add: real_data scripts
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#!/usr/bin/env Rscript
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## data source: https://archive.ics.uci.edu/ml/datasets/Beijing+Multi-Site+Air-Quality+Data
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library(mda)
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Sys.setenv(TF_CPP_MIN_LOG_LEVEL = "3") # Suppress `tensorflow` notes/warnings
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suppressPackageStartupMessages({
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library(NNSDR)
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})
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## Configuration
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d <- 4L # reduction dimension
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epochs = c(2L, 3L) # training epochs (OPG, Refinement)
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## Loading one site of the "Beijing Air Quality" data set
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files <- list.files('data/Beijing\ Multi\ Site\ Air\ Quality\ Data/',
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pattern = '*.csv', full.names = TRUE)
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ds <- na.omit(Reduce(rbind, lapply(files, read.csv)))
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## Create model matrix with dummy variables for factors (One-Hot encoded) for
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## regression of PM2.5 (and dropping PM10)
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X <- model.matrix(~ year + month + day + hour + SO2 + NO2 + CO + O3 + TEMP +
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PRES + DEWP + RAIN + wd + WSPM + station + 0, ds)
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Y <- as.matrix(ds$PM2.5)
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## Build Dimension Reduction Neuronal Network model (matching the data)
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nn <- nnsdr$new(
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input_shapes = list(x = ncol(X)),
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d = d, # Reduction dimension
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hidden_units = 512L,
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activation = 'relu'
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)
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## Open simulation log file, write simulation configuration and header
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log <- file(format(Sys.time(), "results/Beijing_Air_Quality.csv"), "w", blocking = FALSE)
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cat('# d = ', d, '\n# epochs = ', epochs[1], ',', epochs[2], '\n',
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'method,fold,mse,var(Y.test),time.user,time.system,time.elapsed\n',
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sep = '', file = log, append = TRUE)
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## K-Fold Cross Validation
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K <- 10
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for (i in 1:K) {
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## Split into train/test sets
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train <- (1:K) != i
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X.train <- scale(X[train, ])
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Y.train <- Y[train, , drop = FALSE]
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X.test <- scale(X[!train, ], center = attr(X.train, 'scaled:center'),
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scale = attr(X.train, 'scaled:scale'))
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Y.test <- Y[!train, , drop = FALSE]
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## Training
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time <- system.time(nn$fit(X.train, Y.train, epochs = epochs, initializer = 'fromOPG'))
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mse <- mean((nn$predict(X.test) - Y.test)^2)
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cat('"nn.ref",', i, ',', mse, ',', c(var(Y.test)), ',',
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time['user.self'], ',', time['sys.self'], ',', time['elapsed'], '\n',
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sep = '', file = log, append = TRUE)
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## Linear Model
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time <- system.time(lm.mod <- lm(y ~ ., data.frame(X.train, y = Y.train)))
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mse <- mean((predict(lm.mod, data.frame(X.test, y = Y.test)) - Y.test)^2)
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cat('"lm",', i, ',', mse, ',', c(var(Y.test)), ',',
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time['user.self'], ',', time['sys.self'], ',', time['elapsed'], '\n',
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sep = '', file = log, append = TRUE)
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## MARS
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time <- system.time(mars.mod <- mars(X.train, Y.train))
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mse <- mean((predict(mars.mod, X.test) - Y.test)^2)
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cat('"mars",', i, ',', mse, ',', c(var(Y.test)), ',',
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time['user.self'], ',', time['sys.self'], ',', time['elapsed'], '\n',
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sep = '', file = log, append = TRUE)
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## Reset model
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nn$reset()
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}
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## Finished, close simulation log file
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close(log)
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#!/usr/bin/env Rscript
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library(MAVE)
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library(CVarE)
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library(mlbench)
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Sys.setenv(TF_CPP_MIN_LOG_LEVEL = "3") # Suppress `tensorflow` notes/warnings
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suppressPackageStartupMessages({
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library(NNSDR)
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})
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## Configuration
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d <- 1L
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## Load Boston Housing data set
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data('BostonHousing')
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ds <- BostonHousing[, names(BostonHousing) != 'chas']
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## Build Dimension Reduction Neuronal Network model (matching the data)
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nn <- nnsdr$new(
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input_shapes = list(x = ncol(ds) - 1L),
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d = d, # Reduction dimension
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hidden_units = 512L,
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activation = 'relu'
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)
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## Open simulation log file, write simulation configuration and header
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log <- file(format(Sys.time(), "results/BostonHousing.csv"), "w", blocking = FALSE)
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cat('# d = ', d, '\n', 'method,fold,mse\n', sep = '', file = log, append = TRUE)
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## K-Fold Cross Validation
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K <- nrow(ds) # with K == nrow(ds) -> LOO CV
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for (i in 1:K) {
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ds.train <- ds[(1:K) != i, ]
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ds.test <- ds[(1:K) == i, , drop = FALSE]
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X.train <- as.matrix(ds.train[, names(ds) != 'medv'])
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Y.train <- as.matrix(ds.train[, 'medv'])
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X.test <- as.matrix(ds.test[, names(ds) != 'medv'])
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Y.test <- as.matrix(ds.test[, 'medv'])
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## Fit `DR` Neuronal Network model
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nn$fit(X.train, Y.train, epochs = c(200L, 400L), initializer = 'fromOPG')
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Y.pred <- nn$predict(X.test)
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mse <- mean((Y.pred - Y.test)^2)
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cat('"nn.ref",', i, ',', mse, '\n',
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sep = '', file = log, append = TRUE)
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## MAVE as reference
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dr <- mave.compute(X.train, Y.train, method = 'meanMAVE', max.dim = d)
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Y.pred <- predict(dr, X.test, d)
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mse <- mean((Y.pred - Y.test)^2)
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cat('"mave",', i, ',', mse, '\n',
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sep = '', file = log, append = TRUE)
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## and CVE
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X.scaled <- scale(X.train)
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dr <- cve.call(X.scaled, Y.train, k = d)
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Y.pred <- predict(dr, scale(X.test,
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scale = attr(X.scaled, 'scaled:scale'),
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center = attr(X.scaled, 'scaled:center')),
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k = d)
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mse <- mean((Y.pred - Y.test)^2)
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cat('"cve",', i, ',', mse, '\n',
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sep = '', file = log, append = TRUE)
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## Reset model
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nn$reset()
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}
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## Finished, close simulation log file
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close(log)
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#!/usr/bin/env Rscript
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## data source: the `MAVE` R-package
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library(MAVE)
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library(CVarE)
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Sys.setenv(TF_CPP_MIN_LOG_LEVEL = "3") # Suppress `tensorflow` notes/warnings
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suppressPackageStartupMessages({
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library(NNSDR)
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})
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## Configuration
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d <- 1L # reduction dimension
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epochs = c(50L, 100L) # training epochs
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dropped <- c('id', 'date', 'zipcode') #, 'sqft_basement') # columns to be dropped
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## Loading the "House Price in King Counte, USA" data set provided by MAVE
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data('kc_house_data')
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ds <- kc_house_data[, !(names(kc_house_data) %in% dropped)]
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## Build Dimension Reduction Neuronal Network model (matching the data)
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nn <- nnsdr$new(
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input_shapes = list(x = ncol(ds) - 1L),
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d = d, # Reduction dimension
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hidden_units = 512L,
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activation = 'relu'
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)
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## Open simulation log file, write simulation configuration and header
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log <- file(format(Sys.time(), "results/kc_house_data.csv"), "w", blocking = FALSE)
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cat('# d = ', d, '\n# epochs = ', epochs[1], ',', epochs[2], '\n',
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'# dropped = ', paste(dropped, collapse = ', '), '\n',
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'method,fold,mse,var(Y.test),time.user,time.system,time.elapsed\n',
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sep = '', file = log, append = TRUE)
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## K-Fold Cross Validation
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K <- 10
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for (i in 1:K) {
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ds.train <- ds[(1:K) != i, ]
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ds.test <- ds[(1:K) == i, , drop = FALSE]
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X.train <- as.matrix(ds.train[, names(ds) != 'price'])
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Y.train <- as.matrix(ds.train[, 'price'])
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X.test <- as.matrix(ds.test[, names(ds) != 'price'])
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Y.test <- as.matrix(ds.test[, 'price'])
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## Fit `DR` Neuronal Network model
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time <- system.time(nn$fit(X.train, Y.train, epochs = epochs, initializer = 'fromOPG'))
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mse <- mean((nn$predict(X.test) - Y.test)^2)
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cat('"nn.ref",', i, ',', mse, ',', c(var(Y.test)), ',',
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time['user.self'], ',', time['sys.self'], ',', time['elapsed'], '\n',
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sep = '', file = log, append = TRUE)
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## `MAVE`
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time <- system.time(dr <- mave.compute(X.train, Y.train, method = 'meanMAVE', max.dim = d))
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# Sometimes the `mda` package fails -> predict with NA/NaN/Inf value error.
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mse <- tryCatch(mean((predict(dr, X.test, d) - Y.test)^2),
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error = function(err) NA)
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cat('"mave",', i, ',', mse, ',', c(var(Y.test)), ',',
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time['user.self'], ',', time['sys.self'], ',', time['elapsed'], '\n',
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sep = '', file = log, append = TRUE)
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# Current implementation requires too much memory (CVarE v1.1). Run on `VSC`.
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# ## and CVE
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# X.scaled <- scale(X.train)
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# time <- system.time(dr <- cve.call(X.scaled, Y.train, k = d))
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# # Might have the same problem as MAVE since we use `mda` as well.
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# mse <- tryCatch({
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# Y.pred <- predict(dr, scale(X.test,
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# scale = attr(X.scaled, 'scaled:scale'),
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# center = attr(X.scaled, 'scaled:center')),
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# k = d)
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# mean((Y.pred - Y.test)^2)
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# },
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# error = function(err) NA)
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# cat('"cve",', i, ',', mse, ',', c(var(Y.test)), ',',
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# time['user.self'], ',', time['sys.self'], ',', time['elapsed'], '\n',
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# sep = '', file = log, append = TRUE)
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## Reset model
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nn$reset()
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}
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## Finished, close simulation log file
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close(log)
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