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Visualize bivariate tensor products

Source: R/plot_tensor.R
plotTensor.Rd

This function visualizes the contribution of a bivariate tensor product.

Usage

plotTensor(cboost, tname, npoints = 100L, nbins = 15L)

Arguments

cboost

(Compboost)
A trained Compboost object.

tname

(character(2L))
Name of the tensor base learner.

npoints

(integer(1L))
Number of grid points per numerical feature. Note: For two numerical features, the overall number of grid points is npoints^2. For a numerical and categorical feature it is npoints * ncat with ncat the number of categories. For two categorical features ncat^2 grid points are drawn.

nbins

(logical(1L))
Number of bins for the surface. Only applies in the case of two numerical features. A smooth surface is drawn if nbins = NULL.

Value

ggplot object containing the graphic.

Examples

# \donttest{
cboost = Compboost$new(data = iris, target = "Petal.Length",
  learning_rate = 0.1)

cboost$addTensor("Sepal.Width", "Sepal.Length", df1 = 4, df2 = 4, n_knots = 10)
cboost$addTensor("Sepal.Width", "Species", df1 = 4, df2 = 2, n_knots = 10)

cboost$train(100L)
#>   1/100   risk = 1.3  
#>   2/100   risk = 1.1  
#>   4/100   risk = 0.77  
#>   6/100   risk = 0.56  
#>   8/100   risk = 0.43  
#>  10/100   risk = 0.34  
#>  12/100   risk = 0.28  
#>  14/100   risk = 0.24  
#>  16/100   risk = 0.21  
#>  18/100   risk = 0.19  
#>  20/100   risk = 0.18  
#>  22/100   risk = 0.17  
#>  24/100   risk = 0.17  
#>  26/100   risk = 0.16  
#>  28/100   risk = 0.16  
#>  30/100   risk = 0.16  
#>  32/100   risk = 0.15  
#>  34/100   risk = 0.15  
#>  36/100   risk = 0.15  
#>  38/100   risk = 0.15  
#>  40/100   risk = 0.15  
#>  42/100   risk = 0.15  
#>  44/100   risk = 0.15  
#>  46/100   risk = 0.14  
#>  48/100   risk = 0.14  
#>  50/100   risk = 0.14  
#>  52/100   risk = 0.14  
#>  54/100   risk = 0.14  
#>  56/100   risk = 0.14  
#>  58/100   risk = 0.14  
#>  60/100   risk = 0.14  
#>  62/100   risk = 0.13  
#>  64/100   risk = 0.13  
#>  66/100   risk = 0.13  
#>  68/100   risk = 0.13  
#>  70/100   risk = 0.13  
#>  72/100   risk = 0.13  
#>  74/100   risk = 0.13  
#>  76/100   risk = 0.13  
#>  78/100   risk = 0.13  
#>  80/100   risk = 0.13  
#>  82/100   risk = 0.13  
#>  84/100   risk = 0.13  
#>  86/100   risk = 0.12  
#>  88/100   risk = 0.12  
#>  90/100   risk = 0.12  
#>  92/100   risk = 0.12  
#>  94/100   risk = 0.12  
#>  96/100   risk = 0.12  
#>  98/100   risk = 0.12  
#> 100/100   risk = 0.12  
#> 
#> 
#> Train 100 iterations in 0 Seconds.
#> Final risk based on the train set: 0.12
#> 

plotTensor(cboost, "Sepal.Width_Species_tensor")

plotTensor(cboost, "Sepal.Width_Sepal.Length_tensor")

# }