{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e2889168-c959-4143-8a16-47f7e8f9c855",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mon Dec  8 12:36:06 2025       \n",
      "+-----------------------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 580.95.05              Driver Version: 580.95.05      CUDA Version: 13.0     |\n",
      "+-----------------------------------------+------------------------+----------------------+\n",
      "| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |\n",
      "|                                         |                        |               MIG M. |\n",
      "|=========================================+========================+======================|\n",
      "|   0  Tesla V100-SXM2-32GB           On  |   00000000:3B:00.0 Off |                    0 |\n",
      "| N/A   38C    P0             40W /  300W |       0MiB /  32768MiB |      0%      Default |\n",
      "|                                         |                        |                  N/A |\n",
      "+-----------------------------------------+------------------------+----------------------+\n",
      "\n",
      "+-----------------------------------------------------------------------------------------+\n",
      "| Processes:                                                                              |\n",
      "|  GPU   GI   CI              PID   Type   Process name                        GPU Memory |\n",
      "|        ID   ID                                                               Usage      |\n",
      "|=========================================================================================|\n",
      "|  No running processes found                                                             |\n",
      "+-----------------------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2c68ed0-28c2-487c-b3b4-c2f86dcb17d9",
   "metadata": {},
   "source": [
    "# Credit Card Fraud Detection\n",
    "\n",
    "Link: https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud\n",
    "\n",
    "Time taken: 90 minutes (deep work)\n",
    "\n",
    "Author: [Aayush Bajaj](https://abaj.ai)\n",
    "\n",
    "Version control: https://github.com/abaj8494/10khrs-ai-ml-dl/blob/main/projects/interview/credit-card-fraud.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "328bafbc-9a10-4b77-8c80-d023ec98ee28",
   "metadata": {},
   "source": [
    "## Problem Statement"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55b33d34-8a70-4271-a609-5de8b9971243",
   "metadata": {},
   "source": [
    "It is important that credit card companies are able to recognise fraudulent credit card transactions so that customers are not charged for items that they did not purchase.\n",
    "\n",
    "This dataset contains transactions made by credit cards in September 2013 by European cardholders.\n",
    "\n",
    "This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is **highly unbalanced**, the positive class (frauds) account for 0.172% of all transactions. (considering mixture loss, focal perhaps).\n",
    "\n",
    "It contains only numerical input variables which are the result of a PCA transformation (naturally all the feature would be too large of a dataset to share; also confidentiality!). 28 features with only 'Time' and 'Amount' not being transformed.\n",
    "\n",
    "'Time' contains *seconds elapsed* between each transaction and the **first** transaction in the dataset. 'Amount' is the transaction amount (naturally) -- this feature can be used for example-dependant cost-sensitive learning. Feature 'Class' is the response variable and takes value `1` in case of fraud, else `0`.\n",
    "\n",
    "Importantly, given the class imbalance ratio, we should measure accuracy using the **Area Under the Precision-Recall Curve** (AUPRC). Realise that **confusion matrix accuracy** is not meaningful for unbalanced classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bd631168-ca94-41f7-a37c-14cebbd76a4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/scratch/z5362216/.venvs/kits/lib64/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading from https://www.kaggle.com/api/v1/datasets/download/mlg-ulb/creditcardfraud?dataset_version_number=3...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 66.0M/66.0M [00:17<00:00, 3.89MB/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting files...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Path to dataset files: /home/z5362216/.cache/kagglehub/datasets/mlg-ulb/creditcardfraud/versions/3\n"
     ]
    }
   ],
   "source": [
    "import kagglehub\n",
    "\n",
    "path = kagglehub.dataset_download(\"mlg-ulb/creditcardfraud\")\n",
    "\n",
    "print(\"Path to dataset files:\", path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac4373c4-2731-492d-b0fa-07cd6d243996",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "007779a2-db00-487b-863c-faa9ddefbd34",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0173c185-16a3-45ab-98d9-ccaf43451417",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "csv_path = Path(path + '/' + 'creditcard.csv')\n",
    "df = pd.read_csv(csv_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "31384a77-6b18-498d-8f44-123e7688c11f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
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       "      <th>V9</th>\n",
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       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
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       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "612abe7e-93f8-40ea-ac1d-51a599a30ef1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 31)"
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     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6f30fe73-cbf2-451d-94a3-6a22c6687fcc",
   "metadata": {},
   "outputs": [
    {
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      " 2   V2      284807 non-null  float64\n",
      " 3   V3      284807 non-null  float64\n",
      " 4   V4      284807 non-null  float64\n",
      " 5   V5      284807 non-null  float64\n",
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      " 19  V19     284807 non-null  float64\n",
      " 20  V20     284807 non-null  float64\n",
      " 21  V21     284807 non-null  float64\n",
      " 22  V22     284807 non-null  float64\n",
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      " 29  Amount  284807 non-null  float64\n",
      " 30  Class   284807 non-null  int64  \n",
      "dtypes: float64(30), int64(1)\n",
      "memory usage: 67.4 MB\n"
     ]
    }
   ],
   "source": [
    "# ^as expected from the kaggle description brief.\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d554d525-4989-4f99-8d03-8c8e2b4238c6",
   "metadata": {},
   "outputs": [
    {
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       "      <td>-2.406331e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>1.654067e-16</td>\n",
       "      <td>-3.568593e-16</td>\n",
       "      <td>2.578648e-16</td>\n",
       "      <td>4.473266e-15</td>\n",
       "      <td>5.340915e-16</td>\n",
       "      <td>1.683437e-15</td>\n",
       "      <td>-3.660091e-16</td>\n",
       "      <td>-1.227390e-16</td>\n",
       "      <td>88.349619</td>\n",
       "      <td>0.001727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>47488.145955</td>\n",
       "      <td>1.958696e+00</td>\n",
       "      <td>1.651309e+00</td>\n",
       "      <td>1.516255e+00</td>\n",
       "      <td>1.415869e+00</td>\n",
       "      <td>1.380247e+00</td>\n",
       "      <td>1.332271e+00</td>\n",
       "      <td>1.237094e+00</td>\n",
       "      <td>1.194353e+00</td>\n",
       "      <td>1.098632e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>7.345240e-01</td>\n",
       "      <td>7.257016e-01</td>\n",
       "      <td>6.244603e-01</td>\n",
       "      <td>6.056471e-01</td>\n",
       "      <td>5.212781e-01</td>\n",
       "      <td>4.822270e-01</td>\n",
       "      <td>4.036325e-01</td>\n",
       "      <td>3.300833e-01</td>\n",
       "      <td>250.120109</td>\n",
       "      <td>0.041527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.640751e+01</td>\n",
       "      <td>-7.271573e+01</td>\n",
       "      <td>-4.832559e+01</td>\n",
       "      <td>-5.683171e+00</td>\n",
       "      <td>-1.137433e+02</td>\n",
       "      <td>-2.616051e+01</td>\n",
       "      <td>-4.355724e+01</td>\n",
       "      <td>-7.321672e+01</td>\n",
       "      <td>-1.343407e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.483038e+01</td>\n",
       "      <td>-1.093314e+01</td>\n",
       "      <td>-4.480774e+01</td>\n",
       "      <td>-2.836627e+00</td>\n",
       "      <td>-1.029540e+01</td>\n",
       "      <td>-2.604551e+00</td>\n",
       "      <td>-2.256568e+01</td>\n",
       "      <td>-1.543008e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>54201.500000</td>\n",
       "      <td>-9.203734e-01</td>\n",
       "      <td>-5.985499e-01</td>\n",
       "      <td>-8.903648e-01</td>\n",
       "      <td>-8.486401e-01</td>\n",
       "      <td>-6.915971e-01</td>\n",
       "      <td>-7.682956e-01</td>\n",
       "      <td>-5.540759e-01</td>\n",
       "      <td>-2.086297e-01</td>\n",
       "      <td>-6.430976e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.283949e-01</td>\n",
       "      <td>-5.423504e-01</td>\n",
       "      <td>-1.618463e-01</td>\n",
       "      <td>-3.545861e-01</td>\n",
       "      <td>-3.171451e-01</td>\n",
       "      <td>-3.269839e-01</td>\n",
       "      <td>-7.083953e-02</td>\n",
       "      <td>-5.295979e-02</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>84692.000000</td>\n",
       "      <td>1.810880e-02</td>\n",
       "      <td>6.548556e-02</td>\n",
       "      <td>1.798463e-01</td>\n",
       "      <td>-1.984653e-02</td>\n",
       "      <td>-5.433583e-02</td>\n",
       "      <td>-2.741871e-01</td>\n",
       "      <td>4.010308e-02</td>\n",
       "      <td>2.235804e-02</td>\n",
       "      <td>-5.142873e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.945017e-02</td>\n",
       "      <td>6.781943e-03</td>\n",
       "      <td>-1.119293e-02</td>\n",
       "      <td>4.097606e-02</td>\n",
       "      <td>1.659350e-02</td>\n",
       "      <td>-5.213911e-02</td>\n",
       "      <td>1.342146e-03</td>\n",
       "      <td>1.124383e-02</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>139320.500000</td>\n",
       "      <td>1.315642e+00</td>\n",
       "      <td>8.037239e-01</td>\n",
       "      <td>1.027196e+00</td>\n",
       "      <td>7.433413e-01</td>\n",
       "      <td>6.119264e-01</td>\n",
       "      <td>3.985649e-01</td>\n",
       "      <td>5.704361e-01</td>\n",
       "      <td>3.273459e-01</td>\n",
       "      <td>5.971390e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.863772e-01</td>\n",
       "      <td>5.285536e-01</td>\n",
       "      <td>1.476421e-01</td>\n",
       "      <td>4.395266e-01</td>\n",
       "      <td>3.507156e-01</td>\n",
       "      <td>2.409522e-01</td>\n",
       "      <td>9.104512e-02</td>\n",
       "      <td>7.827995e-02</td>\n",
       "      <td>77.165000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>172792.000000</td>\n",
       "      <td>2.454930e+00</td>\n",
       "      <td>2.205773e+01</td>\n",
       "      <td>9.382558e+00</td>\n",
       "      <td>1.687534e+01</td>\n",
       "      <td>3.480167e+01</td>\n",
       "      <td>7.330163e+01</td>\n",
       "      <td>1.205895e+02</td>\n",
       "      <td>2.000721e+01</td>\n",
       "      <td>1.559499e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>2.720284e+01</td>\n",
       "      <td>1.050309e+01</td>\n",
       "      <td>2.252841e+01</td>\n",
       "      <td>4.584549e+00</td>\n",
       "      <td>7.519589e+00</td>\n",
       "      <td>3.517346e+00</td>\n",
       "      <td>3.161220e+01</td>\n",
       "      <td>3.384781e+01</td>\n",
       "      <td>25691.160000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Time            V1            V2            V3            V4  \\\n",
       "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean    94813.859575  1.168375e-15  3.416908e-16 -1.379537e-15  2.074095e-15   \n",
       "std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   \n",
       "min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   \n",
       "25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   \n",
       "50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   \n",
       "75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   \n",
       "max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   \n",
       "\n",
       "                 V5            V6            V7            V8            V9  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   9.604066e-16  1.487313e-15 -5.556467e-16  1.213481e-16 -2.406331e-15   \n",
       "std    1.380247e+00  1.332271e+00  1.237094e+00  1.194353e+00  1.098632e+00   \n",
       "min   -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01   \n",
       "25%   -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01   \n",
       "50%   -5.433583e-02 -2.741871e-01  4.010308e-02  2.235804e-02 -5.142873e-02   \n",
       "75%    6.119264e-01  3.985649e-01  5.704361e-01  3.273459e-01  5.971390e-01   \n",
       "max    3.480167e+01  7.330163e+01  1.205895e+02  2.000721e+01  1.559499e+01   \n",
       "\n",
       "       ...           V21           V22           V23           V24  \\\n",
       "count  ...  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   ...  1.654067e-16 -3.568593e-16  2.578648e-16  4.473266e-15   \n",
       "std    ...  7.345240e-01  7.257016e-01  6.244603e-01  6.056471e-01   \n",
       "min    ... -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00   \n",
       "25%    ... -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01   \n",
       "50%    ... -2.945017e-02  6.781943e-03 -1.119293e-02  4.097606e-02   \n",
       "75%    ...  1.863772e-01  5.285536e-01  1.476421e-01  4.395266e-01   \n",
       "max    ...  2.720284e+01  1.050309e+01  2.252841e+01  4.584549e+00   \n",
       "\n",
       "                V25           V26           V27           V28         Amount  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   \n",
       "mean   5.340915e-16  1.683437e-15 -3.660091e-16 -1.227390e-16      88.349619   \n",
       "std    5.212781e-01  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   \n",
       "min   -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   \n",
       "25%   -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   \n",
       "50%    1.659350e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   \n",
       "75%    3.507156e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   \n",
       "max    7.519589e+00  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   \n",
       "\n",
       "               Class  \n",
       "count  284807.000000  \n",
       "mean        0.001727  \n",
       "std         0.041527  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         0.000000  \n",
       "max         1.000000  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a897ebf-9dbf-487c-8feb-fcb6d0d82236",
   "metadata": {},
   "source": [
    "### Class Imbalances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "551e0184-5a34-4d80-8d71-eeb90126150f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Class\n",
       "0    284315\n",
       "1       492\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# class imbalance details\n",
    "df[\"Class\"].value_counts() # how many of each (raw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7ed8677f-3170-4bec-acf8-b9006c704dc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Class\n",
       "0    0.998273\n",
       "1    0.001727\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Class\"].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "58547e60-2a22-4def-834e-e6c009ceed3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Class     1.000000\n",
       "V11       0.154876\n",
       "V4        0.133447\n",
       "V2        0.091289\n",
       "V21       0.040413\n",
       "V19       0.034783\n",
       "V20       0.020090\n",
       "V8        0.019875\n",
       "V27       0.017580\n",
       "V28       0.009536\n",
       "Amount    0.005632\n",
       "V26       0.004455\n",
       "V25       0.003308\n",
       "V22       0.000805\n",
       "V23      -0.002685\n",
       "V15      -0.004223\n",
       "V13      -0.004570\n",
       "V24      -0.007221\n",
       "Time     -0.012323\n",
       "V6       -0.043643\n",
       "V5       -0.094974\n",
       "V9       -0.097733\n",
       "V1       -0.101347\n",
       "V18      -0.111485\n",
       "V7       -0.187257\n",
       "V3       -0.192961\n",
       "V16      -0.196539\n",
       "V10      -0.216883\n",
       "V12      -0.260593\n",
       "V14      -0.302544\n",
       "V17      -0.326481\n",
       "Name: Class, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr(numeric_only=True)[\"Class\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d0c8375-0165-4e2e-82a6-1f9bc3d266eb",
   "metadata": {},
   "source": [
    "### Plotting Histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "565bab6e-1e34-41c1-9c94-2f3700225f39",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_features = [f\"V{i}\" for i in range(1,29)] # list of strings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e2f657e6-85d7-4966-af3b-93d0f394edb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x2100 with 28 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_features = len(pca_features)\n",
    "n_cols = 4\n",
    "n_rows = int(np.ceil(n_features / n_cols))\n",
    "\n",
    "fig, axes = plt.subplots(n_rows, n_cols, figsize = (4*n_cols, 3*n_rows))\n",
    "axes = axes.flatten()\n",
    "\n",
    "for i, col in enumerate(pca_features):\n",
    "    axes[i].hist(df[col], bins=50)\n",
    "    axes[i].set_title(col, fontsize=9)\n",
    "    axes[i].tick_params(axis=\"both\", which=\"both\", labelsize=7)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91c309fd-72c9-4215-bfd2-0a609cbe7a42",
   "metadata": {},
   "source": [
    "### Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ce9bb5a3-8f49-4ed4-b8a9-ce5aaa671977",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import (\n",
    "    classification_report,\n",
    "    confusion_matrix,\n",
    "    roc_auc_score,\n",
    "    average_precision_score, #AUPRC\n",
    "    precision_recall_curve\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2f7f91aa-e90e-434e-8b1e-50dd5b3b6db7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0cf2285c-2ea7-438d-99e7-a2e6138351b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(columns=[\"Class\"]) # this is what we are trying to predict\n",
    "y = df[\"Class\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ab6f711f-52b1-4914-a200-a91a633ac209",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we need to scale time and amount with a standard scaler, the rest are already standardised by pca\n",
    "cols_to_scale = [\"Time\", \"Amount\"]\n",
    "other_cols = [c for c in X.columns if c not in cols_to_scale]\n",
    "\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        (\"scale\", StandardScaler(), cols_to_scale),\n",
    "        (\"pass\", \"passthrough\", other_cols),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a4d22181-37de-4b6b-bd04-a6f94d08d049",
   "metadata": {},
   "outputs": [],
   "source": [
    "# stratification:\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y,\n",
    "    test_size = 0.2,\n",
    "    stratify=y,\n",
    "    random_state=101\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "07655146-6dbd-4463-b361-9800735c6292",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for model eval:\n",
    "def evaluate_model(name, model, X_test, y_test):\n",
    "    y_pred = model.predict(X_test)\n",
    "    if hasattr(model, \"predict_proba\"):\n",
    "        y_score = model.predict_proba(X_test)[:, 1]\n",
    "    else:\n",
    "        y_score = model.decision_function(X_test)\n",
    "\n",
    "    auprc = average_precision_score(y_test, y_score)\n",
    "    roc_auc = roc_auc_score(y_test, y_score)\n",
    "\n",
    "    print(f\"\\n=== {name} ===\")\n",
    "    print(f\"AUPRC: {auprc:.4f}\")\n",
    "    print(f\"ROC AUC: {roc_auc:.4f}\")\n",
    "    print(\"Confusion Matrix (grain of salt):\")\n",
    "    print(confusion_matrix(y_test, y_pred))\n",
    "    print(\"\\nClassification report:\")\n",
    "    print(classification_report(y_test, y_pred, digits = 4))\n",
    "\n",
    "    return y_score\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a64de8d5-c6fb-4478-8e11-8066ec8a168f",
   "metadata": {},
   "source": [
    "#### Logistic Regression:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "92f218c0-664a-4634-92f1-96876e5d9e26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Logistic Regression (balanced) ===\n",
      "AUPRC: 0.7552\n",
      "ROC AUC: 0.9880\n",
      "Confusion Matrix (grain of salt):\n",
      "[[55463  1401]\n",
      " [    7    91]]\n",
      "\n",
      "Classification report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.9999    0.9754    0.9875     56864\n",
      "           1     0.0610    0.9286    0.1145        98\n",
      "\n",
      "    accuracy                         0.9753     56962\n",
      "   macro avg     0.5304    0.9520    0.5510     56962\n",
      "weighted avg     0.9983    0.9753    0.9860     56962\n",
      "\n"
     ]
    }
   ],
   "source": [
    "log_reg = LogisticRegression(\n",
    "    max_iter=1000,\n",
    "    class_weight = \"balanced\", \n",
    "    n_jobs=-1 # multi-threaded\n",
    ")\n",
    "\n",
    "pipe_lr = Pipeline(\n",
    "    steps = [\n",
    "        (\"prep\", preprocessor),\n",
    "        (\"model\", log_reg)\n",
    "    ]\n",
    ")\n",
    "\n",
    "pipe_lr.fit(X_train, y_train)\n",
    "y_score_lr = evaluate_model(\"Logistic Regression (balanced)\", pipe_lr, X_test, y_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "7ca52f17-4dfb-4b31-b3b8-0d31c61866e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Logistic Regression (unbalanced) ===\n",
      "AUPRC: 0.7861\n",
      "ROC AUC: 0.9795\n",
      "Confusion Matrix (grain of salt):\n",
      "[[56856     8]\n",
      " [   38    60]]\n",
      "\n",
      "Classification report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.9993    0.9999    0.9996     56864\n",
      "           1     0.8824    0.6122    0.7229        98\n",
      "\n",
      "    accuracy                         0.9992     56962\n",
      "   macro avg     0.9408    0.8061    0.8612     56962\n",
      "weighted avg     0.9991    0.9992    0.9991     56962\n",
      "\n"
     ]
    }
   ],
   "source": [
    "log_reg = LogisticRegression(\n",
    "    max_iter=1000,\n",
    "    #class_weight = \"balanced\", \n",
    "    n_jobs=-1 # multi-threaded\n",
    ")\n",
    "\n",
    "pipe_lr = Pipeline(\n",
    "    steps = [\n",
    "        (\"prep\", preprocessor),\n",
    "        (\"model\", log_reg)\n",
    "    ]\n",
    ")\n",
    "\n",
    "pipe_lr.fit(X_train, y_train)\n",
    "y_score_lr = evaluate_model(\"Logistic Regression (unbalanced)\", pipe_lr, X_test, y_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d0fe82ac-a4a8-4948-8404-02bbbbfd8f72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(577.2868020304569)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neg, pos = np.bincount(y_train)\n",
    "scale_pos_weight = neg / pos\n",
    "scale_pos_weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e4b2ea03-41c7-47c0-b285-3e7bfa32b9b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== XGBoost ===\n",
      "AUPRC: 0.8982\n",
      "ROC AUC: 0.9908\n",
      "Confusion Matrix (grain of salt):\n",
      "[[56847    17]\n",
      " [   11    87]]\n",
      "\n",
      "Classification report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.9998    0.9997    0.9998     56864\n",
      "           1     0.8365    0.8878    0.8614        98\n",
      "\n",
      "    accuracy                         0.9995     56962\n",
      "   macro avg     0.9182    0.9437    0.9306     56962\n",
      "weighted avg     0.9995    0.9995    0.9995     56962\n",
      "\n"
     ]
    }
   ],
   "source": [
    "xgb_clf = XGBClassifier(\n",
    "    tree_method = \"auto\",\n",
    "    #device=\"cuda\",\n",
    "    #predictor=\"gpu_predictor\",\n",
    "    max_depth=4,\n",
    "    n_estimators=500,\n",
    "    learning_rate=0.05,\n",
    "    subsample=0.8,\n",
    "    colsample_bytree=0.8,\n",
    "    scale_pos_weight=scale_pos_weight,\n",
    "    eval_metric=\"logloss\", #binary cross entropy\n",
    "    random_state=101,\n",
    "    n_jobs=-1\n",
    ")\n",
    "\n",
    "pipe_xgb = Pipeline(\n",
    "    steps = [\n",
    "        (\"prep\", preprocessor),\n",
    "        (\"model\", xgb_clf)\n",
    "    ]\n",
    ")\n",
    "\n",
    "pipe_xgb.fit(X_train, y_train)\n",
    "\n",
    "y_score_xgb = evaluate_model(\"XGBoost\", pipe_xgb, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "974dc39d-9584-47c8-be46-091f5bb49443",
   "metadata": {},
   "source": [
    "### Comparisons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "4c847b8f-7260-44fb-8b47-9c6db136e8a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prec_lr, rec_lr, _ = precision_recall_curve(y_test, y_score_lr)\n",
    "prec_xgb, rec_xgb, _ = precision_recall_curve(y_test, y_score_xgb)\n",
    "\n",
    "ap_lr = average_precision_score(y_test, y_score_lr)\n",
    "ap_xgb = average_precision_score(y_test, y_score_xgb)\n",
    "\n",
    "plt.figure(figsize=(6,4))\n",
    "plt.plot(rec_lr, prec_lr, label=f\"LogReg (average precision = {ap_lr:.3f})\")\n",
    "plt.plot(rec_xgb, prec_xgb, label=f\"XGBoost (av prec = {ap_xgb:.3f})\")\n",
    "plt.xlabel(\"recall\")\n",
    "plt.ylabel(\"precision\")\n",
    "plt.title(\"precision recall curve\")\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53d73452-13cf-41fd-b1ea-17f0f5d40477",
   "metadata": {},
   "source": [
    "### Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "9d36b425-a1f3-46c9-84b5-bd061b9cea38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float32(1.2455843e-05)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample = X_test.iloc[[0]]\n",
    "fraud_prob = pipe_xgb.predict_proba(sample)[:, 1][0]\n",
    "fraud_prob"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pytorch kits",
   "language": "python",
   "name": "venv_name"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}