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mlgraph

mlgraph provides performance visualizations for standardized ml models with linear, binary or multi tasks. It is implemented as an extension of deeplyr. At the moment several graphics packages are implemented including

  • gg_ ggplot2
  • hc_ highcharter
  • ax_ apexcharter

Computations are based on

Several task specific plots are available:

  • Linear
    • …
  • Binary
    • Confusion Matrix
    • ROC
    • Density
  • Multi
    • Confusion Matrix
    • ROC
    • Density

Installation

Get the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("systats/mlgraph")
preds <- readRDS("data/preds.rds") %>% 
  glimpse
#> Observations: 4,016
#> Variables: 7
#> $ pred   <dbl> 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0,…
#> $ prob0  <dbl> 0.925, 0.152, 0.698, 0.887, 0.750, 0.046, 0.018, 0.193, 0.874,…
#> $ prob1  <dbl> 0.075, 0.848, 0.302, 0.113, 0.250, 0.954, 0.982, 0.807, 0.126,…
#> $ target <dbl> 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0,…
#> $ pol    <dbl> 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0,…
#> $ tweet  <chr> "rt @rzhongnotes: salt fat acid heat long ago the four element…
#> $ split  <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
df <- mlgraph::eval_classifier(preds, target, pred, prob1) %>% glimpse
#> List of 3
#>  $ confusion:Classes 'tbl_df', 'tbl' and 'data.frame':   4 obs. of  6 variables:
#>   ..$ actual     : Factor w/ 2 levels "0","1": 1 1 2 2
#>   ..$ pred       : Factor w/ 2 levels "0","1": 1 2 1 2
#>   ..$ n          : num [1:4] 1831 177 233 1775
#>   ..$ n_actual   : num [1:4] 2008 2008 2008 2008
#>   ..$ perc_actual: num [1:4] 0.912 0.088 0.116 0.884
#>   ..$ perc_all   : num [1:4] 0.456 0.044 0.058 0.442
#>  $ roc      :Classes 'tbl_df', 'tbl' and 'data.frame':   838 obs. of  4 variables:
#>   ..$ .threshold : num [1:838] -Inf 0.016 0.017 0.018 0.019 ...
#>   ..$ specificity: num [1:838] 1 1 1 1 1 1 1 1 1 1 ...
#>   ..$ sensitivity: num [1:838] 0 0.000498 0.000996 0.00498 0.006972 ...
#>   ..$ .level     : num [1:838] 1 1 1 1 1 1 1 1 1 1 ...
#>  $ dens     :Classes 'tbl_df', 'tbl' and 'data.frame':   1024 obs. of  3 variables:
#>   ..$ x     : num [1:1024] -0.0564 -0.0542 -0.052 -0.0499 -0.0477 ...
#>   ..$ y     : num [1:1024] 0.000134 0.000176 0.000229 0.000296 0.000379 ...
#>   ..$ actual: chr [1:1024] "0" "0" "0" "0" ...

ggplot2

gg_plot2 <- gridExtra::grid.arrange(
  gg_plot_confusion(df$confusion), 
  gg_plot_roc(df$roc),
  gg_plot_density(df$dens), nrow = 1
)

ggsave(gg_plot2, filename = "man/figures/gg_plot2.png", width = 18, height = 6)

highcharter

pacman::p_load(shiny, shiny.semantic)
shiny.semantic::semanticPage(
  div(class= "ui three column grid", 
    div(class = "column",
      hc_plot_confusion(df$confusion)
    ),
    div(class = "column",
      hc_plot_roc(df$roc)
    ),
    div(class = "column",
      hc_plot_density(df$dens)
    )
  )
)

apexcharter

pacman::p_load(shiny, shiny.semantic)
shiny.semantic::semanticPage(
  div(class= "ui three column grid", 
    div(class = "column",
      ax_plot_confusion(df$confusion)
    ),
    div(class = "column",
      ax_plot_roc(df$roc)
    ),
    div(class = "column",
      ax_plot_density(df$dens)
    )
  )
)
#htmltools::html_print(out)

sessionInfo()
#> R version 3.6.0 (2019-04-26)
#> Platform: x86_64-apple-darwin15.6.0 (64-bit)
#> Running under: macOS  10.15.1
#> 
#> Matrix products: default
#> BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
#> 
#> locale:
#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#>  [1] mlgraph_0.1.0    forcats_0.4.0    stringr_1.4.0    dplyr_0.8.3     
#>  [5] purrr_0.3.3      readr_1.3.1      tidyr_1.0.0.9000 tibble_2.1.3    
#>  [9] ggplot2_3.2.1    tidyverse_1.3.0  badger_0.0.4    
#> 
#> loaded via a namespace (and not attached):
#>  [1] tidyselect_0.2.5     xfun_0.11            haven_2.2.0         
#>  [4] lattice_0.20-38      colorspace_1.4-1     generics_0.0.2      
#>  [7] vctrs_0.2.99.9000    htmltools_0.4.0      yaml_2.2.0          
#> [10] utf8_1.1.4           rlang_0.4.2          pillar_1.4.3        
#> [13] withr_2.1.2          glue_1.3.1           DBI_1.1.0           
#> [16] RColorBrewer_1.1-2   dbplyr_1.4.2         modelr_0.1.5        
#> [19] readxl_1.3.1         plyr_1.8.5           rvcheck_0.1.3       
#> [22] lifecycle_0.1.0      dlstats_0.1.0        munsell_0.5.0       
#> [25] gtable_0.3.0         cellranger_1.1.0     rvest_0.3.5         
#> [28] evaluate_0.14        knitr_1.26           fansi_0.4.0         
#> [31] broom_0.5.3          Rcpp_1.0.3           scales_1.1.0        
#> [34] backports_1.1.5      jsonlite_1.6         fs_1.3.1            
#> [37] hms_0.5.2            digest_0.6.23        stringi_1.4.3       
#> [40] grid_3.6.0           cli_2.0.0            tools_3.6.0         
#> [43] magrittr_1.5         lazyeval_0.2.2       pacman_0.5.1        
#> [46] crayon_1.3.4         pkgconfig_2.0.3      xml2_1.2.2          
#> [49] pROC_1.15.3          reprex_0.3.0         lubridate_1.7.4     
#> [52] yardstick_0.0.4.9000 rstudioapi_0.10      assertthat_0.2.1    
#> [55] rmarkdown_2.0        httr_1.4.1           R6_2.4.1            
#> [58] nlme_3.1-139         compiler_3.6.0