Imbalanced classification: credit card fraud detection

Original Link : https://keras.io/examples/structured_data/imbalanced_classification/
Last Checked at : 2024-11-22

Author: fchollet
Date created: 2019/05/28
Last modified: 2020/04/17
Description: Demonstration of how to handle highly imbalanced classification problems.

ⓘ This example uses Keras 3

Colab

GitHub

Introduction

This example looks at the Kaggle Credit Card Fraud Detection dataset to demonstrate how to train a classification model on data with highly imbalanced classes.

First, vectorize the CSV data

import csv
import numpy as np

# Get the real data from https://www.kaggle.com/mlg-ulb/creditcardfraud/
fname = "/Users/fchollet/Downloads/creditcard.csv"

all_features = []
all_targets = []
with open(fname) as f:
    for i, line in enumerate(f):
        if i == 0:
            print("HEADER:", line.strip())
            continue  # Skip header
        fields = line.strip().split(",")
        all_features.append([float(v.replace('"', "")) for v in fields[:-1]])
        all_targets.append([int(fields[-1].replace('"', ""))])
        if i == 1:
            print("EXAMPLE FEATURES:", all_features[-1])

features = np.array(all_features, dtype="float32")
targets = np.array(all_targets, dtype="uint8")
print("features.shape:", features.shape)
print("targets.shape:", targets.shape)

Result

HEADER: "Time","V1","V2","V3","V4","V5","V6","V7","V8","V9","V10","V11","V12","V13","V14","V15","V16","V17","V18","V19","V20","V21","V22","V23","V24","V25","V26","V27","V28","Amount","Class"
EXAMPLE FEATURES: [0.0, -1.3598071336738, -0.0727811733098497, 2.53634673796914, 1.37815522427443, -0.338320769942518, 0.462387777762292, 0.239598554061257, 0.0986979012610507, 0.363786969611213, 0.0907941719789316, -0.551599533260813, -0.617800855762348, -0.991389847235408, -0.311169353699879, 1.46817697209427, -0.470400525259478, 0.207971241929242, 0.0257905801985591, 0.403992960255733, 0.251412098239705, -0.018306777944153, 0.277837575558899, -0.110473910188767, 0.0669280749146731, 0.128539358273528, -0.189114843888824, 0.133558376740387, -0.0210530534538215, 149.62]
features.shape: (284807, 30)
targets.shape: (284807, 1)

Prepare a validation set

num_val_samples = int(len(features) * 0.2)
train_features = features[:-num_val_samples]
train_targets = targets[:-num_val_samples]
val_features = features[-num_val_samples:]
val_targets = targets[-num_val_samples:]

print("Number of training samples:", len(train_features))
print("Number of validation samples:", len(val_features))

Result

Number of training samples: 227846
Number of validation samples: 56961

Analyze class imbalance in the targets

counts = np.bincount(train_targets[:, 0])
print(
    "Number of positive samples in training data: {} ({:.2f}% of total)".format(
        counts[1], 100 * float(counts[1]) / len(train_targets)
    )
)

weight_for_0 = 1.0 / counts[0]
weight_for_1 = 1.0 / counts[1]

Result

Number of positive samples in training data: 417 (0.18% of total)

Normalize the data using training set statistics

mean = np.mean(train_features, axis=0)
train_features -= mean
val_features -= mean
std = np.std(train_features, axis=0)
train_features /= std
val_features /= std

Build a binary classification model

import keras

model = keras.Sequential(
    [
        keras.Input(shape=train_features.shape[1:]),
        keras.layers.Dense(256, activation="relu"),
        keras.layers.Dense(256, activation="relu"),
        keras.layers.Dropout(0.3),
        keras.layers.Dense(256, activation="relu"),
        keras.layers.Dropout(0.3),
        keras.layers.Dense(1, activation="sigmoid"),
    ]
)
model.summary()

Result

Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape              ┃    Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩
│ dense (Dense)                   │ (None, 256)               │      7,936 │
├─────────────────────────────────┼───────────────────────────┼────────────┤
│ dense_1 (Dense)                 │ (None, 256)               │     65,792 │
├─────────────────────────────────┼───────────────────────────┼────────────┤
│ dropout (Dropout)               │ (None, 256)               │          0 │
├─────────────────────────────────┼───────────────────────────┼────────────┤
│ dense_2 (Dense)                 │ (None, 256)               │     65,792 │
├─────────────────────────────────┼───────────────────────────┼────────────┤
│ dropout_1 (Dropout)             │ (None, 256)               │          0 │
├─────────────────────────────────┼───────────────────────────┼────────────┤
│ dense_3 (Dense)                 │ (None, 1)                 │        257 │
└─────────────────────────────────┴───────────────────────────┴────────────┘
 Total params: 139,777 (546.00 KB)
 Trainable params: 139,777 (546.00 KB)
 Non-trainable params: 0 (0.00 B)

Train the model with `class_weight` argument

metrics = [
    keras.metrics.FalseNegatives(name="fn"),
    keras.metrics.FalsePositives(name="fp"),
    keras.metrics.TrueNegatives(name="tn"),
    keras.metrics.TruePositives(name="tp"),
    keras.metrics.Precision(name="precision"),
    keras.metrics.Recall(name="recall"),
]

model.compile(
    optimizer=keras.optimizers.Adam(1e-2), loss="binary_crossentropy", metrics=metrics
)

callbacks = [keras.callbacks.ModelCheckpoint("fraud_model_at_epoch_{epoch}.keras")]
class_weight = {0: weight_for_0, 1: weight_for_1}

model.fit(
    train_features,
    train_targets,
    batch_size=2048,
    epochs=30,
    verbose=2,
    callbacks=callbacks,
    validation_data=(val_features, val_targets),
    class_weight=class_weight,
)

Result

Epoch 1/30
112/112 - 3s - 24ms/step - fn: 39.0000 - fp: 25593.0000 - loss: 2.2586e-06 - precision: 0.0146 - recall: 0.9065 - tn: 201836.0000 - tp: 378.0000 - val_fn: 5.0000 - val_fp: 3430.0000 - val_loss: 0.1872 - val_precision: 0.0200 - val_recall: 0.9333 - val_tn: 53456.0000 - val_tp: 70.0000
Epoch 2/30
112/112 - 0s - 991us/step - fn: 32.0000 - fp: 7936.0000 - loss: 1.5505e-06 - precision: 0.0463 - recall: 0.9233 - tn: 219493.0000 - tp: 385.0000 - val_fn: 7.0000 - val_fp: 2351.0000 - val_loss: 0.1930 - val_precision: 0.0281 - val_recall: 0.9067 - val_tn: 54535.0000 - val_tp: 68.0000
Epoch 3/30
112/112 - 0s - 1ms/step - fn: 31.0000 - fp: 6716.0000 - loss: 1.2987e-06 - precision: 0.0544 - recall: 0.9257 - tn: 220713.0000 - tp: 386.0000 - val_fn: 4.0000 - val_fp: 3374.0000 - val_loss: 0.1781 - val_precision: 0.0206 - val_recall: 0.9467 - val_tn: 53512.0000 - val_tp: 71.0000
Epoch 4/30
112/112 - 0s - 1ms/step - fn: 25.0000 - fp: 7348.0000 - loss: 1.1292e-06 - precision: 0.0506 - recall: 0.9400 - tn: 220081.0000 - tp: 392.0000 - val_fn: 6.0000 - val_fp: 1405.0000 - val_loss: 0.0796 - val_precision: 0.0468 - val_recall: 0.9200 - val_tn: 55481.0000 - val_tp: 69.0000
Epoch 5/30
112/112 - 0s - 926us/step - fn: 19.0000 - fp: 6720.0000 - loss: 8.0334e-07 - precision: 0.0559 - recall: 0.9544 - tn: 220709.0000 - tp: 398.0000 - val_fn: 11.0000 - val_fp: 315.0000 - val_loss: 0.0212 - val_precision: 0.1689 - val_recall: 0.8533 - val_tn: 56571.0000 - val_tp: 64.0000
Epoch 6/30
112/112 - 0s - 1ms/step - fn: 19.0000 - fp: 6706.0000 - loss: 8.6899e-07 - precision: 0.0560 - recall: 0.9544 - tn: 220723.0000 - tp: 398.0000 - val_fn: 8.0000 - val_fp: 1262.0000 - val_loss: 0.0801 - val_precision: 0.0504 - val_recall: 0.8933 - val_tn: 55624.0000 - val_tp: 67.0000
Epoch 7/30
112/112 - 0s - 1ms/step - fn: 15.0000 - fp: 5161.0000 - loss: 6.5298e-07 - precision: 0.0723 - recall: 0.9640 - tn: 222268.0000 - tp: 402.0000 - val_fn: 7.0000 - val_fp: 1157.0000 - val_loss: 0.0623 - val_precision: 0.0555 - val_recall: 0.9067 - val_tn: 55729.0000 - val_tp: 68.0000
Epoch 8/30
112/112 - 0s - 1ms/step - fn: 11.0000 - fp: 6381.0000 - loss: 6.7164e-07 - precision: 0.0598 - recall: 0.9736 - tn: 221048.0000 - tp: 406.0000 - val_fn: 10.0000 - val_fp: 346.0000 - val_loss: 0.0270 - val_precision: 0.1582 - val_recall: 0.8667 - val_tn: 56540.0000 - val_tp: 65.0000
Epoch 9/30
112/112 - 0s - 1ms/step - fn: 16.0000 - fp: 7259.0000 - loss: 8.9098e-07 - precision: 0.0523 - recall: 0.9616 - tn: 220170.0000 - tp: 401.0000 - val_fn: 7.0000 - val_fp: 1998.0000 - val_loss: 0.1073 - val_precision: 0.0329 - val_recall: 0.9067 - val_tn: 54888.0000 - val_tp: 68.0000
Epoch 10/30
112/112 - 0s - 999us/step - fn: 19.0000 - fp: 7792.0000 - loss: 9.2179e-07 - precision: 0.0486 - recall: 0.9544 - tn: 219637.0000 - tp: 398.0000 - val_fn: 7.0000 - val_fp: 1515.0000 - val_loss: 0.0800 - val_precision: 0.0430 - val_recall: 0.9067 - val_tn: 55371.0000 - val_tp: 68.0000
Epoch 11/30
112/112 - 0s - 1ms/step - fn: 13.0000 - fp: 5828.0000 - loss: 6.4193e-07 - precision: 0.0648 - recall: 0.9688 - tn: 221601.0000 - tp: 404.0000 - val_fn: 9.0000 - val_fp: 794.0000 - val_loss: 0.0410 - val_precision: 0.0767 - val_recall: 0.8800 - val_tn: 56092.0000 - val_tp: 66.0000
Epoch 12/30
112/112 - 0s - 959us/step - fn: 10.0000 - fp: 6400.0000 - loss: 7.4358e-07 - precision: 0.0598 - recall: 0.9760 - tn: 221029.0000 - tp: 407.0000 - val_fn: 8.0000 - val_fp: 593.0000 - val_loss: 0.0466 - val_precision: 0.1015 - val_recall: 0.8933 - val_tn: 56293.0000 - val_tp: 67.0000
Epoch 13/30
112/112 - 0s - 913us/step - fn: 9.0000 - fp: 5756.0000 - loss: 6.8158e-07 - precision: 0.0662 - recall: 0.9784 - tn: 221673.0000 - tp: 408.0000 - val_fn: 11.0000 - val_fp: 280.0000 - val_loss: 0.0336 - val_precision: 0.1860 - val_recall: 0.8533 - val_tn: 56606.0000 - val_tp: 64.0000
Epoch 14/30
112/112 - 0s - 960us/step - fn: 13.0000 - fp: 6699.0000 - loss: 1.0667e-06 - precision: 0.0569 - recall: 0.9688 - tn: 220730.0000 - tp: 404.0000 - val_fn: 9.0000 - val_fp: 1165.0000 - val_loss: 0.0885 - val_precision: 0.0536 - val_recall: 0.8800 - val_tn: 55721.0000 - val_tp: 66.0000
Epoch 15/30
112/112 - 0s - 1ms/step - fn: 15.0000 - fp: 6705.0000 - loss: 6.8100e-07 - precision: 0.0566 - recall: 0.9640 - tn: 220724.0000 - tp: 402.0000 - val_fn: 10.0000 - val_fp: 750.0000 - val_loss: 0.0367 - val_precision: 0.0798 - val_recall: 0.8667 - val_tn: 56136.0000 - val_tp: 65.0000
Epoch 16/30
112/112 - 0s - 1ms/step - fn: 8.0000 - fp: 4288.0000 - loss: 4.1541e-07 - precision: 0.0871 - recall: 0.9808 - tn: 223141.0000 - tp: 409.0000 - val_fn: 11.0000 - val_fp: 351.0000 - val_loss: 0.0199 - val_precision: 0.1542 - val_recall: 0.8533 - val_tn: 56535.0000 - val_tp: 64.0000
Epoch 17/30
112/112 - 0s - 949us/step - fn: 8.0000 - fp: 4598.0000 - loss: 4.3510e-07 - precision: 0.0817 - recall: 0.9808 - tn: 222831.0000 - tp: 409.0000 - val_fn: 10.0000 - val_fp: 688.0000 - val_loss: 0.0296 - val_precision: 0.0863 - val_recall: 0.8667 - val_tn: 56198.0000 - val_tp: 65.0000
Epoch 18/30
112/112 - 0s - 946us/step - fn: 7.0000 - fp: 5544.0000 - loss: 4.6239e-07 - precision: 0.0689 - recall: 0.9832 - tn: 221885.0000 - tp: 410.0000 - val_fn: 8.0000 - val_fp: 444.0000 - val_loss: 0.0260 - val_precision: 0.1311 - val_recall: 0.8933 - val_tn: 56442.0000 - val_tp: 67.0000
Epoch 19/30
112/112 - 0s - 972us/step - fn: 3.0000 - fp: 2920.0000 - loss: 2.7543e-07 - precision: 0.1242 - recall: 0.9928 - tn: 224509.0000 - tp: 414.0000 - val_fn: 9.0000 - val_fp: 510.0000 - val_loss: 0.0245 - val_precision: 0.1146 - val_recall: 0.8800 - val_tn: 56376.0000 - val_tp: 66.0000
Epoch 20/30
112/112 - 0s - 1ms/step - fn: 6.0000 - fp: 5351.0000 - loss: 5.7495e-07 - precision: 0.0713 - recall: 0.9856 - tn: 222078.0000 - tp: 411.0000 - val_fn: 9.0000 - val_fp: 547.0000 - val_loss: 0.0255 - val_precision: 0.1077 - val_recall: 0.8800 - val_tn: 56339.0000 - val_tp: 66.0000
Epoch 21/30
112/112 - 0s - 1ms/step - fn: 6.0000 - fp: 3808.0000 - loss: 5.1475e-07 - precision: 0.0974 - recall: 0.9856 - tn: 223621.0000 - tp: 411.0000 - val_fn: 10.0000 - val_fp: 624.0000 - val_loss: 0.0320 - val_precision: 0.0943 - val_recall: 0.8667 - val_tn: 56262.0000 - val_tp: 65.0000
Epoch 22/30
112/112 - 0s - 1ms/step - fn: 6.0000 - fp: 5117.0000 - loss: 5.5465e-07 - precision: 0.0743 - recall: 0.9856 - tn: 222312.0000 - tp: 411.0000 - val_fn: 10.0000 - val_fp: 836.0000 - val_loss: 0.0556 - val_precision: 0.0721 - val_recall: 0.8667 - val_tn: 56050.0000 - val_tp: 65.0000
Epoch 23/30
112/112 - 0s - 939us/step - fn: 8.0000 - fp: 5583.0000 - loss: 5.5407e-07 - precision: 0.0683 - recall: 0.9808 - tn: 221846.0000 - tp: 409.0000 - val_fn: 12.0000 - val_fp: 501.0000 - val_loss: 0.0300 - val_precision: 0.1117 - val_recall: 0.8400 - val_tn: 56385.0000 - val_tp: 63.0000
Epoch 24/30
112/112 - 0s - 958us/step - fn: 5.0000 - fp: 3933.0000 - loss: 4.7133e-07 - precision: 0.0948 - recall: 0.9880 - tn: 223496.0000 - tp: 412.0000 - val_fn: 12.0000 - val_fp: 211.0000 - val_loss: 0.0326 - val_precision: 0.2299 - val_recall: 0.8400 - val_tn: 56675.0000 - val_tp: 63.0000
Epoch 25/30
112/112 - 0s - 1ms/step - fn: 7.0000 - fp: 5695.0000 - loss: 7.1277e-07 - precision: 0.0672 - recall: 0.9832 - tn: 221734.0000 - tp: 410.0000 - val_fn: 9.0000 - val_fp: 802.0000 - val_loss: 0.0598 - val_precision: 0.0760 - val_recall: 0.8800 - val_tn: 56084.0000 - val_tp: 66.0000
Epoch 26/30
112/112 - 0s - 949us/step - fn: 5.0000 - fp: 3853.0000 - loss: 4.1797e-07 - precision: 0.0966 - recall: 0.9880 - tn: 223576.0000 - tp: 412.0000 - val_fn: 8.0000 - val_fp: 771.0000 - val_loss: 0.0409 - val_precision: 0.0800 - val_recall: 0.8933 - val_tn: 56115.0000 - val_tp: 67.0000
Epoch 27/30
112/112 - 0s - 947us/step - fn: 4.0000 - fp: 3873.0000 - loss: 3.7369e-07 - precision: 0.0964 - recall: 0.9904 - tn: 223556.0000 - tp: 413.0000 - val_fn: 6.0000 - val_fp: 2208.0000 - val_loss: 0.1370 - val_precision: 0.0303 - val_recall: 0.9200 - val_tn: 54678.0000 - val_tp: 69.0000
Epoch 28/30
112/112 - 0s - 892us/step - fn: 5.0000 - fp: 4619.0000 - loss: 4.1290e-07 - precision: 0.0819 - recall: 0.9880 - tn: 222810.0000 - tp: 412.0000 - val_fn: 8.0000 - val_fp: 551.0000 - val_loss: 0.0273 - val_precision: 0.1084 - val_recall: 0.8933 - val_tn: 56335.0000 - val_tp: 67.0000
Epoch 29/30
112/112 - 0s - 931us/step - fn: 1.0000 - fp: 3336.0000 - loss: 2.5478e-07 - precision: 0.1109 - recall: 0.9976 - tn: 224093.0000 - tp: 416.0000 - val_fn: 9.0000 - val_fp: 487.0000 - val_loss: 0.0238 - val_precision: 0.1193 - val_recall: 0.8800 - val_tn: 56399.0000 - val_tp: 66.0000
Epoch 30/30
112/112 - 0s - 1ms/step - fn: 2.0000 - fp: 3521.0000 - loss: 4.1991e-07 - precision: 0.1054 - recall: 0.9952 - tn: 223908.0000 - tp: 415.0000 - val_fn: 10.0000 - val_fp: 462.0000 - val_loss: 0.0331 - val_precision: 0.1233 - val_recall: 0.8667 - val_tn: 56424.0000 - val_tp: 65.0000

<keras.src.callbacks.history.History at 0x7f22b41f3430>

Conclusions

At the end of training, out of 56,961 validation transactions, we are:

Correctly identifying 66 of them as fraudulent
Missing 9 fraudulent transactions
At the cost of incorrectly flagging 441 legitimate transactions

In the real world, one would put an even higher weight on class 1, so as to reflect that False Negatives are more costly than False Positives.

Next time your credit card gets declined in an online purchase – this is why.

Example available on HuggingFace.

Imbalanced classification: credit card fraud detection

Introduction

First, vectorize the CSV data

Prepare a validation set

Analyze class imbalance in the targets

Normalize the data using training set statistics

Build a binary classification model

Train the model with class_weight argument

Conclusions

Train the model with `class_weight` argument