NNSDR/real_data/Beijing_Multi_Site_Air_Qual...

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R

#!/usr/bin/env Rscript
## data source: https://archive.ics.uci.edu/ml/datasets/Beijing+Multi-Site+Air-Quality+Data
library(mda)
Sys.setenv(TF_CPP_MIN_LOG_LEVEL = "3") # Suppress `tensorflow` notes/warnings
suppressPackageStartupMessages({
library(NNSDR)
})
## Configuration
d <- 4L # reduction dimension
epochs = c(2L, 3L) # training epochs (OPG, Refinement)
## Loading one site of the "Beijing Air Quality" data set
files <- list.files('data/Beijing\ Multi\ Site\ Air\ Quality\ Data/',
pattern = '*.csv', full.names = TRUE)
ds <- na.omit(Reduce(rbind, lapply(files, read.csv)))
## Create model matrix with dummy variables for factors (One-Hot encoded) for
## regression of PM2.5 (and dropping PM10)
X <- model.matrix(~ year + month + day + hour + SO2 + NO2 + CO + O3 + TEMP +
PRES + DEWP + RAIN + wd + WSPM + station + 0, ds)
Y <- as.matrix(ds$PM2.5)
## Build Dimension Reduction Neuronal Network model (matching the data)
nn <- nnsdr$new(
input_shapes = list(x = ncol(X)),
d = d, # Reduction dimension
hidden_units = 512L,
activation = 'relu'
)
## Open simulation log file, write simulation configuration and header
log <- file(format(Sys.time(), "results/Beijing_Air_Quality.csv"), "w", blocking = FALSE)
cat('# d = ', d, '\n# epochs = ', epochs[1], ',', epochs[2], '\n',
'method,fold,mse,var(Y.test),time.user,time.system,time.elapsed\n',
sep = '', file = log, append = TRUE)
## K-Fold Cross Validation
K <- 10
for (i in 1:K) {
## Split into train/test sets
train <- (1:K) != i
X.train <- scale(X[train, ])
Y.train <- Y[train, , drop = FALSE]
X.test <- scale(X[!train, ], center = attr(X.train, 'scaled:center'),
scale = attr(X.train, 'scaled:scale'))
Y.test <- Y[!train, , drop = FALSE]
## Training
time <- system.time(nn$fit(X.train, Y.train, epochs = epochs, initializer = 'fromOPG'))
mse <- mean((nn$predict(X.test) - Y.test)^2)
cat('"nn.ref",', i, ',', mse, ',', c(var(Y.test)), ',',
time['user.self'], ',', time['sys.self'], ',', time['elapsed'], '\n',
sep = '', file = log, append = TRUE)
## Linear Model
time <- system.time(lm.mod <- lm(y ~ ., data.frame(X.train, y = Y.train)))
mse <- mean((predict(lm.mod, data.frame(X.test, y = Y.test)) - Y.test)^2)
cat('"lm",', i, ',', mse, ',', c(var(Y.test)), ',',
time['user.self'], ',', time['sys.self'], ',', time['elapsed'], '\n',
sep = '', file = log, append = TRUE)
## MARS
time <- system.time(mars.mod <- mars(X.train, Y.train))
mse <- mean((predict(mars.mod, X.test) - Y.test)^2)
cat('"mars",', i, ',', mse, ',', c(var(Y.test)), ',',
time['user.self'], ',', time['sys.self'], ',', time['elapsed'], '\n',
sep = '', file = log, append = TRUE)
## Reset model
nn$reset()
}
## Finished, close simulation log file
close(log)