{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* * *\n",
    "<pre> Insea 2025            <i> Statistiques Bayésiennes </i></pre>\n",
    "* * *\n",
    "\n",
    "\n",
    "<h1 align=\"center\"> TP7: Bayesian logistic regression for churn prediction </h1>\n",
    "\n",
    "<pre align=\"right\">                <i> Author: Hicham Janati </i></pre>\n",
    "* * *\n",
    "\n",
    "\n",
    "**Objectives:**\n",
    "\n",
    "- Apply a **Bayesian model** to perform **binary prediction**\n",
    "- Understand how **Bayesian modeling helps avoid underfitting and overfitting**\n",
    "- Compare **Bayesian and frequentist approaches**\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pymc as pm\n",
    "import arviz as az\n",
    "\n",
    "seed = 42\n",
    "rng = np.random.default_rng(seed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Problem Statement\n",
    "\n",
    "In companies that offer services (such as a mobile phone operator), **customer retention** is a major challenge. The **churn rate** refers to the percentage of customers who decide to cancel their subscription (e.g., to switch to another provider). If the company can **predict which customers are likely to churn**, it can take proactive steps — such as offering additional services or special deals — to retain them.\n",
    "\n",
    "We will work with a **real dataset** from a telecom operator (filtered and adapted from: [Kaggle](https://www.kaggle.com/datasets/kapturovalexander/customers-churned-in-telecom-services/data)).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "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>Dependents</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>Contract</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>CustomerID_Region</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>Months</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>MIS-1</td>\n",
       "      <td>78.95</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Two year</td>\n",
       "      <td>DSL</td>\n",
       "      <td>DAL-1</td>\n",
       "      <td>85.95</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Two year</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>SAN-1</td>\n",
       "      <td>104.00</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>No</td>\n",
       "      <td>HOU-1</td>\n",
       "      <td>20.55</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Two year</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>HOU-1</td>\n",
       "      <td>113.10</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Dependents          TechSupport        Contract InternetService  \\\n",
       "0        Yes                   No        One year     Fiber optic   \n",
       "1        Yes                  Yes        Two year             DSL   \n",
       "2         No                  Yes        Two year     Fiber optic   \n",
       "3         No  No internet service  Month-to-month              No   \n",
       "4        Yes                  Yes        Two year     Fiber optic   \n",
       "\n",
       "  CustomerID_Region  MonthlyCharges  Months  Churn  \n",
       "0             MIS-1           78.95    34.0      0  \n",
       "1             DAL-1           85.95    70.0      0  \n",
       "2             SAN-1          104.00    69.0      0  \n",
       "3             HOU-1           20.55     5.0      0  \n",
       "4             HOU-1          113.10    72.0      0  "
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"http://hichamjanati.github.io/data/churn.csv\", index_col=0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Dependents', 'TechSupport', 'Contract', 'InternetService',\n",
       "       'CustomerID_Region', 'MonthlyCharges', 'Months', 'Churn'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the X and y variables\n",
    "y = df.Churn\n",
    "X = df.drop(\"Churn\", axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check if the number of class instances we have:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Churn\n",
       "0    100\n",
       "1    100\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This binary classification task is balanced: (in practice churn rates are significantly lower, churn prediction is very imbalanced in the real world. I made the problem easier here by resampling from the original data for the sake of simplicity). Let's check the type of data variables we have:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dependents            object\n",
       "TechSupport           object\n",
       "Contract              object\n",
       "InternetService       object\n",
       "CustomerID_Region     object\n",
       "MonthlyCharges       float64\n",
       "Months               float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Contract\n",
       "Month-to-month    133\n",
       "One year           37\n",
       "Two year           30\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.Contract.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Data preprocessing\n",
    "### 2.1 One-hot encoding / dummy variables\n",
    "We need to pre-process the data: turn the categorical variables to binary dummy variables. This is called _one-hot encoding_. Here is how it goes:\n",
    "- for a variable like _Contract_ which takes _Month-to-Month_, _One year_ or _Two year_ (three categories) we can transform it to a 3 binary variables: \n",
    "\n",
    "| ... | Contract | ... |\n",
    "|-----|--------------|-----|\n",
    "| ... | Month-to-Month | ... |\n",
    "| ... | One Year | ... |\n",
    "| ... | Two Year | ... |\n",
    "| ... | Month-to-Month | ... |\n",
    "| ... | Two Year | ... |\n",
    "| ... | Two Year | ... |\n",
    "\n",
    "↓\n",
    "\n",
    "| ... | Month-to-Month | One Year | Two Year | ... |\n",
    "|-----|----------------|-----------|-----------|-----| \n",
    "| ... | 1 | 0 | 0 | ... |\n",
    "| ... | 0 | 1 | 0 | ... |\n",
    "| ... | 0 | 0 | 1 | ... |\n",
    "| ... | 1 | 0 | 0 | ... |\n",
    "| ... | 0 | 0 | 1 | ... |\n",
    "| ... | 0 | 0 | 1 | ... |\n",
    "\n",
    "These binary variables are called _dummy variables_ or the one-hot encoding of _Contract_.\n",
    "However you can notice that the sum of these columns will always be 1: the 3 binary variables are linearly dependent which is bad for linear models (particularly if no regularization is used), this is called a _dummy variables trap_. We should drop one of the columns to avoid it. Thus, a categorical variable with K categories is transformed into K-1 binary variables. We can do this with sklearn _transformer_ object called _OneHotEncoder_:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<200x2 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 67 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "encoder = OneHotEncoder(drop='first')\n",
    "encoded_data = encoder.fit_transform(df[[\"Contract\"]])\n",
    "encoded_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output is a sparse matrix (CSR) which is a compressed form of storing matrices with lots of zeros: instead of storing all their entries, we only store in memory necessary information (for e.g the triplets (i, j, v) such that M[i, j] = v and v is not zero). Here the data is small, no need to used sparse matrices: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0.],\n",
       "       [0., 1.],\n",
       "       [0., 1.],\n",
       "       [0., 0.],\n",
       "       [0., 1.],\n",
       "       [0., 0.],\n",
       "       [1., 0.],\n",
       "       [1., 0.],\n",
       "       [0., 0.],\n",
       "       [0., 0.]])"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder = OneHotEncoder(drop='first', sparse_output=False)\n",
    "encoded_data = encoder.fit_transform(X[[\"Contract\"]])\n",
    "encoded_data[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can apply this to all categorical variables: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 1., 0., 1., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.,\n",
       "        0., 0.],\n",
       "       [1., 0., 1., 0., 1., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0.],\n",
       "       [0., 0., 1., 0., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        1., 0.],\n",
       "       [0., 1., 0., 0., 0., 0., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0.],\n",
       "       [1., 0., 1., 0., 1., 1., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0.],\n",
       "       [0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.,\n",
       "        0., 0.],\n",
       "       [0., 0., 0., 1., 0., 1., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.,\n",
       "        0., 0.],\n",
       "       [1., 0., 0., 1., 0., 1., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.,\n",
       "        0., 0.],\n",
       "       [0., 0., 0., 0., 0., 1., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0.],\n",
       "       [0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0.]])"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder = OneHotEncoder(drop='first', sparse_output=False)\n",
    "categorical_features = [\"Dependents\", \"TechSupport\", \"Contract\", \"InternetService\", \"CustomerID_Region\"]\n",
    "encoded_data = encoder.fit_transform(X[categorical_features])\n",
    "encoded_data[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But later we will have a regression coefficient for each one of these columns, how do we know which one belongs to which ?\n",
    "Well we can get their names from the encoder:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Dependents_Yes', 'TechSupport_No internet service',\n",
       "       'TechSupport_Yes', 'Contract_One year', 'Contract_Two year',\n",
       "       'InternetService_Fiber optic', 'InternetService_No',\n",
       "       'CustomerID_Region_CHI-1', 'CustomerID_Region_DAL-1',\n",
       "       'CustomerID_Region_HOU-1', 'CustomerID_Region_LAX-1',\n",
       "       'CustomerID_Region_MIA-1', 'CustomerID_Region_MIS-1',\n",
       "       'CustomerID_Region_NYC-1', 'CustomerID_Region_PHL-1',\n",
       "       'CustomerID_Region_PHX-1', 'CustomerID_Region_SAN-1',\n",
       "       'CustomerID_Region_SEA-1'], dtype=object)"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder.get_feature_names_out(categorical_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Scaling numerical features\n",
    "We also have continuous variables (numerical features): _Months_ and _MonthlyCharges_. As explained in the last class, it's important for them to have the same scale (order of magnitude) in a linear model. Scaling a variable means centering it and dividing by its standard deviation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.326288\n",
       "1    1.850800\n",
       "2    1.808453\n",
       "3   -0.901791\n",
       "4    1.935495\n",
       "Name: Months, dtype: float64"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variable = X[\"Months\"]\n",
    "variable = variable - variable.mean()\n",
    "variable = variable / variable.std()\n",
    "variable.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is tedious to do this for each variable, the best way to do it is to use a sklearn _transformer_ called _StandardScaler_:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.37846895,  0.32710679],\n",
       "       [ 0.63721955,  1.85544479],\n",
       "       [ 1.30442645,  1.81299095],\n",
       "       [-1.78025031, -0.90405438],\n",
       "       [ 1.64080222,  1.94035245],\n",
       "       [ 0.56698725, -1.03141588],\n",
       "       [ 1.37835519,  0.58182979],\n",
       "       [ 1.04937229,  0.66673745],\n",
       "       [ 0.19734354, -0.77669288],\n",
       "       [ 0.79062169, -0.73423905]])"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "numeric_features = [\"MonthlyCharges\", \"Months\"]\n",
    "scaled_data = scaler.fit_transform(X[numeric_features])\n",
    "scaled_data[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Merging both in one transformer:\n",
    "We can handle both categorical and numerical features in one _ColumnTransformer_ object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of the transformed data is (200, 20)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cat__Dependents_Yes</th>\n",
       "      <th>cat__TechSupport_No internet service</th>\n",
       "      <th>cat__TechSupport_Yes</th>\n",
       "      <th>cat__Contract_One year</th>\n",
       "      <th>cat__Contract_Two year</th>\n",
       "      <th>cat__InternetService_Fiber optic</th>\n",
       "      <th>cat__InternetService_No</th>\n",
       "      <th>cat__CustomerID_Region_CHI-1</th>\n",
       "      <th>cat__CustomerID_Region_DAL-1</th>\n",
       "      <th>cat__CustomerID_Region_HOU-1</th>\n",
       "      <th>cat__CustomerID_Region_LAX-1</th>\n",
       "      <th>cat__CustomerID_Region_MIA-1</th>\n",
       "      <th>cat__CustomerID_Region_MIS-1</th>\n",
       "      <th>cat__CustomerID_Region_NYC-1</th>\n",
       "      <th>cat__CustomerID_Region_PHL-1</th>\n",
       "      <th>cat__CustomerID_Region_PHX-1</th>\n",
       "      <th>cat__CustomerID_Region_SAN-1</th>\n",
       "      <th>cat__CustomerID_Region_SEA-1</th>\n",
       "      <th>num__MonthlyCharges</th>\n",
       "      <th>num__Months</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.378469</td>\n",
       "      <td>0.327107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.637220</td>\n",
       "      <td>1.855445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.304426</td>\n",
       "      <td>1.812991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.780250</td>\n",
       "      <td>-0.904054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.640802</td>\n",
       "      <td>1.940352</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cat__Dependents_Yes  cat__TechSupport_No internet service  \\\n",
       "0                  1.0                                   0.0   \n",
       "1                  1.0                                   0.0   \n",
       "2                  0.0                                   0.0   \n",
       "3                  0.0                                   1.0   \n",
       "4                  1.0                                   0.0   \n",
       "\n",
       "   cat__TechSupport_Yes  cat__Contract_One year  cat__Contract_Two year  \\\n",
       "0                   0.0                     1.0                     0.0   \n",
       "1                   1.0                     0.0                     1.0   \n",
       "2                   1.0                     0.0                     1.0   \n",
       "3                   0.0                     0.0                     0.0   \n",
       "4                   1.0                     0.0                     1.0   \n",
       "\n",
       "   cat__InternetService_Fiber optic  cat__InternetService_No  \\\n",
       "0                               1.0                      0.0   \n",
       "1                               0.0                      0.0   \n",
       "2                               1.0                      0.0   \n",
       "3                               0.0                      1.0   \n",
       "4                               1.0                      0.0   \n",
       "\n",
       "   cat__CustomerID_Region_CHI-1  cat__CustomerID_Region_DAL-1  \\\n",
       "0                           0.0                           0.0   \n",
       "1                           0.0                           1.0   \n",
       "2                           0.0                           0.0   \n",
       "3                           0.0                           0.0   \n",
       "4                           0.0                           0.0   \n",
       "\n",
       "   cat__CustomerID_Region_HOU-1  cat__CustomerID_Region_LAX-1  \\\n",
       "0                           0.0                           0.0   \n",
       "1                           0.0                           0.0   \n",
       "2                           0.0                           0.0   \n",
       "3                           1.0                           0.0   \n",
       "4                           1.0                           0.0   \n",
       "\n",
       "   cat__CustomerID_Region_MIA-1  cat__CustomerID_Region_MIS-1  \\\n",
       "0                           0.0                           1.0   \n",
       "1                           0.0                           0.0   \n",
       "2                           0.0                           0.0   \n",
       "3                           0.0                           0.0   \n",
       "4                           0.0                           0.0   \n",
       "\n",
       "   cat__CustomerID_Region_NYC-1  cat__CustomerID_Region_PHL-1  \\\n",
       "0                           0.0                           0.0   \n",
       "1                           0.0                           0.0   \n",
       "2                           0.0                           0.0   \n",
       "3                           0.0                           0.0   \n",
       "4                           0.0                           0.0   \n",
       "\n",
       "   cat__CustomerID_Region_PHX-1  cat__CustomerID_Region_SAN-1  \\\n",
       "0                           0.0                           0.0   \n",
       "1                           0.0                           0.0   \n",
       "2                           0.0                           1.0   \n",
       "3                           0.0                           0.0   \n",
       "4                           0.0                           0.0   \n",
       "\n",
       "   cat__CustomerID_Region_SEA-1  num__MonthlyCharges  num__Months  \n",
       "0                           0.0             0.378469     0.327107  \n",
       "1                           0.0             0.637220     1.855445  \n",
       "2                           0.0             1.304426     1.812991  \n",
       "3                           0.0            -1.780250    -0.904054  \n",
       "4                           0.0             1.640802     1.940352  "
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "categorical_features = [\"Dependents\", \"TechSupport\", \"Contract\", \"InternetService\", \"CustomerID_Region\"]\n",
    "numeric_features = [\"MonthlyCharges\", \"Months\"]\n",
    "\n",
    "categorical_transformer = OneHotEncoder(drop=\"first\", sparse_output=False)\n",
    "numeric_transformer = StandardScaler()\n",
    "\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('cat', categorical_transformer, categorical_features),\n",
    "        ('num', numeric_transformer, numeric_features),\n",
    "    ],\n",
    ")\n",
    "\n",
    "transformed_data = preprocessor.fit_transform(X)\n",
    "print(f\"The shape of the transformed data is {transformed_data.shape}\")\n",
    "\n",
    "# we construct a new pandas with the column names:\n",
    "column_names = preprocessor.get_feature_names_out()\n",
    "transformed_df = pd.DataFrame(transformed_data, columns=column_names)\n",
    "transformed_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 Preprocessing and the train-test split\n",
    "But when developing a machine learing model, we always follow the train-test paradigm where we split train-test data: therefore when transforming / encoding / scaling the variables we should only be using the train data. Otherwise info from the test data will be used by the model: for example, the scaling operation will use test samples to compute the mean and std. Therefore, the column transformer object should be _fit_ on the train only, then we use the _transform_ method on the test data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "categorical_features = [\"Dependents\", \"TechSupport\", \"Contract\", \"InternetService\", \"CustomerID_Region\"]\n",
    "numeric_features = [\"MonthlyCharges\", \"Months\"]\n",
    "\n",
    "categorical_transformer = OneHotEncoder(drop=\"first\", sparse_output=False)\n",
    "numeric_transformer = StandardScaler()\n",
    "\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('cat', categorical_transformer, categorical_features),\n",
    "        ('num', numeric_transformer, numeric_features),\n",
    "    ],\n",
    ")\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(df, y, test_size=0.3, random_state=42, stratify=y)\n",
    "\n",
    "X_train_processed = preprocessor.fit_transform(X_train)\n",
    "\n",
    "X_test_processed = preprocessor.transform(X_test)\n",
    "\n",
    "# we get the column names:\n",
    "column_names = preprocessor.get_feature_names_out()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Logistic regression \n",
    "We can now start doing ML models. First, we use logistic regression without regularization:\n",
    "\n",
    "#### 3.1 Without regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy: 0.8357\n",
      "Test accuracy: 0.6500\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# we can fit the logistic regression model with no regularization:\n",
    "model = LogisticRegression(penalty=None)\n",
    "model.fit(X_train_processed, y_train.values)\n",
    "\n",
    "y_train_pred = model.predict(X_train_processed)\n",
    "y_test_pred = model.predict(X_test_processed)\n",
    "\n",
    "# Evaluate the model accuracy\n",
    "print(f\"Training accuracy: {accuracy_score(y_train_pred, y_train):.4f}\")\n",
    "print(f\"Test accuracy: {accuracy_score(y_test_pred, y_test):.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the model isn't quite good:\n",
    "1. 83% accuracy on the training data means that the model isnt complex enough to predict labels it has already seen\n",
    "2. 65% accuracy on the test data suggest a big difference between train and test: the models learns a bit of noise in the training data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can extract the coefficients and visualize their values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coef = pd.DataFrame(model.coef_, columns=column_names)\n",
    "coef.T.plot(kind=\"bar\", legend=False)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can extract the change in the odd-ratios and visualize their importance in percentages (see TD6):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "odds_changes = (np.exp(model.coef_) - 1) * 100\n",
    "coef = pd.DataFrame(odds_changes, columns=column_names)\n",
    "coef.T.plot(kind=\"bar\", legend=False)\n",
    "plt.grid()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 184.33978368,  -73.24588216,   10.11408019,  -69.01164118,\n",
       "         -42.3604937 , 1867.04450222,  -73.24588216,  -57.30827598,\n",
       "         -67.89164809,  -27.90134379,   87.71595551,  -47.99574828,\n",
       "         -49.09364831,  -69.35050994,   10.37215112,  190.19346862,\n",
       "         -73.42643779,  875.62186833,  -54.79865072,  -77.0485994 ]])"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odds_changes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23.61410818827673, 27.120965039260753)"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[\"Months\"].std(), X[\"MonthlyCharges\"].std()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some odd percentage changes are off-the-charts ! 1867% increase with Optic Fiber and 875% if the customer is in the region SEA-1 ! Given that the performance of the model is poor here and the coefficient values are very large it is very likely the model has learned noise: regularization is needed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 With L2 / L1 regularization\n",
    "We can now try to improve the model by adding regularization. We can use the `LogisticRegressionCV` class which will automatically find the best regularization parameter for us:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy: 0.7857\n",
      "Test accuracy: 0.7167\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# we can fit the logistic regression model with no regularization:\n",
    "model = LogisticRegressionCV(Cs=np.logspace(-3, 3, 100), penalty=\"l2\")\n",
    "model.fit(X_train_processed, y_train.values)\n",
    "\n",
    "y_train_pred = model.predict(X_train_processed)\n",
    "y_test_pred = model.predict(X_test_processed)\n",
    "\n",
    "# Evaluate the model accuracy\n",
    "print(f\"Training accuracy: {accuracy_score(y_train_pred, y_train):.4f}\")\n",
    "print(f\"Test accuracy: {accuracy_score(y_test_pred, y_test):.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The test accuracy is slightly improved. The coefficients look very similar: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coef = pd.DataFrame(model.coef_, columns=column_names)\n",
    "coef.T.plot(kind=\"bar\", legend=False)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "odds_changes = (np.exp(model.coef_) - 1) * 100\n",
    "coef = pd.DataFrame(odds_changes, columns=column_names)\n",
    "coef.T.plot(kind=\"bar\", legend=False)\n",
    "plt.grid()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23.61410818827673, 27.120965039260753)"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[\"Months\"].std(), X[\"MonthlyCharges\"].std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These values are more reasonable ! \n",
    "We can see that the most impactful features are the contract type, months, the monthly charges, internetService with Fiber optic and the SEA-1 region:\n",
    "1. the longer the subscription (contract type and months) the less likely the customer churns.\n",
    "2. In particular, for each additional \"months std\" = 23.6 months -> -45% odds.\n",
    "3. having a Fiber optic internet / high charges makes the customer more likely to churn\n",
    "4. In particular, 26% higher odds of churning if the customer has fiber: it could perhaps more competitiveness between providers.\n",
    "5. For each additional 27$ per month, the customer odds of churning increase by 23.65%.\n",
    "6. Finally, being in SEA-1 increases the odds by 11%.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also try L1 regularization for sparse coefficients (feature selection):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy: 0.8429\n",
      "Test accuracy: 0.6667\n"
     ]
    }
   ],
   "source": [
    "model = LogisticRegressionCV(Cs=np.logspace(-4, 4, 100), penalty=\"l1\", solver=\"liblinear\")\n",
    "model.fit(X_train_processed, y_train.values)\n",
    "\n",
    "y_train_pred = model.predict(X_train_processed)\n",
    "y_test_pred = model.predict(X_test_processed)\n",
    "\n",
    "# Evaluate the model accuracy\n",
    "print(f\"Training accuracy: {accuracy_score(y_train_pred, y_train):.4f}\")\n",
    "print(f\"Test accuracy: {accuracy_score(y_test_pred, y_test):.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coef = pd.DataFrame(model.coef_, columns=column_names)\n",
    "coef.T.plot(kind=\"bar\", legend=False)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coef = pd.DataFrame(100 * (np.exp(model.coef_) - 1), columns=column_names)\n",
    "coef.T.plot(kind=\"bar\", legend=False)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model is similar to the unregularized case, we have 20 dimension with 100 observations: perhaps using feature selection is not a good idea here since the dimension is not that large compared to the number of observations: the Lasso keeps the largest coefficients and reduces the others to near 0. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With all these models, we can obtain the prediction probability (sigmoid) of 10 samples for e.g using:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.22842486, 0.77157514],\n",
       "       [0.29932415, 0.70067585],\n",
       "       [0.22107175, 0.77892825],\n",
       "       [0.45511437, 0.54488563],\n",
       "       [0.28544249, 0.71455751],\n",
       "       [0.85739176, 0.14260824],\n",
       "       [0.67825299, 0.32174701],\n",
       "       [0.91052171, 0.08947829],\n",
       "       [0.78664728, 0.21335272],\n",
       "       [0.50537597, 0.49462403]])"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_proba(X_test_processed[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sigmoid probability of the model corresponds to the second column of the output above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It outputs a vector of probabilites that sums to 1. We get the prediction class using the argmax or comparing the second column to 0.5:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_proba(X_test_processed[:10]).argmax(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(model.predict_proba(X_test_processed[:10])[:, 1] > 0.5).astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Bayesian logistic regression\n",
    "We can now build a bayesian logistic regression with a Gaussian prior using pymc:\n",
    "\n",
    "### 4.1 Fitting the model with MCMC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [sigma, intercept, betas]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9d162efe45194ab2b35aea26fbcceaa0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 2 seconds.\n"
     ]
    },
    {
     "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>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>intercept</th>\n",
       "      <td>-0.288</td>\n",
       "      <td>0.438</td>\n",
       "      <td>-1.101</td>\n",
       "      <td>0.542</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.007</td>\n",
       "      <td>2776.0</td>\n",
       "      <td>2395.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>betas[0]</th>\n",
       "      <td>0.345</td>\n",
       "      <td>0.487</td>\n",
       "      <td>-0.564</td>\n",
       "      <td>1.270</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.007</td>\n",
       "      <td>4078.0</td>\n",
       "      <td>2611.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>betas[1]</th>\n",
       "      <td>-0.544</td>\n",
       "      <td>0.733</td>\n",
       "      <td>-1.884</td>\n",
       "      <td>0.875</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.011</td>\n",
       "      <td>3709.0</td>\n",
       "      <td>2799.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>betas[2]</th>\n",
       "      <td>-0.066</td>\n",
       "      <td>0.457</td>\n",
       "      <td>-0.935</td>\n",
       "      <td>0.788</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.007</td>\n",
       "      <td>4514.0</td>\n",
       "      <td>2947.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>betas[3]</th>\n",
       "      <td>-0.838</td>\n",
       "      <td>0.554</td>\n",
       "      <td>-1.978</td>\n",
       "      <td>0.110</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.007</td>\n",
       "      <td>3821.0</td>\n",
       "      <td>3307.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>betas[4]</th>\n",
       "      <td>-0.381</td>\n",
       "      <td>0.634</td>\n",
       "      <td>-1.627</td>\n",
       "      <td>0.768</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.009</td>\n",
       "      <td>4762.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>betas[5]</th>\n",
       "      <td>1.041</td>\n",
       "      <td>0.618</td>\n",
       "      <td>-0.081</td>\n",
       "      <td>2.221</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.010</td>\n",
       "      <td>2181.0</td>\n",
       "      <td>2661.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>betas[6]</th>\n",
       "      <td>-0.527</td>\n",
       "      <td>0.751</td>\n",
       "      <td>-1.927</td>\n",
       "      <td>0.908</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.011</td>\n",
       "      <td>3877.0</td>\n",
       "      <td>2795.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>betas[7]</th>\n",
       "      <td>-0.058</td>\n",
       "      <td>0.688</td>\n",
       "      <td>-1.452</td>\n",
       "      <td>1.156</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.011</td>\n",
       "      <td>4470.0</td>\n",
       "      <td>2692.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>betas[8]</th>\n",
       "      <td>-0.581</td>\n",
       "      <td>0.590</td>\n",
       "      <td>-1.675</td>\n",
       "      <td>0.533</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.008</td>\n",
       "      <td>3326.0</td>\n",
       "      <td>3316.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>betas[9]</th>\n",
       "      <td>-0.242</td>\n",
       "      <td>0.628</td>\n",
       "      <td>-1.407</td>\n",
       "      <td>0.969</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.010</td>\n",
       "      <td>5203.0</td>\n",
       "      <td>2794.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>betas[10]</th>\n",
       "      <td>0.250</td>\n",
       "      <td>0.626</td>\n",
       "      <td>-0.921</td>\n",
       "      <td>1.442</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.011</td>\n",
       "      <td>6243.0</td>\n",
       "      <td>2790.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>betas[11]</th>\n",
       "      <td>-0.174</td>\n",
       "      <td>0.596</td>\n",
       "      <td>-1.304</td>\n",
       "      <td>0.934</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.009</td>\n",
       "      <td>5396.0</td>\n",
       "      <td>3216.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>betas[12]</th>\n",
       "      <td>-0.254</td>\n",
       "      <td>0.556</td>\n",
       "      <td>-1.341</td>\n",
       "      <td>0.760</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.008</td>\n",
       "      <td>4633.0</td>\n",
       "      <td>2792.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>betas[13]</th>\n",
       "      <td>-0.555</td>\n",
       "      <td>0.563</td>\n",
       "      <td>-1.572</td>\n",
       "      <td>0.517</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.008</td>\n",
       "      <td>3570.0</td>\n",
       "      <td>2551.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>betas[14]</th>\n",
       "      <td>0.069</td>\n",
       "      <td>0.605</td>\n",
       "      <td>-1.032</td>\n",
       "      <td>1.308</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.010</td>\n",
       "      <td>4284.0</td>\n",
       "      <td>2719.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>betas[15]</th>\n",
       "      <td>0.511</td>\n",
       "      <td>0.619</td>\n",
       "      <td>-0.663</td>\n",
       "      <td>1.683</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.009</td>\n",
       "      <td>4406.0</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>betas[16]</th>\n",
       "      <td>-0.596</td>\n",
       "      <td>0.588</td>\n",
       "      <td>-1.736</td>\n",
       "      <td>0.443</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.008</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>2826.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>betas[17]</th>\n",
       "      <td>0.978</td>\n",
       "      <td>0.645</td>\n",
       "      <td>-0.129</td>\n",
       "      <td>2.278</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.009</td>\n",
       "      <td>2661.0</td>\n",
       "      <td>3099.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>betas[18]</th>\n",
       "      <td>0.215</td>\n",
       "      <td>0.381</td>\n",
       "      <td>-0.521</td>\n",
       "      <td>0.927</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.005</td>\n",
       "      <td>3335.0</td>\n",
       "      <td>2299.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>betas[19]</th>\n",
       "      <td>-1.345</td>\n",
       "      <td>0.328</td>\n",
       "      <td>-1.953</td>\n",
       "      <td>-0.729</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.004</td>\n",
       "      <td>2985.0</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma</th>\n",
       "      <td>0.828</td>\n",
       "      <td>0.234</td>\n",
       "      <td>0.439</td>\n",
       "      <td>1.262</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.005</td>\n",
       "      <td>1155.0</td>\n",
       "      <td>1620.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  \\\n",
       "intercept -0.288  0.438  -1.101    0.542      0.008    0.007    2776.0   \n",
       "betas[0]   0.345  0.487  -0.564    1.270      0.008    0.007    4078.0   \n",
       "betas[1]  -0.544  0.733  -1.884    0.875      0.012    0.011    3709.0   \n",
       "betas[2]  -0.066  0.457  -0.935    0.788      0.007    0.007    4514.0   \n",
       "betas[3]  -0.838  0.554  -1.978    0.110      0.009    0.007    3821.0   \n",
       "betas[4]  -0.381  0.634  -1.627    0.768      0.009    0.009    4762.0   \n",
       "betas[5]   1.041  0.618  -0.081    2.221      0.014    0.010    2181.0   \n",
       "betas[6]  -0.527  0.751  -1.927    0.908      0.012    0.011    3877.0   \n",
       "betas[7]  -0.058  0.688  -1.452    1.156      0.010    0.011    4470.0   \n",
       "betas[8]  -0.581  0.590  -1.675    0.533      0.010    0.008    3326.0   \n",
       "betas[9]  -0.242  0.628  -1.407    0.969      0.009    0.010    5203.0   \n",
       "betas[10]  0.250  0.626  -0.921    1.442      0.008    0.011    6243.0   \n",
       "betas[11] -0.174  0.596  -1.304    0.934      0.008    0.009    5396.0   \n",
       "betas[12] -0.254  0.556  -1.341    0.760      0.008    0.008    4633.0   \n",
       "betas[13] -0.555  0.563  -1.572    0.517      0.010    0.008    3570.0   \n",
       "betas[14]  0.069  0.605  -1.032    1.308      0.009    0.010    4284.0   \n",
       "betas[15]  0.511  0.619  -0.663    1.683      0.009    0.009    4406.0   \n",
       "betas[16] -0.596  0.588  -1.736    0.443      0.010    0.008    3500.0   \n",
       "betas[17]  0.978  0.645  -0.129    2.278      0.013    0.009    2661.0   \n",
       "betas[18]  0.215  0.381  -0.521    0.927      0.007    0.005    3335.0   \n",
       "betas[19] -1.345  0.328  -1.953   -0.729      0.006    0.004    2985.0   \n",
       "sigma      0.828  0.234   0.439    1.262      0.007    0.005    1155.0   \n",
       "\n",
       "           ess_tail  r_hat  \n",
       "intercept    2395.0    1.0  \n",
       "betas[0]     2611.0    1.0  \n",
       "betas[1]     2799.0    1.0  \n",
       "betas[2]     2947.0    1.0  \n",
       "betas[3]     3307.0    1.0  \n",
       "betas[4]     3000.0    1.0  \n",
       "betas[5]     2661.0    1.0  \n",
       "betas[6]     2795.0    1.0  \n",
       "betas[7]     2692.0    1.0  \n",
       "betas[8]     3316.0    1.0  \n",
       "betas[9]     2794.0    1.0  \n",
       "betas[10]    2790.0    1.0  \n",
       "betas[11]    3216.0    1.0  \n",
       "betas[12]    2792.0    1.0  \n",
       "betas[13]    2551.0    1.0  \n",
       "betas[14]    2719.0    1.0  \n",
       "betas[15]    2800.0    1.0  \n",
       "betas[16]    2826.0    1.0  \n",
       "betas[17]    3099.0    1.0  \n",
       "betas[18]    2299.0    1.0  \n",
       "betas[19]    2600.0    1.0  \n",
       "sigma        1620.0    1.0  "
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pymc as pm\n",
    "import arviz as az\n",
    "import seaborn as sns\n",
    "\n",
    "# Build the model\n",
    "n_mcmc_samples = 1000\n",
    "coords = dict(var_names=column_names)\n",
    "with pm.Model(coords=coords) as logistic_model:\n",
    "    # Priors for weights and intercept\n",
    "    sigma = pm.HalfCauchy('sigma', beta=1)\n",
    "    intercept = pm.Normal('intercept', mu=0, sigma=sigma)\n",
    "    betas = pm.Normal('betas', mu=0, sigma=sigma, shape=X_train_processed.shape[1])\n",
    "    \n",
    "    # Linear predictor\n",
    "    mu = pm.math.dot(X_train_processed, betas) + intercept\n",
    "    \n",
    "    # Likelihood (observed outcome)\n",
    "    theta = pm.math.sigmoid(mu)\n",
    "    y_obs = pm.Bernoulli('y_obs', p=theta, observed=y_train)\n",
    "    \n",
    "    # Sample from posterior\n",
    "    trace = pm.sample(n_mcmc_samples, tune=1000, return_inferencedata=True)\n",
    "az.summary(trace)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No warnings, all rhats are equal to 1, the ESS are all very large, no red flags of divergences. the MCMC chains pass all diagnostics. To intepret the coefficients, we should check the HDI of the coefficients, if they contain 0 it means that 0 is included in the 94% credible interval: therefore the coefficient is not statistically different from 0:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x1480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_forest(trace)\n",
    "plt.grid()\n",
    "plt.vlines(0, 0, ymax=100, color=\"red\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "None of them are except sigma and beta[19] which corresponds to the last variable \"Months\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'num__Months'"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_names[19]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-74.127770174036"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100 * (np.exp(-1.352) - 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Making predictions\n",
    "How do we make predictions with this model ? Well, we can use the MCMC samples (beta) to compute the sigmoid probabilities on the test data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4000, 20)"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beta_samples = trace.posterior[\"betas\"].values.reshape(-1, 20)\n",
    "beta_samples.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4000, 1)"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intercept_samples = trace.posterior[\"intercept\"].values.reshape(-1, 1)\n",
    "intercept_samples.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((140, 4000), (60, 4000))"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.special import expit as sigmoid\n",
    "\n",
    "def get_bayes_probas(X, trace):\n",
    "    beta_samples = trace.posterior[\"betas\"].values.reshape(-1, 20)\n",
    "    # vector of size 4000 x 20\n",
    "    intercept_samples = trace.posterior[\"intercept\"].values.reshape(1, -1)\n",
    "    # vector of size 4000 x 1\n",
    "\n",
    "    # X test is of size n_samples x 20 so we transpose beta_samples to have a size 20 x 4000\n",
    "    # then we transpose the output to be 4000 x n_samples compatible with intercept_samples of size 1 x 4000\n",
    "    logits = X.dot(beta_samples.T) + intercept_samples\n",
    "    # we have a vector of size n_samples x 4000\n",
    "    return sigmoid(logits)\n",
    "\n",
    "probas_bayes_train = get_bayes_probas(X_train_processed, trace)\n",
    "probas_bayes_test = get_bayes_probas(X_test_processed, trace)\n",
    "\n",
    "probas_bayes_train.shape, probas_bayes_test.shape\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have 4000 different predictions for each of the 60 test samples, we can compute the average prediction and the standard deviation to evaluate our uncertainty:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy: 0.8071\n",
      "Test accuracy: 0.6833\n"
     ]
    }
   ],
   "source": [
    "mean_proba_bayes_train = probas_bayes_train.mean(axis=1)\n",
    "std_proba_bayes_train = probas_bayes_train.std(axis=1)\n",
    "\n",
    "mean_proba_bayes_test = probas_bayes_test.mean(axis=1)\n",
    "std_proba_bayes_test = probas_bayes_test.std(axis=1)\n",
    "\n",
    "bayes_predictions_train = (mean_proba_bayes_train > 0.5).astype(int)\n",
    "bayes_predictions_test = (mean_proba_bayes_test > 0.5).astype(int)\n",
    "\n",
    "print(f\"Training accuracy: {accuracy_score(y_train, bayes_predictions_train):.4f}\")\n",
    "print(f\"Test accuracy: {accuracy_score(y_test, bayes_predictions_test):.4f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems like the performance is similar or even a bit worse than the frequentist approach (Ridge). Why go through the trouble of MCMC then ? Well, because we can compute also uncertainties around those mean predictions of the MCMC samples. For example, with the frequentist approach we would get the the probability of churn is 0.8. With the bayesian approach we have 4000 probabilities of churn for each sample, assume their mean is identical: 0.8. With the 4000 MCMC samples we can also compute an HDI of those probabilities. If the HDI is too large say [0.3, 1.] then we cannot say for sure that 0.8 is statistically significant. If however the HDI is [0.7, 0.9] (it is far from 0.5) then we are more confident in our prediction.\n",
    "\n",
    "In practice, the companies does not want to have many false positives (predict churn for customers who are actually satisfied and won't leave) because it costs money (ads, promo deals to retain them...). So it might use the bayesian approach to only target the customers with predicted churn **and** high certainty. For the predicted churns with low certainty it may send them a satisfaction survey to be more certain. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. How I manipulated the data\n",
    "\n",
    "To illustrate the regularization here I truncated data to only 200 samples (from 10K samples in the kaggle dataset) and kept only a few variables otherwise MCMC would be too slow. And I also added fake variables: the region variable is purely random, completely unrelated to the churn variable. Yet, the regression (and Lasso) found a large coefficient for one of the regions ! This is to illustrate how models with little data can learn noise and lead to wrong intepretations of the coefficients: L2 regularization (and the bayesian approach however correctly reduced their amplitudes). \n",
    "\n",
    "Let's remove the region variable and see what happens. Before, we obtained with the unregularized model using the Regions:\n",
    "\n",
    "- Training accuracy: 0.8357\n",
    "- Test accuracy: 0.6500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_features = [\"Dependents\", \"TechSupport\", \"Contract\", \"InternetService\"]\n",
    "numeric_features = [\"MonthlyCharges\", \"Months\"]\n",
    "\n",
    "categorical_transformer = OneHotEncoder(drop=\"first\", sparse_output=False)\n",
    "numeric_transformer = StandardScaler()\n",
    "\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('cat', categorical_transformer, categorical_features),\n",
    "        ('num', numeric_transformer, numeric_features),\n",
    "    ],\n",
    ")\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42, stratify=y)\n",
    "\n",
    "X_train_processed = preprocessor.fit_transform(X_train)\n",
    "\n",
    "X_test_processed = preprocessor.transform(X_test)\n",
    "\n",
    "# we get the column names:\n",
    "column_names = preprocessor.get_feature_names_out()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy: 0.8214\n",
      "Test accuracy: 0.6833\n"
     ]
    }
   ],
   "source": [
    "# we can fit the logistic regression model with no regularization:\n",
    "model = LogisticRegression(penalty=None)\n",
    "model.fit(X_train_processed, y_train.values)\n",
    "\n",
    "y_train_pred = model.predict(X_train_processed)\n",
    "y_test_pred = model.predict(X_test_processed)\n",
    "\n",
    "# Evaluate the model accuracy\n",
    "print(f\"Training accuracy: {accuracy_score(y_train_pred, y_train):.4f}\")\n",
    "print(f\"Test accuracy: {accuracy_score(y_test_pred, y_test):.4f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Slightly less train accuracy, more test accuracy: the model's overfitting is reduced a little bit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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