mildsvm (version 0.4.1)

predict.misvm: Predict method for misvm object

Description

Predict method for misvm object

Usage

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

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.

Arguments

object

An object of class misvm.

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.

Author

Sean Kent

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.

See Also

• misvm() for fitting the misvm object.

• cv_misvm() for fitting the misvm object with cross-validation.

Examples

mil_data <- generate_mild_df(nbag = 20,
                             positive_prob = 0.15,
                             sd_of_mean = rep(0.1, 3))
df1 <- build_instance_feature(mil_data, seq(0.05, 0.95, length.out = 10))
mdl1 <- misvm(x = df1[, 4:63], y = df1$bag_label,
              bags = df1$bag_name, method = "heuristic")

predict(mdl1, new_data = df1, type = "raw", layer = "bag")

# summarize predictions at the bag layer
library(dplyr)
df1 %>%
  bind_cols(predict(mdl1, df1, type = "class")) %>%
  bind_cols(predict(mdl1, df1, type = "raw")) %>%
  distinct(bag_name, bag_label, .pred_class, .pred)