ML dashboard/Score Selector initial commit
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ml/global.R
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ml/global.R
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library(shiny)
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library(shinyjs)
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library(shinydashboard)
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#library("wesanderson") # ayyyy lmao hipster af
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library(dplyr)
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library(ggplot2)
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library(grid) # for the info box
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library(plotly)
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library(shinycssloaders)
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# make shiny non-stupid
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#options(shiny.launch.browser = FALSE) # i am a big girl and can tie my own laces
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#options(shiny.port = 8000) # don't change the port every time
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#options(shiny.host = '0.0.0.0') # This means "listen to all addresses on all interfaces"
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#options(width=120)
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#options(DT.options = list(scrollX = TRUE))
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# FIXME: get rid of this hardcoded thing which i'm only reading in to have resampling types ahead of loading the real files
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thing = read.csv("/srv/shiny-server/git/Data/ml_combined/genes/pnca_70_30_actual.csv")
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# list of splits
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split_type = c(
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"7030",
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"8020",
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"sl",
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"cd_7030",
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"cd_8020",
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"cd_sl",
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"cd_none_bts",
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"cd_rt"
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)
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split_file = c(
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"_70_30_actual",
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"_70_30_complete",
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"_80_20_actual",
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"_80_20_complete",
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"_sl_actual",
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"_sl_complete",
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"_none_bts_complete",
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"_rt_complete"
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)
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# necessary because the names will be wrong otherwise
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split_map = data.frame(
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files=c(
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"_70_30_actual",
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"_70_30_complete",
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"_80_20_actual",
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"_80_20_complete",
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"_sl_actual",
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"_sl_complete",
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"_none_bts_complete",
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"_rt_complete"
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),
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splits=c(
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"7030",
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"cd_7030",
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"8020",
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"cd_8020",
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"sl",
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"cd_sl",
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"cd_none_bts",
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"cd_rt"
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)
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)
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metadata_cols = c("n_training_size", "n_test_size", "n_trainingY_ratio", "n_testY_ratio", "resampling", "n_features")
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# hardcoded list of drugs
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drug = c("ethambutol", "isoniazid", "pyrazinamide", "rifampicin", "streptomycin")
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gene = c("embb", "katg", "pnca", "rpob", "gid")
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combo = data.frame(drug, gene)
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# Loader for per-gene CSVs
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#"/home/sethp/git/Data/ml_combined/genes/pnca_70_30_complete.csv"
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loaded_files=list()
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for (x in gene) {
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#x=tolower(x)
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for (split in split_file){
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filedata = paste0(x, split)
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filename = paste0('/srv/shiny-server/git/LSHTM_ML/output/genes/',x,split,'.csv')
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#print(c(filename))
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#load_name=paste0(combo[gene==x,"drug"],'_',split_map['splits'][split_map['files']==split])
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load_name=paste0(x,'_baselineC_',split_map['splits'][split_map['files']==split])
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#print(load_name)
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#try({loaded_files[[filedata]] = read.csv(filename)})
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try({loaded_files[[load_name]] = read.csv(filename)})
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}
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}
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# Funky loader for combined data
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for (x in gene) {
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for (ac in c('_actual','_complete')){
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for (gene_count in c(6,5)){
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load_name=paste0(gene_count, "genes_logo_skf_BT_", x, ac)
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filename = paste0('/srv/shiny-server/git/LSHTM_ML/output/combined/',load_name, ".csv")
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print(filename)
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# if (ac=='') {
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# ac2 <- '_complete'
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# } else {
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# ac2 = ac
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# }
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store_name=paste0(gene_count, "genes_logo_skf_BT_", x, ac)
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print(store_name)
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try({temp_df = read.csv(filename)})
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temp_df=temp_df[, 2:ncol(temp_df)] # throw away first column
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loaded_files[[store_name]] = temp_df
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}
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}
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}
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#
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# loaded_files_old=list()
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# for (x in drug) {
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# for (split in split_type){
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# filename = paste0('/home/sethp/git/Data/',
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# x,
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# '/output/ml/tts_',
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# split,
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# '/',
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# combo[drug==x,"gene"],
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# '_baselineC_',
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# split,
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# '.csv')
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# filedata = paste0(combo[drug==x,"gene"],
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# '_baselineC_',
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# split
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# )
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# print(c(filename, filedata))
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#
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# try({loaded_files_old[[filedata]] = read.csv(filename)})
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# }
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# }
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#plot_data = thing[thing$resampling=='none',]
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# FIXME commented out for the moment because we need to use
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# this before the data is actually loaded :-(
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# scores = colnames(thing %>% dplyr::select(-c("Model_name",
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# "source_data",
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# "resampling"
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# )
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# )
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# )
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scores=c("F1", "ROC_AUC", "JCC", "MCC", "Accuracy", "Recall", "Precision")
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resample_types <<- unique(thing$resampling)
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makeplot = function(x, # the DataFrame to plot
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selection, # scoring method e.g. 'MCC'
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resampler, # resampling type e.g. 'none'
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display_infobox = TRUE, # display the infobox on top of the plot
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display_combined = TRUE, # show stuff that only applies to "combined model" plots
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gene = 'NOT SET', # used only for the info box
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drug = 'NOT SET', # used only for the info box
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combined_training_genes = '999' # used only for the info box
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){
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plot_data = x[x$resampling==resampler,]
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y_coord_min = min(plot_data[selection])
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if (y_coord_min > 0) {
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y_coord_min = 0
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}
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if (display_infobox) {
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metadata=t(plot_data[1,metadata_cols])
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if (display_combined){
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metatext=paste0("Train/Test: ",
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metadata[1], "/", metadata[2],
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"\nTrain/Test Target Ratio: ", metadata[3], "/", metadata[4],
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"\nResampling: ", metadata[5],
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"\nFeatures: ", metadata[6],
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"\nGenes Trained: ", combined_training_genes,
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"\nTest Gene: ", gene
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)
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} else {
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metatext=paste0("Train/Test: ",
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metadata[1], "/", metadata[2],
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"\nTrain/Test Target Ratio: ", metadata[3], "/", metadata[4],
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"\nResampling: ", metadata[5],
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"\nFeatures: ", metadata[6],
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"\nTest Gene: ", gene
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)
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}
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#print(metatext)
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grob <- grobTree(textGrob(metatext,
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x=0.01,
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y=0.90,
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hjust=0,
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gp=gpar(col="black")
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)
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)
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}
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ggplot(data=plot_data, aes_string(x="Model_name",
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y=selection,
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fill="source_data",
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group=selection) ) +
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geom_bar(stat="identity"
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, width = 0.75
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, position=position_dodge2(padding=0.1, preserve='total', reverse=TRUE)
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) +
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coord_cartesian(ylim = c(y_coord_min, 1)) +
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scale_fill_manual(values = c("BT" = "#605ca8",
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"CV" = "#bebddb") ) +
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#guides=guide_legend(reverse=TRUE) +
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annotation_custom(grob) +
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# doesn't work with plotly but looks nice :-(
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geom_label(aes_string(label=selection),
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position=position_dodge(width = -0.75),
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#position=position_dodge2(padding=0.1),
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vjust = 1.5,
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alpha=0.75,
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fill="white"
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) +
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# works with plotly but i can't figure out the background yet
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# geom_text(aes_string(label=selection, group=selection),
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# position=position_dodge(width = -0.75),
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# vjust = 1.5,
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# alpha=0.75,
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#
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# ) +
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# add little numbers for the BT bars only
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labs(x="",y=paste(selection,"Score")) +
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theme(
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axis.text.x = element_text(angle = 90),
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)
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# ggplotly()
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}
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