| ams_iforest <- function(url, train_start, train_end, test_start, test_end, threshold_score) { |
| |
| res <- get_data(url) |
| num_metrics <- length(res$metrics) |
| anomalies <- data.frame() |
| |
| metricname <- res$metrics[[1]]$metricname |
| data <- data.frame(as.numeric(names(res$metrics[[1]]$metrics)), as.numeric(res$metrics[[1]]$metrics)) |
| names(data) <- c("TS", res$metrics[[1]]$metricname) |
| |
| for (i in 2:num_metrics) { |
| metricname <- res$metrics[[i]]$metricname |
| df <- data.frame(as.numeric(names(res$metrics[[i]]$metrics)), as.numeric(res$metrics[[i]]$metrics)) |
| names(df) <- c("TS", res$metrics[[i]]$metricname) |
| data <- merge(data, df) |
| } |
| |
| algo_data <- data[ which(df$TS >= train_start & df$TS <= train_end) , ][c(1:num_metrics+1)] |
| iForest <- IsolationTrees(algo_data) |
| test_data <- data[ which(df$TS >= test_start & df$TS <= test_end) , ] |
| |
| if_res <- AnomalyScore(test_data[c(1:num_metrics+1)], iForest) |
| for (i in 1:length(if_res$outF)) { |
| index <- test_start+i-1 |
| if (if_res$outF[i] > threshold_score) { |
| anomaly <- c(test_data[i,1], if_res$outF[i], if_res$pathLength[i]) |
| anomalies <- rbind(anomalies, anomaly) |
| } |
| } |
| |
| if(length(anomalies) > 0) { |
| names(anomalies) <- c("TS", "Anomaly Score", "Path length") |
| } |
| return (anomalies) |
| } |
