predict.omisvm {mildsvm}

Predict method for omisvm object

Description

Predict method for omisvm object

Usage

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

Arguments

object

An object of class omisvm

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

omisvm() for fitting the omisvm object.

Examples

if (require(gurobi)) {
  data("ordmvnorm")
  x <- ordmvnorm[, 3:7]
  y <- ordmvnorm$bag_label
  bags <- ordmvnorm$bag_name

  mdl1 <- omisvm(x, y, bags, weights = NULL)

  # summarize predictions at the bag layer
  library(dplyr)
  df1 <- bind_cols(y = y, 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)
}