---
title: "Benchmarking"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Benchmarking}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
readRDS("benchmark_res.rds") -> res
knitr::opts_chunk$set(
  collapse = TRUE,
  eval = FALSE,
  comment = "#>"
)
```

Based on community detection to automatically classify the keywords, \CRANpkg{akc} can utilize different algorithms for clustering. In this vignette, a benchmark is provided to show the difference for various algorithms on multiple sizes of networks. 

First, we'll load the needed packages.

```{r setup}
library(akc)
library(dplyr)
```

Then, we prepare the needed data. The built-in data table `biblio_data_table` would be used here.

```{r}
bibli_data_table %>% 
  keyword_clean() %>% 
  keyword_merge() -> clean_data
```

Next, a combination of network size and community detection algorithms are designed to be tested:

```{r}
100:300 -> topn_sample
ls("package:akc") %>% 
  str_extract("^group.+") %>% 
  na.omit() %>% 
  setdiff(c("group_biconnected_component",
            "group_components",
            "group_optimal")) -> com_detect_fun_list
```

Finally, we'll implement the computation and record the results.

```{r,eval=FALSE}
all = tibble()
for(i in com_detect_fun_list){
    for(j in topn_sample){
      system.time({
        clean_data %>% 
          keyword_group(top = j,com_detect_fun = get(i)) %>% 
          as_tibble -> grouped_network_table
      }) %>% na.omit-> time_info
      grouped_network_table %>% nrow -> node_no
      grouped_network_table %>% distinct(group) %>% nrow -> group_no
      grouped_network_table %>% 
        count(group) %>% 
        summarise(mean(n)) %>% 
        .[[1]] -> group_avg_node_no
      grouped_network_table %>% 
        count(group) %>% 
        summarise(sd(n)) %>% 
        .[[1]] -> group_sd_node_no
      c(com_detect_fun = i, 
        topn = j,
        node_no = node_no,group_no = group_no,
        avg = group_avg_node_no,
        sd = group_sd_node_no,time_info[1:3]) %>% 
        bind_rows(all,.) -> all
    }
}

res = all %>% 
  mutate_at(2:9,function(x) as.numeric(x) %>% round(2)) %>% 
  distinct(com_detect_fun,node_no,.keep_all = T) %>% 
  select(-topn,-contains("self")) %>% 
  setNames(c("com_detect_fun","No. of total nodes","No. of total groups",
             "Average node number in each group","Standard deviation of node number",
             "Computer running time for keyword_group function")) 
```

The results are displayed in the following table.

```{r,eval=TRUE}
knitr::kable(res)
```

The session information is displayed as below:

```{r,eval=TRUE}
sessionInfo()
```

