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CARET2.R
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library(caret)
library(doMC)
library(here)
source(here::here('libraries.R'))
# OBTENEMOS INFORMACION DE MODELOS DISPONIBLES ----------------------------
x<- getModelInfo()
type<- lapply(x, function(y) {if(length(y$type > 1)){
return(paste(y$type, collapse = "_"))
}else{return(y$type)}})
label<- lapply(x, function(y) {if(length(y$label > 1)){
return(paste(y$label, collapse = "_"))
}else{return(y$label)}})
Tabla_method<- as.data.frame(cbind(names(x),unlist(type), unlist(label)) , row.names = F)
colnames(Tabla_method)<- c("Method", "type", "Label")
Tabla_both<- Tabla_method[which(str_detect(Tabla_method$type,
"Regression")),] %>%
.[str_detect(.$type,"_"),]
Tabla_regresion<- Tabla_method[which(str_detect(Tabla_method$type,
"Regression")),] %>%
.[!str_detect(.$type,"_"),]
sum(str_detect(Tabla_method$Method, "[m5]"))
Tabla_method$Label[str_detect(Tabla_method$Label, "Neural")]
M5_alg<- Tabla_method[str_detect(Tabla_method$Method, "M5"),] %>% .[which(str_detect(.$type,
"Regression")),]
Neural_alg<- Tabla_method[str_detect(Tabla_method$Label, "Neural"),]%>% .[which(str_detect(.$type,
"Regression")),]
SVM_alg<- Tabla_method[str_detect(Tabla_method$Method, "svm"),] %>% .[which(str_detect(.$type,
"Regression")),]
# IMPORTAMOS DATOS --------------------------------------------------------
RDS_down_path<- list.files(here::here('Data/Parques/Belesar/Historico/WEB'),
full.names = T) %>%
.[str_detect(.,"WRF_WEB_")] %>% .[str_detect(., ":")]
download_day<- sapply(str_split(RDS_down_path, "_"),
function(x) paste0(x[3], " ",
str_remove(x[4], ".RDS")))%>% ymd_hms(.)
clean_data<- readRDS(RDS_down_path[which.max(download_day)])
clean_data1<- lapply(clean_data, function(x){
y<- lapply(x, function(r){
r$dif_nivel<- c(diff(r$nivel)[],0)
r$dif_nivel[diff(r$Date)>3600]<- 0
return(r)
})
})
# CORTAMOS DATOS EN ENTRENAMIENTO Y PREDICCION ---------------------------
fecha_cut<- ymd("2019/04/15")
#cortar en entrenamiento y predicción
cut_train<- lapply(clean_data1, function(x,fecha_end){
y<- lapply(x, function(r){
fecha_ini<- ymd("2018/12/01")
Jan_data<- r[which(r$Date<fecha_end & r$Date>fecha_ini),]
return(Jan_data)
})
return(y)
},fecha_end=fecha_cut)
cut_train<- lapply(cut_train, function(x){
r<- x[[1]][,c("Date","LON", "LAT", "prep_hourly", "lluvia",
"nivel", "dif_nivel")]
return(r[complete.cases(r),])
})
cut_predict<- lapply(clean_data1, function(x, fecha_ini){
y<- lapply(x, function(r){
Jan_data<- r[which(r$Date>fecha_ini),]
return(Jan_data)
})
return(y)
}, fecha_ini=fecha_cut)
cut_predict<- lapply(cut_predict, function(x){
r<- x[[1]][,c("Date","LON", "LAT", "prep_hourly", "lluvia",
"nivel", "dif_nivel")]
return(r[complete.cases(r), ])
})
# MODELOS DE PREDICCION ---------------------------------------------------------------------
## SERIA INTERESANTE HECHAR UN VISTAZO AL TEMA DE DOMC
# PARALEL COMPUTATION Y DEMÁS
#registerDoMC(cores = 4)
# GENERAMOS VARIABLES PARA USAR EN EL MODELO -----------------------------------------------------
# i= SMA SOBRE LOS DATOS DE LLUVIA
# j= LAG SOBRE LOS DATOS DE LLUVIA PARA METER AL MODELO
i<- 48
j<- 54
entrenamiento<- cut_train[[23]]
entrenamiento$SMA_prep<- SMA(entrenamiento$prep_hourly,i)
entrenamiento$SMA_difnivel<- SMA(entrenamiento$dif_nivel, 36)
entrenamiento$SMA_prep_lag1<- lag(entrenamiento$SMA_prep,j)
entrenamiento<- entrenamiento[complete.cases(entrenamiento),]
prediccion<- cut_predict[[23]]
prediccion$SMA_prep<- SMA(prediccion$prep_hourly,i)
prediccion$SMA_difnivel<- SMA(prediccion$dif_nivel, 36)
prediccion$SMA_prep_lag1<- lag(prediccion$SMA_prep,j)
prediccion<- prediccion[complete.cases(prediccion),]
# PARÁMETROS TRAIN CARET --------------------------------------------------
particiones <- 10
repeticiones <- 5
# Hiperparámetros
hiperparametros <- data.frame(C = seq(1,500,by=50))
set.seed(123)
seeds <- vector(mode = "list", length = (particiones * repeticiones) + 1)
for (i in 1:(particiones * repeticiones)) {
seeds[[i]] <- sample.int(1000, nrow(hiperparametros))
}
seeds[[(particiones * repeticiones) + 1]] <- sample.int(1000, 1)
# DEFINICIÓN DEL ENTRENAMIENTO
#===============================================================================
control_train <- trainControl(method = "repeatedcv", number = particiones,
repeats = repeticiones, seeds = seeds,
returnResamp = "final", verboseIter = FALSE,
allowParallel = TRUE)
# AJUSTE DEL MODELO
# ==============================================================================
set.seed(342)
# METODOS SVM -------------------------------------------------------------
####SIMPLE
for (nmodel in 1:length(SVM_alg$Method)) {
modelo_knn<-tryCatch({train(SMA_difnivel ~ SMA_prep_lag1, data = entrenamiento,
method = as.character(SVM_alg$Method[nmodel]),
trControl = control_train)},error=function(e){
cat("Errorzzzito")
return("Modelo vacio")
})
modelo_knn1 <- tryCatch({train(SMA_difnivel ~ SMA_prep_lag1 + SMA_prep, data = entrenamiento,
method = as.character(SVM_alg$Method[nmodel]),
trControl = control_train) },error=function(e){
cat("Errorzzzito")
return("Modelo vacio")
})
modelo_knn2 <- tryCatch({train(SMA_difnivel ~ SMA_prep_lag1 * SMA_prep, data = entrenamiento,
method = as.character(SVM_alg$Method[nmodel]),
trControl = control_train)},error=function(e){
cat("Errorzzzito")
return("Modelo vacio")
})
uncorrected<-prediccion$dif_nivel
gg_graph<- ggplot(data = prediccion)+
geom_line(aes(y=uncorrected,
x=prediccion$Date),
alpha=0.5)+
xlab(paste0(range(prediccion$Date)))+
ylab ("nivel [msnm]")+
ggtitle(paste0("Predicción de nivel \n solo con lluvia WRF \n con un lag de ", j, " horas"))+
ylim( c(-0.1,0.1))+
geom_line(aes(y=SMA_difnivel,
x=Date),
col="black", lty=2)+
geom_line(aes(y=ifelse(rep(is.character(modelo_knn), length(prediccion$Date)),
rep(0, length(prediccion$Date)),
predict(modelo_knn,
newdata = prediccion)),
x=Date),
col="red") +
geom_line(aes(y=ifelse(rep(is.character(modelo_knn1), length(prediccion$Date)),
rep(0, length(prediccion$Date)),
predict(modelo_knn1,
newdata = prediccion)),
x=Date),
col="blue")+
geom_line(aes(y=ifelse(rep(is.character(modelo_knn2), length(prediccion$Date)),
rep(0, length(prediccion$Date)),
predict(modelo_knn2,
newdata = prediccion)),
x=Date),
col="green")+
theme_light()+theme(plot.title = element_text(hjust = 0.5))
print(gg_graph)
if(!dir.exists(here::here('Data/Parques/Belesar/Modelos'))){dir.create(here::here('Data/Parques/Belesar/Modelos'))}
path_modelo<- here::here('Data/Parques/Belesar/Modelos/')
name_base<- ifelse(str_detect(as.character(SVM_alg$Method[nmodel]), "."),
str_replace_all(as.character(SVM_alg$Method[nmodel]), "[.]", "_"),
as.character(SVM_alg$Method[nmodel]))
nombre1<- paste0(name_base, "_1.RDS")
nombre2<- paste0(name_base, "_2.RDS")
nombre3<- paste0(name_base, "_3.RDS")
nombre_img<- paste0(name_base, ".PNG")
ggsave( filename = paste0(path_modelo, nombre_img),dpi = 200, device = "png" )
saveRDS(modelo_knn, file=paste0(path_modelo, nombre1))
saveRDS(modelo_knn1, file=paste0(path_modelo, nombre2))
saveRDS(modelo_knn2, file=paste0(path_modelo, nombre3))
}
###SVM cambiando epsilon O COSTE---- NO VALE PARA NADA..
particiones <- 10
repeticiones <- 5
# Hiperparámetros
hiperparametros <- data.frame(C = seq(1,500,by=50))
set.seed(123)
seeds <- vector(mode = "list", length = (particiones * repeticiones) + 1)
for (i in 1:(particiones * repeticiones)) {
seeds[[i]] <- sample.int(1000, nrow(hiperparametros))
}
seeds[[(particiones * repeticiones) + 1]] <- sample.int(1000, 1)
# DEFINICIÓN DEL ENTRENAMIENTO
#===============================================================================
control_train <- trainControl(method = "repeatedcv", number = particiones,
repeats = repeticiones, seeds = seeds,
returnResamp = "final", verboseIter = FALSE,
allowParallel = TRUE)
for (nmodel in as.numeric(as.character(SVM_alg$Method)
%>% str_detect(.,"Linear") %>%
which(.))) {
modelo_knn<-tryCatch({train(SMA_difnivel ~ SMA_prep_lag1, data = entrenamiento,
method = as.character(SVM_alg$Method[nmodel]),
trControl = control_train,
tuneGrid=hiperparametros)},error=function(e){
cat("Errorzzzito")
return("Modelo vacio")
})
modelo_knn1 <- tryCatch({train(SMA_difnivel ~ SMA_prep_lag1 + SMA_prep, data = entrenamiento,
method = as.character(SVM_alg$Method[nmodel]),
trControl = control_train,
tuneGrid=hiperparametros) },error=function(e){
cat("Errorzzzito")
return("Modelo vacio")
})
modelo_knn2 <- tryCatch({train(SMA_difnivel ~ SMA_prep_lag1 * SMA_prep, data = entrenamiento,
method = as.character(SVM_alg$Method[nmodel]),
trControl = control_train,
tuneGrid=hiperparametros)},error=function(e){
cat("Errorzzzito")
return("Modelo vacio")
})
uncorrected<-prediccion$dif_nivel
gg_graph<- ggplot(data = prediccion)+
geom_line(aes(y=uncorrected,
x=prediccion$Date),
alpha=0.5)+
xlab(paste0(range(prediccion$Date)))+
ylab ("nivel [msnm]")+
ggtitle(paste0("Predicción de nivel \n solo con lluvia WRF \n con un lag de ", j, " horas"))+
ylim( c(-0.1,0.1))+
geom_line(aes(y=SMA_difnivel,
x=Date),
col="black", lty=2)+
geom_line(aes(y=ifelse(rep(is.character(modelo_knn), length(prediccion$Date)),
rep(0, length(prediccion$Date)),
predict(modelo_knn,
newdata = prediccion)),
x=Date),
col="red") +
geom_line(aes(y=ifelse(rep(is.character(modelo_knn1), length(prediccion$Date)),
rep(0, length(prediccion$Date)),
predict(modelo_knn1,
newdata = prediccion)),
x=Date),
col="blue")+
geom_line(aes(y=ifelse(rep(is.character(modelo_knn2), length(prediccion$Date)),
rep(0, length(prediccion$Date)),
predict(modelo_knn2,
newdata = prediccion)),
x=Date),
col="green")+
theme_light()+theme(plot.title = element_text(hjust = 0.5))
print(gg_graph)
if(!dir.exists(here::here('Data/Parques/Belesar/Modelos'))){dir.create(here::here('Data/Parques/Belesar/Modelos'))}
path_modelo<- here::here('Data/Parques/Belesar/Modelos/')
name_base<- ifelse(str_detect(as.character(SVM_alg$Method[nmodel]), "."),
str_replace_all(as.character(SVM_alg$Method[nmodel]), "[.]", "_"),
as.character(SVM_alg$Method[nmodel]))
nombre1<- paste0(name_base, "_1_tune.RDS")
nombre2<- paste0(name_base, "_2_tune.RDS")
nombre3<- paste0(name_base, "_3_tune.RDS")
nombre_img<- paste0(name_base, ".PNG")
ggsave( filename = paste0(path_modelo, nombre_img),dpi = 200, device = "png" )
saveRDS(modelo_knn, file=paste0(path_modelo, nombre1))
saveRDS(modelo_knn1, file=paste0(path_modelo, nombre2))
saveRDS(modelo_knn2, file=paste0(path_modelo, nombre3))
}
# M5 methods --------------------------------------------------------------
###PARA ESTE MÉTODO EMPLEAMOS RWEKA Y RJAVA
### MUCHOS PROBLEMAS
library(RWeka)
library(rJava)
for (nmodel in 1:length(M5_alg$Method)) {
modelo_knn<-tryCatch({train(SMA_difnivel ~ SMA_prep_lag1, data = entrenamiento,
method = as.character(M5_alg$Method[nmodel]),
trControl = control_train)},error=function(e){
cat("Errorzzzito")
return("Modelo vacio")
})
modelo_knn1 <- tryCatch({train(SMA_difnivel ~ SMA_prep_lag1 + SMA_prep, data = entrenamiento,
method = as.character(M5_alg$Method[nmodel]),
trControl = control_train) },error=function(e){
cat("Errorzzzito")
return("Modelo vacio")
})
modelo_knn2 <- tryCatch({train(SMA_difnivel ~ SMA_prep_lag1 * SMA_prep, data = entrenamiento,
method = as.character(M5_alg$Method[nmodel]),
trControl = control_train)},error=function(e){
cat("Errorzzzito")
return("Modelo vacio")
})
uncorrected<-prediccion$dif_nivel
gg_graph<- ggplot(data = prediccion)+
geom_line(aes(y=uncorrected,
x=prediccion$Date),
alpha=0.5)+
xlab(paste0(range(prediccion$Date)))+
ylab ("nivel [msnm]")+
ggtitle(paste0("Predicción de nivel \n solo con lluvia WRF \n con un lag de ", j, " horas"))+
ylim( c(-0.1,0.1))+
geom_line(aes(y=SMA_difnivel,
x=Date),
col="black", lty=2)+
geom_line(aes(y=ifelse(rep(is.character(modelo_knn), length(prediccion$Date)),
rep(0, length(prediccion$Date)),
predict(modelo_knn,
newdata = prediccion)),
x=Date),
col="red") +
geom_line(aes(y=ifelse(rep(is.character(modelo_knn1), length(prediccion$Date)),
rep(0, length(prediccion$Date)),
predict(modelo_knn1,
newdata = prediccion)),
x=Date),
col="blue")+
geom_line(aes(y=ifelse(rep(is.character(modelo_knn2), length(prediccion$Date)),
rep(0, length(prediccion$Date)),
predict(modelo_knn2,
newdata = prediccion)),
x=Date),
col="green")+
theme_light()+theme(plot.title = element_text(hjust = 0.5))
print(gg_graph)
if(!dir.exists(here::here('Data/Parques/Belesar/Modelos'))){dir.create(here::here('Data/Parques/Belesar/Modelos'))}
path_modelo<- here::here('Data/Parques/Belesar/Modelos/')
name_base<- ifelse(str_detect(as.character(M5_alg$Method[nmodel]), "."),
str_replace_all(as.character(M5_alg$Method[nmodel]), "[.]", "_"),
as.character(M5_alg$Method[nmodel]))
nombre1<- paste0(name_base, "_1.RDS")
nombre2<- paste0(name_base, "_2.RDS")
nombre3<- paste0(name_base, "_3.RDS")
nombre_img<- paste0(name_base, ".PNG")
ggsave( filename = paste0(path_modelo, nombre_img),dpi = 200, device = "png" )
saveRDS(modelo_knn, file=paste0(path_modelo, nombre1))
saveRDS(modelo_knn1, file=paste0(path_modelo, nombre2))
saveRDS(modelo_knn2, file=paste0(path_modelo, nombre3))
}
# NEURAL NET METHODS ------------------------------------------------------
for (nmodel in 1:length(Neural_alg$Method)) {
modelo_knn<-tryCatch({train(SMA_difnivel ~ SMA_prep_lag1, data = entrenamiento,
method = as.character(Neural_alg$Method[nmodel]),
trControl = control_train)},error=function(e){
cat("Errorzzzito")
return("Modelo vacio")
})
modelo_knn1 <- tryCatch({train(SMA_difnivel ~ SMA_prep_lag1 + SMA_prep, data = entrenamiento,
method = as.character(Neural_alg$Method[nmodel]),
trControl = control_train) },error=function(e){
cat("Errorzzzito")
return("Modelo vacio")
})
modelo_knn2 <- tryCatch({train(SMA_difnivel ~ SMA_prep_lag1 * SMA_prep, data = entrenamiento,
method = as.character(Neural_alg$Method[nmodel]),
trControl = control_train)},error=function(e){
cat("Errorzzzito")
return("Modelo vacio")
})
uncorrected<-prediccion$dif_nivel
gg_graph<- ggplot(data = prediccion)+
geom_line(aes(y=uncorrected,
x=prediccion$Date),
alpha=0.5)+
xlab(paste0(range(prediccion$Date)))+
ylab ("nivel [msnm]")+
ggtitle(paste0("Predicción de nivel \n solo con lluvia WRF \n con un lag de ", j, " horas"))+
ylim( c(-0.1,0.1))+
geom_line(aes(y=SMA_difnivel,
x=Date),
col="black", lty=2)+
geom_line(aes(y=ifelse(rep(is.character(modelo_knn), length(prediccion$Date)),
rep(0, length(prediccion$Date)),
predict(modelo_knn,
newdata = prediccion)),
x=Date),
col="red") +
geom_line(aes(y=ifelse(rep(is.character(modelo_knn1), length(prediccion$Date)),
rep(0, length(prediccion$Date)),
predict(modelo_knn1,
newdata = prediccion)),
x=Date),
col="blue")+
geom_line(aes(y=ifelse(rep(is.character(modelo_knn2), length(prediccion$Date)),
rep(0, length(prediccion$Date)),
predict(modelo_knn2,
newdata = prediccion)),
x=Date),
col="green")+
theme_light()+theme(plot.title = element_text(hjust = 0.5))
print(gg_graph)
if(!dir.exists(here::here('Data/Parques/Belesar/Modelos'))){dir.create(here::here('Data/Parques/Belesar/Modelos'))}
path_modelo<- here::here('Data/Parques/Belesar/Modelos/')
name_base<- ifelse(str_detect(as.character(Neural_alg$Method[nmodel]), "."),
str_replace_all(as.character(Neural_alg$Method[nmodel]), "[.]", "_"),
as.character(Neural_alg$Method[nmodel]))
nombre1<- paste0(name_base, "_1.RDS")
nombre2<- paste0(name_base, "_2.RDS")
nombre3<- paste0(name_base, "_3.RDS")
nombre_img<- paste0(name_base, ".PNG")
ggsave( filename = paste0(path_modelo, nombre_img),dpi = 200, device = "png" )
saveRDS(modelo_knn, file=paste0(path_modelo, nombre1))
saveRDS(modelo_knn1, file=paste0(path_modelo, nombre2))
saveRDS(modelo_knn2, file=paste0(path_modelo, nombre3))
}
# RONDA 2.... -------------------------------------------------------------
#HACEMOS EXACTAMENTE LOS MISMO PERO AÑADIENDO DIFERENCIA DE NIVEL
i<- 48
j<- 54
entrenamiento<- cut_train[[23]]
entrenamiento$SMA_prep<- SMA(entrenamiento$prep_hourly,i)
entrenamiento$SMA_difnivel<- SMA(entrenamiento$dif_nivel, 36)
entrenamiento$SMA_prep_lag1<- lag(entrenamiento$SMA_difnivel,j)
entrenamiento<- entrenamiento[complete.cases(entrenamiento),]
prediccion<- cut_predict[[23]]
prediccion$SMA_prep<- SMA(prediccion$prep_hourly,i)
prediccion$SMA_difnivel<- SMA(prediccion$dif_nivel, 36)
prediccion$SMA_prep_lag1<- lag(prediccion$SMA_difnivel,j)
prediccion<- prediccion[complete.cases(prediccion),]
# PARÁMETROS TRAIN CARET --------------------------------------------------
particiones <- 10
repeticiones <- 5
# Hiperparámetros
hiperparametros <- data.frame(C = seq(1,500,by=50))
set.seed(123)
seeds <- vector(mode = "list", length = (particiones * repeticiones) + 1)
for (i in 1:(particiones * repeticiones)) {
seeds[[i]] <- sample.int(1000, nrow(hiperparametros))
}
seeds[[(particiones * repeticiones) + 1]] <- sample.int(1000, 1)
# DEFINICIÓN DEL ENTRENAMIENTO
#===============================================================================
control_train <- trainControl(method = "repeatedcv", number = particiones,
repeats = repeticiones, seeds = seeds,
returnResamp = "final", verboseIter = FALSE,
allowParallel = TRUE)
# AJUSTE DEL MODELO
# ==============================================================================
set.seed(342)
# METODOS SVM -------------------------------------------------------------
####SIMPLE
for (nmodel in 1:length(SVM_alg$Method)) {
modelo_knn<-tryCatch({train(SMA_difnivel ~ SMA_prep_lag1, data = entrenamiento,
method = as.character(SVM_alg$Method[nmodel]),
trControl = control_train)},error=function(e){
cat("Errorzzzito")
return("Modelo vacio")
})
modelo_knn1 <- tryCatch({train(SMA_difnivel ~ SMA_prep_lag1 + SMA_prep, data = entrenamiento,
method = as.character(SVM_alg$Method[nmodel]),
trControl = control_train) },error=function(e){
cat("Errorzzzito")
return("Modelo vacio")
})
modelo_knn2 <- tryCatch({train(SMA_difnivel ~ SMA_prep_lag1 * SMA_prep, data = entrenamiento,
method = as.character(SVM_alg$Method[nmodel]),
trControl = control_train)},error=function(e){
cat("Errorzzzito")
return("Modelo vacio")
})
uncorrected<-prediccion$dif_nivel
gg_graph<- ggplot(data = prediccion)+
geom_line(aes(y=uncorrected,
x=prediccion$Date),
alpha=0.5)+
xlab(paste0(range(prediccion$Date)))+
ylab ("nivel [msnm]")+
ggtitle(paste0("Predicción de nivel \n nivel -x y WRF \n con un lag de ", j, " horas"))+
ylim( c(-0.1,0.1))+
geom_line(aes(y=SMA_difnivel,
x=Date),
col="black", lty=2)+
geom_line(aes(y=ifelse(rep(is.character(modelo_knn), length(prediccion$Date)),
rep(0, length(prediccion$Date)),
predict(modelo_knn,
newdata = prediccion)),
x=Date),
col="red") +
geom_line(aes(y=ifelse(rep(is.character(modelo_knn1), length(prediccion$Date)),
rep(0, length(prediccion$Date)),
predict(modelo_knn1,
newdata = prediccion)),
x=Date),
col="blue")+
geom_line(aes(y=ifelse(rep(is.character(modelo_knn2), length(prediccion$Date)),
rep(0, length(prediccion$Date)),
predict(modelo_knn2,
newdata = prediccion)),
x=Date),
col="green")+
theme_light()+theme(plot.title = element_text(hjust = 0.5))
print(gg_graph)
if(!dir.exists(here::here('Data/Parques/Belesar/Modelos'))){dir.create(here::here('Data/Parques/Belesar/Modelos'))}
path_modelo<- here::here('Data/Parques/Belesar/Modelos/')
name_base<- ifelse(str_detect(as.character(SVM_alg$Method[nmodel]), "."),
str_replace_all(as.character(SVM_alg$Method[nmodel]), "[.]", "_"),
as.character(SVM_alg$Method[nmodel]))
nombre1<- paste0(name_base, "_1_DIF.RDS")
nombre2<- paste0(name_base, "_2_DIF.RDS")
nombre3<- paste0(name_base, "_3_DIF.RDS")
nombre_img<- paste0(name_base, ".PNG")
ggsave( filename = paste0(path_modelo, nombre_img),dpi = 200, device = "png" )
saveRDS(modelo_knn, file=paste0(path_modelo, nombre1))
saveRDS(modelo_knn1, file=paste0(path_modelo, nombre2))
saveRDS(modelo_knn2, file=paste0(path_modelo, nombre3))
}
# M5 methods --------------------------------------------------------------
###PARA ESTE MÉTODO EMPLEAMOS RWEKA Y RJAVA
### MUCHOS PROBLEMAS
library(RWeka)
library(rJava)
for (nmodel in 1:length(M5_alg$Method)) {
modelo_knn<-tryCatch({train(SMA_difnivel ~ SMA_prep_lag1, data = entrenamiento,
method = as.character(M5_alg$Method[nmodel]),
trControl = control_train)},error=function(e){
cat("Errorzzzito")
return("Modelo vacio")
})
modelo_knn1 <- tryCatch({train(SMA_difnivel ~ SMA_prep_lag1 + SMA_prep, data = entrenamiento,
method = as.character(M5_alg$Method[nmodel]),
trControl = control_train) },error=function(e){
cat("Errorzzzito")
return("Modelo vacio")
})
modelo_knn2 <- tryCatch({train(SMA_difnivel ~ SMA_prep_lag1 * SMA_prep, data = entrenamiento,
method = as.character(M5_alg$Method[nmodel]),
trControl = control_train)},error=function(e){
cat("Errorzzzito")
return("Modelo vacio")
})
uncorrected<-prediccion$dif_nivel
gg_graph<- ggplot(data = prediccion)+
geom_line(aes(y=uncorrected,
x=prediccion$Date),
alpha=0.5)+
xlab(paste0(range(prediccion$Date)))+
ylab ("nivel [msnm]")+
ggtitle(paste0("Predicción de nivel \n nivel -x y WRF \n con un lag de ", j, " horas"))+
ylim( c(-0.1,0.1))+
geom_line(aes(y=SMA_difnivel,
x=Date),
col="black", lty=2)+
geom_line(aes(y=ifelse(rep(is.character(modelo_knn), length(prediccion$Date)),
rep(0, length(prediccion$Date)),
predict(modelo_knn,
newdata = prediccion)),
x=Date),
col="red") +
geom_line(aes(y=ifelse(rep(is.character(modelo_knn1), length(prediccion$Date)),
rep(0, length(prediccion$Date)),
predict(modelo_knn1,
newdata = prediccion)),
x=Date),
col="blue")+
geom_line(aes(y=ifelse(rep(is.character(modelo_knn2), length(prediccion$Date)),
rep(0, length(prediccion$Date)),
predict(modelo_knn2,
newdata = prediccion)),
x=Date),
col="green")+
theme_light()+theme(plot.title = element_text(hjust = 0.5))
print(gg_graph)
if(!dir.exists(here::here('Data/Parques/Belesar/Modelos'))){dir.create(here::here('Data/Parques/Belesar/Modelos'))}
path_modelo<- here::here('Data/Parques/Belesar/Modelos/')
name_base<- ifelse(str_detect(as.character(M5_alg$Method[nmodel]), "."),
str_replace_all(as.character(M5_alg$Method[nmodel]), "[.]", "_"),
as.character(M5_alg$Method[nmodel]))
nombre1<- paste0(name_base, "_1_DIF.RDS")
nombre2<- paste0(name_base, "_2_DIF.RDS")
nombre3<- paste0(name_base, "_3_DIF.RDS")
nombre_img<- paste0(name_base, ".PNG")
ggsave( filename = paste0(path_modelo, nombre_img),dpi = 200, device = "png" )
saveRDS(modelo_knn, file=paste0(path_modelo, nombre1))
saveRDS(modelo_knn1, file=paste0(path_modelo, nombre2))
saveRDS(modelo_knn2, file=paste0(path_modelo, nombre3))
}
# NEURAL NET METHODS ------------------------------------------------------
for (nmodel in 1:length(Neural_alg$Method)) {
modelo_knn<-tryCatch({train(SMA_difnivel ~ SMA_prep_lag1, data = entrenamiento,
method = as.character(Neural_alg$Method[nmodel]),
trControl = control_train)},error=function(e){
cat("Errorzzzito")
return("Modelo vacio")
})
modelo_knn1 <- tryCatch({train(SMA_difnivel ~ SMA_prep_lag1 + SMA_prep, data = entrenamiento,
method = as.character(Neural_alg$Method[nmodel]),
trControl = control_train) },error=function(e){
cat("Errorzzzito")
return("Modelo vacio")
})
modelo_knn2 <- tryCatch({train(SMA_difnivel ~ SMA_prep_lag1 * SMA_prep, data = entrenamiento,
method = as.character(Neural_alg$Method[nmodel]),
trControl = control_train)},error=function(e){
cat("Errorzzzito")
return("Modelo vacio")
})
uncorrected<-prediccion$dif_nivel
gg_graph<- ggplot(data = prediccion)+
geom_line(aes(y=uncorrected,
x=prediccion$Date),
alpha=0.5)+
xlab(paste0(range(prediccion$Date)))+
ylab ("nivel [msnm]")+
ggtitle(paste0("Predicción de nivel \n nivel -x y WRF \n con un lag de ", i, " horas"))+
ylim( c(-0.1,0.1))+
geom_line(aes(y=SMA_difnivel,
x=Date),
col="black", lty=2)+
geom_line(aes(y=ifelse(rep(is.character(modelo_knn), length(prediccion$Date)),
rep(0, length(prediccion$Date)),
predict(modelo_knn,
newdata = prediccion)),
x=Date),
col="red") +
geom_line(aes(y=ifelse(rep(is.character(modelo_knn1), length(prediccion$Date)),
rep(0, length(prediccion$Date)),
predict(modelo_knn1,
newdata = prediccion)),
x=Date),
col="blue")+
geom_line(aes(y=ifelse(rep(is.character(modelo_knn2), length(prediccion$Date)),
rep(0, length(prediccion$Date)),
predict(modelo_knn2,
newdata = prediccion)),
x=Date),
col="green")+
theme_light()+theme(plot.title = element_text(hjust = 0.5))
print(gg_graph)
if(!dir.exists(here::here('Data/Parques/Belesar/Modelos'))){dir.create(here::here('Data/Parques/Belesar/Modelos'))}
path_modelo<- here::here('Data/Parques/Belesar/Modelos/')
name_base<- ifelse(str_detect(as.character(Neural_alg$Method[nmodel]), "."),
str_replace_all(as.character(Neural_alg$Method[nmodel]), "[.]", "_"),
as.character(Neural_alg$Method[nmodel]))
nombre1<- paste0(name_base, "_1_DIF.RDS")
nombre2<- paste0(name_base, "_2_DIF.RDS")
nombre3<- paste0(name_base, "_3_DIF.RDS")
nombre_img<- paste0(name_base, ".PNG")
ggsave( filename = paste0(path_modelo, nombre_img),dpi = 200, device = "png" )
saveRDS(modelo_knn, file=paste0(path_modelo, nombre1))
saveRDS(modelo_knn1, file=paste0(path_modelo, nombre2))
saveRDS(modelo_knn2, file=paste0(path_modelo, nombre3))
}
# METODO 3 ----------------------------------------------------------------
## LO MISMO, PERO AÑADIENDO LLUVIA LAG Y NIVEL LAG
i<- 48
j<- 54
entrenamiento<- cut_train[[23]]
entrenamiento$SMA_prep<- SMA(entrenamiento$prep_hourly,i)
entrenamiento$SMA_difnivel<- SMA(entrenamiento$dif_nivel, 36)
entrenamiento$SMA_difnivel_lag1<- lag(entrenamiento$SMA_difnivel,j)
entrenamiento$SMA_prep_lag1<- lag(entrenamiento$SMA_prep,j)
entrenamiento<- entrenamiento[complete.cases(entrenamiento),]
prediccion<- cut_predict[[23]]
prediccion$SMA_prep<- SMA(prediccion$prep_hourly,i)
prediccion$SMA_difnivel<- SMA(prediccion$dif_nivel, 36)
prediccion$SMA_difnivel_lag1<- lag(prediccion$SMA_difnivel,j)
prediccion$SMA_prep_lag1<- lag(prediccion$SMA_prep,j)
prediccion<- prediccion[complete.cases(prediccion),]
# PARÁMETROS TRAIN CARET --------------------------------------------------
particiones <- 10
repeticiones <- 5
# Hiperparámetros
hiperparametros <- data.frame(C = seq(1,500,by=50))
set.seed(123)
seeds <- vector(mode = "list", length = (particiones * repeticiones) + 1)
for (i in 1:(particiones * repeticiones)) {
seeds[[i]] <- sample.int(1000, nrow(hiperparametros))
}
seeds[[(particiones * repeticiones) + 1]] <- sample.int(1000, 1)
# DEFINICIÓN DEL ENTRENAMIENTO
#===============================================================================
control_train <- trainControl(method = "repeatedcv", number = particiones,
repeats = repeticiones, seeds = seeds,
returnResamp = "final", verboseIter = FALSE,
allowParallel = TRUE)
# AJUSTE DEL MODELO
# ==============================================================================
set.seed(342)
# METODOS SVM -------------------------------------------------------------
####SIMPLE
for (nmodel in 1:length(SVM_alg$Method)) {
modelo_knn<-tryCatch({train(SMA_difnivel ~ SMA_prep_lag1 + SMA_difnivel_lag1 + SMA_difnivel_lag1, data = entrenamiento,
method = as.character(SVM_alg$Method[nmodel]),
trControl = control_train)},error=function(e){
cat("Errorzzzito")
return("Modelo vacio")
})
modelo_knn1 <- tryCatch({train(SMA_difnivel ~ SMA_prep_lag1 + SMA_prep + SMA_difnivel_lag1, data = entrenamiento,
method = as.character(SVM_alg$Method[nmodel]),
trControl = control_train) },error=function(e){
cat("Errorzzzito")
return("Modelo vacio")
})
modelo_knn2 <- tryCatch({train(SMA_difnivel ~ SMA_prep_lag1 * SMA_prep * SMA_difnivel_lag1, data = entrenamiento,
method = as.character(SVM_alg$Method[nmodel]),
trControl = control_train)},error=function(e){
cat("Errorzzzito")
return("Modelo vacio")
})
uncorrected<-prediccion$dif_nivel
gg_graph<- ggplot(data = prediccion)+
geom_line(aes(y=uncorrected,
x=prediccion$Date),
alpha=0.5)+
xlab(paste0(range(prediccion$Date)))+
ylab ("nivel [msnm]")+
ggtitle(paste0("Predicción de nivel \n nivel -x y WRF -x \n con un lag de ", j, " horas"))+
ylim( c(-0.1,0.1))+
geom_line(aes(y=SMA_difnivel,
x=Date),
col="black", lty=2)+
geom_line(aes(y=ifelse(rep(is.character(modelo_knn), length(prediccion$Date)),
rep(0, length(prediccion$Date)),
predict(modelo_knn,
newdata = prediccion)),
x=Date),
col="red") +
geom_line(aes(y=ifelse(rep(is.character(modelo_knn1), length(prediccion$Date)),
rep(0, length(prediccion$Date)),
predict(modelo_knn1,
newdata = prediccion)),
x=Date),
col="blue")+