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Draws samples from the conditional predictive distribution of latent factors

Source: R/Mod_Predict_LF.R
Mod_Predict_LF.Rd

This function is optimized for speed using parallel processing and optionally TensorFlow for matrix operations. This function is adapted from Hmsc::predictLatentFactor with equivalent results to the original function when predictMean = TRUE.

Usage

Mod_Predict_LF(
  Units_pred,
  Units_model,
  postEta,
  postAlpha,
  LF_rL,
  LF_NCores = 8L,
  Temp_Dir = "TEMP_Pred",
  LF_Temp_Cleanup = TRUE,
  Model_Name = NULL,
  UseTF = TRUE,
  TF_Environ = NULL,
  TF_use_single = FALSE,
  LF_OutFile = NULL,
  LF_Return = FALSE,
  LF_Check = FALSE,
  LF_Commands_Only = FALSE,
  solve_max_attempts = 5L,
  solve_chunk_size = 50L,
  Verbose = TRUE
)

Arguments

Units_pred

a factor vector with random level units for which predictions are to be made

Units_model

a factor vector with random level units that are conditioned on

postEta

Character. Path of postEta; a list containing samples of random factors at conditioned units

postAlpha

a list containing samples of range (lengthscale) parameters for latent factors

LF_rL

a HmscRandomLevel-class object that describes the random level structure

LF_NCores

Integer. Number of cores to use for parallel processing of latent factor prediction. Defaults to 8L.

Temp_Dir

Character. Path for temporary storage of intermediate files.

LF_Temp_Cleanup

Logical. Whether to delete temporary files in the Temp_Dir directory after finishing the LF predictions.

Model_Name

Character. Prefix for temporary file names. Defaults to NULL, in which case no prefix is used.

UseTF

Logical. Whether to use TensorFlow for calculations. Defaults to TRUE.

TF_Environ

Character. Path to the Python environment. This argument is required if UseTF is TRUE under Windows. Defaults to NULL.

TF_use_single

Logical. Whether to use single precision for the TensorFlow calculations. Defaults to FALSE.

LF_OutFile

Character. Path to save the outputs. If NULL (default), the predicted latent factors are not saved to a file. This should end with either *.qs2 or *.RData.

LF_Return

Logical. Whether the output should be returned. Defaults to FALSE. If LF_OutFile is NULL, this parameter cannot be set to FALSE because the function needs to return the result if it is not saved to a file.

LF_Check

Logical. If TRUE, the function checks if the output files are already created and valid. If FALSE, the function will only check if the files exist without checking their integrity. Default is FALSE.

LF_Commands_Only

Logical. If TRUE, returns the command to run the Python script. Default is FALSE.

solve_max_attempts

Integer. Maximum number of attempts to run solve and crossprod internal function run_crossprod_solve. Default is 5L.

solve_chunk_size

Integer. Chunk size for solve_and_multiply Python function. Default is 50L.

Verbose

Logical. If TRUE, logs detailed information during execution. Default is TRUE.

Details

The function is expected to be faster than the original function in the Hmsc package, especially when using TensorFlow for calculations and when working on parallel.

The main difference is that this function:

  • allow for parallel processing (LF_NCores argument);

  • when TensorFlow is used (UseTF = TRUE), matrix calculations are much faster, particularly when used on GPU. The following Python modules are needed: numpy, tensorflow, rdata, xarray, and pandas. To use TensorFlow under Windows, the argument TF_Environ should be set to the path of a Python environment with TensorFlow installed;

  • if UseTF is set to FALSE, the function uses R (supported by relatively faster CPP functions) in the calculations;

  • D11 and D12 matrices are processed only once and saved to disk and called when needed.

See also

Hmsc::predictLatentFactor