# count numbers for ML

#source("~/git/LSHTM_analysis/config/alr.R")
#source("~/git/LSHTM_analysis/config/embb.R")
#source("~/git/LSHTM_analysis/config/gid.R")
#source("~/git/LSHTM_analysis/config/katg.R")
source("~/git/LSHTM_analysis/config/pnca.R")
#source("~/git/LSHTM_analysis/config/rpob.R")

source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")

gene
gene_match

nrow(merged_df3)
##############################################
#=============
# mutation_info: revised labels
#==============
table(merged_df3$mutation_info)
sum(table(merged_df3$mutation_info))
table(merged_df3$mutation_info_orig)
##############################################

#=============
# <drug>, dst_mode: revised labels
#==============
table(merged_df3$dst) # orig
sum(table(merged_df3$dst))

table(merged_df3$dst_mode)
#table(merged_df3[dr_muts_col])
sum(table(merged_df3$drtype_mode))

##############################################
#=============
# drtype: revised labels
#==============
table(merged_df3$drtype) #orig

table(merged_df3$drtype_mode)
# mapping 2.1: numeric
# drtype_map = {'XDR': 5
#   , 'Pre-XDR': 4
#   , 'MDR': 3
#   , 'Pre-MDR': 2
#   , 'Other': 1
#   , 'Sensitive': 0}

# create a labels col that is mapped based on drtype_mode
merged_df3$drtype_mode_labels = merged_df3$drtype_mode
merged_df3$drtype_mode_labels = as.factor(merged_df3$drtype_mode)

levels(merged_df3$drtype_mode_labels)

levels(merged_df3$drtype_mode_labels) <- c('Sensitive', 'Other'
                                    , 'Pre-MDR', 'MDR'
                                    , 'Pre-XDR', 'XDR')
levels(merged_df3$drtype_mode_labels)

# check
#table(merged_df3$drtype)
table(merged_df3$drtype_mode)
table(merged_df3$drtype_mode_labels)
sum(table(merged_df3$drtype_mode_labels))
##############################################
# lineage
table(merged_df3$lineage)
sum(table(merged_df3$lineage_labels))

# write file
outfile_merged_df3 = paste0(outdir, '/', tolower(gene), '_merged_df3.csv')
outfile_merged_df3
write.csv(merged_df3, outfile_merged_df3)

outfile_merged_df2 = paste0(outdir, '/', tolower(gene), '_merged_df2.csv')
outfile_merged_df2
write.csv(merged_df2, outfile_merged_df2)

###################################################
###################################################
###################################################

source("~/git/LSHTM_analysis/config/alr.R")
source("~/git/LSHTM_analysis/config/embb.R")
source("~/git/LSHTM_analysis/config/gid.R")
source("~/git/LSHTM_analysis/config/katg.R")
source("~/git/LSHTM_analysis/config/pnca.R")
source("~/git/LSHTM_analysis/config/rpob.R")

df3_filename = paste0("/home/tanu/git/Data/", drug, "/output/", tolower(gene), "_merged_df3.csv")
df3 = read.csv(df3_filename)

# mutationinformation
length(unique((df3$mutationinformation)))

#dm _om
table(df3$mutation_info)
table(df3$mutation_info_labels)
table(df3$mutation_info_orig)
table(df3$mutation_info_labels_orig)

# test_set
na_count <-sapply(df3, function(y) sum(length(which(is.na(y)))))
na_count[drug]

# training set
table(df3[drug])

# drtype: MDR and XDR
#table(df3$drtype) orig i.e. incorrect ones!
table(df3$drtype_mode_labels)

############################
#       Training data
############################
tr_df = df3[!is.na(df3[drug]),]
table(tr_df$dst)
table(tr_df[drug])

bts_df = df3[is.na(df3[drug]),]
table(bts_df$dst_mode)