table generator
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2 changed files with 304 additions and 1 deletions
303
scripts/thesis_summary_table.R
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303
scripts/thesis_summary_table.R
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library(xtable)
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library(dplyr)
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library(tidyverse)
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if (interactive()){
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print("Interactive Session, using home directories")
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data_dir = "~/git/"
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} else {
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data_dir = "/srv/shiny-server/git/"
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}
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latex_outdir = "~/git/Writing/thesis/tex/appendices/"
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thing = read.csv(paste0(data_dir, "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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# "cd_7030",
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# "cd_8020",
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"cd_sl"
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# "none"
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)
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split_choicenames=c(
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# "70:30",
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# "80:20",
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"Scaling law"
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# "CV thresholds"
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)
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split_file = c(
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# "_70_30_complete",
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# "_80_20_complete",
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"_sl_complete"
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# "_none_complete"
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)
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split_file_FS = c(
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# "_70_30_complete",
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# "_80_20_complete",
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"_sl_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_complete",
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# "_80_20_complete",
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"_sl_complete"
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# "_none_complete"
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),
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splits=c(
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# "cd_7030",
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# "cd_8020",
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"cd_sl"
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# "none"
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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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drug_choicenames = c("EmbB-ethambutol", "KatG-isoniazid", "PncA-pyrazinamide", "RpoB-rifampicin", "GidB-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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score_cols = c(
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'MCC',
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'F1',
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'Accuracy',
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'JCC',
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'Recall',
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'Precision',
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'source_data',
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'Model_name'
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)
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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(data_dir,'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() on its own is fine here because we don't need to do anything if it fails
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try({loaded_files[[load_name]] = read.csv(filename)})
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}
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}
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# Loader for per-gene Feature Selection CSVs
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for (x in gene) {
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#x=tolower(x)
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for (split in split_file_FS){
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filedata = paste0(x, split)
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filename = paste0(data_dir,'LSHTM_ML/output/genes/',x,split,'_FS.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], '_FS')
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print(load_name)
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# try() on its own is fine here because we don't need to do anything if it fails
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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('_complete', '_FS')){
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for (gene_count in c(1:5)){
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load_name=paste0(gene_count, "genes_logo_skf_BT_", x, ac)
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filename = paste0(data_dir,'LSHTM_ML/output/combined/',load_name, ".csv")
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store_name=paste0(gene_count, "genes_logo_skf_BT_", x, ac)
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# tryCatch is necessary here rather than try() because we need to do more
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# manipulation afterwards (throwing away the column after loading)
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load_successful=TRUE
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tryCatch({temp_df = read.csv(filename)},error=function(e){load_successful<<-FALSE})
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if (load_successful){
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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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print(paste0("loaded file: ", filename, "into var: ", store_name))
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}
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}
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}
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}
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loaded_files$embb_baselineC_cd_sl = loaded_files$embb_baselineC_cd_sl[loaded_files$embb_baselineC_cd_sl$resampling=='none',score_cols]
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loaded_files$katg_baselineC_cd_sl = loaded_files$katg_baselineC_cd_sl[loaded_files$katg_baselineC_cd_sl$resampling=='none',score_cols]
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loaded_files$pnca_baselineC_cd_sl = loaded_files$pnca_baselineC_cd_sl[loaded_files$pnca_baselineC_cd_sl$resampling=='none',score_cols]
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loaded_files$rpob_baselineC_cd_sl = loaded_files$rpob_baselineC_cd_sl[loaded_files$rpob_baselineC_cd_sl$resampling=='none',score_cols]
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loaded_files$gid_baselineC_cd_sl = loaded_files$gid_baselineC_cd_sl[loaded_files$gid_baselineC_cd_sl$resampling=='none',score_cols]
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loaded_files$embb_baselineC_cd_sl_FS = loaded_files$embb_baselineC_cd_sl_FS[loaded_files$embb_baselineC_cd_sl_FS$resampling=='none',score_cols]
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loaded_files$katg_baselineC_cd_sl_FS = loaded_files$katg_baselineC_cd_sl_FS[loaded_files$katg_baselineC_cd_sl_FS$resampling=='none',score_cols]
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loaded_files$pnca_baselineC_cd_sl_FS = loaded_files$pnca_baselineC_cd_sl_FS[loaded_files$pnca_baselineC_cd_sl_FS$resampling=='none',score_cols]
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loaded_files$rpob_baselineC_cd_sl_FS = loaded_files$rpob_baselineC_cd_sl_FS[loaded_files$rpob_baselineC_cd_sl_FS$resampling=='none',score_cols]
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loaded_files$gid_baselineC_cd_sl_FS = loaded_files$gid_baselineC_cd_sl_FS[loaded_files$gid_baselineC_cd_sl_FS$resampling=='none',score_cols]
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loaded_files$`5genes_logo_skf_BT_embb_complete` = loaded_files$`5genes_logo_skf_BT_embb_complete`[loaded_files$`5genes_logo_skf_BT_embb_complete`$resampling=='none',score_cols]
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loaded_files$`5genes_logo_skf_BT_katg_complete` = loaded_files$`5genes_logo_skf_BT_katg_complete`[loaded_files$`5genes_logo_skf_BT_katg_complete`$resampling=='none',score_cols]
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loaded_files$`5genes_logo_skf_BT_pnca_complete` = loaded_files$`5genes_logo_skf_BT_pnca_complete`[loaded_files$`5genes_logo_skf_BT_pnca_complete`$resampling=='none',score_cols]
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loaded_files$`5genes_logo_skf_BT_rpob_complete` = loaded_files$`5genes_logo_skf_BT_rpob_complete`[loaded_files$`5genes_logo_skf_BT_rpob_complete`$resampling=='none',score_cols]
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loaded_files$`5genes_logo_skf_BT_gid_complete` = loaded_files$`5genes_logo_skf_BT_gid_complete`[loaded_files$`5genes_logo_skf_BT_gid_complete`$resampling=='none',score_cols]
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loaded_files$`5genes_logo_skf_BT_embb_complete`[loaded_files$`5genes_logo_skf_BT_embb_complete`$source_data=="Train","source_data"]="0Train"
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loaded_files$`5genes_logo_skf_BT_katg_complete`[loaded_files$`5genes_logo_skf_BT_katg_complete`$source_data=="Train","source_data"]="0Train"
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loaded_files$`5genes_logo_skf_BT_pnca_complete`[loaded_files$`5genes_logo_skf_BT_pnca_complete`$source_data=="Train","source_data"]="0Train"
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loaded_files$`5genes_logo_skf_BT_rpob_complete`[loaded_files$`5genes_logo_skf_BT_rpob_complete`$source_data=="Train","source_data"]="0Train"
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loaded_files$`5genes_logo_skf_BT_gid_complete`[loaded_files$`5genes_logo_skf_BT_gid_complete`$source_data=="Train","source_data"]="0Train"
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loaded_files$`1genes_logo_skf_BT_embb_FS` = loaded_files$`1genes_logo_skf_BT_embb_FS`[loaded_files$`1genes_logo_skf_BT_embb_FS`$resampling=='none',score_cols]
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loaded_files$`1genes_logo_skf_BT_katg_FS` = loaded_files$`1genes_logo_skf_BT_katg_FS`[loaded_files$`1genes_logo_skf_BT_katg_FS`$resampling=='none',score_cols]
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loaded_files$`1genes_logo_skf_BT_pnca_FS` = loaded_files$`1genes_logo_skf_BT_pnca_FS`[loaded_files$`1genes_logo_skf_BT_pnca_FS`$resampling=='none',score_cols]
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loaded_files$`1genes_logo_skf_BT_rpob_FS` = loaded_files$`1genes_logo_skf_BT_rpob_FS`[loaded_files$`1genes_logo_skf_BT_rpob_FS`$resampling=='none',score_cols]
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loaded_files$`1genes_logo_skf_BT_gid_FS` = loaded_files$`1genes_logo_skf_BT_gid_FS`[loaded_files$`1genes_logo_skf_BT_gid_FS`$resampling=='none',score_cols]
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loaded_files$`1genes_logo_skf_BT_embb_FS`[loaded_files$`1genes_logo_skf_BT_embb_FS`$source_data=="Train","source_data"]="0Train"
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loaded_files$`1genes_logo_skf_BT_katg_FS`[loaded_files$`1genes_logo_skf_BT_katg_FS`$source_data=="Train","source_data"]="0Train"
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loaded_files$`1genes_logo_skf_BT_pnca_FS`[loaded_files$`1genes_logo_skf_BT_pnca_FS`$source_data=="Train","source_data"]="0Train"
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loaded_files$`1genes_logo_skf_BT_rpob_FS`[loaded_files$`1genes_logo_skf_BT_rpob_FS`$source_data=="Train","source_data"]="0Train"
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loaded_files$`1genes_logo_skf_BT_gid_FS`[loaded_files$`1genes_logo_skf_BT_gid_FS`$source_data=="Train","source_data"]="0Train"
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# Rewrite CV/BT to Train/Test and ensure that Train gets presented first
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loaded_files$embb_baselineC_cd_sl_FS[loaded_files$embb_baselineC_cd_sl_FS$source_data=="CV","source_data"]="0Train"
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loaded_files$katg_baselineC_cd_sl_FS[loaded_files$katg_baselineC_cd_sl_FS$source_data=="CV","source_data"]="0Train"
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loaded_files$pnca_baselineC_cd_sl_FS[loaded_files$pnca_baselineC_cd_sl_FS$source_data=="CV","source_data"]="0Train"
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loaded_files$rpob_baselineC_cd_sl_FS[loaded_files$rpob_baselineC_cd_sl_FS$source_data=="CV","source_data"]="0Train"
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loaded_files$gid_baselineC_cd_sl_FS[loaded_files$gid_baselineC_cd_sl_FS$source_data=="CV","source_data"]="0Train"
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loaded_files$embb_baselineC_cd_sl_FS[loaded_files$embb_baselineC_cd_sl_FS$source_data=="BT","source_data"]="Test"
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loaded_files$katg_baselineC_cd_sl_FS[loaded_files$katg_baselineC_cd_sl_FS$source_data=="BT","source_data"]="Test"
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loaded_files$pnca_baselineC_cd_sl_FS[loaded_files$pnca_baselineC_cd_sl_FS$source_data=="BT","source_data"]="Test"
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loaded_files$rpob_baselineC_cd_sl_FS[loaded_files$rpob_baselineC_cd_sl_FS$source_data=="BT","source_data"]="Test"
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loaded_files$gid_baselineC_cd_sl_FS[loaded_files$gid_baselineC_cd_sl_FS$source_data=="BT","source_data"]="Test"
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loaded_files$embb_baselineC_cd_sl[loaded_files$embb_baselineC_cd_sl$source_data=="Train","source_data"]="0Train"
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loaded_files$katg_baselineC_cd_sl[loaded_files$katg_baselineC_cd_sl$source_data=="Train","source_data"]="0Train"
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loaded_files$pnca_baselineC_cd_sl[loaded_files$pnca_baselineC_cd_sl$source_data=="Train","source_data"]="0Train"
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loaded_files$rpob_baselineC_cd_sl[loaded_files$rpob_baselineC_cd_sl$source_data=="Train","source_data"]="0Train"
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loaded_files$gid_baselineC_cd_sl[loaded_files$gid_baselineC_cd_sl$source_data=="Train","source_data"]="0Train"
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# Normal tables
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for (x in gene) {
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out_tex=paste0(latex_outdir,"ml_table-", x, ".tex")
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df_name=paste0(x,"_baselineC_cd_sl")
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processed_df = loaded_files[[df_name]] %>% pivot_wider(names_from=source_data, values_from=c(
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'MCC',
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'F1',
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'Accuracy',
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'Recall',
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'Precision',
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'JCC'
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),
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names_sep = " ",
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names_sort = TRUE
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)
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processed_df=as.data.frame(processed_df)
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colnames(processed_df)=gsub('0Train','Train',colnames(processed_df))
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rownames(processed_df)=processed_df$Model_name
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#colnames(processed_df)=gsub('Model_name','Model name',colnames(processed_df))
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processed_df=subset(processed_df, select=-Model_name)
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assign(paste0(x,"_out"),processed_df)
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print(out_tex)
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print.xtable(xtable(
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processed_df, type="latex"),
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file=out_tex
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)
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}
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quick_latex(huxtable(processed_df), file="/tmp/foo.tex", open=FALSE)
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# Feature Selection tables
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for (x in gene) {
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out_tex=paste0(latex_outdir,"ml_table_FS-", x, ".tex")
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df_name=paste0(x,"_baselineC_cd_sl_FS")
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processed_df = loaded_files[[df_name]] %>% pivot_wider(names_from=source_data, values_from=c(
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'MCC',
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'F1',
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'Accuracy',
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'Recall',
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'Precision',
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'JCC'
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),
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names_sep = " ",
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names_sort = TRUE
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)
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processed_df=as.data.frame(processed_df)
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colnames(processed_df)=gsub('0Train','Train',colnames(processed_df))
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rownames(processed_df)=processed_df$Model_name
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#colnames(processed_df)=gsub('Model_name','Model name',colnames(processed_df))
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processed_df=subset(processed_df, select=-Model_name)
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assign(paste0(x,"_out"),processed_df)
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print(out_tex)
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print.xtable(xtable(
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processed_df, type="latex"),
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file=out_tex
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)
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}
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# Combined Normal
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for (x in gene) {
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out_tex=paste0(latex_outdir,"ml_table_combined-", x, ".tex")
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df_name=paste0("5genes_logo_skf_BT_",x,"_complete")
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processed_df = loaded_files[[df_name]] %>% pivot_wider(names_from=source_data, values_from=c(
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'MCC',
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'F1',
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'Accuracy',
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'Recall',
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'Precision',
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'JCC'
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),
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names_sep = " ",
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names_sort = TRUE
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)
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processed_df=as.data.frame(processed_df)
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colnames(processed_df)=gsub('0Train','Train',colnames(processed_df))
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rownames(processed_df)=processed_df$Model_name
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#colnames(processed_df)=gsub('Model_name','Model name',colnames(processed_df))
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processed_df=subset(processed_df, select=-Model_name)
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assign(paste0(x,"_out"),processed_df)
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print.xtable(xtable(
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processed_df, type="latex"),
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file=out_tex
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)
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}
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# Combined Feature Selection
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for (x in gene) {
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out_tex=paste0(latex_outdir,"ml_table_combined-FS-", x, ".tex")
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df_name=paste0("1genes_logo_skf_BT_",x,"_FS")
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processed_df = loaded_files[[df_name]] %>% pivot_wider(names_from=source_data, values_from=c(
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'MCC',
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'F1',
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'Accuracy',
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'Recall',
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'Precision',
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'JCC'
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),
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names_sep = " ",
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names_sort = TRUE
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)
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processed_df=as.data.frame(processed_df)
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colnames(processed_df)=gsub('0Train','Train',colnames(processed_df))
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rownames(processed_df)=processed_df$Model_name
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#colnames(processed_df)=gsub('Model_name','Model name',colnames(processed_df))
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processed_df=subset(processed_df, select=-Model_name)
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assign(paste0(x,"_out"),processed_df)
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print(out_tex)
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print.xtable(xtable(
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processed_df, type="latex"),
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file=out_tex
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)
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}
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