intro_to_ml/19_b_nystroem_approximation...

111 lines
3.5 KiB
R

source("05_d_svd_mca_code.R")
source("04_validation_croisee_code.R")
source("19_nystroem_approximation_code.R")
datasetHousing.nakr <-
function()
{
dat <- read.csv(file="data/housing.csv", header=TRUE)
dat$ocean_proximity <- as.factor(dat$ocean_proximity)
levels(dat$ocean_proximity)[levels(dat$ocean_proximity)=="<1H OCEAN"] <- "O:<1H"
levels(dat$ocean_proximity)[levels(dat$ocean_proximity)=="ISLAND"] <- "O:ISL"
levels(dat$ocean_proximity)[levels(dat$ocean_proximity)=="INLAND"] <- "O:INL"
levels(dat$ocean_proximity)[levels(dat$ocean_proximity)=="NEAR BAY"] <- "O:NB"
levels(dat$ocean_proximity)[levels(dat$ocean_proximity)=="NEAR OCEAN"] <- "O:NO"
dat$total_bedrooms[is.na(dat$total_bedrooms)] <- median(dat$total_bedrooms, na.rm=TRUE)
dat <- dat[dat$ocean_proximity != "O:ISL", ]
# suppression des modalités vides (ici "O:ISL")
dat <- droplevels(dat)
dat['rooms'] <- dat['total_rooms'] / dat['households']
dat['bedrooms'] <- dat['total_bedrooms'] / dat['households']
dat['pop'] <- dat['population'] / dat['households']
dat <- dat[dat$median_house_value < 500001, ]
dat <- dat[c('longitude', 'latitude', 'housing_median_age', 'households',
'median_income', 'median_house_value', 'ocean_proximity',
'rooms', 'bedrooms', 'pop')]
Z <- onehot_enc(dat[c('ocean_proximity')])
dat <- cbind(dat, as.data.frame(Z))
dat <- dat[,!(colnames(dat) %in% c('ocean_proximity'))]
dat.all <- dat
X <- dat[,!(colnames(dat) %in% c('median_house_value'))]
Y <- dat[,c('median_house_value')]
names(Y) <- rownames(X)
dat <- list(X = X, Y = Y)
split <- splitdata(dat, 0.8)
entr <- split$entr
test <- split$test
r <- list( dat=dat.all, entr=entr, test=test )
return(r)
}
# test.tbl <- table(c(4,2,4,2,1,1,1))
# all(intersperse(test.tbl) == c(1, 2, 4, 1, 2, 4, 1))
intersperse <-
function(tbl)
{
n <- sum(tbl)
values <- as.numeric(names(tbl))
r <- numeric(n)
i <- 1
while(i <= n)
{
for(j in 1:length(tbl))
{
if(tbl[j] != 0)
{
r[i] <- values[j]
i <- i + 1
tbl[j] <- tbl[j] - 1
}
}
}
return(r)
}
# sample the landmarks from the clusters of individuals
# obtained after correspondence analysis
landmarks.by.ca.clst <-
function(cam, X, nbLandmarks)
{
if(nbLandmarks > nrow(X))
{
stop("The number of landmarks must be less than the number of training samples.")
}
landmarks <- numeric(nbLandmarks)
clst <- cam$clsti$cluster[as.numeric(rownames(X))]
clst.tbl <- table(clst)
nb.by.clst <- table((intersperse(clst.tbl))[1:nbLandmarks])
clst.id <- as.numeric(names(nb.by.clst))
set.seed(1123)
clst <- sample(clst)
offset <- 0
for (i in 1:length(nb.by.clst))
{
k <- as.numeric(nb.by.clst[i])
landmarks[(offset+1):(offset+k)] <- as.numeric(names((clst[clst==clst.id[i]])[1:k]))
offset <- offset+k
}
return(landmarks)
}
hous.dat.nakr <- datasetHousing.nakr()
X.entr <- hous.dat.nakr$entr$X
Y.entr <- hous.dat.nakr$entr$Y
X.test <- hous.dat.nakr$test$X
Y.test <- hous.dat.nakr$test$Y
# hous.dat.ca <- datasetHousing.mca()
# hous.cam <- mca(hous.dat.ca)
# nb.landmarks <- round(sqrt(nrow(X.entr)))
# landmarks <- landmarks.by.ca.clst(hous.cam, X.entr, nb.landmarks)
# nakrm <- kfold.nakr(X.entr, Y.entr, landmarks=landmarks)
# nakrm.yh <- predict(nakrm, X.test)
# nakrm.mae <- mean(abs(nakrm.yh - Y.test))
# nakrm.yh.train <- predict(nakrm, X.entr)
# rev(order(abs(nakrm.yh.train - Y.entr)))[1:20]
# hist(Y.entr[rev(order(abs(nakrm.yh.train - Y.entr)))[1:200]])