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Forward chaining

Source: R/validation.R
forward_chaining.Rd

In a forward chaining setting, we retrain/update the model every horizon day/week/months/etc. and test the model on the next horizon day/week/months/etc., i.e. the data that is included at the next training step. We call this training/testing steps iterations and start at iteration 0 where only the first timepoint is only included. At iteration 1, timepoints 1 to 1 + horizon are included.

Usage

get_fc_iteration(t, horizon)

split_fc_dataset(df, it)

detail_fc_training(df, horizon)

get_fc_training_iteration(it_test)

Arguments

t

Vector of timepoints

horizon

Updating horizon

df

Dataframe with columns Patient, Time, Horizon, Iteration. Only the columns Time is required for detail_fc_training.

it

Iteration number

it_test

Vector of testing iteration numbers

Value

  • get_fc_iteration returns a vector corresponding to iteration numbers

  • split_fc_dataset returns a named list of dataframes (Training and Testing)

  • get_fc_training_iteration returns a vector of unique training iteration number

  • detail_fc_training returns a dataframe with columns: Iteration, N, Proportion, LastTime

Details

  • get_fc_iteration associate a vector of timepoints t to the corresponding iteration.

  • split_fc_dataset split dataset df into a training and testing set, and computes prediction horizon and last available score in the test set.

  • get_fc_training_iteration identify training iterations such as test set is not empty

  • detail_fc_training derive training data characteristics for each iteration in df, including the number of training observations, the proportion of training observations on the total number of observations, and the maximum timepoint in the training sets.

Time=t is in Iteration=i means that:

  • Time=t is in the new training data of iteration i

  • Time=t is not in the training data of iterations < i

  • Time=t is in the testing data of iteration i-1

  • Time=t is in the training data of iterations >= i

Examples

h <- 2
df <- get_index2(t_max = rpois(10, 10))
df$Score <- rnorm(nrow(df))
df$Iteration <- get_fc_iteration(df$Time, h)
sp <- split_fc_dataset(df, 1)
train_it <- get_fc_training_iteration(df$Iteration)
fc_char <- detail_fc_training(df, h)