</td>\n",
       "      <td>101.0</td>\n",
       "      <td>157</td>\n",
       "      <td>93.2</td>\n",
       "      <td>38.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.8598</td>\n",
       "      <td>87</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>21.6</td>\n",
       "      <td>87.0</td>\n",
       "      <td>183</td>\n",
       "      <td>103.2</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.8918</td>\n",
       "      <td>69</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>72</td>\n",
       "      <td>2</td>\n",
       "      <td>30.5</td>\n",
       "      <td>93.0</td>\n",
       "      <td>156</td>\n",
       "      <td>93.6</td>\n",
       "      <td>41.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.6728</td>\n",
       "      <td>85</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>25.3</td>\n",
       "      <td>84.0</td>\n",
       "      <td>198</td>\n",
       "      <td>131.4</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.8903</td>\n",
       "      <td>89</td>\n",
       "      <td>206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>23.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>192</td>\n",
       "      <td>125.4</td>\n",
       "      <td>52.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.2905</td>\n",
       "      <td>80</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AGE  SEX   BMI     BP   S1     S2    S3   S4      S5  S6    Y\n",
       "0   59    2  32.1  101.0  157   93.2  38.0  4.0  4.8598  87  151\n",
       "1   48    1  21.6   87.0  183  103.2  70.0  3.0  3.8918  69   75\n",
       "2   72    2  30.5   93.0  156   93.6  41.0  4.0  4.6728  85  141\n",
       "3   24    1  25.3   84.0  198  131.4  40.0  5.0  4.8903  89  206\n",
       "4   50    1  23.0  101.0  192  125.4  52.0  4.0  4.2905  80  135"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"https://www4.stat.ncsu.edu/~boos/var.select/diabetes.tab.txt\", sep=\"\\t\")\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AGE</th>\n",
       "      <th>SEX</th>\n",
       "      <th>BMI</th>\n",
       "      <th>BP</th>\n",
       "      <th>S1</th>\n",
       "      <th>S2</th>\n",
       "      <th>S3</th>\n",
       "      <th>S4</th>\n",
       "      <th>S5</th>\n",
       "      <th>S6</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>442.000000</td>\n",
       "      <td>442.000000</td>\n",
       "      <td>442.000000</td>\n",
       "      <td>442.000000</td>\n",
       "      <td>442.000000</td>\n",
       "      <td>442.000000</td>\n",
       "      <td>442.000000</td>\n",
       "      <td>442.000000</td>\n",
       "      <td>442.000000</td>\n",
       "      <td>442.000000</td>\n",
       "      <td>442.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>48.518100</td>\n",
       "      <td>1.468326</td>\n",
       "      <td>26.375792</td>\n",
       "      <td>94.647014</td>\n",
       "      <td>189.140271</td>\n",
       "      <td>115.439140</td>\n",
       "      <td>49.788462</td>\n",
       "      <td>4.070249</td>\n",
       "      <td>4.641411</td>\n",
       "      <td>91.260181</td>\n",
       "      <td>152.133484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.109028</td>\n",
       "      <td>0.499561</td>\n",
       "      <td>4.418122</td>\n",
       "      <td>13.831283</td>\n",
       "      <td>34.608052</td>\n",
       "      <td>30.413081</td>\n",
       "      <td>12.934202</td>\n",
       "      <td>1.290450</td>\n",
       "      <td>0.522391</td>\n",
       "      <td>11.496335</td>\n",
       "      <td>77.093005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>41.600000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.258100</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>38.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>23.200000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>164.250000</td>\n",
       "      <td>96.050000</td>\n",
       "      <td>40.250000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.276700</td>\n",
       "      <td>83.250000</td>\n",
       "      <td>87.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>25.700000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>186.000000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.620050</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>140.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>59.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>29.275000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>209.750000</td>\n",
       "      <td>134.500000</td>\n",
       "      <td>57.750000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.997200</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>211.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>79.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>42.200000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>242.400000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>9.090000</td>\n",
       "      <td>6.107000</td>\n",
       "      <td>124.000000</td>\n",
       "      <td>346.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              AGE         SEX         BMI          BP          S1          S2  \\\n",
       "count  442.000000  442.000000  442.000000  442.000000  442.000000  442.000000   \n",
       "mean    48.518100    1.468326   26.375792   94.647014  189.140271  115.439140   \n",
       "std     13.109028    0.499561    4.418122   13.831283   34.608052   30.413081   \n",
       "min     19.000000    1.000000   18.000000   62.000000   97.000000   41.600000   \n",
       "25%     38.250000    1.000000   23.200000   84.000000  164.250000   96.050000   \n",
       "50%     50.000000    1.000000   25.700000   93.000000  186.000000  113.000000   \n",
       "75%     59.000000    2.000000   29.275000  105.000000  209.750000  134.500000   \n",
       "max     79.000000    2.000000   42.200000  133.000000  301.000000  242.400000   \n",
       "\n",
       "               S3          S4          S5          S6           Y  \n",
       "count  442.000000  442.000000  442.000000  442.000000  442.000000  \n",
       "mean    49.788462    4.070249    4.641411   91.260181  152.133484  \n",
       "std     12.934202    1.290450    0.522391   11.496335   77.093005  \n",
       "min     22.000000    2.000000    3.258100   58.000000   25.000000  \n",
       "25%     40.250000    3.000000    4.276700   83.250000   87.000000  \n",
       "50%     48.000000    4.000000    4.620050   91.000000  140.500000  \n",
       "75%     57.750000    5.000000    4.997200   98.000000  211.500000  \n",
       "max     99.000000    9.090000    6.107000  124.000000  346.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'AGE'}>,\n",
       "        <AxesSubplot:title={'center':'SEX'}>,\n",
       "        <AxesSubplot:title={'center':'BMI'}>],\n",
       "       [<AxesSubplot:title={'center':'BP'}>,\n",
       "        <AxesSubplot:title={'center':'S1'}>,\n",
       "        <AxesSubplot:title={'center':'S2'}>],\n",
       "       [<AxesSubplot:title={'center':'S3'}>,\n",
       "        <AxesSubplot:title={'center':'S4'}>,\n",
       "        <AxesSubplot:title={'center':'S5'}>],\n",
       "       [<AxesSubplot:title={'center':'S6'}>,\n",
       "        <AxesSubplot:title={'center':'Y'}>, <AxesSubplot:>]], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(bins=25, figsize=(20,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>AGE</th>\n",
       "      <th>SEX</th>\n",
       "      <th>BMI</th>\n",
       "      <th>BP</th>\n",
       "      <th>S1</th>\n",
       "      <th>S2</th>\n",
       "      <th>S3</th>\n",
       "      <th>S4</th>\n",
       "      <th>S5</th>\n",
       "      <th>S6</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>101.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>93.2</td>\n",
       "      <td>38.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.8598</td>\n",
       "      <td>87.0</td>\n",
       "      <td>151.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>87.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>103.2</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.8918</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>72.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.5</td>\n",
       "      <td>93.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>93.6</td>\n",
       "      <td>41.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.6728</td>\n",
       "      <td>85.0</td>\n",
       "      <td>141.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.3</td>\n",
       "      <td>84.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>131.4</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.8903</td>\n",
       "      <td>89.0</td>\n",
       "      <td>206.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>125.4</td>\n",
       "      <td>52.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.2905</td>\n",
       "      <td>80.0</td>\n",
       "      <td>135.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    AGE  SEX   BMI     BP     S1     S2    S3   S4      S5    S6      Y\n",
       "0  59.0  2.0   NaN  101.0  157.0   93.2  38.0  NaN  4.8598  87.0  151.0\n",
       "1  48.0  1.0   NaN   87.0  183.0  103.2  70.0  3.0  3.8918   NaN   75.0\n",
       "2  72.0  NaN  30.5   93.0  156.0   93.6  41.0  4.0  4.6728  85.0  141.0\n",
       "3  24.0  1.0  25.3   84.0  198.0  131.4  40.0  5.0  4.8903  89.0  206.0\n",
       "4  50.0  1.0  23.0  101.0  192.0  125.4  52.0  4.0  4.2905  80.0  135.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add some nulls\n",
    "\n",
    "df = df * np.random.choice([1, np.nan], size=df.shape, p=[0.9, 0.1])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# add unlikely ages\n",
    "\n",
    "age_sample_indices = df.sample(10).index\n",
    "\n",
    "df.loc[age_sample_indices[:5], \"AGE\"] = np.random.randint(0, 5, 5)\n",
    "\n",
    "df.loc[age_sample_indices[5:], \"AGE\"] = np.random.randint(90, 110, 5)\n",
    "\n",
    "df[\"AGE\"].hist(bins=25, figsize=(20,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# force BMI to string dtype\n",
    "\n",
    "df[\"BMI\"].fillna(\" \", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a lognormal feature\n",
    "\n",
    "df[\"S5\"] = (df[\"S5\"] * np.random.lognormal(3, 1, df.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"diabetes.csv\", index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}