Commit 672838f3 authored by John Doe's avatar John Doe
Browse files
parents ef42830b d33cbc4b
# Surrogate experiments controller
# Author: Janis Jatnieks, janis.jatnieks@gfz-potsdam.de, +49(0)157 3245 1188
# This calls all the other dependencies
source("Surrogate_playground.R")
# run the model fitting, validation and selection work-flow with given methods
# if no modelss are specified as first agrument, then all will be tried, this could take a while!
Main(c()
,preprocessing_ind = c(17,18,19,20,21,22,23,24) # most useful caret preporcessors
,input_data = "train/IN.csv"
,output_data = "train/OUT.csv"
,seed = 105
# use of parallelization for training, validation and preprocessing steps
,preproc_para = T, train_para = T, run_para = T
# mase is a nice error measrue - not subject to div/0 and comparable across different columns
,selection_criteria = "MASE"
# run caret tuning routines, if tuning grid enabled
,tuner = T
# use this much for training
,training_samples = 0.7
)
\ No newline at end of file
......@@ -20,12 +20,12 @@ of surrogate models in mind, it can be re-used for more general
prupose ML model creation. See the file example files to get an
idea of how to quickly launch a large number of ML model fitting
experiments:
* `Experiment_controller_simple_example_general.R` for a general purpose ML fitting approcah example for a regression experiment
* `Experiment_controller_simple_demo.R` demo using Boston housing dataset, screening 448 models and selecting the best one, saved under models. The models/brnn_range_0.7_all.csv file contains a table with all error and speed measures for all models that were run using the simple demo. This iz a good example to adapat for your own needs.
* `Experiment_controller_surrogate_example.R` for an example of how to use this for surrogate model fitting the way it was designed to be used (model coupling not included)
The key difference between coupled surrogate models and the gen
purpose examples are that for couped simulation experiemtns there are sometimes special fields and filtering considerations that you can use for the fitting experiements. Among them is the
The key difference between coupled surrogate models and the general purpose examples are
that for couped simulation experiemtns there are sometimes special fields and filtering considerations that you can use for the fitting experiements. Among them is the
* ability to ensure that concentration values can never be negative as some models incorrectly output slighly negative values, but this is not possible in the real world
* that some fields that are expected by the ML model as input,
will be supplied at run-time from the hydrofynamics or thermal
......
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