classification-results-boxplot.R

Resultados clasificación

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 <- read_csv("classification-results.csv")

## Parsed with column specification:
## cols(
##   .default = col_double(),
##   dataset = col_character(),
##   var = col_character(),
##   config.train = col_character(),
##   config.vars = col_character(),
##   T = col_integer(),
##   alg = col_character()
## )

## See spec(...) for full column specifications.

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

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) 

Comportamiento general de local vs all según métrica

p <-ggplot(aes(y = Sensitivity, x = var, fill = config.vars), data = df) + 
  geom_boxplot()  + coord_flip()
print(p)

p <-ggplot(aes(y = F1, x = var, fill = config.vars), data = df) + geom_boxplot() + coord_flip()
print(p)

p <-ggplot(aes(y = Precision , x = var, fill = config.vars), data = df) + geom_boxplot() + coord_flip()
print(p)

Según cuantos días anteriores de información sumamos.

p <-ggplot(aes(y = Precision , x = var, fill = as.factor(T)), data = df) + geom_boxplot() + coord_flip()
print(p)

p <-ggplot(aes(y = Sensitivity , x = var, fill = as.factor(T)), data = df) + geom_boxplot() + coord_flip()
print(p)

p <-ggplot(aes(y = F1 , x = var, fill = as.factor(T)), data = df) + 
  geom_boxplot() + coord_flip() + labs(fill="T")
print(p)

Comportamiento general de local vs all por cada estación según métrica

for(s in stations){
  
  df1 <- df %>% filter( var == s ) 
  
  p <-ggplot(aes(y = F1 , x = alg, fill = config.vars), data = df1) + 
    geom_boxplot() + coord_flip() + labs(title=paste("Estación",s,sep="  "))
  print(p)
  p <-ggplot(aes(y = Sensitivity , x = alg, fill = config.vars), data = df1) + 
    geom_boxplot() + coord_flip() + labs(title=paste("Estación",s,sep="  "))
  print(p)
  p <-ggplot(aes(y = Precision , x = alg, fill = config.vars), data = df1) + 
    geom_boxplot() + coord_flip() + labs(title=paste("Estación",s,sep="  "))
  print(p)

}

library(reshape)

## 
## Attaching package: 'reshape'

## The following object is masked from 'package:dplyr':
## 
##     rename

## The following objects are masked from 'package:tidyr':
## 
##     expand, smiths

df3 <-  df %>% filter( dataset == "dacc") %>% select(-one_of(c("FAR")))

#  melt(df3,id.vars="label")
df4 <- melt(as.data.frame(df3),
            id.vars =(c("label","dataset","var","config.train","config.vars","T","alg")),
            measure.vars = metrics[-1])

p <-ggplot(aes(y = value , x = var, fill = variable), data = df4) + 
  geom_boxplot() + coord_flip() + labs(title="Variabilidad de las métricas por las estaciones")
print(p)

df4 <- df %>% filter( dataset == "dacc")   
df4 <- melt(as.data.frame(df4),
            id.vars =(c("label","dataset","var","config.train","config.vars","T","alg")),
            measure.vars = metrics)

for(a in algoritmos)
{
  for(m in metrics)
  {
    df5 <- df4 %>% filter( alg == a & variable == m) 
    p <-ggplot(aes(y = value , x = var, fill = config.vars), data = df5) + 
      geom_boxplot() + coord_flip() + labs(title=paste("Comportamiento de ",m," en modelo ",a,sep=""))
    print(p)
  }
  # for(s in stations){
  #      
  #   df5 <- df4 %>% filter( var == s & alg == a) 
  #   p <-ggplot(aes(y = value , x = variable, fill = config.vars), data = df5) + 
  #     geom_boxplot() + coord_flip() + labs(title=paste("Estacion ",s," y algoritmo ",a,sep=""))
  #   print(p)
  #  }
}