Anomaly Detection With Deep Learning in R With H2O [Code Snippet]
Anomaly Detection With Deep Learning in R With H2O [Code Snippet]
With this code snippet, you'll be able to download an ECG dataset from the internet and perform deep learning-based anomaly detection on it.
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The following R script downloads an ECG dataset (training and validation) from the internet and performs deep learning-based anomaly detection on it.
library(h2o)
h2o.init()
# Import ECG train and test data into the H2O cluster
train_ecg <- h2o.importFile(
path = "http://h2o-public-test-data.s3.amazonaws.com/smalldata/anomaly/ecg_discord_train.csv",
header = FALSE,
sep = ",")
test_ecg <- h2o.importFile(
path = "http://h2o-public-test-data.s3.amazonaws.com/smalldata/anomaly/ecg_discord_test.csv",
header = FALSE,
sep = ",")
# Train deep autoencoder learning model on "normal"
# training data, y ignored
anomaly_model <- h2o.deeplearning(
x = names(train_ecg),
training_frame = train_ecg,
activation = "Tanh",
autoencoder = TRUE,
hidden = c(50,20,50),
sparse = TRUE,
l1 = 1e-4,
epochs = 100)
# Compute reconstruction error with the Anomaly
# detection app (MSE between output and input layers)
recon_error <- h2o.anomaly(anomaly_model, test_ecg)
# Pull reconstruction error data into R and
# plot to find outliers (last 3 heartbeats)
recon_error <- as.data.frame(recon_error)
recon_error
plot.ts(recon_error)
# Note: Testing = Reconstructing the test dataset
test_recon <- h2o.predict(anomaly_model, test_ecg)
head(test_recon)
h2o.saveModel(anomaly_model, "/Users/avkashchauhan/learn/tmp/anomaly_model.bin")
h2o.download_pojo(anomaly_model, "/Users/avkashchauhan/learn/tmp/", get_jar = TRUE)
h2o.shutdown(prompt= FALSE)That's it — enjoy!
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Published at DZone with permission of Avkash Chauhan , DZone MVB. See the original article here.
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