Communicates with EWSNet (https://ewsnet.github.io), a deep learning framework for modelling and anticipating regime shifts in dynamical systems, and finetunes the model to match the inputted training data. This overwrites the Pretrained weights bundled with EWSmethods. See reset_ewsnet() on how to reset these trained weights.
Usage
ewsnet_finetune(
x,
y,
scaling = TRUE,
envname,
weights_path = default_weights_path()
)Arguments
- x
A numeric matrix to finetune EWSNet on. Each column represents a separate timeseries and each row is a timestep.
- y
A numeric vector consisting of target labels for each training time series. Labels include: 0 (no transition), 1 (smooth transition) or 2 (critical transition).
- scaling
Boolean. If
TRUE, the time series will be scaled between 1 and 2 and scaled EWSNet model weights will be used. This is the recommended setting.- envname
A string naming the Python environment prepared by
ewsnet_init().- weights_path
A string naming the path to model weights installed by
ewsnet_reset().
Examples
#Activate python environment (only necessary
#on first opening of R session).
if (FALSE) { # \dontrun{
ewsnet_init(envname = "EWSNET_env")
} # }
#A dummy dataset of a hedgerow bird population
#monitored over 50 years that needs to be tuned.
abundance_data <- data.frame(time = seq(1:50),
abundance = rnorm(50,mean = 20))
#Generate training data (this is random data as
#an example).
x <- matrix(nrow = 50, ncol = 10)
x <- sapply(1:dim(x)[2], function(i){
x[,i] <- rnorm(50,mean=20,sd=10)})
#Label each time series.
y <- sample(0:2,10,replace = TRUE)
#Finetune EWSNet.
if (FALSE) { # \dontrun{
ewsnet_finetune(
x = x,
y = y,
scaling = TRUE,
envname = "EWSNET_env")
} # }
#Generate new EWSNet predictions.
if (FALSE) { # \dontrun{
pred <- ewsnet_predict(
abundance_data$abundance,
scaling = TRUE,
ensemble = 15,
envname = "EWSNET_env")
} # }