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knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
library(outlierensembles) library(ggplot2) library(outlierensembles)
Let us add some anomalies inside an ring.
set.seed(1) r1 <-runif(803) r2 <-rnorm(803, mean=5) theta = 2*pi*r1; R1 <- 2 R2 <- 2 dist = r2+R2; x = dist * cos(theta) y = dist * sin(theta) X <- data.frame( x1 = x, x2 = y ) labs <- c(rep(0,800), rep(1,3)) nn <- dim(X)[1] knn_auc <- lof_auc <- cof_auc <- rep(0, 10) mu <- 0 z <- cbind(rnorm(3,mu, sd=0.2), rnorm(3,0, sd=0.2)) X[801:803, 1:2] <- z ggplot(X, aes(x1, x2)) + geom_point()
Let us find outliers using DDoutlier R package and use the IRT ensemble to construct an ensemble score.
y1 <- DDoutlier::KNN_AGG(X, k_min=10, k_max=20) y2 <- DDoutlier::LOF(X, k=10) y3 <- DDoutlier::COF(X, k=10) y4 <- DDoutlier::INFLO(X, k=10) y5 <- DDoutlier::KDEOS(X, k_min=10, k_max=20) y6 <- DDoutlier::LDF(X, k=10) y7 <- DDoutlier::LDOF(X, k=10) Y <- cbind.data.frame(y1, y2, y3, y4, y5, y6, y7) ens1 <- irt_ensemble(Y) df <- cbind.data.frame(X, ens1$scores) colnames(df)[3] <- "IRT" ggplot(df, aes(x1, x2)) + geom_point(aes(color=IRT)) + scale_color_gradient(low="yellow", high="red")
Then we do the greedy ensemble.
ens2 <- greedy_ensemble(Y) df <- cbind.data.frame(X, ens2$scores) colnames(df)[3] <- "Greedy" ggplot(df, aes(x1, x2)) + geom_point(aes(color=Greedy)) + scale_color_gradient(low="yellow", high="red")
We do the ICWA ensemble next.
ens3 <- icwa_ensemble(Y) df <- cbind.data.frame(X, ens3) colnames(df)[3] <- "ICWA" ggplot(df, aes(x1, x2)) + geom_point(aes(color=ICWA)) + scale_color_gradient(low="yellow", high="red")
Next, we use the maximum scores to build the ensemble.
ens4 <- max_ensemble(Y) df <- cbind.data.frame(X, ens4) colnames(df)[3] <- "Max" ggplot(df, aes(x1, x2)) + geom_point(aes(color=Max)) + scale_color_gradient(low="yellow", high="red")
Then, we use the a threshold sum to construct the ensemble.
ens5 <- threshold_ensemble(Y) df <- cbind.data.frame(X, ens5) colnames(df)[3] <- "Threshold" ggplot(df, aes(x1, x2)) + geom_point(aes(color=Threshold)) + scale_color_gradient(low="yellow", high="red")
Finally, we use the mean values as the ensemble score.
ens6 <- average_ensemble(Y) df <- cbind.data.frame(X, ens6) colnames(df)[3] <- "Average" ggplot(df, aes(x1, x2)) + geom_point(aes(color=Average)) + scale_color_gradient(low="yellow", high="red")
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