vis2rfi.RdThe main function that leverages packages forestFloor and plotly in order to construct a 3d representation of the interaction effects of two variables.
vis2rfi(impInd = c(1, 2), ffObj = NULL, maxDissim = FALSE, seed = 875, rsf = 0.25, rsf_sub = 1/3, scaleXY = TRUE, outer = list(FUN = "*"), smoother = FALSE, df = c(4, 4), length_out = NULL, cfcLabel = "Combined FCs", colors = "Blues", alpha = 1, highlightcolor = "#ff0000", ...)
| impInd | Importance index. An integer vector of length 2 containing the variable order of importance as given by the random forest algorithm. Integer values should be less than 20 so that the interaction effect between pairs of features in the top 20 set of random forest importance is shown. The default is 1:2 or the first two most important features. |
|---|---|
| ffObj | The object of class forestFloor_regression or forestFloor_multiClass
(where the latter has not been tested yet) obtained after running the
|
| maxDissim | Whether to subsample the data using a maximum dissimilarity
approach as per the |
| seed | An integer value for setting the seed at the subsampling step. Default is 875. Setting a different seed will snatch a different sample of cases from the data, thus experimenting with this parameter can produce various visualizations of from the same pool of data. |
| rsf | Random sample frequency of the data. Numeric between 0 and 1. Since a subsampling approach is used, this value is expected to be small (depending on the data row size), and if chosen large then an error or a warning message is given. Default is 25% of the data row size. |
| rsf_sub | Applicable if maxDissim = TRUE. The percentage of rsf which is set aside for extracting maximally dissimilar cases. Numeric. Default is 33% of rsf. |
| scaleXY | Should the variables in the interaction be standardized? Logical. Default is TRUE. |
| outer | Further arguments passed to outer function, mainly FUN, as default is "*" or to multiply the effects. In addition, since effects of 2way interactions are seeked, this should be a two variable function, else an error is thrown. See the examples section. |
| smoother | Whether a smoother should be applied to the construction of the 3d 2way
interaction surface. Logical with default FALSE. Currently GAMs are used with the built-in
nonparametric smoothing terms that are indicated by s for smoothing splines. See |
| df | The target equivalent degrees of freedom, used as a smoothing parameter. Similarly
as in |
| length_out | The number of equally spaced values within the subsampled range of each
of the two variables forming the interaction. Same as in |
| cfcLabel | A character label of the z-axis in the 3d plot, which corresponds to the combined feature contribution function as given in the outer argument above. Default value is "Combined FCs". |
| colors | Same as in
|
| alpha | Same as in |
| highlightcolor | Same as in |
| ... | Further arguments passed to |
A 3d plotly visualization showing the combined random forest feature contribution of the two-way interaction. If the run is assigned, similarly an object of class "plotly" and "htmlwidget".
See vignette("vis2rfi") for a tutorial/guide
on how the package can be used.
# NOT RUN { # See vignette for a more detailed introduction. # Using example and data from the forestFloor::forestFloor documentation -------------------------------- ### Start of forestFloor::forestFloor documentation : #3 - binary regression example #classification of two classes can be seen as regression in 0 to 1 scale set.seed(1234) library(forestFloor) library(randomForest) data(iris) X = iris[-1:-50,!names(iris) %in% "Species"] #drop third class virginica Y = iris[-1:-50,"Species"] Y = droplevels((Y)) #drop unused level virginica rf = randomForest( X,Y, keep.forest=TRUE, # mandatory keep.inbag=TRUE, # mandatory samp=20, # reduce complexity of mapping structure, with same OOB%-explained importance = TRUE # recommended, else giniImpurity ) ff = forestFloor(rf,X, calc_np=TRUE, # mandatory to recalculate binary_reg=TRUE) # binary regression, scale direction is printed Col = fcol(ff,1) # color by most important feature plot(ff,col=Col) # plot features #interfacing with rgl::plot3d show3d(ff,1:2,col=Col,plot.rgl.args = list(size=2,type="s",alpha=.5)) # End of forestFloor::forestFloor documentation. # Note that RGL device is already slow to enlarge and to manipulate, even with only 100 data points. # In addition, this isn't the most representative example as in practice we might have a larger data set. # Repeated usage of show3d might even crash the R session. # Interaction effect : As Petal.Length and Petal.Width both increase in value, their combined contribution # to the Random Forest prediction also rises. library(vis2rfi) # Let's take a 25% subsample of the data, and similarly examine the interaction effect of the first two # most important variables i.e. as per Random Forest output. # By default forestFloor sums the feature contributions to obtain the combined contribution. Hence, we # update the outer value similarly. # Also we do not standardize the variables so that we can more easily compare to the show3d result. vis2rfi(impInd = c(1,2), ffObj = ff, rsf = 0.25, scaleXY = FALSE, outer = list(FUN = "+")) # ✔ Taking the random subsample. An rsf value of 0.25 corresponds to 25 cases, out of the 100 ones. # ✔ Computing the 3d visualization. # ...examining... # Notes & Diffs : # - Visualization is a structure-like object composed of various data slices. This is in contrast to a # single surface object, and as per package premise. # - Easy to turn around and examine at any angle. # - Rich in colorfulness and interactivity, since leveraging the plotly package. # - Contour lines, as well as data point interactivity, help in discovering areas of both low and high # contribution. # - The interaction effect is similarly found as before but with an important difference: There are also # data points exhibiting a high contribution but with both Petal.Length and Petal.Width having mid-to-low # values. Place for example the mouse cursor on the object's apex and observe the red lined contours appearing on the feature plane. # Such cases are more evident when viewing the interaction effect in this manner since their contribution is # not as attenuated as when plotting all of the data. # The premise underlying *vis2rfi* is that 2 way interaction effects, when viewed as a 3d object as opposed # to a single surface, can be easier discovered and further examined in a richer manner. # Furthermore, taking data subsamples and visually experimenting with them is an idea well rooted in # statistical thinking. # Let's take a different subsample, by changing the seed. vis2rfi(impInd = c(1,2), ffObj = ff, rsf = 0.25, scaleXY = FALSE, seed = 007, outer = list(FUN = "+")) # ✔ Taking the random subsample. An rsf value of 0.25 corresponds to 25 cases, out of the 100 ones. # ✔ Computing the 3d visualization. # # A bit different shape is observed but with similar conclusions to the last plot above. This is due to the # small size of this data set. In less contrived scenarios the difference would be more intense. # Taking a random subsample does not guarantee having a variety of contribution values in the subsample. # To account for this we can use the maxDissim argument so as to include in the subsample data points that # better span the variety of values observed in the initial data set. vis2rfi(impInd = c(1,2), ffObj = ff, rsf = 0.25, scaleXY = FALSE, maxDissim = TRUE, outer = list(FUN = "+")) # Taking the random subsample. # An rsf value of 0.25 corresponds to 25 cases, out of the 100 ones. # Taking the random base 0.67 & pool 0.33 subsample percentage of rsf = 0.25 for maxDissim. # Base subsample size is : # 17 cases # Pool subsample size is : # 83 cases # Computing the maxDissim object. # Adding to base subsample 8 cases from the pool subsample, for a total of 25 cases. # Computing the 3d visualization. # Now the distinction in combined contribution between high and low values of the variables is also # visible in other parts of the feature plane. Similarly as before, these can be viewed by placing # the mouse cursor on the 3d object's apex. # Note also how the depicted object has altered it's structure over these newly identified regions # of interest at say point (x = 6.9, y = 1.4). # If we take another data view, by changing the seed value, we can see this distinction a bit better. vis2rfi(impInd = c(1,2), ffObj = ff, rsf = 0.25, scaleXY = FALSE, maxDissim = TRUE, seed = 007, outer = list(FUN = "+")) # Taking the random subsample. # An rsf value of 0.25 corresponds to 25 cases, out of the 100 ones. # Taking the random base 0.67 & pool 0.33 subsample percentage of rsf = 0.25 for maxDissim. # Base subsample size is : # 17 cases # Pool subsample size is : # 83 cases # Computing the maxDissim object. # Adding to base subsample 8 cases from the pool subsample, for a total of 25 cases. # Computing the 3d visualization. # Observe how at the same data point i.e. (x = 6.9, y = 1.4), the object's "roof" tilts more vividly # than in the previous visualisation. # The main theme of vis2rfi is to repeatedly alter the function's parameters so as to view the 2 way # contribution from a variety of perspectives, as shown for example here. The added benefit of doing # this is expected to be more prominent in situations having a larger data size and/or a less easily # established 2 way contribution to the predicted target. # Visualising 3d objects can be helpful in extracting detailed insights of an important 2 way contribution. # Sometimes though we would like to isolate the interaction effect so as obtain a clearer picture, which # can then be shown to a less technical audience. # We can portray the effect by a smoothed 3d surface that immediately shows us the important interaction # and with a tilt towards the variable that contributes the most i.e. Petal.Length here, having a median # marginal feature contribution of 0.85% as opposed to Petal.Width with a median of -15.34%. # GAM technology is utilized in order to conduct the smoothing, and the default smoothing parameters in # argument df are 4 degrees of freedom for each of the features. vis2rfi(impInd = c(1,2), ffObj = ff, rsf = 0.25, scaleXY = FALSE, maxDissim = TRUE, seed = 007, outer = list(FUN = "+"), smoother = TRUE) # Taking the random subsample. # An rsf value of 0.25 corresponds to 25 cases, out of the 100 ones. # Taking the random base 0.67 & pool 0.33 subsample percentage of rsf = 0.25 for maxDissim. # Base subsample size is : # 17 cases # Pool subsample size is : # 83 cases # Computing the maxDissim object. # Adding to base subsample 8 cases from the pool subsample, for a total of 25 cases. # Fitting the GAM smoothing spline with 4 and 4 degrees of freedom respectively on each of the two interaction variables. # Computing the 3d visualization. # More succinctly than without smoothing, the surface's high upwards incline depicts the increase in the # contribution to the prediction. In addition, the interaction effect has been established with only a # 25% subset of the data. In many practical applications, this is what is mainly needed to convey the # message. # If we would like to further emphasize the interaction effect, we can flatten the 3d surface by lowering # the smoothing spline degrees of freedom in the GAM fit to unit values i.e. vis2rfi(impInd = c(1,2), ffObj = ff, rsf = 0.25, scaleXY = FALSE, maxDissim = TRUE, seed = 007, outer = list(FUN = "+"), smoother = TRUE, df = c(1,1)) # Resulting in a 3d plane for this data set while the tilt towards Petal.Length is more evident. ## Example with caret: rm(list=ls()) library(caret) # Loading required package: lattice # # Attaching package: ‘caret’ # # The following objects are masked from ‘package:vis2rfi’: # # minDiss, sumDiss library(forestFloor) N = 1000 vars = 7 noise_factor = .3 bankruptcy_baserate = 0.2 X = data.frame(replicate(vars,rnorm(N))) y.signal = with(X,X1^2+X2^2+X3*X4+X5+X6^3+sin(X7*pi)*2) #some non-linear f y.noise = rnorm(N) * sd(y.signal) * noise_factor y.total = y.noise+y.signal y = factor(y.total>=quantile(y.total,1-bankruptcy_baserate)) set.seed(1) caret_train_obj <- train( x = X, y = y, method = "rf", keep.inbag = TRUE, #always set keep.inbag=TRUE passed as ... parameter to randomForest ntree=50, #speed up this example, if set too low, forestFloor will fail ) rf = caret_train_obj$finalModel #extract model if(!all(rf$y==y)) warning("seems like training set have been resampled, using smote?") ff = forestFloor(rf,X,binary_reg = T) #... or simply pass train ff = forestFloor(caret_train_obj,X,binary_reg = T) plot(ff,1:6,plot_GOF = TRUE) # Using vis2rfi: library(vis2rfi) # Focus on the 6th in order of importance variable, having the lowest R-squared value with the 5th one. # These are the x3 and x4 variables known to interact: vis2rfi(impInd = c(6,7), ffObj = ff, scaleXY = FALSE, outer = list(FUN = "+")) vis2rfi(impInd = c(6,7), ffObj = ff, scaleXY = FALSE, maxDissim = TRUE, outer = list(FUN = "+"), smoother = TRUE) # }