mikropml (version 1.6.1)

create_grouped_data_partition: Split into train and test set while splitting by groups. When group_partitions is NULL, all samples from each group will go into either the training set or the testing set. Otherwise, the groups will be split according to group_partitions

Description

Split into train and test set while splitting by groups. When group_partitions is NULL, all samples from each group will go into either the training set or the testing set. Otherwise, the groups will be split according to group_partitions

Usage

create_grouped_data_partition(
  groups,
  group_partitions = NULL,
  training_frac = 0.8
)

Value

vector of row indices for the training set

Arguments

groups

Vector of groups to keep together when splitting the data into train and test sets. If the number of groups in the training set is larger than kfold, the groups will also be kept together for cross-validation. Length matches the number of rows in the dataset (default: NULL).

group_partitions

Specify how to assign groups to the training and testing partitions (default: NULL). If groups specifies that some samples belong to group "A" and some belong to group "B", then setting group_partitions = list(train = c("A", "B"), test = c("B")) will result in all samples from group "A" being placed in the training set, some samples from "B" also in the training set, and the remaining samples from "B" in the testing set. The partition sizes will be as close to training_frac as possible. If the number of groups in the training set is larger than kfold, the groups will also be kept together for cross-validation.

training_frac

Fraction of data for training set (default: 0.8). Rows from the dataset will be randomly selected for the training set, and all remaining rows will be used in the testing set. Alternatively, if you provide a vector of integers, these will be used as the row indices for the training set. All remaining rows will be used in the testing set.

Author

Zena Lapp, [e-mail]

Kelly Sovacool, [e-mail]

Examples

if (FALSE) {
groups <- c("A", "B", "A", "B", "C", "C", "A", "A", "D")
set.seed(0)
create_grouped_data_partition(groups, training_frac = 0.8)
groups <- rep.int(c("A", "B", "C"), 3)
create_grouped_data_partition(groups,
  group_partitions = list(train = c("A"), test = c("A", "B", "C"))
)
}