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library(readr)
library(ggplot2)
library(gridExtra)
library(tidyverse)

## ── Attaching packages ───────────────────────────────────────────────────── tidyverse 1.2.1 ──

## ✔ tibble  1.4.2     ✔ dplyr   0.7.6
## ✔ tidyr   0.8.1     ✔ stringr 1.3.1
## ✔ purrr   0.2.5     ✔ forcats 0.3.0

## ── Conflicts ──────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::combine() masks gridExtra::combine()
## ✖ dplyr::filter()  masks stats::filter()
## ✖ dplyr::lag()     masks stats::lag()

library(lazyeval)

## 
## Attaching package: 'lazyeval'

## The following objects are masked from 'package:purrr':
## 
##     is_atomic, is_formula

dataset <-suppressMessages(read_csv("classification-results.csv"))
dataset[,8:ncol(dataset)] <- round(dataset[,8:ncol(dataset)],digits = 3)

#' ESTACION: valores posibles en la columna vars. Son las estaciones meteorológicas
print(unique(dataset$var))

## [1] "tunuyan.temp_min"     "la_llave.temp_min"    "junin.temp_min"      
## [4] "las_paredes.temp_min" "agua_amarga.temp_min"

#' 

DATASET: valores posibles “dacc”, faltan casos de “dacc-temp”,“dacc-spring”

Caso columna o dato que evalue si colabora o no el enfoque agregar vecinos.

ESTACION <- params$station
DATASET <- "dacc"
ALGORITMO <- params$algs

stations <- unique(dataset$var)
algoritmos <- unique(dataset$alg)
metrics <- c("FAR","Sensitivity","Specificity","Accuracy","Kappa","F1","Precision")
#' ## Carga dataset
#' Creación de dataset genérico para resultados clasificación
df <- dataset %>%
  unite(dataset, 
        col=label,c("dataset","var","config.train","config.vars","T","alg"),
        sep = "-",remove=FALSE) %>% 
  select(label,dataset,var,config.train,config.vars,T,alg,FAR,Sensitivity,Specificity,Accuracy,Kappa,F1,Precision) 

df

df %>%
  select(dataset,var,config.train,config.vars,T,alg,FAR) %>%
  filter(dataset == DATASET & var == ESTACION ) %>% # & alg=="glm" & T == 1 ) %>%
  group_by(dataset,var,config.train,T,alg) %>% 
  arrange(dataset,var,config.train,T,alg,desc(config.vars)) %>% 
  mutate(local_vs_all = lag(FAR) - FAR)

#' para crear columna local vs all
mutate_call_ <- function(df, col1, col2, new_col_name) {
  mutate_call = lazyeval::interp(~ round(lag(a) - b,2), a = as.name(col1), b = as.name(col2))
 df %>% mutate_(.dots = setNames(list(mutate_call), new_col_name))
}
#' df data.frame
#' 
#' NO IMPLEMENTADO filtro: filtro a pasar a ggplot en filter
#' 
#' m metrica, valores posibles o referencia array metrics o mirar dataset.
#' 
#' s: character, nombre de la estacion o variable predecida
plot_local_vs_all <- function(df,m,s){

  df1 <- df %>%
    select(dataset,var,config.train,config.vars,T,alg,m) %>%
    group_by(dataset,var,config.train,T,alg) %>% 
    arrange(dataset,var,config.train,T,alg,desc(config.vars)) %>% 
    mutate_call_(m, m, "local_vs_all") %>% 
    filter(!is.na(local_vs_all))  %>%
    unite(col=label,c("dataset","var","config.train","T","alg"),
          sep = "-",remove=FALSE) 
  
  
  p <- ggplot(data=df1, aes(x= reorder(label,-local_vs_all), y=local_vs_all)) +
          geom_bar(stat="identity",fill="green")+ 
          geom_text(aes(label=local_vs_all), vjust=1.3, color="black", size=3) + 
          coord_flip()+
          theme_minimal() +
          labs(x = "Models",title=paste(s,m,sep="--"))
  print(p)
}

 df1 <- df %>%
    #select(dataset,var,config.train,config.vars,T,alg,m) %>%
    filter(dataset == DATASET & var == ESTACION ) # & alg=="glm" & T == 1 ) %>%
  for(m in metrics){
    plot_local_vs_all(df1,m,ESTACION)
  }

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