Benchmarking.R
In akc: Automatic Knowledge Classification

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

## ----setup--------------------------------------------------------------------
#  library(akc)
#  library(dplyr)

## -----------------------------------------------------------------------------
#  bibli_data_table %>%
#    keyword_clean() %>%
#    keyword_merge() -> clean_data

## -----------------------------------------------------------------------------
#  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

## ----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"))

## ----eval=TRUE----------------------------------------------------------------
knitr::kable(res)

## ----eval=TRUE----------------------------------------------------------------
sessionInfo()