r.learn.parallel.predict

NAME

r.learn.parallel.predict - Applies a classification model in parallel using r.learn.predict.

KEYWORDS

raster, classification, regression, machine learning, scikit-learn, prediction, parallel

SYNOPSIS

r.learn.parallel.predict

r.learn.parallel.predict --help

r.learn.parallel.predict [-pzv] group=name load_model=name output=name [chunksize=integer] [nprocs=integer] [grid=rows,columns] [--overwrite] [--help] [--verbose] [--quiet] [--ui]

Flags:

-p: Output class membership probabilities; A raster layer is created for each class. For the case of a binary classification, only the positive (maximum) class is output
-z: Only predict class probabilities
-v: Create a VRT (Virtual Raster) as output
--overwrite: Allow output files to overwrite existing files
--help: Print usage summary
--verbose: Verbose module output
--quiet: Quiet module output
--ui: Force launching GUI dialog

Parameters:

group=name [required]: Group of raster layers used for prediction; GRASS imagery group of raster maps representing feature variables to be used in the machine learning model
load_model=name [required]: Load model from file; File representing pickled scikit-learn estimator model
output=name [required]: Output Map; Raster layer name to store result from classification or regression model. The name will also used as a perfix if class probabilities or intermediate of cross-validation results are ordered as maps.
chunksize=integer: Number of pixels to pass to the prediction method; Number of pixels to pass to the prediction method. GRASS GIS reads raster by-row so chunksize is rounded down based on the number of columns; Default: 100000
nprocs=integer: Number of parallel processes used for band importing in sen2cor; Number of cores for multiprocessing, -2 is n_cores-1; Default: -2
grid=rows,columns: Number of rows and columns in grid