notebooks/week6_lecture.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "industrial-facing",
   "metadata": {},
   "source": [
    "# Week 6  - An introduction to machine learning (Part II)\n",
    "### Motivation\n",
    "\n",
    "Continuation of last week's lecture on ML regression techniques to classification: first we'll look at the Titanic dataset, then the classic [MNIST](http://yann.lecun.com/exdb/mnist/) digit recognition problem, introducing cross validation and hyperparameter search along the way.\n",
    "\n",
    "In the recap lecture, I'll briefly show how these techniques can be applied to natural language, by analysing the 100K MovieLens [dataset](https://grouplens.org/datasets/movielens/).\n",
    "\n",
    "\n",
    "### Aims\n",
    "\n",
    "- Examine how use ML learning to predict the survival chances of Titanic passengers.\n",
    "- Show how machine learning techniques are not restricted to working with tables of numbers, by automatically classifying digits in the classic ML image recognition problem on the \"MNIST\" dataset.\n",
    "- Show how we can be more systematic about how we pick the 'best' model, but introducting metrics, cross-validation, hyperparameter search.\n",
    "- Introduce the homework with a hands-on with data from a [nature machine intelligence](https://www.nature.com/natmachintell/) paper, [\"An interpretable mortality prediction model for COVID-19 patients\"](https://www.nature.com/articles/s42256-020-0180-7)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "oriental-disclaimer",
   "metadata": {},
   "source": [
    "## 0. Setup\n",
    "\n",
    "Refer to setup instructions from Lecture 5."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "outdoor-generator",
   "metadata": {},
   "source": [
    "## 1. Predicting passenger survival on the Titanic\n",
    "\n",
    "We base the example on an example analysis from [GitHub](https://github.com/geodra/Titanic-Dataset)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "peripheral-outreach",
   "metadata": {},
   "source": [
    "### Titanic datasets\n",
    "\n",
    "https://web.stanford.edu/class/archive/cs/cs109/cs109.1166/stuff/titanic.csv\n",
    "\n",
    "https://www.openml.org/d/40945\n",
    "\n",
    "https://www.openml.org/data/get_csv/16826755/phpMYEkMl\n",
    "\n",
    "http://bit.ly/1juuXIr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "laughing-barbados",
   "metadata": {},
   "source": [
    "Download and take a look at the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "extended-township",
   "metadata": {},
   "outputs": [],
   "source": [
    "import urllib, os \n",
    "\n",
    "titanic_path = '../data/titanic.csv'\n",
    "if not os.path.exists(titanic_path):\n",
    "    urllib.request.urlretrieve('https://www.openml.org/data/get_csv/16826755/phpMYEkMl', titanic_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "celtic-glossary",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "titanic = pd.read_csv(\"../data/titanic.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "variable-remove",
   "metadata": {},
   "source": [
    "Let's first explore the dataset to see what we're working with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "manufactured-consensus",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pclass</th>\n",
       "      <th>survived</th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>ticket</th>\n",
       "      <th>fare</th>\n",
       "      <th>cabin</th>\n",
       "      <th>embarked</th>\n",
       "      <th>boat</th>\n",
       "      <th>body</th>\n",
       "      <th>home.dest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Taussig, Mrs. Emil (Tillie Mandelbaum)</td>\n",
       "      <td>female</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>110413</td>\n",
       "      <td>79.65</td>\n",
       "      <td>E67</td>\n",
       "      <td>S</td>\n",
       "      <td>8</td>\n",
       "      <td>?</td>\n",
       "      <td>New York, NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Butt, Major. Archibald Willingham</td>\n",
       "      <td>male</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113050</td>\n",
       "      <td>26.55</td>\n",
       "      <td>B38</td>\n",
       "      <td>S</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>Washington, DC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>901</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Johnston, Miss. Catherine Helen 'Carrie'</td>\n",
       "      <td>female</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.45</td>\n",
       "      <td>?</td>\n",
       "      <td>S</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1053</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Nasr, Mr. Mustafa</td>\n",
       "      <td>male</td>\n",
       "      <td>?</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2652</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>?</td>\n",
       "      <td>C</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>714</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Chip, Mr. Chang</td>\n",
       "      <td>male</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1601</td>\n",
       "      <td>56.4958</td>\n",
       "      <td>?</td>\n",
       "      <td>S</td>\n",
       "      <td>C</td>\n",
       "      <td>?</td>\n",
       "      <td>Hong Kong New York, NY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      pclass  survived                                      name     sex age  \\\n",
       "291        1         1    Taussig, Mrs. Emil (Tillie Mandelbaum)  female  39   \n",
       "45         1         0         Butt, Major. Archibald Willingham    male  45   \n",
       "901        3         0  Johnston, Miss. Catherine Helen 'Carrie'  female   ?   \n",
       "1053       3         0                         Nasr, Mr. Mustafa    male   ?   \n",
       "714        3         1                           Chip, Mr. Chang    male  32   \n",
       "\n",
       "      sibsp  parch      ticket     fare cabin embarked boat body  \\\n",
       "291       1      1      110413    79.65   E67        S    8    ?   \n",
       "45        0      0      113050    26.55   B38        S    ?    ?   \n",
       "901       1      2  W./C. 6607    23.45     ?        S    ?    ?   \n",
       "1053      0      0        2652   7.2292     ?        C    ?    ?   \n",
       "714       0      0        1601  56.4958     ?        S    C    ?   \n",
       "\n",
       "                   home.dest  \n",
       "291             New York, NY  \n",
       "45            Washington, DC  \n",
       "901                        ?  \n",
       "1053                       ?  \n",
       "714   Hong Kong New York, NY  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "soviet-indianapolis",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1309, 14)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "surface-banana",
   "metadata": {},
   "source": [
    "So we can count the missing data, let's replace the `'?'` characters with `None`. We should also extract variables that 'leak' information about the `survived` response (`body`, `boat`); and information we're going to ignore (`home.dest`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "accompanied-proposition",
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic = titanic.replace({'?': None}).drop(['body', 'boat', 'home.dest'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "geographic-bridge",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1309 entries, 0 to 1308\n",
      "Data columns (total 11 columns):\n",
      " #   Column    Non-Null Count  Dtype \n",
      "---  ------    --------------  ----- \n",
      " 0   pclass    1309 non-null   int64 \n",
      " 1   survived  1309 non-null   int64 \n",
      " 2   name      1309 non-null   object\n",
      " 3   sex       1309 non-null   object\n",
      " 4   age       1046 non-null   object\n",
      " 5   sibsp     1309 non-null   int64 \n",
      " 6   parch     1309 non-null   int64 \n",
      " 7   ticket    1309 non-null   object\n",
      " 8   fare      1308 non-null   object\n",
      " 9   cabin     295 non-null    object\n",
      " 10  embarked  1307 non-null   object\n",
      "dtypes: int64(4), object(7)\n",
      "memory usage: 112.6+ KB\n"
     ]
    }
   ],
   "source": [
    "titanic.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "presidential-reynolds",
   "metadata": {},
   "outputs": [
    {
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       "      <th>survived</th>\n",
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       "      <th>count</th>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309</td>\n",
       "      <td>1309</td>\n",
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       "      <td>295</td>\n",
       "      <td>1307</td>\n",
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       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1307</td>\n",
       "      <td>2</td>\n",
       "      <td>98</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>929</td>\n",
       "      <td>281</td>\n",
       "      <td>186</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>8.05</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>843</td>\n",
       "      <td>47</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>60</td>\n",
       "      <td>6</td>\n",
       "      <td>914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.294882</td>\n",
       "      <td>0.381971</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.498854</td>\n",
       "      <td>0.385027</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.837836</td>\n",
       "      <td>0.486055</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.041658</td>\n",
       "      <td>0.865560</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      ],
      "text/plain": [
       "             pclass     survived              name   sex   age        sibsp  \\\n",
       "count   1309.000000  1309.000000              1309  1309  1046  1309.000000   \n",
       "unique          NaN          NaN              1307     2    98          NaN   \n",
       "top             NaN          NaN  Kelly, Mr. James  male    24          NaN   \n",
       "freq            NaN          NaN                 2   843    47          NaN   \n",
       "mean       2.294882     0.381971               NaN   NaN   NaN     0.498854   \n",
       "std        0.837836     0.486055               NaN   NaN   NaN     1.041658   \n",
       "min        1.000000     0.000000               NaN   NaN   NaN     0.000000   \n",
       "25%        2.000000     0.000000               NaN   NaN   NaN     0.000000   \n",
       "50%        3.000000     0.000000               NaN   NaN   NaN     0.000000   \n",
       "75%        3.000000     1.000000               NaN   NaN   NaN     1.000000   \n",
       "max        3.000000     1.000000               NaN   NaN   NaN     8.000000   \n",
       "\n",
       "              parch    ticket  fare        cabin embarked  \n",
       "count   1309.000000      1309  1308          295     1307  \n",
       "unique          NaN       929   281          186        3  \n",
       "top             NaN  CA. 2343  8.05  C23 C25 C27        S  \n",
       "freq            NaN        11    60            6      914  \n",
       "mean       0.385027       NaN   NaN          NaN      NaN  \n",
       "std        0.865560       NaN   NaN          NaN      NaN  \n",
       "min        0.000000       NaN   NaN          NaN      NaN  \n",
       "25%        0.000000       NaN   NaN          NaN      NaN  \n",
       "50%        0.000000       NaN   NaN          NaN      NaN  \n",
       "75%        0.000000       NaN   NaN          NaN      NaN  \n",
       "max        9.000000       NaN   NaN          NaN      NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "vertical-cabinet",
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic['age'] = titanic.age.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "blond-blond",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    809\n",
       "1    500\n",
       "Name: survived, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.survived.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "speaking-equity",
   "metadata": {},
   "source": [
    "Think - what variables might be relevant here, and how might we go about looking at the data to identify relevant features."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "latin-wealth",
   "metadata": {},
   "source": [
    "Always a good idea to eyeball the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ruled-sydney",
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic['person'] = titanic.apply(lambda x: 'child' if x.age < 18 else x.sex, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "experimental-mustang",
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_data = pd.crosstab(index=titanic['person'], columns=titanic['pclass'], values=titanic.index, aggfunc=pd.Series.count).stack().rename(\"counts\").reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "verbal-blank",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (8765817693389)>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import plotnine as p9\n",
    "dodge_text = p9.position_dodge(width=0.9) \n",
    "(\n",
    "    p9.ggplot(plot_data, p9.aes(x='pclass', y='counts', fill='person'))\n",
    "    + p9.geom_col(stat='identity', position='dodge', show_legend=False)\n",
    "    + p9.geom_text(p9.aes(y=-.5, label='person'),\n",
    "             position=dodge_text,\n",
    "             color='gray', size=8, angle=45, va='top')\n",
    "         + p9.lims(y=(-30, 450))  \n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "shaped-position",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic.age.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "domestic-gentleman",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import re\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "immediate-monday",
   "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>pclass</th>\n",
       "      <th>survived</th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>ticket</th>\n",
       "      <th>fare</th>\n",
       "      <th>cabin</th>\n",
       "      <th>embarked</th>\n",
       "      <th>person</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24160</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>B5</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Allison, Master. Hudson Trevor</td>\n",
       "      <td>male</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.55</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>child</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Miss. Helen Loraine</td>\n",
       "      <td>female</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.55</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>child</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
       "      <td>male</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.55</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.55</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass  survived                                             name     sex  \\\n",
       "0       1         1                    Allen, Miss. Elisabeth Walton  female   \n",
       "1       1         1                   Allison, Master. Hudson Trevor    male   \n",
       "2       1         0                     Allison, Miss. Helen Loraine  female   \n",
       "3       1         0             Allison, Mr. Hudson Joshua Creighton    male   \n",
       "4       1         0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female   \n",
       "\n",
       "       age  sibsp  parch  ticket      fare    cabin embarked  person  \n",
       "0  29.0000      0      0   24160  211.3375       B5        S  female  \n",
       "1   0.9167      1      2  113781    151.55  C22 C26        S   child  \n",
       "2   2.0000      1      2  113781    151.55  C22 C26        S   child  \n",
       "3  30.0000      1      2  113781    151.55  C22 C26        S    male  \n",
       "4  25.0000      1      2  113781    151.55  C22 C26        S  female  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "varied-property",
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_title(data):\n",
    "    titles = data.name.apply(lambda x: re.search('([A-Za-z]+)\\.', x).group(0))\n",
    "    l = ['Miss.','Master.','Mr.','Mrs.']\n",
    "    titles = (\n",
    "        titles.replace('Mlle.', 'Miss.')\n",
    "        .replace('Ms.', 'Miss.')\n",
    "        .replace('Mme.', 'Mrs.')\n",
    "        .apply(lambda x: 'Rare' if x not in l else x)\n",
    "    )\n",
    "    return titles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "growing-administrator",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         Miss.\n",
       "1       Master.\n",
       "2         Miss.\n",
       "3           Mr.\n",
       "4          Mrs.\n",
       "         ...   \n",
       "1304      Miss.\n",
       "1305      Miss.\n",
       "1306        Mr.\n",
       "1307        Mr.\n",
       "1308        Mr.\n",
       "Name: name, Length: 1309, dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extract_title(titanic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "graphic-monthly",
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute_age(data):\n",
    "    from random import randrange, uniform\n",
    "    \n",
    "    grouped_means = data.groupby(['sex','pclass','title']).mean().age.reset_index()\n",
    "    grouped_stds = data.groupby(['sex','pclass','title']).std().age.reset_index()\n",
    "    \n",
    "    def fill_age(row):\n",
    "        condition = (\n",
    "            (grouped_means['sex'] == row['sex']) & \n",
    "            (grouped_means['title'] == row['title']) & \n",
    "            (grouped_means['pclass'] == row['pclass'])\n",
    "        ) \n",
    "        mean = grouped_means[condition]['age'].values[0]\n",
    "        std = grouped_stds[condition]['age'].values[0]\n",
    "        return uniform(mean-std, mean+std)\n",
    "\n",
    "    return data.apply(lambda row: fill_age(row) if np.isnan(row['age']) else row['age'], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "another-choice",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       29.000000\n",
       "1        0.916700\n",
       "2        2.000000\n",
       "3       30.000000\n",
       "4       25.000000\n",
       "          ...    \n",
       "1304    14.500000\n",
       "1305    10.586361\n",
       "1306    26.500000\n",
       "1307    27.000000\n",
       "1308    29.000000\n",
       "Length: 1309, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic['title'] = extract_title(titanic)\n",
    "impute_age(titanic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "atomic-brake",
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute_fare(data):\n",
    "    fare = data.fare.astype(float)\n",
    "    return fare.fillna(fare.mean())\n",
    "\n",
    "def impute_embarked(data):\n",
    "    return data.embarked.fillna(data.embarked.mode()[0])\n",
    "\n",
    "def binarize_sex(data):\n",
    "    return data['sex'].apply(lambda x: 1 if x=='male' else 0)\n",
    "\n",
    "def impute_cabin(data):\n",
    "    \"\"\"Fill with model and map each cabin value with the cabin letter.\"\"\"\n",
    "    return data.cabin.fillna(data.cabin.mode()).map(lambda c: str(c)[0])\n",
    "\n",
    "def categorise_pclass(data):\n",
    "    return data.pclass.astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bulgarian-islam",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    C23 C25 C27\n",
       "dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.cabin.mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "medieval-noise",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "n    1014\n",
       "C      94\n",
       "B      65\n",
       "D      46\n",
       "E      41\n",
       "A      22\n",
       "F      21\n",
       "G       5\n",
       "T       1\n",
       "Name: cabin, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "impute_cabin(titanic).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "electoral-rocket",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_family_features(data):\n",
    "    familysize = data['parch'] + data['sibsp'] + 1\n",
    "    singleton = familysize.apply(lambda s: 1 if s == 1 else 0)\n",
    "    smallfamily = familysize.apply(lambda s: 1 if 2 <= s <= 4 else 0)\n",
    "    largefamily = familysize.apply(lambda s: 1 if 5 <= s else 0)\n",
    "    return pd.DataFrame({\n",
    "        'familysize': familysize,\n",
    "        'singleton': singleton,\n",
    "        'smallfamily': smallfamily,\n",
    "        'largefamily': largefamily\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "urban-spanish",
   "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>familysize</th>\n",
       "      <th>singleton</th>\n",
       "      <th>smallfamily</th>\n",
       "      <th>largefamily</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1309 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      familysize  singleton  smallfamily  largefamily\n",
       "0              1          1            0            0\n",
       "1              4          0            1            0\n",
       "2              4          0            1            0\n",
       "3              4          0            1            0\n",
       "4              4          0            1            0\n",
       "...          ...        ...          ...          ...\n",
       "1304           2          0            1            0\n",
       "1305           2          0            1            0\n",
       "1306           1          1            0            0\n",
       "1307           1          1            0            0\n",
       "1308           1          1            0            0\n",
       "\n",
       "[1309 rows x 4 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generate_family_features(titanic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "specialized-graduation",
   "metadata": {},
   "outputs": [],
   "source": [
    "def categorise_ticket(data):\n",
    "    return data.ticket.astype('category').cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "exempt-miracle",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        24160\n",
       "1       113781\n",
       "2       113781\n",
       "3       113781\n",
       "4       113781\n",
       "         ...  \n",
       "1304      2665\n",
       "1305      2665\n",
       "1306      2656\n",
       "1307      2670\n",
       "1308    315082\n",
       "Name: ticket, Length: 1309, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.ticket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "saving-performer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "778    11\n",
       "104     8\n",
       "775     8\n",
       "847     7\n",
       "824     7\n",
       "       ..\n",
       "531     1\n",
       "530     1\n",
       "529     1\n",
       "528     1\n",
       "422     1\n",
       "Length: 929, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorise_ticket(titanic).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "directed-cleanup",
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_ticket_prefix(data):\n",
    "    \"\"\"Extracts each prefix of the ticket, returns 'XXX' if no prefix.\"\"\"\n",
    "    def prefix(ticket):\n",
    "        ticket = ticket.split()\n",
    "        if ticket[0].isdigit():\n",
    "            return 'XXX'\n",
    "        return ticket[0]\n",
    "\n",
    "    return data.ticket.map(prefix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "processed-deployment",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XXX           957\n",
       "PC             92\n",
       "C.A.           46\n",
       "SOTON/O.Q.     16\n",
       "W./C.          14\n",
       "STON/O         14\n",
       "A/5            12\n",
       "CA.            12\n",
       "SC/PARIS       11\n",
       "CA             10\n",
       "A/5.           10\n",
       "F.C.C.          9\n",
       "SOTON/OQ        8\n",
       "C               8\n",
       "S.O./P.P.       7\n",
       "STON/O2.        7\n",
       "S.O.C.          7\n",
       "A/4             6\n",
       "SC/AH           5\n",
       "SC/Paris        5\n",
       "PP              4\n",
       "LINE            4\n",
       "SOTON/O2        3\n",
       "F.C.            3\n",
       "A/4.            3\n",
       "S.C./PARIS      3\n",
       "A.5.            3\n",
       "A./5.           3\n",
       "WE/P            2\n",
       "SC              2\n",
       "P/PP            2\n",
       "W.E.P.          2\n",
       "S.C./A.4.       1\n",
       "S.P.            1\n",
       "Fa              1\n",
       "SC/A4           1\n",
       "SW/PP           1\n",
       "S.O.P.          1\n",
       "SCO/W           1\n",
       "SC/A.3          1\n",
       "SO/C            1\n",
       "S.W./PP         1\n",
       "AQ/4            1\n",
       "AQ/3.           1\n",
       "A.              1\n",
       "STON/OQ.        1\n",
       "W/C             1\n",
       "A4.             1\n",
       "A/S             1\n",
       "C.A./SOTON      1\n",
       "LP              1\n",
       "Name: ticket, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extract_ticket_prefix(titanic).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "pharmaceutical-fossil",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_categorical_features(data):\n",
    "    data['title'] = extract_title(data)\n",
    "    data['pclass'] = categorise_pclass(data)\n",
    "    data['age'] = impute_age(data)\n",
    "    data['fare'] = impute_fare(data)\n",
    "    data['embarked'] = impute_embarked(data)\n",
    "    data['sex'] = binarize_sex(data)\n",
    "    data['cabin'] = impute_cabin(data)\n",
    "    data = data.join(generate_family_features(data))\n",
    "    return data.drop(columns=['name', 'ticket'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "tight-czech",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       211.3375\n",
       "1       151.5500\n",
       "2       151.5500\n",
       "3       151.5500\n",
       "4       151.5500\n",
       "          ...   \n",
       "1304     14.4542\n",
       "1305     14.4542\n",
       "1306      7.2250\n",
       "1307      7.2250\n",
       "1308      7.8750\n",
       "Name: fare, Length: 1309, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "impute_fare(titanic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "automotive-alloy",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = generate_categorical_features(titanic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "orange-intermediate",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1309, 15)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "unauthorized-likelihood",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1\n",
       "1       1\n",
       "2       1\n",
       "3       1\n",
       "4       1\n",
       "       ..\n",
       "1304    3\n",
       "1305    3\n",
       "1306    3\n",
       "1307    3\n",
       "1308    3\n",
       "Name: pclass, Length: 1309, dtype: category\n",
       "Categories (3, int64): [1, 2, 3]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.pclass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "paperback-poultry",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived           1.0000\n",
       "sex                0.0000\n",
       "age               29.0000\n",
       "sibsp              0.0000\n",
       "parch              0.0000\n",
       "fare             211.3375\n",
       "familysize         1.0000\n",
       "singleton          1.0000\n",
       "smallfamily        0.0000\n",
       "largefamily        0.0000\n",
       "pclass_1           1.0000\n",
       "pclass_2           0.0000\n",
       "pclass_3           0.0000\n",
       "cabin_A            0.0000\n",
       "cabin_B            1.0000\n",
       "cabin_C            0.0000\n",
       "cabin_D            0.0000\n",
       "cabin_E            0.0000\n",
       "cabin_F            0.0000\n",
       "cabin_G            0.0000\n",
       "cabin_T            0.0000\n",
       "cabin_n            0.0000\n",
       "embarked_C         0.0000\n",
       "embarked_Q         0.0000\n",
       "embarked_S         1.0000\n",
       "person_child       0.0000\n",
       "person_female      1.0000\n",
       "person_male        0.0000\n",
       "title_Master.      0.0000\n",
       "title_Miss.        1.0000\n",
       "title_Mr.          0.0000\n",
       "title_Mrs.         0.0000\n",
       "title_Rare         0.0000\n",
       "Name: 0, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = pd.get_dummies(features)\n",
    "features.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "continuous-archive",
   "metadata": {},
   "outputs": [],
   "source": [
    "def categorize_age(data, bins=5): \n",
    "     return pd.cut(data.age, bins).astype('category').cat.codes\n",
    "\n",
    "def categorize_fare(data, bins=4):    \n",
    "     return pd.qcut(data.fare, bins).astype('category').cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "signed-scanner",
   "metadata": {},
   "outputs": [],
   "source": [
    "features['age_cat'] = categorize_age(features)\n",
    "features['fare_cat'] = categorize_fare(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "optical-jungle",
   "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>age_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>29.0000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.9167</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30.0000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25.0000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  age_cat\n",
       "0  29.0000        1\n",
       "1   0.9167        0\n",
       "2   2.0000        0\n",
       "3  30.0000        1\n",
       "4  25.0000        1"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features[['age', 'age_cat']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "maritime-attribute",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_features(data):\n",
    "    features = generate_categorical_features(data)\n",
    "    features = pd.get_dummies(features)\n",
    "    features['age_cat'] = categorize_age(features)\n",
    "    features['fare_cat'] = categorize_fare(features)\n",
    "    return features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "aware-gnome",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.linear_model import LogisticRegression, LogisticRegressionCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "middle-engineer",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = generate_features(titanic)\n",
    "train, test = train_test_split(X, test_size=0.3, shuffle=True, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "paperback-denver",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = train.survived\n",
    "X_train = train.drop(columns = 'survived').reset_index().drop(['index'], axis=1)\n",
    "y_test = test.survived\n",
    "X_test = test.drop(columns = 'survived').reset_index().drop(['index'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "robust-morocco",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((916, 34), (393, 34))"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "supreme-federal",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['sex', 'age', 'sibsp', 'parch', 'fare', 'familysize', 'singleton',\n",
       "       'smallfamily', 'largefamily', 'pclass_1', 'pclass_2', 'pclass_3',\n",
       "       'cabin_A', 'cabin_B', 'cabin_C', 'cabin_D', 'cabin_E', 'cabin_F',\n",
       "       'cabin_G', 'cabin_T', 'cabin_n', 'embarked_C', 'embarked_Q',\n",
       "       'embarked_S', 'person_child', 'person_female', 'person_male',\n",
       "       'title_Master.', 'title_Miss.', 'title_Mr.', 'title_Mrs.', 'title_Rare',\n",
       "       'age_cat', 'fare_cat'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "bound-commercial",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = RandomForestClassifier(n_estimators=50, max_features='sqrt')\n",
    "clf = clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "fiscal-mumbai",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='feature'>"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importances = pd.DataFrame()\n",
    "feature_importances['feature'] = X_train.columns\n",
    "feature_importances['importance'] = clf.feature_importances_\n",
    "feature_importances.sort_values(by=['importance'], ascending=True, inplace=True)\n",
    "feature_importances.set_index('feature', inplace=True)\n",
    "\n",
    "feature_importances.plot(kind='barh', figsize=(25, 25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "alpine-undergraduate",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(916, 9) (393, 9)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import SelectFromModel\n",
    "model = SelectFromModel(clf, prefit=True)\n",
    "X_train = model.transform(X_train)\n",
    "X_test = model.transform(X_test)\n",
    "print(X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "impressed-beads",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.linear_model import LogisticRegression, LogisticRegressionCV\n",
    "\n",
    "logreg = LogisticRegression(max_iter=1e5)\n",
    "rf = RandomForestClassifier()\n",
    "gboost = GradientBoostingClassifier()\n",
    "\n",
    "models = [logreg, rf, gboost]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "subtle-accountability",
   "metadata": {},
   "outputs": [],
   "source": [
    "cross_validation = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "periodic-terry",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation of : <class 'sklearn.linear_model._logistic.LogisticRegression'>\n",
      "CV score = 0.808945117605132\n",
      "****\n",
      "Cross-validation of : <class 'sklearn.ensemble._forest.RandomForestClassifier'>\n",
      "CV score = 0.7947137087194107\n",
      "****\n",
      "Cross-validation of : <class 'sklearn.ensemble._gb.GradientBoostingClassifier'>\n",
      "CV score = 0.8089213589926348\n",
      "****\n"
     ]
    }
   ],
   "source": [
    "for model in models:\n",
    "    print('Cross-validation of : {0}'.format(model.__class__))\n",
    "    score = np.mean(cross_val_score(model, X_train, y_train, cv=cross_validation, scoring='accuracy'))\n",
    "    print('CV score = {0}'.format(score))\n",
    "    print('****')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "russian-concert",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=42, shuffle=True),\n",
       "             estimator=RandomForestClassifier(random_state=42),\n",
       "             param_grid={'max_depth': [3, 4, 5, 6, 7, 8],\n",
       "                         'max_features': ['auto', 'sqrt', 'log2'],\n",
       "                         'n_estimators': [100, 150, 200]})"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "rfc=RandomForestClassifier(random_state=42)\n",
    "param_grid = { \n",
    "    'n_estimators': [100, 150, 200],\n",
    "    'max_features': ['auto', 'sqrt', 'log2'],\n",
    "    'max_depth' : [3,4,5,6,7,8]}\n",
    "\n",
    "rfc_cv = GridSearchCV(estimator=rfc, param_grid=param_grid, cv=cross_validation)\n",
    "rfc_cv.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "improved-ridge",
   "metadata": {},
   "source": [
    "Tuned parameters and score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "underlying-commerce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tuned Random Forest Parameters: {'max_depth': 6, 'max_features': 'auto', 'n_estimators': 100}\n",
      "Best score is 0.8274887146590639\n",
      "Score on test data is 0.7938931297709924\n"
     ]
    }
   ],
   "source": [
    "print(\"Tuned Random Forest Parameters: {}\".format(rfc_cv.best_params_))\n",
    "print(\"Best score is {}\".format(rfc_cv.best_score_))\n",
    "print(\"Score on test data is {}\".format(rfc_cv.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "casual-worship",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[209,  15],\n",
       "       [ 66, 103]])"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html\n",
    "y_hat_test = rfc_cv.predict(X_test)\n",
    "confusion_matrix(y_test, y_hat_test, labels=[0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "structured-folder",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    224\n",
       "1    169\n",
       "Name: survived, dtype: int64"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "joint-kazakhstan",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "suited-grocery",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      "[[209  15]\n",
      " [ 66 103]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "disp = metrics.plot_confusion_matrix(rfc_cv, X_test, y_test)\n",
    "disp.figure_.suptitle(\"Confusion Matrix\")\n",
    "print(f\"Confusion matrix:\\n{disp.confusion_matrix}\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "nuclear-accountability",
   "metadata": {},
   "source": [
    "## MNIST handwritten digits\n",
    "\n",
    "Based on sklearn documentation [example](https://scikit-learn.org/stable/auto_examples/classification/plot_digits_classification.html).\n",
    "\n",
    "The images attribute of the dataset stores 8x8 arrays of grayscale values for each image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "aboriginal-banner",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets, svm\n",
    "digits = datasets.load_digits()\n",
    "\n",
    "_, axes = plt.subplots(nrows=1, ncols=4, figsize=(10, 3))\n",
    "for ax, image, label in zip(axes, digits.images, digits.target):\n",
    "    ax.set_axis_off()\n",
    "    ax.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')\n",
    "    ax.set_title('Training: %i' % label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "satellite-robert",
   "metadata": {},
   "outputs": [],
   "source": [
    "# flatten the images\n",
    "n_samples = len(digits.images)\n",
    "data = digits.images.reshape((n_samples, -1))\n",
    "\n",
    "# Create a classifier: a support vector classifier\n",
    "clf = svm.SVC(gamma=0.001)\n",
    "\n",
    "# Split data into 50% train and 50% test subsets\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    data, digits.target, test_size=0.5, shuffle=False)\n",
    "\n",
    "# Learn the digits on the train subset\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "# Predict the value of the digit on the test subset\n",
    "predicted = clf.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "oriental-carrier",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, axes = plt.subplots(nrows=1, ncols=4, figsize=(10, 3))\n",
    "for ax, image, prediction in zip(axes, X_test, predicted):\n",
    "    ax.set_axis_off()\n",
    "    image = image.reshape(8, 8)\n",
    "    ax.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')\n",
    "    ax.set_title(f'Prediction: {prediction}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "saving-think",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report for classifier SVC(gamma=0.001):\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.99      0.99        88\n",
      "           1       0.99      0.97      0.98        91\n",
      "           2       0.99      0.99      0.99        86\n",
      "           3       0.98      0.87      0.92        91\n",
      "           4       0.99      0.96      0.97        92\n",
      "           5       0.95      0.97      0.96        91\n",
      "           6       0.99      0.99      0.99        91\n",
      "           7       0.96      0.99      0.97        89\n",
      "           8       0.94      1.00      0.97        88\n",
      "           9       0.93      0.98      0.95        92\n",
      "\n",
      "    accuracy                           0.97       899\n",
      "   macro avg       0.97      0.97      0.97       899\n",
      "weighted avg       0.97      0.97      0.97       899\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f\"Classification report for classifier {clf}:\\n\"\n",
    "      f\"{metrics.classification_report(y_test, predicted)}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "automotive-tanzania",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      "[[87  0  0  0  1  0  0  0  0  0]\n",
      " [ 0 88  1  0  0  0  0  0  1  1]\n",
      " [ 0  0 85  1  0  0  0  0  0  0]\n",
      " [ 0  0  0 79  0  3  0  4  5  0]\n",
      " [ 0  0  0  0 88  0  0  0  0  4]\n",
      " [ 0  0  0  0  0 88  1  0  0  2]\n",
      " [ 0  1  0  0  0  0 90  0  0  0]\n",
      " [ 0  0  0  0  0  1  0 88  0  0]\n",
      " [ 0  0  0  0  0  0  0  0 88  0]\n",
      " [ 0  0  0  1  0  1  0  0  0 90]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "disp = metrics.plot_confusion_matrix(clf, X_test, y_test)\n",
    "disp.figure_.suptitle(\"Confusion Matrix\")\n",
    "print(f\"Confusion matrix:\\n{disp.confusion_matrix}\")\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}