summary.PUGS: Gather samples of predictions

In bearloga/MLPUGS: Multi-Label Prediction Using Gibbs Sampling (and Classifier Chains)

Description

Collapses the multi-label predictions across sets of classifier chains and iterations into a single set of predictions, either binary or probabilistic.

Usage

## S3 method for class 'PUGS'
summary(object, ...)

Arguments

object	A pugs object generated by predict.ECC.
...	type = "prob" for probabilistic predictions, type = "class" for binary (0/1) predictions

Value

A matrix of predictions.

Examples

x <- movies_train[, -(1:3)]
y <- movies_train[, 1:3]

model_glm <- ecc(x, y, m = 1, .f = glm.fit, family = binomial(link = "logit"))

predictions_glm <- predict(model_glm, movies_test[, -(1:3)],
.f = function(glm_fit, newdata) {

  # Credit for writing the prediction function that works
  # with objects created through glm.fit goes to Thomas Lumley
  
  eta <- as.matrix(newdata) %*% glm_fit$coef
  output <- glm_fit$family$linkinv(eta)
  colnames(output) <- "1"
  return(output)
  
}, n.iters = 10, burn.in = 0, thin = 1)

summary(predictions_glm, movies_test[, 1:3])

## Not run: 

model_c50 <- ecc(x, y, .f = C50::C5.0)
predictions_c50 <- predict(model_c50, movies_test[, -(1:3)],
                           n.iters = 10, burn.in = 0, thin = 1,
                           .f = C50::predict.C5.0, type = "prob")
summary(predictions_c50, movies_test[, 1:3])
  
model_rf <- ecc(x, y, .f = randomForest::randomForest)
predictions_rf <- predict(model_rf, movies_test[, -(1:3)],
                          n.iters = 10, burn.in = 0, thin = 1,
                          .f = function(rF, newdata){
                            randomForest:::predict.randomForest(rF, newdata, type = "prob")
                          })
summary(predictions_rf, movies_test[, 1:3])

## End(Not run)

bearloga/MLPUGS documentation built on March 7, 2020, 11:02 a.m.