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add: real_data scripts

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Daniel Kapla 1 year ago
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3 changed files with 235 additions and 0 deletions
  1. +79
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      real_data/Beijing_Multi_Site_Air_Quality_Data.R
  2. +70
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      real_data/BostonHousing.R
  3. +86
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      real_data/kc_house_data.R

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real_data/Beijing_Multi_Site_Air_Quality_Data.R View File

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#!/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)

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real_data/BostonHousing.R View File

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#!/usr/bin/env Rscript

library(MAVE)
library(CVarE)
library(mlbench)
Sys.setenv(TF_CPP_MIN_LOG_LEVEL = "3") # Suppress `tensorflow` notes/warnings
suppressPackageStartupMessages({
library(NNSDR)
})

## Configuration
d <- 1L

## Load Boston Housing data set
data('BostonHousing')
ds <- BostonHousing[, names(BostonHousing) != 'chas']

## Build Dimension Reduction Neuronal Network model (matching the data)
nn <- nnsdr$new(
input_shapes = list(x = ncol(ds) - 1L),
d = d, # Reduction dimension
hidden_units = 512L,
activation = 'relu'
)

## Open simulation log file, write simulation configuration and header
log <- file(format(Sys.time(), "results/BostonHousing.csv"), "w", blocking = FALSE)
cat('# d = ', d, '\n', 'method,fold,mse\n', sep = '', file = log, append = TRUE)

## K-Fold Cross Validation
K <- nrow(ds) # with K == nrow(ds) -> LOO CV
for (i in 1:K) {
ds.train <- ds[(1:K) != i, ]
ds.test <- ds[(1:K) == i, , drop = FALSE]
X.train <- as.matrix(ds.train[, names(ds) != 'medv'])
Y.train <- as.matrix(ds.train[, 'medv'])
X.test <- as.matrix(ds.test[, names(ds) != 'medv'])
Y.test <- as.matrix(ds.test[, 'medv'])

## Fit `DR` Neuronal Network model
nn$fit(X.train, Y.train, epochs = c(200L, 400L), initializer = 'fromOPG')
Y.pred <- nn$predict(X.test)
mse <- mean((Y.pred - Y.test)^2)
cat('"nn.ref",', i, ',', mse, '\n',
sep = '', file = log, append = TRUE)

## MAVE as reference
dr <- mave.compute(X.train, Y.train, method = 'meanMAVE', max.dim = d)
Y.pred <- predict(dr, X.test, d)
mse <- mean((Y.pred - Y.test)^2)
cat('"mave",', i, ',', mse, '\n',
sep = '', file = log, append = TRUE)

## and CVE
X.scaled <- scale(X.train)
dr <- cve.call(X.scaled, Y.train, k = d)
Y.pred <- predict(dr, scale(X.test,
scale = attr(X.scaled, 'scaled:scale'),
center = attr(X.scaled, 'scaled:center')),
k = d)
mse <- mean((Y.pred - Y.test)^2)
cat('"cve",', i, ',', mse, '\n',
sep = '', file = log, append = TRUE)

## Reset model
nn$reset()
}

## Finished, close simulation log file
close(log)

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real_data/kc_house_data.R View File

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#!/usr/bin/env Rscript
## data source: the `MAVE` R-package

library(MAVE)
library(CVarE)
Sys.setenv(TF_CPP_MIN_LOG_LEVEL = "3") # Suppress `tensorflow` notes/warnings
suppressPackageStartupMessages({
library(NNSDR)
})

## Configuration
d <- 1L # reduction dimension
epochs = c(50L, 100L) # training epochs
dropped <- c('id', 'date', 'zipcode') #, 'sqft_basement') # columns to be dropped

## Loading the "House Price in King Counte, USA" data set provided by MAVE
data('kc_house_data')
ds <- kc_house_data[, !(names(kc_house_data) %in% dropped)]

## Build Dimension Reduction Neuronal Network model (matching the data)
nn <- nnsdr$new(
input_shapes = list(x = ncol(ds) - 1L),
d = d, # Reduction dimension
hidden_units = 512L,
activation = 'relu'
)

## Open simulation log file, write simulation configuration and header
log <- file(format(Sys.time(), "results/kc_house_data.csv"), "w", blocking = FALSE)
cat('# d = ', d, '\n# epochs = ', epochs[1], ',', epochs[2], '\n',
'# dropped = ', paste(dropped, collapse = ', '), '\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) {
ds.train <- ds[(1:K) != i, ]
ds.test <- ds[(1:K) == i, , drop = FALSE]
X.train <- as.matrix(ds.train[, names(ds) != 'price'])
Y.train <- as.matrix(ds.train[, 'price'])
X.test <- as.matrix(ds.test[, names(ds) != 'price'])
Y.test <- as.matrix(ds.test[, 'price'])

## Fit `DR` Neuronal Network model
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)

## `MAVE`
time <- system.time(dr <- mave.compute(X.train, Y.train, method = 'meanMAVE', max.dim = d))

# Sometimes the `mda` package fails -> predict with NA/NaN/Inf value error.
mse <- tryCatch(mean((predict(dr, X.test, d) - Y.test)^2),
error = function(err) NA)
cat('"mave",', i, ',', mse, ',', c(var(Y.test)), ',',
time['user.self'], ',', time['sys.self'], ',', time['elapsed'], '\n',
sep = '', file = log, append = TRUE)

# Current implementation requires too much memory (CVarE v1.1). Run on `VSC`.
# ## and CVE
# X.scaled <- scale(X.train)
# time <- system.time(dr <- cve.call(X.scaled, Y.train, k = d))

# # Might have the same problem as MAVE since we use `mda` as well.
# mse <- tryCatch({
# Y.pred <- predict(dr, scale(X.test,
# scale = attr(X.scaled, 'scaled:scale'),
# center = attr(X.scaled, 'scaled:center')),
# k = d)
# mean((Y.pred - Y.test)^2)
# },
# error = function(err) NA)
# cat('"cve",', 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)

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