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Experiments. Where is the error in nakr?

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This commit is contained in:
Pierre-Edouard Portier 2023-01-29 17:52:53 +01:00
parent 472b35e38c
commit 38d8f0a401
2 changed files with 36 additions and 10 deletions
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20
19_b_nystroem_approximation_housing_experiment_code.R
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@ -99,13 +99,13 @@ 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]])
# 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]])

26
pad.R
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@ -27,3 +27,29 @@
# adj = 0, cex = 0.6)
# points(0, 0, pch = 3)
# ```
# source("19_b_nystroem_approximation_housing_experiment_code.R")
# rdat <- hous.dat.nakr$dat[sample(nrow(hous.dat.nakr$dat), size=2000, replace=FALSE),]
# X <- rdat[,!(colnames(rdat) %in% c('median_house_value'))]
# Y <- rdat[,c('median_house_value')]
# names(Y) <- rownames(X)
# rsplt <- splitdata(list(X = X, Y = Y), 0.8)
# X.entr <- rsplt$entr$X
# Y.entr <- rsplt$entr$Y
# X.test <- rsplt$test$X
# Y.test <- rsplt$test$Y
# source("18_kernel_ridge_regression_code.R")
# krm <- krr(X.entr, Y.entr)
# krm.yh <- predict(krm, X.test)
# krm.mae <- mean(abs(krm.yh - Y.test)) # 35445.1
# source("15_loocv_code.R")
# rm <- ridge(X.entr, Y.entr)
# rm.yh <- predict(rm, X.test)
# rm.mae <- mean(abs(rm.yh - Y.test)) # 45786.62
# library(randomForest)
# rfm <- randomForest(X.entr, Y.entr)
# rfm.yh <- predict(rfm, X.test)
# rfm.mae <- mean(abs(rfm.yh - Y.test)) # 34229.02
# nakrm <- kfold.nakr(X.entr, Y.entr, nb.landmarks=500)
# nakrm.yh <- predict(nakrm, X.test)
# nakrm.mae <- mean(abs(nakrm.yh - Y.test)) # 65454.18