normal25_small.Rmd

---
title: "Normal 25"
author: "Ernesto Carrella"
date: "February 16, 2019"
output: html_document
editor_options: 
  chunk_output_type: console
---

Sample 25 observations from a normal distribution and consider each one a summary statistic (without sorting or anything)

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE, warning= FALSE, message=FALSE,
                      cache=TRUE,
                      dpi = 300, fig.width = 13.3, fig.height = 10)
library(abctools)
source('./setup.R')
library(tidyverse)
```

```{r dataprep}
NOBS<-25 #observations for each call in rgk

DATA_SET_SIZE<-SMALL_DATA_SET_SIZE

total_data<-
  data.frame(
    mean = runif(DATA_SET_SIZE,-5,5),
    sd= runif(DATA_SET_SIZE,1,10)
  )

# run gk model and returns deciles as summary statistics
run_normal25<-function(mean,sd){
  simulation<-
    rnorm(n=25,mean = mean,sd = sd)
  names(simulation) <- paste("obs",1:25,sep="")
  
  
   return(
   
    simulation %>% as.list() %>% as.data.frame()
    
  )
}

total_data<-pmap_df(total_data,run_normal25) %>% cbind(total_data)
param_names<-c("mean","sd")
x_names<- setdiff(colnames(total_data),param_names) 


```

# ABC

## Rejection


```{r rejection}
rejection_cross<-
  cross_validate_abc(total_data,ngroup=5,method="rejection",parameter_colnames = param_names)
print_table(rejection_cross)

```

## Rejection - semiauto


```{r rejection-sabc}
rejection_cross_sabc<-
  cross_validate_abc(total_data,ngroup=5,method="rejection",parameter_colnames = param_names,
                     semiauto = TRUE)
print_table(rejection_cross_sabc)

```

## Rejection - 1D


```{r rejection-sabc1d}
rejection_cross_sabc1d<-
  cross_validate_abc(total_data,ngroup=5,method="rejection",parameter_colnames = param_names,
                     semiauto = TRUE,
                     satr=list(function(x){outer(x,Y=1,"^")}))
print_table(rejection_cross_sabc1d)

```

## Loclin


```{r loclin}
loclin_cross<-
  cross_validate_abc(total_data,ngroup=5,method="loclinear",
                     hcorr=TRUE,parameter_colnames = param_names,
                     semiauto = FALSE)
print_table(loclin_cross)

```


## Neural-Network

```{r neuralnet, eval=TRUE, message= FALSE, results="hide"}
neuralnet<-
  cross_validate_abc(total_data,ngroup=5,method="neuralnet",
                     hcorr=TRUE,parameter_colnames = param_names)

```
```{r}
print_table(neuralnet)

```


# Linear regression


## Degree 1

```{r lm}
lm_cross<-
  cross_validate_lm(total_data,ngroup=5,
                    parameter_colnames = param_names,x_names=x_names,
                     degree=1)
print_table(lm_cross)

```

# Random Forest



```{r rf}
rf_cross<-
  cross_validate_rf(total_data,ngroup=5,
                    parameter_colnames = param_names,x_names=x_names)
print_table(rf_cross)

```


# Loess Fit



```{r loess}
loess_cross<- cross_validate_loess(total_data,ngroup=5,
                    parameter_colnames = param_names,
                    x_names=x_names)
print_table(loess_cross)

```

```{r}
FILENAME<-"normal25_small"
```

# Random Forest (boot)



```{r rfboot, results="hide"}
rfboot_cross<-
  cross_validate_rfboot(total_data,ngroup=5,
                    parameter_colnames = param_names,x_names=x_names)

```

```{r rfboottable}
print_table(rfboot_cross)
```


# Average



```{r average}
average_cross<- cross_validate_average(total_data,ngroup=5,
                    parameter_colnames = param_names,
                    x_names=x_names)
print_table(average_cross)

```

```{r, writetocsv}

crosses<-
  list(
    rejection = rejection_cross,
    sabc_4d = rejection_cross_sabc,
    sabc_1d = rejection_cross_sabc1d, 
    loclin = loclin_cross,
    neuralnet = neuralnet,
    lm = lm_cross,
    rf = rf_cross,
    loess = loess_cross,
    rfboot = rfboot_cross,
    average = average_cross
  )

write_csv(
  map2_df(crosses,names(crosses),~ as_data_frame(t(.x$error)) %>% 
            mutate(method=.y)) %>% gather(parameter,value,-method), 
  paste("./errors/",FILENAME,".csv",sep="")
)

write_csv(
  map2_df(crosses,names(crosses),~ as_data_frame(t(.x$contained)) %>% 
            mutate(method=.y)) %>% gather(parameter,value,-method),   paste("./contained/",FILENAME,".csv",sep="")
)

write_csv(
  map2_df(crosses,names(crosses),~ as_data_frame(t(.x$interval)) %>% 
            mutate(method=.y)) %>% gather(parameter,value,-method), 
  paste("./intervals/",FILENAME,".csv",sep="")
)
```