80 lines
2.9 KiB
R
80 lines
2.9 KiB
R
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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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