outqrf is an R package used for outlier detection. Each numeric variable is regressed onto all other variables using a quantile random forest (QRF). We use ranger to perform the fitting and prediction of quantile regression forests (QRF). Next, we will compute the rank of the observed values in the predicted results' quantiles. If the rank of the observed value exceeds the threshold, the observed value is considered an outlier.
Since the same predicted value might be distributed across multiple quantiles in the predicted quantile results, this affects our location finding for the observed value. Therefore, we also used a method similar to the outForest package to compare the observed value with the 50% quantile value again to determine the final quantile result.
# Development version
devtools::install_github("flystar233/outqrf")We first generate a data set with about 5% outliers values in each numeric column.
#Generate data with outliers in numeric columns
irisWithOutliers <- generateOutliers(iris, p = 0.05,seed =2024)
# Find outliers by quantile random forest regressions
out <- outqrf(irisWithOutliers,quantiles_type=400)
out$outliers
row col observed predicted rank
1 37 Sepal.Length 9.8 5.10 1.000000000
2 45 Sepal.Length 7.4 5.10 1.000000000
3 105 Sepal.Length 4.2 6.80 0.002506266
4 109 Sepal.Length -1.7 6.40 0.000000000
5 122 Sepal.Length 13.6 6.40 1.000000000
6 131 Sepal.Length 7.4 5.80 0.987468672
7 145 Sepal.Length 10.4 6.70 1.000000000
8 20 Sepal.Width 4.5 3.50 1.000000000
9 42 Sepal.Width 2.3 3.20 0.000000000
10 87 Sepal.Width 2.0 2.95 0.000000000
11 106 Sepal.Width 5.6 3.00 1.000000000
12 116 Sepal.Width 6.3 3.00 1.000000000
13 5 Petal.Length 14.8 1.50 1.000000000
14 49 Petal.Length -2.4 1.50 0.000000000
15 73 Petal.Length -7.0 4.60 0.000000000
16 98 Petal.Length 2.7 4.40 0.002506266
17 108 Petal.Length 14.5 5.30 1.000000000
18 131 Petal.Length 3.4 6.40 0.000000000
19 134 Petal.Length 12.6 5.10 0.989974937
20 16 Petal.Width 1.9 0.20 1.000000000
21 27 Petal.Width 2.8 0.20 1.000000000
22 31 Petal.Width -9.5 0.20 0.000000000
23 59 Petal.Width 0.8 1.50 0.000000000
24 66 Petal.Width 6.5 1.40 1.000000000
25 101 Petal.Width 2.5 1.80 0.989974937
26 105 Petal.Width 3.2 1.80 1.000000000
27 113 Petal.Width 0.9 2.10 0.000000000
28 130 Petal.Width 1.6 2.10 0.002506266
29 134 Petal.Width 1.5 2.00 0.017543860
30 135 Petal.Width 1.4 2.30 0.012531328
First, let's simply detect outliers in the data using a box plot.
# find outliers use boxplot
# 32
irisWithOutliers <- outqrf::generateOutliers(iris, p = 0.05,seed =2024)
boxplot_num = 0
for (i in names(irisWithOutliers)[sapply(irisWithOutliers,is.numeric)]){
q1 <- quantile(irisWithOutliers[,i], 0.25)
q3 <- quantile(irisWithOutliers[,i], 0.75)
iqr <- q3 - q1
lower_bound <- q1 - 1.5 * iqr
upper_bound <- q3 + 1.5 * iqr
num <- sum(irisWithOutliers[,i]<lower_bound|irisWithOutliers[,i]>upper_bound)
boxplot_num<-boxplot_num+num
}
boxplot_num
# 28
Then, use outqtf and outForest respectively to detect outliers.
qrf <- outqrf(irisWithOutliers,quantiles_type=400)
rf <- outForest(irisWithOutliers)
evaluateOutliers(iris,irisWithOutliers,qrf$outliers)
#Actual Predicted Cover Coverage Efficiency
# 32.00 30.00 24.00 0.75 0.8
evaluateOutliers(iris,irisWithOutliers,rf$outliers)
#Actual Predicted Cover Coverage Efficiency
# 32.00 19.00 19.00 0.59 1.00
We can even display the original values and outliers of the data using a paired box plot.
plot(qrf)
data <- diamonds|>select(price,carat,cut,color,clarity)
data2 <- outqrf::generateOutliers(data, p = 0.001,seed =2024)
qrf <- outqrf(data2,num.threads=8,quantiles_type=400)
# The process can be slow because it needs to predict the value at 400|1000 quantiles for each observation.
rf <- outForest(data2)
evaluateOutliers(data,data2,qrf$outliers)
#Actual Predicted Cover Coverage Efficiency
#108.00 336.00 103.00 0.95 0.31
evaluateOutliers(data,data2,rf$outliers)
#Actual Predicted Cover Coverage Efficiency
#108.00 687.00 104.00 0.96 0.15