Uses a pre-trained ARF model to estimate leaf and distribution parameters.
Usage
forde(
arf,
x,
oob = FALSE,
family = "truncnorm",
finite_bounds = FALSE,
alpha = 0,
epsilon = 0,
parallel = TRUE
)Arguments
- arf
Pre-trained
adversarial_rf. Alternatively, any object of classranger.- x
Training data for estimating parameters.
- oob
Only use out-of-bag samples for parameter estimation? If
TRUE,xmust be the same dataset used to trainarf.- family
Distribution to use for density estimation of continuous features. Current options include truncated normal (the default
family = "truncnorm") and uniform (family = "unif"). See Details.- finite_bounds
Impose finite bounds on all continuous variables?
- alpha
Optional pseudocount for Laplace smoothing of categorical features. This avoids zero-mass points when test data fall outside the support of training data. Effectively parametrizes a flat Dirichlet prior on multinomial likelihoods.
- epsilon
Optional slack parameter on empirical bounds when
family = "unif"orfinite_bounds = TRUE. This avoids zero-density points when test data fall outside the support of training data. The gap between lower and upper bounds is expanded by a factor of1 + epsilon.- parallel
Compute in parallel? Must register backend beforehand, e.g. via
doParallel.
Value
A list with 5 elements: (1) parameters for continuous data; (2)
parameters for discrete data; (3) leaf indices and coverage; (4) metadata on
variables; and (5) the data input class. This list is used for estimating
likelihoods with lik and generating data with forge.
Details
forde extracts leaf parameters from a pretrained forest and learns
distribution parameters for data within each leaf. The former includes
coverage (proportion of data falling into the leaf) and split criteria. The
latter includes proportions for categorical features and mean/variance for
continuous features. The result is a probabilistic circuit, stored as a
data.table, which can be used for various downstream inference tasks.
Currently, forde only provides support for a limited number of
distributional families: truncated normal or uniform for continuous data,
and multinomial for discrete data. Future releases will accommodate a larger
set of options.
Though forde was designed to take an adversarial random forest as
input, the function's first argument can in principle be any object of class
ranger. This allows users to test performance with alternative
pipelines (e.g., with supervised forest input). There is also no requirement
that x be the data used to fit arf, unless oob = TRUE.
In fact, using another dataset here may protect against overfitting. This
connects with Wager & Athey's (2018) notion of "honest trees".
References
Watson, D., Blesch, K., Kapar, J., & Wright, M. (2023). Adversarial random forests for density estimation and generative modeling. In Proceedings of the 26th International Conference on Artificial Intelligence and Statistics, pp. 5357-5375.
Wager, S. & Athey, S. (2018). Estimation and inference of heterogeneous treatment effects using random forests. J. Am. Stat. Assoc., 113(523): 1228-1242.
Examples
arf <- adversarial_rf(iris)
#> Iteration: 0, Accuracy: 86.05%
#> Iteration: 1, Accuracy: 45.67%
psi <- forde(arf, iris)
head(psi)
#> $cnt
#> f_idx variable min max mu sigma
#> 1: 1 Petal.Length -Inf 1.55 1.35 0.07071068
#> 2: 1 Petal.Width -Inf Inf 0.25 0.07071068
#> 3: 1 Sepal.Length -Inf Inf 4.45 0.07071068
#> 4: 1 Sepal.Width -Inf 2.95 2.60 0.42426407
#> 5: 2 Petal.Length 4.05 4.30 4.15 0.07071068
#> ---
#> 720: 180 Sepal.Width 2.90 3.45 3.40 0.11456157
#> 721: 181 Petal.Length -Inf Inf 4.96 0.13416408
#> 722: 181 Petal.Width 1.65 Inf 1.96 0.26076810
#> 723: 181 Sepal.Length -Inf 6.15 5.88 0.19235384
#> 724: 181 Sepal.Width 2.75 Inf 2.92 0.10954451
#>
#> $cat
#> f_idx variable val prob
#> 1: 1 Species setosa 1.0
#> 2: 2 Species versicolor 1.0
#> 3: 3 Species versicolor 1.0
#> 4: 4 Species versicolor 0.5
#> 5: 4 Species virginica 0.5
#> ---
#> 211: 177 Species versicolor 1.0
#> 212: 178 Species setosa 1.0
#> 213: 179 Species virginica 1.0
#> 214: 180 Species setosa 1.0
#> 215: 181 Species virginica 1.0
#>
#> $forest
#> f_idx tree leaf cvg
#> 1: 1 1 14 0.01333333
#> 2: 2 1 20 0.01333333
#> 3: 3 1 26 0.10000000
#> 4: 4 1 28 0.01333333
#> 5: 5 1 29 0.02000000
#> ---
#> 177: 177 10 48 0.03333333
#> 178: 178 10 50 0.15333333
#> 179: 179 10 52 0.02666667
#> 180: 180 10 55 0.02000000
#> 181: 181 10 56 0.03333333
#>
#> $meta
#> variable class family decimals
#> 1: Sepal.Length numeric truncnorm 1
#> 2: Sepal.Width numeric truncnorm 1
#> 3: Petal.Length numeric truncnorm 1
#> 4: Petal.Width numeric truncnorm 1
#> 5: Species factor multinom NA
#>
#> $levels
#> variable val
#> 1: Species virginica
#> 2: Species setosa
#> 3: Species versicolor
#>
#> $input_class
#> [1] "data.frame"
#>