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feat: Add AI Surrogate functions to V.03

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hans 2024-05-27 15:22:29 +02:00
parent a55a7ae095
commit 99d0b8c70d
3 changed files with 1 additions and 8 deletions
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2
bench/barite/barite_200ai_surrogate_input_script.R
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@ -38,7 +38,7 @@ mass_balance <- function(predictors, prediction) {
} }
validate_predictions <- function(predictors, prediction) { validate_predictions <- function(predictors, prediction) {
epsilon <- 0.00003 epsilon <- 0.000000003
mb <- mass_balance(predictors, prediction) mb <- mass_balance(predictors, prediction)
msgm("Mass balance mean:", mean(mb)) msgm("Mass balance mean:", mean(mb))
msgm("Mass balance variance:", var(mb)) msgm("Mass balance variance:", var(mb))

1
src/CMakeLists.txt
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@ -83,4 +83,3 @@ target_link_libraries(poet_init PRIVATE POETLib RRuntime)
target_include_directories(poet_init PRIVATE "${CMAKE_CURRENT_BINARY_DIR}") target_include_directories(poet_init PRIVATE "${CMAKE_CURRENT_BINARY_DIR}")
install(TARGETS poet poet_init DESTINATION bin) install(TARGETS poet poet_init DESTINATION bin)

6
src/poet.cpp
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@ -289,9 +289,6 @@ static Rcpp::List RunMasterLoop(RInsidePOET &R, const RuntimeParameters &params,
// Predict // Predict
R.parseEval("predictors_scaled <- preprocess(predictors)"); R.parseEval("predictors_scaled <- preprocess(predictors)");
R.parseEval("print('PREDICTORS:')");
R.parseEval("print(head(predictors))");
R.parseEval("prediction <- preprocess(prediction_step(model, predictors_scaled),\ R.parseEval("prediction <- preprocess(prediction_step(model, predictors_scaled),\
backtransform = TRUE,\ backtransform = TRUE,\
outputs = TRUE)"); outputs = TRUE)");
@ -327,9 +324,6 @@ static Rcpp::List RunMasterLoop(RInsidePOET &R, const RuntimeParameters &params,
// TODO: Check how to get the correct columns // TODO: Check how to get the correct columns
R.parseEval("target_scaled <- preprocess(targets, outputs = TRUE)"); R.parseEval("target_scaled <- preprocess(targets, outputs = TRUE)");
R.parseEval("print('TARGET:')");
R.parseEval("print(head(target_scaled))");
R.parseEval("training_step(model, predictors_scaled, target_scaled, validity_vector)"); R.parseEval("training_step(model, predictors_scaled, target_scaled, validity_vector)");
double ai_end_t = MPI_Wtime(); double ai_end_t = MPI_Wtime();