View source: R/GetDetectorScore_GetLabels.R
| GetLabels | R Documentation |
GetLabels Calculates the start and end positions of each window that
are focused on the real anomalies. This windows can be used to know if the detected anomaly is a
true positive or not.
GetLabels(data)
data |
All dataset with training and test datasets with at least |
data must be a data.frame with timestamp, value, is.anomaly
and is.real.anomaly columns. timestamp column can be numeric, of type POSIXct, or a
character type date convertible to POSIXct. see GetWindowsLimits to know more
about how to get start.limit and end.limit columns.
Same data set with two additional columns label and first.tp.
first.tp indicates for each window Which is the position of first true positive.
label indicates for each detection if it is a TP, FP, TN or FN.
A. Lavin and S. Ahmad, “Evaluating Real-time Anomaly Detection Algorithms – the Numenta Anomaly Benchmark,” in 14th International Conference on Machine Learning and Applications (IEEE ICMLA’15), 2015.
## Generate data set.seed(100) n <- 180 x <- sample(1:100, n, replace = TRUE) x[70:90] <- sample(110:115, 21, replace = TRUE) x[25] <- 200 x[150] <- 170 df <- data.frame(timestamp = 1:n, value = x) # Add is.real.anomaly column df$is.real.anomaly <- 0 df[c(25,80,150), "is.real.anomaly"] <- 1 ## Calculate anomalies result <- CpSdEwma( data = df$value, n.train = 5, threshold = 0.01, l = 3 ) res <- cbind(df, result) # Get Window Limits data <- GetWindowsLimits(res) data[data$is.real.anomaly == 1,] # Get labels data <- GetLabels(data) data[data$is.real.anomaly == 1 | data$is.anomaly == 1,] # Plot results PlotDetections(res, print.real.anomaly = TRUE, print.time.window = TRUE)
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