R_Scripts/NN Model.R
In John-R-Wallace-NOAA/FishNIRS: NIRS on fish structures.
# ./cpulimit --exe /more_home/h_jwallace/Python/tf_cpu_only/bin/python --limit 1000 &
# ./cpulimit --exe /opt/R/64-bit/R-4.1.2_MKL/lib64/R/bin/exec/R --limit 1000 > /dev/null 2>&1 &
# nice -19 /opt/R/64-bit/R-4.1.2_MKL/bin/R
# <ctrl-z>
# $ jobs -l
# [1]+ ##### Stopped R
# $ ./cpulimit --pid 37921 --limit 1000 &
# echo "source('NN Model.R')" | /opt/R/64-bit/R-4.1.2_MKL/bin/R --no-save
# setwd(ifelse(.Platform$OS.type == 'windows', "C:/ALL_USR/JRW/SIDT/Train_NN_Model", "/more_home/h_jwallace/SIDT/Train_NN_Model"))
# source('NN Model.R')
# --- Initial setup (The curly brakets without indented lines directly below are there to enable the hiding of code sections using Notepad++.) ---
# (For those who dislike Rstudio and others, Notepad++ is here: https://notepad-plus-plus.org/ and NppToR that passes R code from Notepad++ to R is here: https://sourceforge.net/projects/npptor/)
if(interactive())
setwd(ifelse(.Platform$OS.type == 'windows', "C:/ALL_USR/JRW/SIDT/Predict_NN_Ages", "/more_home/h_jwallace/SIDT/Predict_NN_Ages")) # Change path to the Spectra Set's .GlobalEnv as needed
if(!interactive()) options(width = 120)
Spectra_Set <- c("Hake_2019", "Sable_2017_2019", "Sable_Combo_2022")[2]
Spectra_Path <- "New_Scans" # Put new spectra scans in a separate folder and enter the name of the folder below
Predicted_Ages_Path <- "Predicted_Ages" # The NN predicted ages will go in the path defined below
dir.create(Predicted_Ages_Path, showWarnings = FALSE)
TMA_Ages <- c(TRUE, FALSE)[2] # Are TMA ages available and are they to be used?
verbose <- c(TRUE, FALSE)[1]
plot <- c(TRUE, FALSE)[1]
Max_N_Spectra <- list(50, 200, 'All')[[3]] # Max number of new spectra to be plotted in the spectra figure. (All spectra in the 'New_Scans' folder will be assigned an age regardless of the number plotted in the figure.)
spectraInterp = c('stats_splinefun_lowess', 'prospectr_resample')[1]
opusReader = c('pierreroudier_opusreader', 'philippbaumann_opusreader2')[2]
# (1) Hake 2019, BMS
if(Spectra_Set == "Hake_2019") {
shortNameSegments <- c(2, 4) # Segments 2 and 4 of the spectra file name, e.g.: (PACIFIC, HAKE, BMS201906206C, 1191, OD1) => (HAKE, 1191)
shortNameSuffix <- 'BMS'
opusReader <- 'pierreroudier_opusreader'
fineFreqAdj <- 150
}
# (2) Sablefish 2017 & 2019, Combo survey
if(Spectra_Set == "Sable_2017_2019") {
shortNameSegments <- c(1, 3) # Segments 1 and 3 of the spectra file name, e.g.: (SABLEFISH, COMBO201701203A, 28, OD1) => (SABLEFISH, 28)
shortNameSuffix <- 'Year'
yearPosition <- c(6, 9) # e.g. COMBO201701203A => 2017 (Segment used (see above) is: shortNameSegments[1] + 1)
fineFreqAdj <- 0
opusReader <- 'pierreroudier_opusreader'
if(TMA_Ages)
TMA_Meta <- "C:/ALL_USR/JRW/SIDT/Sablefish/Keras_CNN_Models/Sable_2017_2019 21 Nov 2022.RData" # If used, change path to the main sepectra/metadata save()'d data frame which contains TMA ages. Matching done via 'filenames'.
}
# (3) Sablefish 2022, Combo survey
if(Spectra_Set == "Sable_Combo_2022") {
shortNameSegments <- c(1,5) # Segments 1 and 3 of the spectra file name, e.g.: (SABLEFISH, COMBO201701203A, 28, OD1) => (SABLEFISH, 28)
shortNameSuffix <- 'Year'
yearPosition <- c(6, 9) # e.g. COMBO201701203A => 2017 (Segment used (see above) is: shortNameSegments[1] + 1)
fineFreqAdj <- 0
if(TMA_Ages)
TMA_Meta <- "C:/ALL_USR/JRW/SIDT/Sablefish/Keras_CNN_Models/Sable_2017_2019 21 Nov 2022.RData" # If used, change path to the main sepectra/metadata save()'d data frame which contains TMA ages. Matching done via 'filenames'.
}
# Load functions and packages
{ ###
sourceFunctionURL <- function (URL, type = c("function", "script")[1]) {
" # For more functionality, see gitAFile() in the rgit package ( https://github.com/John-R-Wallace-NOAA/rgit ) which includes gitPush() and git() "
if (!any(installed.packages()[, 1] %in% "httr")) install.packages("httr")
File.ASCII <- tempfile()
if(type == "function")
on.exit(file.remove(File.ASCII))
getTMP <- httr::GET(gsub(' ', '%20', URL))
if(type == "function") {
write(paste(readLines(textConnection(httr::content(getTMP))), collapse = "\n"), File.ASCII)
source(File.ASCII)
}
if(type == "script") {
fileName <- strsplit(URL, "/")[[1]]
fileName <- rev(fileName)[1]
write(paste(readLines(textConnection(httr::content(getTMP))), collapse = "\n"), fileName)
}
}
# Toolbox functions
if (any(installed.packages()[, 1] %in% "JRWToolBox")) {
library(JRWToolBox)
} else {
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/Ls.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/openwd.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/lib.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/get.subs.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/sort.f.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/predicted_observed_plot.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/residuals_plot.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/as.num.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/match.f.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/Table.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/renum.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/agg.table.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/r.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/gof.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/Date.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/timeStamp.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/dec.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/loess.line.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/plot.loess.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/browserPlot.R")
}
# FishNIRS funtion
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/FishNIRS/master/R/plotly.Spec.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/FishNIRS/master/R/plotly_spectra.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/FishNIRS/master/R/predicted_observed_plot.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/FishNIRS/master/R/residuals_plot.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/FishNIRS/master/R/Correlation_R_squared_RMSE_MAE_SAD.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/FishNIRS/master/R/Mode.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/FishNIRS/master/R/agreementFigure.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/FishNIRS/master/R/FCNN_Model.R"); FCNN_model_ver_1 <- FCNN_model
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/FishNIRS/master/R/CNN_model_ver_5.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/FishNIRS/master/R/Read_OPUS_Spectra.R")
# sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/FishNIRS/master/R/CNN_model_2D.R") # Not working yet
lib(lattice)
lib(dplyr)
# remotes::install_github("r-lib/ragg@bc501c9951c5934afa55da6d36cdf03c2705d99f")
# lib(tidyverse)
lib(recipes)
lib(rsample)
lib(GGally)
lib(skimr)
lib(e1071)
lib(mdatools)
lib(plotly)
lib(reticulate)
lib(tensorflow)
lib(keras)
lib(prospectr)
lib(openxlsx)
# install.packages('RcppArmadillo') # Need to use 4.0 version
lib(RcppArmadillo)
# Configuration failed to find one of freetype2 libpng libtiff-4. Try installing:
# * deb: libfreetype6-dev, libpng-dev libtiff5-dev libjpeg-dev (Debian, Ubuntu, etc)
# * rpm: freetype-devel libpng-devel libtiff-devel libjpeg-turbo-devel (Fedora, CentOS, RHEL)
# * csw: libfreetype_dev libpng16_dev libtiff_dev libjpeg_dev (Solaris)
# If freetype2 libpng libtiff-4 is already installed, check that 'pkg-config' is in your
# PATH and PKG_CONFIG_PATH contains a freetype2 libpng libtiff-4.pc file. If pkg-config
# is unavailable you can set INCLUDE_DIR and LIB_DIR manually via:
# R CMD INSTALL --configure-vars='INCLUDE_DIR=... LIB_DIR=...'
# -------------------------- [ERROR MESSAGE] ---------------------------
# <stdin>:1:22: fatal error: ft2build.h: No such file or directory
# compilation terminated.
# --- Setup for TensorFlow and Keras ---
# You will need a 'GITHUB_PAT' from GitHub set somewhere in R (If you need help, search the Web how to get one from GitHub.)
# Sys.setenv(GITHUB_PAT = "**************") # If you set GITHUB_PAT here, uncomment this line. Note, do not share your GITHUB_PAT, nor load it onto GitHib.
Sys.getenv("GITHUB_PAT")
# --- Conda TensorFlow environment ---
Conda_TF_Eniv <- ifelse(.Platform$OS.type == 'windows', "C:/m3/envs/tf", "/more_home/h_jwallace/Python/tf_cpu_only/bin") # Change this path as needed
Sys.setenv(RETICULATE_PYTHON = Conda_TF_Eniv)
Sys.getenv("RETICULATE_PYTHON")
# https://github.com/rstudio/tensorflow/issues/412
# tf$config$threading$set_intra_op_parallelism_threads(1L)
# tf$config$threading$set_intra_op_parallelism_threads(11L)
# cat("\nintra_op_parallelism_threads =", tf$config$threading$get_intra_op_parallelism_threads(), "\n")
# tf$config$threading$set_inter_op_parallelism_threads(11L)
# cat("\nintra_op_parallelism_threads =", tf$config$threading$get_inter_op_parallelism_threads(), "\n\n")
# tf$executing_eagerly()
# https://github.com/rstudio/tensorflow/issues/412
# config <- tf$compat$v1$ConfigProto(intra_op_parallelism_threads = 2L, inter_op_parallelism_threads = 2L)
# session = tf$compat$v1$Session(config=config)
# tf$compat$v1$keras$backend$set_session(session)
# Test to see if TensorFlow is working in R
a <- tf$Variable(5.56)
b <- tf$Variable(2.7)
a + b
k_clear_session()
Seed_Fold <- c(777, 747, 727, 787, 797)[3]
Seed_Model <- c(777, 747, 727, 787, 797)[3]
# Pick the NN model to use (CNN_model_2D currently not working.)
model_Name <- c('FCNN_model_ver_1', 'CNN_model_ver_5', 'CNN_model_2D')[1]
Disable_GPU <- model_Name == 'FCNN_model_ver_1' # Only using the CPU is faster for the FCNN model but slower for CNN_model_ver_5, at least on Sablefish with data from 2017 and 2019.
tensorflow::set_random_seed(Seed_Model, disable_gpu = Disable_GPU)
} ###
# ------ NN Model -------
{ ###
# Load the data
load(paste0(Spectra_Set, '_Model_Spectra.sg.iPLS.RData'))
load(paste0(Spectra_Set, '_TMA_Vector.RData')
set.seed(Seed_Fold)
TMA_Tab <- Table(TMA_Vector)
(Low_strata <- sum(TMA_Tab[1:3]))
(Mid_strata <- sum(TMA_Tab[4:20]))
(High_strata <- sum(TMA_Tab[21:length(TMA_Tab)]))
(SaveOutOties <- c(sample(order(TMA_Vector)[1:Low_strata], 5), sample(order(TMA_Vector)[1:Mid_strata], 5), sample(order(TMA_Vector)[1:High_strata], 5)) ) # Save out 15 oties for final model check.
Model_Spectra.sg.iPLS_SaveOutOties <- Model_Spectra.sg.iPLS[SaveOutOties, ]
save(Model_Spectra.sg.iPLS_SaveOutOties, file = paste0(Spectra_Set, '_Model_Spectra.sg.iPLS_SaveOutOties.RData'))
TMA_Vector_SaveOutOties <- TMA_Vector[SaveOutOties]
save(TMA_Vector_SaveOutOties, file = paste0(Spectra_Set, '_TMA_Vector_SaveOutOties.RData'))
dim(Model_Spectra.sg.iPLS)
Model_Spectra.sg.iPLS <- Model_Spectra.sg.iPLS[-(SaveOutOties), ]
dim(Model_Spectra.sg.iPLS)
TMA_Vector <- TMA_Vector[-(SaveOutOties)]
# Use full file names (with '.0' at the end)
fileNames <- dir(path = Spectra_Path)
# fileNames <- get.subs(fileNames, sep = ".")[1, ]
fileNames[1:5]
# These are the oties that are not used in the NN model and are saved out for testing
sort(fileNames[SaveOutOties])
dir.create(paste0(Spectra_Set, '/Sable_Combo_2022_Saved_Out'), showWarnings = FALSE)
file.copy(paste0(Spectra_Path, "/", fileNames[SaveOutOties]), paste0(Spectra_Set, '_Saved_Out'), recursive = TRUE, copy.date = TRUE)
# = = = = = = = = = = = = = = = = = Intial setup to run the NN code between the '= = =' lines = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =
# Split the data into folds, spitting the remainder of an un-even division into the first folds, one otie per fold until finished
set.seed(Seed_Fold)
num_folds <- 10
index_org <- 1:nrow(Model_Spectra.sg.iPLS)
(fold_size_min <- floor(length(index_org)/num_folds))
(num_extra <- num_folds * dec(length(index_org)/num_folds))
index <- index_org
folds_index <- list()
for(i in 1:(num_folds - 1)) {
print(c(fold_size_min, i, num_extra, i <= num_extra, fold_size_min + ifelse(i <= num_extra, 1, 0), i - num_extra))
folds_index[[i]] <- sample(index, fold_size_min + ifelse(i < (num_extra + 0.1), 1, 0)) # Finite math - grr!
index <- index[!index %in% folds_index[[i]]]
}
folds_index[[num_folds]] <- index
lapply(folds_index, length) # Check the binning result
c(sum(unlist(lapply(folds_index, length))), length(index_org)) # Check that the number of oties is the same
graphics.off()
dev.new(width = 14, height = 6) #2
dev.new() # 3
dev.new(width = 11, height = 8) # 4
dev.new(width = 11, height = 8) # 5
dev.new(width = 10, height = 10) # 6
dev.new(width = 10, height = 10) # 7
# = = = = = = = = = = = = = = = = = Run the NN code between the '= = =' lines and expect long run times = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =
(Rdm_reps <- ifelse(model_Name == 'FCNN_model_ver_1', 20, 10))
Seed_Main <- 707 # Reducing the number of seeds will be considered later
set.seed(Seed_Main)
Seed_reps <- sample(1e7, Rdm_reps)
# Start fresh or continue by loading a file with model iterations already finished (see the commented line with an example model file).
Rdm_models <- list()
Rdm_folds_index <- list()
# load("C:\\ALL_USR\\JRW\\SIDT\\Sablefish\\Keras_CNN_Models\\Hake_2019_FCNN_10_Rdm_model_1_May_2023_13_34_20.RData")
file.create('Run_NN_Model_Flag', showWarnings = TRUE) # Stopping the model with this flag is broken by the nested loops, but left for now.
# Note that errors from plot.loess() are trapped by try() and are normal early in the iteration loop since there are not enough data to smooth.
for(j in (length(Rdm_folds_index) + 1):Rdm_reps) {
cat(paste0("\n\nStart of Random Rep = ", j , "\n\n"))
Seed_Data <- Seed_reps[j]
num_folds <- 10
# Split the data into folds based on the current seed which is dictated by Seed_Main (see above)
set.seed(Seed_Data)
index_org <- 1:nrow(Model_Spectra.sg.iPLS)
(fold_size_min <- floor(length(index_org)/num_folds))
(num_extra <- num_folds * dec(length(index_org)/num_folds))
index <- index_org
folds_index <- list()
for(i in 1:(num_folds - 1)) {
print(c(fold_size_min, i, num_extra, i <= num_extra, fold_size_min + ifelse(i <= num_extra, 1, 0), i - num_extra))
folds_index[[i]] <- sample(index, fold_size_min + ifelse(i < (num_extra + 0.1), 1, 0)) # Finite math - grr!
index <- index[!index %in% folds_index[[i]]]
}
folds_index[[num_folds]] <- index # Remainder from the above for() loop goes into the last fold index
lapply(folds_index, length)
c(sum(unlist(lapply(folds_index, length))), length(index_org))
Fold_models <- list()
for (i in 1:num_folds) {
Model_Spectra.sg.iPLS.F <- Model_Spectra.sg.iPLS[-folds_index[[i]], ]
TMA_Vector.F <- TMA_Vector[-folds_index[[i]]]
# Split the data into training set (2/3) and test set (1/3)
set.seed(Seed_Data)
index <- 1:nrow(Model_Spectra.sg.iPLS.F)
testindex <- sample(index, trunc(length(index)/3))
x.test <- 1000 * Model_Spectra.sg.iPLS.F[testindex, ]
x.train <- 1000 * Model_Spectra.sg.iPLS.F[-testindex, ]
y.test <- TMA_Vector.F[testindex]
y.train <- TMA_Vector.F[-testindex]
cat(paste0("\n\nDimension of x.train = ", paste(dim(x.train), collapse = ' '), '\n\n')) # 906 380; 905 380 with crystallized otie removed
# Same learning rate for all models
learningRate <- c(0.00088, 0.0009)[2]
layer_dropout_rate <- NULL
# layer_dropout_rate <- 0.2
if(model_Name == 'FCNN_model_ver_1') model <- FCNN_model_ver_1(layer_dropout_rate = layer_dropout_rate)
if(model_Name == 'CNN_model_ver_5') model <- CNN_model_ver_5()
if(model_Name == 'CNN_model_2D') model <- CNN_model_2D()
# -- Don't reset Iter, Cor, CA_diag, SAD, or .Random.seed when re-starting the same run ---
tensorflow::set_random_seed(Seed_Model, disable_gpu = Disable_GPU); Seed_Model # Trying to this here and above (see the help for: tensorflow::set_random_seed)
set.seed(Seed_Data); Seed_Data # Re-setting the 'data' seed here to know where the model starts, also the Keras backend needs to cleared and the model reloaded - see above.
Iter_Num <- 8
Iter <- 0
Cor <- RMSE <- CA_diag <- SAD <- saveModels <- NULL
saveModels_List <- list()
while(file.exists('Run_NN_Model_Flag')) { # The multiple full fold version breaks the stop by removing the file flag, but it remains for now
# R memory garbage collection
gc()
# Clear TensorFlow's session
k_clear_session()
(Iter <- Iter + 1)
cat(paste0("\n\nRandom Replicates = ", j, ": Fold number = ", i, ": Iter = ", Iter,"\n"))
viewMetrics <- c(TRUE, FALSE)[2]
# config <- tf$compat.v1.ConfigProto(intra_op_parallelism_threads = 2L, inter_op_parallelism_threads = 2L)
# session <- tf$Session(config = config)
# k_set_session(session)
# blas_set_num_threads(4)
# blas_get_num_procs()
# FCNN model
if(model_Name == 'FCNN_model_ver_1') {
x.train.array <- as.matrix(x.train)
history <- fit(model, x.train.array, y.train, epochs = 1, batch_size = 32, validation_split = 0.2, verbose = 2,
# callbacks = list(callback_tensorboard(histogram_freq = 1, profile_batch = 2)),
view_metrics = viewMetrics)
history <- fit(model, x.train.array, y.train, epochs = 198, batch_size = 32, validation_split = 0.2, verbose = 0, view_metrics = viewMetrics)
history <- fit(model, x.train.array, y.train, epochs = 1, batch_size = 32, validation_split = 0.2, verbose = 2, view_metrics = viewMetrics)
history <- fit(model, x.train.array, y.train, epochs = 99, batch_size = 32, validation_split = 0.2, verbose = 0, view_metrics = viewMetrics)
history <- fit(model, x.train.array, y.train, epochs = 1, batch_size = 32, validation_split = 0.2, verbose = 2, view_metrics = viewMetrics)
history <- fit(model, x.train.array, y.train, epochs = 200, batch_size = 32, validation_split = 0.2, verbose = 0, view_metrics = viewMetrics)
x.test.array <- as.matrix(x.test)
}
# CNN_model ver 1,3,4,5
if(model_Name == 'CNN_model_ver_5') {
x.train.array <- array(as.matrix(x.train), c(nrow(x.train), ncol(x.train), 1))
history <- fit(model, x.train.array, y.train, epochs = 50, batch_size = 32, validation_split = 0.2, verbose = 2,
view_metrics = viewMetrics)
# view_metrics = viewMetrics, callbacks = list(callback_tensorboard(histogram_freq = 1, profile_batch = 2))) # profile_batch = c(1, 5)
x.test.array <- array(as.matrix(x.test), c(nrow(x.test), ncol(x.test), 1))
}
# CNN_model ver 2
# x.train.array <- array(as.matrix(x.train), c(nrow(x.train), ncol(x.train), 1))
# history <- fit(model, x.train.array, y.train, epochs = 1, batch_size = 32, validation_split = 0.2, verbose = 2, view_metrics = viewMetrics)
# history <- fit(model, x.train.array, y.train, epochs = 99, batch_size = 32, validation_split = 0.2, verbose = 0, view_metrics = viewMetrics)
# history <- fit(model, x.train.array, y.train, epochs = 1, batch_size = 32, validation_split = 0.2, verbose = 2, view_metrics = viewMetrics)
# history <- fit(model, x.train.array, y.train, epochs = 199, batch_size = 32, validation_split = 0.2, verbose = 0, view_metrics = viewMetrics)
# x.test.array <- array(as.matrix(x.test), c(nrow(x.test), ncol(x.test), 1))
if(model_Name == 'CNN_model_2D') {
x.train.array <- array(as.matrix(x.train), c(nrow(x.train), ncol(x.train), 1))
history <- fit(model, x.train.array, y.train * diag(length(y.train)), epochs = 50, batch_size = 32, validation_split = 0.2, verbose = 2, view_metrics = viewMetrics)
x.test.array <- array(as.matrix(x.test), c(nrow(x.test), ncol(x.test), 1))
}
evaluate(model, x.test.array, y.test, verbose = 0)
cat("\n")
print(summary(history))
dev.set(3)
print(plot(history))
# Predict using the test set; plot, create statistics, and create an agreement table
y.test.pred <- predict(model, x.test.array)
if(model_Name == 'FCNN_model_ver_1' & is.null(layer_dropout_rate)) Delta <- -0.05 # Delta is a previous estimate or guess for now, which varies by species.
if(model_Name == 'FCNN_model_ver_1' & !is.null(layer_dropout_rate)) Delta <- -0.3
if(model_Name == 'CNN_model_ver_5') Delta <- -0.2
if(model_Name == 'CNN_model_2D') Delta <- 0
y.test.pred.rd <- round(y.test.pred + Delta) # Rounding with a added delta (which is a negative number)
dev.set(4)
# plot(y.test, y.test.pred)
# abline(0, 1, col = 'green', lty = 2)
print(predicted_observed_plot(y.test, y.test.pred, xlab = 'y.test', ylab = 'y.test.pred'))
# SAD vector the Sum of absolute differences plot
# SAD <- c(SAD, sqrt(sum((y.test - y.test.pred.rd)^2)/(length(y.test) - 1))) # RMSE
SAD <- c(SAD, sum(abs(y.test - y.test.pred.rd)))
# Correlation, R_squared, RMSE, MAE, SAD (Sum of Absolute Differences)
cat("\n\n")
print(Correlation_R_squared_RMSE_MAE_SAD(y.test, y.test.pred.rd))
cat("(Prediction has been rounded to the nearest integer)\n")
# Correlation vector for the iterations plot
Cor <- c(Cor, cor(y.test, y.test.pred))
# RMSE vector for the iterations plot
RMSE <- c(RMSE, sqrt(mean((y.test - y.test.pred)^2, na.rm = TRUE)))
# e1071::classAgreement diagonal
CA_diag <- c(CA_diag, e1071::classAgreement(Table(y.test.pred.rd, y.test), match.names = FALSE)$diag)
cat("\nclassAgreement Diagonal =", rev(CA_diag)[1], "\n")
cat("\n\n")
# print(e1071::classAgreement(Table(y.test.pred.rd, y.test), match.names = TRUE)$diag) # match.names = TRUE option
# Correlation Between Sum of Absolute Differences and the classAgreement diagonal
if(length(SAD) >= 10)
# cat("\nCorrelation between sum of Absolute Differences and the classAgreement Diagonal =", signif(cor(SAD[5:length(SA)], CA_diag[5:length(CA_diag)]), 6), "\n")
cat("\nCorrelation between sum of Absolute Differences and the classAgreement Diagonal =", signif(cor(SAD, tail(CA_diag, length(SAD))), 6), "\n")
# dev.new(width = 14, height = 10)
# agreementFigure(y.test, y.test.pred, Delta, full = TRUE)
dev.set(5)
agreementFigure(y.test, y.test.pred, Delta, main = paste0("Random Reps = ", j, ": Fold Num = ", i, ": Iter = ", Iter))
dev.set(2)
par(mfrow = c(3, 1))
# plot(1:length(Cor), sqrt(Cor), col = 'green', ylim = c(-0.03, 1.03), ylab = "Correlation (green)", xlab = "Iteration Number")
# abline(h = c(0.2, 0.9), lty = 2, col ='grey39', lwd = 1.25)
plot(1:length(RMSE), RMSE, col = 'green', type = 'b', ylab = "RMSE (green)", xlab = "Iteration Number")
abline(h = 4, lty = 2, col ='grey39', lwd = 1.25)
try(plot.loess(1:length(CA_diag), CA_diag, col = 'red', line.col = 'deeppink', type = 'b', ylab = "Diagonal of Class Agreement (red)", xlab = "Iteration Number"))
abline(h = 0.2, lty = 2, col ='grey39', lwd = 1.25)
# Avoiding high SAD values at the beginning, and rarely during, a run.
SAD_plot <- SAD
SAD_plot[SAD_plot > 1400] <- NA # Extreme model runs can, on a very rare occasion, put the value of SAD above 1,400 beyond the initial runs
try(plot.loess(1:length(SAD_plot), SAD_plot, col = 'blue', line.col = 'dodgerblue', type = 'b', ylab = "Sum of Absolute Differences (blue)", xlab = "Iteration Number"))
abline(h = 950, lty = 2, col ='grey39', lwd = 1.25)
print(saveName <- paste0(Spectra_Set, '_', paste(get.subs(model_Name, "_")[-2], collapse = "_"), '_SM_', Seed_Model, '_RI_', j, '_LR_',
format(learningRate, sci = FALSE), '_LD_', ifelse(is.null(layer_dropout_rate), 0, layer_dropout_rate), '_It_', length(SAD),
'_SAD_', rev(SAD)[1], '_', timeStamp()))
assign(saveName, serialize_model(model, include_optimizer = TRUE))
# save(Iter, Cor, CA_diag, SAD, learningRate, layer_dropout_rate, .Random.seed, list = saveName, file = paste0(saveName, '.RData'))
saveModels <- c(saveModels, saveName)
saveModels_List[[saveName]] <- serialize_model(model, include_optimizer = TRUE)
if(Iter == Iter_Num)
break
} # Iter while() loop
if(!file.exists('Run_NN_Model_Flag'))
break
Iter_Best_Model <- sort.f(data.frame(SAD, RMSE, CA_diag, Iter = 1:Iter_Num), c(1, 3))[1, 4] # Best model is when SAD is lowest, with ties broken by CA_diag
# Iter_Best_Model <- sort.f(data.frame(SAD, RMSE, CA_diag, Iter = 1:Iter_Num), c(3, 1))[1, 4] # Best model is when SAD is lowest, with ties broken by CA_diag
print(sort.f(data.frame(SAD, RMSE, CA_diag, Iter = 1:Iter_Num), c(1, 3)))
cat(paste0('\n\nBest_Model Number = ', Iter_Best_Model, '\n\n'))
Fold_models[[i]] <- saveModels_List[[Iter_Best_Model]]
cat(paste0('\nBest Model Name = ', saveModels[Iter_Best_Model], "\n\n"))
rm(list = saveModels)
x.fold.test <- as.matrix(1000 * Model_Spectra.sg.iPLS[folds_index[[i]], ])
y.fold.test <- TMA_Vector[folds_index[[i]]]
y.fold.test.pred <- predict(unserialize_model(Fold_models[[i]], custom_objects = NULL, compile = TRUE), x.fold.test)
dev.set(6)
agreementFigure(y.fold.test, y.fold.test.pred, Delta = Delta, full = TRUE, main = paste0("Random Rep = ", j, ": Fold Num = ", i))
dev.set(7)
agreementFigure(y.fold.test, y.fold.test.pred, Delta = Delta, full = FALSE, main = paste0("Random Rep = ", j, ": Fold Num = ", i))
} # j Fold loop
if(!file.exists('Run_NN_Model_Flag'))
break
Rdm_models[[j]] <- Fold_models # List of lists being assigned to an element of a list - the best model for each fold (10 or other used) within the jth random rep
Rdm_folds_index[[j]] <- folds_index # List of vectors being assigned to an element of a list - the index for each fold (10 or other used) within the jth random rep
save(Iter, i, j, Cor, CA_diag, SAD, learningRate, layer_dropout_rate, Seed_Fold, Seed_Model, Seed_Main, Rdm_models,
Rdm_folds_index, file = paste0(Spectra_Set, '_', model_Name, '_', length(Rdm_folds_index), '_Rdm_model_', timeStamp(), '.RData'))
x.fold.test.ALL <- NULL
y.fold.test.ALL <- NULL
y.fold.test.pred.ALL <- NULL
for (k in 1:length(Fold_models)) {
x.fold.test <- as.matrix(1000 * Model_Spectra.sg.iPLS[folds_index[[k]], ])
x.fold.test.ALL <- rbind(x.fold.test.ALL, x.fold.test)
y.fold.test.ALL <- c(y.fold.test.ALL, TMA_Vector[folds_index[[k]]])
print(len(predict(unserialize_model(Fold_models[[k]], custom_objects = NULL, compile = TRUE), x.fold.test)))
y.fold.test.pred.ALL <- c(y.fold.test.pred.ALL, predict(unserialize_model(Fold_models[[k]], custom_objects = NULL, compile = TRUE), x.fold.test))
}
browserPlot('agreementFigure(y.fold.test.ALL, y.fold.test.pred.ALL, Delta = Delta, full = TRUE, main = paste0("Random Rep = ", j)) ')
} # k Random Replicate loop
# = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =
} ###
# Find Median over all Rdm_reps Models and create figures
{ ###
(Rdm_reps <- length(Rdm_folds_index))
y.fold.test.pred_RDM <- NULL
for (j in 1:Rdm_reps) {
folds_index <- Rdm_folds_index[[j]]
Fold_models <- Rdm_models[[j]]
y.fold.test.pred.ALL <- NULL
for (i in 1:length(Fold_models)) {
x.fold.test <- as.matrix(1000 * Model_Spectra.sg.iPLS[folds_index[[i]], ])
y.fold.test.pred <- as.vector(predict(unserialize_model(Fold_models[[i]], custom_objects = NULL, compile = TRUE), x.fold.test))
print(c(length(folds_index[[i]]), length(y.fold.test.pred)))
y.fold.test.pred.ALL <- rbind(y.fold.test.pred.ALL, cbind(Index = folds_index[[i]], y.test.fold.pred = y.fold.test.pred))
}
y.test.pred <- sort.f(data.frame(y.fold.test.pred.ALL))[, 2] # Sort on the Index to match back to the order of the full TMA_Vector and Model_Spectra.sg.iPLS
y.fold.test.pred_RDM <- rbind(y.fold.test.pred_RDM, y.test.pred)
browserPlot('agreementFigure(TMA_Vector, y.test.pred, Delta = -0.05, full = TRUE, main = paste0("Random Rep = ", j))') # Delta is a previous estimate or guess for now
# Full figure only needed for a long-lived species like Sablefish
# dev.new(width = 11, height = 8)
# agreementFigure(TMA_Vector, y.test.pred, Delta = -0.25, full = FALSE, main = paste0("Random Rep = ", j))
}
} ###
John-R-Wallace-NOAA/FishNIRS documentation built on April 12, 2025, 12:59 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
# ./cpulimit --exe /more_home/h_jwallace/Python/tf_cpu_only/bin/python --limit 1000 &
# ./cpulimit --exe /opt/R/64-bit/R-4.1.2_MKL/lib64/R/bin/exec/R --limit 1000 > /dev/null 2>&1 &
# nice -19 /opt/R/64-bit/R-4.1.2_MKL/bin/R
# <ctrl-z>
# $ jobs -l
# [1]+ ##### Stopped R
# $ ./cpulimit --pid 37921 --limit 1000 &
# echo "source('NN Model.R')" | /opt/R/64-bit/R-4.1.2_MKL/bin/R --no-save
# setwd(ifelse(.Platform$OS.type == 'windows', "C:/ALL_USR/JRW/SIDT/Train_NN_Model", "/more_home/h_jwallace/SIDT/Train_NN_Model"))
# source('NN Model.R')
# --- Initial setup (The curly brakets without indented lines directly below are there to enable the hiding of code sections using Notepad++.) ---
# (For those who dislike Rstudio and others, Notepad++ is here: https://notepad-plus-plus.org/ and NppToR that passes R code from Notepad++ to R is here: https://sourceforge.net/projects/npptor/)
if(interactive())
setwd(ifelse(.Platform$OS.type == 'windows', "C:/ALL_USR/JRW/SIDT/Predict_NN_Ages", "/more_home/h_jwallace/SIDT/Predict_NN_Ages")) # Change path to the Spectra Set's .GlobalEnv as needed
if(!interactive()) options(width = 120)
Spectra_Set <- c("Hake_2019", "Sable_2017_2019", "Sable_Combo_2022")[2]
Spectra_Path <- "New_Scans" # Put new spectra scans in a separate folder and enter the name of the folder below
Predicted_Ages_Path <- "Predicted_Ages" # The NN predicted ages will go in the path defined below
dir.create(Predicted_Ages_Path, showWarnings = FALSE)
TMA_Ages <- c(TRUE, FALSE)[2] # Are TMA ages available and are they to be used?
verbose <- c(TRUE, FALSE)[1]
plot <- c(TRUE, FALSE)[1]
Max_N_Spectra <- list(50, 200, 'All')[[3]] # Max number of new spectra to be plotted in the spectra figure. (All spectra in the 'New_Scans' folder will be assigned an age regardless of the number plotted in the figure.)
spectraInterp = c('stats_splinefun_lowess', 'prospectr_resample')[1]
opusReader = c('pierreroudier_opusreader', 'philippbaumann_opusreader2')[2]
# (1) Hake 2019, BMS
if(Spectra_Set == "Hake_2019") {
shortNameSegments <- c(2, 4) # Segments 2 and 4 of the spectra file name, e.g.: (PACIFIC, HAKE, BMS201906206C, 1191, OD1) => (HAKE, 1191)
shortNameSuffix <- 'BMS'
opusReader <- 'pierreroudier_opusreader'
fineFreqAdj <- 150
}
# (2) Sablefish 2017 & 2019, Combo survey
if(Spectra_Set == "Sable_2017_2019") {
shortNameSegments <- c(1, 3) # Segments 1 and 3 of the spectra file name, e.g.: (SABLEFISH, COMBO201701203A, 28, OD1) => (SABLEFISH, 28)
shortNameSuffix <- 'Year'
yearPosition <- c(6, 9) # e.g. COMBO201701203A => 2017 (Segment used (see above) is: shortNameSegments[1] + 1)
fineFreqAdj <- 0
opusReader <- 'pierreroudier_opusreader'
if(TMA_Ages)
TMA_Meta <- "C:/ALL_USR/JRW/SIDT/Sablefish/Keras_CNN_Models/Sable_2017_2019 21 Nov 2022.RData" # If used, change path to the main sepectra/metadata save()'d data frame which contains TMA ages. Matching done via 'filenames'.
}
# (3) Sablefish 2022, Combo survey
if(Spectra_Set == "Sable_Combo_2022") {
shortNameSegments <- c(1,5) # Segments 1 and 3 of the spectra file name, e.g.: (SABLEFISH, COMBO201701203A, 28, OD1) => (SABLEFISH, 28)
shortNameSuffix <- 'Year'
yearPosition <- c(6, 9) # e.g. COMBO201701203A => 2017 (Segment used (see above) is: shortNameSegments[1] + 1)
fineFreqAdj <- 0
if(TMA_Ages)
TMA_Meta <- "C:/ALL_USR/JRW/SIDT/Sablefish/Keras_CNN_Models/Sable_2017_2019 21 Nov 2022.RData" # If used, change path to the main sepectra/metadata save()'d data frame which contains TMA ages. Matching done via 'filenames'.
}
# Load functions and packages
{ ###
sourceFunctionURL <- function (URL, type = c("function", "script")[1]) {
" # For more functionality, see gitAFile() in the rgit package ( https://github.com/John-R-Wallace-NOAA/rgit ) which includes gitPush() and git() "
if (!any(installed.packages()[, 1] %in% "httr")) install.packages("httr")
File.ASCII <- tempfile()
if(type == "function")
on.exit(file.remove(File.ASCII))
getTMP <- httr::GET(gsub(' ', '%20', URL))
if(type == "function") {
write(paste(readLines(textConnection(httr::content(getTMP))), collapse = "\n"), File.ASCII)
source(File.ASCII)
}
if(type == "script") {
fileName <- strsplit(URL, "/")[[1]]
fileName <- rev(fileName)[1]
write(paste(readLines(textConnection(httr::content(getTMP))), collapse = "\n"), fileName)
}
}
# Toolbox functions
if (any(installed.packages()[, 1] %in% "JRWToolBox")) {
library(JRWToolBox)
} else {
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/Ls.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/openwd.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/lib.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/get.subs.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/sort.f.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/predicted_observed_plot.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/residuals_plot.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/as.num.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/match.f.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/Table.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/renum.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/agg.table.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/r.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/gof.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/Date.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/timeStamp.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/dec.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/loess.line.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/plot.loess.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/browserPlot.R")
}
# FishNIRS funtion
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/FishNIRS/master/R/plotly.Spec.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/FishNIRS/master/R/plotly_spectra.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/FishNIRS/master/R/predicted_observed_plot.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/FishNIRS/master/R/residuals_plot.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/FishNIRS/master/R/Correlation_R_squared_RMSE_MAE_SAD.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/FishNIRS/master/R/Mode.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/FishNIRS/master/R/agreementFigure.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/FishNIRS/master/R/FCNN_Model.R"); FCNN_model_ver_1 <- FCNN_model
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/FishNIRS/master/R/CNN_model_ver_5.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/FishNIRS/master/R/Read_OPUS_Spectra.R")
# sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/FishNIRS/master/R/CNN_model_2D.R") # Not working yet
lib(lattice)
lib(dplyr)
# remotes::install_github("r-lib/ragg@bc501c9951c5934afa55da6d36cdf03c2705d99f")
# lib(tidyverse)
lib(recipes)
lib(rsample)
lib(GGally)
lib(skimr)
lib(e1071)
lib(mdatools)
lib(plotly)
lib(reticulate)
lib(tensorflow)
lib(keras)
lib(prospectr)
lib(openxlsx)
# install.packages('RcppArmadillo') # Need to use 4.0 version
lib(RcppArmadillo)
# Configuration failed to find one of freetype2 libpng libtiff-4. Try installing:
# * deb: libfreetype6-dev, libpng-dev libtiff5-dev libjpeg-dev (Debian, Ubuntu, etc)
# * rpm: freetype-devel libpng-devel libtiff-devel libjpeg-turbo-devel (Fedora, CentOS, RHEL)
# * csw: libfreetype_dev libpng16_dev libtiff_dev libjpeg_dev (Solaris)
# If freetype2 libpng libtiff-4 is already installed, check that 'pkg-config' is in your
# PATH and PKG_CONFIG_PATH contains a freetype2 libpng libtiff-4.pc file. If pkg-config
# is unavailable you can set INCLUDE_DIR and LIB_DIR manually via:
# R CMD INSTALL --configure-vars='INCLUDE_DIR=... LIB_DIR=...'
# -------------------------- [ERROR MESSAGE] ---------------------------
# <stdin>:1:22: fatal error: ft2build.h: No such file or directory
# compilation terminated.
# --- Setup for TensorFlow and Keras ---
# You will need a 'GITHUB_PAT' from GitHub set somewhere in R (If you need help, search the Web how to get one from GitHub.)
# Sys.setenv(GITHUB_PAT = "**************") # If you set GITHUB_PAT here, uncomment this line. Note, do not share your GITHUB_PAT, nor load it onto GitHib.
Sys.getenv("GITHUB_PAT")
# --- Conda TensorFlow environment ---
Conda_TF_Eniv <- ifelse(.Platform$OS.type == 'windows', "C:/m3/envs/tf", "/more_home/h_jwallace/Python/tf_cpu_only/bin") # Change this path as needed
Sys.setenv(RETICULATE_PYTHON = Conda_TF_Eniv)
Sys.getenv("RETICULATE_PYTHON")
# https://github.com/rstudio/tensorflow/issues/412
# tf$config$threading$set_intra_op_parallelism_threads(1L)
# tf$config$threading$set_intra_op_parallelism_threads(11L)
# cat("\nintra_op_parallelism_threads =", tf$config$threading$get_intra_op_parallelism_threads(), "\n")
# tf$config$threading$set_inter_op_parallelism_threads(11L)
# cat("\nintra_op_parallelism_threads =", tf$config$threading$get_inter_op_parallelism_threads(), "\n\n")
# tf$executing_eagerly()
# https://github.com/rstudio/tensorflow/issues/412
# config <- tf$compat$v1$ConfigProto(intra_op_parallelism_threads = 2L, inter_op_parallelism_threads = 2L)
# session = tf$compat$v1$Session(config=config)
# tf$compat$v1$keras$backend$set_session(session)
# Test to see if TensorFlow is working in R
a <- tf$Variable(5.56)
b <- tf$Variable(2.7)
a + b
k_clear_session()
Seed_Fold <- c(777, 747, 727, 787, 797)[3]
Seed_Model <- c(777, 747, 727, 787, 797)[3]
# Pick the NN model to use (CNN_model_2D currently not working.)
model_Name <- c('FCNN_model_ver_1', 'CNN_model_ver_5', 'CNN_model_2D')[1]
Disable_GPU <- model_Name == 'FCNN_model_ver_1' # Only using the CPU is faster for the FCNN model but slower for CNN_model_ver_5, at least on Sablefish with data from 2017 and 2019.
tensorflow::set_random_seed(Seed_Model, disable_gpu = Disable_GPU)
} ###
# ------ NN Model -------
{ ###
# Load the data
load(paste0(Spectra_Set, '_Model_Spectra.sg.iPLS.RData'))
load(paste0(Spectra_Set, '_TMA_Vector.RData')
set.seed(Seed_Fold)
TMA_Tab <- Table(TMA_Vector)
(Low_strata <- sum(TMA_Tab[1:3]))
(Mid_strata <- sum(TMA_Tab[4:20]))
(High_strata <- sum(TMA_Tab[21:length(TMA_Tab)]))
(SaveOutOties <- c(sample(order(TMA_Vector)[1:Low_strata], 5), sample(order(TMA_Vector)[1:Mid_strata], 5), sample(order(TMA_Vector)[1:High_strata], 5)) ) # Save out 15 oties for final model check.
Model_Spectra.sg.iPLS_SaveOutOties <- Model_Spectra.sg.iPLS[SaveOutOties, ]
save(Model_Spectra.sg.iPLS_SaveOutOties, file = paste0(Spectra_Set, '_Model_Spectra.sg.iPLS_SaveOutOties.RData'))
TMA_Vector_SaveOutOties <- TMA_Vector[SaveOutOties]
save(TMA_Vector_SaveOutOties, file = paste0(Spectra_Set, '_TMA_Vector_SaveOutOties.RData'))
dim(Model_Spectra.sg.iPLS)
Model_Spectra.sg.iPLS <- Model_Spectra.sg.iPLS[-(SaveOutOties), ]
dim(Model_Spectra.sg.iPLS)
TMA_Vector <- TMA_Vector[-(SaveOutOties)]
# Use full file names (with '.0' at the end)
fileNames <- dir(path = Spectra_Path)
# fileNames <- get.subs(fileNames, sep = ".")[1, ]
fileNames[1:5]
# These are the oties that are not used in the NN model and are saved out for testing
sort(fileNames[SaveOutOties])
dir.create(paste0(Spectra_Set, '/Sable_Combo_2022_Saved_Out'), showWarnings = FALSE)
file.copy(paste0(Spectra_Path, "/", fileNames[SaveOutOties]), paste0(Spectra_Set, '_Saved_Out'), recursive = TRUE, copy.date = TRUE)
# = = = = = = = = = = = = = = = = = Intial setup to run the NN code between the '= = =' lines = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =
# Split the data into folds, spitting the remainder of an un-even division into the first folds, one otie per fold until finished
set.seed(Seed_Fold)
num_folds <- 10
index_org <- 1:nrow(Model_Spectra.sg.iPLS)
(fold_size_min <- floor(length(index_org)/num_folds))
(num_extra <- num_folds * dec(length(index_org)/num_folds))
index <- index_org
folds_index <- list()
for(i in 1:(num_folds - 1)) {
print(c(fold_size_min, i, num_extra, i <= num_extra, fold_size_min + ifelse(i <= num_extra, 1, 0), i - num_extra))
folds_index[[i]] <- sample(index, fold_size_min + ifelse(i < (num_extra + 0.1), 1, 0)) # Finite math - grr!
index <- index[!index %in% folds_index[[i]]]
}
folds_index[[num_folds]] <- index
lapply(folds_index, length) # Check the binning result
c(sum(unlist(lapply(folds_index, length))), length(index_org)) # Check that the number of oties is the same
graphics.off()
dev.new(width = 14, height = 6) #2
dev.new() # 3
dev.new(width = 11, height = 8) # 4
dev.new(width = 11, height = 8) # 5
dev.new(width = 10, height = 10) # 6
dev.new(width = 10, height = 10) # 7
# = = = = = = = = = = = = = = = = = Run the NN code between the '= = =' lines and expect long run times = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =
(Rdm_reps <- ifelse(model_Name == 'FCNN_model_ver_1', 20, 10))
Seed_Main <- 707 # Reducing the number of seeds will be considered later
set.seed(Seed_Main)
Seed_reps <- sample(1e7, Rdm_reps)
# Start fresh or continue by loading a file with model iterations already finished (see the commented line with an example model file).
Rdm_models <- list()
Rdm_folds_index <- list()
# load("C:\\ALL_USR\\JRW\\SIDT\\Sablefish\\Keras_CNN_Models\\Hake_2019_FCNN_10_Rdm_model_1_May_2023_13_34_20.RData")
file.create('Run_NN_Model_Flag', showWarnings = TRUE) # Stopping the model with this flag is broken by the nested loops, but left for now.
# Note that errors from plot.loess() are trapped by try() and are normal early in the iteration loop since there are not enough data to smooth.
for(j in (length(Rdm_folds_index) + 1):Rdm_reps) {
cat(paste0("\n\nStart of Random Rep = ", j , "\n\n"))
Seed_Data <- Seed_reps[j]
num_folds <- 10
# Split the data into folds based on the current seed which is dictated by Seed_Main (see above)
set.seed(Seed_Data)
index_org <- 1:nrow(Model_Spectra.sg.iPLS)
(fold_size_min <- floor(length(index_org)/num_folds))
(num_extra <- num_folds * dec(length(index_org)/num_folds))
index <- index_org
folds_index <- list()
for(i in 1:(num_folds - 1)) {
print(c(fold_size_min, i, num_extra, i <= num_extra, fold_size_min + ifelse(i <= num_extra, 1, 0), i - num_extra))
folds_index[[i]] <- sample(index, fold_size_min + ifelse(i < (num_extra + 0.1), 1, 0)) # Finite math - grr!
index <- index[!index %in% folds_index[[i]]]
}
folds_index[[num_folds]] <- index # Remainder from the above for() loop goes into the last fold index
lapply(folds_index, length)
c(sum(unlist(lapply(folds_index, length))), length(index_org))
Fold_models <- list()
for (i in 1:num_folds) {
Model_Spectra.sg.iPLS.F <- Model_Spectra.sg.iPLS[-folds_index[[i]], ]
TMA_Vector.F <- TMA_Vector[-folds_index[[i]]]
# Split the data into training set (2/3) and test set (1/3)
set.seed(Seed_Data)
index <- 1:nrow(Model_Spectra.sg.iPLS.F)
testindex <- sample(index, trunc(length(index)/3))
x.test <- 1000 * Model_Spectra.sg.iPLS.F[testindex, ]
x.train <- 1000 * Model_Spectra.sg.iPLS.F[-testindex, ]
y.test <- TMA_Vector.F[testindex]
y.train <- TMA_Vector.F[-testindex]
cat(paste0("\n\nDimension of x.train = ", paste(dim(x.train), collapse = ' '), '\n\n')) # 906 380; 905 380 with crystallized otie removed
# Same learning rate for all models
learningRate <- c(0.00088, 0.0009)[2]
layer_dropout_rate <- NULL
# layer_dropout_rate <- 0.2
if(model_Name == 'FCNN_model_ver_1') model <- FCNN_model_ver_1(layer_dropout_rate = layer_dropout_rate)
if(model_Name == 'CNN_model_ver_5') model <- CNN_model_ver_5()
if(model_Name == 'CNN_model_2D') model <- CNN_model_2D()
# -- Don't reset Iter, Cor, CA_diag, SAD, or .Random.seed when re-starting the same run ---
tensorflow::set_random_seed(Seed_Model, disable_gpu = Disable_GPU); Seed_Model # Trying to this here and above (see the help for: tensorflow::set_random_seed)
set.seed(Seed_Data); Seed_Data # Re-setting the 'data' seed here to know where the model starts, also the Keras backend needs to cleared and the model reloaded - see above.
Iter_Num <- 8
Iter <- 0
Cor <- RMSE <- CA_diag <- SAD <- saveModels <- NULL
saveModels_List <- list()
while(file.exists('Run_NN_Model_Flag')) { # The multiple full fold version breaks the stop by removing the file flag, but it remains for now
# R memory garbage collection
gc()
# Clear TensorFlow's session
k_clear_session()
(Iter <- Iter + 1)
cat(paste0("\n\nRandom Replicates = ", j, ": Fold number = ", i, ": Iter = ", Iter,"\n"))
viewMetrics <- c(TRUE, FALSE)[2]
# config <- tf$compat.v1.ConfigProto(intra_op_parallelism_threads = 2L, inter_op_parallelism_threads = 2L)
# session <- tf$Session(config = config)
# k_set_session(session)
# blas_set_num_threads(4)
# blas_get_num_procs()
# FCNN model
if(model_Name == 'FCNN_model_ver_1') {
x.train.array <- as.matrix(x.train)
history <- fit(model, x.train.array, y.train, epochs = 1, batch_size = 32, validation_split = 0.2, verbose = 2,
# callbacks = list(callback_tensorboard(histogram_freq = 1, profile_batch = 2)),
view_metrics = viewMetrics)
history <- fit(model, x.train.array, y.train, epochs = 198, batch_size = 32, validation_split = 0.2, verbose = 0, view_metrics = viewMetrics)
history <- fit(model, x.train.array, y.train, epochs = 1, batch_size = 32, validation_split = 0.2, verbose = 2, view_metrics = viewMetrics)
history <- fit(model, x.train.array, y.train, epochs = 99, batch_size = 32, validation_split = 0.2, verbose = 0, view_metrics = viewMetrics)
history <- fit(model, x.train.array, y.train, epochs = 1, batch_size = 32, validation_split = 0.2, verbose = 2, view_metrics = viewMetrics)
history <- fit(model, x.train.array, y.train, epochs = 200, batch_size = 32, validation_split = 0.2, verbose = 0, view_metrics = viewMetrics)
x.test.array <- as.matrix(x.test)
}
# CNN_model ver 1,3,4,5
if(model_Name == 'CNN_model_ver_5') {
x.train.array <- array(as.matrix(x.train), c(nrow(x.train), ncol(x.train), 1))
history <- fit(model, x.train.array, y.train, epochs = 50, batch_size = 32, validation_split = 0.2, verbose = 2,
view_metrics = viewMetrics)
# view_metrics = viewMetrics, callbacks = list(callback_tensorboard(histogram_freq = 1, profile_batch = 2))) # profile_batch = c(1, 5)
x.test.array <- array(as.matrix(x.test), c(nrow(x.test), ncol(x.test), 1))
}
# CNN_model ver 2
# x.train.array <- array(as.matrix(x.train), c(nrow(x.train), ncol(x.train), 1))
# history <- fit(model, x.train.array, y.train, epochs = 1, batch_size = 32, validation_split = 0.2, verbose = 2, view_metrics = viewMetrics)
# history <- fit(model, x.train.array, y.train, epochs = 99, batch_size = 32, validation_split = 0.2, verbose = 0, view_metrics = viewMetrics)
# history <- fit(model, x.train.array, y.train, epochs = 1, batch_size = 32, validation_split = 0.2, verbose = 2, view_metrics = viewMetrics)
# history <- fit(model, x.train.array, y.train, epochs = 199, batch_size = 32, validation_split = 0.2, verbose = 0, view_metrics = viewMetrics)
# x.test.array <- array(as.matrix(x.test), c(nrow(x.test), ncol(x.test), 1))
if(model_Name == 'CNN_model_2D') {
x.train.array <- array(as.matrix(x.train), c(nrow(x.train), ncol(x.train), 1))
history <- fit(model, x.train.array, y.train * diag(length(y.train)), epochs = 50, batch_size = 32, validation_split = 0.2, verbose = 2, view_metrics = viewMetrics)
x.test.array <- array(as.matrix(x.test), c(nrow(x.test), ncol(x.test), 1))
}
evaluate(model, x.test.array, y.test, verbose = 0)
cat("\n")
print(summary(history))
dev.set(3)
print(plot(history))
# Predict using the test set; plot, create statistics, and create an agreement table
y.test.pred <- predict(model, x.test.array)
if(model_Name == 'FCNN_model_ver_1' & is.null(layer_dropout_rate)) Delta <- -0.05 # Delta is a previous estimate or guess for now, which varies by species.
if(model_Name == 'FCNN_model_ver_1' & !is.null(layer_dropout_rate)) Delta <- -0.3
if(model_Name == 'CNN_model_ver_5') Delta <- -0.2
if(model_Name == 'CNN_model_2D') Delta <- 0
y.test.pred.rd <- round(y.test.pred + Delta) # Rounding with a added delta (which is a negative number)
dev.set(4)
# plot(y.test, y.test.pred)
# abline(0, 1, col = 'green', lty = 2)
print(predicted_observed_plot(y.test, y.test.pred, xlab = 'y.test', ylab = 'y.test.pred'))
# SAD vector the Sum of absolute differences plot
# SAD <- c(SAD, sqrt(sum((y.test - y.test.pred.rd)^2)/(length(y.test) - 1))) # RMSE
SAD <- c(SAD, sum(abs(y.test - y.test.pred.rd)))
# Correlation, R_squared, RMSE, MAE, SAD (Sum of Absolute Differences)
cat("\n\n")
print(Correlation_R_squared_RMSE_MAE_SAD(y.test, y.test.pred.rd))
cat("(Prediction has been rounded to the nearest integer)\n")
# Correlation vector for the iterations plot
Cor <- c(Cor, cor(y.test, y.test.pred))
# RMSE vector for the iterations plot
RMSE <- c(RMSE, sqrt(mean((y.test - y.test.pred)^2, na.rm = TRUE)))
# e1071::classAgreement diagonal
CA_diag <- c(CA_diag, e1071::classAgreement(Table(y.test.pred.rd, y.test), match.names = FALSE)$diag)
cat("\nclassAgreement Diagonal =", rev(CA_diag)[1], "\n")
cat("\n\n")
# print(e1071::classAgreement(Table(y.test.pred.rd, y.test), match.names = TRUE)$diag) # match.names = TRUE option
# Correlation Between Sum of Absolute Differences and the classAgreement diagonal
if(length(SAD) >= 10)
# cat("\nCorrelation between sum of Absolute Differences and the classAgreement Diagonal =", signif(cor(SAD[5:length(SA)], CA_diag[5:length(CA_diag)]), 6), "\n")
cat("\nCorrelation between sum of Absolute Differences and the classAgreement Diagonal =", signif(cor(SAD, tail(CA_diag, length(SAD))), 6), "\n")
# dev.new(width = 14, height = 10)
# agreementFigure(y.test, y.test.pred, Delta, full = TRUE)
dev.set(5)
agreementFigure(y.test, y.test.pred, Delta, main = paste0("Random Reps = ", j, ": Fold Num = ", i, ": Iter = ", Iter))
dev.set(2)
par(mfrow = c(3, 1))
# plot(1:length(Cor), sqrt(Cor), col = 'green', ylim = c(-0.03, 1.03), ylab = "Correlation (green)", xlab = "Iteration Number")
# abline(h = c(0.2, 0.9), lty = 2, col ='grey39', lwd = 1.25)
plot(1:length(RMSE), RMSE, col = 'green', type = 'b', ylab = "RMSE (green)", xlab = "Iteration Number")
abline(h = 4, lty = 2, col ='grey39', lwd = 1.25)
try(plot.loess(1:length(CA_diag), CA_diag, col = 'red', line.col = 'deeppink', type = 'b', ylab = "Diagonal of Class Agreement (red)", xlab = "Iteration Number"))
abline(h = 0.2, lty = 2, col ='grey39', lwd = 1.25)
# Avoiding high SAD values at the beginning, and rarely during, a run.
SAD_plot <- SAD
SAD_plot[SAD_plot > 1400] <- NA # Extreme model runs can, on a very rare occasion, put the value of SAD above 1,400 beyond the initial runs
try(plot.loess(1:length(SAD_plot), SAD_plot, col = 'blue', line.col = 'dodgerblue', type = 'b', ylab = "Sum of Absolute Differences (blue)", xlab = "Iteration Number"))
abline(h = 950, lty = 2, col ='grey39', lwd = 1.25)
print(saveName <- paste0(Spectra_Set, '_', paste(get.subs(model_Name, "_")[-2], collapse = "_"), '_SM_', Seed_Model, '_RI_', j, '_LR_',
format(learningRate, sci = FALSE), '_LD_', ifelse(is.null(layer_dropout_rate), 0, layer_dropout_rate), '_It_', length(SAD),
'_SAD_', rev(SAD)[1], '_', timeStamp()))
assign(saveName, serialize_model(model, include_optimizer = TRUE))
# save(Iter, Cor, CA_diag, SAD, learningRate, layer_dropout_rate, .Random.seed, list = saveName, file = paste0(saveName, '.RData'))
saveModels <- c(saveModels, saveName)
saveModels_List[[saveName]] <- serialize_model(model, include_optimizer = TRUE)
if(Iter == Iter_Num)
break
} # Iter while() loop
if(!file.exists('Run_NN_Model_Flag'))
break
Iter_Best_Model <- sort.f(data.frame(SAD, RMSE, CA_diag, Iter = 1:Iter_Num), c(1, 3))[1, 4] # Best model is when SAD is lowest, with ties broken by CA_diag
# Iter_Best_Model <- sort.f(data.frame(SAD, RMSE, CA_diag, Iter = 1:Iter_Num), c(3, 1))[1, 4] # Best model is when SAD is lowest, with ties broken by CA_diag
print(sort.f(data.frame(SAD, RMSE, CA_diag, Iter = 1:Iter_Num), c(1, 3)))
cat(paste0('\n\nBest_Model Number = ', Iter_Best_Model, '\n\n'))
Fold_models[[i]] <- saveModels_List[[Iter_Best_Model]]
cat(paste0('\nBest Model Name = ', saveModels[Iter_Best_Model], "\n\n"))
rm(list = saveModels)
x.fold.test <- as.matrix(1000 * Model_Spectra.sg.iPLS[folds_index[[i]], ])
y.fold.test <- TMA_Vector[folds_index[[i]]]
y.fold.test.pred <- predict(unserialize_model(Fold_models[[i]], custom_objects = NULL, compile = TRUE), x.fold.test)
dev.set(6)
agreementFigure(y.fold.test, y.fold.test.pred, Delta = Delta, full = TRUE, main = paste0("Random Rep = ", j, ": Fold Num = ", i))
dev.set(7)
agreementFigure(y.fold.test, y.fold.test.pred, Delta = Delta, full = FALSE, main = paste0("Random Rep = ", j, ": Fold Num = ", i))
} # j Fold loop
if(!file.exists('Run_NN_Model_Flag'))
break
Rdm_models[[j]] <- Fold_models # List of lists being assigned to an element of a list - the best model for each fold (10 or other used) within the jth random rep
Rdm_folds_index[[j]] <- folds_index # List of vectors being assigned to an element of a list - the index for each fold (10 or other used) within the jth random rep
save(Iter, i, j, Cor, CA_diag, SAD, learningRate, layer_dropout_rate, Seed_Fold, Seed_Model, Seed_Main, Rdm_models,
Rdm_folds_index, file = paste0(Spectra_Set, '_', model_Name, '_', length(Rdm_folds_index), '_Rdm_model_', timeStamp(), '.RData'))
x.fold.test.ALL <- NULL
y.fold.test.ALL <- NULL
y.fold.test.pred.ALL <- NULL
for (k in 1:length(Fold_models)) {
x.fold.test <- as.matrix(1000 * Model_Spectra.sg.iPLS[folds_index[[k]], ])
x.fold.test.ALL <- rbind(x.fold.test.ALL, x.fold.test)
y.fold.test.ALL <- c(y.fold.test.ALL, TMA_Vector[folds_index[[k]]])
print(len(predict(unserialize_model(Fold_models[[k]], custom_objects = NULL, compile = TRUE), x.fold.test)))
y.fold.test.pred.ALL <- c(y.fold.test.pred.ALL, predict(unserialize_model(Fold_models[[k]], custom_objects = NULL, compile = TRUE), x.fold.test))
}
browserPlot('agreementFigure(y.fold.test.ALL, y.fold.test.pred.ALL, Delta = Delta, full = TRUE, main = paste0("Random Rep = ", j)) ')
} # k Random Replicate loop
# = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =
} ###
# Find Median over all Rdm_reps Models and create figures
{ ###
(Rdm_reps <- length(Rdm_folds_index))
y.fold.test.pred_RDM <- NULL
for (j in 1:Rdm_reps) {
folds_index <- Rdm_folds_index[[j]]
Fold_models <- Rdm_models[[j]]
y.fold.test.pred.ALL <- NULL
for (i in 1:length(Fold_models)) {
x.fold.test <- as.matrix(1000 * Model_Spectra.sg.iPLS[folds_index[[i]], ])
y.fold.test.pred <- as.vector(predict(unserialize_model(Fold_models[[i]], custom_objects = NULL, compile = TRUE), x.fold.test))
print(c(length(folds_index[[i]]), length(y.fold.test.pred)))
y.fold.test.pred.ALL <- rbind(y.fold.test.pred.ALL, cbind(Index = folds_index[[i]], y.test.fold.pred = y.fold.test.pred))
}
y.test.pred <- sort.f(data.frame(y.fold.test.pred.ALL))[, 2] # Sort on the Index to match back to the order of the full TMA_Vector and Model_Spectra.sg.iPLS
y.fold.test.pred_RDM <- rbind(y.fold.test.pred_RDM, y.test.pred)
browserPlot('agreementFigure(TMA_Vector, y.test.pred, Delta = -0.05, full = TRUE, main = paste0("Random Rep = ", j))') # Delta is a previous estimate or guess for now
# Full figure only needed for a long-lived species like Sablefish
# dev.new(width = 11, height = 8)
# agreementFigure(TMA_Vector, y.test.pred, Delta = -0.25, full = FALSE, main = paste0("Random Rep = ", j))
}
} ###
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.