predict_dt: Performs inference over a test dataset with a GBN

In dbnR: Dynamic Bayesian Network Learning and Inference

Performs inference over a test dataset with a GBN

Description

This function performs inference over each row of a folded data.table, plots the results and gives metrics of the accuracy of the predictions. Given that only a single row is predicted, the horizon of the prediction is at most 1. This function is also called by the generic predict method for "dbn.fit" objects. For long term forecasting, please refer to the forecast_ts function.

Usage

predict_dt(fit, dt, obj_nodes, verbose = T, look_ahead = F)

Arguments

fit	the fitted bn
dt	the test dataset
obj_nodes	the nodes that are going to be predicted. They are all predicted at the same time
verbose	if TRUE, displays the metrics and plots the real values against the predictions
look_ahead	boolean that defines whether or not the values of the variables in t_0 should be used when predicting, even if they are not present in obj_nodes. This decides if look-ahead bias is introduced or not.

Value

a data.table with the prediction results for each row

Examples

size = 3
data(motor)
dt_train <- motor[200:900]
dt_val <- motor[901:1000]

# With a DBN
obj <- c("pm_t_0")
net <- learn_dbn_struc(dt_train, size)
f_dt_train <- fold_dt(dt_train, size)
f_dt_val <- fold_dt(dt_val, size)
fit <- fit_dbn_params(net, f_dt_train, method = "mle-g")
res <- suppressWarnings(predict_dt(fit, f_dt_val, obj_nodes = obj, verbose = FALSE))

# With a Gaussian BN directly from bnlearn
obj <- c("pm")
net <- bnlearn::mmhc(dt_train)
fit <- bnlearn::bn.fit(net, dt_train, method = "mle-g")
res <- suppressWarnings(predict_dt(fit, dt_val, obj_nodes = obj, verbose = FALSE))

dbnR documentation built on Oct. 5, 2022, 1:07 a.m.