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I have a set of (2-dimensional) data points that I run through a classifier that uses higher order polynomial transformations. I want to visualize the results as a 2 dimensional scatterplot of the points with the classifier superimbosed on top, preferably using ggplot2 as all other visualizations are made by this. Pretty much like this one that was used in the ClatechX online course on machine learning (the background color is optional).

Example

I can display the points with colors and symbols and all, that's easy but I can't figure out how to draw anything like the classifiers (the intersection of the classifiing hyperplane with the plane representing my threshold). The only thing I found was stat_function and that only takes a function with a single argument.

Edit:

The example that was asked for in the comments:

sample data:

"","x","y","x","x","y","value"
"1",4.17338115745224,0.303530843229964,1.26674990184152,17.4171102853774,0.0921309727918932,-1
"2",4.85514814266935,3.452660451876,16.7631779801937,23.5724634872656,11.9208641959486,1
"3",3.51938610081561,3.41200957307592,12.0081790673332,12.3860785266141,11.6418093267617,1
"4",3.18545089452527,0.933340128976852,2.97310914874565,10.1470974014319,0.87112379635852,-16
"5",2.77556006214581,2.49701633118093,6.93061880335166,7.70373365857888,6.23509055818427,-1
"6",2.45974169578403,4.56341833807528,11.2248303614692,6.05032920997851,20.8247869282818,1
"7",2.73947941488586,3.35344674880616,9.18669833727041,7.50474746458339,11.2456050970786,-1
"8",2.01721803518012,3.55453519499861,7.17027250203368,4.06916860145595,12.6347204524838,-1
"9",3.52376445778646,1.47073399974033,5.1825201951431,12.4169159539591,2.1630584979922,-1
"10",3.77387718763202,0.509284208528697,1.92197605658768,14.2421490273294,0.259370405056702,-1
"11",4.15821685106494,1.03675272315741,4.31104264382058,17.2907673804804,1.0748562089743,-1
"12",2.57985028671101,3.88512040604837,10.0230289934507,6.65562750184287,15.0941605694935,1
"13",3.99800728890114,2.39457673509605,9.5735352407471,15.9840622821066,5.73399774026327,1
"14",2.10979392635636,4.58358959294856,9.67042948411309,4.45123041169019,21.0092935565863,1
"15",2.26988795562647,2.96687697409652,6.73447830932721,5.15239133109813,8.80235897942413,-1
"16",1.11802248633467,0.114183261757717,0.127659454208164,1.24997427994995,0.0130378172656312,-1
"17",0.310411276295781,2.09426849964075,0.650084557879535,0.0963551604515758,4.38596054858751,-1
"18",1.93197490065359,1.72926536411978,3.340897280049,3.73252701675543,2.99035869954433,-1
"19",3.45879891654477,1.13636834081262,3.93046958599847,11.9632899450912,1.29133300600123,-1
"20",0.310697768582031,0.730971727753058,0.227111284709427,0.0965331034018534,0.534319666774291,-1
"21",3.88408110360615,0.915658151498064,3.55649052359657,15.0860860193904,0.838429850404852,-1
"22",0.287852146429941,2.16121324687265,0.622109872005114,0.0828588582043242,4.67084269845782,-1
"23",2.80277011333965,1.22467750683427,3.4324895146344,7.85552030822994,1.4998349957458,-1
"24",0.579150241101161,0.57801398797892,0.334756940497835,0.335415001767533,0.334100170299295-,1
"25",2.37193428212777,1.58276639413089,3.7542178708388,5.62607223873297,2.50514945839009,-1
"26",0.372461311053485,2.51207412336953,0.935650421453748,0.138727428231681,6.31051640130279,-1
"27",3.56567220995203,1.03982002707198,3.70765737388213,12.7140183088242,1.08122568869998,-1
"28",0.634770628530532,2.26303249713965,1.43650656059435,0.402933750845047,5.12131608311011,-1
"29",2.43812176748179,1.91849716124125,4.67752968967431,5.94443775306852,3.68063135769073,-1
"30",1.08741064323112,3.01656032912433,3.28023980783858,1.18246190701233,9.0996362192467,-1
"31",0.98,2.74,2.6852,0.9604,7.5076,1
"32",3.16,1.78,5.6248,9.9856,3.1684,1
"33",4.26,4.28,18.2328,18.1476,18.3184,-1

The code to generate a classifier:

perceptron_train <- function(data, maxIter=10000) {
    set.seed(839)
    X <- as.matrix(data[1:5])
    Y <- data["value"]
    d <- dim(X)
    X <- cbind(rep(1, d[1]), X)
    W <- rep(0, d[2] + 1)
    count <- 0
    while (count < maxIter){
        H <- sign(X %*% W)
        indexs <- which(H != Y)
        if (length(indexs) == 0){
            break
        } else {
            i <- sample(indexs, 1)
            W <- W + 0.1 * (X[i,] * Y[i,])
        }
        count <- count + 1
        point <- as.data.frame(data[i,])
        plot_it(data, point, W, paste("plot", sprintf("%05d", count), ".png", sep=""))
    }
    W
}

The code to generate the plot:

plot_it <- function(data, point, weights, name = "plot.png") {
    line <- weights_to_line(weights)
    point <- point
    png(name)
    p = ggplot() + geom_point(data = data, aes(x, y, color = value, size = 2)) + theme(legend.position = "none")
    p = p + geom_abline(intercept = line[2], slope = line[1])
    print(p)
    dev.off()
}
Nicktar
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    Have a look at `stat_contour(...)`. Beyond that, you would need to post a *reproducible example* containing (at least) a representative sample of your data, and the code you used to generate the classification. See: http://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example/5963610#5963610. – jlhoward Dec 14 '14 at 18:39
  • I'm not sure how this is supposed to help answering the question, as it's not about the data or the code generating the classifiers but there you are. – Nicktar Dec 14 '14 at 19:23
  • This example is still incomplete. It's missing function definitions like `weights_to_line`. It's also odd that you've included the plot within the iterations of the classifier. As jlhoward said, a plot like that will end up looking like a contour plot. You're going to need to threshold a response value for each combination of x/y input to know where the boundaries are. There is simply insufficient information here to help you any further. – MrFlick Dec 15 '14 at 06:24
  • @MrFlick the current example will be used in a presentation to show how these boundaries evolve over iterations. I hoped to avoid calculating thoudands of datapoints over and over again as this seems awfully inefficient and impractical but I'll give it a try. Thank you – Nicktar Dec 15 '14 at 09:50

1 Answers1

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This was solved using material from the question and answers from Issues plotting a fitted SVM model's decision boundary using ggplot2's stat_contour(). I skipped the call to geom_point for the grid-entires and some of the aesthetical definitions like scale_fill_manual and scale_colour_manual. Removing the dots for the grid entries solved the problem with the vanishing contour-line in my case.

train_and_plot_svm <- function(train, kernel = "sigmoid", type ="C", cost, gamma) {
    fit <- svm(as.factor(value) ~ x + y, data = train, kernel = kernel, type = type, cost = cost)
    grid <- expand.grid (x = seq(from = -0.1, to = 15, length = 100), y = seq(from = -0.1, to = 15, length = 100))
    decisionValues <- as.vector(attributes(predict(fit, grid, decision.values = TRUE))$decision)
    p <- predict(fit, grid)
    grid$value <- p
    grid$z <- decisionValues
    p <- ggplot() + stat_contour(data = grid, aes(x = x, y = y, z = z), breaks = c(0)) 
    p <- p + geom_point(data = train, aes(x, y, colour = as.factor(value)), alpha = 0.7)
    p <- p + xlim(0,15) + ylim(0,15) + theme(legend.position="none")
}

Note that this function doesn't return the result of the svm training but the ggplot2 object.

This is, what I got: The resulting plot

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Nicktar
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