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. You can download the data by clicking “Download” at the link, or if you’re setup with a kaggle API key at "~/.kaggle/kagle.json", you can run the following:
reticulate::py_install("kaggle", pip = TRUE)
system("kaggle datasets download -d mlg-ulb/creditcardfraud")
zip::unzip("creditcardfraud.zip", files = "creditcard.csv")library(tensorflow)
library(keras)
set.seed(1234)df <- readr::read_csv("creditcard.csv")
tibble::glimpse(df)Rows: 284,807
Columns: 31
$ Time <dbl> 0, 0, 1, 1, 2, 2, 4, 7, 7, 9, 10, 10, 10, 11, 12, 12, 12, 1…
$ V1 <dbl> -1.3598071, 1.1918571, -1.3583541, -0.9662717, -1.1582331, …
$ V2 <dbl> -0.07278117, 0.26615071, -1.34016307, -0.18522601, 0.877736…
$ V3 <dbl> 2.53634674, 0.16648011, 1.77320934, 1.79299334, 1.54871785,…
$ V4 <dbl> 1.37815522, 0.44815408, 0.37977959, -0.86329128, 0.40303393…
$ V5 <dbl> -0.33832077, 0.06001765, -0.50319813, -0.01030888, -0.40719…
$ V6 <dbl> 0.46238778, -0.08236081, 1.80049938, 1.24720317, 0.09592146…
$ V7 <dbl> 0.239598554, -0.078802983, 0.791460956, 0.237608940, 0.5929…
$ V8 <dbl> 0.098697901, 0.085101655, 0.247675787, 0.377435875, -0.2705…
$ V9 <dbl> 0.3637870, -0.2554251, -1.5146543, -1.3870241, 0.8177393, -…
$ V10 <dbl> 0.09079417, -0.16697441, 0.20764287, -0.05495192, 0.7530744…
$ V11 <dbl> -0.55159953, 1.61272666, 0.62450146, -0.22648726, -0.822842…
$ V12 <dbl> -0.61780086, 1.06523531, 0.06608369, 0.17822823, 0.53819555…
$ V13 <dbl> -0.99138985, 0.48909502, 0.71729273, 0.50775687, 1.34585159…
$ V14 <dbl> -0.31116935, -0.14377230, -0.16594592, -0.28792375, -1.1196…
$ V15 <dbl> 1.468176972, 0.635558093, 2.345864949, -0.631418118, 0.1751…
$ V16 <dbl> -0.47040053, 0.46391704, -2.89008319, -1.05964725, -0.45144…
$ V17 <dbl> 0.207971242, -0.114804663, 1.109969379, -0.684092786, -0.23…
$ V18 <dbl> 0.02579058, -0.18336127, -0.12135931, 1.96577500, -0.038194…
$ V19 <dbl> 0.40399296, -0.14578304, -2.26185710, -1.23262197, 0.803486…
$ V20 <dbl> 0.25141210, -0.06908314, 0.52497973, -0.20803778, 0.4085423…
$ V21 <dbl> -0.018306778, -0.225775248, 0.247998153, -0.108300452, -0.0…
$ V22 <dbl> 0.277837576, -0.638671953, 0.771679402, 0.005273597, 0.7982…
$ V23 <dbl> -0.110473910, 0.101288021, 0.909412262, -0.190320519, -0.13…
$ V24 <dbl> 0.06692807, -0.33984648, -0.68928096, -1.17557533, 0.141266…
$ V25 <dbl> 0.12853936, 0.16717040, -0.32764183, 0.64737603, -0.2060095…
$ V26 <dbl> -0.18911484, 0.12589453, -0.13909657, -0.22192884, 0.502292…
$ V27 <dbl> 0.133558377, -0.008983099, -0.055352794, 0.062722849, 0.219…
$ V28 <dbl> -0.021053053, 0.014724169, -0.059751841, 0.061457629, 0.215…
$ Amount <dbl> 149.62, 2.69, 378.66, 123.50, 69.99, 3.67, 4.99, 40.80, 93.…
$ Class <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…val_idxs <- nrow(df) %>% sample.int(., ceiling( . * 0.2))
val_df <- df[val_idxs, ]
train_df <- df[-val_idxs, ]
sprintf("Number of training samples: %s", nrow(train_df))[1] "Number of training samples: 227845"sprintf("Number of validation samples: %s", nrow(val_df))[1] "Number of validation samples: 56962"train_df$Class %>% {
cat(sprintf(
"Number of positive samples in training data: %s (%.2f%% of total)\n",
sum(.), 100 * mean(.)))
}Number of positive samples in training data: 395 (0.17% of total)weight_for_0 <- 1 / sum(train_df$Class == 0)
weight_for_1 <- 1 / sum(train_df$Class == 1)feature_names <- colnames(train_df) %>% setdiff("Class")
means <- lapply(train_df[feature_names], mean)
stds <- lapply(train_df[feature_names], sd)
for (name in feature_names) {
train_df[[name]] %<>% { (. - means[[name]]) / stds[[name]] }
val_df[[name]] %<>% { (. - means[[name]]) / stds[[name]] }
}model <- keras_model_sequential(input_shape = c(length(feature_names))) %>%
layer_dense(256, activation = "relu") %>%
layer_dense(256, activation = "relu") %>%
layer_dropout(0.3) %>%
layer_dense(256, activation = "relu") %>%
layer_dropout(0.3) %>%
layer_dense(1, activation = "sigmoid")
modelModel: "sequential"
____________________________________________________________________________
Layer (type) Output Shape Param #
============================================================================
dense_3 (Dense) (None, 256) 7936
dense_2 (Dense) (None, 256) 65792
dropout_1 (Dropout) (None, 256) 0
dense_1 (Dense) (None, 256) 65792
dropout (Dropout) (None, 256) 0
dense (Dense) (None, 1) 257
============================================================================
Total params: 139,777
Trainable params: 139,777
Non-trainable params: 0
____________________________________________________________________________class_weight argument
metrics <- list(
metric_false_negatives(name = "fn"),
metric_false_positives(name = "fp"),
metric_true_negatives(name = "tn"),
metric_true_positives(name = "tp"),
metric_precision(name = "precision"),
metric_recall(name = "recall")
)
model %>% compile(
optimizer = optimizer_adam(1e-2),
loss = "binary_crossentropy",
metrics = metrics
)
class_weight <- list("0" = weight_for_0,
"1" = weight_for_1)
callbacks <- list(
callback_model_checkpoint("fraud_model_at_epoch_{epoch}.h5"))
train_features <- as.matrix(train_df[feature_names])
train_targets <- as.matrix(train_df$Class)
validation_data <- list(
as.matrix(val_df[feature_names]),
as.matrix(val_df$Class))
model %>%
fit(train_features, train_targets,
validation_data = validation_data,
class_weight = class_weight,
batch_size = 2048, epochs = 30,
callbacks = callbacks,
verbose = 2)val_pred <- model %>%
predict(as.matrix(val_df[feature_names])) %>%
{ ifelse(. > .5, 1, 0) }
pred_correct <- val_df$Class == val_pred
cat(sprintf("Validation accuracy: %.2f", mean(pred_correct)))Validation accuracy: 0.99fraudulent <- val_df$Class == 1
n_fraudulent_detected <- sum(fraudulent & pred_correct)
n_fraudulent_missed <- sum(fraudulent & !pred_correct)
n_legitimate_flagged <- sum(!fraudulent & !pred_correct)At the end of training, out of 56,962 validation transactions, we are:
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.
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