predict.mior {mildsvm}

Predict method for mior object

Description

Predict method for mior object

Usage

## S3 method for class 'mior'
predict(
  object,
  new_data,
  type = c("class", "raw"),
  layer = c("bag", "instance"),
  new_bags = "bag_name",
  ...
)

Arguments

object

An object of class mior

new_data

A data frame to predict from. This needs to have all of the features that the data was originally fitted with.

type

If 'class', return predicted values with threshold of 0 as -1 or +1. If 'raw', return the raw predicted scores.

layer

If 'bag', return predictions at the bag level. If 'instance', return predictions at the instance level.

new_bags

A character or character vector. Can specify a singular character that provides the column name for the bag names in new_data (default 'bag_name'). Can also specify a vector of length nrow(new_data) that has bag name for each row.

...

Arguments passed to or from other methods.

Details

When the object was fitted using the formula method, then the parameters new_bags and new_instances are not necessary, as long as the names match the original function call.

Value

A tibble with nrow(new_data) rows. If type = 'class', the tibble will have a column .pred_class. If type = 'raw', the tibble will have a column .pred.

Author(s)

Sean Kent

See Also

mior() for fitting the mior object.

Examples

if (require(gurobi)) {
  set.seed(8)
  # make some data
  n <- 15
  X <- rbind(
    mvtnorm::rmvnorm(n/3, mean = c(4, -2, 0)),
    mvtnorm::rmvnorm(n/3, mean = c(0, 0, 0)),
    mvtnorm::rmvnorm(n/3, mean = c(-2, 1, 0))
  )
  score <- X %*% c(2, -1, 0)
  y <- as.numeric(cut(score, c(-Inf, quantile(score, probs = 1:2 / 3), Inf)))
  bags <- 1:length(y)

  # add in points outside boundaries
  X <- rbind(
    X,
    mvtnorm::rmvnorm(n, mean = c(6, -3, 0)),
    mvtnorm::rmvnorm(n, mean = c(-6, 3, 0))
  )
  y <- c(y, rep(-1, 2*n))
  bags <- rep(bags, 3)
  repr <- c(rep(1, n), rep(0, 2*n))

  y_bag <- classify_bags(y, bags, condense = FALSE)

  mdl1 <- mior(X, y_bag, bags)
  # summarize predictions at the bag layer
  library(dplyr)
  df1 <- bind_cols(y = y_bag, bags = bags, as.data.frame(X))
  df1 %>%
    bind_cols(predict(mdl1, df1, new_bags = bags, type = "class")) %>%
    bind_cols(predict(mdl1, df1, new_bags = bags, type = "raw")) %>%
    distinct(y, bags, .pred_class, .pred)
}