mtl-qsar:

install.packages('dplyr')
install.packages('readr')
install.packages('ranger')
install.packages('foreach')

#### Library functions ====
library(dplyr)
library(readr)
library(ranger)
library(foreach)

#### FUnctions ====
'%ni%' <- Negate('%in%')

#### Load in the data ====
data_splits = list.files('/shared/mtl-qsar/data_splits/', full.names = T) # List of the datasets
# ID shows the row number in a given dataset

assist_data = list.files('/shared/mtl-qsar/datasets/assist_data_v190709', full.names = T) # List with fold information
# row numbers for 10 fold cv

original = list.files('/shared/mtl-qsar/datasets/originals', full.names = T) # lists the original datasets

## Only included to test the 10 completed assistant task sets
data_splits = data_splits[1:10]
original = original[1:10]

## Length of data in use
len = length(data_splits)

#### Sorting the data ====
tst.list = list()
trn.list = list()
a.trn = list()
ex.data = list()

tst.out = list()
stl.out = list()
mtl.out = list()
ext.data = list()

outputs = list()

###### RANDOM FOREST ######
RF.mult = foreach(trains_iter = 1:len, .combine = "rbind") %do% {
  
  split = read_csv(data_splits[trains_iter]) # pulls out the split information for the primary task 
  assist = read_csv(assist_data[trains_iter]) # pull out fold info for given dataset
  orig = read_csv(original[trains_iter])
  
  names(split)[1] = 'molecule_id'
  
  foreach (iter = 1:10, .combine = "rbind") %do% {
    tst = split[split$fold == iter,] # pulls out data matching the CV folds
    tst = merge(orig, tst, by = 'molecule_id')
    tst = select(tst, -c(fold))
    
    trn = split[split$fold != iter,] # pulls out not matching CV folds
    trn = merge(orig, trn, by = 'molecule_id')
    trn = select(trn, -c(fold))
    
    tst.list[[iter]] = tst # stores tst set in list
    trn.list[[iter]] = trn # stores training set in a list
    
    a_trn = assist[assist$fold == iter,]
    a_trn = select(a_trn, -c(fold, dataset_id))
    a.trn[[iter]] = a_trn
  }
  
  stl.out[[trains_iter]] = trn.list # stores training sets for whole dataset in list - reference with trn.out[['a']]
  mtl.out[[trains_iter]] = a.trn # stores the assistant task for whole dataset in list
  tst.out[[trains_iter]] = tst.list # stores test set for whole dataset in list form
  
  ##### Create the extended dataset ====
  for(outer in 1:10){
    for(inner in 1:10){
      
      tmp1 = stl.out[[outer]][inner]
      tmp1 = do.call(rbind.data.frame, tmp1)
      tmp2 = mtl.out[[outer]][inner]
      tmp2 = do.call(rbind.data.frame, tmp2)
      
      ext = rbind(tmp1, tmp2)
      
      ex.data[[inner]] = ext
      
    }
    
    ext.data[[outer]] = ex.data
    
  }
  
  for(inloop in 1:10){
    trn.data = ext.data[[trains_iter]][inloop] # Select given datafold 
    trn.data = do.call(rbind.data.frame, trn.data)
    trn.data = trn.data %>% select(-molecule_id) # removes the molecule column
    
    tst.data = tst.out[[trains_iter]][inloop]# Select corresponding datafold
    tst.data = do.call(rbind.data.frame, tst.data)
    tst.dat = tst.data %>% select(-molecule_id)
    row_id_tst = tst.data$molecule_id
    
    mdl = ranger(pXC50 ~ ., trn.data) # makes the random frest model, with the pXC50 as the output and the remainder inputs
    
    preds = predict(mdl, data = tst.dat)
    
    names = original[trains_iter]
    names = substr(names, 37, nchar(names))
    names = substr(names, 1, nchar(names)-4)
    
    print(inloop) # To chek progress
    
    outputs[[inloop]] = data_frame(iter = inloop, row_id = row_id_tst, true = tst.data$pXC50, predicted = preds$predictions, dataset_id = names) # stores the output with the actual values and the predicted values in seperate columns

  }
  outp = data.frame(Reduce(rbind, outputs))
}

write_csv(RF.mult, '/shared/mtl-qsar/predictions/MTL/RF_MTL_perf.csv')