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bestsubsets.R
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bestsubsets.R
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rm(list = ls())
source("formulas.R")
source("hyperparametersfunctions.R")
source("bestsubsetsformulas.R")
library(leaps)
library(bestglm)
library(glmnet)
library(plotmo)
library(glmulti)
library(caret)
library(ggplot2)
library(dplyr)
library(readr)
threshold <- 1e-9
train_modelwithRatings <- read.table("Data/datasets/train_modelWithRatings.csv"
, header = T, sep = ",", quote = "\"", fill = TRUE)
cv_withRatings <- read.table("Data/datasets/cvWithRatings.csv"
, header = T, sep = ",", quote = "\"", fill = TRUE)
train_modelwithRatings <- train_modelwithRatings %>% filter(!is.na(matched))
cv_withRatings <- cv_withRatings %>% filter(!is.na(matched))
Nt <- nrow(train_modelwithRatings)
#apparantly there is one NA in BestOF, temporarily removal needs to be data cleansed
cv_withRatings <- cv_withRatings[!is.na(cv_withRatings$Best.of), ]
Ncv <- nrow(cv_withRatings)
set.seed(42)
yt_m <- as.numeric(runif(Nt, 0, 1) > 0.5)
ycv <- as.numeric(runif(Ncv, 0, 1) > 0.5)
xt_m <- regressorvariables(yt_m, train_modelwithRatings)
xcv <- regressorvariables(ycv, cv_withRatings)
q <- 27
xtmHard <- getXThisSurface(xt_m, "Hard")
xtmGrass <- getXThisSurface(xt_m, "Grass")
xtmClay <- getXThisSurface(xt_m, "Clay")
#quantileHard = quantile(xt_mHard$UncertaintyCOSurface, q / 100)
quantileHard <- quantile(xtmHard$UncertaintySurface, q / 100)
#xt_mHard = removeUncertainMatches(xt_mHard, quantileHard, "COSurface")
xtmHard <- removeUncertainMatches(xtmHard, quantileHard, "Surface")
#quantile = quantile(xcv$Uncertainty, q / 100)
xcvHard <- getXThisSurface(xcv, "Hard")
xcvGrass <- getXThisSurface(xcv, "Grass")
xcvClay <- getXThisSurface(xcv, "Clay")
#xcvHard = removeUncertainMatches(xcvHard, quantileHard, "COSurface")
xcvHard <- removeUncertainMatches(xcvHard, quantileHard, "Surface")
#xtmHardRel <- relevantVariables(xtmHard)
#xcvHardRel <- relevantVariables(xcvHard)
xtmHardRel <- xtmHard
xcvHardRel <- xcvHard
#Normalization of variance
sdtrain <- apply(xtmHardRel[, 1:(length(xtmHardRel) - 1)] , 2, sd)
xtmHardRel[, 1:(length(xtmHardRel) - 1)] <-
as.data.frame(scale(xtmHardRel[, 1:(length(xtmHardRel) - 1)], center = FALSE,scale = sdtrain))
xcvHardRel[, 1:(length(xcvHardRel) - 1)] <-
as.data.frame(scale(xcvHardRel[, 1:(length(xcvHardRel) - 1)], center = FALSE,scale = sdtrain))
#Rating results
regLamRating <- glm(y ~ 0 + ratingdiff + ratingHarddiff
, data = xtmHardRel, family = binomial)
summary(regLamRating)
cvpredRating <- predict(regLamRating, xcvHardRel, type = "response")
LogLoss( xcvHardRel$y, cvpredRating)
testProbs <- data.frame(Class = as.factor(xcvHardRel$y),
LogReg = 1 - cvpredRating)
plot(calibration(Class ~ LogReg, data = testProbs), type = "l")
###ServeReturnRating
regServeReturnRating <- glm(y ~ 0 + ratingservereturndiff + ratingservereturnHarddiff
, data = xtmHardRel, family = binomial)
summary(regServeReturnRating)
cvpredServeReturnRating <- predict(regServeReturnRating, xcvHardRel, type = "response")
LogLoss( xcvHardRel$y, cvpredServeReturnRating)
##Improvement
regMixRating <- glm(y ~ 0 + ratingHarddiff + ratingservereturndiff
, data = xtmHardRel, family = binomial)
summary(regMixRating)
cvpredMixRating <- predict(regMixRating, xcvHardRel, type = "response")
LogLoss( xcvHardRel$y, cvpredMixRating)
#Rating Glicko Break Games Result
regGlicko <- glm(y ~ 0 + glickodiff + glickoHarddiff
, data = xtmHardRel, family = binomial)
summary(regGlicko)
cvpredGlicko <- predict(regGlicko, xcvHardRel, type = "response")
LogLoss(xcvHardRel$y, cvpredGlicko)
#Modelling
regLamMin <- glm(y ~ 0 + ratingNotHarddiff + ratingHarddiff + DummyBo5TimesAvgRatingdiff2
#+ RetiredDiff + FatigueDiff
, data = xtmHardRel, family = binomial)
summary(regLamMin)
cvpredLamMin <- predict(regLamMin, xcvHardRel, type = "response")
LogLoss(cvpredLamMin, xcvHardRel$y)
#Common opponents
regLamPoints <- glm(y ~ 0 + COPercentPointsThisSurfaceDiff + COPercentPointsDiff
, data = xtmHard, family = binomial)
summary(regLamPoints)
cvpredPoints <- predict(regLamPoints, xcvHard, type = "response")
LogLoss(xcvHardRel$y, cvpredPoints)
##SEEMS LIKE A STRONG COMBI, but this probably means a boosted tree is better
regLamPointsProb <- glm(y ~ 0 + ratingdiff + COPercentPointsThisSurfaceDiff
, data = xtmHard, family = binomial)
summary(regLamPointsProb)
cvpredPointsProb <- predict(regLamPointsProb, xcvHard, type = "response")
LogLoss(xcvHardRel$y, cvpredPointsProb)
grid = 10 ^ seq(0, -10, length = 100)
#####RIDGE
ridge.mod <- glmnet(as.matrix(xtmHardRel[ , 1:(length(xtmHardRel) - 1)]), xtmHardRel$y,
family = "binomial", alpha = 0, lambda = grid, intercept = FALSE, thresh = threshold)
plot_glmnet(ridge.mod, xvar = "lambda")
ridge.out <- cv.glmnet(as.matrix(xtmHardRel[ , 1:(length(xtmHardRel) - 1)])
, xtmHardRel$y, alpha = 0, nfolds = 10, family = "binomial", lambda = grid,
intercept = FALSE , thresh = threshold)
plot(ridge.out)
##random stackoverflow says the second one is preferred!!!
##https://stats.stackexchange.com/questions/58531/using-lasso-from-lars-or-glmnet-package-in-r-for-variable-selection
bestridgelam <- ridge.out$lambda.min
#bestlam = ridge.out$lambda.1se
ridge <- glmnet(as.matrix(xtmHardRel[ , 1:(length(xtmHardRel)-1)]), xtmHardRel$y, alpha = 0, lambda = bestridgelam, family = "binomial",
intercept = FALSE, thresh = threshold)
ridge.coef <- predict(ridge.out, type = "coefficients", s = bestridgelam)
ridge.coef
bestridgelam
cvpredRidgeMinTrue <- predict.cv.glmnet(ridge.out, newx = as.matrix(xcvHardRel[, 1:(length(xcvHardRel)-1)])
, type = "response", s = bestridgelam, thresh = threshold)
LogLoss(actual = xcvHardRel$y, predicted = cvpredRidgeMinTrue)
#####LASSO
cvlam.out = cv.glmnet(as.matrix(xtmHardRel[ , 1:(length(xtmHardRel)-1)]), xtmHardRel$y, alpha = 0, nfolds = 10, family = "binomial",
intercept = FALSE, thresh = threshold, lambda = grid)
plot(cvlam.out)
lasso.mod = glmnet(as.matrix(xtmHardRel[ , 1:(length(xtmHardRel)-1)]), xtmHardRel$y, family = "binomial", alpha = 1, lambda = grid
, intercept = FALSE, thresh = threshold)
plot_glmnet(lasso.mod, xvar = "lambda")
#Lambda subset selection
cvlas.out = cv.glmnet(as.matrix(xtmHardRel[ , 1:(length(xtmHardRel)-1)]), xtmHardRel$y, alpha = 1, nfolds = 10, family = "binomial",
lambda = grid, intercept = FALSE, thresh = threshold)
plot(cvlas.out)
##random stackoverflow says the second one is preferred!!!
##https://stats.stackexchange.com/questions/58531/using-lasso-from-lars-or-glmnet-package-in-r-for-variable-selection
bestlam = cvlas.out$lambda.min
#bestlam = cvlas.out$lambda.1se
lasso = glmnet(as.matrix(xtmHardRel[ , 1:(length(xtmHardRel)-1)]), xtmHardRel$y, alpha = 1, lambda = bestlam, family = "binomial",
intercept = FALSE, thresh = threshold)
lasso.coef = predict(cvlas.out, type = "coefficients", s = bestlam)
lasso.coef
bestlam
cvpredLamMinTrue = predict.cv.glmnet(cvlas.out, newx = as.matrix(xcvHardRel[ , 1:(length(xcvHardRel)-1)])
, type = "response", s = bestlam)
LogLoss(actual = xcvHardRel$y, predicted = cvpredLamMinTrue)
#################Interaction
f <- as.formula( ~ .^2)
xtmHardRelInteraction = as.data.frame(model.matrix(f, xtmHardRel[ , 1:(length(xtmHardRel)-1)])[, -1])
xcvHardRelInteraction = as.data.frame(model.matrix(f, xcvHardRel[ , 1:(length(xcvHardRel)-1)])[, -1])
#################Ridge
lasso.modINT = glmnet(as.matrix(xtmHardRelInteraction), xtmHardRel$y, family = "binomial", alpha = 0, lambda = grid
, intercept = FALSE, thresh = threshold)
cv.outINT = cv.glmnet(as.matrix(xtmHardRelInteraction), xtmHardRel$y, alpha = 0, nfolds = 10, family = "binomial",
intercept = FALSE)
plot(cv.outINT)
##random stackoverflow says the second one is preferred!!!
##https://stats.stackexchange.com/questions/58531/using-lasso-from-lars-or-glmnet-package-in-r-for-variable-selection
bestlam = cv.outINT$lambda.min
#bestlam = cv.outINT$lambda.1se
lasso = glmnet(as.matrix(xtmHardRelInteraction), xtmHardRel$y, alpha = 0, lambda = bestlam, family = "binomial",
intercept = FALSE, thresh = threshold)
lasso.coef = predict(cv.outINT, type = "coefficients", s = bestlam)
lasso.coef
bestlam
cvpredINTLamMinTrue = predict.cv.glmnet(cv.outINT, newx = as.matrix(xcvHardRelInteraction), type = "response", s = bestlam)
LogLoss(cvpredINTLamMinTrue, xcvHardRel$y)
################Lasso
lasso.modINT = glmnet(as.matrix(xtmHardRelInteraction), xtmHardRel$y, family = "binomial", alpha = 1, lambda = grid
, intercept = FALSE, thresh = threshold)
cv.outINT = cv.glmnet(as.matrix(xtmHardRelInteraction), xtmHardRel$y, alpha = 1, nfolds = 10, family = "binomial",
intercept = FALSE)
plot(cv.outINT)
##random stackoverflow says the second one is preferred!!!
##https://stats.stackexchange.com/questions/58531/using-lasso-from-lars-or-glmnet-package-in-r-for-variable-selection
bestlam = cv.outINT$lambda.min
#bestlam = cv.outINT$lambda.1se
lasso = glmnet(as.matrix(xtmHardRelInteraction), xtmHardRel$y, alpha = 1, lambda = bestlam, family = "binomial",
intercept = FALSE, thresh = threshold)
lasso.coef = predict(cv.outINT, type = "coefficients", s = bestlam)
lasso.coef
bestlam
cvpredINTLamMinTrue = predict.cv.glmnet(cv.outINT, newx = as.matrix(xcvHardRelInteraction),
type = "response", s = bestlam)
LogLoss(cvpredINTLamMinTrue, xcvHardRel$y)
#lambda min interaction
regLamMinInter = glm(y ~ 0 + ratingdiff + ratingHarddiff + DummyBo5TimesAvgRatingdiff + RetiredDiff
+ FatigueDiff + FatigueDiffTimesBo5 + ratingdiff:ratingClaydiff
+ ratingClaydiff:DummyBo5TimesratingHarddiff + ratingClaydiff:WalkoverDiff
+ ratingHarddiff:DummyBo5TimesratingClaydiff + ratingGrassdiff:RetiredDiff
+ ratingGrassdiff:FatigueDiffTimesBo5 + DummyBo5TimesAvgRatingdiff:WalkoverDiff
+ DummyBo5TimesratingGrassdiff:FatigueDiff + DummyBo5TimesratingfClaydiff:LastHeadtoHead
+ RetiredDiff:FatigueDiffTimesBo5 + WalkoverDiff:RetiredOrWalkoverDiff
+ WalkoverDiff:HeadtoHead
, data = as.data.frame(xtmHardRel), family = binomial)
bestsubxcvHard = relevantVariables(xcvHard)
cvpredLamMin = predict(regLamMinInter, bestsubxcvHard, type = "response")
LogLoss(cvpredLamMin, ycvHard)
#lambda min
regLamMin = glm(y ~ 0 + ratingdiff + ratingHarddiff + DummyBo5TimesAvgRatingdiff + RetiredDiff
+ FatigueDiff, data = xtmHardRel, family = binomial)
summary(regLamMin)
cvpredLamMin = predict(regLamMin, xcvHardRel, type = "response")
LogLoss(cvpredLamMin, xcvHardRel$y)
#regLamMin2 = glm(xt_mHard$y ~ 0 + ratingdiff + ratingHarddiff + DummyBo5TimesAvgRatingdiff +
# DummyBo5TimesratingHarddiff + RetiredDiff + FatigueDiff,
# data = xtmHardRel, family = binomial)
#
#regLamMin3 = glm(xt_mHard$y ~ 0 + ratingdiff + ratingClaydiff + ratingHarddiff + ratingGrassdiff +
# DummyBo5TimesAvgRatingdiff + DummyBo5TimesratingHarddiff + DummyBo5TimesratingClaydiff +
# RetiredDiff + WalkoverDiff + FatigueDiff, data = xtmHardRel, family = binomial)
#cvpredLamMin2 = predict(regLamMin2, xcvHardRel, type = "response")
#LogLoss(cvpredLamMin2, ycvHard)
#cvpredLamMin3 = predict(regLamMin3, xcvHardRel, type = "response")
#LogLoss(cvpredLamMin3, ycvHard)
#lamda 1se
regLam1se = glm(xtmHard$y ~ 0 + ratingdiff + ratingHarddiff + DummyBo5TimesAvgRatingdiff,
data = xtmHardRel, family = binomial)
cvpredLam1se = predict(regLam1se, xcvHard, type = "response")
LogLoss(cvpredLam1se, ycvHard)
reg = glm(xtmHard$y ~ 0 + ratingClaydiff + ratingHarddiff + ratingGrassdiff +
DummyBo5TimesAvgRatingdiff + RetiredOrWalkoverDiff
+FatigueDiff, data = xtmHardRel, family = binomial)
xtmHardRel$y = xtmHard$y
reg = bestglm(Xy = xtmHardRel, family = binomial, IC = "AIC", intercept = FALSE)
#Best GLM with intercept removed for this tho
reg = glm(xtmHard$y ~ 0 + ratingdiff + ratingHarddiff + DummyBo5TimesAvgRatingdiff, data = xtmHardRel,
family = binomial)
summary(reg)
cvpred = predict(reg, xcvHard, type = "response")
LogLoss(cvpred, ycvHard)
#Grass
BestSubsetGrasst_m = relevantVariables(xtmGrass)
xcvGrassRel = relevantVariables(xcvGrass)
lasso.mod = glmnet(as.matrix(BestSubsetGrasst_m), yt_mGrass, family = "binomial", alpha = 1, lambda = grid
, intercept = FALSE)
plot(lasso.mod)
plot_glmnet(lasso.mod, xvar = "lambda")
#Lambda subset selection
cv.out = cv.glmnet(as.matrix(BestSubsetGrasst_m), yt_mGrass, alpha = 1, nfolds = 10, family = "binomial",
intercept = FALSE)
plot(cv.out)
##random stackoverflow says the second one is preferred!!!
##https://stats.stackexchange.com/questions/58531/using-lasso-from-lars-or-glmnet-package-in-r-for-variable-selection
bestlam = cv.out$lambda.min
#bestlam = cv.out$lambda.1se
lasso = glmnet(as.matrix(BestSubsetGrasst_m), yt_mGrass, alpha = 1, lambda = bestlam, family = "binomial",
intercept = FALSE)
lasso.coef = predict(cv.out, type = "coefficients", s = bestlam)
lasso.coef
bestlam
cvpredLamMinTrue = predict.cv.glmnet(cv.out, newx = as.matrix(xcvGrassRel), type = "response", s = bestlam)
LogLoss(cvpredLamMinTrue, ycvGrass)
##random stackoverflow says the second one is preferred!!!
##https://stats.stackexchange.com/questions/58531/using-lasso-from-lars-or-glmnet-package-in-r-for-variable-selection
bestlam = cv.out$lambda.min
#bestlam = cv.out$lambda.1se
#lambda min
regLamMin = glm(yt_mGrass ~ 0 + ratingdiff + ratingHarddiff + ratingGrassdiff + DummyBo5TimesAvgRatingdiff
, data = BestSubsetGrasst_m, family = binomial)
summary(regLamMin)
#adding fatigue diff does improve the validation logloss and remove ratingHarddiff too, since it is so
#insignificant
regLamImprove = glm(y ~ 0 + ratingdiff + ratingGrassdiff + DummyBo5TimesAvgRatingdiff + FatigueDiff
, data = BestSubsetGrasst_m, family = binomial)
summary(regLamImprove)
#adding fatigue or not is a difficult decision, since it is still not significant at all,
#yet when you add the cv data it becomes closer so I guess I am just gonna add it
cvpredLamMin = predict(regLamMin, xcvGrassRel, type = "response")
LogLoss(cvpredLamMin, xcvGrassRel$y)
cvpredLamImprove = predict(regLamImprove, xcvGrassRel, type = "response")
LogLoss(cvpredLamImprove, ycvGrass)
#lamda 1se is the same as lamda min
BestSubsetGrasst_m$y = yt_mGrass
reg = bestglm(BestSubsetGrasst_m, family = binomial, IC = "CV", intercept = TRUE)
#Best GLM with intercept removed for this tho
#oh no, BESTGLM is retarded, not the lowest AIC if you compare the bestGLM (AIC) with regLamMin
reg = glm(yt_mGrass ~ 0 + ratingHarddiff +DummyBo5TimesAvgRatingdiff, data = BestSubsetGrasst_m,
family = binomial)
#Best GLM with IC = AIC seems aight tho
reg = bestglm(BestSubsetGrasst_m, family = binomial, IC = "AIC", intercept = TRUE)
reg = glm(yt_mGrass ~ 0 + ratingdiff + ratingClaydiff + ratingGrassdiff + DummyBo5TimesAvgRatingdiff,
data = BestSubsetGrasst_m, family = binomial)
summary(reg)
cvpred = predict(reg, xcvGrass, type = "response")
LogLoss(cvpred, ycvGrass)
#################CLAY$##############
BestSubsetClayt_m = relevantVariables(xtmClay)
xcvClayRel = relevantVariables(xcvClay)
#interaction
f <- as.formula( ~ .^2)
BestSubsetClayt_mInteraction = as.data.frame(model.matrix(f, BestSubsetClayt_m)[, -1])
xcvClayRelInteraction = as.data.frame(model.matrix(f, xcvClayRel)[, -1])
grid = 10 ^ seq(1, -5, length = 600)
lasso.mod = glmnet(as.matrix(BestSubsetClayt_m), yt_mClay, family = "binomial", alpha = 1, lambda = grid
, intercept = FALSE)
plot(lasso.mod)
plot_glmnet(lasso.mod, xvar = "lambda")
#Lambda subset selection
cv.out = cv.glmnet(as.matrix(BestSubsetClayt_m), yt_mClay, alpha = 1, nfolds = 10, family = "binomial",
intercept = FALSE)
plot(cv.out)
##random stackoverflow says the second one is preferred!!!
##https://stats.stackexchange.com/questions/58531/using-lasso-from-lars-or-glmnet-package-in-r-for-variable-selection
bestlam = cv.out$lambda.min
#bestlam = cv.out$lambda.1se
lasso = glmnet(as.matrix(BestSubsetClayt_m), yt_mClay, alpha = 1, lambda = bestlam, family = "binomial",
intercept = FALSE)
lasso.coef = predict(cv.out, type = "coefficients", s = bestlam)
lasso.coef
bestlam
cvpredLamMinTrue = predict.cv.glmnet(cv.out, newx = as.matrix(xcvClayRel), type = "response", s = bestlam)
LogLoss(cvpredLamMinTrue, ycvClay)
lasso.modINT = glmnet(as.matrix(BestSubsetClayt_mInteraction), yt_mClay, family = "binomial", alpha = 1, lambda = grid
, intercept = FALSE)
cv.outINT = cv.glmnet(as.matrix(BestSubsetClayt_mInteraction), yt_mClay, alpha = 1, nfolds = 10, family = "binomial",
intercept = FALSE)
plot(cv.outINT)
#lambda min
regLamMin = glm(y ~ 0 + ratingdiff + ratingClaydiff + ratingGrassdiff + DummyBo5TimesAvgRatingdiff +
DummyBo5TimesratingClaydiff + WalkoverDiff + RetiredOrWalkoverDiff
+ FatigueDiff + HeadtoHead + LastHeadtoHead, data = BestSubsetClayt_m, family = binomial)
#regLamMin2 = glm(xt_mHard$y ~ 0 + ratingdiff + ratingHarddiff + DummyBo5TimesAvgRatingdiff +
# DummyBo5TimesratingHarddiff + RetiredDiff + FatigueDiff,
# data = xtmHardRel, family = binomial)
#
#regLamMin3 = glm(xt_mHard$y ~ 0 + ratingdiff + ratingClaydiff + ratingHarddiff + ratingGrassdiff +
# DummyBo5TimesAvgRatingdiff + DummyBo5TimesratingHarddiff + DummyBo5TimesratingClaydiff +
# RetiredDiff + WalkoverDiff + FatigueDiff, data = xtmHardRel, family = binomial)
summary(regLamMin)
cvpredLamMin = predict(regLamMin, xcvClayRel, type = "response")
LogLoss(cvpredLamMin, xcvClayRel$y)