Solved – Use of Random Forests for variable importance as preprocess before another analysis

Note: this answer is incomplete.

Toy Problem:
This is a trivial problem that is typically small in dimension and as accessible as possible to human intuition and learning. Personally, I find this (link, link) demo to be accessible for my intuition and learning. So do the folks at the Max Planck Institute for Biological Cybernetics.

The form of the "non-augmented" data is: $$ begin{bmatrix} Class & X & Y\ A& x_1 & y_1\ A& x_2 & y_2\ vdots & vdots & vdots\ B& x_n & y_n\ end{bmatrix}$$

The "physics" of the "good" class is a spiral starting at the origin while the bad class is uniformly random. The human eye can see that quickly. When evaluating "variable importance" we are trying to reduce the number of columns, but the non-augmented has no columns to reduce, thus we augment with random. There is the problem of overlap, it would be better to reclassify some of the uniform random within a range of "ideal" to class "A".

So here is the code that makes the "non-augmented" data:

#housekeeping rm(list=ls())  #library library(randomForest)  #for reproducibility set.seed(08012015)  #basic n <- 1:2000 r <- 0.05*n +1  th <- n*(4*pi)/max(n)  #polar to cartesian x1=r*cos(th)  y1=r*sin(th)  #add noise x2 <- x1+0.1*r*runif(min = -1,max = 1,n=length(n)) y2 <- y1+0.1*r*runif(min = -1,max = 1,n=length(n))  #append salt and pepper x3 <- runif(min = min(x2),max = max(x2),n=length(n)) y3 <- runif(min = min(y2),max = max(y2),n=length(n))  X <- c(x2,x3) Y <- c(y2,y3) myClass <- as.factor(c(as.vector(matrix(1,nrow=length(y2))),         as.vector(matrix(2,nrow=length(y3))) ))  #plot class "A" derivation plot(x1,y1,pch=18,type="l",col="Red", lwd=2) points(x2,y2,pch=18) points(x3,y3,pch=1,col="Blue") legend(x = 65,y=65,        legend = c("true","sampled A","sampled B"),        col = c("Red","Black","Blue"),        lty = c(1,-1,-1),        pch=c(-1,18,1)) 

Here is a plot of the non-augmented data.

Here is the code to augment the "toy" for variable importance detection, and assemble into a single data frame.

#Create bad columns class of uniform randomized good columns x5 <- sample(x = X, size = length(X),replace = T) y5 <- sample(x = Y, size = length(Y),replace = T)   #assemble data into frame  data <- data.frame(myClass,                    c(X),c(Y),c(x5),c(y5) ) names(data) <- c("myclass","x","y","n1","n2") 

First a random forest (not yet with t-tests as in the Tuv reference) is used on all input columns to determine relative variable importance, and to get a sense of sufficient number of trees. It is assumed that more trees are required to get a decent fit using low importance data than with uniformly higher importance data.

#train random forest - I like h2o, but this is textbook Breimann fit.rf_imp <- randomForest(data[2:5],data$myclass,                        ntree = 2000, replace=TRUE, nodesize = 1,                        localImp=T )  varImpPlot(fit.rf_imp) plot(fit.rf_imp)  grid() importance(fit.rf_imp) 

The results for importance (in plot form) are:

The mean decrease in accuracy and mean decrease in gini have a consistent message: "n1 and n2 are low importance columns".

The results for the convergence plot are:

Although somewhat qualitative, it appears that some acceptable level of convergence has occurred by 500 trees. It is also worth noting that the converged error rate is about 22%. This leads to the inference that the "classification error" within the region of "A" is about 1 in 5.

The code for an updated forest, one not including low-importance columns, is:

fit.rf <- randomForest(data[2:3],data$myclass,                            ntree = 500, replace=TRUE, nodesize = 1,                            localImp=T ) 

A plot of actual vs. predicted has excellent accuracy. Code to derive the plot follows:

data2 <- predict(fit.rf,newdata=data[data$myclass==1,c(2,3,4,5)],                  type="response")  #separate class "1" from training data idx1a <- which(data[,1]==1)  #separate class "1" from the predicted data idx1b <- which(data2==1)  #separate class "2" from training data idx2a <- which(data[,1]==2)  #separate class "2" from the predicted data idx2b <- which(data2==2)  #show the difference in classes before and after RF based filter #class "B" aka 2, uniform background plot(data[idx2a,2],data[idx2a,3]) points(data[idx2b,2],data[idx2b,3],col="Blue")  #class "A" aka 1, red spiral points(data[idx1a,2],data[idx1a,3])                         points(data[idx1b,2],data[idx1b,3],col="Red",pch=18) 

The actual plot follows.

For a very simple toy problem, a basic randomForest has been used to determine importance of variables, and to attempt to classify "in" versus "out".

I have an older laptop. It is a Dell Latitude E-7440 with an i7-4600 and 16 GB of RAM running Windows 7. You might have something fancy, or something even older than mine. You could have different OS, R version, or hardware. Your results are likely to differ from mine in absolute scale, but relative scale should still be informative.

Here is the code I used to benchmark the "variable importance" random forest:

res1 <- microbenchmark(randomForest(data[2:5],data$myclass,                                    ntree = 2000, replace=TRUE, nodesize = 1,                                    localImp=T ),                       times=100L) print(res1) 

and here is the code I used to benchmark the fit of a random forest to the important variables only:

res2 <- microbenchmark(randomForest(data[2:3],data$myclass,                                     ntree = 500, replace=TRUE, nodesize = 1,                                     localImp=T ),                        times=100L)  print(res2) 

The time-result for the variable importance was:

       min       lq     mean  median       uq      max neval 1 9.323244 9.648383 9.967486 9.84808 10.05356 12.12949   100 

Over 100 iterations the mean time-to-compute was 9.96 seconds. This is the "time to beat" for "incomparably faster" applied to the toy problem.

The time-result for the reduced model was:

      min       lq     mean   median      uq      max neval  1.515134 1.598504 1.638809 1.634209 1.67372 2.038021   100 

When computed over 100 iterations, the mean time-to-compute was 1.64 seconds. Running on the important data, and only for "reasonable" number of trees, reduced the run-time by about 84%.

INCOMPLETE.

References:

• http://www.statistik.uni-dortmund.de/useR-2008//slides/Strobl+Zeileis.pdf
• https://arxiv.org/pdf/1804.03515.pdf (updated 11/27/2018)

Awaiting:

• random Forest on non-toy, with timing. HIVA is not the right data, even though I asked for it. I need an intermediate set.

• random Forest + t-test solution on toy, with timing

• random Forest + t-test solution on non-toy, with timing
• svm solution on toy, with timing
• svm solution on non-toy, with timing