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Modelos.R
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Modelos.R
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# Carga de librerías
library(Boruta)
library(caret)
library(corrplot)
library(cowplot)
library(doParallel)
library(dplyr)
library(dummies)
library(gam)
library(ggplot2)
library(gridExtra)
library(klaR)
library(lubridate)
library(MASS)
library(mlbench)
library(missForest)
library(MXM)
library(naniar)
library(parallel)
#library(plyr) similar a dplyr
library(psych)
library(randomForest)
library(reshape2)
library(RColorBrewer)
library(sas7bdat)
library(VIM)
# Directorio de trabajo
#setwd("")
# Número de núcleos para paralelizar partes del código
cl <- makeCluster(detectCores() - 1)
registerDoParallel(cl)
# Carga de datos
df <- read.csv("2_datos_con_dummies_y_missings_eliminados.csv")
dput(names(df))
str(df)
# Lista con todas las variables del dataset sin dummies
lista <- c("overall", "potential", "value_eur_m", "wage_eur_m", "age",
"height_cm", "weight_kg", "league_level", "club_position",
"weak_foot", "skill_moves", "international_reputation", "work_rate",
"release_clause_eur_m", "player_traits", "pace", "shooting",
"passing", "dribbling", "defending", "physic", "attacking_crossing",
"attacking_finishing", "attacking_heading_accuracy", "attacking_short_passing",
"attacking_volleys", "skill_dribbling", "skill_curve", "skill_fk_accuracy",
"skill_long_passing", "skill_ball_control", "movement_acceleration",
"movement_sprint_speed", "movement_agility", "movement_reactions",
"movement_balance", "power_shot_power", "power_jumping", "power_stamina",
"power_strength", "power_long_shots", "mentality_aggression",
"mentality_interceptions", "mentality_positioning", "mentality_vision",
"mentality_penalties", "mentality_composure", "defending_marking_awareness",
"defending_standing_tackle", "defending_sliding_tackle", "goalkeeping_diving",
"goalkeeping_handling", "goalkeeping_kicking", "goalkeeping_positioning",
"goalkeeping_reflexes", "years_remaining", "years_in_club", "preferred_foot", "category", "is_internacional")
base<-df[,lista]
# Selección de las columnas numéricas (NO TARGET)
listconti <- c("overall", "potential", "wage_eur_m", "age",
"height_cm", "weight_kg", "league_level",
"weak_foot", "skill_moves", "international_reputation",
"release_clause_eur_m", "pace", "shooting",
"passing", "dribbling", "defending", "physic", "attacking_crossing",
"attacking_finishing", "attacking_heading_accuracy", "attacking_short_passing",
"attacking_volleys", "skill_dribbling", "skill_curve", "skill_fk_accuracy",
"skill_long_passing", "skill_ball_control", "movement_acceleration",
"movement_sprint_speed", "movement_agility", "movement_reactions",
"movement_balance", "power_shot_power", "power_jumping", "power_stamina",
"power_strength", "power_long_shots", "mentality_aggression",
"mentality_interceptions", "mentality_positioning", "mentality_vision",
"mentality_penalties", "mentality_composure", "defending_marking_awareness",
"defending_standing_tackle", "defending_sliding_tackle", "goalkeeping_diving",
"goalkeeping_handling", "goalkeeping_kicking", "goalkeeping_positioning",
"goalkeeping_reflexes", "years_remaining", "years_in_club")
listclass <- c("category", "preferred_foot", "is_internacional")
vardep <- c("value_eur_m")
# 61 variables
base <- base[,c(listconti,listclass,vardep)]
# Estandarización
means <-apply(base[,listconti],2,mean,na.rm=TRUE)
sds<-sapply(base[,listconti],sd,na.rm=TRUE)
base2<-scale(base[,listconti], center = means, scale = sds)
base<-data.frame(cbind(base2,base[,c(listclass,vardep)]))
str(base)
basebis<-dummy.data.frame(base, listclass, sep = ".")#nuevo dataframe con las dummys
# Matriz para algunas selección de variables:
nombres1 <- c("overall", "potential", "wage_eur_m", "age",
"height_cm", "weight_kg", "league_level",
"weak_foot", "skill_moves", "international_reputation",
"release_clause_eur_m", "pace", "shooting",
"passing", "dribbling", "defending", "physic", "attacking_crossing",
"attacking_finishing", "attacking_heading_accuracy", "attacking_short_passing",
"attacking_volleys", "skill_dribbling", "skill_curve", "skill_fk_accuracy",
"skill_long_passing", "skill_ball_control", "movement_acceleration",
"movement_sprint_speed", "movement_agility", "movement_reactions",
"movement_balance", "power_shot_power", "power_jumping", "power_stamina",
"power_strength", "power_long_shots", "mentality_aggression",
"mentality_interceptions", "mentality_positioning", "mentality_vision",
"mentality_penalties", "mentality_composure", "defending_marking_awareness",
"defending_standing_tackle", "defending_sliding_tackle", "goalkeeping_diving",
"goalkeeping_handling", "goalkeeping_kicking", "goalkeeping_positioning",
"goalkeeping_reflexes", "years_remaining", "years_in_club", "category.Defensa",
"category.Delantero", "category.Medio", "preferred_foot.Left", "preferred_foot.Right",
"is_internacional.0", "is_internacional.1")
length(nombres1) #60 variables
vardep <- vardep <- c("value_eur_m")
archivo1 <- basebis
y<-archivo1[,vardep]
x<-archivo1[,nombres1]
# Semilla 1
set.seed(123456)
# Selección de variables: método 1
filtro <- sbf(x, y, sbfControl = sbfControl(functions = rfSBF,method = "cv", verbose = FALSE))
a1 <- dput(filtro$optVariables)
length(a1) #52
variables_a1 <- c("overall", "potential", "wage_eur_m", "age", "weight_kg", "league_level",
"weak_foot", "skill_moves", "international_reputation", "release_clause_eur_m",
"pace", "shooting", "passing", "dribbling", "defending", "physic",
"attacking_crossing", "attacking_finishing", "attacking_heading_accuracy",
"attacking_short_passing", "attacking_volleys", "skill_dribbling",
"skill_curve", "skill_fk_accuracy", "skill_long_passing", "skill_ball_control",
"movement_acceleration", "movement_sprint_speed", "movement_agility",
"movement_reactions", "movement_balance", "power_shot_power",
"power_jumping", "power_stamina", "power_strength", "power_long_shots",
"mentality_aggression", "mentality_interceptions", "mentality_positioning",
"mentality_vision", "mentality_penalties", "mentality_composure",
"defending_marking_awareness", "defending_standing_tackle", "defending_sliding_tackle",
"years_remaining", "years_in_club", "category.Defensa", "category.Delantero",
"category.Medio", "is_internacional.0", "is_internacional.1")
# Selección de variables: método 2
control_rfe <- rfeControl(functions = rfFuncs, method = "cv", number = 10)
results <- rfe(x, y, sizes = c(1:8), rfeControl = control_rfe)
selecrfe <- results$optVariables
a2 <- dput(results$optVariables)
length(a2) #6
variables_a2 <- c("release_clause_eur_m", "overall", "potential", "age", "wage_eur_m", "movement_reactions")
# Selección de variables: método 3
full <- glm(value_eur_m~., data=base, family = gaussian(link = "identity"))
null <- glm(value_eur_m~1, data=base, family = gaussian(link = "identity"))
selec1 <- stepAIC(null, scope=list(upper=full), direction="both", family = gaussian(link = "identity"), trace=FALSE)
vec <- (names(selec1[[1]]))
length(vec) #28
a3 <- dput(vec)
variables_a3 <- c("(Intercept)", "release_clause_eur_m", "international_reputation",
"power_stamina", "attacking_volleys", "weight_kg", "potential",
"overall", "age", "is_internacional", "movement_balance", "categoryDelantero",
"category.Medio", "mentality_interceptions", "skill_ball_control",
"years_remaining", "years_in_club", "mentality_penalties", "power_jumping",
"height_cm", "movement_acceleration", "attacking_heading_accuracy",
"power_strength", "skill_dribbling", "dribbling", "league_level",
"skill_fk_accuracy", "skill_curve")
# Selección de variables: método 4 stepAIC
#ponemos k=log(n) en stepAIC, en este caso n = 15129 observaciones, k = 9.62
full<-glm(value_eur_m~., data=base, family = gaussian(link = "identity"))
null<-glm(value_eur_m~1, data=base, family = gaussian(link = "identity"))
selec1<-stepAIC(null, scope=list(upper=full),
direction="both", family = gaussian(link = "identity"), trace = FALSE, k = 9.62)
vec<-(names(selec1[[1]]))
length(vec) #15
a4 <- dput(vec)
variables_a4 <- c("release_clause_eur_m", "international_reputation",
"power_stamina", "weight_kg", "potential", "overall", "age",
"is_internacional.1", "movement_balance", "mentality_interceptions",
"years_remaining", "years_in_club", "category.Delantero", "category.Medio")
# Selección de variables: método 5. Boruta
# También vale como Filter
out.boruta <- Boruta(value_eur_m~., data = base)
print(out.boruta)
summary(out.boruta)
sal<-data.frame(out.boruta$finalDecision)
sal2<-sal[which(sal$out.boruta.finalDecision=="Confirmed"),,drop=FALSE]
a5 <- dput(row.names(sal2))
length(a5) #31
variables_a5 <- c("overall", "potential", "wage_eur_m", "age", "international_reputation",
"release_clause_eur_m", "pace", "shooting", "passing", "dribbling",
"defending", "physic", "attacking_crossing", "attacking_short_passing",
"skill_dribbling", "skill_curve", "skill_fk_accuracy", "skill_long_passing",
"skill_ball_control", "movement_acceleration", "movement_sprint_speed",
"movement_reactions", "power_stamina", "mentality_interceptions",
"mentality_positioning", "mentality_vision", "mentality_composure",
"defending_marking_awareness", "defending_standing_tackle", "defending_sliding_tackle",
"years_remaining")
# Selección de variables: método 6. MXM
mmpc1 <- MMPC(vardep, archivo1, max_k = 2, hash = TRUE,test = "testIndFisher")
mmpc1@selectedVars
a6 <- dput(names(archivo1[,c(mmpc1@selectedVars)]))
length(a6) #10
variables_a6 <- c("overall", "wage_eur_m", "skill_moves", "international_reputation",
"release_clause_eur_m", "movement_agility", "power_stamina",
"years_remaining", "is_internacional.0", "is_internacional.1")
# Selección de variables: método 7
SES1 <- SES(vardep, archivo1, max_k = 3, hash = TRUE,
test = "testIndFisher")
SES1@selectedVars
dput(names(archivo1[,c(SES1@selectedVars)]))
a7 <-dput(names(archivo1[,c(SES1@selectedVars)]))
length(a7) #6
variables_a7 <- c("overall", "international_reputation", "release_clause_eur_m",
"attacking_volleys", "years_remaining", "is_internacional.1")
# Selección de variables: método 8
source("funcion steprepetido.R")
lista <- steprepetido(data=archivo1, vardep = c("value_eur_m"),
listconti = nombres1, sinicio=12345, sfinal = 12385, porcen = 0.8, criterio="AIC")
tabla <- lista[[1]]
a8 <- dput(lista[[2]][[1]])
length(a8) #27
variables_a8 <- c("release_clause_eur_m", "international_reputation", "attacking_volleys",
"potential", "overall", "age", "is_internacional.1", "weight_kg",
"movement_balance", "category.Delantero", "mentality_interceptions",
"years_remaining", "height_cm", "skill_ball_control", "power_jumping",
"movement_acceleration", "physic", "attacking_heading_accuracy",
"mentality_aggression", "weak_foot", "years_in_club", "skill_dribbling",
"dribbling", "mentality_penalties", "league_level", "category.Defensa",
"mentality_composure")
# Selección de variables: método 9. STEP REPETIDO BIC
lista <- steprepetido(data=archivo1, vardep=c("value_eur_m"),
listconti = nombres1,
sinicio=12345, sfinal=12385, porcen = 0.8, criterio="BIC")
tabla<-lista[[1]]
dput(lista[[2]][[1]])
variables_9_1 <- c("release_clause_eur_m", "international_reputation", "power_stamina",
"potential", "overall", "age", "weight_kg", "movement_balance",
"category.Delantero", "mentality_interceptions", "is_internacional.0")
length(variables_9_1) #11
dput(lista[[2]][[2]])
variables_9_2 <- c("release_clause_eur_m", "international_reputation", "power_stamina",
"defending_sliding_tackle", "age", "is_internacional.1", "years_remaining",
"years_in_club", "category.Delantero")
length(variables_9_2) #9
# COMPROBADO LO ANTERIOR, EJECUTAR DESDE AQUÍ PARA NO TARDAR UN SIGLO
# VALIDACIÓN CRUZADA
source("cruzadas avnnet y lin.R")
data <- basebis
# Variables elegidas en método AIC
medias1<-cruzadalin(data=data,
vardep="value_eur_m",listconti=c("release_clause_eur_m", "international_reputation",
"power_stamina", "attacking_volleys", "weight_kg", "potential",
"overall", "age", "is_internacional.1", "movement_balance", "category.Delantero",
"category.Medio", "mentality_interceptions", "skill_ball_control",
"years_remaining", "years_in_club", "mentality_penalties", "power_jumping",
"height_cm", "movement_acceleration", "attacking_heading_accuracy",
"power_strength", "skill_dribbling", "dribbling", "league_level",
"skill_fk_accuracy", "skill_curve"),
listclass=c(""),grupos=4,sinicio=1234,repe=25)
medias1$modelo="STEPAIC"
# intercept RMSE Rsquared MAE RMSESD RsquaredSD MAESD
# 1 TRUE 0.7436055 0.9912201 0.3043772 0.05348571 0.001029595 0.008124472
# Variables elegidas en método BIC
medias2<-cruzadalin(data=data,
vardep="value_eur_m",listconti=c("release_clause_eur_m", "international_reputation",
"power_stamina", "weight_kg", "potential", "overall", "age",
"is_internacional.1", "movement_balance", "mentality_interceptions",
"years_remaining", "years_in_club", "category.Delantero", "category.Medio"),
listclass=c(""),grupos=4,sinicio=1234,repe=25)
medias2$modelo="STEPBIC"
# intercept RMSE Rsquared MAE RMSESD RsquaredSD MAESD
# 1 TRUE 0.745583 0.9911734 0.3024513 0.05373192 0.001039662 0.008219088
medias3<-cruzadalin(data=data,
vardep="value_eur_m",listconti=c("release_clause_eur_m", "international_reputation", "attacking_volleys",
"potential", "overall", "age", "is_internacional.1", "weight_kg",
"movement_balance", "category.Delantero", "mentality_interceptions",
"years_remaining", "height_cm", "skill_ball_control", "power_jumping",
"movement_acceleration", "physic", "attacking_heading_accuracy",
"mentality_aggression", "weak_foot", "years_in_club", "skill_dribbling",
"dribbling", "mentality_penalties", "league_level", "category.Defensa",
"mentality_composure"),
listclass=c(""),grupos=4,sinicio=1234,repe=25)
medias3$modelo="STEPrep1"
# intercept RMSE Rsquared MAE RMSESD RsquaredSD MAESD
# 1 TRUE 0.7436124 0.9912198 0.3040198 0.0534674 0.001029419 0.008176405
medias4<-cruzadalin(data=data,
vardep="value_eur_m",listconti=c("release_clause_eur_m", "international_reputation",
"power_stamina", "weight_kg", "potential", "overall", "age",
"is_internacional.1", "movement_balance", "mentality_interceptions",
"years_remaining", "years_in_club", "category.Delantero", "category.Medio"),
listclass=c(""),grupos=4,sinicio=1234,repe=25)
medias4$modelo="STEPrep2"
# intercept RMSE Rsquared MAE RMSESD RsquaredSD MAESD
# 1 TRUE 0.745583 0.9911734 0.3024513 0.05373192 0.001039662 0.008219088
medias5<-cruzadalin(data=data,
vardep="value_eur_m",listconti=c("overall", "potential", "wage_eur_m", "age", "weight_kg", "league_level",
"weak_foot", "skill_moves", "international_reputation", "release_clause_eur_m",
"pace", "shooting", "passing", "dribbling", "defending", "physic",
"attacking_crossing", "attacking_finishing", "attacking_heading_accuracy",
"attacking_short_passing", "attacking_volleys", "skill_dribbling",
"skill_curve", "skill_fk_accuracy", "skill_long_passing", "skill_ball_control",
"movement_acceleration", "movement_sprint_speed", "movement_agility",
"movement_reactions", "movement_balance", "power_shot_power",
"power_jumping", "power_stamina", "power_strength", "power_long_shots",
"mentality_aggression", "mentality_interceptions", "mentality_positioning",
"mentality_vision", "mentality_penalties", "mentality_composure",
"defending_marking_awareness", "defending_standing_tackle", "defending_sliding_tackle",
"years_remaining", "years_in_club", "category.Defensa", "category.Delantero",
"category.Medio", "is_internacional.0", "is_internacional.1"),
listclass=c(""),grupos=4,sinicio=1234,repe=25)
medias5$modelo="SBF"
# intercept RMSE Rsquared MAE RMSESD RsquaredSD MAESD
# 1 TRUE 0.7457822 0.9911675 0.3059785 0.05343494 0.001039633 0.007890434
medias6<-cruzadalin(data=data,
vardep="value_eur_m",listconti=c("release_clause_eur_m", "overall", "potential", "age", "wage_eur_m", "movement_reactions"),
listclass=c(""), grupos=4, sinicio=1234, repe=25)
medias6$modelo="RFE"
# intercept RMSE Rsquared MAE RMSESD RsquaredSD MAESD
# 1 TRUE 0.7710036 0.9905551 0.3045512 0.05873291 0.001201108 0.009003541
medias7<-cruzadalin(data=data,
vardep="value_eur_m",listconti=c("overall", "potential", "wage_eur_m", "age", "international_reputation",
"release_clause_eur_m", "pace", "shooting", "passing", "dribbling",
"defending", "physic", "attacking_crossing", "attacking_short_passing",
"skill_dribbling", "skill_curve", "skill_fk_accuracy", "skill_long_passing",
"skill_ball_control", "movement_acceleration", "movement_sprint_speed",
"movement_reactions", "power_stamina", "mentality_interceptions",
"mentality_positioning", "mentality_vision", "mentality_composure",
"defending_marking_awareness", "defending_standing_tackle", "defending_sliding_tackle",
"years_remaining"),
listclass=c(""),grupos=5,sinicio=1234,repe=25)
medias7$modelo="Boruta"
# intercept RMSE Rsquared MAE RMSESD RsquaredSD MAESD
# 1 TRUE 0.7467337 0.9911047 0.3024539 0.06089944 0.001436395 0.01040839
medias8<-cruzadalin(data=data,
vardep="value_eur_m",listconti=c("overall", "wage_eur_m", "skill_moves", "international_reputation",
"release_clause_eur_m", "movement_agility", "power_stamina",
"years_remaining", "is_internacional.0", "is_internacional.1"),
listclass=c(""),grupos=5,sinicio=1234,repe=25)
medias8$modelo="MXM"
# intercept RMSE Rsquared MAE RMSESD RsquaredSD MAESD
# 1 TRUE 0.7495236 0.9910333 0.3024811 0.06100133 0.001456637 0.0103531
# Hasta aquí muy rápido
# Union para el boxplot
union1<-rbind(medias1,medias2,medias3,medias4,medias5,medias6,medias7,medias8)
par(cex.axis=0.9, las = 2)
boxplot(data=union1,col="cyan",error~modelo)
union1$error2<-sqrt(union1$error)
par(cex.axis=0.9, las = 2)
boxplot(data=union1,col="cyan",error2~modelo)
# Test con los diferentes conjuntos de variables
# con StepAIC (500 obs / parametro)
# h = 20
stepAIC <- c("release_clause_eur_m", "international_reputation",
"power_stamina", "attacking_volleys", "weight_kg", "potential",
"overall", "age", "is_internacional.1", "movement_balance", "category.Delantero",
"category.Medio", "mentality_interceptions", "skill_ball_control",
"years_remaining", "years_in_club", "mentality_penalties", "power_jumping",
"height_cm", "movement_acceleration", "attacking_heading_accuracy",
"power_strength", "skill_dribbling", "dribbling", "league_level",
"skill_fk_accuracy", "skill_curve")
#value_eur_m~release_clause_eur_m+international_reputation+power_stamina+attacking_volleys+weight_kg+potential+overall+age+is_internacional.1+movement_balance+category.Delantero+category.Medio+mentality_interceptions+skill_ball_control+years_remaining+years_in_club+mentality_penalties+power_jumping+height_cm+movement_acceleration+attacking_heading_accuracy+power_strength+skill_dribbling+dribbling+league_level+skill_fk_accuracy+skill_curve
control <- trainControl(method = "cv", number = 7, savePredictions = "all")
avnnetgrid <- expand.grid(size = c(20),
decay = c(0.01, 0.1), bag = FALSE)
redavnnet <- train(value_eur_m ~ release_clause_eur_m + international_reputation + power_stamina + attacking_volleys + weight_kg + potential + overall + age + is_internacional.1 + movement_balance + category.Delantero + category.Medio + mentality_interceptions + skill_ball_control + years_remaining + years_in_club + mentality_penalties + power_jumping + height_cm + movement_acceleration + attacking_heading_accuracy + power_strength + skill_dribbling + dribbling + league_level + skill_fk_accuracy + skill_curve,
data = data, method = "avNNet", linout = TRUE, maxit = 100,
trControl = control, tuneGrid = avnnetgrid, repeats = 5,
allowParallel = TRUE)
redavnnet
# Resampling: Cross-Validated (7 fold)
# Summary of sample sizes: 12968, 12969, 12968, 12967, 12968, 12967, ...
# Resampling results across tuning parameters:
#
# size decay RMSE Rsquared MAE
# 17 0.001 1.236849 0.9770822 0.3691273
# 17 0.010 1.128893 0.9800349 0.3483474
# 17 0.100 1.219989 0.9772936 0.3704919
# 20 0.001 1.157392 0.9778660 0.3685027
# 20 0.010 1.193045 0.9779097 0.3494402
# 20 0.100 1.151851 0.9791794 0.3545829
# 23 0.001 1.147541 0.9810943 0.3520308
# 23 0.010 1.130553 0.9800053 0.3614498
# 23 0.100 1.136573 0.9801561 0.3582076
# 27 0.001 1.194860 0.9776985 0.3804899
# 27 0.010 1.114979 0.9801026 0.3676952
# 27 0.100 1.169854 0.9782067 0.3721630
#
# Tuning parameter 'bag' was held constant at a value of FALSE
# RMSE was used to select the optimal model using the smallest value.
# The final values used for the model were size = 33, decay = 0.001 and bag = FALSE.
medias9 <- cruzadaavnnet(data = data,
vardep = "value_eur_m",
listconti = c("release_clause_eur_m", "international_reputation",
"power_stamina", "attacking_volleys", "weight_kg", "potential",
"overall", "age", "is_internacional.1", "movement_balance", "category.Delantero",
"category.Medio", "mentality_interceptions", "skill_ball_control",
"years_remaining", "years_in_club", "mentality_penalties", "power_jumping",
"height_cm", "movement_acceleration", "attacking_heading_accuracy",
"power_strength", "skill_dribbling", "dribbling", "league_level",
"skill_fk_accuracy", "skill_curve"),
listclass = c(""),
grupos = 10,
sinicio = 1235,
repe = 25,
repeticiones = 5,
itera = 100,
size = c(20),
decay = c(0.01))
# Con tamaño 20 y decay 0.01
# size decay bag RMSE Rsquared MAE RMSESD RsquaredSD MAESD
# 1 20 0.01 FALSE 1.229625 0.9761021 0.4014488 0.5918416 0.01756561 0.04332425
medias9$modelo="zr_STEPAIC" # 20 nodos y 27 variables seleccionadas del método AIC
union1<-rbind(medias1,medias2,medias3,medias4,medias5,medias6,medias7,medias8,medias9)
par(cex.axis=1.0, las=2)
boxplot(data=union1,col="cyan",error~modelo)
# con stepBIC (14 variables)
# El numero de nodos optimo es 34
paste(c("release_clause_eur_m", "international_reputation",
"power_stamina", "weight_kg", "potential", "overall", "age",
"is_internacional.1", "movement_balance", "mentality_interceptions",
"years_remaining", "years_in_club", "category.Delantero", "category.Medio"),collapse = "+")
value_eur_m~release_clause_eur_m+international_reputation+power_stamina+weight_kg+potential+overall+age+is_internacional.1+movement_balance+mentality_interceptions+years_remaining+years_in_club+category.Delantero+category.Medio
control<-trainControl(method = "cv", number = 5, savePredictions = "all")
set.seed(123)
avnnetgrid <-expand.grid(size=c(28,32,34),
decay=c(0.01,0.1),bag=FALSE)
redavnnet<- train(value_eur_m~release_clause_eur_m+international_reputation+power_stamina+weight_kg+potential+overall+age+is_internacional.1+movement_balance+mentality_interceptions+years_remaining+years_in_club+category.Delantero+category.Medio,
data=data,method="avNNet",linout = TRUE,maxit=100,
trControl=control,tuneGrid=avnnetgrid, repeats=5)
redavnnet
# 15129 samples
# 14 predictor
#
# No pre-processing
# Resampling: Cross-Validated (5 fold)
# Summary of sample sizes: 12103, 12103, 12103, 12105, 12102
# Resampling results across tuning parameters:
#
# size decay RMSE Rsquared MAE
# 28 0.001 0.8686732 0.9883076 0.2763148
# 28 0.010 0.8787242 0.9878734 0.2713265
# 28 0.100 0.9394300 0.9859383 0.2734086
# 30 0.001 0.8814992 0.9877642 0.2861368
# 30 0.010 0.8787462 0.9884073 0.2676507
# 30 0.100 0.8466426 0.9888089 0.2685555
# 33 0.001 0.8638832 0.9879644 0.2723132
# 33 0.010 0.8882455 0.9875489 0.2707596
# 33 0.100 0.8305828 0.9892712 0.2568360
# 34 0.001 0.9235949 0.9866821 0.2668244
# 34 0.010 0.8509376 0.9885076 0.2734488
# 34 0.100 0.8368521 0.9892319 0.2530016
# 35 0.001 0.8575092 0.9885932 0.2653762
# 35 0.010 0.8301164 0.9891227 0.2712058
# 35 0.100 0.8632437 0.9884798 0.2703303
medias10<-cruzadaavnnet(data=data,
vardep="value_eur_m", c("release_clause_eur_m", "international_reputation",
"power_stamina", "weight_kg", "potential", "overall", "age",
"is_internacional.1", "movement_balance", "mentality_interceptions",
"years_remaining", "years_in_club", "category.Delantero", "category.Medio"),
listclass=c(""),grupos=10,sinicio=1235,repe=25,repeticiones=5,itera=100,
size=c(33),decay=c(0.01))
medias10$modelo="zr_STEPBIC" #33 nodos y variables seleccionadas con STEPBIC
# size decay bag RMSE Rsquared MAE RMSESD RsquaredSD MAESD
# 1 33 0.1 FALSE 0.9827395 0.9850804 0.316356 0.4863367 0.01087616 0.03164791
union1<-rbind(medias1,medias2,medias3,medias4,medias5,medias6,medias7,medias8,medias9,medias10)
par(cex.axis=1.0, las=2)
boxplot(data=union1,col="cyan",error~modelo)
# Red con STEP REPETIDO1
# Variables de steprep1 (9 variables)
# Número de nodos 51
paste(c("release_clause_eur_m", "international_reputation", "power_stamina",
"defending_sliding_tackle", "age", "is_internacional.1", "years_remaining",
"years_in_club", "category.Delantero"),collapse = "+")
value_eur_m~release_clause_eur_m + international_reputation + power_stamina + defending_sliding_tackle + age + is_internacional.1 + years_remaining + years_in_club + category.Delantero
control<-trainControl(method = "cv",number=5,savePredictions = "all")
set.seed(123)
avnnetgrid <-expand.grid(size=c(49),
decay=c(0.01,0.1),bag=FALSE)
redavnnet<- train(value_eur_m~release_clause_eur_m + international_reputation + power_stamina + defending_sliding_tackle + age + is_internacional.1 + years_remaining + years_in_club + category.Delantero,
data=data,method="avNNet",linout = TRUE,maxit=100,
trControl=control,tuneGrid=avnnetgrid, repeats=5)
redavnnet
# 15129 samples
# 9 predictor
#
# No pre-processing
# Resampling: Cross-Validated (5 fold)
# Summary of sample sizes: 12103, 12103, 12103, 12105, 12102
# Resampling results across tuning parameters:
#
# size decay RMSE Rsquared MAE
# 47 0.001 0.9173642 0.9870806 0.3197883
# 47 0.010 0.8878302 0.9880077 0.3130992
# 47 0.100 0.9226121 0.9869393 0.3193348
# 49 0.001 0.8682853 0.9884530 0.3122436
# 49 0.010 0.8744016 0.9880053 0.3120502
# 49 0.100 0.8920008 0.9876894 0.3197015
# 51 0.001 0.8669497 0.9885350 0.3110278
# 51 0.010 0.8751612 0.9880865 0.3061546
# 51 0.100 0.8917000 0.9876773 0.3103530
#
# Tuning parameter 'bag' was held constant at a value of FALSE
# RMSE was used to select the optimal model using the smallest value.
# The final values used for the model were size = 51, decay = 0.001 and bag = FALSE.
medias11<-cruzadaavnnet(data=data,
vardep="value_eur_m",listconti=c("release_clause_eur_m", "international_reputation", "power_stamina",
"defending_sliding_tackle", "age", "is_internacional.1", "years_remaining",
"years_in_club", "category.Delantero"),
listclass=c(""),grupos=10,sinicio=1235,repe=25,repeticiones=5,itera=100,
size=c(51),decay=c(0.01))
medias11$modelo="zr_SETPrep1"#51 nodos y variables seleccionadas con STEPrep1
union1<-rbind(medias1,medias2,medias3,medias4,medias5,medias6,medias7,medias8,medias9, medias11)
par(cex.axis=1.0, las=2)
boxplot(data=union1,col="cyan",error~modelo)
# SELECCIÓN DE VARIABLES CON RF
# PRUEBA CON RFOREST 30 AUNQUE SE PUEDE PROBAR CON BAGGING
rfgrid<-expand.grid(mtry=c(30))
control<-trainControl(method = "cv",number = 5,savePredictions = "all")
rf<- train(value_eur_m~.,data=data,
method="rf",trControl=control,tuneGrid=rfgrid,
linout = T,ntree=1200,nodesize=10,replace=TRUE,
importance=TRUE)
rf
# CON IMPORTANCIA DE VARIABLES RANDOM FOREST
final<-rf$finalModel
tabla<-as.data.frame(importance(final))
tabla<-tabla[order(-tabla$IncNodePurity),]
tabla
# tabla<-tabla[order(-tabla$"%IncMSE"),]
# tabla
barplot(tabla$IncNodePurity,names.arg=rownames(tabla))
lista<-dput(rownames(tabla))
vardep<-"value_eur_m"
vacio2<-data.frame()
#Dejo comentado porque solo es para el gráfico
for (i in (2:20))
{
varis<-lista[1:i]
data2<-data[,c(varis,vardep)]
rfgrid<-expand.grid(mtry=c(i))
control<-trainControl(method = "cv",number=5,savePredictions = "all")
rf<- train(value_eur_m~.,data=data2,
method="rf",trControl=control,tuneGrid=rfgrid,
linout = T,ntree=300,nodesize=10,replace=TRUE,
importance=TRUE)
a<-rf$results$MAE
vacio <- data.frame(Variables = i, MAE = a)
vacio2<-rbind(vacio,vacio2)
}
vacio2 <- arrange(vacio2, Variables)
ggplot(vacio2,aes(y=MAE, x=Variables))+geom_point()+geom_line()+
scale_x_continuous(breaks = vacio2$Variables)+
scale_y_continuous(breaks = vacio2$MAE) + labs(title="RANDOM FOREST")
selecrandomforest<-lista[1:7]
dput(selecrandomforest)
# release_clause_eur_m 71.47823342 5.300338e+05
# overall 28.21689722 2.059854e+05
# potential 24.38681375 7.243883e+04
# wage_eur_m 12.66219001 5.753354e+04
# movement_reactions 10.79225757 4.401533e+04
# skill_ball_control 7.24879656 7.986305e+03
# age 15.50655442 3.659775e+03
# dribbling 2.48139615 2.837130e+03
# movement_sprint_speed 0.14850823 2.155152e+03
# attacking_finishing 1.99305653 1.472433e+03
# attacking_short_passing 4.04805855 1.368844e+03
# skill_dribbling 2.02849976 9.294926e+02
# pace -1.03695530 8.313058e+02
# mentality_positioning 0.76375866 7.716361e+02
# international_reputation 1.71755816 7.049665e+02
# mentality_composure 5.39114181 6.480652e+02
# shooting 1.75195196 6.300259e+02
# movement_acceleration 1.50334564 5.917670e+02
# skill_long_passing 3.76120571 5.687084e+02
# power_shot_power 1.27040321 5.315719e+02
# years_remaining 4.08340320 5.001679e+02
# defending_sliding_tackle 2.03969792 4.816922e+02
# movement_agility -1.77013956 4.112183e+02
# power_stamina 5.22520187 3.614607e+02
# defending_marking_awareness 2.26096005 3.157170e+02
# goalkeeping_diving -0.98658616 2.778378e+02
# height_cm 2.79387576 2.726568e+02
# skill_fk_accuracy 2.76088684 2.548992e+02
# defending_standing_tackle 3.81813789 2.525836e+02
# passing 3.57995951 2.460104e+02
# defending 4.37542214 2.313474e+02
# mentality_interceptions 3.42562738 2.224515e+02
# attacking_heading_accuracy 2.56479881 2.191522e+02
# physic 0.65369217 2.117475e+02
# power_jumping 2.37095375 2.116375e+02
# attacking_crossing 3.19710178 2.075376e+02
# goalkeeping_handling -0.64241081 2.045059e+02
# mentality_penalties 0.73679590 2.011886e+02
# movement_balance 1.68320054 1.997846e+02
# mentality_vision 2.92824411 1.952934e+02
# attacking_volleys 0.96790027 1.948163e+02
# goalkeeping_kicking -1.31073161 1.945144e+02
# goalkeeping_reflexes -0.79650829 1.839437e+02
# power_long_shots 3.83292145 1.793039e+02
# weight_kg 1.80802476 1.786964e+02
# weak_foot 1.58879248 1.703217e+02
# power_strength 2.64062303 1.636469e+02
# mentality_aggression 1.94471017 1.627831e+02
# years_in_club -1.23569337 1.596232e+02
# skill_curve 2.67516332 1.580573e+02
# goalkeeping_positioning 0.66082695 1.321738e+02
# skill_moves -0.07071613 1.054955e+02
# is_internacional.1 1.51120782 4.641138e+01
# is_internacional.0 2.08338113 3.243959e+01
# preferred_foot.Right -0.13419155 1.704140e+01
# category.Medio 0.36427061 1.404676e+01
# category.Delantero -0.97007640 1.255207e+01
# preferred_foot.Left -0.18205662 1.231115e+01
# category.Defensa 0.06416935 1.096760e+01
# league_level 0.03999268 5.821629e+00
variables_rf <- c("release_clause_eur_m", "overall", "potential", "wage_eur_m", "movement_reactions", "skill_ball_control", "age")
paste(c("release_clause_eur_m", "overall", "potential", "wage_eur_m", "movement_reactions", "skill_ball_control", "dribbling"), collapse = " + ")
# Lineal con random_forest seleccion de variables
medias12 <-cruzadalin(data=data,
vardep="value_eur_m",listconti=variables_rf,
listclass=c(""), grupos=10, sinicio=1234, repe=25)
medias12$modelo="rf_lineal"
# intercept RMSE Rsquared MAE RMSESD RsquaredSD MAESD
# 1 TRUE 0.7695829 0.9905504 0.3050161 0.06350201 0.001565473 0.01081121
union1 <-rbind(medias1,medias2,medias3,medias4,medias5,medias6,medias7,medias8,medias12)
par(cex.axis=0.9, las=2)
boxplot(data=union1,col="cyan",error~modelo)
# Red con random_forest selección de variables
# Tenemos 7, por lo que 55 nodos
control<-trainControl(method = "cv", number = 5, savePredictions = "all")
set.seed(123)
avnnetgrid <-expand.grid(size=c(45,53),
decay=c(0.01,0.1,0.001),bag=FALSE)
redavnnet<- train(value_eur_m~release_clause_eur_m + overall + potential + wage_eur_m + movement_reactions + skill_ball_control + age,
data=data,method="avNNet",linout = TRUE,maxit=100,
trControl=control,tuneGrid=avnnetgrid, repeats=5)
redavnnet
# No pre-processing
# Resampling: Cross-Validated (5 fold)
# Summary of sample sizes: 12103, 12103, 12103, 12105, 12102
# Resampling results across tuning parameters:
#
# size decay RMSE Rsquared MAE
# 50 0.001 0.8240752 0.9894396 0.2975340
# 50 0.010 0.8241754 0.9894730 0.2954904
# 50 0.100 0.8315770 0.9893029 0.2920147
# 53 0.001 0.8136922 0.9898322 0.2947727
# 53 0.010 0.7722893 0.9908583 0.2872407
# 53 0.100 0.7984537 0.9900008 0.2902439
# 55 0.001 0.7895871 0.9905337 0.2905865
# 55 0.010 0.7785420 0.9905775 0.2843074
# 55 0.100 0.8076598 0.9899902 0.2889998
#
# Tuning parameter 'bag' was held constant at a value of FALSE
# RMSE was used to select the optimal model using the smallest value.
# The final values used for the model were size = 53, decay = 0.01 and bag = FALSE.
medias13<-cruzadaavnnet(data=data,
vardep="value_eur_m",listconti=variables_rf,
listclass=c(""),grupos=4,sinicio=1234,repe=25, size=c(53),decay=c(0.1),repeticiones=5,itera=1000)
medias13$modelo="zrf_red"
# size decay bag RMSE Rsquared MAE RMSESD RsquaredSD MAESD
# 1 53 0.01 FALSE 0.7414321 0.9911114 0.164906 0.3217185 0.007714562 0.01499751
union1<-rbind(medias1,medias2,medias3,medias4,medias5,medias6,medias7,medias8,medias9, medias13)
par(cex.axis=1.0, las=2)
boxplot(data=union1,col="cyan",error~modelo)
# Comprobación de los nodos:
# Red con random_forest selección de variables
# Tenemos 7, por lo que 55 nodos
variables_rf <- c("release_clause_eur_m", "overall", "potential", "wage_eur_m", "movement_reactions", "skill_ball_control", "age")
control<-trainControl(method = "cv", number = 5, savePredictions = "all")
set.seed(123)
avnnetgrid <-expand.grid(size=c(5,10,15,20,30,40,50),
decay=c(0.01,0.1),bag=FALSE)
redavnnet<- train(value_eur_m~release_clause_eur_m + overall + potential + wage_eur_m + movement_reactions + skill_ball_control + age,
data=data,method="avNNet",linout = TRUE,maxit=100,
trControl=control,tuneGrid=avnnetgrid, repeats=5)
redavnnet
# TUNEO DEL PARÁMETRO maxit con la mejor red
# Validación cruzada con avNNet
control<-trainControl(method = "cv",
number=5, savePredictions = "all")
set.seed(123)
nnetgrid <- expand.grid(size=c(5, 20, 40, 50), decay=c(0.1,0.01, 0.01/2), bag=F)
completo<-data.frame()
listaiter<-c(10, 20, 50, 100, 500, 1000, 1200)
# Cambiar variables por el modelo elegido en este caso el mejor era con las 7 de rf (modelo rf_red)
for (iter in listaiter)
{
rednnet<- train(value_eur_m~release_clause_eur_m + overall + potential + wage_eur_m + movement_reactions + skill_ball_control + dribbling,
data=data,
method="avNNet",linout = TRUE,maxit=iter,
trControl=control,repeats=5, tuneGrid=nnetgrid,trace=F)
rednnet$results$itera<-iter
completo<-rbind(completo,rednnet$results)
}
completo<-completo[order(completo$RMSE),]
ggplot(completo, aes(x=factor(itera), y=RMSE,
color=factor(decay),pch=factor(size))) +
geom_point(position=position_dodge(width=0.5),size=3)
# Llega un momento en que se ve que se sobreajusta, es necesario early stopping
# size decay bag RMSE Rsquared MAE RMSESD RsquaredSD MAESD itera
# 1 10 0.01 FALSE 0.6000679 0.9943066 0.1892569 0.13713812 0.0016728044 0.013391603 1500
# 2 10 0.10 FALSE 0.5800655 0.9947278 0.1929375 0.04773237 0.0004135345 0.005895104 1500
# 3 20 0.01 FALSE 0.6030690 0.9943352 0.1670892 0.10165305 0.0012523884 0.015866184 1500
# 4 20 0.10 FALSE 0.6564546 0.9936167 0.1722719 0.09905182 0.0008988284 0.008085325 1500
# 5 30 0.01 FALSE 0.6425461 0.9937049 0.1591170 0.16897165 0.0021690293 0.014575135 1500
# 6 30 0.10 FALSE 0.7154135 0.9923849 0.1655306 0.17267599 0.0021882349 0.008994776 1500
# 7 53 0.01 FALSE 0.8463020 0.9890927 0.1658147 0.33762533 0.0069161524 0.018127941 1500
# 8 53 0.10 FALSE 0.8151749 0.9897772 0.1680668 0.29919137 0.0056591160 0.016485926 1500
# Añadimos la caja de la red:
medias17<-cruzadaavnnet(data=data,
vardep="value_eur_m",listconti=variables_rf,
listclass=c(""),grupos=10,sinicio=1234,repe=25,repeticiones=5,itera=1000,
size=c(5),decay=c(0.01))
medias17$modelo="Red_cv10"
union1<-rbind(medias1,medias2,medias3,medias4,medias5,medias6,medias7,medias8,medias9, medias10, medias11, medias12, medias13, medias1, medias17)
par(cex.axis=1.0, las = 2)#las=2 si lo ponemos aqui caben los nombres de las variables
boxplot(data=union1,col="cyan",error~modelo)
union1$error2<-sqrt(union1$error)
par(cex.axis=1.0, las = 2)
boxplot(data=union1,col="cyan",error2~modelo)
medias18<-cruzadaavnnet(data=data,
vardep="value_eur_m",listconti=variables_rf,
listclass=c(""),grupos=5,sinicio=1234,repe=25,repeticiones=5,itera=1000,
size=c(5),decay=c(0.01))
medias18$modelo="Red_cv5"
union1<-rbind(medias1,medias2,medias3,medias4,medias5,medias6,medias7,medias8,medias9, medias10, medias11, medias12, medias13, medias1, medias17, medias18)
par(cex.axis=1.0, las = 2)#las=2 si lo ponemos aqui caben los nombres de las variables
boxplot(data=union1,col="cyan",error~modelo)
union1$error2<-sqrt(union1$error)
par(cex.axis=1.0, las = 2)
boxplot(data=union1,col="cyan",error2~modelo)
# otra de prueba
medias16<-cruzadaavnnet(data=data,
vardep="value_eur_m",listconti=variables_rf,
listclass=c(""),grupos=4,sinicio=1234,repe=25,repeticiones=5,itera=500,
size=c(50),decay=c(0.1))
medias16$modelo="Red2"
union1<-rbind(medias1,medias2,medias3,medias4,medias5,medias6,medias7,medias8,medias9, medias10, medias11, medias12, medias13, medias15, medias16)
par(cex.axis=1.0, las = 2)#las=2 si lo ponemos aqui caben los nombres de las variables
boxplot(data=union1,col="cyan",error~modelo)
union1$error2<-sqrt(union1$error)
par(cex.axis=1.0, las = 2)
boxplot(data=union1,col="cyan",error2~modelo)
##############################################################################
#---------------------------COMPARACIÓN R Y SAS------------------------------#
data#datos limpios
basebis
# Hay que tener en cuenta que la evaluación más precisa de la performance
# se obtiene con los resultados de cv repetida y boxplot.
#
#
# 1) Crearemos archivo train-test en R y crearemos los archivos sas correspondientes.
# 2) Construiremos el mejor modelo con train en SAS y el mejor modelo en R
# 3) Predeciremos test y evaluaremos y compararemos la performance.
# Obviamente los mismos sets de variables con regresión tendrán las mismas performance en R y SAS.
# Pero las redes neuronales no.
# 1) Crearemos archivo train-test en R y crearemos los archivos sas correspondientes.
# Dividiremos en train test el archivo, 70%ag train, 30% test
##################################################3
##############################################################################
#---------------------------COMPARACIÓN R Y SAS------------------------------#
# Establecer la semilla para reproducibilidad
set.seed(12345)
sample_size <- floor(0.3 * nrow(basebis))
test_indices <- sample(seq_len(nrow(basebis)), size = sample_size)
test_data <- basebis[test_indices, ]
train_data <- basebis[-test_indices, ]
rf_sec <- c("release_clause_eur_m", "overall", "potential", "wage_eur_m", "movement_reactions", "skill_ball_control", "age", "value_eur_m")
test_selected_rf <- test_data[, rf_sec]
train_selected_rf <- train_data[, rf_sec]
# Guarda los conjuntos de prueba y entrenamiento en archivos CSV
write.csv(test_selected_rf, file = "rf_train.csv", row.names = FALSE)
write.csv(train_selected_rf, file = "rf_test.csv", row.names = FALSE)
st_sec <- c("release_clause_eur_m", "international_reputation", "power_stamina",
"defending_sliding_tackle", "age", "is_internacional.1", "years_remaining",
"years_in_club", "category.Delantero", "value_eur_m")
test_selected_st <- test_data[, st_sec]
train_selected_st <- train_data[, st_sec]
write.csv(test_selected_st, file = "st_train.csv", row.names = FALSE)
write.csv(train_selected_st, file = "st_test.csv", row.names = FALSE)
# ******************
# FUNCIÓN PREDICT
# ******************
# En principio el mejor modelo obtenido con R es con las variables rf
# 1) Construimos la red con los datos train
# 2) Evaluamos la red construida sobre datos test con la función predict.
set.seed(136919)
test <- test_selected_rf
train <- train_selected_rf
library(caret)
control<-trainControl(method = "none")
nnetgrid <- expand.grid(size=c(5),decay=c(0.01),bag=F)
rednnet<- train(value_eur_m~release_clause_eur_m + overall + potential + wage_eur_m + movement_reactions + skill_ball_control + age,
data=train,method="avNNet",linout = TRUE,
trControl=control,repeats=5,tuneGrid=nnetgrid,maxit=1000,trace=T)
# Se aplica la función predict:
predicciones<-predict(rednnet,test)
# Se añaden las predicciones al archivo test y se calcula el error:
comple<-cbind(test,predicciones)
comple$error<-(comple$value_eur_m-comple$predicciones)^2
MSE<-mean(comple$error); MSE #0.261676
RMSE<-sqrt(MSE); RMSE #0.5115428
# Predicciones con la semilla 2
set.seed(123456)
test <- test_selected_rf
train <- train_selected_rf
library(caret)
control<-trainControl(method = "none")
nnetgrid <- expand.grid(size=c(5),decay=c(0.01),bag=F)
rednnet<- train(value_eur_m~release_clause_eur_m + overall + potential + wage_eur_m + movement_reactions + skill_ball_control + age,