Commit ee60501b authored by Janis Jatnieks's avatar Janis Jatnieks
Browse files

Update Experiment_conrtoller_general_example.R

parent 6006c439
......@@ -7,35 +7,19 @@ require(data.table)
# This calls all the other dependencies
source("Surrogate_playground.R")
# read the data tables
IN_training_data_path = "train/100_IN.csv"
OUT_training_data_path ="train/100_OUT.csv"
INtable = fread(IN_training_data_path)
OUTtable = fread(OUT_training_data_path)
# subset columns
INselection =c("C","Ca","Cl","Mg","N","Na","S","Si","H*","O*","Charge","Pressure","MatUnsatPorosity","UD_p4f","UD_pH","UD_pe","UD_mass_H2O","UD_charge(eq)","UD_O(0)(mol/kgw)","UD_Ca(mol/kgw)","UD_Calcite","UD_Kaolinite","UD_si_Calcite","UD_si_Gypsum","UD_si_K-Feldspar", "UD_si_Kaolinite","Time")
OUTselection=c("C","Ca","Cl","Mg","N","Na","S","Si","H*","O*","Charge","Pressure","MatUnsatPorosity","UD_p4f","UD_pH","UD_pe","UD_mass_H2O","UD_charge(eq)","UD_O(0)(mol/kgw)","UD_Ca(mol/kgw)","UD_Calcite","UD_Kaolinite","UD_si_Calcite","UD_si_Gypsum","UD_si_K-Feldspar", "UD_si_Kaolinite","Time")
INtable = subset(INtable, select=INselection )
OUTtable = subset(OUTtable,select=OUTselection)
# run the model fitting, validation and selection work-flow with given methods
Main(c("MARS","MARS3","PolyMARS","PolyMARS3","mda_p01","polymars_p01","brnn","qrnn","lars2","lasso","lars")
,preprocessing_ind = c(17) # no pre-processing
,input_data = INtable
,output_data = OUTtable
# 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) # no pre-processing works often fairly well
,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 = F
,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"
# drops any such named column from input as well as output
,exclude_columns = c("Head")
# makes sure concentration values are positive
,allow_neg_cols= c("Pressure","H*","O*")
# assumes this will be passed in from the model coupling to the Surrogate at run-time
,external = c("Time","Pressure","MatUnsatPorosity","Saturation","Temp","MatHeatPorosity","MatFlowStorativity","UD_p4f")
,allow_neg_cols = T
# dump error tables
,write_full_residuals = F
# run caret tuning routines
......
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